PRESCRIPTIONS FOR REDUCING THE PLANNING FALLACY: THE OUTSIDE VIEW VERSUS UNPACKING
SITI ZUBAIDAH OTHMAN
BA Psychology (I/O Psychology), MSc Human Resources
This thesis is presented for the degree of Doctor of Philosophy
of
The University of Western Australia
UWA Business School
Management and Organizations
2009
Declaration
Prescriptions for reducing the planning fallacy: The outside view versus unpacking
This thesis is my own composition, all sources have been acknowledged and my
contribution is clearly identified in the thesis.
______________________________________________________________________
Siti Zubaidah Othman
i
Abstract
This thesis examines two theories of the planning fallacy, the inside / outside
view and the unpacking approach, and extends planning fallacy explanations to projects
involving Human Resource Information Systems (HRIS). This is the first empirical
research to compare these theories on the same tasks and in the same study.
The first study tested the two theories in a controlled experiment with 708
student participants working on small group project assignments. Individuals were
nested within 162 project groups, and data was collected on a weekly basis for eight
weeks. The hypotheses were tested using three-level hierarchical linear modeling
(HLM). Both approaches showed opposite effects on predictions made towards
completion time and outcome success, but had no effect on changes in confidence in
predictions of completion time and outcome success.
The second study explored whether the outside view, inside view or unpacking
planning approaches are commonly used by planners for 45 real HRIS projects in
organizations, and their effects on the accuracy of predictions about the success or
failure of the project in terms of project completion time, outcome achieved, and
satisfaction with the outcome. Hypotheses were tested using correlations, and results are
also presented using descriptive statistics. The reported extent of use of the unpacking
approach was positively related to the success of the project in terms of percentage of
outcome achieved, the satisfaction with the HRIS system, which includes the content,
format, accuracy, timeliness and ease of use of the system and the reported affective
response towards outcome success.
Recommendations for practice include support for the unpacking approach and
the use of work breakdown structure in project planning. Suggestions are made for
further research on differentiating the inside view and unpacking, and on the
ii
undesirable effects of both the outside view and unpacking in increasing rather than
reducing optimistic bias for outcome success.
iii
Table of Contents
Abstract .............................................................................................................................. i Table of Contents ............................................................................................................. iii Dedication ....................................................................................................................... vii Acknowledgements ........................................................................................................ viii List of Figures .................................................................................................................. ix List of Tables..................................................................................................................... x List of Appendices ......................................................................................................... xiii CHAPTER 1 ..................................................................................................................... 1 INTRODUCTION ............................................................................................................ 1
1.1 Context and Significance ........................................................................................ 1 1.2 Scope and Aim of Research .................................................................................... 6 1.3 Organization of Chapters in the Thesis ................................................................... 8
CHAPTER 2 ................................................................................................................... 11 A REVIEW OF PROJECT SUCCESS, PROJECT PLANNING AND PLANNING BIASES ........................................................................................................................... 11
2.1 Introduction ........................................................................................................... 11 2.2 The Characteristics of Projects.............................................................................. 11 2.3 The Concepts of Information System (IS) Project Success and Failure ............... 12 2.4 Past Research on Causes of Information System (IS) Project Failure .................. 14 2.5 The Role of Planning ............................................................................................ 16 2.6 The Planning Process: Issues and Challenges ....................................................... 17 2.7 Planning Under Uncertainty .................................................................................. 19
2.7.1 Biases in Completion Time Predictions ......................................................... 21 2.7.2 Biases in Outcome Predictions....................................................................... 28 2.7.3 Confidence in Predictions .............................................................................. 32 2.7.4 Biases in Affective Predictions ...................................................................... 35
2.8 Conclusion and Research Issues to be Addressed ................................................. 38 CHAPTER 3 ................................................................................................................... 41 HYPOTHESES - STUDY 1............................................................................................ 41
3.1 Introduction ........................................................................................................... 41 3.2 Testing the Theories of the Planning Fallacy ........................................................ 41 3.3 Theories of the Planning Fallacy and Outcome Prediction ................................... 42 3.4 Theories of the Planning Fallacy and Confidence in Predictions ......................... 43 3.5 Theories of the Planning Fallacy and Prediction of Affective Reactions towards Outcome Success and Failure ..................................................................................... 44 3.6 Conclusion ............................................................................................................ 46
CHAPTER 4 ................................................................................................................... 48 METHOD – STUDY 1 ................................................................................................... 48
4.1 Introduction ........................................................................................................... 48 4.2 Research Design .................................................................................................... 48 4.3 Data Collection Process ........................................................................................ 49 4.4 Participants ............................................................................................................ 49 4.5 Experimental Materials ......................................................................................... 50 4.6 Experimental Procedure ........................................................................................ 52 4.7 Measures ............................................................................................................... 55
4.7.1 Time Completion Measure ............................................................................. 55 4.7.2 Outcome Success Measures ........................................................................... 55 4.7.3 Confidence Measures ..................................................................................... 56 4.7.4 Affective Response for Success and Failure Measures.................................. 56
4.8 Data Analysis Strategy .......................................................................................... 57
iv
4.8.1 Level of Analysis ........................................................................................... 57 4.8.2 Data Centering using Grand Means ............................................................... 58 4.8.3 Time Centering .............................................................................................. 59
4.9 Sample Size for Multilevel Modeling Analyses ................................................... 60 4.10 Data Cleaning ...................................................................................................... 62 4.11 Conclusion .......................................................................................................... 62
CHAPTER 5 ................................................................................................................... 63 RESULTS – STUDY 1 ................................................................................................... 63
5.1 Introduction ........................................................................................................... 63 5.2 Demographic Characteristics of the Participants .................................................. 63 5. 3 Hypothesis Testing ............................................................................................... 63
5.3.1 Completion Time Predictions ........................................................................ 64 5.3.1.1 Unconditional Three-level HLM Models ............................................... 67 5.3.1.2 Conditional Three-level Model for Completion Time Prediction Bias .. 71
5.3.2 Outcome Predictions ...................................................................................... 78 5.3.2.1 Unconditional Three-level Model ........................................................... 79 5.3.2.2 Conditional Three-level Model ............................................................... 81
5.3.3 Confidence in Predictions of Project Completion Time ................................ 87 5.3.3.1 Unconditional Three-level Model ........................................................... 88 5.3.3.2 Conditional Three-level Model ............................................................... 90
5.3.4 Confidence in Predictions of Project Outcome .............................................. 95 5.3.4.1 Unconditional Three-level Model ........................................................... 96 5.3.4.2 Conditional Three-level Model ............................................................... 98
5.3.5 Predictions of Affective Response towards Outcome Success .................... 103 5.3.5.1 Unconditional Three-level Model ......................................................... 105 5.3.5.2 Conditional Three-level Model ............................................................. 107
5.3.6 Predictions of Affective Response towards Outcome Failure ..................... 113 5.3.6.1 Unconditional Three-level Model ......................................................... 115 5.3.6.2 Conditional Three-level Model ............................................................. 117
5.4 Conclusion .......................................................................................................... 122 CHAPTER 6 ................................................................................................................. 124 DISCUSSION – STUDY 1 ........................................................................................... 124
6.1 Introduction ......................................................................................................... 124 6.2 Completion Time Predictions ............................................................................. 124 6.3 Outcome Predictions ........................................................................................... 127 6.4 Confidence in Predictions ................................................................................... 128 6.5 Predictions of Affective Response towards Outcome Success and Failure ........ 128 6.6 Study 1 Limitations ............................................................................................. 130 6.7 Conclusion .......................................................................................................... 131
CHAPTER 7 ................................................................................................................. 133 INTRODUCTION TO STUDY 2 AND DEVELOPMENT OF THE HYPOTHESES 133
7.1 Introduction ......................................................................................................... 133 7.2 Background of the Second Study ........................................................................ 134 7.3 Research on HRIS Effectiveness ........................................................................ 138 7.4 Reasons for Unsuccessful HRIS Projects ........................................................... 141 7.4 Importance and Challenges of HRIS Project Planning ....................................... 145 7.5 The Planning Fallacy Theories and HRIS Projects: Extending Study 1 ............. 147
7.5.1 Project Completion Predictions ................................................................... 147 7.5.2 Project Outcome Prediction ......................................................................... 147 7.5.3 Predictions of Affective Response towards Outcome Success and Failure . 149
7.6 Conclusion .......................................................................................................... 150 CHAPTER 8 ................................................................................................................. 151
v
METHOD – STUDY 2 ................................................................................................. 151 8.1 Introduction ......................................................................................................... 151 8.2 Sampling Design ................................................................................................. 151
8.2.1 The Participating Organizations ................................................................... 151 8.2.2 The Participants ............................................................................................ 153
8.3 Survey Materials ................................................................................................. 154 8.4 Procedure............................................................................................................. 158 8.5 Research Measures .............................................................................................. 159
8.5.1 Planning Method Measures .......................................................................... 159 8.5.2 Project Completion Time Measures ............................................................. 161 8.5.3 Outcome Success Measures ......................................................................... 162 8.5.4 Measures of Affective Response to Success and Failure ............................. 165 8.5.5 Demographic Measures ................................................................................ 166
8.6 Data Analysis Strategy ........................................................................................ 166 8.6.1 Open-ended Questions ................................................................................. 167
8.7 Conclusion .......................................................................................................... 168 CHAPTER 9 ................................................................................................................. 169 RESULTS – STUDY 2 ................................................................................................. 169
9.1 Introduction ......................................................................................................... 169 9.2. Demographic Characteristics of the Participants ............................................... 169 9.3 HRIS Projects in the Survey ............................................................................... 170 9.4 Planning Methods and Project Outcomes ........................................................... 173 9.5 Correlation Analysis............................................................................................ 176
9.5.1 Bias in Project Completion Prediction ......................................................... 176 9.5.2 Bias in Project Outcome Prediction ............................................................. 179 9.5.3 Other Correlation Results ............................................................................. 179
9.6 Hypothesis Testing .............................................................................................. 180 9.6.1 Project Completion Time Predictions .......................................................... 180 9.6.2 Project Outcome Predictions ........................................................................ 181 9.6.3 Predictions of Affective Response towards Outcome Success and Failure . 184
9.7 Conclusion .......................................................................................................... 186 CHAPTER 10 ............................................................................................................... 187 DISCUSSION – STUDY 2 ........................................................................................... 187
10.1 Introduction ....................................................................................................... 187 10.2 Project Completion Time Predictions ............................................................... 187 10.3 Project Outcome Predictions ............................................................................. 188 10.4 Predictions of Affective Reactions towards Outcome Success and Failure ..... 190 10.5 Study 2 Limitations ........................................................................................... 192 10.6 Conclusion ........................................................................................................ 193
CHAPTER 11 ............................................................................................................... 194 GENERAL DISCUSSION............................................................................................ 194
11.1 Introduction ....................................................................................................... 194 11.2 Theories of the Planning Fallacy....................................................................... 194
11.2.1 Completion Time Predictions .................................................................... 195 11.3 Extending the Explanations of the Planning Fallacy ........................................ 196
11.3.1 Outcome Predictions .................................................................................. 196 11.3.2 Confidence in Predictions .......................................................................... 197 11.3.3 Predictions of Affective Reactions towards Outcome Success and Failure ............................................................................................................................... 198 11.3.4 Further Theoretical Development .............................................................. 200
11.4 Implications for Practice ................................................................................... 200 11.5 Limitations and Directions for Future Research ............................................... 202
vi
11.6 Conclusion ........................................................................................................ 204 REFERENCES.............................................................................................................. 206 APPENDICES .............................................................................................................. 222
vii
Dedication
To my mother, Aminah Kontak, my father, Othman Ali, my husband, Abdul Razak Abd
Manaf, and all my siblings, especially Suzana, Hamidah, Norhayati and Noraini.
viii
Acknowledgements
Without the dedication and support from these people, the completion of this
thesis would not have been possible. I would like to thank Universiti Utara Malaysia,
for sponsoring my study, and to UWA Business School for awarding me the ad-hoc
scholarship. I owe an immense debt to Dr. Catherine Lees, my main supervisor for all
the energy, care and enthusiasm she devoted to this effort. Her brilliant ideas,
suggestions and above all, her belief in my potential have made me feel confident and
gave me a strong focus from the very beginning of my study. Without her professional
guidance and support, I would not be where I am today. I am also deeply grateful to
Professor John Cordery, my second supervisor, for giving me invaluable support
throughout my candidature.
To my loving and supporting husband, Abdul Razak, my beloved parents,
Aminah and Othman, my parent in-laws, Abd Manaf and Mukhayani, my siblings, in-
laws and all my nephews and nieces, thank you for all your prayers, patience, support,
and word of encouragement for me to keep going till the final end of this journey.
I also would like to thank my colleague at Universiti Utara Malaysia, Dr
Khulida, Sharima, Jannatul, Safinas and my wonderful PhD friends Naomi, Karen,
Hairul, Nazlida, Hazarina, Aspalella, Norizan, Rusmawati, Nor Azlin, Nor Haniza,
Nashrah, Shamsuri and Norhisham Nordin for providing me with many discussions,
constructive comments and suggestions during this PhD journey.
Finally yet importantly, I would like to express my gratitude to all the lecturers
and students during the second semester session 2005 / 2006, in the Faculty of Human
and Social Development, Universiti Utara Malaysia, for participating in my first study.
A word of appreciation also goes to all HR staffs in organizations in Perak, Kedah,
Perlis, Penang and Kuala Lumpur, for their involvement in my second study. Without
their sincere participation, this study will not be as successful as today.
ix
List of Figures
Figure 5.1 Mean initial predicted and actual completion times. (n=708) 64
Figrue 5.2 Mean predicted and actual completion times for the eight weeks project. (n=702)
65
Figure 5.3 Mean prediction bias of individuals’ project completion time
predictions for the four conditions. Bias is actual minus predicted completion time. (n=708)
66
Figure 5.4 Mean prediction bias of individuals’ project outcome
predictions for the four conditions. Actual outcome (in marks) minus predicted. (n=689)
78
Figure 5.5 Mean confidence in predictions of project completion time for
the four conditions. (n=708)
87
Figure 5.6 Mean confidence in predictions of project outcome for the four conditions. (n=689)
95
Figure 5.7 Mean rating of predicted and experienced affective response
for success. (n=422) 104
Figure 5.8 Mean prediction bias of individuals’ affective response
towards project outcome success for the four conditions. Actual response minus predicted response. (n=422)
105
Figure 5.9 Mean rating of predicted and experienced affective response
for failure. (n=130) 114
Figure 5.10 Mean prediction bias of individuals’ affective response
towards project outcome failure for the four conditions. Actual response minus predicted response. (n=130)
115
x
List of Tables
Table 5.1 Prediction bias for project completion time: A three-level fully unconditional model
70
Table 5.2a Prediction bias for project completion time: Three-level
model of the effects of manipulations relative to the packed (control) condition
74
Table 5.2b Prediction bias for project completion time: Three-level
model of the effects of manipulations relative to the outside view condition
76
Table 5.3 Prediction bias for project outcome: A three-level fully
unconditional model
80
Table 5.4a Prediction bias for project outcome: Three-level model of the effects of manipulations relative to the packed (control) condition
83
Table 5.4b Prediction bias for project outcome: Three-level model of
the effects of manipulations relative to the outside view condition
85
Table 5.5 Confidence in predictions of project completion time: A
three-level fully unconditional model
89
Table 5.6a Confidence in predictions of project completion time: Three-level model of the effects of manipulations relative to the packed (control) condition
91
Table 5.6b Confidence in predictions of project completion time:
Three-level model of the effects of manipulations relative to the outside view condition
93
Table 5.7 Confidence in predictions of project outcome: A three-
level fully unconditional model
97
Table 5.8a Confidence in predictions of project outcome: Three-level model of the effects of manipulations relative to the packed (control) condition
99
Table 5.8b Confidence in predictions of project outcome: Three-
level model of the effects of manipulations relative to the outside view condition
101
Table 5.9 Prediction bias for affective response towards project
outcome success: A three-level fully unconditional model
106
xi
Table 5.10a Prediction bias for affective response towards project outcome success: Three-level model of the effects of manipulations relative to the packed (control) condition
109
Table 5.10b Prediction bias for affective response towards project
outcome success: Three-level model of the effects of manipulations relative to the outside view condition
111
Table 5.11 Prediction bias for affective response towards project
outcome failure: A three-level fully unconditional model
116
Table 5.12a Prediction bias for affective response towards project outcome failure: Three-level model of the effects of manipulations relative to the packed (control) condition
118
Table 5.12b Prediction bias for affective response towards project
outcome failure: Three-level model of the effects of manipulations relative to the outside view condition
120
Table 7.1 Definitions of human resource information systems
(HRIS) 134
Table 7.2 Summary of potential values of human resource
information systems (HRIS) 137
Table 8.1 The four versions of the questionnaires 156
Table 8.2 The nine activity statements 160
Table 8.3 Descriptions of the three approaches 161
Table 8.4 Original and adapted versions of end-user satisfaction
items 165
Table 9.1 HR computer-based applications reported as presently
implemented in surveyed organizations 170
Table 9.2 Participants’ involvement in the project phases (all the 45
projects) 172
Table 9.3a Aspects of system that participants felt most satisfied
with 174
Table 9.3b Aspects of system that participants felt least satisfied
with 175
Table 9.4 Descriptive statistics, scale reliabilities, and correlations
of variables for completed projects 177
Table 9.5 The planning approach used and project completion time
(forced choice questions) 181
xii
Table 9.6 The planning approach used and mean reported percentage of outcome achieved (standard deviations in brackets)
182
Table 9.7 The planning approach used and mean predicted of
outcome achieved (standard deviations in brackets)
182
Table 9.8 The planning approach used and mean end-user satisfaction (standard deviations in brackets)
183
Table 9.9 The planning approach used and mean of overall
satisfaction with the system (standard deviations in brackets)
184
Table 9.10 Descriptive statistics of participants’ affective response
towards project outcome success and failure
185
Table 9.11 The planning approach used and mean predicted and actual affective response towards project outcome success (standard deviations in brackets)
186
xiii
List of Appendices
APPENDIX A SAMPLE OF THE EXPERIMENTAL MATERIALS (ENGLISH AND TRANSLATED VERSION) – STUDY 1
223
APPENDIX B EXPERIMENTAL DESIGN AND WEEKLY RESPONDENTS
320
APPENDIX C RESULTS OF DEMOGRAPHIC CHARACTERISTICS OF THE PARTICIPANTS – STUDY 1
321
APPENDIX D SAMPLE OF THREE-LEVEL HIERARCHICAL REGRESSION EQUATIONS – STUDY 1
322
APPENDIX E RESULTS FOR CONDITIONAL THREE-LEVEL HLM MODELS – STUDY 1
Appendix E-1 Prediction bias for project completion time: Three-level model of the effects of manipulation relative to the inside view condition
340
Appendix E-2 Prediction bias for project outcome: Three-level model of the effects of manipulation relative to the inside view condition
342
Appendix E-3 Confidence in predictions of project completion time: Three-level model of the effects of manipulation relative to the inside view condition
344
Appendix E-4 Confidence in predictions of project outcome: Three-level model of the effects of manipulation relative to the inside view condition
346
Appendix E-5 Prediction bias for affective response towards outcome success: Three-level model of the effects of manipulations relative to the inside view condition
348
Appendix E-6 Prediction bias for affective response towards outcome failure: Three-level model of the effects of manipulations relative to the inside view condition
350
APPENDIX F LEGENDS FOR GRAPHICAL ANALYSIS RESULTS – STUDY 1
352
APPENDIX G SAMPLE OF THE SURVEY MATERIALS – STUDY 2 354
APPENDIX H OPEN-ENDED QUESTION MATERIALS – STUDY 2 430
APPENDIX I DEMOGRAPHIC CHARACTERISTICS OF THE PARTICIPANTS – STUDY 2
444
xiv
APPENDIX J DESCRIPTIVE STATISTICS OF PROJECTS IN THE SURVEY – STUDY 2
445
APPENDIX K MEAN PREDICTION BIAS FOR PROJECT COMPLETION TIME – STUDY 2
Appendix K-1 Mean prediction bias for completed projects 447
Appendix K-2 Mean prediction bias for project at the planning stage
448
1
CHAPTER 1
INTRODUCTION
1.1 Context and Significance
Making accurate predictions about when a project can be completed, what a
project can deliver, and how successful it will be are important aspects of project
planning and project management. This research investigates how to improve the
accuracy of such predictions made during the planning stages of project management.
Overestimating task completion time and budget may result in failure to convince top
management to approve the project, but underestimating these things may lead to failure
to deliver the expected outcome within time and budget, even if the project is approved
(Morgenshtern, Raz, & Dvir, 2007). Making inaccurate completion time and outcome
estimates can have economic, social and personal costs including disputes, litigation,
and total abandonment of a project (Aibinu & Jagboro, 2002; Calisir & Gumussoy,
2005; Ewusi-Mensah & Przasnyski, 1991; Manavazhi & Adhikari, 2002; Sambasivan &
Soon, 2007).
In information systems (IS) development projects such as human resource
information systems (HRIS), inaccurate predictions could lead to a number of problems
such as failure to provide what the organization needs, inaccurate or insufficient data,
disappointing end-functionalities, and lack of interaction with other systems, which in
turn can lead to further problems. Thus, finding ways to improve the accuracy of project
outcome predictions will assist organizations to achieve greater economic certainty and
effectiveness in the use of their funds.
In practice, various methods and techniques have been tried to improve the
performance of project prediction, and these include among others network techniques
like the critical path model (CPM) and the program evaluation and review technique
2
(PERT) (Cleland & Ireland, 2002; Hardie, 2001; Lock, 2003); estimation by analogy,
parametric models such as the constructive cost model (COCOMO), expert estimation,
and artificial intelligence techniques (Hill, Thomas, & Allen, 2000; Hughes, 1996;
Morgenshtern et al., 2007); work breakdown structure (WBS); milestones schedules,
and bar chart or Gantt charts (Cleland & Ireland, 2002; Lock, 2003; Meredith & Mantel,
2006; Rosenau, 1998).
Despite the wide range of project planning techniques and methodologies being
introduced and utilized, evidence of prediction problems among IS projects continues to
appear. Cats-Baril and Thompson (1995) reported that 20 percent of all IT projects are
scrapped before completion, and 80 percent of the completed projects finished late, over
budget or failed to meet users’ expectations. In 1999, Ambler reported that since the
1980s the failure rate in the development of large-scale software projects was 85
percent. Keil, Mann and Rai (2000) found that 30 to 40 percent of IS projects showed
some degree of escalation, and in a sample of 76 projects analyzed, Calisir and
Gumussoy (2005) found that the average IT budget overrun was 19 percent and
schedule overrun 49 percent.
The formal methods for reducing prediction error in project management have
been widely discussed in the project management literature, but very little research has
been devoted to the behavioral aspects of the problem. Research into how project
managers make plans and decisions during project planning, and their impact on the
success of the project is still limited.
The main concern of this thesis is the role of human judgment in making
accurate project planning predictions, with particular application to human resource
information system (HRIS) projects. Even though formal planning tools can be useful,
McFarland (1981) argued that these tools contribute little to reducing uncertainty and
highlighting problems at the early stage of the planning. The planning tools do allow
3
project managers to structure the sequence of tasks, but it is very difficult to predict the
time, cost and technical performance simultaneously. As argued by Hill, Thomas and
Allen (2000), the effectiveness of project management techniques depends heavily on
accurate duration prediction. Making predictions or forecasts is essentially a human
task, and therefore, the way projects are run needs to be considered from a perspective
that includes the psychological and behavioral aspects of the problem.
Predictions are guesses regarding aspects of future events, made on the basis of
available knowledge. The accuracy of predictions about performance of a task, such as a
project, may be affected by the extent of uncertainties associated with the task to be
predicted. The uncertainties are often associated, as in the case of an IS project like
HRIS, with the definition of requirements, choice of technology available, and the
users’ needs. To make accurate predictions planners need to cope with many vague and
conflicting requirements, as well as diverse users’ skills and expectations. According to
Smith (2007), the uncertain nature of a project always leaves planners to deal with the
unknown, and subject to streams of unpredictable and unexplainable events that may
prevent project success.
Because of the uncertain nature of events that might delay or otherwise disrupt a
project, Lovallo and Kahneman (2003) believed that many planners tend to establish
what they think would be the “most likely” scenario and assume that its outcome is, in
fact, the most likely outcome. This phenomenon is known as the planning fallacy. Even
though the concept of the planning fallacy is relatively new to the project management
literature, it has been researched and documented in the psychology literature.
The planning fallacy has been described as “the tendency to hold a confident
belief that one’s own project will proceed as planned, even while knowing that the vast
majority of similar projects have run late” (Buehler, Griffin, & Ross, 1994, p.366).
According to Lovallo and Kahneman (2003), it is a tendency to make decisions based
4
on delusional optimism rather than on a rational weighting of gains, losses and
probabilities. In trying to complete tasks, many of us tend to be optimistic and
overconfident.
Two compelling but conflicting theories have been put forward in the literature
to explain the planning fallacy. One line of research, termed the inside-outside
approach, has shown that focusing on the outside perspective, that is, a broader context
that includes relevant past experiences, the experiences of others, and background
events, produces more accurate predictions regarding task completion time than using
the inside perspective, which involves focusing only on the project at hand to make the
prediction (Lovallo & Kahneman, 2003). In contrast, research on an approach termed
enumerating, or unpacking, has suggested that taking an inside perspective that
specifically involves thinking more about the task at hand and its separate components
also results in a reduction of the planning fallacy (Kruger & Evans, 2004). As theories
of the planning fallacy are contradictory, and logically lead to different prescriptions for
planning processes, understanding of the planning fallacy can be extended by testing the
two theories against each other in the same study, to investigate the strategy that is
better in reducing the planning fallacy. These two theories are tested against each other
for the first time in the experiment that forms Study 1 in this thesis.
Moreover, the planning fallacy has been observed in a wide variety of activities,
from novel laboratory tasks to large-scale industrial projects (Buehler et al., 1994;
Camerer & Lovallo, 1999; Kahneman & Lovallo, 1993; Lovallo & Kahneman, 2003),
but to date there has been no research to understand the planning fallacy with respect to
the detailed planning of specific types of projects, such as IS projects. For example,
projects involving human resource information systems (HRIS). There is evidence
showing that many HRIS projects have encountered problems during implementation
and fail to deliver the expected outcome, where the systems are said to have low
5
benefits, inaccurate or insufficient data, to be difficult to access, have disappointing end
functionalities, and a lack of interaction with other systems (Caplan, 2004; Kavanagh,
Gueutal, & Tannenbaum, 1990; Macy, 2004; Russell, 2006). Little is known about
whether issues in planning and development in HRIS projects contribute to these
failures. The second study reported in this thesis is a field study that applies theories of
the planning fallacy to a sample of HRIS projects and measures the extent to which their
outcomes fall short of expectations.
The data for both studies were collected in Malaysia. The rapid economic
development in Malaysia made it an ideal setting for this research as it provides a
context in which there is rapid change in the spread and adoption of IT. As Malaysia is
moving forward towards achieving a fully developed and industrialized country by the
year 2020, the effort of making science and technology an integral component of socio-
economic planning and development continues. During the Eighth Malaysia Plan (2001-
2005), efforts were on providing a stronger platform for the country’s transition towards
a knowledge-based economy by promoting information and communication
technologies (ICT) as a strategic driver to support and contribute directly to the growth
of the economy as well as to enhance quality of life. These efforts continue in the Ninth
Malaysia Plan (2006-2010), but with greater adoption and usage of ICT to allow greater
expansion of ICT-related industries and services.
A total of RM12.9 billion has been allocated for ICT-related programs and
projects during this plan, with a major portion of the allocation for computerization of
government ministries and agencies as well as for the supply and maintenance of
computers and Internet access. Providing adequate and reliable ICT infrastructure with
extensive capacity to support access and delivery of information remains a major focus
in this plan, and is seen as an effort to strengthen Malaysia’s position as a preferred
global location for ICT investment and as a market leader for ICT solutions.
6
The Malaysian government also acknowledges that having technology per se is
not sufficient if the human resources are not technologically fluent. Therefore, various
education, training, and skills development programs have been planned and expanded
to cover schools, pre-university levels, and the higher education institutions to keep up
with the demand for high quality, skilled and creative ICT personnel. It is expected that
the total ICT workforce will increase at a rate of 10.4 percent per year from 183,204 in
2005 to 300,000 in 2010 (EPU, 2006). At the same time, both public and private sectors
are also encouraged to provide a conducive environment for e-learning as a work
culture, which allows for knowledge sharing and knowledge application at the
workplace.
In summary, the Malaysian government has acknowledged the importance of IT
in meeting future challenges. Thus, IT projects have high strategic expectations
associated with them in the context of a developing economy such as Malaysia. This
research can make an effective contribution to our understanding of the best way to plan
for successful IT projects. This is a broader contribution that extends beyond the
Malaysian context. This study should benefit both scholars and practitioners regarding
methods for reducing prediction errors during project planning, and should also apply
beyond the HRIS context to project planning generally. A literature search reveals
limited empirical studies on the issue of judgmental biases in project management, and
none to my knowledge has been conducted on HRIS projects.
1.2 Scope and Aim of Research
The main focus of this study is twofold: first, to examine and compare two
theories of the planning fallacy - the inside / outside view and the unpacking approach,
and second, to extend the application of planning fallacy explanations to real projects in
organizations involving HRIS projects. Specifically, I aim to identify which approaches
are best utilized to reduce the planning fallacy, and how these approaches affect the
7
success of the project in terms of accuracy of predictions of project duration and
outcome.
The study was conducted in two phases using two different research designs.
The first study involved a longitudinal experiment with 162 student group projects
(involving 772 student participants). The second study involved a survey of 45 HRIS
projects in place in organizations.
The first study was experimental because this is the first empirical study to
examine and compare the effects of the inside / outside view and the unpacking
approach to the planning fallacy in a single study. The first study also compares these
theories for the first time on predictions other than time to completion of a project,
specifically on prediction of project outcome, on confidence in predictions of project
completion time and outcome, and on predictions of affective reactions to project
outcome success and failure.
It was also important for the first study to demonstrate that the planning fallacy
occurs in a Malaysian context and culture, and to show that previous effects of the
inside / outside views and unpacking approaches could be replicated in that context. The
sample for the first study was recruited from the Faculty of Human and Social
Development, Universiti Utara Malaysia. Students’ predictions about their projects were
collected on a weekly basis over a two-month period.
For the second study, which was a cross-sectional study, data were collected
from firms located in the four states of Peninsular Malaysia: Perak, Penang, Kedah and
Perlis. In selecting the research sample, there were no restrictions on the types of
industry, but the firms had to have an HRIS in place, and the respondents had to be
involved directly with the HRIS project planning. The aim for the second study is to
explore which of the planning approaches are commonly used by planners during HRIS
project planning, and to examine how the approach used during the planning process
8
relates to the accuracy of predictions about the success or failure of the project in terms
of project completion time and outcome.
1.3 Organization of Chapters in the Thesis
This chapter is the first of the eleven chapters in this thesis. Chapter 2 gives a
general review of the literature on project management in general and IT/IS projects in
particular. The concepts of project success and failure, and how these can be measured
are also presented. Discussion in Chapter 2 continues with an explanation of the role
and challenges as well as issues in project planning. The chapter concludes with a
discussion on biases that often influence the predictions made during planning.
Based on the literature reviewed in Chapter 2, Chapter 3 proposes testing
theories of the planning fallacy using predictions made during project planning. Two
theories of the planning fallacy, the inside versus outside view (Lovallo & Kahneman,
2003) and the unpacking approach (Kruger & Evans, 2004) are explored, leading to the
development of the research hypotheses for Study 1. Several propositions are made
regarding the effect of the inside/outside view and the unpacking approach on
predictions of project completion time and outcome, on the confidence in prediction of
project completion time and outcome, and on the prediction of affective reactions to
project outcome success and failure.
Chapter 4 describes the method for Study 1, namely the research design and
procedure. The chapter reports the selection of the participants, the experimental
materials used, and the experimental process and data collection procedure. Chapter 4
concludes with a brief outline of the strategies and procedures that were used to analyze
data collected from the experiment.
Chapter 5 presents the results from testing the research hypotheses for Study 1.
There are reports of the multilevel hierarchical linear modeling (HLM) analyses and
9
graphical analyses. The hypotheses were tested using HLM, and their results are
summarized in a number of tables to facilitate interpretation.
Chapter 6 discusses the interpretation of the research findings from Study 1. The
findings from this study are compared to those found in past research reviewed in
Chapter 2. New findings are also discussed. Chapter 6 concludes with a discussion on
the limitations of Study 1.
Chapter 7 presents an introduction of Study 2. Literature on Human Resource
Information Systems (HRIS) is reviewed, explaining their definition and purpose,
potential value and usage in the organization. The challenges of planning for an HRIS
system are also discussed. The chapter concludes with the development of the research
hypotheses for Study 2.
Chapter 8 describes the research method for Study 2. The chapter reports the
selection of the respondents, sample types and size, the development of the
questionnaire for the research, and the survey process and data collection procedure.
Chapter 8 ends with a brief description of the strategies and procedures that were used
to analyze data collected from the survey.
Chapter 9 reports the results for Study 2. There are reports of the descriptive
statistical analysis and bivariate correlation analysis. The results are summarized in a
number of tables to facilitate interpretation.
Chapter 10 discusses the interpretation of the research findings for Study 2. The
findings are compared to those found in the past research reviewed in Chapters 2 and 7.
New findings are also discussed. The chapter ends with a discussion on limitations of
Study 2.
Chapter 11, the final chapter, presents the general discussion and conclusion of
the two studies and their implications for both researchers and practitioners. Chapter 11
10
concludes with a discussion of the benefits of the two studies and some suggestions for
future research.
11
CHAPTER 2
A REVIEW OF PROJECT SUCCESS, PROJECT PLANNING AND
PLANNING BIASES
2.1 Introduction
This chapter sets out issues related to project planning as presented and
discussed in the project management and psychology literatures. These issues are
reviewed to provide theoretical foundation for the research. The chapter begins by
describing the characteristics of projects in general and information systems (IS)
projects in particular. Then, how project success and failure are commonly measured in
the literature is discussed, followed by findings from past studies on the causes of IS
project failure. The chapter then reviews the role of planning as well as issues and
challenges surrounding the planning process. Common biases associated with planning
are also discussed. The chapter concludes by highlighting areas of research to be
addressed.
2.2 The Characteristics of Projects
A project is a unique endeavor - a special task that has not been done before
(Dvir, Raz, & Shenhar, 2003). Projects have three-dimensional objectives –
performance specification, the time schedule and the cost budget; they involve
resources; are accomplished within an organization (Rosenau, 1998); and are designed
to attain specific results (Katagiri & Turner, 2004). Gemimo, Reich and Sauer (2007)
view projects as complex, multidimensional phenomena with many factors interacting
in their execution. Even though all projects are in a sense unique, Perminova,
Gustafsson and Wikstrom (2008) argued that projects do share some similarities. All
projects have some restriction in time, costs and scope as well as demand for quality,
and involve a high level of uncertainty.
12
With regards to information systems (IS) projects, some authors believe that IS
projects possess certain characteristics that make them different from other projects and
increase the chances of their failure. IS by definition is a “combination of computer
hardware, communication technology and software designed to handle information
related to one or more business processes” (Flowers, 1996, p.3). Examples include
among others accounting systems, personnel systems, and sales order processing
systems.
Ewusi-Mensah (1997) has identified three main characteristics of IS projects,
and these include a requirement for an intense collaboration of three groups of
stakeholders, namely IS staff, end-users, and management; inherent risks and
uncertainties that are difficult to assess at the start of the project; and their capital
intensive nature as IS become increasingly critical to the survival and well-being of
companies.
Because the practical management of IS projects is difficult and challenging, and
I will argue that their outcomes have often been disappointing, it is important to
consider how IS project success and failure can be measured.
2.3 The Concepts of Information System (IS) Project Success and Failure
Projects are more successful when criteria for judging success are defined and
agreed upon by all the participants (Wateridge, 1995, 1998). If the project team and
major stakeholders fail to understand or have differing views on what success
constitutes, the project will end up with unclear scope, inappropriate measurement,
specification changes, delays and other issues (Hartman & Ashrafi, 2002).
But what does project success or failure mean? Though many have written about
IS success and failure, there is no generally agreed definition of these terms. This is
because success or failure is subjective in nature and the definition often depends on
who is asked. A project can be a success for one party and a disaster for another.
13
Many authors have suggested time, cost and quality outcome as success criteria
(M. C. Jones & Harrison, 1996; Rai & Al-Hindi, 2000; Robey, Smith, & Vijayasarathy,
1993). However, Wateridge (1998) disagreed that using these three criteria (being on
time, within budget and to technical specification) is enough to define success, even if
time, cost and specifications are important for IS projects. He argued that there are
instances where these three criteria have not been met but the project is considered to be
successful. Success and failure, he argued, cannot be seen as “black and white,” because
a project may not always be seen as completely successful or a complete failure as
different people may see the project outcome differently.
Based on his study on the meaning of project success with project managers,
sponsors, users, and system analysts, Wateridge found that all parties agreed that
budget, timescale, and user requirements were important in judging the success of IS
projects. However, users were more concerned about the system meeting their needs
and being happy with the system, while project managers viewed commercial success
and meeting quality as more important criteria. In other words, project managers focus
on the project process, such as meeting the time and budget constraints, while users
focus on the project product, such as delivering a system that makes them happy.
Looking from the failure perspective, Yeo (2002) considered an IS project as a
failure if the system is under-utilized or abandoned. Flowers (1996, p.4) regards an IS
project as a failure if any of the following situations occur: 1) when the system as a
whole does not operate as expected and its overall performance is sub-optimal; 2) if, on
implementation, it does not perform as originally intended or if it is so user-hostile that
it is rejected by users and is under-utilized; 3) the cost of IS development exceeds the
benefits the system may bring throughout its useful life; or 4) the information system
development is abandoned due to either problems with complexity of the system or the
management of the project.
14
Lyytinen and Hirschheim (1987) classified the notion of IS failure into four
major categories: correspondence failure, process failure, interaction failure and
expectation failure. An IS project is considered failed if the system design objectives are
not met (correspondence failure); the IS cannot be developed within the allocated
budget and / or time (process failure); the level of end-user usage of the information
system is low (interaction failure); or the system is unable to meet its stakeholders’
requirements, expectations or values (expectation failure).
To summarize, project success and failure are difficult to define as they mean
different things to different people. Project managers may view success as the survival
of their project whereas users are more concerned whether the system meets their need
or not. Therefore, how success and failure are defined and who evaluates them, affects
the final judgment of success or failure. In the following section, causes of project
failure are reviewed.
2.4 Past Research on Causes of Information System (IS) Project Failure
According to Pinto and Mantel (1990), it is difficult to gain a complete
understanding of what causes a project to fail. The difficulties are due to factors such as
the difficulty for people to agree on how project failure is defined; much of the research
conducted has been based on theoretical concepts or anecdote; causes of failure may
vary by type of project being studied; and the causes may depend on the stage of project
life cycle where different stages might have different causes of failure.
A review of studies on IS project failure reveals that failure could be caused by a
combination of factors such as unrealistic expectations; lack of resources; uncooperative
customers; weak management of contractors (Brown & Jones, 1998); poor risk
management (Jiang & Klein, 1999; McFarlan, 1981); lack of user involvement; poor
organizational communication and information sharing (Peterson & Kim, 2003); lack of
general agreement on what the new system’s goals and objectives should be in order to
15
satisfy the organization’s requirements; a weak or problematic project team which is
caused by lack of leadership and active interaction among all the three IS project groups
– IS staff, end-users, and management; poor project management where there is lack of
a measurement system to measure progress and to identify risks; lack of experience and
knowledge of the relevant application; lack of senior management involvement where
there is no active participation in monitoring progress and in making decisions at critical
stages; and escalation of project cost and time to completion (Ewusi-Mensah, 1997;
Ewusi-Mensah & Przasnyski, 1991, 1994).
Though the causes for IS project failure portrayed in the literature are
multidimensional issues, Murray (2001) argued that many of the causes of project
failure are universal with the majority of the causes generally being similar from project
to project. The similarity is due to the nature of the project itself. Sharing a similar view,
Raymond and Bergeron (2008) also believe that all projects share a number of things in
common: all need to be managed, where they need to be planned, staffed, organized,
monitored, controlled and evaluated. In fact, planning has been mentioned repeatedly in
studies as being critical for project success (Aladwani, 2002; Morgenshtern et al., 2007;
Pinto & Prescott, 1990; Yeo, 2002).
Pinto and Prescott (1990) argued that a variety of project critical success factors
studied in the literature often fall into two distinct subgroups: one is related to initial
project planning and the other is concerned with subsequent tactical operationalization.
Project mission, top management support, schedules and client consultation for
instance, are all consistent with the role of planning whereas personnel, technical tasks,
client acceptance, monitoring and feedback, communication and trouble-shooting are
concerned with project development and operationalization once initial plans have been
determined. Based on their survey of 408 project managers, Pinto and Prescott found
16
that planning factors were of great relative importance for project success and continue
to drive tactics throughout the project life cycle.
The impact of planning on project success has also been illustrated in other
studies. Yeo (2002) found that a significant proportion of problems faced in information
system projects are related to project planning issues. Aladwani (2002) also found that
planning is a major contributor to IT project success, in that project planning is a
significant mediator of the project uncertainty-success link. Dvir, Raz and Shenhar
(2003) discovered that the amount of effort spent in defining the project’s goals and
functional requirements has a significant positive relationship with project success.
Planning at a detailed level (shorter activities, smaller tasks) has also been found to
contribute to the accuracy of estimates and reduces the size of estimation errors
(Morgenshtern et al., 2007).
In summary, planning is important in ensuring project success as numerous
decisions are made at this stage. Task duration, types and number of activities needed to
be carried out, and the amount of resources and budget are all determined and estimated
during the initiation and planning stage of the project. These estimations then provide a
basis for further planning, control and decision making. In the following section, the
importance and role of planning in projects are explained.
2.5 The Role of Planning
Planning is “the process of thinking through and making explicit the objectives,
goals, and strategies necessary to bring the project through its life cycle to a successful
termination when the project’s product, service or process takes its rightful place in the
execution of project owner strategies” (Cleland & Ireland, 2002, p.310).It is a rational
determination of how a project can be initiated, sustained and terminated.
In the project management literature, planning has been considered as an
essential element that determines the success or failure of a project. Although planning
17
does not guarantee project success and some believe that forward planning may
sometimes reduce the accuracy of time prediction (Buehler, Griffin, & MacDonald,
1997), lack of planning will probably guarantee failure (Dvir et al., 2003). As
highlighted in most project management literature, plans are useful, even necessary
because the complete absence of plans would make project completion impossible.
The main purpose of project planning is to reduce uncertainty (Dvir et al., 2003),
and to ensure that projects are delivered on time, within budget and to the quality
expected by the customer (Yeates, 1991). A plan is needed to coordinate and
communicate project tasks, to satisfy requirements imposed by others such as customers
or top management, to provide a basis for monitoring activity, and to help avoid
problems (Rosenau, 1998). Plans also help “to establish a set of directions in sufficient
detail to tell the project team what exactly must be done, when it must be done, and
what resources to use in order to produce the deliverables of the project successfully”
(Meredith & Mantel, 2006, p.235).
Because project planning is intended to determine whether a project is able to
accomplish the performance specifications on or before the time limit and within
budget, a detailed plan usually contains all the tasks to be done, the resources needed,
the methods to be followed in the project and its management and control, the tools to
be used in support of the methods, the work schedule, the budget, and an identification
of potential risks and contingencies (Lientz & Rea, 1998).
Though planning is considered an important element that determines the success
or failure of the project, it is also one of the most challenging activities. What makes
planning a challenging process is discussed next.
2.6 The Planning Process: Issues and Challenges
Planning processes “define and refine objectives and select the best of the
alternative courses of action to attain the objective that the project was undertaken to
18
address” (Zwikael & Sadeh, 2007, p.756). Maylor (2005) has identified four main
stages of planning processes namely identifying the elements of activities, determining
the sequence of the activities, estimating time and resources, and presenting the plan in
an understandable format. To have a good likelihood of survival, all phases must be
carefully planned out at the beginning of the project. This is because decisions made at
the initiation and planning stages are said to impact the organization more compared to
decisions at other times during the project management process (McCray, Purvis, &
McCray, 2002).
Because planning deals with the uncertainty of the project, Maylor (2005)
argued that estimation is the key part of planning. If estimation is considered to be the
important element in planning, perhaps the most common project-related problems arise
due to incorrect estimations for example, of the amount of effort that must go into the
project. Problems such as project overrun, unrealistic expectations, underestimation of
the project’s scope, and hardware and software budget short falls, which have often
been highlighted in the literature, may result from poor estimation.
The complexity and scale of the project has been found to be related to the
estimation error. In a study conducted by Morgenshtern, Raz and Dvir (2007), it was
found that more complex projects with higher uncertainty, as indicated by the project
size (duration, effort, and number of potential users), the complexity of the application
and the level of innovativeness needed, are more likely to entail larger error in
estimation of duration and effort.
Some researchers believe that the complexity of projects has made the intuitive
process of decision making more attractive rather than the rational (Bukszar, 1988;
McCray et al., 2002; McFarlan, 1981; Purvis, McCray, & Roberts, 2004; White &
Fortune, 2002). In a survey of 118 project managers and team members who were
directly and actively involved in IT projects, Purvis, McCray and Roberts (2004)
19
discovered that no models or formal methods were mentioned when constructing
estimates for project cost or durations. In fact, respondents did not deny using intuition
for critical decision making. Whilst many would expect less experienced managers to
utilize intuitive processes, Leybourne and Sadler-Smith (2006) found that more
experienced managers use intuition and improvisation more than do less experienced
managers.
The complexity and mixed success of formal planning tools may also explain
why people prefer the intuitive process. White and Fortune (2002) found only small
numbers of project management methods, tools and techniques are used, and
respondents reported drawbacks to the methods, tools and techniques they had used. In
another study, Raymond and Bergeron (2008) found that using formal techniques like a
project management information system (PMIS) does not lead to greater impacts on
project performance and the PMIS itself has no direct influence upon project success.
These empirical findings suggest that judgment under uncertainty continues to
play an essential role in project decisions especially in predicting events such as the
success of a project. However, judgment can be subject to biases, which may underlie
errors in predictions made during planning. The kinds of biases that have been found to
occur in planning processes are discussed next.
2.7 Planning Under Uncertainty
Though project planners realize that projects are risky undertakings that may not
always end as planned and have the tendency to suffer unexpected outcomes, many are
still overly optimistic in their predictions of the success of their projects. Evidence of
optimistic prediction bias in planning has been observed in projects from everyday
events such as completing major school assignments (Buehler et al., 1997; Buehler et
al., 1994; Newby-Clark, Ross, Buehler, Koehler, & Griffin, 2000) to large scale
20
industrial and commercial projects like the completion of hydroelectric dams and public
transportation systems (Hall, 1980; Schnaars, 1989).
The evidence for unrealistic optimism is considerable. People are found to
underestimate how long it will take them to accomplish tasks and projects (Buehler et
al., 1997; Buehler et al., 1994; Buehler, Griffin, & Ross, 2002; Byram, 1997; Newby-
Clark et al., 2000), overestimate the impact of future events on their future feelings
(Lam, McFarland, Ross, & Cheung, 2005), and overestimate how likely they are to
experience a wide variety of positive life events (Perloff & Fetzer, 1986; Weinstein,
1980). There is also evidence that people tend to shift from optimism in their personal
predictions across time relative to the event being predicted. Most people tend to be
very optimistic in the beginning but as the outcome draws near, they tend to abandon
their optimism by displaying realism or even pessimism (Gilovich, Kerr, & Mecdvec,
1993; Liberman & Trope, 1998; Shepperd, Findley-Klein, Kwavnick, Walker, & Perez,
2000; Shepperd, Grace, Cole, & Klein, 2005).
According to researchers, these distortions of reality occur because of errors in
the way people process information (Buehler & Griffin, 2003; Buehler et al., 1994;
Weinstein, 1980). Specifically, the lack of certain information needed to make accurate
future predictions tends to introduce this error. Human ability to process the information
is also found to be limited and bounded by various biases. These combinations of
information search and processing biases introduce significant error to human judgment.
Thus, predictions can be characterized by two general shortcomings: people tend to
make overly optimistic predictions, and people tend to be overly confident in their
predictions (Dunning, 2007).
In the next sections, several biases that relate to specific predictions and their
causes are discussed.
21
2.7.1 Biases in Completion Time Predictions
To plan and schedule a project, planners must predict the time needed to perform
the project-related activities. However, more often than not, planners tend to make
predictions that are overly optimistic, claiming that they need less time to complete
tasks than actually turns out to be the case. Even when they are aware that their past
projects took a longer time to finish, they still believe their current project will finish as
planned. This specific form of optimistic bias is known as the planning fallacy. The
Planning fallacy is where people stick to an optimistic completion prediction even while
knowing that similar projects have run late in the past (Buehler et al., 1994). Evidence
of the planning fallacy has been observed in many studies involving a wide variety of
tasks such as completing income tax forms, school assignments, and holiday shopping,
and in each case, a longer time was taken than initially predicted (e.g., Buehler et al.,
1997; Buehler et al., 1994).
One explanation that has been offered for the occurrence of the planning fallacy
is that people tend to focus on the specific task at hand and regard it as a unique event,
disassociated from previous similar tasks (Buehler & Griffin, 2003; Buehler et al., 1997;
Buehler et al., 1994, 2002). Lovallo and Kahneman (2003) note that in 1979 Kahneman
and Tversky suggested two types of information that people can use to predict task
completion time, singular information or distributional information. Singular
information, also termed the inside perspective or the case-based view, is information
related to the specific task at hand whereas distributional information, or the outside
perspective or the class-based view, is information about previous similar tasks.
Neglecting distributional information or the outside perspective when predicting task
completion time has been suggested as a possible cause of the planning fallacy, because
prediction that is based on the inside view, a view that portrays the progression of a
22
specific scenario from the present to the future, could lead people to neglect the vast
number of ways in which their plan may go wrong.
Lovallo and Kahneman (2003) further argued that if individuals view their task
in a broader context that includes relevant past experiences, the experiences of others
and background events that may influence progress, a more realistic prediction can be
made. This is because the outside, distributional, or class-based view focuses on the set
of “comparable instances and produces estimates that are less extremely optimistic than
the singular estimates” (Griffin & Buehler, 2005, p. 758). In other words, these
researchers have claimed that a more realistic prediction can be made if individuals
think more about past experiences similar to the event at hand, that is, if they make
more connection between past experiences and possible future experiences. The general
difference between these two approaches is then “whether individuals treat the target
task as a unique case or as an instance of a category of similar problems” (Buehler et al.,
2002, p. 253).
There are a number of findings that support this inside / outside explanation of
the planning fallacy. When people are encouraged to consider past similar experiences,
they tend to become more accurate in their prediction and the planning fallacy is
reduced (Buehler et al., 1994, 2002). According to Buehler and Griffin (2003) a detailed
plan such as a planner’s concrete step-by-step plan for implementation actually
heightens the tendency to make overly optimistic forecasts and projections. This is
because planning processes are tied more directly to stated predictions than to actual
behavior as the planner has greater control over a predicted completion time than over
actual completion time. These findings also indicate that people make overly optimistic
predictions, partly because they overweight their specific plans for a given future
project and underweight more general distributional information. For these reasons,
Buehler and Griffin (2003), like Lovallo and Kahneman (2003) suggested that
23
individuals who are seeking a more realistic forecast are advised to adopt an approach to
prediction that involves considering the distribution of completion times for related
projects or the views of natural, outside observers and give less weight to their own
plans.
Though the inside / outside view suggested that the planning fallacy occurs
because people neglect past relevant experience, Roy, Christenfeld and McKenzie
(2005) believe that underestimation is not due to people neglecting or ignoring accurate
past experiences, but to inaccurate recall, or what they termed “memory bias.” Memory
bias is based on the assumption that “people use memories of duration when making
predictions of future duration and that biases in these memories cause biases in
prediction” (Roy et al., 2005, p. 749). In other words, the incorrect prediction of future
duration is caused by the incorrect memories of past durations. They argue that people
rarely check the precise starting and ending time for particular tasks, and therefore
people base predictions on memories of past estimated duration instead of past actual
duration. Because people forget some events in the past, they remember previous tasks
as taking less time than they actually did, and so they tend to underestimate similar
future durations. Griffin and Buehler (2005, p.759) however, disagreed with Roy,
Christenfeld and McKenzie’s proposition. They argued that general beliefs about past
projects do not just depend on memory, but could be from observations of other
people’s experiences or from general knowledge.
On the other hand, Thomas, Newstead and Handley (2003) and Thomas and
Handley (2008) found that people tend to misestimate future task duration even when
they consider information about their previous task performance, and suggested an
anchoring and adjustment explanation. Anchoring has been known to be one of the
strongest and most prevalent of cognitive biases. Anchoring happens when people make
their best guess, and this anchors their thinking, which prevents them from imagining
24
circumstances that differ substantially from it. In their studies, they found that using
previous task duration as an anchor for prediction lead to misestimating of future task
duration: underestimation occurred with the shorter duration (low) anchor and
overestimation occurred with the longer (high) anchor (Thomas & Handley, 2008;
Thomas et al., 2003).
Another explanation for the occurrence of the planning fallacy which seems
conflict with the inside / outside view was offered by Kruger and Evans (2004). Kruger
and Evans (2004) argue that taking an inside perspective, which involves thinking more
about the specific task at hand, does not always result in a planning fallacy. Indeed, they
found that prompting individuals to think more about the specific task at hand
sometimes caused the planning fallacy to be reduced, not increased. Kruger and Evans
(2004) believe that the reason why people are unable to make accurate predictions
regarding task completion time is because they do not unpack those tasks into their
various subcomponents. The activity of enumerating and breaking down tasks into their
various subcomponents has been termed “unpacking” (Tversky & Koehler, 1994). The
idea of enumerating is to make the subcomponents of the task salient when making
estimations instead of looking at the task as a whole. By doing that, it should increase
the overall completion time estimate and, as a result, decrease the planning fallacy.
Kruger and Evans (2004) found that when individuals are induced to unpack
multifaceted tasks like preparing for a holiday, shopping, getting ready for a date,
formatting a document or preparing food, completion time estimates increased and as a
result, the planning fallacy was decreased.
Kruger and Evans (2004) based their explanations on the logic of support theory
(Rottenstreich & Tversky, 1997; Tversky & Koehler, 1994). Support theory is a
descriptive theory of subjective probability where the subjective probability of an event
depends on the explicitness of its description (Tversky & Koehler, 1994). “Like the
25
measured length of a coastline, which increases as a map becomes more detailed, the
perceived likelihood of an event increases as its description becomes more specific”
(Tversky & Koehler, 1994, p. 565). Rather than attaching probabilities to the event,
support theory attaches subjective probability to the event’s descriptions called
hypotheses. Each hypothesis has a support value, and to increase this support, the
hypothesis should be unpacked into its exclusive components. For example, when
people are asked to unpack a multifaceted category like “death due to natural causes”
into its subcomponents (e.g., death due to heart attack, cancer or other related natural
cause) the subjective probability of the event increases.
Like Kruger and Evans, Wilson, Wheatley, Meyers, Gilbert and Axsom (2000)
suggest that it is the way people think about the focal event that causes the planning
fallacy, not a failure to think about past experiences. According to this focalism
hypothesis, thinking more about non-focal events, even ones that will occure in the
future, such as other assignments they have that week, or how busy their life will be that
could impede their progress, should result in more realistic time completion predictions.
They believe that people could correct their prediction in this manner even if they do
not have any past experience with the task.
It is important to note that Kruger and Evans’s (2004) notion of enumerating
activities is in accordance with prescriptions on project planning in the project
management literature. In project planning, work breakdown structure (WBS) is used to
divide the overall project into work elements that represent singular work units,
assigned either to the organization or to an outside agency, such as a contractor (Cleland
& Ireland, 2002). Work breakdown structure is also known as “chunking” or
“unbundling” (Maylor, 2005). According to Maylor (2005), projects can be broken
down in several ways. The first level of breakdown is known as activity breakdown,
where activities to be undertaken are broken down into major groups. Functional
26
breakdown is the second form of breakdown, where the project is divided into its
functional areas. Another type of breakdown is known as physical grouping, where in
the example of IT projects, the hardware and the software issues are split. Apart from
breaking down tasks, Smith (2007) has introduced the concept of a “risk register”, a
schedule that lists all the hazards faced by a project, whatever their form, when dealing
with uncertainties.
The underlying philosophy of the work breakdown structure is to divide the
project into work packages, the smallest manageable work elements of the project, that
are assignable, and for which accountability can be expected. Also, it is a way of
dividing a project to ensure the completeness, compatibility, and continuity of all work
that is required for successful completion of the project. The work breakdown structure
helps for better planning as it provides necessary information to “assign responsibilities
within the team, allocate tasks to specific resources, estimate time against tasks to help
understand the overall timing within the project, create a budget for the project, provide
a structure for monitoring and reporting progress against budgets, predict spending to
the end of the project / milestones, and recognize and understand problems within the
plan” (R. Jones, 2007, p.89).
Even though unpacking seems similar to the idea of decomposition in judgment,
Kruger and Evans (2004) noted that unpacking and decomposition are different in terms
of their operationalization, underlying theory and predictions. Decomposition involves
breaking a category down literally and asking participants to make separate estimates
for each of several components of the category, which are then aggregated. Unpacking
involves breaking down the task only symbolically to change the way the task is
described or represented. Unlike decomposition, which involves combination of
multiple judgments, unpacking only involves a single prediction judgment.
Decomposition infers that breaking down a category into simple components ought to
27
reduce bias because it should be easier to make accurate predictions for smaller, simpler
components than for a large, complex aggregate. It is not based on any explanation for
the direction of prediction bias. In contrast, unpacking is based on support theory, and
infers only an increase in overall estimates, which may or may not translate into reduced
bias.
There is other evidence supporting the unpacking approach to reducing bias.
Sanna and Schwarz (2004) studied the impact of a thought listing task on the planning
fallacy. When people are asked to consider factors that could lead to failure or success,
their predictions depend on the number of failure or success components they are asked
to generate. For example, they argued that when people are asked to generate three ways
to succeed, they find this easy, and infer that success is therefore a likely outcome. But
when they are asked to think of 12 ways to succeed they find this difficult to bring to
mind and infer that success is unlikely. They believed that the ability to recall events
depends on the subjective experience of ease where subjective experience can override
the amount of information that comes to mind. The interaction of thought content and
accessibility experiences (how easy or difficult it is for thoughts to come to mind) also
influences task completion time predictions.
In their study, Sanna and Schwarz (2004) found that students who did not list
their thoughts were overly optimistic regarding their study completion time before an
exam. However, when students were asked to list their thoughts implying success (3
success thoughts or 12 failure thoughts) 28 days before the exam, the planning fallacy
did not increase but when students were asked to list their thoughts implying failure (3
failure thoughts or 12 success thoughts) at the same time, the planning fallacy was
significantly reduced, even though it did not completely eliminate the fallacy.
Thinking along a similar line, Russo and Schoemaker (1992) also suggested that
people underestimate future project duration because they have difficulty imagining all
28
the ways that events could unfold. Those scenarios that do come to mind are regarded as
most likely and therefore, people tend to assume that everything will go accordingly.
Similarly, Byram (1997) argued that people tend to underestimate because of the
difficulty of thinking of things that may go wrong during task performance. This is
known as availability bias, and for time prediction, the availability bias may account for
underestimation if people do not consider things that can go wrong.
In summary, the problem of overly optimistic prediction in task completion time
is often associated with the planning fallacy. I will focus on two conflicting
explanations of how to reduce the planning fallacy. The inside / outside view suggests
that the possible cause for the planning fallacy is because people focus on the specific
task at hand and neglect past distributional information. However, planning in real
project often involves planning in great details and utilizing the workbreakdown
structure which seems similar to focusing on the project at hand and the unpacking
approach. Both of these explanations have empirical support from the literature for
predictions of task completion time.
2.7.2 Biases in Outcome Predictions
Optimism is not limited to predictions of task completion time described by the
planning fallacy. Studies have shown that people in general display “optimistic bias” in
their predictions about future events and outcomes, believing that they are more likely
than others to experience positive events and less likely than others to experience
negative events (Shepperd et al., 2000; Weinstein, 1980). Weinstein (1980) found that
when student participants were asked to rate their likelihood of experiencing various
positive and negative life events, the average likelihood of responses given by
participants tended to be above average for positive events but below average for
negative events. People were also found optimistically biased for a wide variety of
events such as lung cancer (Lee, 1989; McKenna, Warburton, & Winwood, 1993),
29
unplanned pregnancy (Burger & Burns, 1988; Whitley & Hern, 1991), criminal
victimization (Perloff & Fetzer, 1986), illness (Weinstein, 1980, 1982, 1987), and
automobile accidents (McKenna et al., 1993). The optimism in personal predictions
may arise from errors in the way people process information or may be motivated by
self-enhancement needs. Several explanations have been advanced to account for the
optimistic biases in future outcome predictions.
Some authors regard optimistic bias as evidence of wishful thinking and the
illusion of control (Makridakis & Wheelwright, 1989; Weinstein, 1980). In wishful
thinking bias, people tend to exaggerate the likelihood of the outcomes that they prefer.
Weinstein (1980) believed that exaggerating the likelihood of the preferred outcomes
tends to produce positive affect, whereas underestimating the likelihood of positive
outcomes would produce negative affect. Therefore, Weinstein (1980) assumed that the
stronger the affect, the stronger the distortion of reality.
The illusion of control bias is the tendency for individuals to believe that they
are able to control or at least influence outcomes even when they clearly cannot. In most
cases, people fail to regard the possibility of uncontrollable occurrences that may hinder
their progress toward a goal. Instead of focusing on the uncertainty of the events, most
individuals tend to focus on the aspects that they believed they can control, and thus,
overestimate their own capabilities of achieving those outcomes. According to
Weinstein (1980), if individuals perceive an event can be controllable, they tend to
believe that there are steps that they could take to increase the likelihood of a desirable
outcome. Since it is easier to bring to mind their own action than the actions of others,
people make assumptions that the desired outcomes are more likely to happen to them
than to other people. Apart from focusing on the aspect that they can control, observing
an early sequence of success can also lead people to believe they have some control
over outcomes (Langer, 1975). Individuals are also found to regard risk as ca hallenge
30
to be met where by trying harder, people may believe they have more control than is
realistic over the tasks they are trying to predict (Lovallo & Kahneman, 2003).
Optimistic outcome prediction can also be explained by the representativeness
heuristic (Kahneman & Tversky, 1972; Tversky & Kahneman, 1974).
Representativeness refers to the process of judging the probability that an individual
case fits into a particular category by evaluating the degree to which the individual case
is representative of, or similar to the category, but ignoring the base rate of category
membership. For many events, people may have a stereotyped conception about the
kind of person to whom this event typically happens (Weinstein, 1980). The
representativeness heuristic suggests that if the persons do not see themselves fitting
with the stereotype, then they will conclude that the event would not happen to them.
Weinstein further argued that if stereotypes are defensive, the image of a person who
experiences positive events would overemphasize a person’s own characteristics, and
this tendency would exaggerate optimistic biases.
Another explanation is that people’s thoughts about the future are dominated by
goals and plans and rarely include considerations of failure or unpleasant episodes.
Newby-Clark and Ross (2003) found in their study that participants thoughts of the
future often included desirable goals such as graduation, gainful employment, marriage,
children, and exotic travel. Participants took longer to record negative future outcomes
than a pleasant future scenario, and participants were slower to generate negative future
events than to generate positive future events.
Optimistic outcome prediction is not just caused by having a thought about the
future; past personal experience may influence beliefs about the chances of
experiencing an event. Personal experience provides the material for individuals to
recall past occurrences of similar events and to imagine situations in which such events
31
could occur, leading to greater perceived probability through the mechanism of
“availability” (Tversky & Kahneman, 1973, 1974).
Though there is evidence of people being optimistically biased towards future
outcomes, there is also evidence showing that people tend to be very optimistic in the
beginning, but as the outcome draws near they tend to abandon their optimism by
displaying realism or even pessimism (Liberman & Trope, 1998; Shepperd et al., 2000;
Shepperd et al., 2005). Liberman and Trope (1998) found that individuals tend to be
more optimistic about distant future than near future outcomes.
There are several explanations for this shift from optimism suggested in the
literature. Optimism is believed to reduce as the outcome draws near because people
gain new information that may help them to see more clearly what is likely to happen
(Shepperd et al., 2005). In fact, some evidence suggests that people will abandon their
optimism, displaying realism or even pessimism as defensive mechanism, if they
anticipate that information or feedback might soon challenge their optimistic stance.
People are also found to shift away from optimism in response to a shift in their
understanding of the event from abstract to concrete (Liberman & Trope, 1998).
Abstract features are more likely to be used when explaining distant future than near
future events, because in many situations relevant information regarding the distant
future is unavailable or uncertain. As the outcome draws near, this optimism will soon
change when more concrete information has been gathered.
The role of affective information processing has also been suggested to be
responsible for this transition from optimism to pessimism in predictions. Schwarz and
Clore (1983) found that people used their momentary affective states as a source of
information when making various kinds of judgments. However, it was found that only
unpleasant affective states motivate people to seek explanations as opposed to
individuals in pleasant affective states. This is because the unpleasant affective state is
32
often associated with the deviation from the individual’s usual feelings and thus, more
attempts are generated to seek reasonable explanations. As highlighted by Gilovich,
Kerr and Medvec (1993), people might regard their level of anxiety about an impending
outcome as an important source of information about the outcomes’ status. That is, if
people feel anxious as the outcome draws near, it must be because they will do poorly.
Apart from gaining new information, Shepperd et al. (2005) proposed that
another category of explanation for this shift from optimism is because people brace for
a possible undesired outcome. People tend to display realism or even pessimism as a
way to ready themselves for unpleasant surprises or undesired outcomes and most
likely, to avoid the disappointment (Shepperd et al., 2000; Shepperd et al., 2005). Since
people are normally trying to avoid unpleasant feelings associated with unexpected
negative outcomes, they are motivated to make pessimistic predictions regarding future
outcomes as those outcomes draw nearer.
In conclusion, people tend to render predictions that are overly optimistic:
underestimate the amount of time needed to complete a task or over predict the
occurrence of positive events and under predict negative ones. In the following section,
people’s confidence in their predictions is discussed.
2.7.3 Confidence in Predictions
The terms confidence and optimism are both used in the literature. Both
optimism and confidence have been used to refer to a prediction of a successful
outcome. For example, Gilovich, Kerr and Medvec (1993) and Sanna and Schwarz
(2004) both use the concept of confidence as belief in a successful outcome, whereas
Shepperd, Findley-Klein, Kwavnick, Walker, & Perez (2000) refer to this as optimism.
In this study, the term confidence refers to the strength of belief or degree of conviction
in a prediction, regardless of the content of the prediction. Confidence indicates the
33
strength of belief that the prediction is correct. The term optimism is used to indicate the
level of success participants predict, that is, the content of the prediction.
Research has shown that people do not just make predictions that are overly
optimistic; they tend to be overconfident in the accuracy of their beliefs. When
participants were asked about how certain they were that they will finish by the time
that they had predicted, Buehler, Griffin and Ross (1994) found that participants were
quite confident that they would meet their predictions. Based on the scale of 0% (not at
all certain) to 100% (completely certain), participants rated their confidence in meeting
a predicted time and date at 74% for an academic project and 69.9% for non-academic
tasks. For each case, approximately 40% of the participants actually finished by the
predicted time.
Russo and Schoemaker (1992) explored the sources of optimism, referring to it
as overconfidence. They proposed four cognitive causes of overconfidence: availability,
anchoring, confirmation bias and hindsight. They believed that most people tend to be
optimistic in predictions, because they have difficulties in imagining all the ways events
could unfold. Normally, people tend to rely on data and scenarios that come easily to
hand and mind (availability bias). Those scenarios that do come to mind are regarded as
most likely and therefore, they tend to assume that everything will go accordingly.
Second, when people make their best guess, they tend to anchor their thinking, which
prevents them from imagining circumstances that differ substantially from it (anchoring
bias). Anchoring bias is persistent in many aspects of our daily lives, and is known to be
one of the strongest and most prevalent of cognitive biases. Third, people tend to lean
toward one perspective when making their predictions (confirmation bias) and seek
support for their initial view rather than looking for disconfirming evidence. Fourth,
people always believe that the world is more predictable than it really is (hindsight
bias), due to the failure to appreciate the full uncertainty that existed at the time. They
34
are rarely surprised by events that have already happened, even though they may have
had considerable difficulty in predicting the same events.
People are not always optimismistic, their optimism may change over time.
Sanna (1999) offers an explanation of temporal changes in optimism (Sanna used the
term confidence) based on the reciprocal influences between mental simulation and
affect. He proposed that affect can serve as both cause and consequence of simulations.
One reason why he believes people are more optimistic in success when they view a
performance from a distal perspective than from a proximal perspective is because
upward simulation (or upward counterfactual, simulations that are better than reality)
may increase as performance gets proximal. Since upward simulation is preparative,
people would likely have preparative thoughts as a performance gets closer. In other
words, as performance approaches, a person may think more about everything that still
requires doing, and this may decrease their confidence. Decrease in optimism may also
result from increased negative mood or anxiety. People may try to buffer themselves
from failure as performance approaches and upward simulation can generate negative
affect. Sanna (1999) found that change in optimism was related to both mental
simulation and affect, either increase in upward simulations, increase in negative affect
and anxiety, or both (given the reciprocal relation between simulation and affect).
In another study, Sanna and Schwarz (2004) examined the impact of thought
listing on change in optimism. They altered people’s change in optimism by
manipulating the number of failure or success components that they asked people to
generate. They found that asking students to generate thoughts implying success (3
success thoughts, 12 failure thoughts) 28 days before exam eliminated the otherwise
observed low optimism for proximal outcome and asking students to generate thoughts
implying failure (3 failure thoughts, 12 success thoughts) eliminated the otherwise
observed high optimism for distal outcomes.
35
2.7.4 Biases in Affective Predictions
People frequently think about how they will feel when particular events take
place. They may consider the kinds of emotions they will experience, the intensity of
their emotional reactions, and how long those feelings will last (Buehler, McFarland,
Spyropoulos, & Lam, 2007; Gilbert & Wilson, 2000; Wilson et al., 2000). Such
affective predictions are important because people base many decisions on them.
Decisions about whether to pursue a project or not for example, are based at least in
part, on predictions about how these decisions will make one feel. People may
reasonably choose events that they predict will produce positive feelings and avoid
those that might distress them.
Generally, people know exactly what will make them feel happy or sad. But,
knowing what one wants is not the same as knowing how much one will like it once it
happens. In fact, past studies on affective predictions (e.g., Lam et al., 2005; Sanna &
Schwarz, 2004) have shown that people often mispredict their own future feelings and
overestimate the impact of future events on their feelings. People always think that they
will be happier after a positive outcome and will be sadder after a negative outcome
even when the future outcome is similar to ones they have experienced in the past. But,
the results turn out not as they initially expected them to be.
The tendency for people to overestimate the power and persistence of emotional
reactions to events has been termed impact bias, which is one of the most prevalent
biases in affective predictions (Wilson & Gilbert, 2003). Impact bias has been observed
for affective prediction concerning a wide range of positive and negative events and
outcomes such as wining or losing sporting events, learning about exam grades,
receiving positive or negative feedback over an aptitude test, being granted or denied
tenure, and living in a pleasant or an unpleasant climate (Buehler & McFarland, 2001;
Gilbert, Pinel, Wilson, Blumberg, & Wheatley, 1998; Sanna & Schwarz, 2004; Schkade
36
& Kahneman, 1998; Van Dijk, 2009; Wilson, Meyers, & Gilbert, 2001; Wilson et al.,
2000). However, Finkenauer, Gallucci, Dijk and Pollman (2007) found that the bias is
more pronounced for negative affect than for positive affect. In their study, participants
highly overestimated their disappointment toward failing their driver’s license exam,
while they only slightly overestimated their happiness following success of the exam.
Focalism has been suggested to be one of the key sources of impact bias, at least
for positive events (Wilson et al., 2000). Focalism is where people focus intensely on
the event concerned and fail to consider other events that are likely to occur that could
also influence emotions. Because people are focusing too much on some events and too
little on other events, they fail to consider the consequences these other events might
bring about. Wilson, Wheatley, Meyers, Gilbert and Axsom (2000) found that when
college students were asked to consider a wider range of life events that would be
occurring at the same time as the event, they generated more moderate affective
predictions over an upcoming football game. Relatedly, Schkade and Kahneman (1998)
found that people incorrectly predicted that they would be happier living in California
than the Midwest when they focused on the salient differences in climate.
Another suggested cause of impact bias is related to the misconstrual problem in
predicting one’s reaction to events. While focalism involves neglecting other events that
will occur at the same time as the target event, construal involves imagining the target
event in a particular manner (Buehler & McFarland, 2001). Buehler and McFarland
(2001) argued that each of the future events can occur in many different ways regarding
the particular manner in which it unfolds, and each will evoke different emotional
reactions. Giving an example of getting an A in midterm, they argued that it can give
different meanings and consequences depending on the details of the situations and the
context in which it occurs.
37
According to Wilson and Gilbert (2003), to know how a future event will impact
one’s feelings one needs to bring to mind an accurate representation of that event. If the
person does not have experience with those events before, they need to construct a
representation of what the event is likely to entail. Because people often fail to
appreciate the fact that a future event may not occur exactly the way they imagine,
people are prone to errors in predicting how they will feel about those events.
Neglecting a set of past relevant experiences, that is, failing to adopt the outside
view, is also suggested as one of the reasons why people tend to exaggerate the
emotional impact of future events. In their study, Buehler and McFarland (2001) found
that participants who were asked to recall several of their past experiences with
Christmas generated more realistic positive affective predictions than those who focused
exclusively on the upcoming holidays.
The problems of mispredicted affective reactions to future events can also be
attributed to one’s memory of the events. If people have experienced the event before,
they can predict their feelings by recalling how they felt in the past. However, Robinson
and Clore (2002) note that emotional experiences are not stored in the memory in a
form that can be retrieved directly later. They further argued that because the details of
an experience fade over time, people depend more on their theories about how the event
will make them feel rather than on the actual experience. Poor recall and incorrect
theories have been said to lead to error in predicting how future events will affect one’s
feelings.
Accordingly, Morewedge, Gilbert and Wilson (2005) also believe that people
generally do not realize how, and how often, they think about the events that have
already happened, and therefore, mispredict their affective reactions to future events.
But, knowing how and how often one thinks about the event will not be enough if one
fails to imagine the event accurately. According to Morewedge et al. (2005), people
38
naturally depend on their memories of past events to generate their affective reactions
toward future events. However, people tend to remember more of the unusual events
than the more routine, everyday events. The tendency to remember the best and the
worst of times instead of the most typical of times has also been found to contribute to
impact bias. Morewedge, Gilbert and Wilson (2005) found that participants who were
asked to recall a single instance of an event, or to recall no events at all, made more
extreme predictions about the future than participants who were asked to recall several
events or to recall atypical events. However, the recall of events also involves the
subjective experience of ease of recall where subjective experience can override the
amount of information that comes to mind. Sanna and Schwarz (2004) found that the
impact bias was eliminated for reactions to failure when success is easy to recall and the
impact was also eliminated for reactions to success when failure is easy to recall.
2.8 Conclusion and Research Issues to be Addressed
The above literature review indicates that the process of planning and prediction
are important for any project. A considerable amount of money and effort is spent in
attempts to predict how long projects will take to complete and certainly, whether the
projects are possible to complete. However, the uncertain nature of projects often makes
planning, especially making predictions about the project’s future, a challenging
activity. Some researchers believe that the uncertain nature of project environments has
made intuitive decision making rather than rational decision unavoidable. But, intuitive
decision making is vulnerable to various temporal biases such as the planning fallacy,
confidence changes, and impact bias that may influence the accuracy of the predictions
made. Therefore, finding ways to neutralize these biases could generate significant
benefits for organizations.
Several researchers have suggested that the combination of information search
and processing biases tends to introduce significant error to human judgment which
39
often leads people to make overly optimistic predictions, and to be overly confident in
their predictions (Buehler & Griffin, 2003; Buehler et al., 1994; Dunning, 2007;
Weinstein, 1980). From the above discussions, Lovallo and Kahneman (2003) noted
that in 1979 Kahneman and Tversky have distinguished between two approaches to
intuitive predictions, which they label the inside and outside view. Focusing on the
outside view, that is, a broader context that includes relevant past experiences, the
experiences of others and background events, produces more accurate predictions
regarding task completion time than using the inside view, focusing only on the project
at hand to make the prediction Several prediction biases such as the tendency to make
overconfident and overly optimistic predictions of completion time can be understood
from the results of people’s tendency to favor the inside view, and underweight relevant
past experiences. In contrast, research on an approach termed enumerating or unpacking
has suggested that taking an inside perspective that specifically involves thinking more
about the task at hand and its components may also result in a reduction of the planning
fallacy (Kruger & Evans, 2004). If both the outside view and the unpacking approach
were able to reduce bias in completion time prediction, which of these methods would
produce a greater effect? A second issue is that the outside view and unpacking
approach have been used to explain the occurrence of the planning fallacy. Would their
prescriptions result in similar effects for other types of predictions like outcome and
affective reactions towards outcome success and failure, and on the confidence of those
predictions?
In this thesis I examine and compare the effects of the inside-outside and
unpacking approaches on task completion time prediction. There are practical reasons to
compare the effectiveness of these two methods for reducing the planning fallacy.
Detailed planning is unavoidable in project work. Indeed, the preparation of a complete
work breakdown structure is an essential part of project management (Cleland &
40
Ireland, 2002). If this detailed focus on the project at hand exacerbates the planning
fallacy, there is a conundrum that the more we know about a project, the worse our
predictions about it will be. In contrast, the enumerating approach of Kruger and Evans
(2004) accords with the work breakdown structure concept in project management. If
the enumerating or unpacking approach is effective in reducing the planning fallacy, it
could be utilized to improve prediction of project duration and outcomes.
The study was also designed to explore whether individuals exhibit an optimistic
bias when predicting a project’s outcome, and tested whether the explanations of the
planning fallacy can be applied to optimistic outcome predictions. Also, I examined
how the outside perspective and enumerating approach influence the confidence in
estimation and the prediction of affective reactions toward the success of the project’s
completion time and outcome.
In the next chapter, Chapter 3, hypotheses for Study 1 are proposed for project
completion time and outcome, for confidence in prediction and for prediction of
affective reactions towards outcome success or failure.
41
CHAPTER 3
HYPOTHESES - STUDY 1
3.1 Introduction
This chapter presents the twelve research hypotheses for Study 1, based on the
two dominant theoretical frameworks of the planning fallacy reviewed in Chapter 2.
The extension of these two theories to predictions other than time to completion of a
project, specifically on prediction of project outcome, confidence in prediction of
project completion time and outcome, and predictions of affective reactions to project
outcome success and failure are also considered and hypotheses are prepared.
3.2 Testing the Theories of the Planning Fallacy
Chapter 2 has reviewed two compelling, but conflicting explanations about the
occurrence of the planning fallacy. One line of research, the inside/outside view
proposes that neglecting distributional information or the outside perspective when
predicting task completion time is the cause of the planning fallacy, because prediction
that is based on the inside view, a view that portrays the progression of a specific
scenario from the present to the future, leads people to neglect the vast number of ways
in which their plan may go wrong. In contrast, the second line of research proposed that
taking an inside perspective that involves thinking more about the specific task at hand
does not always result in a planning fallacy, provided that those tasks are unpacked into
their various subcomponents.
There is an apparent contradiction between the inside / outside view and
unpacking approaches and the main aim of this study is to compare these as methods for
reducing the planning fallacy. These two theories have not been tested on the same task
before. It is possible that the conflicting results for these two explanations are due to the
different tasks with which they have been tested. Therefore, testing these two theories
42
on the same task allows for the outside perspective and the unpacking approach to be
compared. However, no hypothesis contrasting the outside perspective and the
unpacking approach was prepared in this study, as there was no basis on which to
hypothesize the existence or direction of differences between them, so the tests I
conduct to make comparisons between those approaches are exploratory. Hypothesis 1
was proposed to replicate the separate findings from previous studies on the inside /
outside perspective and the unpacking approach. It was also necessary to demonstrate
that the planning fallacy occurs in this research context, so that further hypotheses may
be tested.
H1.1a: People who make predictions regarding task completion time using the outside
perspective are more accurate than people who use the inside perspective.
H1.1b: People who make predictions regarding task completion time after unpacking
the task are more accurate than people who make a prediction for the task as a whole.
3.3 Theories of the Planning Fallacy and Outcome Prediction
Working from the two theoretical frameworks of the planning fallacy, I now
extend them to outcome prediction. First, the outside view suggests that outcome
predictions should be more accurate if individuals compare their chances of achieving
the predicted outcome with others, use their own past experiences as a basis to visualize
how the outcome would transpire or take into consideration the probability of the events
for the general population. In other words, gathering information from various sources
and viewing the future outcomes in a broader context should increase the chances of
making more accurate outcome predictions.
Second, support theory suggested that for an unpacked multifaceted task such as
a project, individuals should be able to make a more accurate outcome prediction
because the unpacking process changes the way the project is represented or described.
The process of enumerating helps people to better understand what they are supposed to
43
deliver in a project and thus, they should be able to make a more accurate outcome
prediction regarding whether they can deliver it or not.
Thus, the following hypotheses are proposed which are parallel with hypotheses
for prediction of completion time.
H1.2a: People who use the outside perspective are more accurate in their prediction of
project outcome success than people who use the inside perspective.
H1.2b: People who unpack the project components are more accurate in their prediction
of project outcome success than people who do not unpack.
3.4 Theories of the Planning Fallacy and Confidence in Predictions
Applying the theoretical analysis of the planning fallacy to the occurrence of
confidence changes, it is suggested that people who adopt an inside perspective or fail
to unpack the future event will demonstrate greater changes in their confidence. This is
because people who adopt the outside perspective should have more information in the
early stage of the prediction process than people who adopt the inside perspective, and
those who unpack the events early in the prediction process should also have more
information than those who do not. Therefore, the difference between the information
on hand early in the process and the information on hand later, as the outcome of event
draws near, will not be as great for these groups as for people who use the inside
approach or do not unpack. Their changes in confidence will also not be as great. Four
hypotheses are proposed to test the effect of inside / outside perspectives and unpacking
on confidence change in predictions of completion time and outcome success.
H1.3a: People who use the outside perspective will demonstrate smaller change in their
confidence of predicted completion time compared to those who use the inside
perspective.
H1.3b: People who unpack will demonstrate smaller change in their confidence of
predicted completion time compared to those who do not unpack.
44
H1.4a: People who use the outside perspective will demonstrate smaller change in their
confidence of predicted outcome compared to those who use the inside perspective.
H1.4b: People who unpack will demonstrate smaller change in their confidence of
predicted outcome as compared to those who do not unpack.
3.5 Theories of the Planning Fallacy and Prediction of Affective Reactions towards
Outcome Success and Failure
Research on the impact bias suggests that reasons for people to overestimate
their emotional reaction when making affective predictions can be attributed to people’s
failure to consider the consequences of other events that will influence their emotions
(Wilson & Gilbert, 2003; Wilson et al., 2000); the misconstrual problem that involves
imagining the target event in a particular manner, and neglecting a set of past
experiences (Buehler & McFarland, 2001) which seems to be consistent with the
explanation of inside / outside perspective on the planning fallacy.
Based on these explanations, I suggest that the reason why people tend to
overestimate the emotional reactions they will experience is because they frequently
adopt the inside perspective when making their affective predictions. Adopting the
inside perspective makes people prone to the focalism problem because the scenario-
based approach tends to lead people to neglect a vast majority of other events that might
influence their feelings. In contrast, if people adopt the outside perspective, a more
moderate affective prediction should be generated. This is because they have a set of
relevant past experiences as their reference, and taking consideration of how previous
events have influenced their feelings may actually help them to make a more accurate
prediction.
However, using the outside perspective can be cognitively demanding especially
when a number of different events must be evaluated within a short period of time,
making the process of connecting the past experiences and the specific prediction of
45
future events difficult. A person must first select an appropriate standard for
comparison, a similar past experience. In many cases, individuals find it difficult to
detect the similarities in their past experiences when there are various instances that
seem so different from each other. Besides, prediction by its very nature, focuses on the
future rather than the past and this may prevent individuals from looking backward
(Buehler et al., 2002).
Wilson, Wheatley and Meyers (2000) argued that people don’t have to think
about past experiences to correct their predictions of affective reactions, instead they
can think more about non-focal events, which refer to many other events that will
influence emotions. This may seem to contradict the inside/outside perspective, but
working with time to completion predictions, Kruger and Evans (2004) argued that if
the inside perspective pays attention to non-focal subcomponents of the task, people
will generate more accurate predictions.
Based on the above analysis, I explored the effects of the inside/outside
perspectives and the unpacking approach on accuracy of affective predictions. I
proposed hypotheses for affective prediction that parallel the hypotheses for prediction
of completion time. Separate hypotheses are necessary to deal with successful and
unsuccessful outcomes. A more accurate, that is, less extreme prediction of affective
reaction was expected to occur in the presence of the additional information provided by
the outside view or the unpacking procedure. Therefore, the following hypotheses are
proposed:
H1.5a: People who use the outside perspective will make more accurate predictions of
positive affective reactions towards outcome success than people who use the inside
perspective.
H1.5b: People who unpack will make more accurate predictions of positive affective
reactions towards outcome success than those who do not unpack.
46
H1.6a: People who use the outside perspective will make more accurate predictions of
negative affective reactions towards outcome failure than people who use the inside
perspective.
H1.6b: People who unpack will make more accurate predictions of negative affective
reactions towards outcome failure than those who do not unpack.
In organizational projects, planning and prediction are normally carried out not
by individuals, but by groups. Even if the project managers make the final prediction
alone, they usually interact with others to collect information, opinions and advice
(Heath & Gonzalez, 1995). This new information, which is not available to them prior
to making their initial prediction, could cause them to revise their decision making or
influence the degree of bias in their predictions. Since this study is examining the
planning predictions in a project, a group project is used. Data are collected on measures
of the confidence changes hypotheses which involve a number of time points as the
project deadline draws closer.
The design involved repeated weekly observations of prediction behavior,
among student participants in a group project over a period of time. Taking into
consideration the hierarchically nested data structures in this study, the repeated
measures (time at level 1) are nested within participants (level 2) and the participants in
turn are nested within project groups (level 3). The research hypotheses in this
longitudinal study are tested using Hierarchical Linear Models (HLM). Detailed
discussions on the multilevel analysis approach used in Study 1 are presented in the
next chapter, Chapter 4.
3.6 Conclusion
This chapter has focused on two theories of the planning fallacy namely the
inside / outside view and the unpacking approach, and the development of 12 research
hypotheses. It is proposed that the adoption of either the outside perspective or the
47
unpacking approach would generate a more accurate prediction of task completion time
and outcome, a smaller change in confidence in predictions of task completion time and
outcome, and a more accurate prediction of affective response towards outcome success
or failure.
The next chapter, Chapter 4, discusses the method used to test the hypotheses
presented in this chapter.
48
CHAPTER 4
METHOD – STUDY 1
4.1 Introduction
This chapter describes the research method for the first study, including the
design of the experiment, the data collection process, participants, the experimental
materials, the procedure involved and the research measures used in the study. The
chapter concludes with strategies for analyzing the data.
4.2 Research Design
The hypotheses stated in Chapter 3 were tested in a controlled experiment. The
experiment involved four conditions in which participants’ initial planning
considerations about their projects were manipulated with instructions that were
consistent with the inside view, outside view, unpacked or packed (control) approaches.
Responses were collected on eight measures – predicted project completion time,
predicted project outcome, confidence in prediction of project completion time,
confidence in prediction of the success of the outcome and predicted affective response
toward outcome success and failure and reported affective response towards outcome
success and failure.
As the design required experimental manipulation of the prediction and planning
procedure for a large number of projects, it was not practical to use projects in
organizations. To provide control of the environment I have used students’ group
project assignments. Using students’ project assignments made it possible to gather a
large amount of data, ensured that the projects would not be abandoned, and that the
duration of the projects fits within this study time. The eight weeks duration and small
group nature of the projects is a realistic parallel for small organizational projects.
Furthermore, these projects were important with real consequential outcomes that could
49
be expected to engender affective reactions in the participants, an aspect that is difficult
to achieve in laboratory studies.
4.3 Data Collection Process
Approval for this study was granted by both the Human Research Ethics
Committee of The University of Western Australia, and the Malaysia Economic
Planning Unit (EPU). The fieldwork was conducted from December 2005 until April
2006. During that period, the following activities were conducted.
The plan for recruitment of participants began in December before the second
semester session 2005 / 2006 started, in the Faculty of Human and Social Development,
Universiti Utara Malaysia. Lecturers were first contacted through email and the purpose
and nature of the study were explained. Through the first contact, twelve lecturers
agreed to allow their students to participate in the study. To ensure that no student
would be involved in the experiment twice, classes were selected for this study through
manually checking each of the twelve lecturers’ student lists. At the end of the process,
eleven classes conducted by ten different lecturers were chosen. The experiment was
conducted between January and April 2006. Detailed procedure of the experiment is
provided in Section 4.6.
4.4 Participants
A total of 799 undergraduate students from eleven classes agreed to participate.
At the conclusion of the experiment all participants were given a small gift for their
voluntary participation. Participants were working in one of 162 student project groups
(ranging from 3 to 10 members per group), but completed all the questionnaire
materials individually. The 162 project groups were randomly assigned to one of the
four between-participant conditions: inside view, outside view, unpacked and packed
(control), as described in detail in Section 4.6.
50
Twenty-three participants failed to continue with the experiment because they
dropped the relevant class and were not included in the final sample. Another four
students’ data were also excluded as the information given was not feasible. For
example, one participant reported that the project assignment would be completed
within a day, with the predicted date of completion given as the day after the experiment
was conducted. Therefore, data of 772 participants are potentially available for analysis.
4.5 Experimental Materials
Acknowledging the cultural differences and since teaching is carried out in both
English and Bahasa Malaysia at Universiti Utara Malaysia, all materials prepared for
the participants were provided in both English and Bahasa Malaysia. Participants were
given the choice between the two versions so that they could express their ideas freely.
The Bahasa Malaysia version was translated using back translation. I first translated the
English versions of the materials (which have been approved by the Human Research
Ethics Committee of The University of Western Australia) to Bahasa Malaysia as I am a
bilingual native speaker of Bahasa Malaysia. The Bahasa Malaysia versions were given
to two bilingual native speakers who then translated them back into English (working
independently). The final translated version was consistent with the original. Both
versions of the experimental materials are shown in Appendix A. All participants in this
study chose the Bahasa Malaysia version.
Throughout the experiment, each participant received the following
experimental materials: an information sheet, a consent form, experimental
questionnaire (with cover letter attached) for one of the four conditions [inside view,
outside view, unpacked or packed (control)], eight copies of a weekly diary form and a
short affective questionnaire. The information sheet, consent form, experimental
questionnaire and a first week’s diary were given on the first day of the experiment. A
weekly diary was then given on a weekly basis until week 8. A short affective
51
questionnaire was given on the last day of the experiment, after participants received
their project assignment marks.
The information sheet provides participants with information such as the
background and general aim of the study, what they would be asked to do as part of
their involvement in the study, and benefits gained by participating in the study. Contact
phone numbers were also provided in case participants have any questions or they need
further information pertaining to the study. The consent form assured participants that
their participation in this study was voluntary and confidential. They were advised that
they were free to withdraw at any time without reason and without prejudice.
The experimental questionnaire asked about participants’ predictions regarding
project completion time and project assignment marks that they anticipated receiving,
level of confidence regarding those predictions and their expected affective response
towards project success and failure should those occur. Before answering these
questions, the focus of participants’ thoughts was varied according to one of the four
conditions with instructions in the first section of the questionnaire.
The weekly diary entry asked participants to record any activities and group
meetings that were conducted in that particular week that were related to their project
assignments. Specific information such as date, start time and finish time for each of the
activities and matters discussed during group meetings were also asked. For the last
diary entry, participants were also asked to indicate their contribution and other group
members’ contributions towards the project assignment. The affective questionnaire on
the last day asked participants to rate their actual feelings regarding the marks that they
received. Each of the experimental materials had a coded ID number based on the
participants’ student number, to identify participants and their different classes and
group projects.
52
4.6 Experimental Procedure
The experiment was conducted during the weeks of the semester where students
are required to complete a project assignment. In each class, after lecturers had assigned
students to groups, students were asked to sit together in their own groups before the
first part of the experimental process took place. Then, I addressed the students by
introducing the experiment and invited them to participate in the experiment. Students
were provided with a sheet of information about the experiment and a consent form.
Students were invited to ask questions and then consent forms were collected. Each
participant was then given a questionnaire for one of the four conditions: inside view,
outside view, unpacked and packed (control). All members of a group received the
instructions for the same condition. The questionnaires for the four conditions were
randomly distributed among the groups in each class in the following manner. Group
one was given questionnaires with the inside view condition, group two was given
questionnaires with the outside view condition, group three was given questionnaires
with the unpacked condition and group four was given questionnaires with the packed
(control) condition. The process was repeated with the rest of the groups in the class.
The materials for the four conditions differed as follows.
Participants in the inside and outside perspective conditions were asked in the
written instructions to spend a few minutes considering their plans for completing the
project. The instructions were designed to vary the focus of their thoughts towards
either the outside or inside view. The instructions for the outside view were as follows
(the inside view version of the instructions is in parentheses):
Please take a few minutes to consider your plans for completing your project
assignment. Recall (think about) the kind of events that have happened (are likely to
happen) in your past (future) project assignment and those of people that you know
about (as you work on your project assignment). Recall (think about) all the problems
53
that have happened (are likely to happen) in the past (future), that have impeded (will
impede) your project progress and performance, and any aspects of the project or
situation in the past (future) that have facilitated (will facilitate) your project progress
and performance.
The next page of the questionnaire asked participants to “Please describe all the
problems that have happened (are likely to happen) in the past (future) that have
impeded (will impede) your project progress and performance.” Ample space was
provided on which participants could write their descriptions. The next page asked
participants to “Please describe any aspects of the projects or situations in the past
(future) that have facilitated (will facilitate) your past project (your project’s) progress
and performance.”
For participants in the unpacked and packed (control) conditions, the instructions
asked participants to “Please take a few minutes to consider your plans for completing
your project assignment.” On the next page, where they were asked to write down their
descriptions, participants in the unpacked condition were asked to do the following.
Participants in packed (control) condition did not go through this part of the procedure.
a) “Please list down components that are needed for project progress and
performance.”
And on the page after that, they were asked,
b) “Please list down each and every thing that you plan to do in completing this
project assignment.”
Participants in all conditions were then asked to “Try to predict as accurately and
realistically as possible when you will finish the project assignment and what marks you
anticipate to get.” Participants were also asked to indicate how confident or certain they
were that they would finish this project assignment by the date that they predicted and
how confident or certain they were that they would get the marks that they predicted.
54
Then, they were asked to indicate how they would feel after “a successful project” and
if the project were to “succeed” and how they would feel after “not a successful project”
and if “you do not succeed,” should that occur. Apart from answering prediction
questions, participants were also asked to fill in their demographic information: gender,
age, race and number of semesters that they have completed.
After participants finished answering the questions, they were supplied with a
prepared form (like a diary) on which they were to report their project progress for the
next week. Participants were requested to write down their student number as their
personal identification code and to record every occasion and the times (date, hour and
minutes; from beginning to end) when they worked on the project assignment. Apart
from reporting the progress of their project, participants were also requested to record
every meeting their group held outside of class time (if any, during that particular week)
and to report all the matters discussed during those meetings. The final questions on the
diary form were similar prediction questions to those on the initial questionnaire and
were to be answered at the end of the week.
Participants were assured that all the information given will remain confidential at
all times and will be used for the study only. It would not affect their project
assignment’s grade and would not be seen by their lecturer. Any identifying information
such as student number will be destroyed after the data analysis has been completed and
the data will be completely anonymous in the analysis and reports of the study. Each
week I went to every class in the study and personally collected their week’s diaries and
issued new ones, for the eight weeks of the projects. The last diary entry was collected
on the day the participants handed in their project assignments. Once the lecturers
finished marking the project assignments, the marks were given to me. The participants
collected their project assignment marks from me in person, and they were asked to fill
out a short questionnaire asking their feelings towards the marks they had received.
55
4.7 Measures
The major dependent constructs in this study were biases in the participants’
prediction of project completion time, and outcome success, confidence in prediction of
project’s completion time and outcome success, and their predicted and actual affective
reactions towards project success and failure.
4.7.1 Time Completion Measure
The actual completion time was measured by analyzing participants’ final
diaries. Based on the report written by the participants, the date and time that the
participants stated they finally finished the project was taken as the actual completion
time. The start time was the time of the class day when the project was distributed and
the experimental manipulation was conducted.
Prediction bias was indexed by the signed time difference between predicted and
actual completion dates, with the predicted subtracted from actual. If the predicted
completion time falls before the actual time completion, the difference is positive. If the
predicted completion time falls after the actual completion time the difference is
negative. In previous empirical studies, Buehler, Griffin and MacDonald (1997) and
Sanna and Schwarz (2004) have used a similar measure and subtracted the predicted
from the actual to operationalize time completion prediction accuracy measures.
4.7.2 Outcome Success Measures
There were two measures of predicted outcome success. Based on the points
allocated for the project assignment, participants were asked to predict their percentile
performance using a 13-point scale ranging from 40% to 100%. For the second
measure, participants were asked to state the exact marks they anticipated receiving (in
percentage) in a box provided. The actual project outcome was measured by the score
participants received from their respective lecturer converted to a percentage. Although
the projects were completed in groups, the marks included individual components that
56
ensured that individuals in a group did not all receive the same marks. Prediction bias
for the project outcome was indexed by the signed difference between predicted and
actual scores, with predicted subtracted from actual.
4.7.3 Confidence Measures
Confidence in predictions of completion time and outcome was assessed on a
weekly basis by asking participants to provide their confidence level by marking a 15
cm line, which had labeled anchors of absolutely impossible (0%), 50/50 chance (50%)
and completely certain (100%). For analysis, the response was measured from the 0%
end of the line and centimeters were converted to a number out of 100. A similar
measure was taken for confidence in the predicted assignment mark.
4.7.4 Affective Response for Success and Failure Measures
Measures for affective response were adapted from Sanna and Schwarz (2004).
In the questionnaire completed at the first meeting, but after the manipulations of the
four conditions, participants were asked to estimate on two 11-point scales how they
would feel after “a successful project” and if the project were to “succeed” (1 = not
good, 11 = very good). They were also asked to estimate how they would feel after “a
not successful project” and if the project “do not succeed” (1 = very bad, 11 = not bad).
Immediately after receiving the grades, participants were asked to answer a
dichotomous question on whether they viewed the mark they received for the project as
a successful project or not a successful project. If they answered successful they then
turned to two questions asking the degree to which they feel good (1 = not good, 11 =
very good), and if they answered not successful they turned to questions asking the
degree to which they feel bad (1 = not bad, 11 = very bad) about their obtained project
assignment marks. This scale is as described by Sanna and Schwarz (2004), and
following their practices, before conducting the analysis, the responses to feeling “bad”
were reverse coded.
57
4.8 Data Analysis Strategy
Multilevel regression analysis was used to test research hypotheses developed in
Chapter 3. The data consisted of repeated weekly observations obtained from
individuals working on group projects over a two-month period. The repeated measures
(time at level 1) are nested within 772 participants (level 2), and the participants in turn
are nested within 162 project groups (level 3). Multilevel modeling provides a way to
analyze nested data simultaneously at all levels. The HLM 6.02 program by
Raudenbush, Bryk, Cheong, Congdon and du Toit (2004) was used.
4.8.1 Level of Analysis
The first HLM analysis examined the intercept-only unconditional models with
only time (WEEK) included in the regression equations. Estimation of the unconditional
models allowed the investigation of the overall change trajectory across all participants
and groups. These include the estimation of initial status (G000) and the average
prediction bias rate per week (G100). The unconditional model also distributed the
variance components into participants within groups for initial status (R0) and weekly
prediction bias rates (R1) and between groups for mean initial status (U00) and mean
prediction bias rates (U10). These variance component estimates allowed the percentage
of variation that lies between groups for both the initial status and prediction rates to be
considered.
The next set of analyses examined the conditional model. The Level-1 model
remains the same as in the unconditional model. But now entered into the model at
Level-2 are participant-level variables such as the four conditions – inside, outside,
unpacked and packed (control), and participants’ demographic characteristics such as
age, gender, race and total number of semesters taken. At Level-3, the project group-
level variables such as group size, teacher, group mean for age, group mean for gender
and group mean for semester were entered. The manipulations in this study are entered
58
at the participant level (level-2) rather than at the project group level (level-3) because
the manipulations are applied to the individuals, through instructions read by the
individuals and responded to with predictions made by individuals.
The Type I error rate or alpha value in the multilevel regression analyses was set
at .10, which is consistent with what past multilevel authors have used (e.g., Krull &
MacKinnon, 2001; Raudenbush & Bryk, 2002; Singer & Willet, 2003).
4.8.2 Data Centering using Grand Means
Centering is an important consideration in multilevel models as it facilitates the
interpretation of the intercepts (Enders & Tofighi, 2007; Hofman & Gavin, 1998; Kreft
& de Leeuw, 1998; Kreft, de Leeuw, & Aiken, 1995; Raudenbush & Bryk, 2002).
Without centering (the raw score models), the intercepts can be interpreted as the
expected value of the dependent variable when the predictor variables are equal to zero.
For some predictor variables such as age, the value of zero is meaningless. Thus,
centering is critical in making the interpretation of the coefficient and variance
components more meaningful, especially in HLM, which focuses on explaining
variance in the intercept and regression coefficients.
Data that have a multilevel structure usually give researchers a range of
possibilities regarding the choice of reference values. For example, in this study, a
variable concerning participants (e.g. age, gender and semester) nested within group
projects can be centered by subtracting the grand mean of the variable or by subtracting
the group mean. With grand mean centering, each of the participant’s age, gender and
semester values is expressed as its deviation from the variable’s grand mean, which is
the overall mean of the variable of all participants in the sample under study. Grand
mean centering involves a linear transformation of the values for the centered variables,
and this will change the intercept but not the slope, thus, the intercept can be interpreted
as an adjusted mean (Raudenbush & Bryk, 2002). Since grand mean centering allows
59
consideration of an effect after partialling out or controlling for other effects, the
variance of the intercept is then the variance in the adjusted means.
The choice of centering depends on the model and research question under
investigation rather than on a statistical basis. Raudenbush and Bryk (2002) and Kreft,
de Leeuw and Aiken (1995) recommended the used of grand mean-centering as it
makes interpretation of intercepts clearer. Though using grand mean centering produces
nearly equivalent results with the raw metric scaling, Kreft, de Leeuw and Aiken (1995)
found that grand mean centered models provide a “computational advantage,” because
in most cases grand mean centering reduces the correlation between the intercept and
slope estimates across groups. Unlike grand mean centering, using group mean
centering produces results that differ from other approaches. Although group mean
centering can introduce complexity into model specification and interpretation,
Raudenbush and Bryk (2002), Kreft, de Leeuw and Aiken (1995), Hofmann and Gavin
(1998) note that group mean centering is useful when studying contextual effects.
In this longitudinal study, I am not investigating how groups (contexts) affect
individual participants’ predictions. The level-1 predictor (the WEEK variable) was
centered on day 1 when the experiment started and the manipulation was conducted.
When used, level-3 predictor (group size) was centered around its grand means. Level-2
predictors (age, gender and semester) were centered around their grand means.
4.8.3 Time Centering
As mentioned in Section 4.8.1, the data collected for this study consist of
repeated weekly observations where each participant is measured on an identical set of
occasions. Because the measurement occasions were equally and identically spaced for
all participants, the WEEK variable (at level-1) took on integer values between 0 (at the
initial assessment) and 8 (at the final assessment). According to Peugh and Enders
(2005) and Singer and Willet (2003) centering WEEK in this manner allows the
60
intercept to be interpreted as the true estimated initial status (i.e., the expected value of
the outcome variable when WEEK = 0).
4.9 Sample Size for Multilevel Modeling Analyses
In multilevel analysis, the maximum likelihood estimation methods assume that
the sample size is large (Hox, 2002). Thus, in this experimental study, 162 groups (from
772 individuals respondents), with three to ten participants per group, and with nine
waves of data were initially collected in the analysis to test the research hypotheses.
Since this is a longitudinal study, the presence of missing data is practically
unavoidable even if it is well-controlled. The missing data in longitudinal studies tends
to accumulate over time due to either item non-response, wave non-response or
participant drop out. Participants might respond on only a subset of the study variables,
miss a particular measurement wave or fail to follow-up or completely drop out from
the study (Hedeker & Gibbons, 1997). In this study, missing data occurred primarily
due to participants who failed to return their weekly diary for a particular week. The
experimental design and the weekly number of respondents involved in this study are
presented in Appendix B.
For this study, cases with missing data were not included in the analyses. Even
though the multi-level analysis procedure would permit the inclusion of cases with
missing values, it was considered that students who missed some weeks of classes
might not have all the necessary information to make informed predictions about the
project, compared to students who attended all classes.
From the 772 initial participant cases, 64 cases were deleted from the analysis.
This comprised 56 cases that contained missing data on at least one occasion, 6
participants who failed to return the diary on week eight indicating the actual
completion time, and 2 cases that were deleted because the project group had only one
group member remaining. The multilevel analysis cannot be conducted on groups with
61
only one member. Therefore, to test the hypotheses concerning completion time
prediction, data from 708 participants were used. Completion time predictions were
assessed until week 6 of the 8-week project. The seventh week data were not included in
the analysis as it was so close to the project submission deadline, by which time
participants knew they must finish the project.
For the outcome predictions, data from 689 participants were used to test the
hypotheses. Participants’ outcome predictions were measured until week 8 (final week)
of the project. During weeks 7 and 8, more respondents failed to hand in their diaries.
Therefore, out of 772 the initial participant cases, 83 cases were deleted with 79 cases
involving missing data on at least one occasion and 4 cases were deleted as the project
group had only one group member remaining.
For the confidence in prediction of project completion time and outcome, the
same number of cases was used as in testing the hypotheses for completion time and
outcome prediction. A total number of 708 and 689 participants were used respectively.
To test the hypotheses concerning the affective response towards project
outcome success and failure, 552 participant cases were used. At the end of the
experiment, where actual affective response was collected, 629 participants (from the
total of 772 students) took part. Of these 629 participants, 461 viewed their assignment
grade as a success, while 168 viewed their assignment grade as a failure. Thirty-nine
cases were deleted from the participants who viewed their assignment grade as a
success, of which 33 cases were deleted due to missing data on at least one occasion and
6 cases were deleted as only one group member remained. For participants who view
their assignment grade as a failure, 38 cases were deleted, comprising 15 cases deleted
due to missing data on at least on one occasion, and 23 cases deleted as only one group
member remained.
62
4.10 Data Cleaning
Prior to the primary analyses, the data were examined for data entry accuracy,
outliers, and distributional properties. Outliers were detected using both z-scores (with a
cut-off point of + 3SD) and Mahalanobis distance (a cut-off point of .001). At the end of
these procedures, 60 outliers were detected. Since there was no difference in the results
if the outliers were taken out or not, all the participant cases were used for further
analysis.
4.11 Conclusion
In this chapter, the method and the analysis strategy for the first study have been
described, including the design of the experiment, the data collection process, the
selection of the participants, the experimental procedure and the research measures.
This chapter also explains the adoption of the three-level modeling approach to test the
research hypotheses. The results of these tests are reported in the next chapter, Chapter
5.
63
CHAPTER 5
RESULTS – STUDY 1
5.1 Introduction
Chapter 5 reports results for the first study. The chapter begins with an overview
of the demographic characteristics of the participants in the experiment. It then reports
the results as graphs of mean responses and this is followed by the hierarchical linear
modeling (HLM) analyses, which test the hypotheses proposed in Chapter 3.
5.2 Demographic Characteristics of the Participants
78.6% of those who participated in this experiment were female. The average
age of the participants was 21 years old. Malays constituted 71.6% of the participants,
followed by 19.8% Malaysian Chinese, and 5.7% Malaysian Indians. The remaining
2.8% of the respondents reported that they were from other ethnic groups. On average,
participants in this experiment had completed 3 semesters of university study. The
detailed demographic characteristics of the participants are presented in Appendix C.
5. 3 Hypothesis Testing
Results for the first study are presented according to each category of the
dependent variables, starting with predictions made regarding task completion time,
followed by results for outcome predictions, confidence in predictions of completion
time, confidence in predictions of outcome, and predictions of affective response
towards outcome success and outcome failure. The results are first described through
graphical analysis, where means of responses over weeks of the experiment are plotted,
and then statistical analyses are presented.
64
5.3.1 Completion Time Predictions
Figure 5.1 plots means of completion time predictions made by 708 participants
on day 1 of the experiment and means for their actual project assignment completion
times. Standard error bars were not included in the graph because of the multilevel
nature of the data. Inspection of Figure 5.1 suggests that on the first day of the
experiment, participants in each of the four conditions predicted that they would take
less days to complete their project assignments than they actually did take, consistent
with the planning fallacy
46.00
47.00
48.00
49.00
50.00
51.00
52.00
53.00
54.00
55.00
56.00
Day 1 Actual
Time
Mea
n co
mpl
etio
n tim
e (in
day
s)
InsideOutsideUnpackedControl
Figure 5.1. Mean initial predicted and actual completion time for the four conditions. (n=708)
Participants continued to make predictions each week. Figure 5.2 plots mean
completion time predictions made by 702 participants over the seven weeks of the
project and their actual completion times. There are fewer participants because some did
not complete all weeks. Inspection of the graph suggests that the predicted number of
65
days increases over time for all the four conditions. On average, participants in the
inside view and packed (control) conditions appear to be more optimistic about their
project completion time than participants in the unpacked and outside conditions.
46.00
47.00
48.00
49.00
50.00
51.00
52.00
53.00
54.00
55.00
56.00
day 1 week1
week2
week3
week4
week5
week6
week7
actual
Week
Mea
n co
mpl
etio
n tim
e (in
day
s)
InsideOutsideUnpackedControl
Figure 5.2. Mean predicted and actual completion time for the four conditions. (n=702)
The hypotheses concerned the bias in predictions and not the actual predictions
made, as shown in Figures 5.1 and 5.2. Figure 5.3 plots mean prediction bias for the
completion time predictions made by 708 individuals over the first 6 weeks of the 8-
week project. The data for the seventh week were not included as it was so close to the
project submission deadline, by which time participants knew they must finish the
project. Completion time prediction bias was measured by subtracting predicted
completion time from actual completion time, so the positive numbers on the vertical
axis of Figure 5.3 indicate that participants underestimated how long the project would
take to complete, which is consistent with the planning fallacy. Inspection of Figure 5.3
66
suggests that the bias is less for the outside view than for the inside view, which is
consistent with Hypothesis 1.1a, and that the bias is less for the unpacked condition than
for the packed (control) condition, which is consistent with Hypothesis 1.1b. There
appears to be little difference between the outside view condition and the unpacked
condition. Inspection of the slopes of the graph suggests that the completion time
prediction bias was reduced as the project submission deadline drew closer. These
effects are tested in the following sections using multilevel models.
The results of the unconditional three-level HLM model are described first,
followed by the conditional three-level HLM model.
0.00
1.00
2.00
3.00
4.00
5.00
6.00
day 1 week 1 week 2 week 3 week 4 week 5 week 6
Weeks
Mea
n pr
edic
tions
bia
s (in
day
s)
Inside
Outside
Unpacked
Control
Figure 5.3. Mean prediction bias of individuals’ project completion time predictions for the four conditions. Bias is actual minus predicted completion time. (n=708)
67
5.3.1.1 Unconditional Three-level HLM Models
In an initial analysis, the unconditional HLM model was estimated for the
completion time prediction bias dependent variable, with time (WEEK) as the only
predictor in the regression equation. The following are the equations for the
unconditional model which was adapted from Raudenbush and Bryk (2002, p.238).
Level-1 Model
Ytij = π0ij + π1ij(WEEK) + etij (1)
Level-2 Model
π0ij = β00j + r0ij (2a)
π1ij = β10j + r1ij (2b)
Level-3 Model
β00j = γ000 + u00j (3a)
β10j = γ100 + u10j (3b)
where
Ytij is the outcome at time t for participant i in group j;
π0ij is the initial status of participant ij, that is, the expected outcome for that participant
on the first day (when WEEK = 0);
π1ij is the prediction bias rate for participant ij during the weeks;
etij is a random “time effect”. These random time effects are assumed to be normally
distributed with mean of 0, and variance σ2;
β00j represents the mean initial status within group j;
γ000 is the overall mean initial status;
r0ij and r1ij are random “participant effects”. These random participant effects are
assumed to be normally distributed with mean of 0, and variance σ2;
β10j is the mean weekly prediction bias rate within group j;
γ100 is the overall mean weekly prediction bias rate;
68
u00j and u10j are the random “group effects”. These random group effects are assumed to
be normally distributed with mean of 0, and variance σ2.
The results for this unconditional HLM model are presented in Table 5.1. The
estimated initial status (G000) for completion time predictions bias was 4.400 and was
significant, indicating that there was bias in completion time predictions. The average
weekly prediction bias rate (G100) was estimated at -0.454 and was significant. This
indicates that the bias became smaller as the project completion date drew nearer. For
completion time prediction, the variance among participants within groups for initial
status and weekly prediction bias rates (i.e., R0 and R1) and the variance between
groups for mean initial status and mean weekly prediction bias rates (i.e., U00 and U10)
were statistically significant.
Based on these variance component estimates, the percentage of variation that
lies between groups for both initial status and weekly prediction bias rate can be
computed. The following formula was used to estimate the percentage of variation that
lies between groups for initial status and prediction bias rate. It was adapted from
Raudenbush and Bryk (2002, p.239).
% variance between groups on πpjk = τβpp / τβpp + τπpp (4)
where
p = 0, …, P, in this case, p = 0,1
πpjk is either the initial status (π0ij) or the prediction bias rate (π1ij)
τβpp is the random group effect (u00j or u10j)
τπpp is the random individual effect (r0ij or r1ij)
69
The results show that 16.03% of the variance in initial status for completion time
prediction bias lies between project groups. For completion time prediction bias rate,
18.14% of the variance is between groups. In summary, the unconditional model for
completion time predictions revealed significant variance of participants for initial
status and weekly prediction bias rates and between groups for mean initial status and
mean prediction bias rates. The following section reports results for conditional HLM
models that examined the effects of the four conditions [inside, outside, unpacked and
packed (control)] on participants’ predictions regarding task completion time.
70
Table 5.1 Prediction bias for project completion time: A three-level fully unconditional model Prediction bias for project completion time Fixed Effect Coefficient S.Error t-ratio p-value For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 4.400 0.302 14.566 0.000*** For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 -0.454 0.051 -8.943 0.000*** Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
10.950
Level-2 INTERCEPT1, (Individual initial status), R0 24.861 358 2094.508 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.521 358 803.215 0.000*** INTERCEPT1/INTERCEPT2,(Group mean status),U00 4.748 157 243.467 0.000*** WEEK / INTERCEPT2, (Group mean prediction bias rate),U10 0.116 157 235.897 0.000*** Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 16.03 Prediction bias rate, P1 18.14 Deviance = 20455.174 Number of estimated parameters = 9 A three-level model with week = level - 1, individual participants = level - 2, and group = level - 3. Number of participants = 708 and groups = 158. The dependent variable is measured as the difference between participants’ predicted and actual project completion time (in days). †p<0.10,*p <.05, **p<.01, ***p<.001
71
5.3.1.2 Conditional Three-level Model for Completion Time Prediction Bias
To test hypotheses 1.1a and 1.1b, the intercepts of the multilevel model were
tested to compare the effects of the outside and unpacking approaches against the inside
and packed (control) approaches respectively, for completion time predictions. These
four conditions were dummy coded and separate analyses were conducted with different
conditions coded as the reference condition. Since there were no strong theories on
which control variables should be included in the model, an exploratory procedure was
used. As suggested by Hox (2002), the procedure begins by adding the control variables
step by step. At each step, the results were inspected to see if the control variables were
significant and how much residual error remained. Fixed parameters were estimated
first before introducing random parameters.
The final full model that describes the effects of the four conditions [with
packed (control) as the reference condition] on completion time predictions is as
follows. Other versions of the equation with outside and inside conditions as the
reference conditions are presented in Appendix D. A three-level model with time
(WEEK) at level 1, and the three conditions (inside, outside and unpacked) at level 2 as
predictors of completion time predictions was estimated. The equation at level 3 had no
predictors. All the level-2 participants’ potential control variables (e.g. age, semester,
gender and race) were tested as were those at level 3 for the project groups (group size
and teacher), but they had no significant effects. Therefore, they were excluded from the
final model.
Level-1 Model
Ytij = P0 + P1*(WEEK) + E
Level-2 Model
P0 = B00 + B01*(Inside) + B02*(Outside) + B03*(Unpacked) + R0
P1 = B10 + B11*(Inside) + B12*(Outside) + B13*(Unpacked) + R1
72
Level-3 Model
B00 = G000 + U00
B01 = G010
B02 = G020
B03 = G030
B10 = G100 + U10
B11 = G110
B12 = G120
B13 = G130
where
Ytij is the outcome at time t for participant i in group j;
P0 is the initial status of participant ij, that is, the expected outcome for that participant
on the first day (when WEEK = 0);
P1 is the prediction bias rate for participant ij during the weeks;
E is a random “time effect”. These random time effects are assumed to be normally
distributed with mean of 0, and variance σ2;
B00 represents the mean initial status within group j;
G000 is the overall mean initial status;
R0 and R1 are random “participant effects”. These random participant effects are
assumed to be normally distributed with mean of 0, and variance σ2;
B10 is the mean weekly prediction bias rate within group j;
G100 is the overall mean weekly prediction bias rate;
U00 and U10 are the random “group effects”. These random group effects are assumed
to be normally distributed with mean of 0, and variance σ2.
73
The results for this three-level hierarchical regression for the completion time
bias data, with the packed (control) condition as the reference are summarized in Table
5.2a. The intercepts for initial status in Table 5.2a show that the outside view and
unpacked condition were both significantly less biased than the packed (control)
condition in their completion time predictions at the start of the project. This result for
the unpacked condition supports Hypothesis 1.1b. The inside view condition was not
significantly different to the packed (control) condition.
To test Hypothesis 1.1a, which compares the inside and outside view conditions,
and to explore whether there was a difference between the outside view and unpacked
conditions, an analysis of the same model was conducted with the outside view
condition as the reference. The results are reported in Table 5.2b. Though inspection of
Figure 5.1 suggests that the bias is less for the outside view than that for the inside view,
the intercepts for initial status in Table 5.2b were not significantly different between the
outside and the inside view condition, failing to provide statistical support for
Hypothesis 1.1a. The outside view condition was not significantly different to the
unpacked condition in the initial status intercept. Results with the inside view condition
as the reference are presented in Appendix E-1 on page 337-338.
74
Table 5.2a Prediction bias for project completion time: Three-level model of the effects of manipulations relative to the packed (control) condition Prediction bias for project completion time Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 5.695 0.529 10.767 0.000*** For Inside (UP1), B01 INTERCEPT3, G010 -1.260 0.815 -1.545 0.123 For Outside (UP2), B02 INTERCEPT3, G020 -2.070 0.798 -2.593 0.010* For Unpacked (UP3) INTERCEPT3, G030 -1.904 0.792 -2.403 0.017* Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 -0.552 0.106 -5.195 0.000*** For Inside (UP1), B11 INTERCEPT3, G110 -0.025 0.150 -0.167 0.868 For Outside (UP2), B12 INTERCEPT3, G120 0.239 0.139 1.720 0.086† For Unpacked (UP3), B13 INTERCEPT3, G130 0.183 0.144 1.267 0.206 Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
10.950
Level-2 (student within groups) INTERCEPT1, (Individual initial status), R0 24.958 355 2094.523 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.520 355 803.221 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status),U00 3.889 157 228.716 0.000*** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.107 157 230.811 0.000***
75
Table 5.2a (Continued) Random effect Variance Component df Chi-square p-value Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 13.48 Prediction bias rate, P1 17.06 Deviance = 20444.451
Number of estimated parameters = 15 A Three-level model with time = level-1, individual participants = level-2 and group = level-3. Number of participants = 708 and groups = 158 The dependent variable is measured as the difference between participants’ predicted and actual project completion time (in days). †p<0.10,*p <.05, **p<.01, ***p<.001
76
Table 5.2b Prediction bias for project completion time: Three-level model of the effects of manipulations relative to the outside view condition Prediction bias for project completion time Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 3.625 0.598 6.060 0.000*** For Inside (IO1), B01 INTERCEPT3, G010 0.810 0.862 0.940 0.348 For Unpacked (IO2), B02 INTERCEPT3, G020 0.166 0.840 0.198 0.843 For Packed (IO3) INTERCEPT3, G030 2.070 0.798 2.593 0.010* Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 -0.313 0.089 -3.501 0.001** For Inside (IO1), B11 INTERCEPT3, G110 -0.264 0.138 -1.913 0.056† For Unpacked (IO2), B12 INTERCEPT3, G120 -0.056 0.132 -0.422 0.673 For Packed (IO3), B13 INTERCEPT3, G130 -0.239 0.139 -1.720 0.086† Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
10.950
Level-2 (student within groups) INTERCEPT1, (Individual initial status), R0 24.958 355 2094.523 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.520 355 803.221 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status),U00 3.889 157 228.716 0.000*** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.107 157 230.811 0.000***
77
Table 5.2b (Continued) Random effect Variance Component df Chi-square p-value Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 13.48 Prediction bias rate, P1 17.06 Deviance = 20444.451
Number of estimated parameters = 15 A Three-level model with time = level-1, individual participants = level-2 and group = level-3. Number of participants = 708 and groups = 158 The dependent variable is measured as the difference between participants’ predicted and actual project completion time (in days). †p<0.10,*p <.05, **p<.01, ***p<.001
78
5.3.2 Outcome Predictions
Figure 5.4 plots mean prediction bias for the outcome predictions made by
individuals over the entire eight weeks of the project. Outcome prediction bias was
measured by subtracting the predicted project mark from the actual project mark, so the
negative numbers on the vertical axis of Figure 5.4 indicate that, on average,
participants overestimated the marks they would receive for their projects. As for
completion time prediction, this bias is optimistic, but it is in the opposite direction to
the underestimation of completion time.
Importantly, the outside view and unpacked conditions again have similar
results, but while they were less biased than the inside view and packed conditions for
completion time predictions, they appear to be more biased for outcome predictions.
Therefore, inspection of Figure 5.4 suggests that Hypotheses 1.2a and 1.2b are not
supported, and actually in the wrong direction.
-12.00
-10.00
-8.00
-6.00
-4.00
-2.00
0.00day 1 week
1week
2week
3week
4week
5week
6week
7week
8
Weeks
Mea
n pr
edic
tion
bias
InsideOutsideUnpackedControl
Figure 5.4. Mean prediction bias of individuals’ project outcome predictions for the four conditions. Actual outcome (in marks) minus predicted. (n=689)
79
5.3.2.1 Unconditional Three-level Model
An unconditional HLM model was estimated for the outcome prediction bias dependent
variable, with time (WEEK) as the only predictor in the regression equations. The
results for this unconditional HLM model are presented in Table 5.3. For outcome
prediction bias, the estimated initial status (G000) was -7.778 and was significant,
indicating that bias did occur. The average weekly prediction bias rate (G100) was
estimated at -0.147, and was also significant, indicating that participants’ outcome
predictions became more biased as the end of the project drew nearer. The variance
among participants within groups for initial status and weekly prediction bias rates (i.e.,
R0 and R1), and the variance between groups for mean initial status and mean weekly
prediction bias rates (i.e., U00 and U10) were statistically significant. 69.52% of the
variance in initial status for outcome prediction bias lies between groups, and 14.98% of
the variance for the outcome prediction bias rate is between groups.
In short, the unconditional model for outcome predictions showed significant
variance of participants for initial status and weekly prediction bias rates and between
groups for mean initial status and mean prediction bias rates. Results for the conditional
HLM model are reported next.
80
Table 5.3 Prediction bias for project outcome: A three-level fully unconditional model Prediction bias for project outcome Fixed Effect Coefficient S.Error t-ratio p-value For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 -7.778 0.859 -9.059 0.000*** For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 -0.147 0.036 -4.079 0.000*** Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
10.105
Level-2 INTERCEPT1, (Individual initial status), R0 42.861 348 4261.302 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.297 348 951.367 0.000*** INTERCEPT1/INTERCEPT2,(Group mean status),U00 97.775 155 1233.888 0.000*** WEEK / INTERCEPT2, (Group mean prediction bias rate),U10 0.052 155 214.865 0.001** Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 69.52 Prediction bias rate, P1 14.98 Deviance = 26030.334 Number of estimated parameters = 9 A three-level model with week = level-1, individual participants = level -2, and group = level - 3. Number of participants = 689 and groups = 156. The dependent variable is measured as the difference between participants’ predicted and actual project marks (in percentage). †p<0.10,*p <.05, **p<.01 ***p<.001
81
5.3.2.2 Conditional Three-level Model
Hypotheses 1.2a and 1.2b were tested by examining the intercepts of the
multilevel model to compare the effects of the outside and unpacked approaches against
the inside and packed (control) approaches for outcome prediction bias. These four
conditions were dummy coded and again separate analyses were conducted with
different conditions coded as the reference condition. Similar steps were taken with the
outcome predictions as for the completion time predictions, where the procedure begins
by adding the control variables step by step. At each step, the results were inspected to
see which control variables were significant and how much residual error remained.
Fixed parameters were estimated first before using random parameters.
The final full models that describe the effect of the four conditions [with packed
(control), outside and inside as the reference condition] on outcome predictions are
presented in Appendix D. For outcome predictions, I estimated a three-level model with
time (WEEK) as the only predictor at level 1, the three conditions (inside, outside and
unpacked) and semester as predictors at level 2, and teachers and group mean for
semester as predictors at level 3. Unlike the predictions of completion time, significant
effects of the teacher were found for the student predictions of their mark, and effect-
coded teacher variables are included in this analysis. In this three-level hierarchical
linear model, semester was grand mean centered.
82
Table 5.4a presents the results for the three-level hierarchical regressions with
the packed (control) condition as the reference. Initial status was not significantly
different from the packed condition for any of the experimental conditions. Thus, while
inspection of Figure 5.2 suggests that the unpacked condition was more biased than the
packed condition, the statistical analysis does not confirm this.
In an analysis of the same model with the outside view condition as the
reference (shown in Table 5.4b), the intercept for the inside view condition was
significantly different to that for the outside view condition, but not in the direction
stated in Hypothesis 1.2a. The intercept for the unpacked condition did not differ
significantly from the outside view condition. Results with the inside view condition as
the reference is presented in Appendix E-2 on page 339-340.
83
Table 5.4a Prediction bias for project outcome: Three-level model of the effects of manipulations relative to the packed (control) condition Prediction bias for project outcome Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 -5.308 3.490 -1.521 0.130 TEACHER1(T1), G001 6.322 2.123 2.978 0.004** TEACHER2(T2), G002 -9.667 2.852 -3.389 0.001** TEACHER3(T3), G003 -12.551 4.309 -2.913 0.005** TEACHER4(T4), G004 8.468 4.023 2.105 0.037* TEACHER5(T5), G005 6.626 3.008 2.203 0.029* TEACHER6(T6), G006 2.433 3.312 0.735 0.464 TEACHER7(T7), G007 -0.631 1.804 -0.350 0.727 TEACHER8(T8), G008 -10.376 2.253 -4.606 0.000*** TEACHER9(T9), G009 2.732 2.317 1.179 0.241 SEM_MEAN, G0010 -0.615 1.014 -0.606 0.545 For Inside (UP1), B01 INTERCEPT3, G010 1.999 1.752 1.141 0.255 For Outside (UP2), B02 INTERCEPT3, G020 -1.696 1.830 -0.927 0.355 For Unpacked (UP3), B03 INTERCEPT3, G030 -2.450 1.891 -1.296 0.196 For semester, B04 INTERCEPT3, G040 -2.055 0.461 -4.459 0.000*** Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 -0.147 0.036 -4.079 0.000*** Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
10.105
Level-2 (student within groups)
84
Table 5.4a (Continued) Random effect Variance Component df Chi-square p-value INTERCEPT1, (Individual initial status), R0 41.380 344 4124.282 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.297 348 951.367 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status),U00 52.197 145 755.179 0.000*** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.052 155 214.727 0.001** Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 55.78 Prediction bias rate, P1 14.87 Deviance = 25933.896 Number of estimated parameters = 23 A Three-level model with time = level-1, individual participants = level-2 and group = level-3. Number of participants = 689 and groups = 156 The dependent variable is measured as the difference between participants’ predicted and actual project marks (in percentage). †p<0.10,*p <.05, **p<.01, ***p<.001
85
Table 5.4b Prediction bias for project outcome: Three-level model of the effects of manipulations relative to the outside view condition Prediction bias for project outcome Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 -7.005 3.476 -2.015 0.045* TEACHER1(T1), G001 6.322 2.123 2.978 0.004** TEACHER2(T2), G002 -9.667 2.852 -3.389 0.001** TEACHER3(T3), G003 -12.551 4.309 -2.913 0.005** TEACHER4(T4), G004 8.468 4.023 2.105 0.037* TEACHER5(T5), G005 6.626 3.008 2.203 0.029* TEACHER6(T6), G006 2.433 3.312 0.735 0.464 TEACHER7(T7), G007 -0.631 1.804 -0.350 0.727 TEACHER8(T8), G008 -10.376 2.253 -4.606 0.000*** TEACHER9(T9), G009 2.732 2.317 1.179 0.241 SEM_MEAN, G0010 -0.615 1.014 -0.606 0.545 For Inside (IO1), B01 INTERCEPT3, G010 3.696 1.853 1.994 0.046*
For Unpacked (IO2), B02 INTERCEPT3, G020 -0.754 1.991 -0.379 0.705 For Packed (IO3), B03 INTERCEPT3, G030 1.696 1.830 0.927 0.355 For semester, B04 INTERCEPT3, G040 -2.055 0.461 -4.459 0.000*** Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 -0.147 0.036 -4.079 0.000*** Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
10.105
Level-2 (student within groups)
86
Table 5.4b (Continued) Random effect Variance Component df Chi-square p-value INTERCEPT1, (Individual initial status), R0 41.380 344 4124.282 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.297 348 951.367 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status),U00 52.197 145 755.179 0.000*** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.052 155 214.727 0.001** Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 55.78 Prediction bias rate, P1 14.87 Deviance = 25933.896 Number of estimated parameters = 23 A Three-level model with time = level-1, individual participants = level-2 and group = level-3. Number of participants = 689 and groups = 156 The dependent variable is measured as the difference between participants’ predicted and actual project marks (in percentage). †p<0.10,*p <.05, **p<.01, ***p<.001
87
5.3.3 Confidence in Predictions of Project Completion Time
Figure 5.5 plots means of participants’ ratings of confidence in their predictions
of completion time in each of the four conditions over the first 6 weeks of the 8-week
project. As shown in Figure 5.5, there appears to be little change in confidence over
time for participants in each of the four conditions, contrary to Hypotheses 1.3a and
1.3b. Inspection of Figure 5.5 also suggests that participants in the outside view
condition were less confident in their predictions of completion time than participants in
the inside view condition. In contrast, participants in the unpacked condition appear to
be more confident in their predictions of completion time than participants in the packed
(control) condition. Once again there appears to be little difference between the outside
view condition and the unpacked conditions.
72.00
74.00
76.00
78.00
80.00
82.00
84.00
day 1 week 1 week 2 week 3 week 4 week 5 week 6
Weeks
Mea
n co
nfid
ence
in p
redi
ctio
ns (i
n pe
rcen
tage
)
Inside
Outside
Unpacked
Control
Figure 5.5. Mean confidence in predictions of project completion time for the four conditions. (n=708)
88
5.3.3.1 Unconditional Three-level Model
An unconditional multilevel model was estimated for the confidence in
predictions of completion time dependent variables, with time (WEEK) as the only
predictor in the regression equations. The results for this unconditional HLM model are
presented in Table 5.5. The estimated initial status (G000) for confidence in predictions
of completion time was 77.897. The average weekly confidence change rate (G100) was
estimated at 0.204, which was marginally significant. The variance among participants
within groups for initial status and weekly confidence change rates (i.e., R0 and R1) and
the variance components between groups for mean initial status and mean weekly
confidence change rates (i.e., U00 and U10) were statistically significant.
Regarding the percentage that lies between groups for both initial status and
confidence change rate, the results show that 20.67% of the variance in the initial status
for confidence in predictions of completion time lies between groups. 15.59% of the
variance for confidence change rate is between groups. In summary, the unconditional
models revealed significant variance of participants for initial status and weekly
confidence change rate and between groups for mean initial status and mean confidence
change rates. Results for the conditional HLM model are reported in the next section.
89
Table 5.5 Confidence in predictions of project completion time: A three-level fully unconditional model Confidence in predictions of project completion time Fixed Effect Coefficient S.Error t-ratio p-value For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 77.897 0.871 89.446 0.000*** For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly confidence change rate), G100 0.204 0.116 1.757 0.080†
Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
63.493
Level-2 INTERCEPT1, (Individual initial status), R0 186.347 358 2640.614 0.000*** WEEK slope, (Individual weekly confidence change rate), R1 2.768 358 821.164 0.000*** INTERCEPT1/INTERCEPT2,(Group mean status),U00 48.554 157 272.232 0.000*** WEEK / INTERCEPT2, (Group mean confidence change rate),U10 0.512 157 199.396 0.012* Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 20.67 Confidence change rate, P1 15.59 Deviance = 27343.061 Number of estimated parameters = 9 A three-level model with week = level-1, individual participants = level -2, and group = level - 3. Number of participants = 708 and groups = 158. The dependent variable is measured from the 0% end of the line and centimeters were converted to a number out of 100%. †p<0.10,*p <.05, **p<.01 ***p<.001
90
5.3.3.2 Conditional Three-level Model
Hypotheses 1.3a and 1.3b were tested by examining the slopes of the multilevel
model to compare the effects of the outside and unpacking approaches against the inside
and packed (control) approaches on changes to confidence in predictions of completion
time. I estimated a three-level model with time (WEEK) as the only predictor at level 1,
and the three conditions (inside, outside, and unpacked) as predictors at level 2. The
equation at level 3 had no predictors. As for the earlier analysis, similar steps have been
conducted by adding each of the participant control variables (e.g. age, semester, gender
and race) including the group size and teacher into the model. There were no significant
effects for those variables, and therefore, they were excluded from the final model. The
model equation for the effect of the three conditions on confidence in predictions of
completion time [with packed (control) as the reference condition] was the same as that
for completion time bias.
Table 5.6a presents the three-level hierarchical regression results for confidence
in predictions of completion time with the packed (control) condition as the reference.
The slopes for weekly confidence change rate were not significantly different from the
packed (control) condition for either the inside view condition or the unpacked
condition. This result for the unpacked condition does not support Hypothesis 1.3b. The
slope for the outside view condition was marginally significantly steeper than the
packed (control) condition.
In an analysis with the outside view condition as the reference (Table 5.6b), the
slopes for weekly confidence change rate were not significantly different from the
outside condition for the inside view condition or the unpacked condition. This result
for the outside condition does not support Hypothesis 1.3a. Results with the inside view
condition as the reference is presented in Appendix E-3 on page 341-342.
91
Table 5.6a Confidence in predictions of project completion time: Three-level model of the effects of manipulations relative to the packed (control) condition Confidence in predictions of project completion time Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 77.335 1.599 48.373 0.000*** For Inside (UP1), B01 INTERCEPT3, G010 2.623 2.424 1.082 0.280 For Outside (UP2), B02 INTERCEPT3, G020 -0.328 2.191 -0.150 0.882 For Unpacked (UP3) INTERCEPT3, G030 -0.033 2.511 -0.013 0.990 Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly confidence change rate), G100 -0.048 0.209 -0.228 0.820 For Inside (UP1), B11 INTERCEPT3, G110 0.219 0.283 0.774 0.439 For Outside (UP2), B12 INTERCEPT3, G120 0.468 0.278 1.680 0.093† For Unpacked (UP3), B13 INTERCEPT3, G130 0.333 0.371 0.898 0.370 Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
63.493
Level-2 (Student within groups) INTERCEPT1, (Individual initial status), R0 186.451 355 2640.617 0.000*** WEEK slope, (Individual weekly confidence change rate), R1 2.760 355 821.165 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status),U00 46.956 157 268.369 0.000*** WEEK / INTERCEPT2, (Group mean confidence change rate), U10 0.494 157 197.163 0.016*
92
Table 5.6a (Continued) Random effect Variance Component df Chi-square p-value Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 20.12 Confidence change rate, P1 15.19 Deviance = 27338.901
Number of estimated parameters = 15 A three-level model with week = level-1, individual participants = level -2, and group = level - 3. Number of participants = 708 and groups = 158. The dependent variable is measured from the 0% end of the line and centimeters were converted to a number out of 100%. †p<0.10,*p <.05, **p<.01 ***p<.001
93
Table 5.6b Confidence in predictions of project completion time: Three-level model of the effects of manipulations relative to the outside view condition Confidence in predictions of project completion time Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 77.008 1.498 51.391 0.000*** For Inside (IO1), B01 INTERCEPT3, G010 2.951 2.359 1.251 0.212 For Unpacked (IO2), B02 INTERCEPT3, G020 0.295 2.448 0.120 0.905 For Packed (IO3) INTERCEPT3, G030 0.328 2.191 0.150 0.882 Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly confidence change rate), G100 0.420 0.184 2.288 0.023* For Inside (IO1), B11 INTERCEPT3, G110 -0.248 0.265 -0.937 0.350 For Unpacked (IO2), B12 INTERCEPT3, G120 -0.135 0.357 -0.378 0.706 For Packed (IO3), B13 INTERCEPT3, G130 -0.468 0.278 -1.680 0.093†
Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
63.493
Level-2 (Student within groups) INTERCEPT1, (Individual initial status), R0 186.451 355 2640.617 0.000*** WEEK slope, (Individual weekly confidence change rate), R1 2.760 355 821.165 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status),U00 46.956 157 268.369 0.000*** WEEK / INTERCEPT2, (Group mean confidence change rate), U10 0.494 157 197.163 0.016*
94
Table 5.6b (Continued) Random effect Variance Component df Chi-square p-value Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 20.12 Confidence change rate, P1 15.19 Deviance = 27338.901
Number of estimated parameters = 15 A three-level model with week = level-1, individual participants = level -2, and group = level - 3. Number of participants = 708 and groups = 158. The dependent variable is measured from the 0% end of the line and centimeters were converted to a number out of 100%. †p<0.10,*p <.05, **p<.01 ***p<.001
95
5.3.4 Confidence in Predictions of Project Outcome
Figure 5.6 plots means of participants’ confidence in their predictions of an
outcome for the assignment mark in each of the four conditions over the entire eight
weeks of the project. Inspection of Figure 5.6 suggests that there is some small increase
in confidence in predictions of outcome over time for all the four conditions. The
amount of change appears to be the same for the four conditions and this is not
consistent with Hypothesis 1.4a and 1.4b. However, on average, participants in the
outside view, inside view and unpacked conditions appear to be more confident than
participants in the packed (control) condition. There appears to be little difference
between the three manipulated conditions.
72.00
74.00
76.00
78.00
80.00
82.00
84.00
day 1 week1
week2
week3
week4
week5
week6
week7
week8
WEek
Mea
n co
nfid
ence
in p
redi
ctio
ns (i
n pe
rcen
tage
Inside
Outside
Unpacked
Control
Figure 5.6. Mean confidence in predictions of project outcome for the four conditions. (n=689)
96
5.3.4.1 Unconditional Three-level Model
An unconditional multilevel model was estimated for the confidence in
predictions of the outcome, with time (WEEK) as the only predictor in the regression
equations. The results for this unconditional HLM model are presented in Table 5.7.
The estimated initial status (G000) for confidence in predictions of outcome was
76.637. The average weekly confidence change rate (G100) was estimated at 0.313 and
was significant indicating that confidence in outcome predictions increased as the
project due date drew nearer. The variance among participants within groups for initial
status and weekly confidence change rates (i.e., R0 and R1) and the variance
components between groups for mean initial status and mean weekly confidence change
rates (i.e., U00 and U10) were statistically significant.
As for the percentage that lies between groups for both initial status and
confidence change rate, the results show that 13.87% of the variance in the initial status
for confidence in predictions of outcome lies between groups. 20.02% of the variance
for confidence change rate is between groups. In summary, the unconditional model for
confidence in predictions of outcome revealed significant variance among individuals
for initial status and weekly confidence change rate and between groups for mean initial
status and mean confidence change rates. The results for the conditional HLM model
are presented next.
97
Table 5.7 Confidence in predictions of project outcome: A three-level fully unconditional model Confidence in predictions of project outcome Fixed Effect Coefficient S.Error t-ratio p-value For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 76.637 0.809 94.695 0.000*** For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 0.313 0.084 3.718 0.000***
Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
61.640
Level-2 INTERCEPT1, (Individual initial status), R0 194.827 348 3334.193 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 1.336 348 809.823 0.000*** INTERCEPT1/INTERCEPT2,(Group mean status),U00 31.378 155 221.333 0.001** WEEK / INTERCEPT2, (Group mean prediction bias rate),U10 0.334 155 223.308 0.000*** Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 13.87 Prediction bias rate, P1 20.02 Deviance = 33761.008 Number of estimated parameters = 9 A three-level model with week = level-1, individual participants = level -2, and group = level - 3. Number of participants = 689 and groups = 156. The dependent variable is measured from the 0% end of the line and centimeters were converted to a number out of 100%. †p<0.10,*p <.05, **p<.01 ***p<.001
98
5.3.4.2 Conditional Three-level Model
Hypotheses 1.4a and 1.4b were tested by examining the slopes of the multilevel
model to compare the effects of the outside and unpacking approaches against the inside
and packed (control) approaches on changes to confidence for predictions of outcome
success. Since adding each of the participants’ control variables (e.g. age, semester,
gender and race) including the group size and teacher gave no significant effects, I
estimated a similar three-level model as in the confidence in predictions of completion
time, with time (WEEK) as the only predictor at level 1, and the three conditions
(inside, outside and unpacked) as predictors at level 2. The equation at level 3 had no
predictors.
Results for this three-level hierarchical regression are summarized in Table 5.8a
with the packed (control) condition as the reference. The weekly confidence change rate
was not significantly different from the packed condition for any of the experimental
conditions. This result for the unpacked condition does not support Hypothesis 1.4b.
To test Hypothesis 1.4a, which compares the slopes for inside and outside view
conditions, and to explore whether there was a difference between the outside view and
unpacked conditions, an analysis was conducted with the outside condition as the
reference. The slopes of weekly confidence change rate (shown in Table 5.8b) were not
significantly different from the outside view condition for any of the experimental
conditions. Therefore, Hypothesis 1.4a is not supported. Results with the inside view
condition as the reference is presented in Appendix E-4 on page 343-344.
99
Table 5.8a Confidence in predictions of project outcome: Three-level model of the effects of manipulations relative to the packed (control) condition Confidence in predictions of project outcome Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 75.580 1.307 57.809 0.000*** For Inside (UP1), B01 INTERCEPT3, G010 1.873 2.220 0.844 0.400 For Outside (UP2), B02 INTERCEPT3, G020 0.836 2.151 0.389 0.697 For Unpacked (UP3) INTERCEPT3, G030 1.509 2.088 0.723 0.470 Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 0.301 0.121 2.484 0.014* For Inside (UP1), B11 INTERCEPT3, G110 0.106 0.208 0.509 0.611 For Outside (UP2), B12 INTERCEPT3, G120 -0.074 0.201 -0.368 0.713 For Unpacked (UP3), B13 INTERCEPT3, G130 0.013 0.241 0.056 0.956 Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
61.640
Level-2 (student within groups) INTERCEPT1, (Individual initial status), R0 194.804 345 3334.196 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 1.334 345 809.824 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status),U00 30.967 155 220.288 0.001** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.332 155 222.706 0.000***
100
Table 5.8a (Continued) Random effect Variance Component df Chi-square p-value Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 13.72 Prediction bias rate, P1 19.93 Deviance = 33759.444
Number of estimated parameters = 15 A three-level model with week = level-1, individual participants = level -2, and group = level - 3. Number of participants = 689 and groups = 156. The dependent variable is measured from the 0% end of the line and centimeters were converted to a number out of 100%. †p<0.10,*p <.05, **p<.01 ***p<.001
101
Table 5.8b Confidence in predictions of project outcome: Three-level model of the effects of manipulations relative to the outside view condition Confidence in predictions of project outcome Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 76.416 1.708 44.734 0.000*** For Inside (IO1), B01 INTERCEPT3, G010 1.036 2.477 0.418 0.675 For Unpacked (IO2), B02 INTERCEPT3, G020 0.673 2.359 0.285 0.776 For Packed (IO3) INTERCEPT3, G030 -0.836 2.151 -0.389 0.697 Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 0.227 0.161 1.412 0.160 For Inside (IO1), B11 INTERCEPT3, G110 0.180 0.233 0.772 0.441 For Unpacked (IO2), B12 INTERCEPT3, G120 0.088 0.263 0.333 0.739 For Packed (IO3), B13 INTERCEPT3, G130 0.074 0.201 0.368 0.713 Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
61.640
Level-2 (student within groups) INTERCEPT1, (Individual initial status), R0 194.804 345 3334.196 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 1.334 345 809.824 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status),U00 30.967 155 220.288 0.001** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.332 155 222.706 0.000***
102
Table 5.8b (Continued) Random effect Variance Component df Chi-square p-value Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 13.72 Prediction bias rate, P1 19.93 Deviance = 33759.444
Number of estimated parameters = 15 A three-level model with week = level-1, individual participants = level -2, and group = level - 3. Number of participants = 689 and groups = 156. The dependent variable is measured from the 0% end of the line and centimeters were converted to a number out of 100%. †p<0.10,*p <.05, **p<.01 ***p<.001
103
5.3.5 Predictions of Affective Response towards Outcome Success
Figure 5.7 plots mean affective response for predicted and experienced success,
for the 422 participants who reported that they viewed their project outcome as a
success. Inspection of Figure 5.7 suggests that on the first day of the experiment,
participants in each of the four conditions predicted more positive feelings after success
than they actually experienced after receiving their grades.
A mixed between-within subjects analysis of variance was conducted to assess
the effect of the four conditions [inside view, outside view, unpacked and packed
(control)] on participant’s predictions of affective reactions towards outcome success,
across two time periods (on day 1 of the experiment and on the day grades were
received). There was a significant main effect for time, F (1,418) = 359.27, p < .001,
partial eta squared = .46, showing a reduction in positive feelings towards outcome
success across the two time periods. The main effect comparing the four conditions was
significant, F (3,418) = 5.04, p < .01, partial eta squared = .04, suggesting there is a
difference in the effect of the four conditions on the prediction of affective reactions
toward outcome success. The greatest difference is between the outside view condition
and the unpacked condition, with a more extreme affective prediction and response for
the outside condition. There was no significant interaction between the four conditions
and time. These results are further tested with a multilevel analysis.
104
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
day 1 after receiving grades
Time
Affe
ctiv
e re
spon
se
Inside
Outside
Unpacked
Control
Not good
Very good
Figure 5.7. Mean rating of predicted and experienced affective response for success. (n=422)
Figure 5.8 plots mean prediction bias for the affective response towards project
outcome success made by individuals over the entire eight weeks of the project.
Affective prediction bias was measured by subtracting predicted feelings from actually
experienced feelings, so the negative numbers on the vertical axis of Figure 5.8 indicate
that participants consistently overestimated their positive feelings to success as shown
in Figure 5.7. Inspection of Figure 5.8 suggests that the bias is less for the outside view
than for the inside view, which would be consistent with Hypothesis 1.5a, and that the
bias is less for the unpacked condition than for the packed (control) condition, which
would be consistent with Hypothesis 1.5b. Also, the outside view condition appears less
biased than the unpacked condition. These effects were tested statistically with
multilevel models.
105
-3.00
-2.00
-1.00
0.00day 1 week
1week
2week
3week
4week
5week
6week
7week
8
Week
Mea
n pr
edic
tions
bia
s
Inside
Outside
Unpacked
Control
Figure 5.8. Mean prediction bias of individuals’ affective response towards outcome success for the four conditions. Actual response minus predicted response. (n=422)
5.3.5.1 Unconditional Three-level Model
An unconditional multilevel model was estimated for predictions of affective
response towards outcome success, with time (WEEK) as the only predictor in the
regression equations. The results for this unconditional HLM model are presented in
Table 5.9. The estimated initial status (G000) for predictions of affective response
towards outcome success was -1.793 and was significant; supporting the ANOVA result
that bias occurred. The average weekly prediction bias rate (G100) was estimated at
0.023 and was significant, indicating that there was a small overall reduction in bias.
The variance among participants within groups for initial status and weekly prediction
bias rates (i.e., R0 and R1) and the variance components between groups for mean
initial status (i.e., U00) were statistically significant. But the variance between groups
for mean weekly prediction bias rate (i.e., U10) was marginally significant.
106
Table 5.9 Predictions bias for affective response towards project outcome success: A three-level fully unconditional model Predictions bias for affective reactions towards project outcome success Fixed Effect Coefficient S.Error t-ratio p-value For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 -1.793 0.144 -12.469 0.000*** For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 0.023 0.007 3.238 0.002**
Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
0.292
Level-2 INTERCEPT1, (Individual initial status), R0 2.698 189 4876.788 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.007 189 482.858 0.000*** INTERCEPT1/INTERCEPT2,(Group mean status),U00 1.219 115 250.059 0.000*** WEEK / INTERCEPT2, (Group mean prediction bias rate),U10 0.001 115 139.208 0.062†
Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 31.13 Prediction bias rate, P1 14.10 Deviance = 6148.771 Number of estimated parameters = 9 A three-level model with week = level-1, individual participants = level -2, and group = level - 3. Number of participants = 422 and groups = 116. The dependent variable is measured as the difference between participants’ predicted and actual feelings towards assignment marks. †p<0.10,*p <.05, **p<.01 ***p<.001
107
As for the percentage that lies between groups for both initial status and
prediction bias rate, the results show that 31.13% of the variance in the initial status for
predictions of affective response towards outcome success lies between groups. 14.10%
of the variance for prediction bias rate is between groups. In short, the unconditional
model for predictions of affective response towards outcome success revealed
significant variance of participants for initial status and weekly prediction bias rates and
between groups for mean initial status. The results for the conditional HLM model are
reported in the following section.
5.3.5.2 Conditional Three-level Model
To test Hypotheses 1.5a and 1.5b, the intercepts of the multilevel model were
examined to compare the effects of the outside and unpacking approaches against the
inside and packed (control) approaches on predictions of affective response towards
outcome success. The three-level hierarchical regression equation for the affective
prediction towards outcome success was estimated with time (WEEK) the only
predictor at level 1, and the three conditions (with packed as the reference condition) as
predictors at level 2 (at P0). The equation at level 3 had no predictors. All participant
control variables such as age, gender, semester and race were tested initially including
the group size and teacher, but the results showed no significant effects for those
variables. Thus, they were excluded from the model. The final full models that describe
the effects of the three conditions [with packed (control), outside and inside as the
reference condition] on affective reaction prediction bias are presented in Appendix D.
108
Table 5.10a summarizes the results of the three-level hierarchical regressions
with the packed (control) condition as the reference. The intercepts for initial status in
Table 5.10a show that the outside view was significantly less biased than the packed
(control) condition in affective predictions towards outcome success at the start of the
project. The unpacked and inside view condition, however, did not differ significantly
from the packed (control) condition. This result for the unpacked condition does not
support Hypothesis 1.5b.
In an analysis with the outside view condition as the reference (Table 5.10b),
initial status was significantly different from the outside view condition for the inside
view, and thus, the results provide some support for Hypothesis 1.5a. The unpacked
condition was marginally significantly different from the outside view condition.
Results with the inside view condition as the reference are presented in Appendix E-5
on page 345-346.
109
Table 5.10a Prediction bias for affective response towards project outcome success: Three-level model of the effects of manipulations relative to the packed (control) condition Predictions bias for affective reactions towards project outcome success Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 -2.168 0.258 -8.395 0.000*** For Inside (UP1), B01 INTERCEPT3, G010 0.282 0.382 0.738 0.461 For Outside (UP2), B02 INTERCEPT3, G020 1.053 0.349 3.013 0.003** For Unpacked (UP3), B03 INTERCEPT3, G030 0.292 0.417 0.701 0.484 Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, G100 0.023 0.007 3.241 0.002** Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
0.292
Level-2 (student within groups) INTERCEPT1, (Individual initial status), R0 2.688 186 4876.781 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.007 189 482.858 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status), U00 1.104 115 236.716 0.000*** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.001 115 139.139 0.062†
110
Table 5.10a (Continued) Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 29.11 Prediction bias rate, P1 13.98 Deviance = 6141.417 Number of estimated parameters = 12 A Three-level model with time = level-1, individual participants = level-2 and group = level-3. Number of participants = 422 and groups = 116 The dependent variable is measured as the difference between participants’ predicted and actual feelings towards assignment marks. †p<0.10,*p <.05, **p<.01, ***p<.001
111
Table 5.10b Prediction bias for affective response towards project outcome success: Three-level model of the effects of manipulations relative to the outside view condition Predictions bias for affective reactions towards project outcome success Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 -1.115 0.237 -4.702 0.000*** For Inside (IO1), B01 INTERCEPT3, G010 -0.771 0.368 -2.097 0.037* For Unpacked (IO2), B02 INTERCEPT3, G020 -0.760 0.404 -1.880 0.061† For Packed (IO3), B03 INTERCEPT3, G030 -1.053 0.349 -3.013 0.003** Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, G100 0.023 0.007 3.241 0.002** Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
0.292
Level-2 (student within groups) INTERCEPT1, (Individual initial status), R0 2.688 186 4876.781 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.007 189 482.858 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status), U00 1.104 115 236.716 0.000*** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.001 115 139.139 0.062†
112
Table 5.10b (Continued) Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 29.11 Prediction bias rate, P1 13.98 Deviance = 6141.417 Number of estimated parameters = 12 A Three-level model with time = level-1, individual participant = level-2 and group = level-3. Number of participants = 422 and groups = 116 The dependent variable is measured as the difference between participants’ predicted and actual feelings towards assignment marks. †p<0.10,*p <.05, **p<.01, ***p<.001
113
5.3.6 Predictions of Affective Response towards Outcome Failure
Mean predicted and experienced affective responses are plotted in Figure 5.9 for
the 130 participants who reported that they viewed their project outcome as a failure.
Inspection of Figure 5.9 suggests that on the first day of the experiment, participants in
each of the four conditions predicted that they would experience more negative feelings
after failure than were actually experienced after receiving grades.
A mixed between-within subjects analysis of variance was conducted to assess
the effect of the four conditions [inside view, outside view, unpacked and packed
(control)] on participant’s affective predictions towards outcome failure, across two
time periods (on day 1 of the experiment and on the day grades were received). There
was a substantial main effect for time, F (1,126) = 37.94, p < .0005, partial eta squared
= .23. The main effect comparing the four conditions was not significant, suggesting no
difference in the effect of the four conditions on the overall level of affective reactions
toward outcome failure. There was no significant interaction between the four
conditions and time.
114
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
day 1 after receiving grades
Time
Affe
ctiv
e re
spon
se
Inside
Outside
Unpacked
Packed
Very bad
Not bad
Figure 5.9. Mean rating of predicted and experienced affective response for failure. (n=130)
Figure 5.10 plots mean prediction bias for affective response towards project
outcome failure made by individuals over the entire eight weeks of the project.
Affective prediction bias was measured by subtracting predicted feelings of failure from
reported actually experienced feelings, so the positive numbers on the vertical axis of
Figure 5.10 indicate that, on average, participants overestimated the extent of their
negative feelings to failure. The graph also suggests that participants in the outside view
condition are less biased than those in the inside view condition, which is consistent
with Hypothesis 1.6a. But participants in the unpacked condition do not seem to be
more or less biased in their predictions of affective response towards outcome failure
than those in the packed (control) condition, which is inconsistent with Hypothesis 1.6b.
Participants in the outside view condition appear to be less biased than participants in
the other three conditions.
115
0.00
1.00
2.00
3.00
day 1 week1
week2
week3
week4
week5
week6
week7
week8
Week
Mea
n pr
edic
tions
bia
s
Inside
Outside
Unpacked
Control
Figure 5.10. Mean prediction bias of individuals’ affective response towards project outcome failure for the four conditions. Actual response minus predicted response. (n=130)
5.3.6.1 Unconditional Three-level Model
An unconditional multilevel model was estimated for predictions of affective
response towards outcome failure, with time (WEEK) as the only predictor in the
regression equations. The results for this unconditional HLM model are presented in
Tables 5.11. The estimated initial status (G000) for predictions of affective response
towards outcome success was 1.971 and was significant. The average weekly prediction
bias rate (G100) was estimated at 0.055 and was also significant, suggesting a slight
increase in bias.
The variance among participants within groups for initial status and weekly
prediction bias rates (i.e., R0 and R1) and the variance components between groups for
mean initial status and mean weekly prediction bias rates (i.e., U00 and U10) were
statistically significant.
116
Table 5.11 Prediction bias for affective response towards project outcome failure: A three-level fully unconditional model Predictions bias for affective reactions towards project outcome failure Fixed Effect Coefficient S.Error t-ratio p-value For INTERCEPT1, P0
INTERCEPT2, B00
INTERCEPT3, (Average initial status),G000 1.971 0.288 6.840 0.000*** For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 0.055 0.013 4.129 0.000***
Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
0.455
Level-2 INTERCEPT1, (Individual initial status), R0 4.385 66 1699.213 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.006 66 112.300 0.001** INTERCEPT1/INTERCEPT2,(Group mean status),U00 1.709 43 84.826 0.000***
WEEK / INTERCEPT2, (Group mean prediction bias rate),U10 0.002 43 65.508 0.015* Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 28.05 Prediction bias rate, P1 31.09 Deviance = 2612.881 Number of estimated parameters = 9 A three-level model with week = level-1, individual participants = level -2, and group = level - 3. Number of participants = 130 and groups = 44. The dependent variable is measured as the difference between participants’ predicted and actual feelings towards assignment marks. †p<0.10,*p <.05, **p<.01 ***p<.001
117
As for the percentage that lies between groups for both initial status and
prediction bias rate, the results show that 28.05% of the variance in the initial status for
predictions of affective response towards outcome failure lies between groups. The
results for prediction bias rate show that 31.09% of the variance is between groups. In
summary, the unconditional model for predictions of affective response towards
outcome failure revealed significant variance of participants for initial status and weekly
prediction bias rates and between groups for mean initial status. The results for the
conditional HLM model are presented next.
5.3.6.2 Conditional Three-level Model
Hypotheses 1.6a and 1.6b were tested by examining the intercepts of the
multilevel model to compare the effects of the outside and unpacking approaches
against the inside and packed (control) approaches respectively on predictions of
affective response towards outcome failure. Similar three-level hierarchical regression
equations as in estimating the predictions of affective response towards outcome
success were used, as adding the control variables into the model gave no significant
effects for those variables.
Results of the three-level hierarchical regression with packed (control) condition
as the reference are summarized in Table 5.12a. Initial status was not significantly
different from the packed condition for any of the experimental conditions. Therefore,
the result for the unpacked condition does not support Hypothesis 1.6b.
Similar results were obtained in an analysis with the outside view condition as
the reference (Table 5.12b). The initial status was not significantly different from the
outside view condition for any of the experimental conditions. Thus, Hypothesis 1.6a is
not supported. Results with the inside view condition as the reference are presented in
Appendix E-6 on page 347-348.
118
Table 5.12a Prediction bias for affective response towards project outcome failure: Three-level model of the effects of manipulations relative to the packed (control) condition Predictions bias for affective reactions towards project outcome failure Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 1.993 0.429 4.460 0.000*** For Inside (UP1), B01 INTERCEPT3, G010 -0.137 0.742 -0.185 0.854 For Outside (UP2), B02 INTERCEPT3, G020 -0.475 0.650 -0.731 0.467 For Unpacked (UP3), B03 INTERCEPT3, G030 0.424 0.526 0.806 0.422 Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, G100 0.055 0.013 4.119 0.000*** Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
0.455
Level-2 (student within groups) INTERCEPT1, (Individual initial status), R0 4.392 63 1699.207 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.006 66 112.300 0.001** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status), U00 1.522 43 80.664 0.001** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.002 43 65.863 0.014*
119
Table 5.12a (Continued) Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 25.73 Prediction bias rate, P1 31.74 Deviance = 2610.588 Number of estimated parameters = 12 A Three-level model with time = level-1, individual participants = level-2 and group = level-3. Number of participants = 130 and groups = 44 The dependent variable is measured as the difference between participants’ predicted and actual feelings towards assignment marks. †p<0.10,*p <.05, **p<.01, ***p<.001
120
Table 5.12b Prediction bias for affective response towards project outcome failure: Three-level model of the effects of manipulations relative to the outside view condition Prediction bias for affective reactions towards project outcome failure Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 1.518 0.532 2.850 0.007** For Inside (IO1), B01 INTERCEPT3, G010 0.338 0.796 0.425 0.671 For Unpacked (IO2), B02 INTERCEPT3, G020 0.899 0.599 1.502 0.136 For Packed (IO3), B03 INTERCEPT3, G030 0.475 0.650 0.731 0.467 Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, G100 0.055 0.013 4.119 0.000*** Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
0.455
Level-2 (student within groups) INTERCEPT1, (Individual initial status), R0 4.392 63 1699.207 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.006 66 112.300 0.001*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status), U00 1.522 43 80.664 0.001** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.002 43 65.863 0.014*
121
Table 5.12b (Continued) Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 25.73 Prediction bias rate, P1 31.74 Deviance = 2610.588 Number of estimated parameters = 12 A Three-level model with time = level-1, individual participant = level-2 and group = level-3. Number of participants = 130 and groups = 44 The dependent variable is measured as the difference between participants’ predicted and actual feelings towards assignment marks. †p<0.10,*p <.05, **p<.01, ***p<.001
122
5.4 Conclusion
This chapter has presented a series of multilevel analyses to test the hypotheses
for Study 1, presented in Chapter 3. The results have also been described through
graphical analyses, where means of responses over weeks of the experiments are
plotted. The results of multilevel analyses show several significant effects of the outside
view and unpacking approaches on the predictions made regarding task completion time
and outcome, and on affective predictions towards outcome success.
There were no hypotheses on the difference between the outside view and the
unpacked conditions, but the exploratory analyses demonstrated some interesting
outcomes. There were similar results for the outside view and unpacked approach on all
except on one dependent measure. There was a marginally significant difference
between the outside view and the unpacked condition on predictions of affective
response towards outcome success.
The results from the graphical analyses suggest that participants in the outside
view and in the unpacking approach were less biased in their prediction of completion
time, but more biased in their prediction of outcome success than participants in the
inside view and in the packed (control) conditions. There was little change in
confidence in prediction of completion time, but there was a more substantial increase
in confidence in predictions of outcome over time for each of the four conditions. The
graphs indicate that participants in each of the four conditions predicted more positive
feelings after success than actually experienced after receiving grades, and the bias was
less for the outside view than for the inside view and was less for the unpacked
condition than for the packed (control) condition. Participants in each of the four
conditions also predicted they would experience more negative feelings after failure
than were actually experienced after receiving grades. There were no significant
123
differences between the conditions for impact bias for failure. These research findings
are discussed in the next chapter (Chapter 6).
124
CHAPTER 6
DISCUSSION – STUDY 1
6.1 Introduction
The aim of Study 1 was to test two theories of the planning fallacy and to extend
the theoretical explanations of the planning fallacy to domains other than time to
completion of a project, specifically to prediction of project outcome, to confidence in
prediction of project completion time and outcome, and to predictions of affective
reactions to project outcome success and failure. The discussion that follows is
organized around the hypotheses presented in Chapter 3.
6.2 Completion Time Predictions
Study 1 replicated the planning fallacy effect on task completion time prediction
for both the outside view and the unpacking approaches. As implied by the two theories
of the planning fallacy, participants who carry out behavior involved either in the
outside view prescription or the unpacking prescription make more accurate task
completion predictions than those who do not, that is, those in the control condition.
The inside/outside views approach claims that it is necessary to consider distributional
information about other projects (the outside view), as this gives information about the
likely duration of a project, and the likelihood of intervening events that could affect
project completion. According to that approach the inside view, focusing on the project
at hand, leads to neglect of relevant distributional information about obstacles to
completion. In contrast, the unpacking or enumerating approach does just that, it
focuses attention on the project at hand, requiring that all steps to be performed are
enumerated and considered in detail before making a prediction regarding completion
time. Unlike prior inside / outside view and unpacking approach research, these two
125
approaches were tested simultaneously on the same task verifying that these very
different prescriptions can both reduce the planning fallacy.
The outside view condition was significantly less biased than the control
condition, providing evidence that an outside view manipulation that only involves
recall is sufficient to reduce the planning fallacy. For the comparison of the inside and
outside views, although examination of Figure 5.1 suggests participants in the outside
view condition were less biased than participants in the inside view condition, this was
not confirmed by the statistical test of the intercepts. Possibly, this difference might
have reached significance if the instructions for the inside and outside views had been
more different. They were designed to be as similar as possible with only the minimal
word differences necessary to provide the manipulation.
The outside manipulation focused attention on past experiences. This
manipulation relies on recall. Participants were asked to recall the kind of events that
have happened in the past, which includes all the problems that have happened that have
impeded their project progress and performance. They were also asked to remember any
situation that has facilitated their project progress and performance. Participants in the
inside manipulation were also asked the same, but focused on the future. They were
asked to anticipate any problems that will impede their project progress and
performance and any situation that will facilitate their project progress and performance.
Buehler, Griffin and Ross (1994) suggest that asking participants to recall past
experiences before making predictions may not be enough to reduce optimistic
prediction unless they are also asked to describe how their past experiences could affect
their current predictions, which amounts to actively moderating their predictions.
Participants in the outside and inside conditions were not asked to do this.
More accurate predictions about project completion were also obtained when
participants were asked to unpack their task components as compared to those who do
126
not unpack the task. Prompting people to unpack a task into its subcomponents actually
changes the manner in which the task is represented. By describing tasks in more detail
through listing down components needed for a future project, and listing all the steps
that they planned to carry out before making the prediction, individuals get an
indication that the task would take a longer time, and thus, increase their time estimates.
In earlier planning fallacy research the importance of unpacking a task into its
subcomponents has been emphasized for its ability to enhance prediction accuracy of
completion time (Kruger & Evans, 2004). The current results provide support for the
unpacking effect on prediction accuracy.
Past research in separate studies has shown that manipulations of both the
outside view and enumerating (unpacked versus packed) reduce the planning fallacy.
Therefore, I was not able to predict which of these methods would produce a greater
effect. The findings that emerge from this study suggest that there was no difference
between them. Both approaches have similar effects and reduce the planning fallacy for
completion time prediction accuracy. It also indicates that for completion time
predictions in project planning applications, a detailed focus on the project at hand that
requires a full enumeration of the component tasks may be as accurate as taking an
outside approach to completion time prediction.
Results from this study also demonstrate that the inside view, which also focuses
on the project at hand, was not significantly different to the unpacked condition for
completion time predictions. This result leads to a question of whether the difference in
wording of instructions would have any implication on the planning fallacy. In this
study, participants in the inside view were asked to think about the kind of events that
would be likely to happen in the future, such as problems that will impede their project
progress and performance and to think about any aspect of the project or situations in
the future that will facilitate their project progress. On the other hand, participants in the
127
unpacked condition were asked to list down components that are needed for project
progress and performance and to list down each and every thing that they plan to do in
completing the project. While the current findings did not reach significance for the
affect of this slight difference of instructions on the planning fallacy, Figure 5.1
suggests there may be an effect, but no hypothesis was framed to compare the
unpacking approach and the inside view. If the slight difference in wording of
instructions contributes to the effect of increasing or decreasing the planning fallacy, it
may also have an implication for interpreting the mixed results of past research, and for
prescriptions for reducing the planning fallacy in practice. In the past, Kruger and Evans
(2004) have attempted to find the similarity between the inside view and the unpacking
approach by highlighting that if the inside view focuses on the “non-focal
subcomponents of the task, they would expect an increase in time estimates, and a
decreased planning fallacy” (p.596).
6.3 Outcome Predictions
It was hypothesized that the two approaches that have been found to reduce the
planning fallacy for completion time predictions could be extended to improve other
types of project related predictions, such as predicting the success of the project
outcome. The manipulations of both the outside view and the unpacking approach,
however, gave opposite effects to what was expected based on their effects on the
planning fallacy. I hypothesized that the two approaches should improve outcome
prediction accuracy, just as they do for completion time, because they contain more
information that can help individuals to make a more accurate outcome prediction. But,
the present study shows that inducing people to consider the outside view or
alternatively, unpacking the project task, led to more optimistic outcome predictions
compared to the control condition, increasing rather than reducing the optimistic bias
for outcome success. This is the first time this effect has been demonstrated. It is
128
difficult to find an explanation for this optimistically biased outcome prediction when
the outside view was adopted. Support theory can explain this phenomenon with respect
to unpacking. While enumerating all the component tasks expands the apparent size of
the project, and therefore increases predictions of how long it will take (reduce the
planning fallacy), enumeration will also increase availability and support occurrence of
the components, and therefore, support for the success of the overall project.
6.4 Confidence in Predictions
Evidence was also obtained on how strongly participants really believed in their
predictions, by assessing their confidence. The present study proposed and tested an
account of changes in confidence based on the two theories of the planning fallacy. It
was hypothesized that participants who adopt the outside view or the unpacking
approach would demonstrate smaller changes in confidence in predictions of project
completion time and outcome than those in the inside view or control conditions. The
results show no support for these two hypotheses. The statistical analyses suggest that
recalling problems of past projects, or alternatively, unpacking tasks does not produce
effects on the change in confidence over time. Participants showed a marginal increase
in confidence across time regarding their prediction of completion time, but showed an
increasing confidence in predictions of outcome success over time.
This study extends past research by showing that the outside view and
unpacking approach produce similar results for confidence in prediction of completion
time. Unfortunately, that result does nothing to distinguish between the competing
theoretical explanations on which these two approaches are based.
6.5 Predictions of Affective Response towards Outcome Success and Failure
For affective reactions I framed hypotheses only for predictions of reactions to
outcome, and did not propose that these would change over time differentially for the
four conditions. I argued that through the distributional information it provides, the
129
outside view should lead to a more realistic expectation for the outcome, and therefore,
a more accurate prediction of affective response to the outcome when it occurs,
compared to the inside view. As the enumerating or unpacking approach also reduces
the planning fallacy, I hypothesized that it also would lead to a more accurate prediction
of affective reaction than the packed (control) condition. The results support the
hypothesis for the outside view for affective predictions towards outcome success but
not for affective predictions towards outcome failure. However, the results failed to
support any of the hypotheses for the unpacking approach. Furthermore, while bias was
reduced for the outside view approach, the absolute level of affective prediction and
response to success was the most extreme in that condition.
Therefore, results from this study show that participants prompted to focus on
relevant past experiences improved affective prediction accuracy towards outcome
success, reducing impact bias for positive events. Prompting participants to focus on
past experiences may help them to consider that positive events can unfold in many
different ways, and thus, moderate the effects of temporal focus on affective prediction
towards outcome success. However, similar effects of recalling past experiences was
not found in affective predictions accuracy towards outcome failure. The findings imply
that encouraging people to consider past experiences may not always result in
improving accuracy of affective predictions, particularly, in predicting negative
reactions towards outcome failure. This may be due to difficulty to extract an
appropriate set of past experiences. This result should be considered cautiously as the
number of participants who viewed their project as a failure was not great.
Prompting participants to unpack project tasks into their subcomponents failed
to have a significant effect on the accuracy of the affective predictions towards outcome
success and failure. The statistical results of intercept suggest that both the unpacking
and pack (control) have similar impact bias. The failure of unpacking to reduce the
130
impact bias is consistent with Van Boven and Epley’s (2003) proposition that “more
detailed descriptions may make it easier to summon evidence that suggests more
extreme evaluation” (p.264). They based their proposition on support theory. In their
study, they found that participants prompted to unpack the category of events results in
more extreme affective predictions.
6.6 Study 1 Limitations
There are limitations in the design of this study that might influence the
interpretations and generalizations of these findings. These issues are discussed next.
The study was aimed at understanding processes involved in planning projects in
organizations, but student projects were used. These were real projects with real
outcomes and consequences, but it could be argued that the predictions made by the
students in an educational context do not have the same kinds of consequences as occur
for projects in business, such as HRIS projects. The ubiquity of the planning fallacy in
practice, however, suggests that the magnitude of consequences does not necessarily
improve accuracy of prediction. Indeed, the planning fallacy has been found to be
exacerbated by incentives (Buehler et al., 1997).
The student group projects were not fully free to vary the completion time
without constraint. This is like real business projects, as many real business projects are
also constraint by the deadline agreed in the contract. In software projects for example,
the process of completing and installing new systems or upgrading the existing system
must be conducted within a specified time to avoid distrupting business operations.
Business projects that fail to deliver as planned may face penalties in terms of financial
implications, and loss of future contracts. Besides, past studies on the planning fallacy
have also used student projects (Buehler & Griffin, 2003; Buehler et al., 1994).
Apart from that, the study used group projects, but the prediction was made
individually. This according to Heath and Gonzalez (1995) is known as “interactive
131
decision making” which is different from the “group decision making.” Interactive
decision making involves making decisions after interactions with others without
involving a consensual group procedure. In this study, participants do interact with their
group members during group meetings, and with their teachers, their seniors and other
friends from other groups in the process of completing their project assignments. I did
not further examine effects of interaction with others as a source of ongoing outside
view or unpacking information on predictions of task completion time and outcomes. A
study conducted by Heath and Gonzalez (1995) has revealed that the information
collected through the interaction process alone helps individuals to make decisions, but
it did not improve decision accuracy in their study.
No manipulation check was included in Study 1. If a manipulation check had
been conducted on day 1 of the experiment, after the manipulation, it may have affected
the behavior in subsequent weeks of the experiment. If a manipulation check was
conducted in week eight of the experiment, the participants’ memories of the
instructions may have faded through the lapse of time, and this would render the
manipulation check irrelevant to the study.
6.7 Conclusion
This study represents the first attempt to compare two theories of the planning
fallacy, the inside / outside view and the unpacking approach in the same study, and to
extend theoretical explanations of the planning fallacy to domains other than predictions
of time to completion of a project, specifically to outcome predictions, to confidence in
predictions and to predictions of affective reactions to project outcomes. In doing so,
these two theories were tested in an experimental study using students’ group project
assignments.
Given the findings and acknowledging the limitations of Study 1, several areas
of potential future research exist. This and the implications of these results for planning
132
fallacy research and practice are discussed in Chapter 11. A further study, which
follows, was conducted to understand processes involved in planning real projects in
organizations. In Study 2, planning approaches that are commonly used during Human
Resource Information System (HRIS) development projects are explored, and their
influence on the accuracy of predictions about the success or failure of the project in
terms of project completion time and outcome are examined. In Chapter 7, the literature
on HRIS is reviewed, with particular attention being paid to the role of planning on the
success of HRIS project implementation.
133
CHAPTER 7
INTRODUCTION TO STUDY 2 AND DEVELOPMENT OF THE
HYPOTHESES
7.1 Introduction
To understand processes involved in planning real projects in organizations, and
since to my knowledge, no study has been conducted on the planning fallacy with
Human Resource Information System (HRIS) projects, the second study was conducted
in an applied field environment involving HRIS development projects in place in
organizations in Malaysia. The aim was to explore which of the planning approaches are
commonly used by planners during HRIS project planning, and to examine how the
approach used during the planning process influences the accuracy of predictions about
the success or failure of the project in terms of project completion time and outcome.
Planners’ predictions regarding their affective response towards their project outcome
success or failure are also tested.
To examine the approaches used in the task of planning an HRIS development
project, it is necessary to understand the nature of human resource information systems,
and to examine whether predictions about the outcomes of those projects are successful,
it is necessary to understand the purposes the human resource information system are
intended to fulfill.
The structure of the remainder of this chapter is as follows. The discussion
begins with the definition and the potential value of HRIS. This is followed with
findings from past studies on HRIS effectiveness and possible reasons for the
unsuccessful outcome of HRIS projects. The discussion continues with HRIS planning
processes and challenges. The chapter ends with the development of the hypotheses for
the second study.
134
7.2 Background of the Second Study
An HRIS is a system for gathering and maintaining the data that describes the
human resources, transforming the data into information and then reporting the
information to users. Some of the HRIS definitions given in the literature are
summarized in Table 7.1. Some authors (Ceriello & Freeman, 1991; Hendrickson,
2003) suggest a broader concept of well functioning HRIS that includes people such as
managers, programmers, analysts, technical support, and users from both within human
resources and outside; policies and procedures that describe how to handle specific data
entry, transaction updates, report generation, system maintenance and related activities;
and data required to manage the HR function, and should not be limited to technical
parts of the system such as computer hardware and software.
Table 7.1 Definitions of human resource information systems (HRIS)
Definitions of Human Resource Information System Authors
“A system used to acquire, store, manipulate, analyze, retrieve and distribute pertinent information regarding an organization’s human resources”.
(Tannenbaum, 1990, p.27)
“A computer-based technique of collecting, storing, maintaining data and retrieving information about employees and their jobs”.
(Targowski & Deshpande, 2001, p.44)
“The composite of data bases, computer applications, and hardware and software that are used to collect, record, store, manage, deliver, present, and manipulate data for Human Resources (HR)”.
(Broderick & Boudreau, 1992, p.17)
“A total sum of all systems that store data, classify data and make it easily available to the decision maker in so far as the human resource is concerned”.
(Sadri & Chatterjee, 2003, p.86)
“Any application of computers that an organization utilizes in order to manage its workforce”.
(Romm, Pliskin, & Weber, 1995, p.63)
“A functional database accessed on site or remotely, designed to hold data on employees and to support HR activities such as recruitment, selection, performance management, training and development”.
(Tansley & Newell, 2007, p.350)
135
Beside HRIS, terms like virtual HR(M), web-based HR, e-HR, e-HRM, HR intranet,
computer-based human resource management systems (CHRIS) and HR portals are also
widely used in the literature. Even though to some people e-HR is perceived as
equivalent to HRIS, some argue that there is a fundamental difference between e-HR
and HRIS. Ruel, Bondaruk and Looise (2004) for example, claimed that while the
implementation of HRIS is basically directed towards the HR department itself where
the users are mainly HR staff, the target group of e-HR is people outside of the HR
department such as employees and managers. Ruel, Bondaruk and Looise argue that the
difference between HRIS and e-HR can be identified “as the switch from the
automation of HR services towards technological support of information on HR
services” (p.365).
Strohmeier (2007) believes that a term such as HRIS is rather broad with respect to
technology application, whereas terms such as virtual HR(M), web-based HR, or e-HR
are all used with direct attention to main characteristics of the same phenomenon but
with narrower intentions. Strohmeier argues that e-HRM is more of the “application of
information technology for both networking and supporting at least two individuals or
collective actors in their shared performing of HR activities” (p.20).
The implementation of HRIS has been seen as an effort towards reducing costs
and improving the quality of HR services. Many are confident that by adopting IT into
HR, HR can deliver better services, build a far more accurate picture of their workforce
and produce more accurate information that can enable both executives and HR to make
better decisions, especially decisions that are related to the workforce. It has been
claimed that with IT, HR services can be delivered at a lower cost and in a consistent,
high quality and timely manner. IT makes it possible to increase transactions without
increasing resources, increase timeliness through processing power, increase
performance (e.g., accuracy, precision, completeness), and simplify processes
136
(Lengnick-Hall & Moritz, 2003). With the Internet, organizations can become more
collaborative, connected and responsive to the changing needs of the workforce, and
work arrangements can be modified so that work can be performed from many locations
at any time (Stone, Stone-Romero, & Lukaszewski, 2006).
Though the potential benefits of adopting IT into HR have been emphasized in
the literature, and these are summarized in Table 7.2, the reality shows that many
organizations are not implementing HRIS as effectively or as widely as expected. In
fact, there is evidence showing that many HRIS projects are not as successful as
expected and users are not as happy with the outcome as they expected they would be,
once the system is implemented. HRIS projects have been reported to be unable to
deliver the expected outcome, such as the system failed to provide what the organization
needs, have inaccurate or insufficient data, are difficult to access, have disappointing
end functionalities or are lacking the important functionalities, the logic of the HRM
administration did not match with the logic of the system, the information needed is not
available on time or it is out of date, or there is a lack of interaction with other systems
(Bondaruk & Ruel, 2008; Caplan, 2004; Chapman & Webster, 2003; Kavanagh et al.,
1990; Macy, 2004; Russell, 2006). It has also been claimed that implementation of
HRIS does not help in increasing accountability, making better decisions and better
communications and shifting the HR focus to strategic issues (Pearson, 2001). Past
studies have also shown that the effectiveness of using IT in HR was mixed, and many
were being underutilized. These are discussed next.
137
Table 7.2 Summary of potential values of human resource information systems (HRIS)
Applications Authors
On-line recruitment or e-recruitment
• enable to reach a wider range of qualified applicants, including those in international markets;
• reduce recruitment costs; cut down the hiring cycle time; • attract the interest of highly competent individuals who are
not currently searching for jobs that is, passive job-seeker; • facilitate the recruitment process for overseas operations; • address specific labor market niches; automate candidate
screening and tracking, manage a larger volume of resumes and applicant information; and
• streamline the recruitment process
(Baillie, 1996; Buckley, Minette, Joy, & Michaels, 2004; Bussler & Davis, 2001-2002; Cappelli, 2001; Cober, Brown, Levy, Cober, & Keeping, 2003; Galanaki, 2002; Greengard, 1998; Lin & Stasinskaya, 2002; Martinsons, 1997; Nink & Chieke, 2004; Shand, 2000; Thaler-Carter, 1998b; Zusman & Landis, 2002).
e-selection
• minimize costs and maximize the utilization of their human capital
(Kehoe, Dickter, Russell, & Sacco, 2005)
e-performance management
• simplifies the process of completing appraisal forms through the use of “canned” sentences and paragraph;
• provide raters with online evaluation of the performance where feedback from multiple raters can be received in a timely manner;
• can track and compare unit performance where the data gathered can be used to identify human resource problems, highlight exceptional performance, uncover potential errors and provide feedback to managers on the incidence of such errors;
• gives a greater span of control without having to observe employees’ behavior directly in the process of assessing their performance;
• data related to number of work units completed, number of key strokes, time spent on the task, and error rates can be gathered
(Cardy & Miller, 2003, 2005; Stone et al., 2006)
e-learning / computer-based learning / on-line learning / distributed learning / web-based training
• provide consistent, worldwide training; reduce delivery cycle time;
• increase learner convenience; reduce information overload; improve tracking;
• lower expenses; save travel cost and time spent away from the office;
• able to practice using pre-recorded learning; can involved with real time chat sessions;
• allows for more flexible management of one’s own growth and learning;
• eliminate bureaucratic controls and procedures; and • provide information storage for easily access and sharing
knowledge
(Berry, 1993; Bussler & Davis, 2001-2002; Kotylar & Saks, 2001; Welsh, Wanberg, Brown, & Simmering, 2003)
138
7.3 Research on HRIS Effectiveness
A study conducted by Gardner, Lepak and Bartol (2003) on 1,969 HR
executives concluded that with more extensive use of IT, HR professionals are able to
provide increased information responsiveness, have more information autonomy and
have more external professional links. Though HR professionals spent less time on
routine tasks, they had to spend more time on IT related activities and on developing IT
related qualifications.
Studies concerning a more specific application such as the effectiveness of
online recruitment or e-recruiting have shown mixed results. While cost and efficiency
are among the advantages reported in using online recruitment, the quality of the
applicant pool has been seen as a potential disadvantage. In Jatuso and Sinar’s (2003)
study, the use of e-recruiting was found able to attract candidates with high levels of
drive, previous achievement and work experience. In terms of cost, results from two
case studies have shown how organizations that used an automated recruiting and
screening system reported substantial cost savings from reducing turnover, staffing cost
and increasing hiring process efficiencies (Buckley et al., 2004). Based on data
collected over a six-year period (December 1999 – June 2006) within the UK, Parry and
Tyson (2008) found not only that online recruitment reduced costs through reducing the
use of papers and reducing agency costs, but also reduced the time taken to hire
employees.
Even though e-recruiting is able to attract more applicants from a larger
geographical area, Chapman and Webster (2003) found that they are not always of
higher quality. McManus and Ferguson (2003) also found a similar outcome, where
candidates recruited through the internet are found to have less favorable background
characteristics than candidates recruited through traditional methods, particularly with
respect to employment stability (candidates are frequent job hoppers). Parry and Tyson
139
(2008) also reported similar findings where organizations that used online recruitment
did receive very large numbers of applications, but a high proportion of them were not
suitable for the position advertised.
In terms of the ability of online recruitment to reach a more diverse population,
studies have shown mixed feelings from the users. Though McManus and Ferguson
(2003) did not find any difference between using online recruitment and traditional
methods, other studies have shown opposite results. Respondents who were surveyed
and interviewed in Galanaki’s (2002) study reported feeling doubtful about the potential
of online recruitment to reach the “passive seeker”. Similar results were also shown in
Parry and Tyson’s (2008) study where users reported feeling doubtful about the ability
of online recruitment to get the kind of candidates that the organization required, to
target the passive job seekers, and to reach a diverse population. Apart from
recruitment, organizations also report mixed success in acquiring and implementing
technologies that support the application, screening and selection process (Chapman &
Webster, 2003). Surprisingly, Chapman and Webster found larger organizations report
having less success than smaller ones.
With regard to training, while potential benefits of e-learning discussed in the
literature are appealing, research on the effectiveness of e-learning shows little
difference between web-based training (WBT) and instructor-led training where scores
in both media show an exceptionally high level of learning (Coppola & Myre, 2002).
Results from interviews conducted with 22 individuals who have been involved with e-
learning efforts in SMEs found that the use of e-learning may not be equally effective
for everyone and for all types of courses (Welsh et al., 2003). Learners, with low
computer skills or with anxiety with the computers, may have difficulties using the
computer as a learning tool. Also, e-learning may be useful for training that emphasizes
cognitive learning outcomes that involve less complex knowledge and intellectual skill,
140
but not suitable for more advanced classes or for those requiring soft skills, and for
classes that teach skills with psychomotor components.
In summary, the mixed results of the effectiveness of using IT in HR processes
may indicate the ability of users to use the system which can be related to the extent and
understanding of functionality and support. Or it may indicate that the shift from using
the manual to a more advanced system is not as simple as hoped, with many
organizations underestimating the challenge of adopting technology solutions in HR
processes. The mixed results may also indicate a failure of the HR function to make
advanced used of the technology.
Based on interviews with HR groups in ten Fortune 500 companies, Broderick
and Boudreau (1992) found that the main focus of HRIS was on managing basic human
resource functions such as record keeping, payroll, compensation and benefits
administration. Other studies have also indicated that most organizations use their HRIS
as no more than an “electronic filing cabinet” for keeping relevant staff information like
age, gender, years of service, classification, qualification and previous work history and
processing routine administrative tasks, rather than facilitating a strategic focus for HR
within the organization (Bassett, Campbell, & Licciardi, 2003; Kinnie & Arthurs,
1996).
Ball (2001) found information in core administration, training and recruitment
was stored for administrative purposes rather than for analysis or decision support. In
the training and development area, HRIS has been used most frequently for monitoring
and administrative purposes such as to store course administration and evaluation
information. The budget control for training was also conducted manually and was kept
away from the HRIS. In the recruitment area, the system was used for tracking
applicants through the recruitment process, interview management and media response
analysis, rather than for more analytical tasks like skills matching.
141
A recent study conducted by Hussain, Wallace and Cornelius (2007) also
showed similar results in terms of HRIS usage. Their report was based on a survey
conducted on HR professionals from 450 organizations in the UK and in-depth semi-
structured interviews conducted with 11 senior organizational executives to whom the
HR professional reported. Slightly less than 50% of the companies they surveyed use
HRIS or other software to support strategic HR tasks, with differences in advanced task
usage or in strategic decision making between small to medium enterprise (SME) and
larger companies.
The problem of underutilized HRIS continues to be a fundamental challenge for
many in HR. Although past studies have made some attempts to consider reasons for
this low usage, there is still a need for an effective approach to increase the use of these
systems. Some of the reasons for unsuccessful HRIS projects, particularly one that
relates to underutilization of the system are discussed next.
7.4 Reasons for Unsuccessful HRIS Projects
Several reasons have been proposed for the low usage of Human Resource
Information Systems (HRIS) in the HR function, and this includes lack of financial
support, lack of top management support, the organizations’ size, human resource
strategy and users’ ability to use the system.
Caplan (2004) and Kovach and Cathcart (1999) for example, believed that lack
of financial support and interest from key people in the organization contribute to the
underutilization problems. According to Huo and Kearns (1992), the introduction of
HRIS always involves a substantial amount of money, and in many cases it is not easy
to justify. If HR tasks have been done well previously in a manual fashion, top
management may not see any good reason for investing in the computerization process,
unless HR people are able to demonstrate tangible return on investment. Apart from
financial support, top management support is related to the adoption of innovative
142
systems (Teo, Lim, & Fedric, 2007; Thong, 1999). Greater support from top
management not only implies getting greater resources for the development of HRIS
applications, but also overcoming user resistance to ensure a quicker pace of change and
more HRIS applications can be adopted.
Financial support also explains why larger organizations have more potential to
use HRIS than smaller organizations. Compared to larger organizations, smaller ones do
not have enough budget or resources to install all the applications needed to assist with
strategic decisions. A survey conducted on 500 companies in Hong Kong found that the
greatest barrier to the adoption of HRIS was insufficient financial support (Ngai & Wat,
2006). In reality, many of these HR systems are partially installed with restrictive
priorities having to be set, and often a simple, off-the-shelf software version is
implemented. Studies have shown that smaller organizations usually go for low cost and
low risk, more flexible software or in-house HRIS development (Ball, 2001; Thaler-
Carter, 1998a). In Hussain, Wallace and Cornelius’s (2007) study, larger companies
were found to have full computerization of strategic HR tasks as compared to small to
medium enterprises (SME).
Larger organizations also have a greater need for computerized HRIS as they
have more complicated tasks of coordination, where information processing
requirements are much higher compared to smaller firms (Thaler-Carter, 1998a). Based
on a postal survey to 470 respondents who were randomly selected from the Financial
Analysis Made Easy (FAME) database, Ball (2001) found that organizational size
determines whether an organization has an HRIS at all, whether it adopts certain
modules over others, and how information is used and analyzed. Different recruitment
practices have also been found between small and large organizations (Hausdorf &
Duncan, 2004). The differences were due to reasons such as larger organizations have
more job openings, and thus, hire larger numbers of candidates; larger organizations
143
need to deal with a greater number of candidates because they are well-established and
known in the marketplace; and larger organizations have more money for recruitment.
A survey conducted on 99 IT companies whose shares are traded in the London Stock
Exchange showed that the size of the organization was the most important factor in
determining the use of online recruitment (Galanaki, 2002). Teo, Lim and Fedric (2007)
also found the size of organizations is the most important discriminator between
adopters and non-adopters of HRIS, and the extent of HRIS adoption.
Broderick and Boudreau (1992) in contrast argue that the nature of HRIS usage
is determined by a firm’s human resources strategy. For example, in order to achieve a
cost leadership strategy, a transaction system is more likely to be developed to best
support more routine, high volume HR decisions with well-defined information needs
and outcomes. An expert system is more likely to be implemented if the strategy is to
achieve quality/ customer satisfaction, where emphasis is more on improving the
existing work methods, products/services and customer relations. Expert systems
possess a developmental focus that is suitable for distribution of knowledge and
experience. Conversely, a decision support style of system is developed if an innovation
strategy is to be achieved. This type of computer application allows users to pull
together information, analyze it and represent it in many forms, and also assists the user
with electronic memory aids and references.
Underutilization has also been attributed to users’ abilities to use HRIS, where
users are said to be unaware of the way an HRIS can be used, or they are lacking the
skill to retrieve information (Macy, 2004).
Even though a number of studies have been conducted to understand the reasons
for the underutilization of HRIS capabilities, one area that has received little attention in
the research of HRIS is the planning aspect of the system. Past studies on HRIS do not
include any attempt at studying decision making during project planning, and the
144
consequent impact on the level or type of usage of HRIS. Specifically, very little effort
has been devoted to examining the problem of a lower usage of the system from the
perspective of the HRIS project itself.
This apparent failure of HRIS projects to deliver systems that meet users’
expectations as planned may explain why the systems often fail to be utilized
strategically. Haines and Petit (1997) note that user satisfaction and system usage are
two common measures for system success. Even though usage may be voluntary, it also
depends on whether the system is perceived to be of value to the end user. They further
argue that one condition that can help explain the levels of user satisfaction and system
usage is in the computerization process itself as many decisions and conditions that
influence the final configuration of the system manifest in this process.
The importance of planning has also been highlighted by the 22 interviewees in
a study examining the successful implementation of e-learning in organizations (Welsh
et al., 2003). They believed that a successful e-learning implementation requires greater
planning effort especially when dealing with the training design issues such as whether
the employees can use the technology, how to ensure good design, and how to ensure
learner motivation; IT infrastructure issues such as whether the users have the
technology necessary to access content and whether the required hardware, software and
technical support are available; and change management issues that include how to
prepare users and training departments for the change and how to gain support from top
management.
In the HR practitioner literature, Ogier (2003) has pointed out that some of the
problems with HRIS implementation arise from a lack of proper planning and
resourcing. Because of the poor planning, managers are found to underestimate the
scope of IT projects, and fail to take into consideration that projects may take longer
than expected and cost more than budgeted.
145
Therefore, project planning may have a significant impact on the quality of the
HRIS that is delivered, and consequently on how well the HRIS is utilized after the
implementation process. In order to further understand the importance of planning in the
development of HRIS, its processes and challenges are discussed next.
7.4 Importance and Challenges of HRIS Project Planning
As in any other project, successful implementation of HRIS requires significant
planning and effort. This is because the overall system requirements and strategies need
to be developed at this stage such as the kind of data, analysis, security, reports, and
other features users need. Also, decisions must be made on whether there is a need for
new applications or just an upgrade to the existing ones by eliminating redundancies in
systems and data. Then the project team must decide whether the planned systems
should be developed internally or acquired from a vendor; whether the system should be
integrated with other systems or be one stand-alone system; whether the users need to
acquire extensive computer experience or none at all; and whether the users have access
to computer support or they have to function in relative isolation (Haines & Petit, 1997).
Apart from determining when the project should start and when it will end,
project teams are also required to estimate the resources needed to achieve their project
objectives such as hardware costs, vendor charges for software and facilities
requirements, consultant time, custom software development, documentation revisions,
and user training (Ceriello & Freeman, 1991). If the project team fails to plan carefully
and gather enough information during this stage, issues related to system design may
not be solved and the project teams may make the wrong kind of decision. These wrong
decisions will then be carried out in the next phase and finally, the wrong kind of
system will be implemented.
Even though planning is crucial for developing a successful HRIS, its process is
becoming increasingly complex as most of the technology associated with HRIS is new.
146
As in other development of information systems projects, HRIS are also becoming more
diversified in terms of size, application domain, and underlying technology. For that
reason, planning for the best technologies that match HR and business’s needs presents
a great challenge. As argued by Bassett (2003), it is not simply finding what the
technology can provide, it is more a matter of ensuring the information facilitated by
HRIS is what the business needs. Also, it is a matter of understanding what specific
aspects of IT support activities are needed for achieving HR and organization strategic
objectives. However, there is limited evidence concerning the kind of technologies
currently available in the HR area. This leaves many planners ignorant of what an HRIS
could or couldn’t do. Thus, many may have expectations of the system that are
unrealistic (Pearson, 2001).
The rapid advancement in HR technology and the increased diversity of HRIS
users and demands undoubtedly has made planning for HRIS projects more complex
and uncertain. Smith (2007) argues that the complexities and uncertainties of the project
leaves planners to make assumptions even though they are in a state of ignorance when
setting out the project plan. But, making accurate prediction is important for the success
of the project. Many of the unsuccessful aspect of IS projects already mentioned, such
as projects that finished late, over budget or failed to meet users’ expectations, indicate
inaccuracy in making predictions. Lovallo and Kahneman (2003) believe that a high
number of project failures are caused by the tendency to make planning decisions based
on delusional optimism rather than on a rational weighting of potential gains, losses and
probabilities. Though project planners know that their past projects have taken longer
than planned, many still believe that their next project will finish as planned. This
phenomenon is known as the planning fallacy, and it was described and explored in the
first study. In this second study, I propose that the planning fallacy is one of the reasons
that may contribute to HRIS project outcomes that are considered relatively
147
unsuccessful by those involved. In the following section, the hypotheses for the second
study are proposed.
7.5 The Planning Fallacy Theories and HRIS Projects: Extending Study 1
The planning fallacy has been introduced and explained in the previous chapters.
According to several researchers, the planning fallacy is the reason for many ill fated
projects.
7.5.1 Project Completion Predictions
Study 1 found that participants who were asked to take the unpacking approach,
and those who were asked to take the outside view approach to planning both made
more accurate predictions regarding project completion time than those who were in the
packed (control) conditions. However, there was no different in terms of accuracy in
prediction made regarding completion time between participants who take the inside
and the outside views. These hypotheses have not before been tested in a field
environment involving HRIS projects. Thus, similar hypotheses will be tested, but the
outside view and the unpacking approach will be tested against the inside view as it is
not realistic to include the control (packed) condition. It is therefore predicted that:
H2.1a: Predictions of task duration generated from an outside perspective are more
accurate than predictions generated from an inside perspective.
H2.1b: Predictions of task duration generated from the unpacking approach are more
accurate than predictions generated from an inside perspective.
7.5.2 Project Outcome Prediction
In Study 1, adopting the outside view and the unpacking approach gave an
opposite effect on the outcome prediction compared to their effect on completion time
prediction. Study 1 showed that inducing people to consider the outside view and the
unpacking approach led to more optimistic outcome prediction rather than reducing the
optimistic bias. However, the hypotheses tested in Study 1 have not been tested in a
148
field environment. IT projects like HRIS are very dynamic in nature, with technology
that evolves rapidly. Whether HRIS planners are optimistic or not in their outcome
prediction is still yet to be discovered. Therefore, similar hypotheses were also proposed
for Study 2.
H2.3a: Predictions of project outcome quality generated from an outside perspective are
more accurate than those generated from an inside perspective.
H2.3b: Predictions of project outcome quality generated from an unpacking approach
are more accurate than those generated from the inside perspective.
In Study 2, the outcome success of this project is tested as satisfaction with the
system implemented. According to DeLone and McLean (1992) and Ives and Olson
(1984), user satisfaction has been considered as one of the most important measures of
information systems success. Generally, systems that meet the needs of the user will
reinforce satisfaction of the system. Using technical quality to measure outcome success
would not be considered successful if it did not meet the needs of the users. The
hypotheses that I frame for Study 2 are to describe the relationship between the
planning approach used (the inside view, outside view or the unpacking approach) and
users’ satisfaction level. Therefore, another three hypotheses related to outcome success
are proposed to test whether the level of satisfaction with the HRIS project outcome is
related to the planning method adopted by the planners.
Specifically:
H2.4a: Planners who adopt the outside perspective are more satisfied with the HRIS
outcome than planners who adopt the inside perspective.
H2.4b: Planners who adopt the unpacking approach are more satisfied with the HRIS
outcome than planners who adopt the inside perspective.
149
7.5.3 Predictions of Affective Response towards Outcome Success and Failure
Research on affective predictions suggests that people often overestimate the
power and persistence of emotional reactions to events and this is known as impact bias
(Wilson & Gilbert, 2003). Wilson and Gilbert (2005) argued that “exaggerating the
impact of emotional events serves as a motivator, making people work hard to obtain
things that they predict will have large positive consequences and avoid things that they
predict will have large negative consequences” (p.134). If this is true, the impact bias
could be found when planning for a project as it serves a motivating function for
obtaining a successful project outcome and to avoid failures. As discussed earlier in this
chapter, HRIS users are not as happy with the system as they predicted they would be
once the system is implemented.
In Study 1, prompting participants to recall past experiences reduced the impact
bias for positive affect after outcome success but no effect was found when participants
were prompted to detail project task at hand. Both the outside view and unpacking
approach failed to have significant effect on reducing the impact bias for negative affect
after outcome failure. However, it is not known whether learning from past experience
(the outside view) or detailing the project at hand (the unpacking approach) will reduce
the impact bias in the context of HRIS projects in organizations. Therefore, similar
hypotheses were also proposed for Study 2, but in this study the outside view and the
unpacking approach will be tested against the inside view, because a control condition
is not available.
H2.5a: People who use the outside perspective will make more accurate predictions of
positive affective reactions towards outcome success than people who use the inside
perspective.
H2.5b: People who unpack will make more accurate predictions of positive affective
reactions towards outcome success than those who use the inside perspective.
150
H2.6a: People who use the outside perspective will make more accurate predictions of
negative affective reactions towards outcome failure than people who use the inside
perspective.
H2.6b: People who unpack will make more accurate predictions of negative affective
reactions towards outcome failure than those who use the inside perspective.
Changes in confidence tested in Study 1 cannot be tested in Study 2 as it
involves a cross-sectional study.
7.6 Conclusion
This chapter has presented an overview of the second study. Literature on HRIS
has been discussed especially as it relates to potential benefits and usage of the systems
in organizations. The aims of the second study were set out, and a series of hypotheses
were presented regarding the impact of the inside / outside view and unpacking
approach on the accuracy of HRIS project completion time and outcome success
predictions, and predictions of affective response towards outcome success and failure.
The following chapter, Chapter 8, describes the method of the second study.
151
CHAPTER 8
METHOD – STUDY 2
8.1 Introduction
Chapter 8 describes the method for the second study. In this chapter, the sample
design, survey materials used in the study, procedure for collecting data and the
research measures are described. The chapter ends with strategies for analyzing the data.
8.2 Sampling Design
The sampling frame for organizations in this study includes Malaysian HR
professionals who are employed in organizations located in the states of Perak, Kedah,
Perlis and Penang. The selection of firms was based on various sources with Federation
of Malaysian Manufacturers (FMM) Directory 2007 as the main reference. The FMM
Directory was used as many large companies are registered under this directory which is
relevant to the study. Also, a list of companies working cooperatively with Universiti
Utara Malaysia’s student practicum unit was used to select companies that are not
registered under the FMM Directory, but located within the same industrial estates
where the study was conducted. Only HR staff who were directly and actively involved
with the planning process of HRIS projects are relevant to this study.
8.2.1 The Participating Organizations
According to the FMM website, there are over 2000 manufacturing and
industrial services companies of varying sizes registered with FMM (Federation of
Malaysian Manufacturers, 2007). For practical reasons, only companies in the
geographical area of the states of Perak, Kedah, Perlis and Penang were included. These
states were chosen as they are among the states that have the most industrial estates in
Malaysia, and to make it practical for me to make repeated visits to each site to
distribute and collect the survey form on site. In total, there are 485 manufacturing and
152
12 services companies registered with FMM in these four states. From that list
companies were further selected based on their size and location.
Several studies (e.g. Ball, 2001; Galanaki, 2002; Teo et al., 2007; Thaler-Carter,
1998a) have shown that size plays a major role in decisions on whether an HRIS should
be implemented or not in an organization. The number of employees in the organization
also determines the need for computerization of HR functions. The larger the number of
the employees, the more HR functions needs to be automated. Following the definition
of small to medium enterprises (SME) given by the Malaysian National SME
Development Council (NSDC), manufacturing companies with a total number of
employees less than 50 and services companies with a total number of employees less
than 19 are considered small (NSDC, 2005). There were 117 manufacturing and 2
services companies that met this definition, and they were excluded from the list. Apart
from that, the location of the companies was also important for the purpose of
scheduling for appointments and making repeated visits to the companies, as the survey
was conducted on site. Thus, for reasons of difficult access to locations, another 204
manufacturing companies and 7 services companies were excluded from the list.
Companies (manufacturing and services) that are not registered in the FMM
Directory, but located within the industrial estates where the study was conducted, were
also included. Thus, another 98 companies were added to the list. This gives a total
number of 265 manufacturing and services companies, altogether. These companies
were arranged according to the four states, and the process of data collection proceeded
from one state to another, starting from the state of Perlis down to Perak.
Acknowledging the cultural context where the study was conducted, a referral or
networking technique was essential. This technique helps to build trust and rapport with
the potential respondents, and increases the chances of being entertained and getting
permission to conduct a study at the respective companies. Through this technique, 124
153
contact persons were identified at companies among the 265. As no contact could be
obtained at the remaining 141 companies, they were deleted from the list. Within the
period of data collection which began from the middle of September until middle of
December 2007, all the 124 HR managers or their representatives were contacted
personally by telephone.
A small number of firms in these four states, especially in the service industries
like banking, telecommunication, insurance and health indicated that the planning and
decision making processes were made by the HR staff at their Headquarters in Kuala
Lumpur. In this case, appointments were set up with the HR staff in Kuala Lumpur.
Among the industries that participated in this study were insurance, education,
hotel, banking and finance, health, construction, telecommunications, information
technology, electric and electronic products, petroleum, textiles, iron and steel products,
automotive parts and components, rubber products (i.e. gloves, tires), cement and
concrete products, and automobile.
8.2.2 The Participants
Out of 124 HR managers or their representatives from the organizations
contacted, 70 of them agreed to participate in the survey. Those who declined to
participate gave reasons such as being too busy with the year end budgeting, planning
and bonus payments; it was against their company’s policy to entertain any studies; they
did not use any HR computer systems as some of the HR functions such as payroll were
outsourced; they had just joined the company where the system already existed in the
company; the person in-charge of the HRIS planning had moved to another company;
and the company was undergoing major restructuring.
From the total of 70 who agreed to participate in the survey, 52 survey forms
were finally collected. Of these, 15 respondents from 9 organizations were at the
planning stage, and 37 respondents from 36 organizations indicated that their firm had
154
already completed their HRIS project. One of the challenges faced during the data
collection process was to get participation of the whole planning team, especially for the
completed projects. The majority of them had moved to other organizations, and some
were reluctant to participate, and recommended more senior team members to
participate in the survey. As there were insufficient respondents for multilevel analysis,
only one representative from each organization is included in the analyses that are
reported here. The representative was chosen based on their seniority in the
organization. All 45 respondents who participated in this study indicated that they were
one of the key decision makers with HRIS responsibility, and therefore would be in a
position to know about major decisions involving the HRIS project.
8.3 Survey Materials
All the survey materials were prepared in English, as professional-level workers
in Malaysia can and often do work in English. Two sets of questionnaires were
developed, one completed by participants who had already finished their HRIS project
development and the other completed by participants who were at the planning stage of
their HRIS project. Each participant in this survey received an information sheet, and
either an eight or a ten-page questionnaire (with cover letter attached). The survey
materials used in this study are shown in Appendix G.
For participants who had completed their HRIS project, the questionnaire asked
about the background of their project, their involvement in the project, methods used
during the planning process, the outcome of the project, their satisfaction level with the
systems and their overall feelings towards the project outcome. The ten-page
questionnaire consisted of five sections. Section 1 asked about the project background
information such as name and nature of the project, the date the project started and
ended, whether the project finished early, on time or late, and their involvement in the
project. Questions such as name and nature of the project, the date the project started
155
and ended, the number of days if the project finished early or late, and participants’
involvement in the project were open-ended questions.
Section 2 asked about the planning approach used by HR staff during the HRIS
project. There were 9 activity statements and 3 descriptions of approaches to project
planning. In Section 3 of the questionnaire, there were 2 items about the actual HRIS
project outcome, 12 items about end-user computing satisfaction and 1 item on
participants’ overall satisfaction with the project outcome. Two open-ended questions
were also included in this section asking participants to list the aspects of the system
that they were most satisfied and least satisfied with. In Section 4 there was a question
on whether participants view their project as a success or a failure. The final section of
the questionnaire, Section 5, sought the demographic characteristics of the participating
staff, their respective organizations and their HR systems.
The presentation order of the activity statements and the descriptions of planning
approaches were both counterbalanced. Four sets of questionnaires were prepared as
shown in Table 8.1. The first set was the original version. For the second set, the order
of the 9 activity statements (questions 6 to 14) in Section 2 of the questionnaire was
reversed. The order of the three descriptions of approaches in Section 2 remained the
same. The third set was the version with the original order of the 9 activity statements in
Section 2, but the order of the three descriptions of approaches was reversed. The last
set was the version with the order of the 9 activity statements as in the second version,
and the three descriptions of approaches were as in the third set of the questionnaire.
These four versions of the questionnaire were then arranged and distributed in such a
way that the first participant would get the first version of the questionnaire, the second
participant would get the second version, the third participant would get the third
version and the fourth participant would get the fourth version. The same procedure was
repeated with the fifth, sixth and the rest of the participants. As shown in the
156
questionnaire for the original version, in Appendix G on page 351, the names of the
planning approaches were not shown on the questionnaire, the approaches were
indicated by the letter A, B, and C. The names are used in Table 8.1 to refer to the
relevant description of the approaches.
Table 8.1 The four versions of the questionnaires
Version one (original version)
Version two Version three Version four
Order of Section 2 questions
Order of Section 2 questions
Order of Section 2 questions
Order of Section 2 questions
9 activity statements 9 activity statements 9 activity statements 9 activity statements
Q6 Q14 Q6 Q14
Q7 Q13 Q7 Q13
Q8 Q12 Q8 Q12
Q9 Q11 Q9 Q11
Q10 Q10 Q10 Q10
Q11 Q9 Q11 Q9
Q12 Q8 Q12 Q8
Q13 Q7 Q13 Q7
Q14 Q6 Q14 Q6
Description of approaches
Description of approaches
Description of approaches
Description of approaches
A Inside A Inside A Unpacked A Unpacked
B Outside B Outside B Outside B Outside
C Unpacked C Unpacked C Inside C Inside
The questionnaire for participants who were at the planning stage asked about
the background of their HRIS project, their involvement in the project, methods used
during the planning process, their predicted outcome of the project and their predicted
157
feelings if the project was to succeed or to fail. In Section 1, participants were asked
about the name and nature of the project, the date the project would start and when it
was expected to finish, their prediction about when the project is likely to finish and
their involvement in the project. As for the completed project version, these questions
were open-ended questions.
The questions in Section 2 were similar to those for the completed project
version of the questionnaire. Counterbalancing of the activity statements and
descriptions of approaches was also used to control for any order effects. In Section 3 of
the questionnaire, there were 2 items about the predicted HRIS project outcome.
Questions in Sections 4 and 5 were similar to those in the completed project version
except that in Section 4, there was no question asking participants to indicate their view
on whether the project was a success or a failure. As for the completed project versions,
four sets of questionnaires were prepared and given to participants using a similar
process to that used for the completed project version.
All participants were informed and reminded (verbally and in writing) that their
participation was completely voluntary and that their responses would be treated with
the strictest confidentiality. They were not requested to identify themselves in that they
did not put their names on the survey forms. They could withdraw from the study, by
discontinuing the completion of the questionnaire, or by not returning it, at any time and
without prejudice. The completion and return of the questionnaire was taken as the
indication to the researcher that the participant agreed to participate. Each of the survey
forms had a coded ID to identify respondents only with respect to their different
organizations, to identify whether they were at the planning stage or already completed
the project and to differentiate between the four sets of questionnaires that were used.
158
8.4 Procedure
Potential organizations listed under the Federation of Malaysia Manufacturers
(FMM) Directory 2007 were contacted personally by telephone. Through the initial
telephone conversation, I introduced myself, explained the purpose of the call and
asked for an appointment with an HR manager or the person involved with the planning
of the HRIS, to conduct the survey. Once the respondent agreed to participate in the
study on behalf of the firm, a date was fixed at the respondent’s convenience.
During the survey sessions with respondents, I personally administered and
collected the completed questionnaire. Each respondent was first briefed about the
purpose and the nature of the survey. They were then provided with a sheet of
information about the survey and were invited to ask questions. Respondents were
assured that all the information given will remain confidential at all times and will be
used for the study only. They were not requested to identify themselves in that they do
not put their names on the survey forms. Respondents were allowed ample time to read
the information sheet to ensure that they understood the instructions and to decide if
they really wanted to participate in the survey. Before answering the survey questions,
each respondent was asked to choose a recently completed HRIS project or the one that
they were currently involved in and respond to the survey questions with respect to that
project. Respondents were then given 30 minutes to complete the forms. Each meeting
lasted between 30 and 60 minutes.
More time was given to those who could not complete the forms in 30 minutes
if they wished. Some participants did not have time to complete the questionnaire at
work, or preferred not to complete the questionnaire at work. These participants were
each given a pre-addressed and postage-paid envelope so they could post the
questionnaire back to me in their own time. There were also respondents who preferred
the survey form to be attached through their email and the completed form was returned
159
through email. For respondents who were not able to fill out the questionnaire during
the meeting, a follow-up telephone call reminder was used to remind respondents about
returning the questionnaire.
8.5 Research Measures
The major dependent measures in this study were bias in HRIS planners’
prediction of project completion time and project outcome success, and impact bias in
their predictions of affective response towards project success or failure.
8.5.1 Planning Method Measures
To explore planning methods used by the HRIS planners, two sets of questions
were prepared: the ratings of use of the three planning approaches and the forced choice
questions. To test the generalizability of Study 1 results to a real-world setting, the
wording used to develop the planning method measures in Study 2 was as similar as
possible to Study 1.
The questions regarding use of the three planning approaches involved 9 activity
statements, with 3 questions each to indicate the three approaches (the inside view, the
outside view and the unpacking approach). As shown in Table 8.2, these activity
statements were developed based on the manipulation instructions used in Study 1.
These nine activity statements were then arranged in a way that the first activity
statement represents the inside view, the second activity statement represents the
outside view and the third activity statement represents the unpacking approach, and the
process was repeated with the rest of the activity statements. Based on a seven-point
scale whereby, 1 = strongly disagree, and 7 = strongly agree, participants rated their
degree of agreement that the statement describes an activity they were doing in
accomplishing the project.
160
Table 8.2 The nine activity statements
Manipulation instructions used in Study 1 Nine activity statements used in Study 2
Inside view:
Please take a few minutes to consider your plans for completing your project assignment. Think about the kind of events that are likely to happen as you work on your project assignment. Think about all the problems that are likely to happen in the future that will impede your project progress and performance, and any aspects of the project or situation in the future that will facilitate your project progress and performance.
Inside view:
1. I think about the kind of events that are likely to happen in this project
2. I think all the problems that are likely to happen in the future that will impede this project’s progress and performance
3. I think about any aspects of this project or situations in the future that will facilitate the project’s progress and performance
Outside view:
Please take a few minutes to consider your plans for completing your project assignment. Recall the kind of events that have happened in your past project assignment and those of other people that you know about. Recall all the problems that have happened in the past that have impeded your project progress and performance, and any aspect of the project or situation in the past that have facilitated your project progress and performance.
Outside view:
1. I recall the kinds of events that have happened in my past projects and those of other people that I know about
2. I recall all the problems that have happened in the past that have impeded other project’s progress and performance
3. I recall any aspects of project or situations in the past that have facilitated other projects’ progress and performance
Unpacking approach:
Please take a few minutes to consider your plans for completing your project assignment. Think about the components that are needed for project progress and performance. Also, think about each and everything that you plan to do in completing this project assignment.
Unpacking approach:
1. I think about the components or elements that need to be included in the project
2. I breakdown the overall project into assignable work elements
3. I list each and every step that I am going to take in order to complete the project.
The forced choice questions involved 3 descriptions of approaches to project
planning. The 3 descriptions were also developed based on the manipulation
instructions used in Study 1. These descriptions are presented in Table 8.3. Participants
were asked to indicate which among the three approaches they commonly used when
planning for their HRIS project.
161
Table 8.3 Descriptions of the threes approaches
Manipulations instructions used in Study 1 Descriptions of approaches used in Study 2
Inside view:
Please take a few minutes to consider your plans for completing your project assignment. Think about the kind of events that are likely to happen as you work on your project assignment. Think about all the problems that are likely to happen in the future that will impede your project progress and performance, and any aspects of the project or situation in the future that will facilitate your project progress and performance.
Inside view:
When planning for an HRIS project, I focus on the kinds of events that are likely to happen. I take into consideration all the problems that are likely to happen in the future that will impede the project’s progress and performance, and any aspect of the project or situations in the future that will facilitate the project’s progress and performance.
Outside view:
Please take a few minutes to consider your plans for completing your project assignment. Recall the kind of events that have happened in your past project assignment and those of other people that you know about. Recall all the problems that have happened in the past that have impeded your project progress and performance, and any aspect of the project or situation in the past that have facilitated your project progress and performance.
Outside view:
When planning for an HRIS project, I recall the kinds of events that have happened in my past projects and those of other people that I know about. I also recall all the problems that have happened in the past that have impeded my project progress and performance, and any aspects of the project or situation in the past that have facilitated my project progress and performance. Apart from learning from my past experience, I also consult with other people who have experience in similar projects.
Unpacking approach:
Please take a few minutes to consider your plans for completing your project assignment. Think about the components that are needed for project progress and performance. Also, think about each and everything that you plan to do in completing this project assignment.
Unpacking approach:
When planning for an HRIS, I think about the components that are needed for my project progress and performance. I also think about each and everything that I plan to do in completing the project. I breakdown all the tasks that need to be carried out in the project into specific tasks.
8.5.2 Project Completion Time Measures
The predicted completion time is measured by taking the difference between the
answer given by participants to indicate when the project started, and when the project
is supposed to end, or did end. For completed projects, the actual completion time was
measured by the date stated by participants who already finished their HRIS project. In
a separate question, participants were further asked to state the total number of days by
which the project either falls short or exceeds the due date for completion.
162
8.5.3 Outcome Success Measures
Outcome success was measured in several ways. First, participants were asked
to indicate the extent to which the predicted or actual outcome of their project falls short
of expectations, meets expectations, or exceeds expectations. Second, by marking a 15
cm line, which had labeled anchors of 50%, 100% and 150%, participants were asked to
indicate their predicted or actual percentage of project outcome achieved. In business
projects, even though the outcome is claimed to meet the expectation, it may not mean
exactly 100% as planned. Thus, this scale was included to gain further understanding on
to what extent participants’ project outcomes really meet, or exceed their expectations.
The scale begins at 50% as most projects may be considered as a failure if the outcome
achieved was less than 50% from what was initially planned. In this study, 100%
indicates that the outcome achieved was exactly what was planned. The scale was
extended up to 150% as in certain situations, the outcome achieved do sometimes
exceed what was initially planned. For analysis, the response was measured from the
50% end of the line and centimeters were converted to a number relative to 100%.
Participants who had finished their HRIS project were asked to answer
additional questions related to their satisfaction with the system. To measure
participants’ satisfaction level towards specific components of the system, an
established 12-item measure of IT system user satisfaction from Doll and Torkzadeh
(1988) was adapted with permission. The scale was chosen because it is conceptualized
as “the affective attitude towards a specific computer application by someone who
interacts with the application directly” (Doll & Torkzadeh, 1988, p.261). Their end-user
computing satisfaction instrument has been shown to be both reliable and valid for
measuring end-user satisfaction involving different types of computer platform and
applications (Doll, Deng, Raghunathan, Torkzadeh, & Xia, 2004; Hendrickson,
Glorfeld, & Cronan, 1994). Several studies have reported that the scale has adequate
163
internal consistency (the Cronbach alphas for each factor ranging from .65 to .98 and for
the 12-item scale ranging from .88 to .96), test-retest reliability (correlation coefficient
for the 12-item scale ranging from .76 to .96), criterion-related validity (.76), and
construct validity (all items have significant loading on their corresponding factors
ranging from .62 to .94) (Doll & Torkzadeh, 1988; Doll, Xia, & Torkzadeh, 1994;
Hendrickson et al., 1994; McHaney & Cronan, 1998; McHaney, Hightower, & Pearson,
2002; McHaney, Hightower, & White, 1999; Torkzadeh & Doll, 1991). The 12 items
represent five underlying dimensions of end-user satisfaction: content, accuracy, format,
ease of use, and timeliness.
The 12 items were rephrased by changing the wording of the original version
from a question format to a statement format to suit the agree-disagree response scale
used for this study. In the past, some authors have also made some changes to the
original version of the Doll and Torkzadeh (1988) end-user satisfaction instrument.
Abdinnour-Helm, Chaparro and Farmer (2005) for example, made minor alterations in
the wording of the original end-user computer satisfaction items such as using past tense
for several items and using “site satisfaction” rather than “system satisfaction” where
appropriate to suit the specifics of Web-site user satisfaction. Otto, Nadjawi and Caron
(2000) replaced the “timeliness” construct with website “responsiveness” or download
time in measuring their web-user satisfaction.
Second, the response scale was changed from its original frequency scale to an
agreement scale taking into consideration the difficulty for participants to remember the
frequency of events that have happened. The original scale does not apply in this study
as some of the questions are not appropriate for the frequency scale. The question of
whether the HRIS is providing precise information, presenting output in a useful format,
providing with up-to-date information, or is easy to use or not, much depends on how
the system is being planned and designed initially. Problems that relate to the system are
164
often detected on the day it is implemented. If an effort is not made to correct them, the
problems will persist every time the system is used. Therefore, it is difficult to justify
measuring feelings of satisfaction towards the system using a frequency of occurrence
scale. In this study, each of the adapted questions asked how strongly the respondents
agreed or disagreed with the user satisfaction statements on a seven-point scale
whereby, 1 = strongly disagree, and 7 = strongly agree. For this measure, the higher the
score, the greater feelings of satisfaction.
Due to changes in the wording and response scale, the reliability of the new
adapted scale in this study was compared to previous studies. Coefficient alpha for the
36 participants for the adapted 12-item end-user satisfaction scale was .97. The
reliabilities (alpha) of the five sub-scales were: content = .93; accuracy = .89; format =
1.00, ease of use = .89 and timeliness = .92. The original and adapted versions of the 12
items are shown in Table 8.4.
Finally, in a separate question participants were asked to indicate their overall
feelings of satisfaction with the system by placing a single slash mark across a 15 cm
line, which had labeled anchors of not at all (0), fair (5) and extremely (10). For
analysis, the response was measured from the 0 end of the line and centimeters were
converted to a number out of 10. Participants were also asked to list aspects of the
system that they were most and least satisfied with (if any).
165
Table 8.4 Original and adapted versions of end-user satisfaction items
Original version Adapted version
Content
Does the system provide the precise information you need?
The system provides the precise information that I need.
Does the information content meet your needs?
The information content meets my needs.
Does the system provide reports that seem to be just about exactly what you need?
The system provides reports that seem to be just about exactly what I need.
Does the system provide sufficient information?
The system provides sufficient information.
Format
Do you think the output is presented in a useful format?
The output is presented in a useful format.
Is the information clear? The information is clear.
Accuracy
Is the system accurate? The system is accurate.
Are you satisfied with the accuracy of the system?
I am satisfied with the accuracy of the system.
Timeliness
Do you get the information you need in time? I get the information I need in time.
Does the system provide up-to-date information?
The system provides up-to-date information.
Ease of use
Is the system user friendly? The system is user friendly.
Is the system easy to use? The system is easy to use.
8.5.4 Measures of Affective Response to Success and Failure
Measures for affective response were adapted from Sanna and Schwarz (2004).
Participants who were at the planning stage of the HRIS project were asked to estimate
on two 11-point scales how they would feel after “a successful project” and if the
project were to “succeed” (1 = not good, 11 = very good). They were also asked to
estimate how they would feel after “not a successful project” and if the project “does
not succeed” (1 = very bad, 11 = not bad). Participants who had finished their HRIS
projects were asked to answer a dichotomous question on whether they viewed their
166
HRIS project as successful or not successful. If they answered successful they then
turned to two questions asking the degree to which they feel good (not good to very
good), and if they answered not successful they turned to questions asking the degree to
which they feel bad (very bad to not bad) about their HRIS project outcome.
8.5.5 Demographic Measures
A number of demographic variables were also measured for descriptive and
control purposes. These included job level, and the extent of experience with HRIS
systems and projects, which may be correlated with the level of expectations of project
outcomes, and with the accuracy of project completion predictions. It is necessary to
control such extraneous sources of variation in the analyses of the results. In Malaysia,
there is an interest in ethnicity, and acceptance of the results of this research in business
circles will require that a full description of the sample is possible, including the
breakdown of ethnic origins and gender of the participants. This information is also
necessary to show that the sample is representative and to ensure that generalizations to
the wider population of firms and employees can be made.
8.6 Data Analysis Strategy
Project data from 45 organizations were included in the analysis. Although the
final sample size for this study is fairly small in an absolute sense, it is nevertheless
comparable with past studies on projects in organizations. Ewusi-Mensah &
Parzasnyski (1991) for example, used 49 out of 566 questionnaires in their exploratory
study on project abandonment. Morgenshtern, Raz and Dvir (2007) based their analysis
on the final sample of 43 projects collected from 24 individuals to study factors that
affect duration and effort estimation errors in software development projects. In another
study, Hyvari (2006) used 25 respondents from among 368 individuals in 78 companies
to examine the success factors of project management in organizations.
167
Taking into consideration the small sample size, analyses were conducted using
descriptive statistics and hypotheses were tested using correlation analysis. The analyses
were conducted using SPSS (version 15) program for Windows. It was planned that if
the sample size of projects was larger, involving the whole project planning team in
each organization, factor analysis would be conducted to test the new adapted version of
the end-user satisfaction instruments and a multilevel regression analysis to test the
research hypotheses. However, the number of respondents was not sufficient for these
techniques.
8.6.1 Open-ended Questions
There were three open-ended questions in this survey. The first open-ended
question was regarding the nature of the participants’ involvement in the HRIS projects.
Responses from participants were first transcribed, before being given to two
individuals for the categorizing process (working independently). Each individual was
asked to sort the responses according to categories that I formulated based on Ceriello
and Freeman’s (1991) work. They suggested that steps in computerizing human
resources basically involve five phases of effort, namely system planning, system
design, vendor selection, system implementation and system maintenance and
evaluation. Thus, for the first open-ended question, these five categories were developed
for the sorting and coding process.
The second open-ended question was regarding the aspects of the system that
participants felt most satisfied with, and the third question was on the aspects of the
system that participants felt least satisfied with. A similar process was performed with
the same coders. The categories of answers for the second and third open-ended
questions were formulated based on Doll and Torkzadeh’s (1988) end-user satisfaction
constructs: content, format, accuracy, timeliness and ease of use. For answers that did
not fit with any of the categories given, they were recorded in the ‘others’ categories.
168
Therefore, six categories were developed for the sorting and coding process for the
second and third open-ended questions.
Each coder was given three instruction sheets with definitions for each category,
the three open-ended questions, participants’ responses for each of the questions and
three sets of coding sheets. For one coder, the process was started with the responses
regarding aspects of the system that participants felt most satisfied with. It then
followed by sorting responses regarding participants’ involvement in the HRIS project,
and finally with the responses regarding the aspects of the system that participants felt
least satisfied with. The other coder did these tasks in the reverse order.
Each coder took between 40 and 70 minutes to finish the whole task. Answers
from both individuals were then compared. For any answers that were not in agreement
for both individuals, consensus was sought. Both individuals were asked to discuss and
decide on the answers. It took approximately an hour to complete the second task. The
open-ended materials and the instructions for coding are presented in Appendix I.
8.7 Conclusion
This chapter has explained the research method and strategy of the second study.
It described how the sample of organizations was obtained, the selection of the
respondents, development of the questionnaire, the research materials, and the survey
procedure. This chapter also briefly explains the adoption of correlation analysis to test
the research hypotheses. The results of the second study are reported in Chapter 9.
169
CHAPTER 9
RESULTS – STUDY 2
9.1 Introduction
Chapter 9 reports results of the second study. The chapter begins by reporting
the demographic characteristics of the respondents and descriptions of the HRIS
projects involved. It then presents the bivariate relationships between the research
variables. The chapter concludes with the descriptive analysis of the findings.
9.2. Demographic Characteristics of the Participants
Detailed descriptive statistics of the participants’ demographic characteristics are
presented in Appendix I. It is noted that 62.2% of the 45 participants in this survey were
females. The average age of the participants was 37 years old. Malays constitute 80% of
the survey participants, followed by 11.1% Malaysian Chinese and 8.9% Malaysian
Indians. The majority of the participants in this survey (93.3%) had higher academic
qualifications of either a tertiary or diploma, first / professional degree or second degree
and above. The remainder of the respondents had either secondary education (11 years
of schooling) or certificate. Senior management staff such as executives, managers and
senior managers made up 80% of the total participants. The rest consisted of
administrative and other technical staff.
On average, the participants had been in their present position for 59 months,
almost 5 years, and had served their organization for 7.5 years. Participants’ average
experience as computer-based system users was 10.7 years, and average experience
with HRIS projects was 6 years.
170
The average number of employees in the organizations and in their HR
departments was 2161 and 21 people respectively. HRIS had been used by the
organizations for an average of 10 years. Computer-based HR system applications that
are presently implemented are summarized in Table 9.1.
Table 9.1 HR computer-based applications reported as presently implemented in surveyed organizations
HR computer-based applications Total number of organizations
Payroll 43
Compensation and benefits 27
Training and career development 24
Performance appraisal 18
Recruitment and selection 15
Human resource planning 12
Time management 11
Employee relations 9
Employee database 8
Leave administration 7
Medical 3
Employee self-service (ESS) 2
Standard operating procedure (SOP) 1
Documentation and organization 1
Organization Management 1
In the next section, a brief description of the HRIS projects is presented.
9.3 HRIS Projects in the Survey
Results presented in this section are based on responses given in the open-ended
questions and descriptive statistics for selected relevant variables from the final sample
of 45 respondents. Most of the projects in the sample (66.7%) were projects to install a
new system. The remainder were projects to upgrade existing systems. Based on the
responses given by the participants, installing a new system in their context did not
mean that it was their first experience using HRIS. It was more about switching to
171
another software provider, changing the operating system used (e.g., from DOS to Web-
based system), or changing from a stand-alone system to a more advanced, integrated
system. Upgrading the old system referred to system maintenance where minor
modifications or adjustments to the system were made. For example, the system was
either upgraded from the old version to a newer version, or changing the calculation
setting in payroll and compensation modules when the government imposed a new
policy, which may affect for example, the calculation of employees’ salary, deduction
of employees’ provident fund (EPF), and employees’ social security organization
(SOCSO).
From the total of 45 projects, 36 projects had been completed and 9 projects
were still at the planning stage. Of the 36 projects completed, 29 were completed as
planned with respect to completion time, and the remaining 7 projects finished later
than planned. On average, the project was completed within 10 months, with the
shortest project completed within 1 month and the longest project completed in 48
months or 4 years. Of the 36 participants with completed projects, 32 of them viewed
their project as successful.
As for the 9 projects at the planning stage, 4 participants predicted that the
project will be completed as planned, 4 participants predicted that the project will finish
later than planned, and 1 participant predicted that the project will finish earlier than
planned. On average, these projects were scheduled to finish within 21 months, with the
shortest project scheduled to finish in 3 months and the longest in 42 months or 3.5
years.
When participants were asked about who initiated the idea to develop the
system, 91% indicated that the ideas were initiated internally by the organization.
Specifically, 40% of the ideas came from the HR department, 37.8% were initiated by
top management, 17.8% came from the IS department, 4.4% came from the accounts
172
and finance department and 2.2% came from the operation side. These percentages do
not total to 100 percent as six respondents indicated more than one source though they
were instructed to indicate only one. Most of the systems implemented were either
acquired from a vendor (46.7%) or through a combination of packaged and custom
software (37.8%), where a lower cost packaged program was purchased and some
modification was done to the package to suit the organization’s needs.
Regarding participants’ involvement in the project in terms of their role and
contribution, the coded responses indicate their involvement in the five phases of effort
during the project. Table 9.2 summarizes the responses given by participants.
Table 9.2 Participants’ involvement in the project phases (all the 45 projects)
Project phases
Total number of participants coded as performing this phase
Among the activities reported
System Planning
18 Confirming the activities to be carried out; determining the requirements and specifications of the systems
System Design
23 Researching, collecting and drafting the system structure; planning and designing new screens, reports and modules
Vendor Selection
12 Contacting vendor; getting quotation; evaluating demonstration and selecting the vendor
System Implementation
28 Monitoring the testing and the transition process; reinstall data into new system; check for accuracy after data was reinstalled; check for reporting issues; solving problems that arise during the transition process
System Maintenance and Evaluation
18 Getting feedback from the end-users; identified problems with the existing system; provide new requirements to be included in the upgrading of the system
173
The descriptive statistics of projects in the survey discussed in this section are
presented in Appendix J.
9.4 Planning Methods and Project Outcomes
All participants were asked a forced choice question regarding the kind of
approach they used when planning for HRIS. Of the 45 participants, 20 indicated that
they used the outside view, followed by the unpacking approach (17 participants) and
the inside view (8 participants). In terms of whether the project outcomes meet their
expectation or not, 30 of 36 participants who had completed their HRIS projects
indicated that their project outcomes did meet their expectations. The remainder
indicated that the project outcome fell short of their expectations (5 participants) or
exceeded their expectations (1 participant). Even though 30 participants stated that the
project outcomes did meet their expectations, only 6 reported that their outcomes met
100% of their expectations as planned. All 9 of the participants at the planning stage
predicted that their project outcomes will meet their expectations. However, when asked
about percentage of planned outcome they expected to be achieved, only 1 participant
predicted his / her project outcome will meet 100% as planned.
Participants who had completed the projects were further asked to list the
aspects of the system that they felt most and least (if any) satisfied with. Their responses
were coded using the categories of content, format, accuracy, timeliness and ease of use.
Table 9.3a and b summarizes the responses given by participants.
174
Table 9.3a Aspects of system that participants felt most satisfied with
Aspects of the system Total number of participants coded as responding to the
aspect of the system
Examples from among the responses reported
Content 13 “Provides comprehensive, precise and sufficient information as needed; able to generate basic information; able to pull out information needed for monthly report”
Format 14 “Allow to use a simpler report”
Accuracy 10 “Generate accurate report; the information / data retrieved are accurate; the system provides accurate information”
Timeliness 8 “Able to retrieve and generate reports in a timely manner; get results in a faster manner; save time; speed up the process of getting employee information”
Ease of use 15 “Simple to use; easy to use; user friendly; easy to update; easy for tracking purposes; easy to access”
Other aspects 7 “complete HR functions; the system integrates with other modules; the system provides good file maintenance; the web-based system allows employees to access their own data which helps in decreasing their workload and saves time; the system provides a sense of internal control; and the system was reliable and efficient”
175
Table 9.3b Aspects of system that participants felt least satisfied with
Aspects of the system Total number of participants coded as responding to the
aspect of the system
Examples from among the responses reported
Content 12 “The system failed to generate the kind of reports requested; the system only provides the most recent data and was not able to retain the archive data, which was needed for referencing and checking”
Format 15 “The report generated does not meet with external demand; the amendments of the results need to be done manually; unable to breakdown the records as needed”
Accuracy 0 None was reported
Timeliness 7 “The system was very slow in processing especially when there were many users concurrently on the system; the system depends too much on human input which often delayed the process; it is a batch processing, not real time system”
Ease of use 6 “The system was not as user friendly as expected; difficult to use; very complicated especially for aged staff and the non-IT savvy”
Other aspects 6 “the system was not integrated with the existing system, other system or modules and outside systems such as the bank auto-pay system; the system was very basic and simple where some functions have to be carried out manually, or it just serves a transaction processing function only and was not helping in decision making; no helpdesk facilities; very bad support service; some of the HR functions were either not available or did not meet their expectations; updating processes have to be done manually; and the system was not stable”
176
Regarding their overall satisfaction with the systems, twenty one participants
rated between good and extremely good (rated between 7.5 and 10 on 10 point scale),
and fifteen participants rated between fair and good (rated between 5 and 7.5 on 10
point scale). The average overall satisfaction was 7.17(SD=1.48).
The descriptive statistics of planning methods and expected project outcomes
summarized in this section are presented in Appendix J. The next section reports the
correlations between the research variables.
9.5 Correlation Analysis
Table 9.4 presents the means, standard deviations, and Pearson correlations of
variables for the 36 participants who had completed projects. The internal consistency
reliabilities (Cronbach’s Alpha) of the research measures are reported in parenthesis
along the diagonal of the correlation table. As shown in Table 9.4, the Cronbach’s
alphas for the three items in each of the planning approach measures were in a range
between .72 and .84. The five sub-scales of the 12 item end-user satisfaction scale
(content, accuracy, format, ease of use and timeliness) also have satisfactory reliability
values ranging from .89 to 1.00. It is noted that Cronbach’s alpha for satisfaction with
format was 1.00 and after checking the data, the answers to the two items did
correspond within the participants. The correlation analysis results were also used to
address the hypotheses for bias in predictions of task duration and outcome.
9.5.1 Bias in Project Completion Prediction
Bias in project completion prediction was not significantly correlated with the
extent of use of any of the three planning approaches, the inside view, the outside view,
and the unpacking approach.
177
Table 9.4 Descriptive statistics, scale reliabilities, and correlations of variables for completed projects
Variables N Mean S.D. 1 2 3 4 5 6 7 8 9 10 11 12 13 1. Gender 36 .61 .49 2. Age 33 37.30 6.58 .11 3. Malay 36 .78 .42 -.15 .13 4. Chinese 36 .14 .35 .16 -.45** -.75** 5. Indian 36 .08 .28 .03 .28 -.56** -.12 6. Education 36 3.75 1.08 -.24 -.37* .00 .02 -.02 7. Tenure in present position 36 60.17 60.46 .12 .52** -.08 -.21 .38* -.48** 8. Tenure 36 95.00 74.80 .30 .57** .11 -.14 .00 -.17 .28 9. Computer-based system experience 36 10.86 5.43 .02 .57** -.05 -.10 .20 -.03 .19 .46** 10. HRIS projects experience 36 75.33 54.17 .19 .32 .00 -.06 .07 .02 .11 .50** .63* 11. Inside – use rating 36 5.22 1.14 -.10 -.33 -.27 .18 .18 .26 -.28 -.03 -.17 -.20 (.83) 12. Outside – use rating 36 5.02 1.15 -.02 -.26 -.21 .09 .20 .12 -.24 -.02 .18 .16 .56** (.84) 13. Unpacked – use rating 36 5.59 .86 -.22 .03 .06 .00 -.09 .31 -.08 .02 .32 .02 .47** .33* (.72) 14. Project duration 36 10.81 9.68 -.20 .24 .23 -.22 -.08 .16 -.16 .30 .33* .18 -.12 -.02 .01 15. Bias in duration prediction 36 1.22 3.06 -.22 -.22 .15 -.08 -.12 .17 -.29 -.11 -.04 .05 .06 .17 -.02 16. Percentage of outcome achieved 36 89.44 16.84 .00 -.08 .35* -.02 -.50** .16 -.29 .14 -.06 -.07 .30 -.02 .35* 17. Satisfaction with content 36 5.48 1.06 -.08 .11 .13 -.15 -.02 .04 .13 .18 .11 -.13 .21 .05 .48** 18. Satisfaction with format 36 5.33 1.21 -.16 .03 .20 -.11 -.16 .17 .00 .18 .05 -.12 .34* .07 .56** 19. Satisfaction with accuracy 36 5.36 1.26 -.16 .02 .08 -.08 -.01 .06 .13 .16 -.02 -.20 .36* .10 .53** 20. Satisfaction with timeliness 36 5.19 1.42 -.05 .06 -.12 .09 .07 -.05 .23 .13 .02 -.20 .43* .18 .52** 21. Satisfaction with ease of use 36 5.25 1.19 -.12 -.03 .09 -.05 -.06 .01 .05 .06 -.13 -.33 .32 -.02 .45** 22. End-user satisfaction (12-item scale) 36 5.35 1.11 -.12 .05 .08 -.07 -.03 .05 .12 .16 .02 -.20 .34* .08 .54** 23. Overall satisfaction 36 7.17 1.48 -.20 .21 .29 -.13 -.28 .05 .01 .25 .00 -.15 .13 -.04 .50** 24. Feeling of success 32 8.30 1.53 -.11 .11 .13 -.08 -.09 -.18 .42* .20 -.07 -.15 .25 -.06 .40* Note: Coefficient alpha reliability estimates are in parentheses on the diagonal of the correlation table
*Correlation is significant at p < 0.05 and **Correlation is significant at p< 0.01
178
Table 9.4 (Continued) Descriptive statistics, scale reliabilities, and correlations of variables for completed projects
Variables 14 15 16 17 18 19 20 21 22 23 24 14. Project duration 15. Bias in duration prediction .40* 16. Percentage of outcome achieved -.21 -.28 17. Satisfaction with content -.15 -.22 .48** (.93) 18. Satisfaction with format -.22 -.20 .64** .90** (1.00) 19. Satisfaction with accuracy -.19 -.13 .42* .90** .89** (.89) 20. Satisfaction with timeliness -.32 -.31 .42* .76** .73** .81** (.92) 21. Satisfaction with ease of use -.27 -.17 .46** .84** .78** .82** .84** (.89) 22. End-user satisfaction (12-item scale) -.24 -.23 .52** .96** .92** .95** .88** .92** (.97) 23. Overall satisfaction -.01 -.14 .46** .68** .67** .64** .69** .78** .74** 24. Feeling of success -.22 -.41* .58** .60** .61** .62** .75** .73** .71** .60** Note: Coefficient alpha reliability estimates are in parentheses on the diagonal of the correlation table
*Correlation is significant at p < 0.05 and **Correlation is significant at p< 0.01
179
9.5.2 Bias in Project Outcome Prediction
Percentage of outcome achieved was significantly positively correlated with the
extent of use of the unpacking approach (r = .35, p<.01). This result indicates that
participants who report higher adoption of the unpacking approach also tend to report a
higher percentage of outcome achieved.
There were also significant positive correlations between end-user satisfaction
and both the extent of use of the inside view (r = .34, p<.05), and the extent of use of the
unpacking approach (r = .54, p<.01). Hence, the more participants report they adopted
the inside view or the unpacking approach, the more satisfied they were with the
system.
Table 9.4 also revealed significant positive relationships between all of the end-
user satisfaction components and the extent of use of the unpacking approach, with
correlation coefficients between .45 and .56. Also, there were significant positive
relationships between all except two of the end-user satisfaction components and the
inside view, with correlation coefficients between .34 and .43. These results imply that
the more participants adopted the unpacking approach, the more satisfied they were
with the content, format, accuracy, timeliness and ease of use of the system, and this
also applied to the inside view for some components.
Participants’ rating of overall satisfaction was significantly positively correlated
with the extent of use of the unpacking approach (r = .50, p<.01) but not with the
reported extent of use of the inside or outside views approaches, suggesting that the
more participants adopted the unpacking approach, the more satisfied they were with
the overall project outcome.
9.5.3 Other Correlation Results
Feelings that the project was a success were positively and significantly
correlated with the extent of use of the unpacking approach (r = .40, p<.05) but again
180
not with reported extent of use of the inside or outside views approaches. This suggests
that participants with higher adoption of the unpacking approach have higher feelings of
project success.
There were other significant correlations that are worth noting. Table 9.4 reveals
positive and significant relationships between reported use of the outside and inside
views (r = .56, p< .01), between unpacking and the inside view (r = .47, p<.01) and
between unpacking and the outside view (r = .33, p<.05). This suggests that the three
approaches were related, or it may reflect a common method variance component. There
are insufficient cases to test this explanation.
9.6 Hypothesis Testing
Two sets of questions explored the approaches used by the HRIS planners; the
ratings of use of the three planning approaches and the forced choice question. To test
hypotheses 2.1a, 2.1b, 2.2a and 2.2b, the three approaches are compared on the
dependent variable. But, with the small sample size it is not possible to conduct a
statistical test to compare the three approaches on the bias in completion time and
outcome. Results from the correlations and descriptive statistics were also taken into
consideration.
9.6.1 Project Completion Time Predictions
Table 9.5 presents the numbers of participants who reported that they used either
the inside view, outside view, or unpacking approach to planning when asked a forced
choice question. As shown in Table 9.5, it appears that the unpacking approach may be
related to finishing on time. From the total of 14 projects for which the respondents
chose the unpacking approach for the forced choice questions, only 1 project was
finished later than planned. However, this result is not significant using a Chi-squared
test of the contingency table.
181
Table 9.5 The planning approach used and project completion time (forced choice questions) Project completion time
Planning approach used Total Inside view Outside view Unpacking
On time 5 11 13 29
Late 2 4 1 7
Total 7 15 14 36
Out of 36 completed projects, 7 projects finished later than was planned. On
average, projects finished 6.28 months late or about 36.08% of the entire project
duration (see Appendix K-1). The biases are substantial.
Four participants at the planning stage predicted that their project would finish
later than planned (see Appendix K-2). On average, they predicted that their project will
take 5 months longer than expected or about 37% of the entire estimated project
duration. One participant believed that his / her project would finish 18 months earlier
than planned.
9.6.2 Project Outcome Predictions
For the completed projects, Table 9.6 presents the mean reported percentage of
outcome achieved for participants who reported that they used either the inside view,
outside view, or unpacking approach to planning for the forced choice question. As
shown in Table 9.6, it seems that the unpacking approach may be related to outcome
success. Out of 100 percent of outcome planned to achieve, on average 92.57 percent of
outcome was successfully achieved by those who adopted the unpacking approach.
However, the number of participants is too small for a statistical test of significance of
this effect.
182
Table 9.6 The planning approach used and mean reported percentage of outcome achieved (standard deviations in brackets) Planning approach used
Inside view (n=7) Outside view (n=15) Unpacking (n=14)
Mean percentage of outcome achieved
87.00(13.08)
87.67(24.37)
92.57(5.24)
For the 9 participants who had not yet completed their project, Table 9.7
presents the mean predicted percentage of outcome expected to be achieved for those
who reported that they used either the outside view or the unpacking approach to
planning when asked the forced choice question. None reported using the inside view.
Results shown in Table 9.7 suggest that participants who adopt the unpacking approach
may be less optimistic regarding the outcome expected to be achieved than those who
adopt the outside view, but the number of participants is too small to establish this
difference statistically.
Table 9.7 The planning approach used and mean predicted percentage of outcome achieved (standard deviations in brackets) Planning approach used
Outside view (n=5) Unpacking (n=4)
Mean predicted percentage of outcome achieved
88.00(10.37)
79.25(13.74)
In Hypothesis 2.4 I predicted that the level of satisfaction with the HRIS project
outcome is related to the planning approach adopted by the planners. Specifically, I
hypothesized that planners who adopt the outside view (H2.4a) or the unpacking
approach (H2.4b) will be more satisfied with the HRIS outcome than planners who
adopt the inside view. Table 9.8 presents the planning approach used and mean end-user
satisfaction. For the unpacking approach, the results in Table 9.8 support H2.4b, with
the direction of the difference in mean satisfaction consistent with that hypothesis. The
183
opposite occurred for the outside view, in that those who adopted the outside view were
less satisfied than those who used the inside view, so H2.4a was not supported.
Table 9.8 The planning approach used and mean end-user satisfaction (standard deviations in brackets) Mean end-user satisfaction with
Planning approach used
Inside view (n=7) Outside view (n=15) Unpacking (n=14)
The system (12-items)
5.38(.90)
5.04(1.36)
5.67(.86)
Content 5.50(.84) 5.17(1.20) 5.80(.95)
Format 5.27(.74) 5.00(1.56) 5.72(.90)
Accuracy 5.36(1.14) 5.07(1.50) 5.68(1.01)
Timeliness 5.21(1.35) 4.90(1.76) 5.50(1.04)
Ease of use 5.43(.93) 4.93(1.44) 5.50(.98)
Table 9.9 shows the mean for the single question about overall satisfaction with
the system for the planning approach used. This was a different question in the
questionnaire, and is not the same as the first line in Table 9.8, which is for the 12-item
end-user satisfaction scale. Overall, participants who adopted the unpacking approach
appear to be more satisfied with the system than those who adopted the inside and
outside view. Out of a 10 point scale (with 0 not at all satisfied and 10 extremely
satisfied), on average participants who adopted the unpacking approach rated the system
between good and extremely good. Participants who adopted the outside view seem to
be less satisfied with the system and rated it between fair and good. Even though the
number of participants is small, the difference in mean overall satisfaction with the
system between those who chose the outside view and unpacking was marginally
significant difference by a two-tailed t-test for independent samples, t(27) = -1.91, p =
.068.
184
Table 9.9 The planning approach used and mean of overall satisfaction with the system (standard deviations in brackets) Planning approach used
Inside view (n=7) Outside view (n=15) Unpacking (n=14)
Mean of overall satisfaction with the system
7.21(1.16)
6.66(1.62)
7.71(1.35)
9.6.3 Predictions of Affective Response towards Outcome Success and Failure
Of the 36 participants who had completed their HRIS projects, 32 viewed their
projects as successful and 4 viewed their project as a failure. All four participants who
viewed the project as a failure reported that they adopted the outside view during the
planning process. The 9 participants who were at the planning stage indicated that they
adopted the outside and unpacking approaches when planning for the project. The size
of these groups is too small to statistically compare between the predicted and actual
feelings of success and failure of the participants in the inside view, outside view and
unpacking groups. Therefore, only descriptive analyses and qualitative comparisons are
used to describe the patterns of participants’ affective response towards project outcome
success and failure in this study.
Table 9.10 presents descriptive statistics of both the predicted and actual
affective response towards outcome success or failure. On average, predicted affective
response was higher than the actual affective response towards outcome success, and
predicted affective response was lower than the actual affective response towards
outcome failure.
185
Table 9.10 Descriptive statistics of participants’ affective response towards project outcome success and failure
Descriptions Frequency Mean Std. Dev Median
Completed project
Actual affective response
Success 32 8.41 1.5 8.0
Failure 4 5.50 2.1 5.5
Total 36
Uncompleted project
Predicted affective response
Success 9 9.56 1.0 9.0
Failure 9 3.78 2.4 3.0
Total 9
Based on the descriptive statistics result shown in Table 9.10, in general
participants do overestimate the positive feelings they will experience upon success and
their negative feelings upon failure. This is consistent with the occurrence of impact
bias.
As no respondents for the uncompleted projects reported using the inside view,
only two groups could be compared (outside view and unpacking approach) for the
patterns of affective prediction and actual experience of positive feelings in response to
success. Table 9.11 presents means of the predicted and actual affective response
towards project outcome success for participants who reported using the outside view
and unpacking approaches. There is a smaller difference between the predicted and
actual affective response towards outcome success for those who used the unpacking
approach, than for those who used the outside view approach.
186
Table 9.11 The planning approach used and mean predicted and actual affective response towards project outcome success (standard deviations in brackets) Planning approach used
Outside view (n=5) Unpacking (n=4)
Mean predicted affective response towards project outcome success for uncompleted project
9.60 (1.14)
9.00 (0.82)
Outside view (n=11) Unpacking (n=13)
Mean actual affective response towards project outcome success for completed project
8.23 (1.89)
8.58(1.40)
9.7 Conclusion
This chapter described the demographic characteristics of the 45 participants, the
open-ended responses, and the results of the correlation and descriptive analyses. The
research hypotheses were considered in the light of those results. The results indicate
that those who adopt the unpacking approach during planning have projects that were
more likely to be completed on time as compared to those who adopt the inside or
outside view.
The results also imply that the unpacking approach is related to outcome
success. The more that participants adopted the unpacking approach the more satisfied
they were with the system, and this includes satisfaction with the content, format,
accuracy, timeliness and ease of use of the system.
As for the prediction of affective response towards outcome success and failure,
participants in general predicted more positive feelings after success and more negative
feeling after failure than were actually experienced. This effect was less for the
participants who used the unpacking approach, although the results cannot be tested for
significance. These research findings for Study 2 are discussed in the next chapter,
Chapter 10.
187
CHAPTER 10
DISCUSSION – STUDY 2
10.1 Introduction
This chapter discusses the findings of Study 2 in light of the literature reviewed
on human resource information systems (HRIS) and the planning fallacy, and the
hypotheses developed in Chapter 7. Study 2 was conducted with two purposes in mind:
First, in Study 2 I aimed to explore approaches commonly used by planners during
HRIS project planning; Second, I aimed to extend explanations of the planning fallacy
to HRIS projects. For Study 2, the two explanations of the planning fallacy were tested
and compared using data on actual projects where participants reflected on the projects
that they had handled or the one they presently handled. Study 2 provides identification
of planning approaches that are used during HRIS projects. The findings, as presented
in Chapter 9, are discussed in the sections below. A more general discussion of the
broader implications of both studies will follow in Chapter 11.
10.2 Project Completion Time Predictions
This is the first time that the planning fallacy has been studied and shown to
occur for HRIS projects. In Study 2, the completion time predictions were measured
using two sets of questions: the rating of use of the three planning approaches and the
forced choice questions. Results from the rating of use of the three planning approaches
shows no association between bias of the completion time and the extent of use of any
of the three planning approaches, the inside view, the outside view and the unpacking
approach.
Results from the forced choice questions, however, suggest that the planning
fallacy does occur for HRIS projects, with 7 of the 36 completed projects finishing later
than planned, and none finishing early. Furthermore, of the completed HRIS projects
188
that used the unpacking approach, only one finished later than planned, fewer than for
projects that used the inside and outside views. This provides support for Kruger and
Evans’s (2004) explanation based on support theory. According to support theory,
unpack multifaceted tasks into their various subcomponents would make the tasks seem
bigger, which would increase estimates of the time the task would require, thus reducing
the planning fallacy. This indicates that for completion time predictions, a detailed
focus on the project at hand that requires a full enumeration of the component tasks will
be at least as accurate as taking an outside view approach to completion time prediction.
For those who adopted the inside or the outside view, there was little difference
in terms of the numbers of projects that finished later than planned. One possibility for
this result is that though HRIS planners may attend to their past, they may fail to
incorporate this information into their predictions. This may be due to the difficulty in
finding suitable similar projects, with previous projects usually not similar enough to
the project under consideration.
Another possibility is that HRIS planners may not have the time to examine
other similar projects even if they know about them, especially when the decision has to
be made in a short period of time. Instead of spending time examining the experiences
of other similar projects, HRIS planners may prefer and use information that is available
and easy to apply.
10.3 Project Outcome Predictions
In Study 2, HRIS implementation success was assessed by reports of the
percentage of outcome achieved and by the level of satisfaction with the system. The
results show that there was an association between the extent of use of the unpacking
approach and the reported percentage of outcome achieved. By listing down and
detailing the specifications and requirements of the systems, HRIS planners would have
189
a clearer picture of what they are supposed to deliver. Also, the detailing process of
determining the requirements needed would make it easier for planners to communicate
with vendors in choosing systems that best suited their organization’s needs. In contrast,
the extent to which the outside view was used, recalling past similar projects, did not
relate to the percentage of outcome achieved. This is an important emerging outcome
worth noting even though there was no hypothesis prepared to test whether the actual
outcome of the HRIS project differed for the different planning approaches.
In terms of the level of satisfaction with the system, HRIS planners who adopt
the unpacking approach or the inside view were more satisfied with the system than
those who adopt the outside view. For those who adopted the unpacking approach, the
higher feelings of satisfaction were possibly due to the higher percentage of outcome
they achieved. For those who adopted the outside view, though they achieved as much
outcome as those who adopt the inside view, they were not as satisfied with the
outcome as those who adopted the inside view. One possible explanation for these
results is that HRIS planners who use the outside view anchor their expected outcome to
other peoples’ experience of their HRIS systems. As highlighted in Chapter 9, most of
the HRIS systems implemented were acquired from a vendor. Based on qualitative
analyses of participants’ responses regarding the sources of potential vendors, the most
common and easiest way of acquiring a vendor was through recommendation by friends
from other organizations. Though the HRIS systems used by their friends worked well
in their friends’ organizations, it might not be the case in the participants’ organization.
In other words, the system may be effective for one organization but not for the other.
For example, certain HRIS modules may be suitable for large companies, but became
complicated when used by smaller companies.
For planners who adopt the unpacking approach, though the potential vendors
were also recommended by their friends, they may not have depended on the experience
190
of their friends in preparing what they needed. They work more on detailing and listing
down their needs to be presented to the vendor, and this may explain the satisfaction
feelings towards the outcome that they achieved.
10.4 Predictions of Affective Reactions towards Outcome Success and Failure
The affective measures were included in this study because it was reported in the
literature that HRIS users were not as happy as they had predicted once the systems
were implemented. This may indicate that HRIS users overestimate the affective
reactions they would experience following the outcome of the HRIS projects; that is
they display an impact bias.
Unlike Study 1, HRIS planners’ actual and predicted affective response towards
outcome success or failure was not assessed within the same person or for the same
projects. HRIS planners who had completed their projects rated their actual affective
response towards the system they implemented, and whether it was a success or not,
which provided the actual affective response information. HRIS planners who were at
the planning stage predicted their future affective response towards both the projects’
outcome success and failure, which provided the predicted affective response
information.
In this study, actual affective response towards outcome success was positively
related to the ratings of use of the unpacking approach, (but not the rating of use of
either the outside or inside views). This suggests that the more planners adopted the
unpacking approach the happier they were with the system they implemented. This is
not surprising, because as discussed in the previous section, HRIS planners who
adopted the unpacking approach achieved more of what they planned for. This might
explain the high positive feelings reported by those who adopted the unpacking
approach.
191
Though the data of actual and predicted affective response toward success and
failure were gathered from different participants for different projects, the results
portray some similar patterns to those in Study 1. Descriptively, HRIS planners who
have completed their project actually experienced less extreme positive feelings after
success and less extreme negative feelings after failure than was predicted by
participants at the planning stage. This finding of impact bias is consistent with past
findings on prediction of affective responses (Sanna & Schwarz, 2004).
In Study 1, shown in the graph in Figure 5.6 on page 105 participants in the
outside view condition were less biased than the unpacked condition for predictions of
affective reactions to outcome success. In Study 2 however, the results in Table 9.11 on
page 184 show the impact bias for positive affective reactions following a successful
HRIS project was less for those who use the unpacking approach than for those who use
the outside view. This result from Study 2 suggests that describing a project in greater
detail may improve the affective predictions towards outcome success more than using
past relevant experiences. Detailing the tasks that need to be carried out and the kind of
system that needs to be delivered may change the manner in which the project is
described. With a detailed description, planners may be able to imagine mentally what
the outcome will be. Having a detailed description before a project has taken place may
make the possibility for success or failure become more visible, giving some indication
to planners of whether or not they can deliver the project outcome. This may moderate
planners’ predictions of, and their actual affective reactions towards the outcome
success of the project.
In contrast, the outside view depends on recalling past relevant experience to
make predictions of future affective reactions. Though HRIS planners may have
experienced past success, it may not be easy to choose the kind of past events that can
be considered as the most relevant to their future reactions. Even if they are able to
192
recall how they felt in the past, they may not be able to apply this knowledge to their
predictions about the future.
Another possible reason may be that planners recall and rely on those memories
that come most easily to mind. According to Morewedge et al.(2005), people tend to
remember more of the unusual events than the more routine, everyday events.
Depending on the memory of the best of times like best project success may have
heightened the impact bias and this may explain why planners who adopt the outside
view overestimate the impact of emotions of future events.
Since this study is unable to examine planners’ affective reactions towards
outcome failure, future research is needed to further investigate the impact bias for
negative affective reactions following unsuccessful HRIS projects.
10.5 Study 2 Limitations
The results discussed in this chapter are exploratory given that the number of
respondents who were prepared to share their experiences in planning for human
resource information systems (HRIS) was relatively small.
The small sample was related to challenges faced in obtaining suitable
organizations in which to conduct data gathering. Many organizations were reluctant to
participate as they were concerned about the kind of information they have to provide
and the amount of time they have to spend answering the survey. As this study sought
participants who had been involved directly with the planning and decision making
regarding the HRIS, many potential respondents were senior in their organizations and
their time was valuable.
Apart from that, respondent availability often became an issue. It was common
to find that HRIS planners or decision makers were no longer with an organization
because of organizational restructuring or professional career advancement. For those
who were still with the organization, they were often reluctant to participate and
193
recommended their more senior team members to participate in the survey. Thus, it was
very difficult to get the whole project team to participate in the study especially for
projects that have been completed. Even though the sample of respondents is small, the
diversity of organizations who actually responded to the survey lends some validity to
the results. Also, the sample size is typical of sample sizes in published research on
project planning.
Another limitation of this study is that some data provided in this study is from a
single source, except for affective predictions and reactions that are provided by
different people for different projects. This includes rating of extent use of the planning
approach, the prediction of completion time, the percentage of outcome planned to
achieve and the satisfaction level with the systems. In future research, it would be
desirable to have some outcomes success measure and a measure of the planning
methods used that are not provided by the same person.
Apart from that, Study 2 involves a correlational design. The correlational
nature of the design has not made it possible to comment on the causal nature of the
relationships, as the discovery of association only suggests the possibility of cause.
However, showing correlation can be a useful first step toward demonstrating causation.
In summary, while there are some limitations associated with the approach used
here and given the exploratory nature of the study, the results of this research provide
useful findings that should be of interest to both researchers and practitioners.
10.6 Conclusion
The current chapter has discussed results from Study 2 in light of the literature
and limitations. The next chapter, Chapter 11, will provide a general discussion of the
findings of both studies and present theoretical and practical implications, as well as
limitations of the methods. Finally, recommendations will be made regarding
applications, and areas of future research.
194
CHAPTER 11
GENERAL DISCUSSION
11.1 Introduction
This chapter presents the overall discussion for the thesis, incorporating both
studies. The two studies reported in this thesis elaborate and extend prior research on
the planning fallacy. This study is the first to compare the inside / outside view and the
unpacking approach on the planning fallacy in the same task; and on predictions other
than time to project completion, specifically prediction of project outcome, confidence
in prediction of project completion time and outcome, and prediction of affective
reactions to project outcome success and failure, thereby extending previous research in
important ways. This is also the first time that the planning fallacy has been studied and
shown to occur for HRIS projects. There are several contributions that can be drawn
from both of the studies.
11.2 Theories of the Planning Fallacy
An important aspect of this thesis was the test of two theories of the planning
fallacy to increase the understanding of their effects on predictions made during project
planning. What is apparent from these studies is that the comparison of these two
theories of the planning fallacy, the inside / outside view and unpacking approach
provides a new and very important basis for understanding how to improve prediction
accuracy when planning for a project.
The discussion that follows begins with the effect of the inside / outside view
and unpacking approach on prediction of completion time. This is followed by a
discussion on extending the explanations of the planning fallacy to other types of
predictions. The implications for practice, limitations and directions for future research
are then discussed.
195
11.2.1 Completion Time Predictions
Studies have shown that people are generally overly optimistic about how long it
will take to complete a project task in the future even while knowing that their past
projects have taken longer than planned (Buehler et al., 1997; Buehler et al., 1994;
Newby-Clark et al., 2000). Past studies have attempted to explain why people commit
this planning fallacy by offering two conflicting prescriptions: the inside / outside view
and the unpacking approach. The current study extends previous research by comparing
both of these approaches in a single study.
Results from Study 1 show that the outside view condition was significantly less
biased than the control condition, providing evidence that an outside view manipulation
that only involves recall is sufficient to reduce the planning fallacy. However, results for
the comparison of the inside and outside view do not support the hypothesis for the
outside view. Though a qualitative examination of Figure 5.1 suggests participants in
the outside view condition were less biased than participants in the inside view
condition, this was not confirmed by the test of the intercepts.
The results however, support the hypothesis for unpacking. More accurate
predictions about project completion time were obtained when participants were asked
to unpack their task components as compared to those who do not unpack their task.
Though they were subject to the planning fallacy, it is to a lesser extent than participants
in the packed (control) condition. This result supports prior research on the planning
fallacy, which found that prompting people to unpack specific tasks would decrease or
eliminate the planning fallacy (Kruger & Evans, 2004).
The findings of Study 2 revealed that HRIS planners who reported that they
adopted the unpacking approach when planning for the system tend to have more
projects completed on time as compared to those who adopted the inside or the outside
view.
196
The results from both studies support current planning practice of using work
breakdown structure. A more accurate completion time prediction can be achieved when
the multifaceted tasks are unpacked into subcomponents as the process makes tasks
seem bigger and more time consuming.
There are two important results from completion time predictions that emerge
from Study 1. Comparing these approaches on the same task shows no significant
difference between the outside view and the unpacking approach. Both approaches have
similar effects and reduce the planning fallacy for completion time prediction accuracy.
This indicates that for completion time predictions in project planning applications, a
detailed focus on the project at hand that requires a full enumeration of the component
tasks, may be as accurate as taking an outside approach to completion time prediction.
Second, the inside view, which also focuses on the project at hand, was not
significantly different to the unpacked conditions. This result leads to a question of
whether the difference in wording of instructions has implications for the planning
fallacy. If it does contribute to the effect of increasing or decreasing the planning
fallacy, it may also have an implication for interpreting the mixed results of past
research, and for prescriptions for reducing the planning fallacy in practice. Further
research is needed to examine the possible effects of differences in wording of
instructions on the planning fallacy.
11.3 Extending the Explanations of the Planning Fallacy
11.3.1 Outcome Predictions
In terms of explaining the outcome prediction, the effect of adopting the outside
perspective and unpacking approach has not been examined and compared in a single
study before. Therefore, the hypotheses proposed in Study 1 were parallel with
hypotheses for prediction of completion time. It was proposed that participants in the
outside view condition or the unpacked condition would have more accurate outcome
197
predictions as compared to participants in the inside view condition or the unpacked
(control) condition respectively.
Results from Study 1 indicated that prompting participants to focus on past
relevant experience or to unpack the task tends to increase not reduce the bias of
outcome predictions. However, Study 2 has shown that adopting the unpacking
approach is related to the actual percentage of outcome achieved, and HRIS planners
who unpacked were also more satisfied with their system as compared to HRIS planners
who adopted the inside or the outside view. The results indicate that when predicting for
outcomes of a real project, planning at greater detail by having proper identification of a
project’s activities helps planners to visualize the outcome that they plan to achieve and
thus, more accurate outcome prediction could be generated.
The mixed results of the effect of unpacking on outcome prediction may be
explained by the logic of support theory. Support theory suggests that the influence of
unpacking is related to the number of elements that are unpacked. Though the number
and the size of the constituent elements will increase time estimates, and thus decrease
the planning fallacy, it might also increase the subjective probability that the outcome
will be achieved, and thus increase the outcome prediction bias as shown in Study 1.
But, changing the manner in which the task is described or represented might also direct
people’s attention to important things in the project. The more detailed the description
of the project, the clearer the amount of the effort that needs to be put in to achieve that
outcome.
11.3.2 Confidence in Predictions
In this study, further evidence was also obtained on how much participants
really believe in their predictions towards project completion time and outcome success
by assessing their confidence. In previous research, Buehler, Griffin and Ross (1994)
found that participants were quite confident that they would meet their predictions;
198
confidence in meeting a predicted date and time was on average 74.1% for academic
project. The current study found a similar level of confidence in Study 1, and extends
this study on confidence in predictions by examining the effects of the inside / outside
view and the unpacking approach on the changes of confidence across time.
In Study 1, it was predicted that people who adopt the outside perspective or the
unpacking approach would have smaller changes in their confidence in the prediction
made towards completion time and outcome success as they have more information in
the early stage of the prediction process. However, results from Study 1 failed to
support any of the hypothesized effects of the outside view and unpacking approach on
changes in confidence. Participants showed stable confidence across time regarding
their prediction of completion time, but showed an increasing confidence in predictions
of outcome success.
The results suggest that recalling problems of past projects or alternatively,
unpacking tasks does not produce effects on the change in confidence over time though
the same mechanism reduced the planning fallacy and increased the bias in outcome
predictions. The study shows that participants consistently reported feeling high
confidence in their predictions regardless of being too optimistic in their predictions.
The same participants who confidently believed that they would complete the project
task before the required date and would achieve the grades they predicted, finished later
than expected and failed to get the grades that they expected. This study implies that
prompting people to focus on past experiences and even other people’s experiences or
alternatively, to unpack tasks into their subcomponents may reduce the bias in
prediction, but it may neither reduce nor increase their confidence in those predictions.
11.3.3 Predictions of Affective Reactions towards Outcome Success and Failure
The effects of the outside view and unpacking approach on affective prediction
accuracy are somewhat mixed. Results from Study 1 supported the hypothesized effect
199
of the outside view but not the unpacking approach on prediction of affective reactions
towards outcome success. Participants who were prompted to focus on relevant past
experiences predicted less positive affective reactions towards outcome success than
those prompted to focus on the inside view future scenario for the project at hand. This
finding supports previous research of the inside / outside views approach on affective
prediction, which found that participants who focused on the event itself and neglected
past experiences, anticipated stronger feelings than those who focused on relevant past
experiences (Buehler & McFarland, 2001).
Comparing the outside view and unpacking approach on the affective
predictions in Study 1 indicated that adopting the outside view generated more accurate
predictions than adopting unpacking. That is, participants prompted to focus on relevant
past experience were found to be more accurate in their prediction of affective reactions
towards outcome success than participants prompted to list down components of the
projects and steps needed to complete the project.
The results of Study 2 however, show the opposite. Though the HRIS planners
overestimated the emotions they would experience following the outcome of project
success just as the participants did in Study 1, the impact bias for positive affective
reactions following the successful HRIS project was reduced in association with the
extent of use of the unpacking approach, suggesting that describing the projects in
greater detail did reduce the impact bias.
As for predictions of negative affect after failure, Study 1 failed to support any
of the hypothesized effects of the outside view and unpacking approaches on predictions
of negative affect after failure.
When one considers findings of both studies, the results show that using either
prescription for reducing the planning fallacy may reduce the impact bias, at least for
positive affective reactions following the outcome success.
200
11.3.4 Further Theoretical Development
Results from this study show that the outside view and unpacking approaches
have similar effects on the planning fallacy. This leads to the question of whether the
approaches have different underlying mechanisms that can lead to smilar outcomes or
they actually have a same underlying mechanism. This thesis does not explore this
question, it remains for future research.
11.4 Implications for Practice
The current research findings have several implications for project managers.
The research results demonstrate that better predictions of completion time can be
achieved when each of the subcomponents of the project tasks are considered. Before
making predictions, project managers are encouraged to unpack project tasks into their
subcomponents and to list all the possible steps that are needed to accomplish the
project. Better predictions can be achieved when planners detail out tasks to be carried
out as detailed description may remind people of constituent elements they would not
have otherwise considered (Van Boven & Epley, 2003), and make it easier for
individuals to mentally simulate what an event will be like (Kahneman & Tversky,
1982).
This finding supports utilizing the work breakdown structure method when
planning for projects (Cleland & Ireland, 2002; R. Jones, 2007; Maylor, 2005).
However, it is important to note the difference between unpacking and decomposition in
terms of their operationalization, underlying theory and predictions. According to
Kruger and Evans (2004), decomposition involves actual breaking down a category
with separate estimates assigned to each of the components of the category, before it is
aggregated arithmetically. Unpacking involves breaking down a category symbolically
to change the way the task is described or represented. Unlike decomposition, which
involves combination of multiple judgments, unpacking only involves a single
201
prediction judgment. Decomposition infers that breaking down a category into simple
components ought to reduce bias because it should be easier to make accurate
predictions for smaller, simpler components than for a large, complex aggregate. But,
unpacking infers only an increase in overall estimates, which only translate into reduced
bias if there is an underestimation bias to start with.
Though there is a difference between the decomposition and unpacking, they
might be used together when planning for a project. It has been shown that prediction
problems among IS projects continues to occur even when various methods and
techniques including the workbreakdown structure are utilized. The findings of this
study show that the unpacking approach helps in improving completion time and
outcome predictions, which project managers could consider when utilizing the work
breakdown structure.
However, certain predictions may not be improved through adopting either the
outside view approach or the unpacking approach. It is discovered through this study
that prescriptions for reducing the planning fallacy have opposite effects on predictions
of project completion time and of project outcome success. This applies to both of the
existing methods for reducing the planning fallacy, the outside view approach and the
enumerating or unpacking approach. A message for managers is to beware of the
potential for project outcomes to fall short of expectations on outcome success measures
when these procedures to reduce the planning fallacy are used to reduce prediction bias
for completion times.
The study also demonstrated that people overestimate the emotions they would
experience following the outcome of project success. This might explain why people are
not as happy as they predicted they would be once the project outcome was delivered.
Though the potential impact of emotional events a around future project outcome may
serve a motivating function, which could make project managers work hard to obtain
202
success and to avoid failure, it may also create unnecessary anxiety about future
performance especially when overestimating the negative impact of an unsuccessful
project outcome. Again, the two existing methods for reducing the planning fallacy
have mixed effects on reducing impact bias. The results from Study 2 showed that if
project managers are to reduce the impact bias for positive affective reactions following
a successful HRIS project, describing the project in greater detail give greater benefits
than using past relevant experiences. However, no results can be provided whether
using the same approach will reduce impact bias for negative reactions following the
unsuccessful HRIS project.
In summary, the prescriptions discussed above are suggestive of the types of
actions that project managers can take, to help minimize the risk of underestimating
project completion time and overestimating project outcome success. It is hoped that
results from both studies will encourage new thinking not just among IS project
managers, but also other project managers. The research results reported in this study
suggest the need for a view of project management which includes a psychological and
behavioral perspective. Most studies in the past on IS problems have focused on
specific reasons for failure and suggested specific remedies such as risk management
techniques, user participation, and tools to represent system abstraction. But, project
success often depends on the validity and accuracy of project decisions made during
planning. Human judgment and decision-making is susceptible to various biases that
may influence the accuracy of the predictions made (Buehler et al., 1997; Buehler et al.,
1994; Byram, 1997; Newby-Clark et al., 2000). Practices designed to neutralize these
biases could generate significant benefits for organizations.
11.5 Limitations and Directions for Future Research
On the basis of current findings, several interesting questions remain. In
particular, the question arises of why these methods reduce optimistic bias for
203
completion time predictions, but exacerbate it for outcome success predictions. Are
there other types of predictions that fall between these? What is the underlying
mechanism, or are there multiple mechanisms? The outside view and the unpacking
approaches had some similar results in research, suggesting that they share an
explanatory mechanism, but they are based on very different arguments. The outside
view approach is based on enhancing the salience of base rate information to improve
prediction accuracy. The unpacking approach is based on exploiting people’s failure to
make the same judgments about a collection of subcomponents as they make about the
super category comprised of those components. Future research is needed either to
reconcile these explanations, or to show that there are conditions under which the
outside view and unpacking approaches do not lead to similar results.
Apart from that, this study used group projects, but the prediction was made
individually. In the first study, participants do interact with their group members during
group meetings, their lecturer, their seniors and other friends from other groups in the
process of completing their project assignments. But, I did not further examine the
effect of interaction with others and the adoption of the outside and unpacking approach
in predicting task completion time and outcome, as the main focus of study was more
on testing and comparing the two conflicting theories of the planning fallacy and
extending the explanation of the planning fallacy on the outcome prediction. This
provides another direction for future research.
There is also a need for future research to extend the exploration of the influence
of the outside view, inside view and unpacking approaches on planning and decision
making on other applications of project management apart from HRIS projects. Since
the study only compares the effects and tests whether there is a difference between the
two approaches, without taking into account the nature of the project involved, future
research should test these two approaches in various contexts to gain their effectiveness.
204
This study was not designed to examine the context in which the outside view approach
may be a more suitable method than the unpacking approach and vice versa in
predicting completion time, outcome, and affective reactions towards outcome success
and failure.
Though the first study is a longitudinal study, the second was cross-sectional as
it was not practical to a conduct a longitudinal study. A cross-sectional design is simple,
inexpensive, and allows for the collection of data in a relatively short period. Although
there are advantages to using a cross-sectional design, this method offers limited
information regarding how the whole planning process take place, starting from its
inception to the implementation stage. Perhaps, in the future, it may be worth
investigating the planning approach used when planning a project using a longitudinal
study.
11.6 Conclusion
The aim of this thesis was to investigate how to improve the accuracy of
predictions made during the planning stages of project management. The main concern
of this thesis is the role of human judgment in making accurate project planning
predictions, with particular applications to human resource information systems (HRIS)
projects. This thesis provides the first attempt to test the two compelling but conflicting
theories of the planning fallacy, the inside / outside view and the unpacking approach
against each other in a single study, and to demonstrate the differences and similarities
between these two approaches. The results indicate that the outside view and the
unpacking approach do affect the predictions made regarding the completion of a
project where the planning fallacy is reduced. When examining these two methods in a
real project environment of HRIS, it seems that the extent to which the unpacking
approach is used may be related to finishing on time and being satisfied with the
outcome, as well as the actual success of the project.
205
An important contribution made by this thesis is the comparison of the planning
fallacy theories for predictions other than time to completion of a project, specifically
prediction of project outcome, confidence in prediction of project completion time and
outcome, and predictions of affective reactions to project outcome success and failure.
Both approaches show opposite effects on predictions of completion time and outcome
success, but have no effect on changes in confidence in predictions of completion time
and outcome success. Testing these two methods in real projects involving HRIS,
suggests that the unpacking approach rather than the outside view is more effective in
predicting project outcome success and satisfaction, and in reducing the impact bias for
positive affective reactions following the success of an HRIS project.
It is hoped that through the examination of both the outside view and the
unpacking approach in reducing biases in prediction during project planning, a more
complete understanding of the effectiveness of these two methods will be achieved.
206
REFERENCES
Abdinnour-Helm, S. F., Chaparro, B. S., & Farmer, S. M. (2005). Using the end-user computing satisfaction (EUCS) instrument to measure satisfaction with a web site. Decision Sciences, 36(2), 341-364.
Aibinu, A. A., & Jagboro, G. O. (2002). The effects of construction delays on project delivery in Nigerian construction industry. International Journal of Project Management, 20, 593-599.
Aladwani, A. M. (2002). IT project uncertainty, planning and success: An empirical investigation from Kuwait. Information Technology & People, 15(3), 210-226.
Baillie, J. (1996, August 29). Attracting employees who surf the internet. People Management, 2, 46-47.
Ball, K. S. (2001). The use of human resource information systems: A survey. Personnel Review, 30(6), 677-693.
Bassett, P., Campbell, G., & Licciardi, R. (2003). Tunnel vision: Limited use of human resource information systems (HRIS): School of Management, Victoria University of Technology.
Berry, W. E. (1993). HRIS can improve performance, empower and motivate knowledge workers. Employment Relations Today, 297-303.
Bondaruk, T. V., & Ruel, H. J. M. (2008). HRM systems for successful information technology implementation: Evidence from three case studies. European Management Journal, 26, 153-165.
Broderick, R., & Boudreau, J. W. (1992). Human resource management information technology and the competitive edge. Academy of Management Executive, 6(2), 7-17.
Brown, A. D., & Jones, M. R. (1998). Doomed to failure: Narratives of inevitability and conspiracy in a failed IS project. Organization Studies, 19(1), 73-88.
Buckley, P., Minette, K., Joy, D., & Michaels, J. (2004). The use of an automated employment recruiting and screening system for temporary professional employees: A case study. Human Resource Management, 43(2 & 3), 233-241.
207
Buehler, R., & Griffin, D. (2003). Planning, personality, and prediction: The role of future focus in optimistic time predictions. Organizational Behavior And Human Decision Processes, 92(1-2), 80-90.
Buehler, R., Griffin, D., & MacDonald, H. (1997). The role of motivated reasoning in optimistic time predictions. Personality and Social Psychology Bulletin, 23(3), 238-247.
Buehler, R., Griffin, D., & Ross, M. (1994). Exploring the "planning fallacy": Why people underestimate their task completion times. Journal of Personality and Social Psychology, 67(3), 366-381.
Buehler, R., Griffin, D., & Ross, M. (2002). Inside the planning fallacy: The causes and consequences of optimistic time predictions. In T. Gilovich, D. Griffin & D. Kahneman (Eds.), Heuristics and biases: The Psychology of Intuitive Judgment (pp. 250-270). Cambridge: Cambridge University Press.
Buehler, R., & McFarland, C. (2001). Intensity bias in affective forecasting: The role of temporal focus. Personality and Social Psychology Bulletin, 27(11), 1480-1493.
Buehler, R., McFarland, C., Spyropoulos, V., & Lam, K. C. H. (2007). Motivated prediction of future feelings: Effects of negative mood and mood orientation on affective forecasts. Personality and Social Psychology Bulletin, 33(9), 1265-1278.
Bukszar, E. (1988). Hindsight bias and strategic choice: Some problems in learning from experience. Academy of Management Journal, 31(3), 628-641.
Burger, J. M., & Burns, L. (1988). The illusion of unique invulnerability and the use of effective contraception. Personality and Social Psychology Bulletin, 14(2), 264-270.
Bussler, L., & Davis, E. (2001-2002). Information systems: The quiet revolution in human resource management. Journal of Computer Information Systems, 17-20.
Byram, S. J. (1997). Cognitive and motivational factors influencing time prediction. Journal of Experimental Psychology: Applied, 3(3), 216-239.
Calisir, F., & Gumussoy, C. A. (2005). Determinants of budget overruns on IT projects. Technovation, 25, 631-636.
Camerer, C., & Lovallo, D. (1999). Overconfidence and excess entry: An experimental approach. The American Economic Review, 89(1), 306-318.
208
Caplan, J. (2004, March). eHR in Greater China: The future of HR takes flight. China Staff, 10, 1-7.
Cappelli, P. (2001). Making the most of on-line recruiting. Harvard Business Review, 79, 139-146.
Cardy, R. L., & Miller, J. S. (2003). Technology implications for HRM. In D. L. Stone (Ed.), Advances in human performance and cognitive engineering research (pp. 99-118). Greenwich, CT: JAI Press.
Cardy, R. L., & Miller, J. S. (2005). eHR and performance management: A consideration of positive potential and the dark side. In H. G. Gueutal & D. L. Stone (Eds.), The brave new world of eHR: Human resources management in the digital age (pp. 138-165). San Francisco, CA: Jossey-Bass.
Cats-Baril, W., & Thompson, R. (1995). Managing information technology projects in the public sector. Public Administration Review, 55(6), 559-566.
Ceriello, V. R., & Freeman, C. (1991). Human resource management systems: Strategies, tactics and techniques. New York: Lexington Books.
Chapman, D. S., & Webster, J. (2003). The use of technologies in the recruiting, screening, and selection processes for job candidates. International Journal of Selection and Assessment, 11(2/3), 113-120.
Cleland, D. I., & Ireland, L. R. (2002). Project management: Strategic design and implementation (4th ed.). New York: McGraw-Hill.
Cober, R. T., Brown, D. J., Levy, P. E., Cober, A. B., & Keeping, L. M. (2003). Organizational web sites: Web site content and style as determinants of organizational attraction. International Journal of Selection and Assessment, 11(2/3), 158-169.
Coppola, N. W., & Myre, R. (2002). Corporate software training: Is web-based training as effective as instructor-led training? IEEE Transactions on Professional Communication, 45(3), 170-186.
DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95.
Doll, W. J., Deng, X., Raghunathan, T. S., Torkzadeh, G., & Xia, W. (2004). The meaning and measurement of user satisfaction: A multigroup invariance analysis of the end-user computing satisfaction instrument. Journal of Management Information Systems, 21(1), 227-262.
209
Doll, W. J., & Torkzadeh, G. (1988). The measurement of end-user computing satisfaction. MIS Quarterly, 12(2), 258-274.
Doll, W. J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 18(4), 453-461.
Dunning, D. (2007). Prediction: The inside view. In A. W. Kruglanski & E. T. Higgins (Eds.), Social Psychology: Handbook of basic principles (2nd ed., pp. 69-90). New York: The Guilford Press.
Dvir, D., Raz, T., & Shenhar, A. J. (2003). An empirical analysis of the relationship between project planning and project success. International Journal of Project Management, 21(2), 89-95.
Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12(2), 121-138.
EPU. (2006). Ninth Malaysia Plan. Retrieved 28th August 2008. from http://www.epu.jpm.my/RM9/html/english.htm.
Ewusi-Mensah, K. (1997). Critical issues in abandoned information systems development projects. Communications of the ACM, 40(9), 74-80.
Ewusi-Mensah, K., & Przasnyski, Z. H. (1991). On Information systems project abandonment: An exploratory study of organizational practices. MIS Quarterly, 15(1), 67-86.
Ewusi-Mensah, K., & Przasnyski, Z. H. (1994). Factors contributing to the abandonment of information systems development projects. Journal of Information Technology, 9, 185-201.
Federation of Malaysian Manufacturers. (2007). About FMM. Retrieved 5th April, 2007, from http://www.fmm.org.my/
Finkenauer, C., Gallucci, M., Dijk, W. W. v., & Pollmann, M. (2007). Investigating the role of time in affective forecasting: Temporal influences on forecasting accuracy. Personality and Social Psychology Bulletin, 33(8), 1152-1166.
Flowers, S. (1996). Software failure: Management failure: Amazing stories and cautionary tales. Chichester: John Wiley & Sons.
210
Galanaki, E. (2002). The decision to recruit online: A descriptive study. Career Development International, 7(4), 243-251.
Gardner, S. D., Lepak, D. P., & Bartol, K. M. (2003). Virtual HR: The impact of information technology on the human resource professional. Journal of Vocational Behavior, 63, 159-179.
Gemino, A., Reich, B. H., & Sauer, C. (2007). A temporal model of information technology project performance. Journal of Management of Information Systems, 24(3), 9-44.
Gilbert, D. T., Pinel, E. C., Wilson, T. D., Blumberg, S. J., & Wheatley, T. P. (1998). Immune neglect: A source of durability bias in affective forecasting. Journal of Personality and Social Psychology, 75(3), 617-638.
Gilbert, D. T., & Wilson, T. D. (2000). Miswanting. In J. P. Forgas (Ed.), Thinking and feeling: The role of affect in social cognition (pp. 178-197). Cambridge, England: Cambridge Press University.
Gilovich, T., Kerr, M., & Mecdvec, V. H. (1993). Effect of temporal perspective on subjective confidence. Journal of Personality and Social Psychology, 64(4), 552-560.
Greengard, S. (1998, August). Putting online recruiting to work. Workforce, 77, 73-77.
Griffin, D., & Buehler, R. (2005). Biases and fallacies, memories and predictions: Comment on Roy, Christenfeld, and McKenzie (2005). Psychological Bulletin, 131(5), 757-760.
Haines, V. Y., & Petit, A. (1997). Conditions for successful human resource information systems. Human Resource Management, 36(2), 261-275.
Hall, P. (1980). Great planning disasters. London: Weidenfeld & Nicolson.
Hardie, N. (2001). The prediction and control of project duration: A recursive model. International Journal of Project Management, 19, 401-409.
Hartman, F., & Ashrafi, R. A. (2002). Project management in the information systems and information technologies industries. Project Management Journal, 33(3), 5-15.
211
Hausdorf, P. A., & Duncan, D. (2004). Firm size and Internet recruiting in Canada: A preliminary investigation. Journal of Small Business Management, 42(3), 325-334.
Heath, C., & Gonzalez, R. (1995). Interaction with others increases decision confidence but not decision quality: Evidence against information collection views of interactive decision making. Organizational Behavior And Human Decision Processes, 61(3), 305-326.
Hedeker, D., & Gibbons, R. D. (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods, 2(1), 64-78.
Hendrickson, A. R. (2003). Human resource information systems: Backbone technology of contemporary human resources. Journal of Labor Research, 24(3), 381-395.
Hendrickson, A. R., Glorfeld, K., & Cronan, T. P. (1994). On the repeated test-retest reliability of the end-user computing satisfaction instrument: A comment. Decision Sciences, 25(4), 655-667.
Hill, J., Thomas, L. C., & Allen, D. E. (2000). Experts' estimates of task durations in software development projects. International Journal of Project Management, 18, 13-21.
Hofman, D. A., & Gavin, M. B. (1998). Centering decisions in hierarchical linear models: Implications for research in organizations. Journal of Management, 24(5), 623-641.
Hox, J. J. (2002). Multilevel analysis: Techniques and applications. New Jersey: Lawrence Erlbaum Associates, Inc. Publishers.
Hughes, R. T. (1996). Expert judgment as an estimating method. Information and Software Technology, 38, 67-75.
Huo, Y. P., & Kearns, J. (1992). Optimizing the job-person match with computerized human resource information system. Personnel Review, 21(2), 3-18.
Hussain, Z., Wallace, J., & Cornelius, N. E. (2007). The use and impact of human resource information systems on human resource management professionals. Information & Management, 44, 74-89.
Hyvari, I. (2006). Success of projects in different organizational conditions. Project Management Journal, 37(4), 31-41.
212
Ives, B., & Olson, M. H. (1984). User involvement and MIS success: A review of research. Management Science, 30(5), 586-603.
Jattuso, M. L., & Sinar, E. F. (2003). Source effects in Internet-based screening procedures. International Journal of Selection and Assessment, 11(2/3), 137-140.
Jiang, J. J., & Klein, G. (1999). Risks to different aspects of system success. Information & Management, 36, 263-272.
Jones, M. C., & Harrison, A. W. (1996). IS project team performance: An empirical assessment. Information and Management, 31(2), 57-65.
Jones, R. (2007). Project management survival: A practical guide to leading, managing & delivering challenging projects. London: Kogan Page Limited.
Kahneman, D., & Lovallo, D. (1993). Timid choice and bold forecasts: A cognitive perspective on risk taking. Management Science, 39(1), 17-31.
Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representative. Cognitive Psychology, 3, 430-454.
Kahneman, D., & Tversky, A. (1982). The simulation heuristic. In D. Kahneman, P. Slovic & A. Tversky (Eds.), Judgment under uncertainty: Heuristics and biases (pp. 201-208). New York: Cambridge University Press.
Katagiri, M., & Turner, R. (Eds.). (2004). Goal directed project management: Effective techniques and strategies (3rd ed.). London: Kogan Page.
Kavanagh, M. J., Gueutal, H. G., & Tannenbaum, S. I. (1990). Human resource information systems: Development and application. USA: PWS-Kent.
Kehoe, J. F., Dickter, D. N., Russell, D. P., & Sacco, J. M. (2005). e-Selection. In H. G. Gueutal & D. L. Stone (Eds.), The brave new world of eHR. San Francissco, CA: Jossey-Bass.
Keil, M., Mann, J., & Rai, A. (2000). Why software projects escalate: An empirical analysis and test of four theoretical models. MIS Quarterly, 24(4), 631-664.
Kinnie, N. J., & Arthurs, A. J. (1996). Personnel specialists' advanced use of information technology: Evidence and explanations. Personnel Review, 25(3), 3-19.
213
Kotylar, I., & Saks, A. M. (2001, October 22). Using technology for knowledge and skill transfer. Canadian HR Reporter, 14, G9.
Kovach, K. A., & Cathcart, C. E. J. (1999). Human resource information systems (HRIS): Providing business with rapid data access, information exchange and strategic advantage. Public Personnel Management, 28(2), 275-282.
Kreft, I. G. G., & de Leeuw, J. (1998). Introducing multilevel modeling. London: Sage Publications.
Kreft, I. G. G., de Leeuw, J., & Aiken, L. S. (1995). The effect of different forms of centering in hierarchical linear models. Multivariate Behavioral Research, 30(1), 1-21.
Kruger, J., & Evans, M. (2004). If you don't want to be late, enumerate: Unpacking reduces the planning fallacy. Journal of Experimental Social Psychology, 40(5), 586-598.
Krull, J. L., & MacKinnon, D. P. (2001). Multilevel modeling of individual and group level mediated effects. Multivariate Behavioral Research, 36(2), 249-277.
Lam, K. C. H., McFarland, C., Ross, M., & Cheung, I. (2005). Cultural differences in affective forecasting: The role of focalism. Personality and Social Psychology Bulletin, 31(9), 1296-1309.
Langer, E. J. (1975). The illusion of control. Journal of Personality and Social Psychology, 32(2), 311-328.
Lee, C. (1989). Perceptions of immunity to disease in adult smokers. Journal of Behavioral Medicine, 12(3), 267-277.
Lengnick-Hall, M. L., & Moritz, S. (2003). The impact of e-HR on the human resource management function. Journal of Labor Research, 24(3), 365-380.
Leybourne, S., & Sadler-Smith, E. (2006). The role of intuition and improvisation in project management. International Journal of Project Management, 24, 483-492.
Liberman, N., & Trope, Y. (1998). The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory. Journal of Personality and Social Psychology, 75(1), 5-18.
214
Lientz, B. P., & Rea, K. P. (1998). Project management for the 21st century. San Diego: Academic Press.
Lin, B., & Stasinskaya, V. S. (2002). Data warehousing management issues in online recruiting. Human Systems Management, 21(1), 1-8.
Lock, D. (2003). Project management (8th ed.). Hampshire, England: Gower Publishing Limited.
Lovallo, D., & Kahneman, D. (2003). Delusions of success: How optimism undermines executives' decisions. Harvard Business Review, 81(7), 56-63.
Lyytinen, K., & Hirschheim, R. (1987). Information system failures: A survey and classification of the empirical literature. In P. Zorkoczy (Ed.), Oxford surveys in information technology (pp. 257-309). Oxford: Oxford University Press.
Macy, J. (2004, September). Data disarray. HRMonthly, 36-38.
Makridakis, S., & Wheelwright, S. C. (1989). Forecasting methods for management (5th ed.). NewYork: John Wiley & Sons.
Manavazhi, M. R., & Adhikari, D. K. (2002). Material and equipment procurement delays in highway projects in Nepal. International Journal of Project Management, 20, 627-632.
Martinsons, M. G. (1997). Human resource management applications of knowledge-based systems. International Journal of Information Management, 17(1), 35-53.
Maylor, H. (2005). Project management (3rd ed.). England: Pearson Education Limited.
McCray, G. E., Purvis, R. L., & McCray, C. G. (2002). Project management under uncertainty: The impact of heuristics and biases. Project Management Journal, 33(1), 49-57.
McFarlan, F. W. (1981). Portfolio approach to information systems. Harvard Business Review, 59(5), 142-150.
McHaney, R., & Cronan, T. P. (1998). Computer simulation success: On the use of the end-user computing satisfaction instrument: A comment. Decision Sciences, 29(2), 525-536.
215
McHaney, R., Hightower, R., & Pearson, J. (2002). A validation of the end-user computing satisfaction instrument in Taiwan. Information & Management, 39(6), 503-511.
McHaney, R., Hightower, R., & White, D. (1999). EUCS test-retest reliability in representational model decision support systems. Information & Management, 36(2), 109-119.
McKenna, F. P., Warburton, D. M., & Winwood, M. (1993). Exploring the limits of optimism: The case of smokers' decision making. British Journal of Psychology, 84, 389-394.
McManus, M. A., & Ferguson, M. W. (2003). Biodata, personality, and demographic differences of recruits from three sources. International Journal of Selection and Assessment, 11(2/3), 175-183.
Meredith, J. R., & Mantel, S. M., Jr. (2006). Project management: A managerial approach. Hoboken, New Jersey: John Wiley & Sons, Inc.
Morewedge, C. L., Gilbert, D. T., & Wilson, T. D. (2005). The least likely of times: How remembering the past biases forecasts of the future. Psychological Science: Research, Theory, & Application in Psychology and Related Sciences, 16(8), 626-630.
Morgenshtern, O., Raz, T., & Dvir, D. (2007). Factors affecting duration and effort estimation errors in software development projects. Information and Software Technology, 49, 827-837.
Murray, J. P. (2001). Recognizing the responsibility of a failed information technology project as a shared failure. Information Systems Management, 18, 25-30.
Newby-Clark, I. R., & Ross, M. (2003). Conceiving the past and future. Personality and Social Psychology Bulletin, 29(7), 807-818.
Newby-Clark, I. R., Ross, M., Buehler, R., Koehler, D. J., & Griffin, D. (2000). People focus on optimistic scenarios and disregard pessimistic scenarios while predicting task completion times. Journal of Experimental Psychology: Applied, 6(3), 171-182.
Ngai, E. W. T., & Wat, F. K. T. (2006). Human resource information systems: A review and empirical analysis. Personnel Review, 35(3), 297-314.
Nink, C., & Chieke, R. K. (2004, August). Cyberspace recruiting: Useful strategies to improve success. Corrections Today, 66, 104-108.
216
NSDC. (2005). Definitions for small and medium enterprises in Malaysia. Retrieved 3rd July, 2007, from http://www.smeinfo.com.my/pdf/sme_definitions_ENGLISH.pdf
Ogier, J. (2003, September). Are you the tech wreck? The trouble with HRIS. HRMonthly, 19-24.
Otto, J. R., Najdawi, M. K., & Caron, K. M. (2000). Web-user satisfaction: An exploratory study. Journal of End User Computing, 12(4), 3-10.
Parry, E., & Tyson, S. (2008). An analysis of the use and success of online recruitment methods in the UK. Human Resource Management Journal, 18(3), 257-274.
Pearson, G. (2001, October 22). HRMS does make a difference. Canadian HR Reporter, 14, G1-2.
Perloff, L. S., & Fetzer, B. K. (1986). Self-other judgments and perceived vulnerability to victimization. Journal of Personality and Social Psychology, 50(3), 502-510.
Perminova, O., Gustafsson, M., & Wikstrom, K. (2008). Defining uncertainty in projects - a new perspective. International Journal of Project Management, 26, 73-79.
Peterson, D. K., & Kim, C. (2003). Perceptions on IS risks and failure types: A comparison of designers from the United States, Japan and Korea. Journal of Global Information Management, 11(3), 19-38.
Peugh, J. L., & Enders, C. K. (2005). Using the SPSS mixed procedure to fit cross-sectional and longitudinal multilevel models. Educational and Psychological Measurement, 65(5), 717-741.
Pinto, J. K., & Mantel, S. I., Jr. (1990). The causes of project failure. IEEE Transactions on Engineering Management, 37(4).
Pinto, J. K., & Prescott, J. E. (1990). Planning and tactical factors in the project implementation process. Journal of Management Studies, 27(3), 305-327.
Purvis, R. L., McCray, G. E., & Roberts, T. L. (2004). Heuristics and biases in information systems project management. Engineering Management Journal, 16(2), 19-27.
217
Rai, A., & Al-Hindi, H. (2000). The effects of development process modeling and task uncertainty on development quality performance. Information and Management, 37(6), 335-346.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oak, CA: Sage Publications.
Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., Congdon, R., & du Toit, M. (2004). HLM6: Hierarchical linear and nonlinear modeling. Lincolnwood, IL: Scientific Software International, Inc.
Raymond, L., & Bergeron, F. (2008). Project management information systems: An empirical study of their impact on project managers and project success. International Journal of Project Management, 26(2), 213-220.
Robey, D., Smith, L. A., & Vijayasarathy, L. R. (1993). Perceptions of conflict and success in information systems development projects. Journal of Management Information Systems, 10(1), 123-139.
Robinson, M. D., & Clore, G. L. (2002). Belief and feeling: Evidence for an accessibility model of emotional self-report. Psychological Bulletin, 128(6), 934-960.
Romm, C. T., Pliskin, N., & Weber, Y. (1995). The relevance of organizational culture to the implementation of human resources information systems. Asia Pacific Journal of Human Resources, 33(2), 63-80.
Rosenau, M. D., Jr. (1998). Successful project management: Step-by-step approach with practical examples (3rd ed.). New York: John Wiley & Sons, Inc.
Rottenstreich, Y., & Tversky, A. (1997). Unpacking, repacking and anchoring: Advances in support theory. Psychological Review, 104(2), 406-415.
Roy, M. M., Christenfeld, N. J. S., & McKenzie, C. R. M. (2005). Underestimating the duration of future events: Memory incorrectly used or memory bias? Psychological Bulletin, 131(5), 738-756.
Ruel, H. J. M., Bondarouk, T. V., & Looise, J. K. (2004). E-HRM: Innovation or irritation. An explorative empirical study in five large companies on web-based HRM. Management Revue, 15(3), 364-380.
Russell, T. (2006, June 27). HR's rage against the machine. Human Resources, 16-17.
218
Russo, J. E., & Schoemaker, P. J. H. (1992). Managing overconfidence. MIT Sloan Management Review, 33(2), 7-19.
Sadri, J., & Chatterjee, V. (2003). Building organisational character through HRIS. International Journal of Human Resources Development and Management, 3(1), 84-98.
Sambasivan, M., & Soon, Y. W. (2007). Causes and effects of delays in Malaysian construction industry. International Journal of Project Management, 25(5), 437-542.
Sanna, L. J. (1999). Mental simulations, affect, and subjective confidence: Timing is everything. Psychological Science, 10(4), 339-345.
Sanna, L. J., & Schwarz, N. (2004). Integrating temporal biases: The interplay of focal thoughts and accessibility experiences. Psychological Science, 15(7), 474-481.
Schkade, D. A., & Kahneman, D. (1998). Does living in California make people happy? A focusing illusion in judgment of life satisfaction. Psychological Science, 9(5), 340-346.
Schnaars, S. P. (1989). Megamistakes: Forecasting and the myth of rapid technological change. New York: Free Press.
Schwarz, N., & Clore, G. L. (1983). Mood, misattribution, and judgments of well-being: Informative and directive functions of affective states. Journal of Personality and Social Psychology, 45(3), 513-523.
Shand, D. (2000, April 17). The passive/aggressive job seeker. Computerworld, 34, 50-51.
Shepperd, J. A., Findley-Klein, C., Kwavnick, K. D., Walker, D., & Perez, S. (2000). Bracing for loss. Journal of Personality and Social Psychology, 78(4), 620-634.
Shepperd, J. A., Grace, J., Cole, L. J., & Klein, C. (2005). Anxiety and outcome predictions. Personality and Social Psychology Bulletin, 31(2), 267-275.
Singer, J. D., & Willet, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press, Inc.
Smith, C. (2007). Making sense of project realities: Theory, practice and the pursuit of performance. England: Gower Publishing Company.
219
Stone, D. L., Stone-Romero, E. F., & Lukaszewski, K. (2006). Factors affecting the acceptance and effectiveness of electronic human resource systems. Human Resource Management Review, 16, 229-244.
Strohmeier, S. (2007). Research in e-HRM: Review and implications. Human Resource Management Review, 17, 19-37.
Tannenbaum, S. I. (1990). Human resource information systems: User group implications. Journal of Systems Management, 41(1), 27-32.
Tansley, C., & Newell, S. (2007). Project social capital, leadership and trust: A study of human resource information system development. Journal of Managerial Psychology, 22(4), 350-368.
Targowski, A. S., & Deshpande, S. P. (2001). The utility and selection of an HRIS. Advances in Competitiveness Research (ACR), 9(1), 42-56.
Teo, T. S. H., Lim, G. S., & Fedric, S. A. (2007). The adoption and diffusion of human resources information systems in Singapore. Asia Pacific Journal of Human Resources, 45(1), 44-62.
Thaler-Carter, R. E. (1998a, July). The HRIS in small companies: Tips for weighing the options. HRMagazine, 43, 30-35.
Thaler-Carter, R. E. (1998b, November). Recruiting through the web: Better or just bigger? HRMagazine, 43, 61-67.
Thomas, K. E., & Handley, S. J. (2008). Anchoring in time estimation. Acta Psychologica, 127(1), 24-29.
Thomas, K. E., Newstead, S. E., & Handley, S. J. (2003). Exploring the time prediction process: The effects of task experience and complexity on prediction accuracy. Applied Cognitive Psychology, 17, 655-673.
Thong, J. Y. L. (1999). An integrated model of information systems adoption in small businesses. Journal of Management Information Systems, 15(4), 187-214.
Torkzadeh, G., & Doll, W. J. (1991). Test-retest reliability of the end-user computing satisfaction instrument. Decision Sciences, 22, 26-37.
Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207-232.
220
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.
Tversky, A., & Koehler, D. J. (1994). Support theory: A nonextensional representation of subjective probability. Psychological Review, 101, 547-567.
Van Boven, L., & Epley, N. (2003). The unpacking effect in evaluative judgments: When the whole is less than the sum of its parts. Journal of Experimental Social Psychology, 39(3), 263-269.
Van Dijk, W. W. (2009). How do you feel? Affective forecasting and the impact bias in track athletics. The journal of Social Psychology, 149(3), 243-248.
Wateridge, J. (1995). IT projects: A basis for success. International Journal of Project Management, 13(3), 169-172.
Wateridge, J. (1998). How can IS/IT projects be measured for success? International Journal of Project Management, 16(1), 59-63.
Weinstein, N. D. (1980). Unrealistic optimism about future life events. Journal of Personality and Social Psychology, 39(5), 806-820.
Weinstein, N. D. (1982). Unrealistic optimism about susceptibility to health problems. Journal of Behavioral Medicine, 5(4), 441-460.
Weinstein, N. D. (1987). Unrealistic optimism about susceptibility to health problems: Conclusions from a community-wide sample. Journal of Behavioral Medicine, 10(5), 481-500.
Welsh, E. T., Wanberg, C. R., Brown, K. G., & Simmering, M. J. (2003). E-learning: emerging uses, empirical results and future directions. International Journal of Training and Development, 7(4), 245-258.
White, D., & Fortune, J. (2002). Current practice in project management - an empirical study. International Journal of Project Management, 20(1), 1-11.
Whitley, B. E., Jr., & Hern, A. L. (1991). Perceptions of vulnerability to pregnancy and the use of effective contraception. Personality and Social Psychology Bulletin, 17(1), 104-110.
Wilson, T. D., & Gilbert, D. T. (2003). Affective forecasting. In M. Zanna (Ed.), Advances in experimental social psychology (Vol. 35, pp. 345-407). New York: Academic Press.
221
Wilson, T. D., & Gilbert, D. T. (2005). Affective forecasting: Knowing what to want. Current Direction in Psychological Science, 14(3), 131-134.
Wilson, T. D., Meyers, J. M., & Gilbert, D. T. (2001). Lesson from the past: Do people learn from experience that emotional reactions are short-lived? Personality and Social Psychology Bulletin, 27(12), 1648-1661.
Wilson, T. D., Wheatley, T., Meyers, J. M., Gilbert, D. T., & Axsom, D. (2000). Focalism: A source of durability bias in affective forecasting. Journal of Personality and Social Psychology, 78(5), 821-836.
Yeates, D. (Ed.). (1991). Project management for information systems. London: Pitman Publishing.
Yeo, K. T. (2002). Critical failure factors in information system projects. International Journal of Project Management, 20, 241-246.
Zusman, R. R., & Landis, R. S. (2002). Applicant preferences for web-based versus traditional job postings. Computers in Human Behavior, 18(3), 285-296.
Zwikael, O., & Sadeh, A. (2007). Planning effort as an effective risk management tool. Journal of Operation Management, 25, 755-767.
222
APPENDICES
223
APPENDIX A
SAMPLE OF THE EXPERIMENTAL MATERIALS (ENGLISH AND TRANSLATED VERSION) – STUDY 1
This appendix contains copies of the experimental materials provided to respondents,
namely the information sheet; consent form; cover letter; experimental questionnaire for
one of the four conditions: inside perspective, outside perspective, unpacked and packed
(control); a weekly diary entry; and a short affective questionnaire.
224
School of Economics and Commerce Dr. Catherine D. Lees Mail Bag 261 35 Stirling Highway, Crawley, WA 6009 Phone +61 8 6488 2877 Fax +61 8 6488 1055 Email [email protected] Web www.biz.uwa.edu
Project Planning and Project Success
INFORMATION SHEET
Background of the study It is known that many Human Resource Information Systems (HRIS) projects have encountered problems during implementation and some were abandoned. Like many other projects, HRIS projects were reported not to be able to finish on time, within budget and more importantly, to deliver the expected outcome. There has been no research on this phenomenon in the HRIS literature, although these effects have been observed in a wide variety of activities. Also, most studies on project planning have been conducted in Western countries. Carrying out the research in Malaysia with cultural, economic, social and political differences from Western countries will provide insight into how Malaysian people make decisions during project planning. This may impact on the HRIS project planning and success in Malaysia in ways that are as yet unknown.
Aims of the study This study is conducted to investigate the best method of improving the planning in a project. This is done by testing two theories of planning accuracy and extending previous researchers’ explanations of project planning problems to the Malaysian context.
Your involvement in this study Participation in this study is totally voluntary and your responses will remain completely confidential. The completion and return of the enclosed Consent Form is taken to constitute your consent to participate in the study You may withdraw from the study, by discontinuing the completion of the questionnaire, at any time and without prejudice. You need not justify your decision to withdraw, and if you choose to withdraw your responses will not be recorded. Your participation in this study does not prejudice any right to compensation that you may have under statute or common law. After all the data have been collected, identifying information such as your name or student ID number will be destroyed and the data will be completely anonymous in the analyses and reports of the research. If the results from this study will be published, only aggregate results will be reported and individual responses will not identifiable.
225
Risks The researcher anticipates that this study will not involve any risk for you. Please be assured that the researcher will give due care in providing support if you experience any distress.
Benefits As a result of participating in this study, you may feel empowered and feel a sense of satisfaction because you have contributed to an important study that will benefit the society. Findings from this study may have significant implications for human resource management practices such as reducing forecasting errors during project planning and provide them with avenues for improving HRIS implementation processes by understanding the factors that might promote feasible and successful HRIS projects.
Experimental process You will be invited to participate in an experiment with the Field Researcher. In the beginning of the experiment, you will be randomly assigned to receive one of four different sets of instructions that ask you to think about how you will complete your project assignment. The Field Researcher will ask you to follow the instructions and to fill out a short questionnaire about when you will finish your project assignment and what mark you anticipate to get, how confident you are with your prediction and your feelings towards the mark you receive. This will take approximately 30 minutes. Once you have completed all the experimental materials, you will receive a form like a diary where you are requested to write a weekly progress report. Your diary will be collected on a weekly basis by the Field Researcher. As you hand in your report, you will receive a new diary to write. The last diary entry will be collected on the day you hand in your project assignment. Once your lecturer has finished marking your project assignment, your mark will be given to the Field Researcher for you to collect. As you collect your project assignment’s mark from the Field Researcher, you will be asked to fill out a short questionnaire about your feelings towards the mark you receive. This will take less than 10 minutes. It is hoped that your participation in this study will provide invaluable information that will help to guide human resource people and people involved in projects in knowing which method is the best to reduce prediction error during project planning processes and enable them to finish the project on time, within budget and deliver the outcome as expected. If you have any questions regarding this study, you may address them to the Field Researcher Siti Zubaidah Othman or to the Chief Investigator, Dr. Catherine Lees at the addresses below. A copy of this Information Sheet and Consent Form is provided for your own records.
Dr. CATHERINE LEES SITI ZUBAIDAH OTHMAN PhD Candidate UWA Business School UWA Business School The University of Western Australia The University of Western Australia 35 Stirling Highway, Crawley, 6009 35 Stirling Highway, Crawley, 6009 Phone: +61 8 6488 2877 Phone: +61 8 6488 3921 Fax: +61 8 6488 1055 Fax: +61 8 6488 1055 Email: [email protected] Email: [email protected]
226
Project planning and Project success
CONSENT FORM
I ________________________________________ have read the information provided and any questions I have asked have been answered to my satisfaction. I agree to participate in this activity, realising that I may withdraw at any time without reason and without prejudice. I understand that all information provided is treated as strictly confidential and will not be released by the investigator unless required to by law. I have been advised as to what data is being collected, what the purpose is, and what will be done with the data upon completion of the research. I agree that research data gathered for this study may be published provided that my name or other identifying information is not used. I understand that any future use that the University makes of the data is subject to separate approval by the Research and Ethics Committees of The University of Western Australia. ___________________________ __________________
Participant Date The Human Research Ethics Committee at the University of Western Australia requires that all participants are informed that, if they have any complaint regarding the manner in which a research project is conducted, it may be given to the researcher or, alternatively, to the Secretary, Human Research Ethics Committee, Registrar’s Office, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009 (telephone number 6488-3703). All study participants will be provided with a copy of the Information Sheet and Consent Form for their personal records.
227
Project Planning and Project Success
Questionnaire
Dear Participant, Thank you for agreeing to participate in this research on project planning. We would appreciate it very much if you could answer the questions carefully as the information you provide will influence the accuracy and the success of this research. It will take no longer than 30 minutes to complete the questionnaire. All answers will be treated with strict confidence and will be used for the purpose of the study only.
If you have any questions regarding this research, you may address them to Siti Zubaidah Othman or to Dr. Catherine Lees at the contact details below. Thank you for your cooperation and the time taken in answering this questionnaire. Yours sincerely, Dr. CATHERINE LEES SITI ZUBAIDAH OTHMAN PhD Candidate School of Economics & Commerce School of Economics & Commerce The University of Western Australia The University of Western Australia 35 Stirling Highway, Crawley, 6009 35 Stirling Highway, Crawley, 6009 Phone: +61 8 6488 2877 Phone: +61 8 6488 3921 Fax: +61 8 6488 1055 Fax: +61 8 6488 1055 Email: [email protected] Email: [email protected] Faculty of Human & Social Development Universiti Utara Malaysia Phone: +604-9283840 Fax: +604-9285754 Email: [email protected]
Project Planning and Project Success
(A1)
228
SECTION 1: Instructions: Please follow the instructions step by step. Do not look ahead until you have completed each question. Step 1: We would like you to write down your student number and today’s date. The purpose of having your student number is that you will be receiving other materials in the future. In order to ensure that you get the right materials, your student number will be used.
Student # : ________________ Today’s Date : ________________ Step 2: After you have filled in your student number and today’s date, please read the following instructions.
Please take a few minutes to consider your plans for completing your
project assignment. Think about the kind of events that are likely to
happen as you work on your project assignment. Think about all the
problems that are likely to happen in the future that will impede your
project progress and performance, and any aspects of the project or
situation in the future that will facilitate your project progress and
performance.
Step 3: Now, you can turn to the next page.
Project Planning and Project Success
(A1)
229
Step 4: To help you answering questions a through c, please focus on the project assignment that you are going to complete for this class. Please use the space provided to write your answer.
a. Please describe all the problems that are likely to happen in the future that will impede your project progress and performance.
Project Planning and Project Success
(A1)
230
b. Please describe any aspects of the project or situation that will facilitate your project progress and performance.
Project Planning and Project Success
(A1)
231
c. When do you start working on your project assignment? Please indicate your predicted date and time even though you are still unsure about it at this stage. Date: ______________ Time: _____________
Step 5: After you have finished writing down your plans for your project assignment, you
can now turn to page 5.
Project Planning and Project Success
(A1)
232
SECTION 2: Instructions: Please answer all the questions below carefully.
Please predict as accurately and realistically as possible in questions 1 and 2: 1. When would you think you will complete your project assignment? Completion Date: ___________ Completion Time: ___________ 2. How many days do you think you will take to finish this project?
Number of days: ____________ Using the scale given below, please indicate how confident or certain you are of the statement 3. Please make a mark (x) across the line to indicate your confidence. 3. I will finish this project assignment by the date and time that I predicted.
Please predict as accurately and realistically as possible in question 4: 4. Considering the total points for this project assignment as 100%, what percentage of
that mark do you anticipate to get for this project assignment? Please tick ( √ ) the given box that represents your most appropriate answer.
40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% Please estimate the actual mark that you anticipate (in percentage).
Absolutely impossible (0%)
50/50 Chance Completely certain (100%)
Project Planning and Project Success
(A1)
233
Using the scale given below, please indicate how confident or certain you are of the statement 5. Please make a mark (x) across the line to indicate your confidence. 5. I will get the mark that I predicted. Questions 6 – 9 are about your feelings toward your project assignment outcome. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer. 6. How would you feel if you do well in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 not good very good 7. How would you feel if you succeed this project assignment? 1 2 3 4 5 6 7 8 9 10 11 not good very good 8. How would you feel if you do poorly in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 very bad not bad 9. How would you feel if you do not succeed in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 very bad not bad
Absolutely impossible (0%)
50/50 Chance Completely certain (100%)
Project Planning and Project Success
(A1)
234
Please tick ( √ ) the given box OR fill in the blank that represents your most appropriate answer. 1. I am: Male Female
2. My age is ___________ years 3. My race is: Malay Chinese Indian Other: ____________ (Please specify)
4. Since I started my course at Universiti Utara Malaysia, I have completed _____
semesters. (This does not include the current semester)
Project Planning and Project Success
(A2)
235
SECTION 1: Instructions: Please follow the instructions step by step. Do not look ahead until you have completed each question. Step 1: We would like you to write down your student number and today’s date. The purpose of having your student number is that you will be receiving other materials in the future. In order to ensure that you get the right materials, your student number will be used.
Student # : ________________ Today’s Date : ________________ Step 2: After you have filled in your student number and today’s date, please read the following instruction.
Please take a few minutes to consider your plans for completing your
project assignment. Recall the kind of events that have happened in
your past project assignment and those of other people that you know
about. Recall all the problems that have happened in the past that
have impeded your project progress and performance, and any
aspects of the project or situation in the past that have facilitated your
project progress and performance.
Step 3: Now, you can turn to the next page.
Project Planning and Project Success
(A2)
236
Step 4: To help you answering questions a through c, you are encourage to recall as many projects (either projects that you have done in the past or other people projects) as you can. Please use the space provided to write your answer.
a. Please describe all the problems that have happened in the past that have impeded your project progress and performance.
Project Planning and Project Success
(A2)
237
b. Please describe any aspects of the projects or situations in the past that have facilitated your past project progress and performance.
Project Planning and Project Success
(A2)
238
c. When do you start working on your project assignment? Please indicate your predicted date and time even though you are still unsure about it at this stage. Date: ______________ Time: _____________
Step 5: After you have finished writing down your plans for your project assignment, you
can now turn to page 5.
Project Planning and Project Success
(A2)
239
SECTION 2: Instructions: Please answer all the questions below carefully.
Please predict as accurately and realistically as possible in questions 1 and 2: 1. When would you think you will complete your project assignment? Completion Date: ___________ Completion Time: ___________ 2. How many days do you think you will take to finish this project?
Number of days: ____________ Using the scale given below, please indicate how confident or certain you are of the statement 3. Please make a mark (x) across the line to indicate your confidence. 3. I will finish this project assignment by the date and time that I predicted. Please predict as accurately and realistically as possible in question 4: 4. Considering the total point for this project assignment as 100%, what percentage of
that mark do you anticipate to get for this project assignment? Please tick ( √ ) the given box that represents your most appropriate answer.
40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% Please estimate the actual mark that you anticipate (in percentage).
Absolutely impossible (0%)
50/50 Chance Completely certain (100%)
Project Planning and Project Success
(A2)
240
Using the scale given below, please indicate how confident or certain you are of the statement 5. Please make a mark (x) across the line to indicate your confidence. 5. I will get the mark that I predicted. Questions 6 – 9 are about your feelings toward your project assignment outcome. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer. 6. How would you feel if you do well in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 not good very good 7. How would you feel if you succeed this project assignment? 1 2 3 4 5 6 7 8 9 10 11 not good very good 8. How would you feel if you do poorly in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 very bad not bad 9. How would you feel if you do not succeed in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 very bad not bad
Absolutely impossible (0%)
50/50 Chance Completely certain (100%)
Project Planning and Project Success
(A2)
241
Please tick ( √ ) the given box OR fill in the blank that represents your most appropriate answer. 1. I am: Male Female
2. My age is ___________ years 3. My race is: Malay Chinese Indian Other: ____________ (Please specify)
4. Since I start my course at Universiti Utara Malaysia, I have completed _____ semesters.
(This does not include the current semester)
Project Planning and Project Success
(B1)
242
SECTION 1: Instructions: Please follow the instructions step by step. Do not look ahead until you have completed each question. Step 1: We would like you to write down your student number and today’s date. The purpose of having your student number is that you will be receiving other materials in the future. In order to ensure that you get the right materials, your student number will be used.
Student # : ________________ Today’s Date : ________________ Step 2: After you have filled in your student number and today’s date, please read the following instruction.
Please take a few minutes to consider your plans for completing your
project assignment. Think about the components that are needed for
project progress and performance. Also, think about each and every
thing that you plan to do in completing this project assignment.
Step 3: Now, you can turn to the next page.
Project Planning and Project Success
(B1)
243
Step 4: To help you answering questions a through c, please refer to your project assignment that you are going to complete for this class. Please write your answer in the space provided.
a. What are the components or elements that need to be included in this project assignment?
Project Planning and Project Success
(B1)
244
b. Please describe in details each and every step that you are going to take in order to complete this project assignment.
Project Planning and Project Success
(B1)
245
c. When do you start working on your project assignment? Please indicate your predicted date and time even though you are still unsure about it at this stage. Date: ______________ Time: _____________
Step 5: After you have listed all the components that are needed for this project
assignment and steps that you plan to take, you can now turn to page 5.
Project Planning and Project Success
(B1)
246
SECTION 2: Instructions: Please answer all the questions below carefully.
Please predict as accurately and realistically as possible in questions 1 and 2: 1. When would you think you will complete your project assignment? Completion Date: ___________ Completion Time: ___________ 2. How many days do you think you will take to finish this project?
Number of days: ____________
Using the scale given below, please indicate how confident or certain you are of the statement 3. Please make a mark (x) across the line to indicate your confidence. 3. I will finish this project assignment by the date and time that I predicted.
Please predict as accurately and realistically as possible in question 4: 4. Considering the total point for this project assignment as 100%, what percentage of
that mark do you anticipate to get for this project assignment? Please tick ( √ ) the given box that represents your most appropriate answer.
40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% Please estimate the actual mark that you anticipate (in percentage).
Absolutely impossible (0%)
50/50 Chance Completely certain (100%)
Project Planning and Project Success
(B1)
247
Using the scale given below, please indicate how confident or certain you are of the statement 5. Please make a mark (x) across the line to indicate your confidence. 5. I will get the mark that I predicted. Questions 6 – 9 are about your feelings toward your project assignment outcome. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer. 6. How would you feel if you do well in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 not good very good 7. How would you feel if you succeed this project assignment? 1 2 3 4 5 6 7 8 9 10 11 not good very good 8. How would you feel if you do poorly in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 very bad not bad 9. How would you feel if you do not succeed in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 very bad not bad
Absolutely impossible (0%)
50/50 Chance Completely certain (100%)
Project Planning and Project Success
(B1)
248
Please tick ( √ ) the given box OR fill in the blank that represents your most appropriate answer. 1. I am: Male Female
2. My age is ___________ years 3. My race is: Malay Chinese Indian Other: ____________ (Please specify)
4. Since I start my course at Universiti Utara Malaysia, I have completed _____ semesters.
(This does not include the current semester)
Project Planning and Project Success
(B2)
249
SECTION 1: Instructions: Please follow the instructions step by step. Do not look ahead until you have completed each question. Step 1: We would like you to write down your student number and today’s date. The purpose of having your student number is that you will be receiving other materials in the future. In order to ensure that you get the right materials, your student number will be used.
Student # : ________________ Today’s Date : ________________ Step 2: After you filled in your student number and today’s date, please read the following instruction.
Please take a few minutes to consider your plans for completing your
project assignment.
Step 3: Now, you can turn to the next page.
Project Planning and Project Success
(B2)
250
Step 4: If you need to write down your thoughts while reviewing your project assignment, use the space provided below
a. You can write down your thoughts as you review your project assignment.
Project Planning and Project Success
(B2)
251
b. When do you start working on your project assignment? Please indicate your predicted date and time even though you are still unsure about it at this stage. Date: ______________ Time: _____________
Step 5: You can now turn to page 4.
Project Planning and Project Success
(B2)
252
SECTION 2: Instructions: Please answer all the questions below carefully.
Please predict as accurately and realistically as possible in questions 1 - 2: 1. When would you think you will complete your project assignment? Completion Date: ___________ Completion Time: ___________ 2. How many days do you think you will take to finish this project?
Number of days: ____________
Using the scale given below, please indicate how confident or certain you are of the statement 3. Please make a mark (x) across the line to indicate your confidence. 3. I will finish this project assignment by the date and time that I predicted.
Please predict as accurately and realistically as possible in question 4: 4. Considering the total point for this project assignment as 100%, what percentage of
that mark do you anticipate to get for this project assignment? Please tick ( √ ) the given box that represents your most appropriate answer.
40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% Please estimate the actual mark that you anticipate (in percentage).
Absolutely impossible (0%)
50/50 Chance Completely certain (100%)
Project Planning and Project Success
(B2)
253
Using the scale given below, please indicate how confident or certain you are of the statement 5. Please make a mark (x) across the line to indicate your confidence. 5. I will get the mark that I predicted. Questions 6 – 9 are about your feelings toward your project assignment outcome. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer. 6. How would you feel if you do well in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 not good very good 7. How would you feel if you succeed this project assignment? 1 2 3 4 5 6 7 8 9 10 11 not good very good 8. How would you feel if you do poorly in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 very bad not bad 9. How would you feel if you do not succeed in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 very bad not bad
Absolutely impossible (0%)
50/50 Chance Completely certain (100%)
Project Planning and Project Success
(B2)
254
Please tick ( √ ) the given box OR fill in the blank that represents your most appropriate answer. 1. I am: Male Female
2. My age is ___________ years 3. My race is: Malay Chinese Indian Other: ____________ (Please specify)
4. Since I start my course at Universiti Utara Malaysia, I have completed _____ semesters.
(This does not include the current semester)
Project planning and project success
255
DIARY ENTRY Instructions:
1. You are requested to make a weekly entry into your diary, explaining about your project progress.
2. You need to write down your student number, name of the courses, class time and date.
3. Please record every occasion and the times (date, hour and minute; from beginning to end) when you have worked on the project assignment.
Notes: All the information given will remain confidential at all times and will be used for the study only.
THANK YOU FOR YOUR COOPERATION.
Student #: ______________ Course: ____________ Class Time: ______________ Date: ________ to _______
(G)
256
DIARY ENTRY WEEK ______
Date / Day Time Activities
Begin End
Student #: ______________ Course: ____________ Class Time: ______________ Date: ________ to _______
(G)
257
Date / Day Time Activities
Begin End
Student #: ______________ Course: ____________ Class Time: ______________ Date: ________ to _______
(G)
258
Please use the space provided to write your answer. Meeting’s date & day Time Things being discussed
Begin End
Student #: ______________ Course: ____________ Class Time: ______________ Date: ________ to _______
(G)
259
Meeting’s date & day Time Things being discussed
Begin End
Student #: ______________ Course: ____________ Class Time: ______________ Date: ________ to _______
(G)
260
Please answer all the questions below carefully. 10. When would you think you will complete your project assignment? Completion Date: ___________ Completion Time: _______________ 11. How many days do you think you will take to finish this project?
Number of days: ____________ Using the scale given below, please indicate how confident or certain you are of the statement 3. Please make a mark (x) across the line to indicate your confidence. 12. I will finish this project assignment by the date and time that I predicted. 13. Considering the total for this project assignment as 100%, what percentage of that mark do you anticipate to get from this project assignment? Please tick ( √ ) the given
box that represents your most appropriate answer. 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% Please estimate the mark that you anticipate (in percentage).
Absolutely impossible (0%)
50/50 Chance Completely certain (100%)
Student #: ______________ Course: ____________ Class Time: ______________ Date: ________ to _______
(G)
261
Using the scale given below, please indicate how confident or certain you are of the statement 5. Please make a mark (x) across the line to indicate your confidence. 14. I will get the mark that I predicted. Questions 6 – 9 are about your feelings toward your project assignment outcome. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer. 15. How would you feel if you do well in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 not good very good 16. How would you feel if you succeed this project assignment? 1 2 3 4 5 6 7 8 9 10 11 not good very good 17. How would you feel if you do poorly in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 very bad not bad 18. How would you feel if you do not succeed in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 very bad not bad
Absolutely impossible (0%)
50/50 Chance Completely certain (100%)
Student #: ______________ Course: ____________ Class Time: ______________ Date: ________ to _______
(G)
262
LAST DIARY ENTRY
Date / Day Time Activities
Begin End
Student #: ______________ Course: ____________ Class Time: ______________ Date: ________ to _______
(G)
263
Date / Day Time Activities
Begin End
Student #: ______________ Course: ____________ Class Time: ______________ Date: ________ to _______
(G)
264
Please use the space provided to write your answer. Meeting’s date & day Time Things being discussed
Begin End
Student #: ______________ Course: ____________ Class Time: ______________ Date: ________ to _______
(G)
265
Meeting’s date & day Time Things being discussed
Begin End
Student #: ______________ Course: ____________ Class Time: ______________ Date: ________ to _______
(G)
266
Please answer all the questions below carefully. 19. When would you think you will complete your project assignment? Completion Date: ___________ Completion Time: _______________ 20. How many days do you think you will take to finish this project?
Number of days: ____________ Using the scale given below, please indicate how confident or certain you are of the statement 3. Please make a mark (x) across the line to indicate your confidence. 21. I will finish this project assignment by the date and time that I predicted. 22. Considering the total for this project assignment as 100%, what percentage of that mark do you anticipate to get from this project assignment? Please tick ( √ ) the given
box that represents your most appropriate answer. 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% Please estimate the actual mark that you anticipate (in percentage).
Absolutely impossible (0%)
50/50 Chance Completely certain (100%)
Student #: ______________ Course: ____________ Class Time: ______________ Date: ________ to _______
(G)
267
Using the scale given below, please indicate how confident or certain you are of the statement 5. Please make a mark (x) across the line to indicate your confidence. 23. I will get the mark that I predicted. Questions 6 – 9 are about your feelings toward your project assignment outcome. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer. 24. How would you feel if you do well in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 not good very good 25. How would you feel if you succeed this project assignment? 1 2 3 4 5 6 7 8 9 10 11 not good very good 26. How would you feel if you do poorly in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 very bad not bad 27. How would you feel if you do not succeed in this project assignment? 1 2 3 4 5 6 7 8 9 10 11 very bad not bad
Absolutely impossible (0%)
50/50 Chance Completely certain (100%)
Student #: ______________ Course: ____________ Class Time: ______________ Date: ________ to _______
(G)
268
28. Your own contribution towards the project assignment (in percentage) : 29. The overall contribution of other members of your team towards the project assignment (in percentage): 30. Each of your team members’ contribution towards the project assignment (in percentage):
(Do not include yourself)
Name Contribution (in percentage) __________________ ______________________ __________________ ______________________ __________________ ______________________ __________________ ______________________ __________________ ______________________
Project planning and Project success
269
Student #: ______________ Date: ______________ Instructions: You have just received your mark for your project assignment. Please state your view about the mark you received by answering question 1.
1. How do you view the mark you received for this project? A successful project Not a successful project If it is a successful project, please turn to page 2. If it is not a successful project, please go to page 3.
Project planning and Project success
270
Instructions: Please answer questions 2 and 3. Using the following scale, please tick ( √ ) the given box that represents your feelings toward your mark.
2. To what degree do you feel good about your mark after doing well in this project
assignment? 1 2 3 4 5 6 7 8 9 10 11 Not good Very good 3. To what degree do you feel good about your mark after your success in this project
assignment? 1 2 3 4 5 6 7 8 9 10 11 Not good Very good
THAT WAS THE FINAL QUESTION. THANK YOU FOR YOUR PARTICIPATION.
Project planning and Project success
271
Instructions: Please answer questions 4 and 5.
Using the following scale, please tick ( √ ) the given box that represents your feelings toward your mark.
4. To what degree do you feel bad about your mark after doing poorly in this project
assignment? 1 2 3 4 5 6 7 8 9 10 11 Not bad Very bad
5. To what degree do you feel bad about your mark after the unsuccessful project assignment?
1 2 3 4 5 6 7 8 9 10 11 Not bad Very bad
THAT WAS THE FINAL QUESTION. THANK YOU FOR YOUR PARTICIPATION.
272
Perancangan Projek dan Kejayaan Projek
RISALAH MAKLUMAT
Latar belakang kajian Seperti yang diketahui projek Sistem Maklumat Sumber Manusia (SMSM) menghadapi banyak masalah ketika pelaksanaannya dan ada antaranya terhenti begitu sahaja. Sama seperti projek lain, projek SMSM juga dilaporkan gagal disiapkan mengikut tempoh masa yang diberi, mengikut bajet yang diperuntukkan dan yang paling utama, hasilnya tidak seperti yang diharapkan. Penyelidikan mengenai fenomena ini masih lagi tiada dalam ulasan karya SMSM, walaupun kesannya dapat dilihat dalam pelbagai jenis aktiviti. Selain daripada itu, kebanyakan kajian yang berkaitan dengan perancangan projek dijalankan di negara-negara barat. Kajian seumpamanya di Malaysia, yang mempunyai budaya, ekonomi, sosial dan politik yang berbeza daripada negara barat, akan dapat memberi kefahaman baru tentang bagaimana orang Malaysia membuat keputusan ketika perancangan projek. Ini mungkin akan memberi impak ke atas kejayaan dan perancangan projek SMSM di Malaysia dalam bentuk yang masih belum diketahui lagi. Matlamat kajian Kajian ini dijalankan bagi mengkaji kaedah terbaik dalam memperbaiki perancangan projek. Ia dijalankan dengan menguji dua teori berkenaan dengan ketepatan perancangan dan mengembangkan penerangan daripada penyelidik terdahulu tentang masalah dalam perancangan projek ke dalam konteks Malaysia. Penglibatan anda dalam kajian ini Penyertaan dalam kajian ini adalah sukarela dan jawapan anda akan dianggap sebagai betul-betul sulit. Borang kebenaran yang telah diisi dan dipulangkan akan dianggap sebagai tanda persetujuan anda untuk menyertai kajian ini. Anda boleh menarik balik penyertaan anda daripada kajian ini, dengan tidak meneruskan pengisian borang soal selidik, pada bila-bila masa dan tanpa menjejaskan kedudukan anda. Anda tidak perlu menyatakan alasan mengapa anda menarik balik penyertaan daripada kajian ini, dan sekiranya anda memilih untuk berbuat demikian, jawapan anda tidak akan direkodkan Penyertaan anda dalam kajian ini tidak akan menjejaskan apa-apa hak pampasan yang ada pada anda di bawah statut atau common law. Setelah semua data selesai dikumpul, maklumat diri seperti nama atau nombor pelajar akan dimusnahkan dan tiada data pengenalan digunakan ketika penganalisaan dan penulisan laporan kajian. Sekiranya hasil kajian ini diterbitkan, hanya hasil keseluruhan kajian akan dilaporkan dan jawapan secara individu tidak akan didedahkan. Risiko Penyelidik mengandaikan bahawa kajian ini tidak melibatkan apa-apa risiko. Penyelidik memberi jaminan bahawa bantuan akan diberikan sekiranya anda mengalami sebarang kesulitan.
273
Faedah Hasil daripada penglibatan anda dalam kajian ini, anda mungkin akan berasa lebih berupaya dan memperolehi kepuasan kerana anda telah memberi sumbangan terhadap satu kajian penting yang akan memberi manfaat kepada masyarakat. Hasil kajian ini mungkin mempunyai implikasi yang signifikan terhadap pengamalan pengurusan sumber manusia seperti mengurangkan kesilapan dalam membuat ramalan ketika perancangan projek dan memberi jalan dalam memperbaiki proses pelaksanaan SMSM dengan memahami faktor-faktor yang boleh mempromosikan kebolehlaksanaan dan kejayaan SMSM. Proses Eksperimen Anda akan dijemput untuk menyertai satu eksperimen oleh penyelidik lapangan. Pada permulaan eksperimen, anda akan secara rawak menerima satu daripada empat set arahan yang meminta anda untuk memikirkan bagaimana anda akan menyiapkan tugasan projek anda. Penyelidik lapangan akan meminta anda untuk mengikut arahan yang diberi dan mengisi satu soal selidik yang pendek tentang bilakah anda akan menyiapkan tugasan projek anda dan apakah markah yang anda andaikan akan diterima, bagaimanakah tahap keyakinan anda terhadap ramalan yang diberi dan perasaan anda terhadap markah yang bakal anda terima. Ini akan mengambil masa lebih kurang 30 minit. Apabila anda selesai mengisi kesemua bahan eksperimen, anda akan menerima satu borang seperti diari yang mengkehendaki anda untuk menulis laporan kemajuan secara mingguan. Diari anda akan dikutip secara mingguan oleh penyelidik lapangan. Anda akan menerima diari yang baru setiap kali anda menghantar laporan anda. Diari terakhir akan dikumpul pada hari anda menyerahkan tugasan projek. Setelah pensyarah anda selesai memeriksa tugasan projek anda, markah anda akan diberikan kepada penyelidik lapangan untuk anda ambil. Semasa anda mengambil markah tugasan projek anda daripada penyelidik lapangan, anda akan diminta untuk mengisi satu soal selidik yang pendek yang mengkehendaki anda untuk menyatakan perasaan anda terhadap markah yang diterima. Ini akan mengambil masa kurang daripada 10 minit. Adalah diharapkan dengan penyertaan anda dalam kajian ini, dapat memberikan maklumat yang berharga yang boleh dijadikan panduan oleh pihak sumber manusia dan mereka yang terlibat dengan mana-mana projek dalam mengetahui kaedah terbaik bagi mengurangkan kesilapan dalam membuat ramalan ketika proses perancangan dan membolehkan mereka menyiapkan projek dalam tempoh yang ditetapkan, mengikut bajet yang diperuntukkan dan dapat memberi hasil seperti yang diharapkan. Sekiranya anda mempunyai apa-apa persoalan berkaitan dengan kajian ini, anda boleh kemukakannya kepada penyelidik lapangan atau ketua penyelidik, Dr. Catherine Lees di alamat seperti di bawah. Satu salinan Risalah maklumat dan Borang kebenaran akan diberikan untuk rekod peribadi anda.
Dr. CATHERINE LEES SITI ZUBAIDAH OTHMAN Calon PhD School of Economics & Commerce School of Economics & Commerce The University of Western Australia The University of Western Australia 35 Stirling Highway, Crawley, 6009 35 Stirling Highway, Crawley, 6009 Tel: +61 8 6488 2877 Tel: +61 8 6488 3921 Faks: +61 8 6488 1055 Faks: +61 8 6488 1055 Email: [email protected] Email: [email protected] Fakulti Pembangunan Sosial dan Manusia Universiti Utara Malaysia Tel: +604-9283840 Faks: +604-9285754 Email: [email protected]
274
Perancangan Projek dan Kejayaan Projek
BORANG KEBENARAN
Saya, ______________________________________ telah membaca maklumat yang disediakan dan bagi setiap soalan yang saya ajukan, jawapan yang memuaskan telah diberikan. Saya bersetuju untuk menyertai aktiviti ini, menyedari bahawa saya boleh menarik balik penyertaan saya pada bila-bila masa tanpa sebab dan tanpa menjejaskan apa-apa hak pampasan yang ada pada saya. Saya faham bahawa semua maklumat yang saya berikan akan dianggap sebagai betul-betul sulit dan tidak akan diberikan kepada mana-mana pihak lain melainkan jika dikehendaki berbuat demikian oleh undang-undang. Saya telah diberitahu tentang jenis data yang akan dikumpul, tujuannya dan tindakan yang akan diambil ke atas data tersebut apabila kajian selesai. Saya bersetuju bahawa data penyelidikan yang dikumpul untuk kajian ini mungkin akan diterbitkan dengan syarat tiada data pengenalan digunakan. Saya faham bahawa apa-apa kegunaan data tersebut oleh Universiti pada masa depan adalah tertakluk kepada kelulusan berasingan daripada Jawatankuasa Kajian dan Etika The Universiti of Western Australia. ___________________________ __________________
Peserta Tarikh Jawatankuasa Etika Kemanusiaan The University of Western Australia mengkehendaki supaya semua peserta dimaklumkan bahawa jika mereka mempunyai apa-apa aduan, tentang cara projek kajian ini dijalankan, ia boleh diberikan kepada penyelidik, atau sebagai pilihan lain, kepada Secretary, Human Research Ethics Committee, Registrar’s Office, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009 (nombor telefon 6488-3703). Semua peserta kajian akan diberikan salinan Risalah maklumat dan Borang kebenaran untuk rekod peribadi mereka.
275
Perancangan projek dan Kejayaan projek
Soal selidik
Peserta yang dihormati, Terima kasih di atas persetujuan anda untuk menyertai penyelidikan tentang perancangan projek ini. Kami amat menghargai sekiranya anda dapat menjawab soalan dengan berhati-hati kerana maklumat yang anda beri akan mempengaruhi ketepatan dan kejayaan penyelidikan ini. Ia akan mengambil masa tidak lebih daripada 30 minit untuk menyiapkan soal selidik ini. Kesemua jawapan akan dianggap sebagai betul-betul sulit dan hanya akan digunakan untuk tujuan kajian ini sahaja.
Sekiranya anda mempunyai apa-apa persoalan mengenai penyelidikan ini, anda boleh kemukakan kepada Siti Zubaidah Othman atau Dr. Catherine Lees seperti alamat di bawah. Terima kasih di atas kerjasama yang diberi dan masa yang diambil untuk menjawab soal selidik ini. Yang benar, Dr. CATHERINE LEES SITI ZUBAIDAH OTHMAN Pelajar PhD School of Economics & Commerce School of Economics & Commerce The University of Western Australia The University of Western Australia 35 Stirling Highway, Crawley, 6009 35 Stirling Highway, Crawley, 6009 Tel: +61 8 6488 2877 Tel: +61 8 6488 3921 Faks: +61 8 6488 1055 Faks: +61 8 6488 1055 Email: [email protected] Email: [email protected]
Fakulti Pembangunan Sosial & Manusia Universiti Utara Malaysia Tel: +604-9283840 Faks: +604-9285754 Email: [email protected]
Perancangan projek dan kejayaan projek
(A1)
276
BAHAGIAN 1: Arahan: Sila ikut arahan langkah demi langkah. Anda tidak dibenarkan untuk melihat soalan lain sehinggalah anda selesai menjawab setiap soalan. Langkah 1: Kami ingin meminta anda untuk menulis nombor pelajar dan tarikh hari ini. Tujuan kami mendapatkan nombor pelajar anda adalah kerana anda akan menerima bahan-bahan lain di masa akan datang. Bagi memastikan anda mendapat bahan yang betul, nombor pelajar anda akan digunakan.
# Pelajar : ________________ Tarikh hari ini : ________________ Langkah 2: Setelah anda menulis nombor pelajar dan tarikh hari ini, sila baca arahan di bawah.
Sila ambil beberapa minit untuk memikirkan rancangan anda untuk
menyiapkan tugasan projek anda. Fikirkan tentang sebarang
peristiwa yang bakal berlaku semasa menyiapkan tugasan projek
anda. Fikirkan tentang semua masalah yang bakal berlaku di masa
akan datang yang boleh menghalang kemajuan dan prestasi projek
anda, dan aspek lain yang berkaitan dengan projek atau keadaan di
masa akan datang yang akan mempercepatkan kemajuan dan prestasi
projek anda.
Langkah 3: Anda boleh ke halaman sebelah sekarang.
Perancangan projek dan kejayaan projek
(A1)
277
Langkah 4: Bagi membantu anda menjawab soalan a hingga c, sila fokus pada tugasan projek yang akan anda siapkan untuk kelas ini. Sila gunakan ruangan yang disediakan bagi menulis jawapan anda.
a. Sila perihalkan semua masalah yang bakal berlaku di masa akan datang yang boleh menghalang kemajuan dan prestasi projek anda.
Perancangan projek dan kejayaan projek
(A1)
278
b. Sila perihalkan apa-apa aspek yang berkaitan dengan projek atau keadaan yang akan mempercepatkan kemajuan dan prestasi projek anda.
Perancangan projek dan kejayaan projek
(A1)
279
c. Bilakah anda akan mula membuat tugasan projek anda? Sila jangkakan tarikh dan masa walaupun anda belum pasti tentangnya pada tahap ini. Tarikh: ______________ Masa: _______________
Langkah 5: Setelah anda selesai menulis rancangan bagi tugasan projek ini, anda boleh ke
halaman 5 sekarang.
Perancangan projek dan kejayaan projek
(A1)
280
BAHAGIAN 2: Arahan: Sila jawab semua soalan berikut dengan teliti.
Sila berikan jawapan dengan seberapa tepat dan realistik yang mungkin bagi soalan 1 dan 2: 1. Bilakah anda fikir anda akan menyiapkan tugasan projek anda? Tarikh Disiapkan: ___________ Masa Disiapkan: ___________ 2. Berapa harikah anda fikir anda akan ambil untuk menyiapkan projek ini?
Jumlah hari: ____________ Dengan menggunakan skala di bawah, sila nyatakan sejauh mana anda yakin atau pasti dengan kenyataan 3. Sila tanda (x) atas garisan berikut bagi menggambarkan keyakinan anda. 3. Saya akan menyiapkan tugasan projek ini pada tarikh dan masa yang saya jangkakan.
Sila berikan jawapan dengan seberapa tepat dan realistik yang mungkin bagi soalan 4: 4. Katakan markah keseluruhan bagi tugasan projek ini ialah 100%, apakah peratus
markah yang anda jangka anda akan dapat daripada tugasan projek ini? Sila tanda ( √ ) pada kotak yang benar-benar menggambarkan jawapan anda.
40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%
Sila buat jangkaan markah sebenar yang anda fikir anda akan peroleh (dalam peratus).
Mustahil sama sekali (0%)
Peluang 50/50 Seratus peratus yakin (100%)
Perancangan projek dan kejayaan projek
(A1)
281
Dengan menggunakan skala di bawah, sila nyatakan sejauh mana anda yakin atau pasti dengan kenyataan 5. Sila tanda (x) atas garisan berikut bagi menggambarkan keyakinan anda. 5. Saya akan mendapat markah yang saya jangkakan. Soalan 6 – 9 adalah tentang perasaan anda terhadap hasil tugasan projek anda. Dengan menggunakan skala berikut, sila tanda ( √ ) pada kotak yang benar -benar menggambarkan jawapan anda. 6. Apakah perasaan anda jika anda cemerlang dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 tidak amat menyenangkan menyenangkan 7. Apakah perasaan anda jika anda berjaya dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 tidak amat menyenangkan menyenangkan 8. Apakah perasaan anda sekiranya anda tidak cemerlang dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 amat teruk tidak teruk 9. Apakah perasaan anda jika anda tidak berjaya dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 amat teruk tidak teruk
Mustahil sama sekali (0%)
Peluang 50/50 Seratus peratus yakin (100%)
Perancangan projek dan kejayaan projek
(A1)
282
Sila tandakan ( √ ) pada kotak yang disediakan ATAU isikan tempat kosong bagi menggambarkan jawapan anda yang paling sesuai. 1. Saya seorang: Lelaki Perempuan
2. Umur saya ___________ tahun 3. Saya berbangsa: Melayu Cina India Lain-lain: _____ (Sila nyatakan)
4. Semenjak saya memulakan kursus di Universiti Utara Malaysia, saya telah
menghabiskan _____ semester. (Tidak termasuk semester sekarang)
Perancangan projek dan kejayaan projek
(A2)
283
BAHAGIAN 1: Arahan: Sila ikut arahan langkah demi langkah. Anda tidak dibenarkan untuk melihat soalan lain sehinggalah anda selesai menjawab setiap soalan. Langkah 1: Kami ingin meminta anda untuk menulis nombor pelajar dan tarikh hari ini. Tujuan kami mendapatkan nombor pelajar anda adalah kerana anda akan menerima bahan-bahan lain di masa akan datang. Bagi memastikan anda mendapat bahan yang betul, nombor pelajar anda akan digunakan.
# Pelajar : ________________ Tarikh hari ini : ________________ Langkah 2: Setelah anda menulis nombor pelajar dan tarikh hari ini, sila baca arahan di bawah.
Sila ambil beberapa minit untuk memikirkan rancangan anda untuk
menyiapkan tugasan projek anda. Ingat kembali semua peristiwa
yang berlaku semasa anda menyiapkan tugasan projek anda yang lalu
dan orang lain yang anda kenali. Ingat kembali semua masalah yang
telah berlaku yang menghalang kemajuan dan pretasi projek anda,
dan apa-apa aspek yang berkaitan dengan projek atau keadaan di
masa lalu yang telah mempercepat kemajuan dan prestasi tugasan
projek anda.
Langkah 3: Anda boleh ke halaman sebelah sekarang.
Perancangan projek dan kejayaan projek
(A2)
284
Langkah 4: Bagi membantu anda menjawab soalan a hingga c, anda digalakkan untuk mengingat kembali seberapa banyak projek yang boleh (sama ada projek yang telah anda lakukan di masa lalu atau projek orang lain). Sila gunakan ruang yang disediakan untuk menulis jawapan anda.
a. Sila perihalkan semua masalah yang berlaku di masa lalu yang telah menghalang kemajuan dan prestasi tugasan projek anda.
Perancangan projek dan kejayaan projek
(A2)
285
b. Sila perihalkan apa-apa aspek yang berkaitan dengan projek atau keadaan yang telah mempercepat kemajuan dan prestasi projek anda di masa lalu.
Perancangan projek dan kejayaan projek
(A2)
286
c. Bilakah anda mula membuat tugasan projek anda? Sila jangkakan tarikh dan masa walaupun anda belum pasti tentangnya pada tahap ini. Tarikh: ______________ Masa: _______________
Langkah 5: Setelah anda selesai menulis rancangan bagi tugasan projek ini, anda boleh ke
halaman 5 sekarang
Perancangan projek dan kejayaan projek
(A2)
287
BAHAGIAN 2: Arahan: Sila jawab semua soalan berikut dengan teliti.
Sila berikan jawapan dengan seberapa tepat dan realistik yang mungkin bagi soalan 1 dan 2: 1. Bilakah anda fikir anda akan menyiapkan tugasan projek anda? Tarikh Disiapkan: ___________ Masa Disiapkan: ___________ 2. Berapa harikah anda fikir anda akan ambil untuk menyiapkan projek ini?
Jumlah hari: ____________ Dengan menggunakan skala di bawah, sila nyatakan sejauh mana anda yakin atau pasti dengan kenyataan 3. Sila tanda (x) atas garisan berikut bagi menggambarkan keyakinan anda. 3. Saya akan menyiapkan tugasan projek ini pada tarikh dan masa yang saya jangkakan.
Sila berikan jawapan dengan seberapa tepat dan realistik yang mungkin bagi soalan 4: 4. Katakan markah keseluruhan bagi tugasan projek ini ialah 100%, apakah peratus
markah yang anda jangka anda akan dapat daripada tugasan projek ini? Sila tanda ( √ ) pada kotak yang benar-benar menggambarkan jawapan anda.
40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%
Sila buat jangkaan markah sebenar yang anda fikir anda akan peroleh (dalam peratus).
Mustahil sama sekali (0%)
Peluang 50/50 Seratus peratus yakin (100%)
Perancangan projek dan kejayaan projek
(A2)
288
Dengan menggunakan skala di bawah, sila nyatakan sejauh mana anda yakin atau pasti dengan kenyataan 5. Sila tanda (x) atas garisan berikut bagi menggambarkan keyakinan anda. 5. Saya akan mendapat markah yang saya jangkakan. Soalan 6 – 9 adalah tentang perasaan anda terhadap hasil tugasan projek anda. Dengan menggunakan skala berikut, sila tanda ( √ ) pada kotak yang benar -benar menggambarkan jawapan anda. 6. Apakah perasaan anda jika anda cemerlang dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 tidak amat menyenangkan menyenangkan 7. Apakah perasaan anda jika anda berjaya dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 tidak amat menyenangkan menyenangkan 8. Apakah perasaan anda jika anda tidak cemerlang dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 amat teruk tidak teruk 9. Apkah perasaan anda jika anda tidak berjaya dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 amat teruk tidak teruk
Mustahil sama sekali (0%)
Peluang 50/50 Seratus peratus yakin (100%)
Perancangan projek dan kejayaan projek
(A2)
289
Sila tandakan ( √ ) pada kotak yang disediakan ATAU isikan tempat kosong bagi menggambarkan jawapan anda yang paling sesuai. 1. Saya seorang: Lelaki Perempuan
2. Umur saya ___________ tahun 3. Saya berbangsa: Melayu Cina India Lain-lain: _____ (Sila nyatakan)
4. Semenjak saya memulakan kursus di Universiti Utara Malaysia, saya telah
menghabiskan _____ semester. (Tidak termasuk semester sekarang)
Perancangan projek dan kejayaan projek
(B1)
290
BAHAGIAN 1: Arahan: Sila ikut arahan langkah demi langkah. Anda tidak dibenarkan untuk melihat soalan lain sehinggalah anda selesai menjawab setiap soalan. Langkah 1: Kami ingin meminta anda untuk menulis nombor pelajar dan tarikh hari ini. Tujuan kami mendapatkan nombor pelajar anda adalah kerana anda akan menerima bahan-bahan lain di masa akan datang. Bagi memastikan anda mendapat bahan yang betul, nombor pelajar anda akan digunakan.
# Pelajar : ________________ Tarikh hari ini : ________________ Langkah 2: Setelah anda menulis nombor pelajar dan tarikh hari ini, sila baca arahan di bawah.
Sila ambil beberapa minit untuk memikirkan rancangan anda untuk
menyiapkan tugasan projek anda. Fikirkan tentang komponen yang
diperlukan untuk kemajuan dan prestasi projek. Fikirkan juga setiap
perkara yang anda cadang untuk lakukan bagi menyiapkan tugasan
projek ini.
Langkah 3: Anda boleh ke halaman sebelah sekarang.
Perancangan projek dan kejayaan projek
(B1)
291
Langkah 4: Bagi membantu anda menjawab soalan a hingga c, sila rujuk projek yang akan anda siapkan untuk kelas ini. Sila guna ruangan yang disediakan bagi menulis jawapan anda.
a. Apakah komponen atau elemen yang perlu dimasukkan dalam tugasan projek ini?
Perancangan projek dan kejayaan projek
(B1)
292
b. Sila perihalkan dengan terperinci setiap langkah yang anda akan ambil untuk menyiapkan tugasan projek ini.
Perancangan projek dan kejayaan projek
(B1)
293
c. Bilakah anda mula membuat tugasan projek anda? Sila jangkakan tarikh dan masa walaupun anda belum pasti tentangnya pada tahap ini. Tarikh: ______________ Masa: _______________
Langkah 5: Setelah anda selesai menyenaraikan semua komponen yang diperlukan untuk
projek ini dan langkah yang anda rancang untuk ambil, anda boleh ke halaman 5 sekarang.
Perancangan projek dan kejayaan projek
(B1)
294
BAHAGIAN 2: Arahan: Sila jawab semua soalan berikut dengan teliti.
Sila berikan jawapan dengan seberapa tepat dan realistik yang mungkin bagi soalan 1 dan 2: 1. Bilakah anda fikir anda akan menyiapkan tugasan projek anda? Tarikh Disiapkan: ___________ Masa Disiapkan: ___________ 2. Berapa harikah anda fikir anda akan ambil untuk menyiapkan projek ini?
Jumlah hari: ____________ Dengan menggunakan skala di bawah, sila nyatakan sejauh mana anda yakin atau pasti dengan kenyataan 3. Sila tanda (x) atas garisan berikut bagi menggambarkan keyakinan anda. 3. Saya akan menyiapkan tugasan projek ini pada tarikh dan masa yang saya jangkakan.
Sila berikan jawapan dengan seberapa tepat dan realistik yang mungkin bagi soalan 4: 4. Katakan markah keseluruhan bagi tugasan projek ini ialah 100%, apakah peratus
markah yang anda jangka anda akan dapat daripada tugasan projek ini? Sila tanda ( √ ) pada kotak yang benar-benar menggambarkan jawapan anda.
40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%
Sila buat jangkaan markah sebenar yang anda fikir anda akan peroleh (dalam peratus).
Mustahil sama sekali (0%)
Peluang 50/50 Seratus peratus yakin (100%)
Perancangan projek dan kejayaan projek
(B1)
295
Dengan menggunakan skala di bawah, sila nyatakan sejauh mana anda yakin atau pasti dengan kenyataan 5. Sila tanda (x) atas garisan berikut bagi menggambarkan keyakinan anda. 5. Saya akan mendapat markah yang saya jangkakan. Soalan 6 – 9 adalah tentang perasaan anda terhadap hasil tugasan projek anda. Dengan menggunakan skala berikut, sila tanda ( √ ) pada kotak yang benar -benar menggambarkan jawapan anda. 6. Apakah perasaan anda jika anda cemerlang dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 tidak amat menyenangkan menyenangkan 7. Apakah perasaan anda jika anda berjaya dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 tidak amat menyenangkan menyenangkan 8. Apakah perasaan anda jika anda tidak cemerlang dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 amat teruk tidak teruk 9. Apakah perasaan anda jika anda tidak berjaya dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 amat teruk tidak teruk
Mustahil sama sekali (0%)
Peluang 50/50 Seratus peratus yakin (100%)
Perancangan projek dan kejayaan projek
(B1)
296
Sila tandakan ( √ ) pada kotak yang disediakan ATAU isikan tempat kosong bagi menggambarkan jawapan anda yang paling sesuai. 1. Saya seorang: Lelaki Perempuan
2. Umur saya ___________ tahun 3. Saya berbangsa: Melayu Cina India Lain-lain: _____ (Sila nyatakan)
4. Semenjak saya memulakan kursus di Universiti Utara Malaysia, saya telah
menghabiskan _____ semester. (Tidak termasuk semester sekarang)
Perancangan projek dan kejayaan projek
(B2)
297
BAHAGIAN 1: Arahan: Sila ikut arahan langkah demi langkah. Anda tidak dibenarkan untuk melihat soalan lain sehinggalah anda selesai menjawab setiap soalan. Langkah 1: Kami ingin meminta anda untuk menulis nombor pelajar dan tarikh hari ini. Tujuan kami mendapatkan nombor pelajar anda adalah kerana anda akan menerima bahan-bahan lain di masa akan datang. Bagi memastikan anda mendapat bahan yang betul, nombor pelajar anda akan digunakan.
# Pelajar : ________________ Tarikh hari ini : ________________ Langkah 2: Setelah anda menulis nombor pelajar dan tarikh hari ini, sila baca arahan di bawah.
Sila ambil beberapa minit untuk memikirkan rancangan anda untuk
menyiapkan tugasan projek anda.
Langkah 3: Anda boleh ke halaman sebelah sekarang.
Perancangan projek dan kejayaan projek
(B2)
298
Langkah 4: Jika anda perlu menulis sebarang perkara yang anda terfikir semasa menyemak semula tugasan projek anda, sila guna ruangan yang disediakan di bawah.
a. Anda boleh menulis sebarang perkara yang anda terfikir semasa menyemak semula tugasan projek anda.
Perancangan projek dan kejayaan projek
(B2)
299
b. Bilakah anda mula membuat tugasan projek anda? Sila jangkakan tarikh dan masa walaupun anda belum pasti tentangnya pada tahap ini. Tarikh: ______________ Masa: _______________
Langkah 5: Anda boleh ke halaman 4 sekarang.
Perancangan projek dan kejayaan projek
(B2)
300
BAHAGIAN 2: Arahan: Sila jawab semua soalan berikut dengan teliti.
Sila berikan jawapan dengan seberapa tepat dan realistik yang mungkin bagi soalan 1 dan 2: 1. Bilakah anda fikir anda akan menyiapkan tugasan projek anda? Tarikh Disiapkan: ___________ Masa Disiapkan: ___________ 2. Berapa harikah anda fikir anda akan ambil untuk menyiapkan projek ini?
Jumlah hari: ____________ Dengan menggunakan skala di bawah, sila nyatakan sejauh mana anda yakin atau pasti dengan kenyataan 3. Sila tanda (x) atas garisan berikut bagi menggambarkan keyakinan anda. 3. Saya akan menyiapkan tugasan projek ini pada tarikh dan masa yang saya jangkakan.
Sila berikan jawapan dengan seberapa tepat dan realistik yang mungkin bagi soalan 4: 4. Katakan markah keseluruhan bagi tugasan projek ini ialah 100%, apakah peratus
markah yang anda jangka anda akan dapat daripada tugasan projek ini? Sila tanda ( √ ) pada kotak yang benar-benar menggambarkan jawapan anda.
40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%
Sila buat jangkaan markah sebenar yang anda fikir anda akan peroleh (dalam peratus).
Mustahil sama sekali (0%)
Peluang 50/50 Seratus peratus yakin (100%)
Perancangan projek dan kejayaan projek
(B2)
301
Dengan menggunakan skala di bawah, sila nyatakan sejauh mana anda yakin atau pasti dengan kenyataan 5. Sila tanda (x) atas garisan berikut bagi menggambarkan keyakinan anda. 5. Saya akan mendapat markah yang saya jangkakan. Soalan 6 – 9 adalah tentang perasaan anda terhadap hasil tugasan projek anda. Dengan menggunakan skala berikut, sila tanda ( √ ) pada kotak yang benar -benar menggambarkan jawapan anda. 6. Apakah perasaan anda jika anda cemerlang dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 tidak amat menyenangkan menyenangkan 7. Apakah perasaan anda jika anda berjaya dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 tidak amat menyenangkan menyenangkan 8. Apakah perasaan anda jika anda tidak cemerlang dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 amat teruk tidak teruk 9. Apakah perasaan anda jika anda tidak berjaya dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 amat teruk tidak teruk
Mustahil sama sekali (0%)
Peluang 50/50 Seratus peratus yakin (100%)
Perancangan projek dan kejayaan projek
(B2)
302
Sila tandakan ( √ ) pada kotak yang disediakan ATAU isikan tempat kosong bagi menggambarkan jawapan anda yang paling sesuai. 1. Saya seorang: Lelaki Perempuan
2. Umur saya ___________ tahun 3. Saya berbangsa: Melayu Cina India Lain-lain: _____ (Sila nyatakan)
4. Semenjak saya memulakan kursus di Universiti Utara Malaysia, saya telah
menghabiskan _____ semester. (Tidak termasuk semester sekarang)
Perancangan projek dan kejayaan projek
303
MASUKAN DIARI Arahan:
1. Anda diminta untuk membuat masukan secara mingguan ke dalam diari, menerangkan tentang perkembangan projek anda.
2. Anda dikehendaki menulis nombor pelajar, nama kursus, masa kelas dan tarikh.
3. Sila rekodkan semua perkara yang dilakukan dan masa yang diambil (tarikh, jam dan minit; dari mula hingga akhir) setiap kali anda melakukan tugasan projek.
Nota: Semua maklumat yang diberi akan kekal sebagai betul-betul sulit pada sepanjang masa dan akan hanya digunakan untuk tujuan kajian semata-mata.
TERIMA KASIH DI ATAS KERJASAMA ANDA.
# Pelajar: ____________ Kursus: ___________ Masa Kelas: ______________ Tarikh: ________ hingga __________
(K)
304
KEMASUKAN DIARI MINGGU ______
Tarikh / Hari Masa Aktiviti
Mula Tamat
# Pelajar: ____________ Kursus: ___________ Masa Kelas: ______________ Tarikh: ________ hingga __________
(K)
305
Tarikh / Hari Masa Aktiviti
Mula Tamat
# Pelajar: ____________ Kursus: ___________ Masa Kelas: ______________ Tarikh: ________ hingga __________
(K)
306
Sila gunakan ruangan yang disediakan untuk menulis jawapan anda. Tarikh & hari perjumpaan
Masa Perkara yang dibincangkan
Mula Tamat
# Pelajar: ____________ Kursus: ___________ Masa Kelas: ______________ Tarikh: ________ hingga __________
(K)
307
Tarikh & hari perjumpaan
Masa Perkara yang dibincangkan
Mula Tamat
# Pelajar: ____________ Kursus: ___________ Masa Kelas: ______________ Tarikh: ________ hingga __________
(K)
308
Sila jawab semua soalan berikut dengan teliti. 31. Bilakah anda fikir anda akan menyiapkan tugasan projek ini? Tarikh Disiapkan: ___________ Masa Disiapkan: _______________ 32. Berapa harikah anda fikir anda akan ambil untuk menyiapkan projek ini?
Jumlah hari: ____________ Dengan menggunakan skala di bawah, sila nyatakan sejauh mana anda yakin atau pasti dengan kenyataan 3. Sila tandakan (x) atas garisan berikut bagi menggambarkan keyakinan anda. 33. Saya akan menyiapkan tugasan projek ini pada tarikh dan masa yang saya jangkakan. 34. Katakan markah keseluruhan bagi tugasan projek ini adalah 100%, apakah peratus markah yang anda jangka anda akan dapat daripada tugasan projek ini? Sila tanda ( √ )
pada kotak yang benar-benar menggambarkan jawapan anda. 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% Sila buat jangkaan markah sebenar yang anda fikir anda akan peroleh (dalam peratus).
Mustahil sama sekali (0%)
Peluang 50/50 Seratus peratus yakin (100%)
# Pelajar: ____________ Kursus: ___________ Masa Kelas: ______________ Tarikh: ________ hingga __________
(K)
309
Dengan menggunakan skala di bawah, sila nyatakan sejauh mana anda yakin atau pasti dengan kenyataan 5. Sila tanda (x) atas garisan berikut bagi menggambarkan keyakinan anda. 35. Saya akan mendapat markah yang saya jangkakan. Soalan 6 – 9 adalah tentang perasaan anda terhadap hasil tugasan projek anda. Dengan menggunakan skala berikut, sila tanda ( √ ) pada kotak yang benar -benar menggambarkan jawapan anda. 36. Apakahkah perasaan anda jika anda cemerlang dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 tidak menyenangkan amat menyenangkan 37. Apakah perasaan anda jika anda berjaya dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 tidak menyenangkan amat menyenangkan 38. Apakah perasaan anda jika anda tidak cemerlang dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 amat teruk tidak teruk 39. Apakah perasaan anda jika anda tidak berjaya dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 amat teruk tidak teruk
Mustahil sama sekali (0%)
Peluang 50/50 Seratus peratus yakin
(100%)
# Pelajar: ______________ Kursus: ____________ Masa Kelas: ______________ Tarikh: ______ hingga _______
(K)
310
KEMASUKAN DIARI AKHIR
Tarikh / Hari Masa Aktiviti
Mula Tamat
# Pelajar: ______________ Kursus: ____________ Masa Kelas: ______________ Tarikh: ______ hingga _______
(K)
311
Tarikh / Hari Masa Aktiviti
Mula Tamat
# Pelajar: ______________ Kursus: ____________ Masa Kelas: ______________ Tarikh: ______ hingga _______
(K)
312
Sila gunakan ruangan yang disediakan untuk menulis jawapan anda. Tarikh & hari perjumpaan
Masa Perkara yang dibincangkan
Mula Tamat
# Pelajar: ______________ Kursus: ____________ Masa Kelas: ______________ Tarikh: ______ hingga _______
(K)
313
Tarikh & hari perjumpaan
Masa Perkara yang dibincangkan
Mula Tamat
# Pelajar: ______________ Kursus: ____________ Masa Kelas: ______________ Tarikh: ______ hingga _______
(K)
314
Sila jawab semua soalan berikut dengan teliti. 40. Bilakah anda fikir anda akan menyiapkan tugasan projek anda? Tarikh Disiapkan: ___________ Masa Disiapkan: _______________ 41. Berapa harikah anda fikir anda akan ambil untuk menyiapkan projek ini?
Jumlah hari: ____________ Dengan menggunakan skala di bawah, sila nyatakan sejauh mana anda yakin atau pasti dengan kenyataan 3. Sila tandakan (x) atas garisan berikut bagi menggambarkan keyakinan anda. 42. Saya akan menyiapkan tugasan projek ini pada tarikh dan masa yang saya jangkakan. 43. Katakan markah keseluruhan tugasan projek ini ialah 100%, apakah peratus markah yang anda jangka anda akan dapat daripada tugasan projek ini? Sila tanda ( √ ) pada
kotak yang benar-benar dapat menggambarkan jawapan anda. 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% Sila buat jangkaan markah sebenar yang anda fikir anda akan peroleh (dalam peratus).
Mustahil sama sekali (0%)
Peluang 50/50 Seratus peratus yakin (100%)
# Pelajar: ______________ Kursus: ____________ Masa Kelas: ______________ Tarikh: ______ hingga _______
(K)
315
Dengan menggunakan skala di bawah, sila nyatakan sejauh mana anda yakin atau pasti dengan kenyataan 5. Sila tanda (x) atas garisan berikut bagi menggambarkan keyakinan anda. 44. Saya akan mendapat markah yang saya jangkakan. Soalan 6 – 9 adalah tentang perasaan anda terhadap hasil tugasan projek anda. Dengan menggunakan skala berikut, sila tanda ( √ ) pada kotak yang benar -benar menggambarkan jawapan anda. 45. Apakah perasaan anda jika anda cemerlang dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 tidak menyenangkan amat menyenangkan 46. Apakah perasaan anda jika anda berjaya dalam tugasan projek ini?
1 2 3 4 5 6 7 8 9 10 11 tidak menyenangkan amat menyenangkan 47. Apkahkah perasaan anda jika anda tidak cemerlang dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 amat teruk tidak teruk 48. Apakah perasaan anda jika anda tidak berjaya dalam tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 amat teruk tidak teruk
Mustahil sama sekali (0%)
Peluang 50/50 Seratus peratus yakin (100%)
# Pelajar: ______________ Kursus: ____________ Masa Kelas: ______________ Tarikh: ______ hingga _______
(K)
316
49. Sumbangan anda terhadap tugasan projek ini (dalam peratus) : 50. Sumbangan keseluruhan ahli lain dalam pasukan anda terhadap tugasan projek ini (dalam peratus): 51. Sumbangan setiap ahli pasukan anda terhadap tugasan projek ini (dalam peratus):
(Tidak termasuk diri anda)
Nama Sumbangan (dalam peratus) __________________ ______________________ __________________ ______________________ __________________ ______________________ __________________ ______________________ __________________ ______________________
317
# Pelajar: ______________ Tarikh: ______________ Arahan: Anda baru sahaja menerima markah bagi tugasan projek anda. Sila nyatakan pandangan anda terhadap markah yang anda terima dengan menjawab soalan 1.
1. Apakah pandangan anda terhadap markah yang anda terima untuk projek ini? Projek ini satu projek yang berjaya Projek ini satu projek yang tidak berjaya Sekiranya projek ini ialah projek yang berjaya, sila ke halaman 2. Sekiranya projek ini ialah projek yang tidak berjaya, sila ke halaman 3.
318
Arahan: Sila jawab soalan 2 dan 3. Dengan menggunakan skala yang diberi, sila tanda ( √ ) pada kotak yang disediakan bagi menggambarkan perasaan anda terhadap markah anda.
2. Setakat mana anda berasa senang dengan markah anda setelah melakukan tugasan
projek ini dengan baik? 1 2 3 4 5 6 7 8 9 10 11 Tidak Amat menyenangkan menyenangkan 3. Setakat mana anda berasa senang dengan markah anda setelah anda berjaya dalam
tugasan projek ini? 1 2 3 4 5 6 7 8 9 10 11 Tidak Amat menyenangkan menyenangkan
ITU ADALAH SOALAN YANG TERAKHIR. TERIMA KASIH DI ATAS KERJASAMA ANDA.
319
Arahan: Sila jawab soalan 4 dan 5.
Dengan menggunakan skala yang diberi, sila tanda ( √ ) pada kotak yang disediakan bagi menggambarkan perasaan anda terhadap markah anda.
4. Setakat mana anda berasa teruk dengan markah anda setelah anda tidak melakukan
tugasan projek dengan baik? 1 2 3 4 5 6 7 8 9 10 11 Tidak teruk Amat teruk
5. Setakat mana anda berasa teruk dengan markah anda setelah anda tidak berjaya dalam tugasan projek ini?
1 2 3 4 5 6 7 8 9 10 11 Tidak teruk Amat teruk
ITU ADALAH SOALAN YANG TERAKHIR. TERIMA KASIH DI ATAS KERJASAMA ANDA.
320
APPENDIX B
EXPERIMENTAL DESIGN AND WEEKLY RESPONDENTS –STUDY 1
Respondents at week
Manipulated
Condition
0
1
2
3
4
5
6
7
8
Inside (n = 193)
193
192
192
189
187
186
188
187
190
Outside (n = 184)
184 180 179 179 176 178 177 178 180
Unpacked (n = 199)
199 198 194 196 193 194 194 192 191
Packed (n = 196)
196 195 193 191 192 190 188 184 186
321
APPENDIX C
RESULTS OF DEMOGRAPHIC CHARACTERISTICS OF THE
PARTICIPANTS – STUDY 1
Descriptions Frequency % Mean Std. Dev Median Min. Max.
Gender Male 165 21.4 Female 607 78.6 Total 772 100 Age Total response 772 100 21.69 1.4 22 18 36 Ethnicity Malay 553 71.6 Chinese 153 19.8 Indians 44 5.7 Others 22 2.8 Total 772 100 Semesters Total response 772 100 2.87 1.7 3 1 8
322
APPENDIX D
SAMPLES OF THREE-LEVEL HIERARCHICAL REGRESSION EQUATIONS
– STUDY 1
This appendix contains samples of the equation that describe the effect of the four
conditions and students’ control variables on project completion time predictions,
project outcome success predictions, confidence in predictions of project completion
time, confidence in predictions of project outcome success, affective predictions
towards outcome success, and affective predictions towards outcome failure, with
outside and inside as the reference conditions.
323
A sample of three-level equations for project completion time predictions, confidence in predictions of project completion time and confidence in predictions of project outcome success 1a. Equations with the outside view as a reference condition
Level-1 Model Ytij = P0 + P1*(WEEK) + E
Level-2 Model P0 = B00 + B01*(Inside) + B02*(Unpacked) + B03*(Packed) + R0
P1 = B10 + B11*(Inside) + B12*(Unpacked) + B13*(Packed) + R1
Level-3 Model B00 = G000 + U00
B01 = G010
B02 = G020
B03 = G030
B10 = G100 + U10
B11 = G110
B12 = G120
B13 = G130
where
Ytij is the outcome at time t for participant i in group j;
P0 is the initial status of participant ij, that is, the expected outcome for that participant
on the first day (when WEEK = 0);
P1 is the prediction bias rate for participant ij during the weeks;
E is a random “time effect”. These random time effects are assumed to be normally
distributed with mean of 0, and variance σ2;
B00 represents the mean initial status within group j;
G000 is the overall mean initial status;
324
R0 and R1 are random “participant effects”. These random participant effects are
assumed to be normally distributed with mean of 0, and variance σ2;
B10 is the mean weekly prediction bias rate within group j;
G100 is the overall mean weekly prediction bias rate;
U00 and U10 are the random “group effects”. These random group effects are assumed
to be normally distributed with mean of 0, and variance σ2.
325
1b. Equations with the inside view as a reference condition
Level-1 Model
Ytij = P0 + P1*(WEEK) + E
Level-2 Model
P0 = B00 + B01*(Outside) + B02*(Unpacked) + B03*(Packed) + R0
P1 = B10 + B11*(Outside) + B12*(Unpacked) + B13*(Packed) + R1
Level-3 Model
B00 = G000 + U00
B01 = G010
B02 = G020
B03 = G030
B10 = G100 + U10
B11 = G110
B12 = G120
B13 = G130
where
Ytij is the outcome at time t for participant i in group j;
P0 is the initial status of participant ij, that is, the expected outcome for that participant
on the first day (when WEEK = 0);
P1 is the prediction bias rate for participant ij during the weeks;
E is a random “time effect”. These random time effects are assumed to be normally
distributed with mean of 0, and variance σ2;
B00 represents the mean initial status within group j;
G000 is the overall mean initial status;
R0 and R1 are random “participant effects”. These random participant effects are
assumed to be normally distributed with mean of 0, and variance σ2;
326
B10 is the mean weekly prediction bias rate within group j;
G100 is the overall mean weekly prediction bias rate;
U00 and U10 are the random “group effects”. These random group effects are assumed
to be normally distributed with mean of 0, and variance σ2.
327
A sample of three-level equations with grand-mean centering for project outcome predictions 2a. Equations with the packed (control) as a references condition Level-1 Model
Ytij = P0 + P1*(WEEK) + E
Level-2 Model
P0 = B00 + B01*(Inside) + B02*(Outside) + B03*(Unpacked) + B04*( SEMESTERxSEMESTER − ) + R0
P1 = B10 + R1
Level-3 Model
B00 = G000 + G001(Teacher1) + G002(Teacher2) + G003(Teacher3) + G004(Teacher4) + G005(Teacher5) + G006(Teacher6) + G007(Teacher7) + G008(Teacher8) + G009(Teacher9) + G0010(SEMESTER_MEAN) + U00
B01 = G010
B02 = G020
B03 = G030
B10 = G100 + U10
where
Ytij is the outcome at time t for participant i in group j;
P0 is the initial status of participant ij, that is, the expected outcome for that participant
on the first day (when WEEK = 0);
P1 is the prediction bias rate for participant ij during the weeks;
E is a random “time effect”. These random time effects are assumed to be normally
distributed with mean of 0, and variance σ2;
B00 represents the mean initial status within group j;
G000 is the overall mean initial status;
R0 and R1 are random “participant effects”. These random participant effects are
assumed to be normally distributed with mean of 0, and variance σ2;
328
B10 is the mean weekly prediction bias rate within group j;
G100 is the overall mean weekly prediction bias rate;
U00 and U10 are the random “group effects”. These random group effects are assumed
to be normally distributed with mean of 0, and variance σ2.
329
2b. Equations with the outside view as a reference condition
Level-1 Model Ytij = P0 + P1*(WEEK) + E
Level-2 Model P0 = B00 + B01*(Inside) + B02*(Unpacked) + B03*(Packed) +
B04*( SEMESTERxSEMESTER − ) + R0
P1 = B10 + R1
Level-3 Model B00 = G000 + G001(Teacher1) + G002(Teacher2) + G003(Teacher3) +
G004(Teacher4) + G005(Teacher5) + G006(Teacher6) + G007(Teacher7) + G008(Teacher8) + G009(Teacher9) + G0010(SEMESTER_MEAN) + U00
B01 = G010
B02 = G020
B03 = G030
B10 = G100 + U10
where
Ytij is the outcome at time t for participant i in group j;
P0 is the initial status of participant ij, that is, the expected outcome for that participant
on the first day (when WEEK = 0);
P1 is the prediction bias rate for participant ij during the weeks;
E is a random “time effect”. These random time effects are assumed to be normally
distributed with mean of 0, and variance σ2;
B00 represents the mean initial status within group j;
G000 is the overall mean initial status;
R0 and R1 are random “participant effects”. These random participant effects are
assumed to be normally distributed with mean of 0, and variance σ2;
B10 is the mean weekly prediction bias rate within group j;
330
G100 is the overall mean weekly prediction bias rate;
U00 and U10 are the random “group effects”. These random group effects are assumed
to be normally distributed with mean of 0, and variance σ2.
331
2c. Equations with the inside view as a reference condition
Level-1 Model
Ytij = P0 + P1*(WEEK) + E
Level-2 Model
P0 = B00 + B01*(Outside) + B02*(Unpacked) + B03*(Packed) + B04*( SEMESTERxSEMESTER − ) + R0
P1 = B10 + R1
Level-3 Model
B00 = G000 + G001(Teacher1) + G002(Teacher2) + G003(Teacher3) + G004(Teacher4) + G005(Teacher5) + G006(Teacher6) + G007(Teacher7) + G008(Teacher8) + G009(Teacher9) + G0010(SEMESTER_MEAN) + U00
B01 = G010
B02 = G020
B03 = G030
B10 = G100 + U10
where
Ytij is the outcome at time t for participant i in group j;
P0 is the initial status of participant ij, that is, the expected outcome for that participant
on the first day (when WEEK = 0);
P1 is the prediction bias rate for participant ij during the weeks;
E is a random “time effect”. These random time effects are assumed to be normally
distributed with mean of 0, and variance σ2;
B00 represents the mean initial status within group j;
G000 is the overall mean initial status;
R0 and R1 are random “participant effects”. These random participant effects are
assumed to be normally distributed with mean of 0, and variance σ2;
332
B10 is the mean weekly prediction bias rate within group j;
G100 is the overall mean weekly prediction bias rate;
U00 and U10 are the random “group effects”. These random group effects are assumed
to be normally distributed with mean of 0, and variance σ2.
333
A sample of three-level equations for predictions of affective response towards outcome success, and affective response towards outcome failure 3a. Equations with the packed (control) as a reference condition Level-1 Model
Ytij = P0 + P1*(WEEK) + E
Level-2 Model
P0 = B00 +B01*(Inside) + B02*(Outside) + B03*(Unpacked) + R0
P1 = B10 + R1
Level-3 Model
B00 = G000 + U00
B01 = G010
B02 = G020
B03 = G030
B04 = G040
B05 = G050
B06 = G060
B10 = G100 + U10
where
Ytij is the outcome at time t for participant i in group j;
P0 is the initial status of participant ij, that is, the expected outcome for that participant
on the first day (when WEEK = 0);
P1 is the prediction bias rate for participant ij during the weeks;
E is a random “time effect”. These random time effects are assumed to be normally
distributed with mean of 0, and variance σ2;
B00 represents the mean initial status within group j;
G000 is the overall mean initial status;
334
R0 and R1 are random “participant effects”. These random participant effects are
assumed to be normally distributed with mean of 0, and variance σ2;
B10 is the mean weekly prediction bias rate within group j;
G100 is the overall mean weekly prediction bias rate;
U00 and U10 are the random “group effects”. These random group effects are assumed
to be normally distributed with mean of 0, and variance σ2.
335
3b. Equations with the outside view as a reference condition
Level-1 Model Ytij = P0 + P1*(WEEK) + E
Level-2 Model P0 = B00 + B01*(Inside) + B02*(Unpacked) + B03*(Packed) + R0
P1 = B10 + R1
Level-3 Model B00 = G000 + U00
B01 = G010
B02 = G020
B03 = G030
B10 = G100 + U10
where
Ytij is the outcome at time t for participant i in group j;
P0 is the initial status of participant ij, that is, the expected outcome for that participant
on the first day (when WEEK = 0);
P1 is the prediction bias rate for participant ij during the weeks;
E is a random “time effect”. These random time effects are assumed to be normally
distributed with mean of 0, and variance σ2;
B00 represents the mean initial status within group j;
G000 is the overall mean initial status;
R0 and R1 are random “participant effects”. These random participant effects are
assumed to be normally distributed with mean of 0, and variance σ2;
B10 is the mean weekly prediction bias rate within group j;
G100 is the overall mean weekly prediction bias rate;
336
U00 and U10 are the random “group effects”. These random group effects are assumed
to be normally distributed with mean of 0, and variance σ2.
337
3c. Equations with the inside view as a reference condition
Level-1 Model
Ytij = P0 + P1*(WEEK) + E
Level-2 Model
P0 = B00 + B01*(Outside) + B02*(Unpacked) + B03*(Packed) + R0
P1 = B10 + R1
Level-3 Model
B00 = G000 + U00
B01 = G010
B02 = G020
B03 = G030
B10 = G100 + U10
where
Ytij is the outcome at time t for participant i in group j;
P0 is the initial status of participant ij, that is, the expected outcome for that participant
on the first day (when WEEK = 0);
P1 is the prediction bias rate for participant ij during the weeks;
E is a random “time effect”. These random time effects are assumed to be normally
distributed with mean of 0, and variance σ2;
B00 represents the mean initial status within group j;
G000 is the overall mean initial status;
R0 and R1 are random “participant effects”. These random participant effects are
assumed to be normally distributed with mean of 0, and variance σ2;
B10 is the mean weekly prediction bias rate within group j;
338
G100 is the overall mean weekly prediction bias rate;
U00 and U10 are the random “group effects”. These random group effects are assumed
to be normally distributed with mean of 0, and variance σ2.
339
APPENDIX E
RESULTS FOR CONDITIONAL THREE-LEVEL HLM MODELS – STUDY 1
This appendix contains six results of conditional three-level HLM models:
1. Prediction bias for project completion time
2. Prediction bias for project outcome
3. Confidence in predictions of project completion time
4. Confidence in predictions of project outcome
5. Prediction bias for affective response towards project outcome success
6. Prediction bias for affective response towards project outcome failure
Appendix E-1
340
Table 5.2c Prediction bias for project completion time: Three-level model of the effects of manipulations relative to the inside view condition Prediction bias for project completion time Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 4.435 0.620 7.149 0.000*** For Outside (IIO1), B01 INTERCEPT3, G010 -0.810 0.862 -0.940 0.348 For Unpacked (IIO2), B02 INTERCEPT3, G020 -0.644 0.856 -0.752 0.452 For Packed (IIO3) INTERCEPT3, G030 1.260 0.815 1.545 0.123 Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 -0.577 0.105 -5.492 0.000*** For Outside (IIO1), B11 INTERCEPT3, G110 0.264 0.138 1.913 0.056† For Unpacked (IIO2), B12 INTERCEPT3, G120 0.208 0.144 1.449 0.148 For Packed (IIO3), B13 INTERCEPT3, G130 0.025 0.150 0.167 0.868 Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
10.950
Level-2 (student within groups) INTERCEPT1, (Individual initial status), R0 24.958 355 2094.523 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.520 355 803.221 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status),U00 3.889 157 228.716 0.000*** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.107 157 230.811 0.000***
Appendix E-1
341
TABLE 5.2c (Continued) Random effect Variance Component df Chi-square p-value Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 13.48 Prediction bias rate, P1 17.06 Deviance = 20483.096
Number of estimated parameters = 15 A Three-level model with time = level-1, individual student = level-2 and group = level-3. Number of students = 708 and groups = 158 The dependent variable is measured as the difference between students’ predicted and actual project completion time (in days). †p<0.10,*p <.05, **p<.01, ***p<.001
Appendix E-2
342
Table 5.4c Prediction bias for project outcome: Three-level model of the effects of manipulations relative to the inside view condition Prediction bias for project outcome Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 -3.309 3.831 -0.864 0.389 TEACHER1(T1), G001 6.322 2.123 2.978 0.004** TEACHER2(T2), G002 -9.667 2.852 -3.389 0.001** TEACHER3(T3), G003 -12.551 4.309 -2.913 0.005** TEACHER4(T4), G004 8.468 4.023 2.105 0.037* TEACHER5(T5), G005 6.626 3.008 2.203 0.029* TEACHER6(T6), G006 2.433 3.312 0.735 0.464 TEACHER7(T7), G007 -0.631 1.804 -0.350 0.727 TEACHER8(T8), G008 -10.376 2.253 -4.606 0.000*** TEACHER9(T9), G009 2.732 2.317 1.179 0.241 SEM_MEAN, G0010 -0.615 1.014 -0.606 0.545 For Outside (IIO1), B01 INTERCEPT3, G010 -3.696 1.853 -1.994 0.046*
For Unpacked (IIO2), B02 INTERCEPT3, G020 -4.449 1.925 -2.311 0.021* For Packed (IIO3), B03 INTERCEPT3, G030 -1.999 1.752 -1.141 0.255 For semester, B04 INTERCEPT3, G040 -2.055 0.461 -4.459 0.000*** Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 -0.147 0.036 -4.079 0.000*** Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
10.105
Level-2 (student within groups)
Appendix E-2
343
Table 5.4c (Continued) Random effect Variance Component df Chi-square p-value INTERCEPT1, (Individual initial status), R0 41.380 344 4124.282 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.297 348 951.367 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status),U00 52.197 145 755.179 0.000*** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.052 155 214.727 0.001** Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 55.78 Prediction bias rate, P1 14.87 Deviance = 25933.896 Number of estimated parameters = 23 A Three-level model with time = level-1, individual student = level-2 and group = level-3. Number of students = 689 and groups = 156 The dependent variable is measured as the difference between students’ predicted and actual project completion time (in days). †p<0.10,*p <.05, **p<.01, ***p<.001
Appendix E-3
344
Table 5.6c Confidence in predictions of project completion time: Three-level model of the effects of manipulations relative to the inside view condition Confidence in predictions of project completion time Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 79.958 1.822 43.879 0.000*** For Outside (IIO1), B01 INTERCEPT3, G010 -2.951 2.359 -1.251 0.212 For Unpacked (IIO2), B02 INTERCEPT3, G020 -2.656 2.659 -0.999 0.319 For Packed (IIO3) INTERCEPT3, G030 -2.623 2.424 -1.082 0.280 Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 0.172 0.191 0.899 0.370 For Outside (IIO1), B11 INTERCEPT3, G110 0.248 0.265 0.937 0.350 For Unpacked (IIO2), B12 INTERCEPT3, G120 0.113 0.361 0.315 0.753 For Packed (IIO3), B13 INTERCEPT3, G130 -0.219 0.283 -0.774 0.439
Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
63.493
Level-2 (student within groups) INTERCEPT1, (Individual initial status), R0 186.451 355 2640.617 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 2.760 355 821.165 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status),U00 46.956 157 268.369 0.000*** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.494 157 197.163 0.016*
Appendix E-3
345
Table 5.6c (Continued) Random effect Variance Component df Chi-square p-value Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 20.12 Prediction bias rate, P1 15.19 Deviance = 27338.901
Number of estimated parameters = 15 A three-level model with week = level-1, individual students = level -2, and group = level - 3. Number of students = 708 and groups = 158. The dependent variable is measured from the 0% end of the line and centimeters were converted to a number out of 100%. †p<0.10,*p <.05, **p<.01 ***p<.001
Appendix E-4
346
Table 5.8c Confidence in predictions of project outcome: Three-level model of the effects of manipulations relative to the inside view condition Confidence in predictions of project outcome Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 77.453 1.794 43.169 0.000*** For Outside (IIO1), B01 INTERCEPT3, G010 -1.036 2.477 -0.418 0.675 For Unpacked (IIO2), B02 INTERCEPT3, G020 -0.364 2.422 -0.150 0.881 For Packed (IIO3) INTERCEPT3, G030 -1.873 2.220 -0.844 0.400 Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, (Average weekly prediction bias rate), G100 0.407 0.169 2.407 0.017* For Outside (IIO1), B11 INTERCEPT3, G110 -0.180 0.233 -0.772 0.441 For Unpacked (IIO2), B12 INTERCEPT3, G120 -0.092 0.268 -0.345 0.730 For Packed (IIO3), B13 INTERCEPT3, G130 -0.106 0.208 -0.509 0.611 Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
61.640
Level-2 (student within groups) INTERCEPT1, (Individual initial status), R0 194.804 345 3334.196 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 1.334 345 809.824 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status),U00 30.967 155 220.288 0.001** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.332 155 222.70 0.000***
Appendix E-4
347
Table 5.8c (Continued) Random effect Variance Component df Chi-square p-value Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 13.72 Prediction bias rate, P1 19.93 Deviance = 33759.444
Number of estimated parameters = 15 A three-level model with week = level-1, individual students = level -2, and group = level - 3. Number of students = 689 and groups = 156. The dependent variable is measured from the 0% end of the line and centimeters were converted to a number out of 100%. †p<0.10,*p <.05, **p<.01 ***p<.001
Appendix E-5
348
Table 5.10c Prediction bias for affective response towards project outcome success: Three-level model of the effects of manipulations relative to the inside view condition Prediction bias for affective reactions towards project outcome success Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 -1.886 0.281 -6.617 0.000*** For Outside (IIO1), B01 INTERCEPT3, G010 0.771 0.368 2.097 0.037* For Unpacked (IIO2), B02 INTERCEPT3, G020 0.010 0.432 0.025 0.981 For Packed (IIO3), B03 INTERCEPT3, G030 -0.282 0.381 -0.738 0.461 Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, G100 0.023 0.007 3.241 0.002** Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
0.292
Level-2 (student within groups) INTERCEPT1, (Individual initial status), R0 2.688 186 4876.781 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.007 189 482.858 0.000*** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status), U00 1.104 115 236.716 0.000*** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.001 115 139.139 0.062†
Appendix E-5
349
Table 5.10c (Continued) Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 29.11 Prediction bias rate, P1 13.98 Deviance = 6141.417 Number of estimated parameters = 12 A Three-level model with time = level-1, individual student = level-2 and group = level-3. Number of students = 422 and groups = 116 The dependent variable is measured as the difference between students’ predicted and actual feelings towards assignment marks. †p<0.10,*p <.05, **p<.01, ***p<.001
Appendix E-6
350
Table 5.12c Prediction bias for affective response towards project outcome failure: Three-level model of the effects of manipulations relative to the inside view condition Prediction bias for affective reactions towards project outcome failure Fixed effect Coefficient S.Error t-ratio p-value Initial status: For INTERCEPT1, P0
INTERCEPT2, B00 INTERCEPT3, (Average initial status),G000 1.856 0.635 2.922 0.006** For Outside (IIO1), B01 INTERCEPT3, G010 -0.338 0.796 -0.425 0.671 For Unpacked (IIO2), B02 INTERCEPT3, G020 0.561 0.698 0.804 0.423 For Packed (IIO3), B03 INTERCEPT3, G030 0.137 0.742 0.185 0.854 Rate of change: For Week slope, P1 INTERCEPT2, B10 INTERCEPT3, G100 0.055 0.013 4.119 0.000*** Random effect Variance Component df Chi-square p-value Level-1 Effect (Week), E
0.455
Level-2 (student within groups) INTERCEPT1, (Individual initial status), R0 4.392 63 1699.207 0.000*** WEEK slope, (Individual weekly prediction bias rate), R1 0.006 66 112.300 0.001** Level-3 (between groups) INTERCEPT1 / INTERCEPT2,(Group mean status), U00 1.522 43 80.664 0.001** WEEK / INTERCEPT2, (Group mean prediction bias rate), U10 0.002 43 65.863 0.014*
Appendix E-6
351
Table 5.12c (Continued) Level-1 Coefficient Percentage of Variance Between Groups Initial status, P0 25.73 Prediction bias rate, P1 31.74 Deviance = 2610.588 Number of estimated parameters = 12 A Three-level model with time = level-1, individual student = level-2 and group = level-3. Number of students = 130 and groups = 44 The dependent variable is measured as the difference between students’ predicted and actual feelings towards assignment marks. †p<0.10,*p <.05, **p<.01, ***p<.001
352
APPENDIX F
LEGENDS FOR GRAPHICAL ANALYSIS RESULTS – STUDY 1
1a. Mean predictions bias for completion time for the four conditions
day 1 week 1 week 2 week 3 week 4 week 5 week 6 Inside 4.97 4.41 3.96 3.41 2.90 2.70 2.30 Outside 3.67 3.25 2.66 2.50 2.31 2.18 1.58 Unpacked 4.07 3.58 3.22 2.57 2.40 2.04 1.56 Packed 5.62 4.43 4.35 3.55 3.56 2.77 2.44
1b. Mean predictions bias for project outcome for the four conditions
day 1 week 1 week 2 week 3 week 4 week 5 week 6 week 7 week 8 Inside -5.29 -6.16 -6.68 -6.45 -6.94 -6.84 -6.86 -7.03 -7.38 Outside -8.32 -8.66 -9.33 -9.30 -9.30 -9.55 -9.48 -9.78 -9.92 Unpacked -9.55 -9.49 -9.56 -9.92 -9.92 -9.96 -10.42 -10.65 -11.07 Packed -6.86 -6.20 -6.16 -6.57 -6.57 -6.60 -6.85 -7.39 -8.13
1c. Mean confidence in predictions of project completion time for the four conditions
day 1 week 1 week 2 week 3 week 4 week 5 week 6 Inside 81.42 82.57 81.22 81.10 81.87 82.56 82.93 Outside 78.98 78.70 79.12 79.44 80.06 80.79 80.28 Unpacked 80.01 78.94 79.87 80.55 80.12 80.65 81.23 Packed 77.25 76.00 76.52 77.00 76.54 76.90 76.30
1d. Mean confidence in predictions of project outcome for the four conditions
day 1 week 1 week 2 week 3 week 4 week 5 week 6 week 7 week 8 Inside 76.74 77.36 78.72 78.86 79.96 79.78 80.50 80.88 82.24 Outside 77.28 77.75 78.15 78.49 77.71 78.37 79.14 79.65 80.20 Unpacked 76.59 78.18 79.17 80.26 78.47 78.96 80.51 80.31 80.84 Packed 74.41 76.04 74.62 75.52 76.15 75.83 76.13 77.69 78.10
353
1e. Mean predictions bias for affective response towards project outcome success for the four conditions
day 1 week 1 week 2 week 3 week 4 week 5 week 6 week 7 week 8 Inside -1.79 -1.56 -1.55 -1.55 -1.45 -1.49 -1.45 -1.53 -1.61 Outside -1.55 -1.25 -1.22 -1.21 -1.19 -1.17 -1.19 -1.17 -1.22 Unpacked -2.08 -1.84 -1.72 -1.80 -1.67 -1.77 -1.82 -1.77 -1.81 Packed -2.17 -2.00 -2.04 -1.96 -1.89 -1.85 -1.89 -1.93 -2.02
1f. Mean predictions bias for affective response towards project outcome failure for the four conditions
day 1 week 1 week 2 week 3 week 4 week 5 week 6 week 7 week 8 Inside 1.90 2.14 2.12 2.26 2.48 2.38 2.05 2.48 2.50 Outside 1.03 0.96 1.32 1.28 1.45 1.38 1.42 1.41 1.46 Unpacked 1.97 2.04 2.20 2.30 2.28 2.20 2.32 2.28 2.50 Packed 1.23 2.16 2.18 2.14 1.98 2.41 2.18 2.20 2.20
354
APPENDIX G
SAMPLE OF THE SURVEY MATERIALS – STUDY 2
This appendix contains copies of the survey materials provided to respondents in the
second study, namely the information sheet, the cover letter; and the two sets of
questionnaire (with four different versions each) - one completed by participants who
have already finished their HRIS project, and the other completed by participants who
were at the planning stage of their HRIS project.
355
UWA Business School Dr. Catherine D. Lees Mail Bag 261 35 Stirling Highway, Crawley, WA 6009 Phone +61 8 6488 2877 Fax +61 8 6488 1055 Email [email protected] Web www.biz.uwa.edu
HRIS Project Planning and Project Success
INFORMATION SHEET
Background of the study Like other IT projects, it is known that Human Resource Information Systems (HRIS) projects are difficult to develop and that planning plays an important part. There has been no research to understand this process for HRIS projects to date, although varied project outcomes have been observed in a wide variety of activities. Also, most studies on project planning have been conducted in Western countries. Carrying out the research in Malaysia with cultural, economic, social and political differences from Western countries will provide insight into how Malaysian people make decisions during project planning. This may impact on the HRIS project planning and success in Malaysia in ways that are as yet unknown. Aims of the study This study is conducted to investigate the best method of improving the planning in an HRIS project. This is done by testing theories of planning and extending previous researchers’ explanations of project planning problems to the Malaysian context.
Your involvement in this study Participation in this study is totally voluntary and your responses will remain completely confidential. The completion and return of the enclosed questionnaire is taken to constitute your consent to participate in the study You may withdraw from the study, by discontinuing the completion of the questionnaire, or by not returning it, at any time and without prejudice. You need not justify your decision to withdraw, and if you choose to withdraw your responses will not be recorded. Your participation in this study does not prejudice any right to compensation that you may have under statute or common law. You are not asked to provide your name, and your responses will be completely anonymous in the analyses and reports of the research. If the results from this study are published, only aggregate results will be reported and individual responses will not be identifiable.
356
Risks The researchers anticipate that this study will not involve any risk for you. Please be assured that the researchers will answer all your questions in full.
Benefits As a result of participating in this study, you may feel empowered and feel a sense of satisfaction because you have contributed to an important study that will benefit the society. Findings from this study may have significant implications for human resource management practices such as improving project planning and HRIS implementation processes by understanding the factors that might promote feasible and successful HRIS projects.
Survey process You will be invited to participate in a survey. In the beginning of the survey session, you will be briefed about the purpose and the nature of the survey. You are then provided with a sheet of information and you will be invited to ask questions. You will be given instructions and asked to fill out a short questionnaire about methods you use during HRIS project planning, about the project completion time and the project outcome, and your feelings towards the level of success of the HRIS project. This will take approximately 30 minutes. It is hoped that your participation in this study will provide valuable information that will help to guide human resource professionals and others involved in projects regarding the best method for planning processes, and enable them to deliver good project outcomes. If you have any questions regarding this study, you may address them to Siti Zubaidah Othman or to the Chief Investigator, Dr. Catherine Lees at the addresses below. A copy of this Information Sheet is provided for your own records.
Dr. CATHERINE LEES SITI ZUBAIDAH OTHMAN PhD Candidate UWA Business School UWA Business School The University of Western Australia The University of Western Australia 35 Stirling Highway, Crawley, 6009 35 Stirling Highway, Crawley, 6009 Phone: +61 8 6488 2877 Phone: +61 8 6488 3669 Fax: +61 8 6488 1055 Fax: +61 8 6488 1055 Email: [email protected] Email: [email protected]
357
HRIS Project Planning and Project Success
Questionnaire
Dear Participant, Thank you for agreeing to participate in this research on project planning. We would appreciate it very much if you could answer the questions carefully as the information you provide will influence the accuracy and the success of this research. It will take no longer than 30 minutes to complete the questionnaire. All answers will be treated with strict confidence and will be used for the purpose of the study only.
If you have any questions regarding this research, you may address them to Siti Zubaidah Othman or to Dr. Catherine Lees at the contact details below. Thank you for your cooperation and the time taken in answering this questionnaire. Yours sincerely, Dr. CATHERINE LEES SITI ZUBAIDAH OTHMAN PhD Candidate UWA Business School UWA Business School The University of Western Australia The University of Western Australia 35 Stirling Highway, Crawley, 6009 35 Stirling Highway, Crawley, 6009 Phone: +61 8 6488 2877 Phone: +61 8 6488 3669 Fax: +61 8 6488 1055 Fax: +61 8 6488 1055 Email: [email protected] Email: [email protected]
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)1
358
SECTION 1: Instructions: We would like you to briefly describe about the HRIS project that you have been involved in. Please answer questions 1 to 5 as accurately as possible. 1. Name and nature of the project (e.g. new HRIS, upgrading payroll system, adding a
new module) ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
2. The project was completed on? _______________ 3. When was the project started? ________________ 4. Was the project finished by the due date? Please tick (√ ) one of the following. The project was completed - Early On time Late
If you responded EARLY, please state how early: Number of days before the due date: __________ months _________ days
If you responded LATE, please state how late: Number of days after the due date: ___________ months _________ days
5. Please describe your involvement in the project in terms of your role and your contribution.
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)1
359
SECTION 2: Instructions: To answer questions 6 to 14, please think about the project that you have mentioned in questions 1 to 5. Please read carefully each of the activity statements below, and indicate how much you agree that the statement describes an activity you did in accomplishing the project. AS I WORK ON THIS PROJECT: 6. I think about the kind of events that are likely to happen in this project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 7. I recall the kinds of events that have happened in my past projects and those of other
people that I know about.
1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 8. I think about the components or elements that need to be included in the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 9. I think about all the problems that are likely to happen in the future that will impede
this project’s progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 10. I recall all the problems that have happened in the past that have impeded other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 11. I breakdown the overall project into assignable work elements. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)1
360
12. I think about any aspects of this project or situations in the future that will facilitate the project’s progress and performance.
1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 13. I recall any aspects of projects or situations in the past that have facilitated other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 14. I list each and every step that I am going to take in order to complete the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)1
361
Instructions: Below are three descriptions of approaches that can be used when planning for a project.
APPROACH A
When planning for an HRIS project, I focus on the kinds of events that are likely
to happen. I take into consideration all the problems that are likely to happen in
the future that will impede the project’s progress and performance, and any
aspect of the project or situations in the future that will facilitate the project’s
progress and performance.
APPROACH B
When planning for an HRIS project, I recall the kinds of events that have
happened in my past projects and those of other people that I know about. I also
recall all the problems that have happened in the past that have impeded my
project progress and performance, and any aspects of the project or situation in
the past that have facilitated my project progress and performance. Apart from
learning from my past experience, I also consult with other people who have
experience in similar projects.
APPROACH C
When planning for an HRIS project, I think about the components that are
needed for my project progress and performance. I also think about each and
everything that I plan to do in completing the project. I break down all the tasks
that need to be carried out in the project into specific tasks.
15. Please indicate which of the above best describes the approach you used:
A B C
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)1
362
SECTION 3:
16. The project outcome: Falls short of expectations
Meets expectations
Exceeds expectations
17. Considering that 100% is the outcome you planned to achieve, please indicate (in percentage terms) the extent to which the HRIS project outcome meets your expectations. Please make a mark across the line to indicate your answer.
For the next set of questions, please think about the same HRIS project. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer. 18. The output is presented in a useful format. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 19. I am satisfied with the accuracy of the system. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 20. The information is clear.
1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
50% 100% 150%
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)1
363
21. The system is accurate. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 22. The system provides sufficient information. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 23. The system provides up-to-date information.
1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 24. I get the information I need in time.
1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 25. The system provides reports that seem to be just about exactly what I need. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 26. The system is easy to use. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 27. The system is user friendly. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)1
364
28. The system provides the precise information I need. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 29. The information content meets my needs. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree For the next question, please think about the same HRIS project. Using the following scale, please place a single slash mark across the line in a position that represents your personal opinion in answer to the question. 30. Overall, how satisfied are you with the system? 31. What aspects of the system, if any, are you MOST satisfied with? ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
32. What aspect of the systems, if any, are you LEAST satisfied with? ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
10 Extremely
5 Fair
0 Not at all
Poor
Good
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)1
365
SECTION 4: Questions 33 – 37 are about your feelings toward your HRIS project outcome. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer. 33. How do you view your HRIS project outcome? A successful project Not a successful project
If it is A SUCCESSFUL PROJECT, please answer questions 34 AND 35. If it is NOT A SUCCESSFUL PROJECT, please answer question 36 AND 37.
34. To what degree do you feel good about your HRIS outcome after this project has
done well? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 35. To what degree do you feel good about your HRIS outcome after the success of this
project? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 36. To what degree do you feel bad about your HRIS outcome after this project has
done poorly?
1 2 3 4 5 6 7 8 9 10 11 Very bad not bad 37. To what degree do you feel bad about your HRIS outcome after the unsuccessful
project? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)1
366
SECTION 5: Please tick ( √ ) the given box or fill in the blank that represents your answer: 1. I am: Male Female 2. My age is _________ years. 3. My ethnic origin is: Malay Chinese Indian Other: ___________ (Please stated) 4. My education level: Secondary Education Certificate Diploma First / Professional Degree Second Degree and Above 5. My position in this organization: _________________ 6. Number of years in present position: _______________ 7. Number of years with present organization: ___________ 8. Number of years experience as a computer-based system user: _________________ 9. Number of years experience with HRIS projects: ___________________________ 10. Estimate of total number of employees working in this organization: ____________ 11. Estimate of total number of employees in your Human Resource Department: _____ 12. Estimate of number of years of HRIS usage by your organization: ______________
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)1
367
13. The idea for the system described in question 1 came from (check the most
important source):
________ Sources internal to the organization (specify which): ______ Top management of the organization ______ Information system department ______ HR department ______Other (please specify)__________________________________
________ Sources external to the organization (specify which): ______ Customers ______ Suppliers ______ Observation of competitors ______Other (please specify) _________________________________ 14. Check the HR computer-based applications presently implemented by your
organization: Payroll operation Training and career development
Compensation and benefits Human resource planning
Performance appraisal Recruitment and selection
Employee / labor relations Others: (please specify) __________________
15. The system described in question 1 has been: Developed internally Acquired from vendor Both of these 16. If acquired from vendor, please state vendor’s company name (Optional question): __________________________________________________________________
Thank you for taking the time to complete this survey.
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)2
368
SECTION 1: Instructions: We would like you to briefly describe about the HRIS project that you have been involved in. Please answer questions 1 to 5 as accurately as possible. 1. Name and nature of the project (e.g. new HRIS, upgrading payroll system, adding a
new module) ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
2. The project was completed on? _______________ 3. When was the project started? ________________ 4. Was the project finished by the due date? Please tick (√ ) one of the following. The project was completed -
Early On time Late
If you responded EARLY, please state how early:
Number of days before the due date: __________ months _________ days
If you responded LATE, please state how late:
Number of days after the due date: ___________ months _________ days
5. Please describe your involvement in the project in terms of your role and your
contribution. ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)2
369
SECTION 2: Instructions: To answer questions 6 to 14, please think about the project that you have mentioned in questions 1 to 5. Please read carefully each of the activity statements below, and indicate how much you agree that the statement describes an activity you did in accomplishing the project. AS I WORK ON THIS PROJECT: 6. I list each and every step that I am going to take in order to complete the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 7. I recall any aspects of projects or situations in the past that have facilitated other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 8. I think about any aspects of this project or situations in the future that will facilitate
the project’s progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 9. I breakdown the overall project into assignable work elements. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 10. I recall all the problems that have happened in the past that have impeded other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 11. I think about all the problems that are likely to happen in the future that will impede
the project’s progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)2
370
12. I think about the components or elements that need to be included in the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 13. I recall the kinds of events that have happened in my past projects and those of other
people that I know about. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 14. I think about the kind of events that are likely to happen in this project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)2
371
Instructions: Below are three descriptions of approaches that can be used when planning for a project.
APPROACH A
When planning for an HRIS project, I focus on the kinds of events that are likely
to happen. I take into consideration all the problems that are likely to happen in
the future that will impede the project’s progress and performance, and any
aspect of the project or situations in the future that will facilitate the project’s
progress and performance.
APPROACH B
When planning for an HRIS project, I recall the kinds of events that have
happened in my past projects and those of other people that I know about. I also
recall all the problems that have happened in the past that have impeded my
project progress and performance, and any aspects of the project or situation in
the past that have facilitated my project progress and performance. Apart from
learning from my past experience, I also consult with other people who have
experience in similar projects.
APPROACH C
When planning for an HRIS project, I think about the components that are
needed for my project progress and performance. I also think about each and
everything that I plan to do in completing the project. I break down all the tasks
that need to be carried out in the project into specific tasks.
15. Please indicate which of the above best describes the approach you used: A B C
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)2
372
SECTION 3:
16. The project outcome: Falls short of expectations
Meets expectations
Exceeds expectations
17. Considering that 100% is the outcome you planned to achieve, please indicate (in percentage terms) the extent to which the HRIS project outcome meets your expectations. Please make a mark across the line to indicate your answer.
For the next set of questions, please think about the same HRIS project. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer. 18. The output is presented in a useful format. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 19. I am satisfied with the accuracy of the system. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 20. The information is clear. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
50% 100% 150%
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)2
373
21. The system is accurate. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 22. The system provides sufficient information. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 23. The system provides up-to-date information. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 24. I get the information I need in time. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 25. The system provides reports that seem to be just about exactly what I need. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 26. The system is easy to use. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 27. The system is user friendly. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)2
374
28. The system provides the precise information I need. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 29. The information content meets my needs. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree For the next question, please think about the same HRIS project. Using the following scale, please place a single slash mark across the line in a position that represents your personal opinion in answer to the question. 30. Overall, how satisfied are you with the system? 31. What aspects of the system, if any, are you MOST satisfied with? ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
32. What aspect of the systems, if any, are you LEAST satisfied with? ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
10 Extremely
5 Fair
0 Not at all
Poor
Good
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)2
375
SECTION 4: Questions 33 – 37 are about your feelings toward your HRIS project outcome. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer. 33. How do you view your HRIS project outcome? A successful project Not a successful project
If it is A SUCCESSFUL PROJECT, please answer questions 34 AND 35. If it is NOT A SUCCESSFUL PROJECT, please answer question 36 AND 37.
34. To what degree do you feel good about your HRIS outcome after this project has
done well? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 35. To what degree do you feel good about your HRIS outcome after the success of this
project? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 36. To what degree do you feel bad about your HRIS outcome after this project has
done poorly? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad 37. To what degree do you feel bad about your HRIS outcome after the unsuccessful
project? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)2
376
SECTION 5: Please tick ( √ ) the given box or fill in the blank that represents your answer: 1. I am:
Male Female
2. My age is _________ years. 3. My ethnic origin is: Malay Chinese Indian Other: ___________ (Please stated)
4. My education level: Secondary Education Certificate Diploma First / Professional Degree Second Degree and Above
5. My position in this organization: _________________ 6. Number of years in present position: _______________ 7. Number of years with present organization: ___________ 8. Number of years experience as a computer-based system user: _________________ 9. Number of years experience with HRIS projects: ___________________________ 10. Estimate of total number of employees working in this organization: ____________ 11. Estimate of total number of employees in your Human Resource Department: _____ 12. Estimate of number of years of HRIS usage by your organization: ______________
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)2
377
13. The idea for the system described in question 1 came from (check the most important source):
________ Sources internal to the organization (specify which):
______ Top management of the organization ______ Information system department ______ HR department ______Other (please specify)__________________________________
________ Sources external to the organization (specify which): ______ Customers ______ Suppliers ______ Observation of competitors ______Other (please specify) _________________________________ 14. Check the HR computer-based applications presently implemented by your
organization: Payroll operation Training and career development
Compensation and benefits Human resource planning
Performance appraisal Recruitment and selection
Employee / labor relations Others: (please specify) __________________
15. The system described in question 1 has been: Developed internally Acquired from vendor Both of these If acquired from vendor, please state vendor’s company name (Optional question): __________________________________________________________________
Thank you for taking the time to complete this survey.
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)3
378
SECTION 1: Instructions: We would like you to briefly describe about the HRIS project that you have been involved in. Please answer questions 1 to 5 as accurately as possible. 1. Name and nature of the project (e.g. new HRIS, upgrading payroll system, adding a
new module)
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
2. The project was completed on? _______________ 3. When was the project started? ________________ 4. Was the project finished by the due date? Please tick (√ ) one of the following. The project was completed -
Early On time Late
If you responded EARLY, please state how early:
Number of days before the due date: __________ months _________ days
If you responded LATE, please state how late:
Number of days after the due date: ___________ months _________ days
5. Please describe your involvement in the project in terms of your role and your
contribution. ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)3
379
SECTION 2: Instructions: To answer questions 6 to 14, please think about the project that you have mentioned in questions 1 to 5. Please read carefully each of the activity statements below, and indicate how much you agree that the statement describes an activity you did in accomplishing the project. AS I WORK ON THIS PROJECT: 6. I think about the kind of events that are likely to happen in this project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 7. I recall the kinds of events that have happened in my past projects and those of other
people that I know about. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 8. I think about the components or elements that need to be included in the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 9. I think about all the problems that are likely to happen in the future that will impede
this project’s progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 10. I recall all the problems that have happened in the past that have impeded other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 11. I breakdown the overall project into assignable work elements. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)3
380
12. I think about any aspects of this project or situations in the future that will facilitate the project’s progress and performance.
1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 13. I recall any aspects of projects or situations in the past that have facilitated other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 14. I list each and every step that I am going to take in order to complete the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)3
381
Instructions: Below are three descriptions of approaches that can be used when planning for a project.
APPROACH A
When planning for an HRIS project, I think about the components that are
needed for my project progress and performance. I also think about each and
everything that I plan to do in completing the project. I break down all the tasks
that need to be carried out in the project into specific tasks.
APPROACH B
When planning for an HRIS project, I recall the kinds of events that have
happened in my past projects and those of other people that I know about. I also
recall all the problems that have happened in the past that have impeded my
project progress and performance, and any aspects of the project or situation in
the past that have facilitated my project progress and performance. Apart from
learning from my past experience, I also consult with other people who have
experience in similar projects.
APPROACH C
When planning for an HRIS project, I focus on the kinds of events that are likely
to happen. I take into consideration all the problems that are likely to happen in
the future that will impede the project’s progress and performance, and any
aspect of the project or situations in the future that will facilitate the project’s
progress and performance.
15. Please indicate which of the above best describes the approach you used:
A B C
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)3
382
SECTION 3:
16. The project outcome:
Falls short of expectations Meets expectations Exceeds expectations
17. Considering that 100% is the outcome you planned to achieve, please indicate (in percentage terms) the extent to which the HRIS project outcome meets your expectations. Please make a mark across the line to indicate your answer.
For the next set of questions, please think about the same HRIS project. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer. 18. The output is presented in a useful format. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 19. I am satisfied with the accuracy of the system. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 20. The information is clear. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
50% 100% 150%
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)3
383
21. The system is accurate. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 22. The system provides sufficient information. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 23. The system provides up-to-date information. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 24. I get the information I need in time. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 25. The system provides reports that seem to be just about exactly what I need. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 26. The system is easy to use. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 27. The system is user friendly. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)3
384
28. The system provides the precise information I need. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 29. The information content meets my needs. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree For the next question, please think about the same HRIS project. Using the following scale, please place a single slash mark across the line in a position that represents your personal opinion in answer to the question. 30. Overall, how satisfied are you with the system? 31. What aspects of the system, if any, are you MOST satisfied with? ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
32. What aspect of the systems, if any, are you LEAST satisfied with? ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
10 Extremely
5 Fair
0 Not at all
Poor
Good
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)3
385
SECTION 4: Questions 33 – 37 are about your feelings toward your HRIS project outcome. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer. 33. How do you view your HRIS project outcome? A successful project Not a successful project
If it is A SUCCESSFUL PROJECT, please answer questions 34 AND 35. If it is NOT A SUCCESSFUL PROJECT, please answer question 36 AND 37.
34. To what degree do you feel good about your HRIS outcome after this project has
done well? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 35. To what degree do you feel good about your HRIS outcome after the success of this
project? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 36. To what degree do you feel bad about your HRIS outcome after this project has
done poorly? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad 37. To what degree do you feel bad about your HRIS outcome after the unsuccessful
project? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)3
386
SECTION 5: Please tick ( √ ) the given box or fill in the blank that represents your answer: 1. I am: Male Female
2. My age is _________ years. 3. My ethnic origin is: Malay Chinese Indian Other: ___________ (Please stated)
4. My education level: Secondary Education Certificate Diploma First / Professional Degree Second Degree and Above
5. My position in this organization: _________________ 6. Number of years in present position: _______________ 7. Number of years with present organization: ___________ 8. Number of years experience as a computer-based system user: _________________ 9. Number of years experience with HRIS projects: ___________________________ 10. Estimate of total number of employees working in this organization: ____________ 11. Estimate of total number of employees in your Human Resource Department: _____ 12. Estimate of number of years of HRIS usage by your organization: ______________
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)3
387
13. The idea for the system described in question 1 came from (check the most important source):
________ Sources internal to the organization (specify which):
______ Top management of the organization ______ Information system department ______ HR department ______Other (please specify)__________________________________
________ Sources external to the organization (specify which): ______ Customers ______ Suppliers ______ Observation of competitors ______Other (please specify) _________________________________ 14. Check the HR computer-based applications presently implemented by your
organization: Payroll operation Training and career development Compensation and benefits Human resource planning Performance appraisal Recruitment and selection Employee / labor relations Others: (please specify) __________________
15. The system described in question 1 has been: Developed internally Acquired from vendor Both of these If acquired from vendor, please state vendor’s company name (Optional question): __________________________________________________________________
Thank you for taking the time to complete this survey.
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)4
388
SECTION 1: Instructions: We would like you to briefly describe about the HRIS project that you have been involved in. Please answer questions 1 to 5 as accurately as possible. 1. Name and nature of the project (e.g. new HRIS, upgrading payroll system, adding a
new module) ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
2. The project was completed on? _______________ 3. When was the project started? ________________ 4. Was the project finished by the due date? Please tick (√ ) one of the following. The project was completed -
Early On time Late
If you responded EARLY, please state how early:
Number of days before the due date: __________ months _________ days
If you responded LATE, please state how late:
Number of days after the due date: ___________ months _________ days
5. Please describe your involvement in the project in terms of your role and your contribution.
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)4
389
SECTION 2: Instructions: To answer questions 6 to 14, please think about the project that you have mentioned in questions 1 to 5. Please read carefully each of the activity statements below, and indicate how much you agree that the statement describes an activity you did in accomplishing the project. AS I WORK ON THIS PROJECT: 6. I list each and every step that I am going to take in order to complete the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 7. I recall any aspects of projects or situations in the past that have facilitated other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 8. I think about any aspects of this project or situations in the future that will facilitate
the project’s progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 9. I breakdown the overall project into assignable work elements. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 10. I recall all the problems that have happened in the past that have impeded other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 11. I think about all the problems that are likely to happen in the future that will impede the project’s progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)4
390
12. I think about the components or elements that need to be included in the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 13. I recall the kinds of events that have happened in my past projects and those of other
people that I know about. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 14. I think about the kind of events that are likely to happen in this project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)4
391
Instructions: Below are three descriptions of approaches that can be used when planning for a project.
APPROACH A
When planning for an HRIS project, I think about the components that are
needed for my project progress and performance. I also think about each and
everything that I plan to do in completing the project. I break down all the tasks
that need to be carried out in the project into specific tasks.
APPROACH B
When planning for an HRIS project, I recall the kinds of events that have
happened in my past projects and those of other people that I know about. I also
recall all the problems that have happened in the past that have impeded my
project progress and performance, and any aspects of the project or situation in
the past that have facilitated my project progress and performance. Apart from
learning from my past experience, I also consult with other people who have
experience in similar projects.
APPROACH C
When planning for an HRIS project, I focus on the kinds of events that are likely
to happen. I take into consideration all the problems that are likely to happen in
the future that will impede the project’s progress and performance, and any
aspect of the project or situations in the future that will facilitate the project’s
progress and performance.
15. Please indicate which of the above best describes the approach you used:
A B C
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)4
392
SECTION 3:
16. The project outcome:
Falls short of expectations
Meets expectations
Exceeds expectations
17. Considering that 100% is the outcome you planned to achieve, please indicate (in percentage terms) the extent to which the HRIS project outcome meets your expectations. Please make a mark across the line to indicate your answer.
For the next set of questions, please think about the same HRIS project. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer. 18. The output is presented in a useful format. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 19. I am satisfied with the accuracy of the system. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 20. The information is clear. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
50% 100% 150%
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)4
393
21. The system is accurate. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 22. The system provides sufficient information. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 23. The system provides up-to-date information. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 24. I get the information I need in time. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 25. The system provides reports that seem to be just about exactly what I need. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 26. The system is easy to use. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 27. The system is user friendly. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)4
394
28. The system provides the precise information I need. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 29. The information content meets my needs. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree For the next question, please think about the same HRIS project. Using the following scale, please place a single slash mark across the line in a position that represents your personal opinion in answer to the question. 30. Overall, how satisfied are you with the system? 31. What aspects of the system, if any, are you MOST satisfied with? ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
32. What aspect of the systems, if any, are you LEAST satisfied with? ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
10 Extremely
5 Fair
0 Not at all
Poor
Good
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)4
395
SECTION 4: Questions 33 – 37 are about your feelings toward your HRIS project outcome. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer. 33. How do you view your HRIS project outcome? A successful project Not a successful project
If it is A SUCCESSFUL PROJECT, please answer questions 34 AND 35. If it is NOT A SUCCESSFUL PROJECT, please answer question 36 AND 37.
34. To what degree do you feel good about your HRIS outcome after this project has
done well? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 35. To what degree do you feel good about your HRIS outcome after the success of this
project? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 36. To what degree do you feel bad about your HRIS outcome after this project has
done poorly? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad 37. To what degree do you feel bad about your HRIS outcome after the unsuccessful
project? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)4
396
SECTION 5: Please tick ( √ ) the given box or fill in the blank that represents your answer: 1. I am: Male Female
2. My age is _________ years. 3. My ethnic origin is: Malay Chinese Indian Other: ___________ (Please stated)
4. My education level: Secondary Education Certificate Diploma
First / Professional Degree Second Degree and Above
5. My position in this organization: _________________ 6. Number of years in present position: _______________ 7. Number of years with present organization: ___________ 8. Number of years experience as a computer-based system user: _________________ 9. Number of years experience with HRIS projects: ___________________________ 10. Estimate of total number of employees working in this organization: ____________ 11. Estimate of total number of employees in your Human Resource Department: _____ 12. Estimate of number of years of HRIS usage by your organization: ______________
HRIS Project Planning and Project Success
Completed project (R: ___ O: ___)4
397
13. The idea for the system described in question 1 came from (check the most important source):
________ Sources internal to the organization (specify which):
______ Top management of the organization ______ Information system department ______ HR department ______Other (please specify)__________________________________
________ Sources external to the organization (specify which): ______ Customers ______ Suppliers ______ Observation of competitors ______Other (please specify) _________________________________ 14. Check the HR computer-based applications presently implemented by your
organization: Payroll operation Training and career development
Compensation and benefits Human resource planning
Performance appraisal Recruitment and selection
Employee / labor relations Others: (please specify) __________________
15. The system described in question 1 has been: Developed internally Acquired from vendor Both of these
If acquired from vendor, please state vendor’s company name (Optional question): __________________________________________________________________
Thank you for taking the time to complete this survey.
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)1
398
SECTION 1: Instructions: We would like you to briefly describe about the HRIS project that you are currently involved in. Please answer questions 1 to 5 as accurately as possible. 1. Name and nature of the project (e.g. new HRIS, upgrading payroll system, adding a
new module) ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
2. The project is scheduled to be completed on _______________ 3. The project started (or will start) on _______________ 4. I predict that this project will finish on ______________ 5. Please describe your involvement in the project in terms of your role and your
contribution.
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)1
399
SECTION 2: Instructions: To answer questions 6 to 14, please think about the project that you have mentioned in questions 1 to 5. Please read carefully each of the activity statements below, and indicate how much you agree that the statement describes an activity you are doing in accomplishing the project. AS I WORK ON THIS PROJECT: 6. I think about the kind of events that are likely to happen in this project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 7. I recall the kinds of events that have happened in my past projects and those of other
people that I know about. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 8. I think about the components or elements that need to be included in the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 9. I think about all the problems that are likely to happen in the future that will impede
the project’s progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 10. I recall all the problems that have happened in the past that have impeded other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 11. I breakdown the overall project into assignable work elements. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)1
400
12. I think about any aspects of this project or situations in the future that will facilitate
the project’s progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 13. I recall any aspects of projects or situations in the past that have facilitated other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 14. I list each and every step that I am going to take in order to complete the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)1
401
Instructions: Below are three descriptions of approaches that can be used when planning for a project.
APPROACH A
When planning for an HRIS project, I focus on the kinds of events that are likely
to happen. I take into the consideration all the problems that are likely to happen
in the future that will impede the project’s progress and performance, and any
aspects of the project or situations in the future that will facilitate the project’s
progress and performance.
APPROACH B
When planning for an HRIS project, I recall the kinds of events that have
happened in my past projects and those of other people that I know about. I also
recall all the problems that have happened in the past that have impeded my
project progress and performance, and any aspects of the project or situations in
the past that have facilitated my project progress and performance. Apart from
learning from my past experience, I also consult with other people who have
experience in similar projects.
APPROACH C
When planning for an HRIS project, I think about the components that are
needed for my project’s progress and performance. I also think about each and
everything that I plan to do in completing the project. I break down all the tasks
that need to be carried out in the project into specific tasks.
15. Please indicate which of the above best describes the approach you are using:
A B C
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)1
402
SECTION 3:
16. The project outcome will: Fall short of expectations
Meet expectations
Exceed expectations
17. Considering that 100% is the outcome you plan to achieve, please indicate (in percentage terms) the extent to which the HRIS project outcome will meet your expectations. Please make a mark across the line to indicate your answer.
50% 100% 150%
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)1
403
SECTION 4: Questions 18 – 21 are about your feelings toward your HRIS project outcome. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer.
18. How would you feel if this project does well? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 19. How would you feel if this project succeeds? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 20. How would you feel if this project does poorly? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad 21. How would you feel if this project does not succeed? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)1
404
SECTION 5: Please tick ( √ ) the given box or fill in the blank that represents your answer: 1. I am: Male Female
2. My age is _________ years. 3. My ethnic origin is: Malay Chinese Indian Other: ___________ (Please stated)
4. My education level: Secondary Education Certificate Diploma
First / Professional Degree Second Degree and Above
5. My position in this organization: _________________ 6. Number of years in present position: _______________ 7. Number of years with present organization: ___________ 8. Number of years experience as a computer-based system user: _________________ 9. Number of years experience with HRIS projects: ___________________________ 10. Estimate of total number of employees working in this organization: ____________ 11. Estimate of total number of employees in your Human Resource Department: _____ 12. Estimate of number of years of HRIS usage by your organization: _____________
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)1
405
13. The idea for the system described in question 1 came from (check the most
important source):
________ Sources internal to the organization (specify which): ______ Top management of the organization ______ Information system department ______ HR department ______ Other (please specify) _________________________________
________ Sources external to the organization (specify which): ______ Customers ______ Suppliers ______ Observation of competitors ______ Other (please specify) _________________________________ 14. Check the HR computer-based applications presently implemented by your
organization: Payroll operation Training and career development
Compensation and benefits Human resource planning
Performance appraisal Recruitment and selection
Employee / labor relations Other (please specify) __________________
15. The system proposed in question 1 will be: Developed internally Acquired from vendor Both of these
If acquired from vendor, please state vendor’s company name (Optional question):
________________________________________________________________
Thank you for taking the time to complete this survey.
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)2
406
SECTION 1: Instructions: We would like you to briefly describe about the HRIS project that you are currently involved in. Please answer questions 1 to 5 as accurately as possible. 1. Name and nature of the project (e.g. new HRIS, upgrading payroll system, adding a
new module) ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
2. The project is scheduled to be completed on _______________ 3. The project started (or will start) on _______________ 4. I predict that this project will finish on ______________ 5. Please describe your involvement in the project in terms of your role and your
contribution. ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)2
407
SECTION 2: Instructions: To answer questions 6 to 14, please think about the project that you have mentioned in questions 1 to 5. Please read carefully each of the activity statements below, and indicate how much you agree that the statement describes an activity you are doing in accomplishing the project. AS I WORK ON THIS PROJECT: 6. I list each and every step that I am going to take in order to complete the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 7. I recall any aspects of projects or situations in the past that have facilitated other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 8. I think about any aspects of this project or situations in the future that will facilitate
the project’s progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 9. I breakdown the overall project into assignable work elements. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 10. I recall all the problems that have happened in the past that have impeded other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 11. I think about all the problems that are likely to happen in the future that will impede
the project’s progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)2
408
12. I think about the components or elements that need to be included in the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 13. I recall the kind of events that have happened in my past projects and those of other
people that I know about. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 14. I think about the kind of events that are likely to happen in this project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)2
409
Instructions: Below are three descriptions of approaches that can be used when planning for a project.
APPROACH A
When planning for an HRIS project, I focus on the kinds of events that are likely
to happen. I take into the consideration all the problems that are likely to happen
in the future that will impede the project’s progress and performance, and any
aspects of the project or situations in the future that will facilitate the project’s
progress and performance.
APPROACH B
When planning for an HRIS project, I recall the kinds of events that have
happened in my past projects and those of other people that I know about. I also
recall all the problems that have happened in the past that have impeded my
project progress and performance, and any aspects of the project or situations in
the past that have facilitated my project progress and performance. Apart from
learning from my past experience, I also consult with other people who have
experience in similar projects.
APPROACH C
When planning for an HRIS project, I think about the components that are
needed for my project’s progress and performance. I also think about each and
everything that I plan to do in completing the project. I break down all the tasks
that need to be carried out in the project into specific tasks.
15. Please indicate which of the above best describes the approach you are using: A B C
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)2
410
SECTION 3: 16. The project outcome will: Fall short of expectations
Meet expectations
Exceed expectations
17. Considering that 100% is the outcome you plan to achieve, please indicate (in percentage terms) the extent to which the HRIS project outcome will meet your expectations. Please make a mark across the line to indicate your answer.
50% 100% 150%
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)2
411
SECTION 4: Questions 18 – 21 are about your feelings toward your HRIS project outcome. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer.
18. How would you feel if this project does well? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 19. How would you feel if this project succeeds? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 20. How would you feel if this project does poorly? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad 21. How would you feel if this project does not succeed? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)2
412
SECTION 5: Please tick ( √ ) the given box or fill in the blank that represents your answer: 1. I am: Male Female
2. My age is _________ years. 3. My ethnic origin is: Malay Chinese Indian Other: ___________ (Please stated)
4. My education level: Secondary Education Certificate Diploma
First / Professional Degree Second Degree and Above
5. My position in this organization: _________________ 6. Number of years in present position: _______________ 7. Number of years with present organization: ___________ 8. Number of years experience as a computer-based system user: _________________ 9. Number of years experience with HRIS projects: ___________________________ 10. Estimate of total number of employees working in this organization: ____________ 11. Estimate of total number of employees in your Human Resource Department: _____ 12. Estimate of number of years of HRIS usage by your organization: _____________
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)2
413
13. The idea for the system described in question 1 came from (check the most important source):
________ Sources internal to the organization (specify which):
______ Top management of the organization ______ Information system department ______ HR department ______ Other (please specify) _________________________________
________ Sources external to the organization (specify which): ______ Customers ______ Suppliers ______ Observation of competitors ______ Other (please specify) _________________________________ 14. Check the HR computer-based applications presently implemented by your
organization: Payroll operation Training and career development
Compensation and benefits Human resource planning
Performance appraisal Recruitment and selection
Employee / labor relations Other (please specify) __________________
15. The system proposed in question 1 will be: Developed internally Acquired from vendor Both of these
If acquired from vendor, please state vendor’s company name (Optional question):
________________________________________________________________
Thank you for taking the time to complete this survey.
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)3
414
SECTION 1: Instructions: We would like you to briefly describe about the HRIS project that you are currently involved in. Please answer questions 1 to 5 as accurately as possible. 1. Name and nature of the project (e.g. new HRIS, upgrading payroll system, adding a
new module) ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
2. The project is scheduled to be completed on _______________ 3. The project started (or will start) on _______________ 4. I predict that this project will finish on ______________ 5. Please describe your involvement in the project in terms of your role and your
contribution. ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)3
415
SECTION 2: Instructions: To answer questions 6 to 14, please think about the project that you have mentioned in questions 1 to 5. Please read carefully each of the activity statements below, and indicate how much you agree that the statement describes an activity you are doing in accomplishing the project. AS I WORK ON THIS PROJECT: 6. I list each and every step that I am going to take in order to complete the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 7. I recall any aspects of projects or situations in the past that have facilitated other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 8. I think about any aspects of this project or situations in the future that will facilitate
the project’s progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 9. I breakdown the overall project into assignable work elements. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 10. I recall all the problems that have happened in the past that have impeded other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 11. I think about all the problems that are likely to happen in the future that will impede
the project’s progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)3
416
12. I think about the components or elements that need to be included in the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 13. I recall the kind of events that have happened in my past projects and those of other
people that I know about. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 14. I think about the kind of events that are likely to happen in this project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)3
417
Instructions: Below are three descriptions of approaches that can be used when planning for a project.
APPROACH A
When planning for an HRIS project, I think about the components that are
needed for my project’s progress and performance. I also think about each and
everything that I plan to do in completing the project. I break down all the tasks
that need to be carried out in the project into specific tasks.
APPROACH B
When planning for an HRIS project, I recall the kinds of events that have
happened in my past projects and those of other people that I know about. I also
recall all the problems that have happened in the past that have impeded my
project progress and performance, and any aspects of the project or situations in
the past that have facilitated my project progress and performance. Apart from
learning from my past experience, I also consult with other people who have
experience in similar projects.
APPROACH C
When planning for an HRIS project, I focus on the kinds of events that are likely
to happen. I take into the consideration all the problems that are likely to happen
in the future that will impede the project’s progress and performance, and any
aspects of the project or situations in the future that will facilitate the project’s
progress and performance.
15. Please indicate which of the above best describes the approach you are using: A B C
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)3
418
SECTION 3: 16. The project outcome will: Fall short of expectations
Meet expectations
Exceed expectations
17. Considering that 100% is the outcome you plan to achieve, please indicate (in percentage terms) the extent to which the HRIS project outcome will meet your expectations. Please make a mark across the line to indicate your answer.
50% 100% 150%
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)3
419
SECTION 4: Questions 18 – 21 are about your feelings toward your HRIS project outcome. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer.
18. How would you feel if this project does well? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 19. How would you feel if this project succeeds? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 20. How would you feel if this project does poorly? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad 21. How would you feel if this project does not succeed? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)3
420
SECTION 5: Please tick ( √ ) the given box or fill in the blank that represents your answer: 1. I am: Male Female
2. My age is _________ years. 3. My ethnic origin is: Malay Chinese Indian Other: ___________ (Please stated)
4. My education level: Secondary Education Certificate Diploma
First / Professional Degree Second Degree and Above
5. My position in this organization: _________________ 6. Number of years in present position: _______________ 7. Number of years with present organization: ___________ 8. Number of years experience as a computer-based system user: _________________ 9. Number of years experience with HRIS projects: ___________________________ 10. Estimate of total number of employees working in this organization: ____________ 11. Estimate of total number of employees in your Human Resource Department: _____ 12. Estimate of number of years of HRIS usage by your organization: _____________
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)3
421
13. The idea for the system described in question 1 came from (check the most important source):
________ Sources internal to the organization (specify which):
______ Top management of the organization ______ Information system department ______ HR department ______ Other (please specify) _________________________________
________ Sources external to the organization (specify which): ______ Customers ______ Suppliers ______ Observation of competitors ______ Other (please specify) _________________________________ 14. Check the HR computer-based applications presently implemented by your
organization: Payroll operation Training and career development
Compensation and benefits Human resource planning
Performance appraisal Recruitment and selection
Employee / labor relations Other (please specify) __________________
15. The system proposed in question 1 will be: Developed internally Acquired from vendor Both of these
If acquired from vendor, please state vendor’s company name (Optional question):
________________________________________________________________
Thank you for taking the time to complete this survey.
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)4
422
SECTION 1: Instructions: We would like you to briefly describe about the HRIS project that you are currently involved in. Please answer questions 1 to 5 as accurately as possible. 1. Name and nature of the project (e.g. new HRIS, upgrading payroll system, adding a
new module) ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
2. The project is scheduled to be completed on _______________ 3. The project started (or will start) on _______________ 4. I predict that this project will finish on ______________ 5. Please describe your involvement in the project in terms of your role and your
contribution. ______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
______________________________________________________________________
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)4
423
SECTION 2: Instructions: To answer questions 6 to 14, please think about the project that you have mentioned in questions 1 to 5. Please read carefully each of the activity statements below, and indicate how much you agree that the statement describes an activity you are doing in accomplishing the project. AS I WORK ON THIS PROJECT: 6. I list each and every step that I am going to take in order to complete the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 7. I recall any aspects of projects or situations in the past that have facilitated other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 8. I think about any aspects of this project or situations in the future that will facilitate
the project’s progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 9. I breakdown the overall project into assignable work elements. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 10. I recall all the problems that have happened in the past that have impeded other
projects’ progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 11. I think about all the problems that are likely to happen in the future that will impede
the project’s progress and performance. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)4
424
12. I think about the components or elements that need to be included in the project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 13. I recall the kind of events that have happened in my past projects and those of other
people that I know about. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree 14. I think about the kind of events that are likely to happen in this project. 1 2 3 4 5 6 7 Strongly Strongly Disagree Agree
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)4
425
Instructions: Below are three descriptions of approaches that can be used when planning for a project.
APPROACH A
When planning for an HRIS project, I think about the components that are
needed for my project’s progress and performance. I also think about each and
everything that I plan to do in completing the project. I break down all the tasks
that need to be carried out in the project into specific tasks.
APPROACH B
When planning for an HRIS project, I recall the kinds of events that have
happened in my past projects and those of other people that I know about. I also
recall all the problems that have happened in the past that have impeded my
project progress and performance, and any aspects of the project or situations in
the past that have facilitated my project progress and performance. Apart from
learning from my past experience, I also consult with other people who have
experience in similar projects.
APPROACH C
When planning for an HRIS project, I focus on the kinds of events that are likely
to happen. I take into the consideration all the problems that are likely to happen
in the future that will impede the project’s progress and performance, and any
aspects of the project or situations in the future that will facilitate the project’s
progress and performance.
15. Please indicate which of the above best describes the approach you are using: A B C
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)4
426
SECTION 3: 16. The project outcome will: Fall short of expectations
Meet expectations
Exceed expectations
17. Considering that 100% is the outcome you plan to achieve, please indicate (in percentage terms) the extent to which the HRIS project outcome will meet your expectations. Please make a mark across the line to indicate your answer.
50% 100% 150%
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)4
427
SECTION 4: Questions 18 – 21 are about your feelings toward your HRIS project outcome. Using the following scale, please tick ( √ ) the given box that represents your most appropriate answer.
18. How would you feel if this project does well? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 19. How would you feel if this project succeeds? 1 2 3 4 5 6 7 8 9 10 11 Not good very good 20. How would you feel if this project does poorly? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad 21. How would you feel if this project does not succeed? 1 2 3 4 5 6 7 8 9 10 11 Very bad not bad
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)4
428
SECTION 5: Please tick ( √ ) the given box or fill in the blank that represents your answer: 1. I am: Male Female
2. My age is _________ years. 3. My ethnic origin is: Malay Chinese Indian Other: ___________ (Please stated)
4. My education level: Secondary Education Certificate Diploma
First / Professional Degree Second Degree and Above
5. My position in this organization: _________________ 6. Number of years in present position: _______________ 7. Number of years with present organization: ___________ 8. Number of years experience as a computer-based system user: _________________ 9. Number of years experience with HRIS projects: ___________________________ 10. Estimate of total number of employees working in this organization: ____________ 11. Estimate of total number of employees in your Human Resource Department: _____ 12. Estimate of number of years of HRIS usage by your organization: _____________
HRIS Project Planning and Project Success
At planning stage (R: __ O: __)4
429
13. The idea for the system described in question 1 came from (check the most important source):
________ Sources internal to the organization (specify which):
______ Top management of the organization ______ Information system department ______ HR department ______ Other (please specify) _________________________________
________ Sources external to the organization (specify which): ______ Customers ______ Suppliers ______ Observation of competitors ______ Other (please specify) _________________________________ 14. Check the HR computer-based applications presently implemented by your
organization: Payroll operation Training and career development
Compensation and benefits Human resource planning
Performance appraisal Recruitment and selection
Employee / labor relations Other (please specify) __________________
15. The system proposed in question 1 will be: Developed internally Acquired from vendor Both of these
If acquired from vendor, please state vendor’s company name (Optional question):
________________________________________________________________
Thank you for taking the time to complete this survey.
430
APPENDIX H
OPEN-ENDED QUESTION MATERIALS – STUDY 2
This appendix contains copies of the open-ended materials provided to two independent
individuals for sorting processes: two sets of instruction, three open-ended questions
and three sets of answer sheet.
431
OPEN-ENDED QUESTIONS
Instruction: Please sort the answer as best as you can into five categories of heading. You can use
the ‘other’ category if you think the answer does not fit with the five categories
mentioned. You are allowed to go back and revise your answer as many times as you
like until you are satisfied with your answer. Please indicate at the end of the answer
sheet, the amount of time you spend to finish this task.
To guide you with the sorting process, below are definitions for each category:
System Planning – often a two-step process – requirement definition and feasibility
analysis. Requirement definition involves several types of investigation to determine
what kinds of data, analysis, security, reports, and other features users need. Feasibility
analysis estimates the resources required to achieve those objectives. It includes
hardware cost, vendor charges for software, facilities requirements, consultant time,
custom software development, documentation revisions and user training.
System Design – the design process has three components: (1) define data needs by
designing data structure, content and control; (2) select, create or adapt software that
will store, process, and provide those data as required; and (3) select hardware on which
that software will function properly or locate software that will run on hardware already
in place.
Vendor Selection – entails identifying potential vendors, communicating with them
clearly and thoroughly, and evaluating their proposals.
432
System Implementation – is a process of bringing the HRIS from a design state to an
operational state. It usually involves a number of overlapping processes such as
implementation planning, policy and procedure development, project team training,
installation, modification, interfaces, conversion, user training, controlled testing and
parallel testing.
System Maintenance – occurs throughout a systems’ life cycle and include any work
done on the system after delivery and operational acceptance. There are three categories
of maintenance: (1) corrective maintenance – fixing problems that prevent the system
from working the way designers and users intended it to work; (2) adaptive maintenance
– modifications to HRIS in responses to changes in technology, government regulations
or external forces such as fixes or new system releases from the vendor; and (3)
perfective maintenance – modifying the system to respond to changes and requests from
users and technicians.
433
Please describe your involvement in the project in terms of your role and your
contribution.
434
INVOLVEMENT IN THE PROJECT
Respondent System planning System Design Vendor Selection System Implementation
System Maintenance and Evaluation
Respondent 1
Respondent 2
Respondent 3
Respondent 4
Respondent 5
Respondent 6
Respondent 7
Respondent 8
Respondent 9
Respondent 10
Respondent 11
Respondent 12
Respondent 13
Respondent 14
Respondent 15
Respondent 16
Respondent 17
Respondent 18
435
Respondent System planning System Design Vendor Selection System Implementation
System Maintenance and Evaluation
Respondent 19
Respondent 20
Respondent 21
Respondent 22
Respondent 23
Respondent 24
Respondent 25
Respondent 26
Respondent 27
Respondent 28
Respondent 29
Respondent 30
Respondent 31
Respondent 32
Respondent 33
Respondent 34
Respondent 35
Respondent 36
Respondent 37
Respondent 38
436
Respondent System planning System Design Vendor Selection System Implementation
System Maintenance and Evaluation
Respondent 39
Respondent 40
Respondent 41
Respondent 42
Respondent 43
Respondent 44
Respondent 45
How long it takes you to finish this task? _______________
437
OPEN-ENDED QUESTIONS
Instruction: Please sort the answer as best as you can into five categories of heading. You can use
the ‘other’ category if you think the answer does not fit with the five categories
mentioned. You are allowed to go back and revise your answer as many times as you
like until you are satisfied with your answer. Please indicate at the end of the answer
sheet, the amount of time you spend to finish this task.
To guide you with the sorting process, below are definitions for each category:
Content – is referring to the information or report generated by the system, whether the
information or reports are precise, sufficient and exactly as one needed.
Accuracy – is referring to the correctness of the output information.
Format – is referring to the way the output is being presented, whether it is clear and in
a useful format.
Ease of use – is referring to the easiness and user-friendliness of using the application.
Timeliness – is referring to how fast you can get the information you need and whether
the system provides up-to-date information.
438
What aspects of the system, if any, are you MOST satisfied with?
439
What aspects of the system, if any, are you LEAST satisfied with?
440
MOST SATISFIED
Respondent Content Format Accuracy Ease of use Timeliness Others
Respondent 1
Respondent 2
Respondent 3
Respondent 4
Respondent 5
Respondent 6
Respondent 7
Respondent 8
Respondent 9
Respondent 10
Respondent 11
Respondent 12
Respondent 13
Respondent 14
Respondent 15
Respondent 16
Respondent 17
Respondent 18
Respondent 19
441
Respondent Content Format Accuracy Ease of use Timeliness Others
Respondent 20
Respondent 21
Respondent 22
Respondent 23
Respondent 24
Respondent 25
Respondent 26
Respondent 27
Respondent 28
Respondent 29
Respondent 30
Respondent 31
Respondent 32
Respondent 33
Respondent 34
Respondent 35
Respondent 36
How long it takes you to finish this task? ______________
442
LEAST SATISFIED
Respondent Content Format Accuracy Ease of use Timeliness Others
Respondent 1
Respondent 2
Respondent 3
Respondent 4
Respondent 5
Respondent 6
Respondent 7
Respondent 8
Respondent 9
Respondent 10
Respondent 11
Respondent 12
Respondent 13
Respondent 14
Respondent 15
Respondent 16
Respondent 17
Respondent 18
Respondent 19
443
Respondent Content Format Accuracy Ease of use Timeliness Others
Respondent 20
Respondent 21
Respondent 22
Respondent 23
Respondent 24
Respondent 25
Respondent 26
Respondent 27
Respondent 28
Respondent 29
Respondent 30
Respondent 31
Respondent 32
Respondent 33
Respondent 34
Respondent 35
Respondent 36
How long it takes you to finish this task? ____________
444
APPENDIX I Demographic characteristics of the participants from forty-five organizations –
Study 2 Descriptions Frequency % Mean Std. Dev Median Min. Max.
Gender Male 17 37.8 Female 28 62.2 Total 45 100 Age Total Response 42 93.3 37.14 6.2 37.0 27.0 49.0 Ethnicity Malay 36 80.0 Chinese 5 11.1 Indians 4 8.9 Total 45 100 Academic Qualification Secondary education 2 4.4 Certificate 1 2.2 Diploma 15 33.3 First/Professional Degree 15 33.3 Second degree and above 12 26.7 Total 45 100 Job Designation Senior Manager 3 6.7 Manager 18 40.0 Assistant Manager 4 8.9 Senior Executive 2 4.4 Executive 9 20.0 Administrative staff / officer 3 6.7 Assist. Admin. staff/ officer 2 4.4 Others 4 8.9 Total 45 100 No. of Mnths in present Position Total Response 45 100 59.4 62.3 36.0 1 240 No. of Mnths in Current Org Total Response 45 100 89.8 76.1 72.0 1 336 No of Yrs as system user Total Response 45 100 10.7 5.0 10.0 1 24 No. of Mnths in HRIS Project Total Response 45 100 72.8 50.8 60.0 0 204 No. of Employees in Org Total Response 45 100 2160.8 4816.2 750.0 30 25000 No. of Employee in HR Dept Total Response 45 100 21.2 27.2 12.0 1 150 No. of Yrs of HRIS usage in Org Total Response 44 97.8 10.07 7.2 8.5 1 30 HRIS presently Implemented Payroll 42 93.3 Compensation and Benefits 27 60.0 Performance Appraisal 18 40.0 Employee Relations 9 20.0 Training and Career Development 24 53.3 Human Resource Planning 12 26.7 Recruitment and Selection 15 33.3 Time Management 11 24.4 Medical 3 6.7 Leave Administration 7 15.6 Employee Database 8 17.8 SOP 1 2.2 Documentation 1 2.2 Organization Management 1 2.2 Employee Self Service (ESS) 2 4.4
445
APPENDIX J
DESCRIPTIVE STATISTICS OF PROJECTS IN THE SURVEY – STUDY 2
Descriptions Frequency % Mean Std. Dev
Median Min. Max.
Nature of the project Installing new system 30 66.7 Upgrading the existing system 15 33.3 Total 45 100 Project status Completed 36 80.0 At the planning stage 9 20.0 Total 45 100 Project success Successful 32 88.9 Not successful 4 11.1 Total 36 100 Actual project completion time On time 29 64.4 Late 7 15.6 Total 36 100 Predicted completion time Early 1 11.1 On time 4 44.4 Late 4 44.4 Total 9 100 Actual project duration (in mths) Total response 36 100 10.5 9.4 9.50 1 48 Predicted project duration(in mths) Total response 9 100 21.7 14.4 24.0 3 42 Idea to develop the system Internally 41 91.1 Top Management 17 37.8 IS Department 8 17.8 HR Department 18 40.0 Account Department 2 4.4 Operation Side 1 2.2 Finance Department 2 4.4 Externally 4 8.9 Suppliers 2 4.4 Observation of Competitors 1 2.2 State Secretary 1 2.2 Development of the System Develop Internally 7 15.6 Acquired from Vendor 21 46.7 Both of these 17 37.8 Total 45 100 Planning method used Inside view 8 17.8 Outside view 20 44.4 Unpacking 17 37.8 Total 45 100 Completed project outcome Falls short of expectation 5 13.9 Meets the expectation 30 83.3 Exceeds expectation 1 2.8 Total 36 100
446
APPENDIX K
MEAN PREDICTION BIAS FOR PROJECT COMPLETION TIME – STUDY 2
This appendix contains mean prediction bias for completed projects and projects at the
planning stage for Study 2.
.
Appendix K-1
447
Mean prediction bias for completed projects
Completed Projects Projects duration (in months)
Total months exceeded % exceeded over entire project duration
7 36 6 16.67 16 12 3 25 19 24 12 50 21 12 3 25 27 11 2 18.18 33 19 12 63.16 34 11 6 54.55
Average 17.86 6.28 36.08
Appendix K-2
448
Mean prediction bias for projects at the planning stage Project at the Planning Stage
Estimated projects duration as planned (in months)
Total months predicted to exceed
% predicted to exceed over entire estimated project duration
1 25 12 48.00 2 3 2 66.67 6 12 3 25.00 7 36 3 8.33
Average 19.00 5.00 37.00
