The Pennsylvania State University
The Graduate School
College of Information Sciences and Technology
Humanitarian Information Management Network
Effectiveness:
An Analysis at the Organizational and Network Levels
A Dissertation in
Information Sciences and Technology
by
Louis-Marie Ngamassi Tchouakeu
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Doctor of Philosophy
August 2011
ii
The dissertation of Louis-Marie Ngamassi Tchouakeu was reviewed and approved* by
the following:
Carleen Maitland
Associate Professor of Information Sciences and Technology
Dissertation Advisor
Chair of Committee
Andrea Tapia
Associate Professor of Information Sciences and Technology
Lynette Kvasny
Associate Professor of Information Sciences and Technology
Wenpin Tsai
Professor of Business Administration
Mary Beth Rosson
Professor of Information Sciences and Technology
Graduate Director for Information Sciences and Technology
*Signatures are on file in the Graduate School
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ABSTRACT
Massive international response to humanitarian crises such as the South Asian Tsunami
in 2004, the Hurricane Katrina in 2005 and the Haiti earthquake in 2010 highlights the
importance of humanitarian inter-organizational collaboration networks, especially in
information management and exchange. Despite more than a decade old call for more
research on the effectiveness of inter-organizational networks in the nonprofit context, to
date limited work has been done. The objective of this dissertation is to develop a theory
that provides a better understanding of organizational and network effectiveness in the
humanitarian relief field. The study deals with two broad research questions. The first
research question focuses on the relationship between network structural characteristics
and network effectiveness. The second research question concerns organizational
effectiveness and focuses on the relationship between organizational internal
characteristics (and especially the availability of information technology), ego-network
characteristics, network structural characteristics and effectiveness. To answer these
research questions, I used a multi-method research design that applies social network
analytic techniques in combination with statistical analyses (correlation and regression)
and content analysis to analyze data collected through multiple sources including a web-
based survey, semi-structured interviews, and database search. At the network level of
analysis, my findings extend a previous model for assessing network effectiveness in the
humanitarian relief field. At the organizational level of analysis, my research proposes
an integrated approach for assessing effectiveness that takes into account the
characteristics of organization but also those of the network in which the organization is
embedded. My study also highlights the catalytic role of information technology on
organizational effectiveness in humanitarian information management and
exchange. The dissertation concludes by highlighting both theoretical and practical
contributions and by suggesting directions for future research.
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TABLE OF CONTENTS
LIST OF FIGURES .............................................................................................................VII
LIST OF TABLES ............................................................................................................... IX
ACKNOWLEDGEMENTS .................................................................................................... X
1 INTRODUCTION ........................................................................................................ 1
1.1 PROBLEM DEFINITION .......................................................................................................... 2 1.2 PREVIOUS STUDIES .............................................................................................................. 3 1.3 MOTIVATION OF THE STUDY .................................................................................................. 6 1.4 RESEARCH OBJECTIVES ......................................................................................................... 7 1.5 RESEARCH DESIGN ............................................................................................................ 10 1.6 KEY FINDINGS .................................................................................................................. 12 1.7 ORGANIZATION OF DISSERTATION ........................................................................................ 13
2 CONTEXT OF THE STUDY ......................................................................................... 14
2.1 INTRODUCTION ................................................................................................................ 14 2.2 HUMANITARIAN RELIEF ...................................................................................................... 14 2.3 HUMANITARIAN INTER-ORGANIZATIONAL COORDINATION ......................................................... 15 2.4 HUMANITARIAN INFORMATION MANAGEMENT AND EXCHANGE ................................................. 18 2.5 HUMANITARIAN COLLABORATION AND COORDINATION CHALLENGES ........................................... 20
3 REVIEW OF RELEVANT LITERATURE ......................................................................... 25
3.1 INTRODUCTION ................................................................................................................ 25 3.2 ORGANIZATIONAL EFFECTIVENESS ......................................................................................... 25
3.2.1 DEFINING ORGANIZATIONAL EFFECTIVENESS .................................................................. 25 3.2.2 MODELS OF ORGANIZATIONAL EFFECTIVENESS ............................................................... 26
3.2.2.1 GOAL MODEL .......................................................................................... 26 3.2.2.2 SYSTEM RESOURCE MODEL ........................................................................ 27 3.2.2.3 INTERNAL PROCESSING MODEL ................................................................... 28 3.2.2.4 MULTIPLE CONSTITUENCIES MODEL ............................................................. 29
3.3 INTER-ORGANIZATIONAL NETWORK EFFECTIVENESS .................................................................. 31 3.3.1 DEFINING NETWORK EFFECTIVENESS ............................................................................ 31 3.3.2 MODEL OF NETWORK EFFECTIVENESS .......................................................................... 33
3.3.2.1 PERFORMANCE GAP MODEL ....................................................................... 33 3.3.2.2 PROVAN & MILWARD MODEL .............................................................................. 33
3.3.2.3 PRINCIPLES AGENTS MODEL ................................................................................. 34
3.3.2.4 STUCTURALIST PERSPECTIVE MODEL ..................................................................... 35
3.3.2.5 ADAPTIVE CAPACITY MODEL................................................................................. 36
3.3.3 PREDICTORS OF NETWORK EFFECTIVENESS IN NONPROFIT ....................................................... 37
3.4 ISSUES IDENTIFIED IN THE LITERATURE ON EFFECTIVENESS .......................................................... 40
4 THEORETICAL FRAMEWORK ................................................................................... 46
4.1 INTRODUCTION ................................................................................................................ 46 4.2 SOCIAL NETWORK THEORIES ............................................................................................... 46 4.3 RESOURCE BASED VIEW ..................................................................................................... 49
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4.4 WORKING DEFINITIONS...................................................................................................... 50 4.4.1 EFFECTIVENESS ....................................................................................................................... 50
4.4.2 NETWORK .............................................................................................................................. 50
4.4.3 LEVEL OF ANALYSIS OF NETWORK EFFECTIVENESS ................................................................... 52
4.4.3.1 NETWORK LEVEL .................................................................................................. 52
4.4.3.2 ORGANIZATIONAL LEVEL ....................................................................................... 52
4.4.3.3 BENEFICIARY LEVEL ............................................................................................... 53
4.5 RESEARCH MODELS AND HYPOTHESES .................................................................................. 54 4.5.1 NETWORK CHARACTERISTICS AND EFFECTIVENESS ................................................................... 54
4.5.1.1 CENTRALITY .......................................................................................................... 54
4.5.1.2 STRUCTURAL HOLES ............................................................................................. 55
4.5.1.3 DENSITY ............................................................................................................... 56
4.5.1.4 CLIQUES ............................................................................................................... 57
4.5.1.5 OVERLAPPING CLIQUE .......................................................................................... 57
4.5.1.6 MULTIPLEXITY ...................................................................................................... 58
4.5.2 ORGANIZATIONAL CHARACTERISTICS AND EFFECTIVENESS ........................................................ 59
4.5.2.1 ORGANIZATION SIZE ............................................................................................. 59
4.5.2.2 RANGE OF SERVICES PROVIDED ............................................................................. 60
4.5.2.3 INFORMATION TECHNOLOGY ................................................................................ 61
5 METHODOLOGY ..................................................................................................... 65
5.1 INTRODUCTION ................................................................................................................ 65 5.2 RESEARCH DESIGN ............................................................................................................ 65 5.3 RESEARCH PARTICIPANTS .................................................................................................... 67 5.4 DATA COLLECTION INSTRUMENTS ........................................................................................ 71
5.4.1 SURVEY .................................................................................................................................. 71
5.4.2 INTERVIEWS ........................................................................................................................... 73
5.4.3 DATABASE SEARCH ................................................................................................................. 74
5.5 DATA COLLECTION ............................................................................................................ 75 5.5.1 SURVEY DATA ........................................................................................................................ 75
5.5.2 INTERVIEW DATA ................................................................................................................... 77
5.5.3 DATABASE DATA ................................................................................................................... 78
5.6 DATA ANALYSIS TECHNIQUES .............................................................................................. 79 5.6.1 SOCIAL NETWORK TECHNIQUES .............................................................................................. 79
5.6.2 CONTENT ANALYSIS ................................................................................................................ 84
5.6.3 STATISTICAL ANALYSIS ............................................................................................................ 87
5.7 METHODOLOGICAL ISSUES .................................................................................................. 87 5.7.1 SOCIAL NETWORK ANALYSIS ISSUES ........................................................................................ 88
5.7.2 CONTENT ANALYSIS ISSUES ..................................................................................................... 89
5.8 SUMMARY ....................................................................................................................... 89
6 ANALYSIS ............................................................................................................... 90
6.1 INTRODUCTION ................................................................................................................ 90 6.2 QUALITATIVE DATA ANALYSIS .............................................................................................. 90
6.2.1 DEDUCTIVE CODES ................................................................................................................. 90
6.2.1.1 NETWORK BENEFIT ............................................................................................... 90
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6.2.1.2 NETWORK EFFECTIVENESS ..................................................................................... 94
6.2.1.3 COLLABORATION FACTORS .................................................................................. 100
6.2.1.4 COLLABORATION BARRIERS ................................................................................. 109
6.2.1.5 MEASURES OF EFFECTIVENESS ............................................................................. 116
6.2.2 INDUCTIVE CODES ................................................................................................................. 118
6.2.2.1 FROM ADVICE TO PROJECT COLLABORATION ........................................................ 118
6.2.2.2 NETWORK SCOPE ............................................................................................... 119
6.2.2.3 NETWORK AUDIENCE ......................................................................................... 120
6.3 EFFECTIVENESS MEASURES ...............................................................................................121 6.3.1 PERCEIVED NETWORK EFFECTIVENESS ................................................................................... 121
6.3.2 LEVEL OF ACTIVITIES AND LEVEL OF COLLABORATION............................................................. 123
6.4 NETWORK STRUCTURAL CHARACTERISTICS AND EFFECTIVENESS ................................................127 6.4.1 DENSITY ............................................................................................................................... 127
6.4.2 CLIQUE ................................................................................................................................ 128
6.4.3 CLIQUE OVERLAP ................................................................................................................. 131
6.4.4 MULTIPLEXITY ...................................................................................................................... 132
6.4.5 DISCUSSION ......................................................................................................................... 136
6.5 EGO-NET CHARACTERISTICS AND EFFECTIVENESS ...................................................................140 6.5.1 MODELS BUILDING .............................................................................................................. 141
6.5.1.1 EFFECTIVENESS MEASURED AS LEVEL OF ACTIVITIES ............................................. 142
6.5.1.2 EFFECTIVENESS MEASURED AS LEVEL OF COLLABORATION .................................... 146
6.5.2 HYPOTHESES TESTING .......................................................................................................... 152
6.5.2.1 MAIN EFFECTS .................................................................................................. 152
6.5.2.2 INFORMATION TECHNOLOGY INTERACTION EFFECTS ............................................ 156
6.5.3 DISCUSSION ......................................................................................................................... 157
7 CONCLUSIONS AND DIRECTIONS FOR FUTURE RESEARCH ...................................... 167
7.1 INTRODUCTION ..............................................................................................................167 7.2 SUMMARY OF THE LITERATURE ..........................................................................................167 7.3 KEY FINDINGS .................................................................................................................168 7.4 CONTRIBUTIONS .............................................................................................................174 7.5 LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH ...........................................................179
8 REFERENCES ......................................................................................................... 183
APPENDIX .................................................................................................................... 199
APPENDIX A: INFORM CONSENT FORM FOR SOCIAL SCIENCE RESEARCH .............................................199 APPENDIX B: LETTER-EMAIL SENT TO POTENTIAL SURVEY PARTICIPANTS .............................................201 APPENDIX C: SURVEY QUESTIONNAIRE ........................................................................................202 APPENDIX D: INTERVIEW GUIDE .................................................................................................226
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LIST OF FIGURES
Figure 1: Level of NGOs coordination .................................................................................... 17
Figure 2. A Preliminary model of network effectiveness ...................................................... 34
Figure 3. Relationships between network effectiveness at different levels of network
analysis and influence by key stakeholders ..................................................................... 35
Figure 4: Research Model for Network Level of Analysis ..................................................... 64
Figure 5: Research Model for Organizatioanl Level of Analysis ............................................ 64
Figure 6: Global Symposium Project Collaboration Sub-Networks ....................................... 70
Figure 7: Global Symposium Advice Sub-Networks .............................................................. 70
Figure 8: United Nations Agencies Network Structure ........................................................... 77
Figure 9: Non-Governmental Organizations Network Structure............................................. 77
Figure 10: Governmental Organizations Network Structure .................................................. 77
Figure 11: Qualitative data analysis coding process (Seidel, 1998) ........................................ 84
Figure 12: Network Benefit Code’s Coverage ........................................................................ 93
Figure 13: Aggregated Benefit Cross Network ....................................................................... 93
Figure 14: Network Effectiveness Code’s Coverage .............................................................. 98
Figure 15: Network Effectiveness Code’s Loudness .............................................................. 99
Figure 16: Network Effectiveness Code’s Loudness Cross Network ..................................... 100
Figure 17. Factor’s Coverage .................................................................................................. 101
Figure 18. Factor’s Loudness ................................................................................................. 103
Figure 19: Loudness of Collaboration Factors Grouped per Category .................................... 106
Figure 20: Loudness of Collaboration Factors Cross Network ............................................... 107
Figure 21: Break Down of Structural Barriers ........................................................................ 113
Figure 22: Loudness of Barriers to Collaboration Grouped per Category .............................. 115
Figure 23: Loudness of Barriers to Collaboration Cross Network .......................................... 116
Figure 24: United Nations Agencies Clique Structure ............................................................ 134
Figure 25: Non-Governmental Organizations Clique Structure .............................................. 134
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Figure 26: Governmental Organizations Clique Structure ..................................................... 135
Figure 27:Residual Plot for Effectiveness Mesuared as the Level of Activities (Model
IVa) .................................................................................................................................. 144
Figure 28: Normal Plot for Effectiveness Mesuared as the Level of Activities (Model
IVa) .................................................................................................................................. 144
Figure 29: Residual Plot for Effectiveness Mesuared as the Level of Activities (Model
IVb) .................................................................................................................................. 145
Figure 30: Normal Plot for Effectiveness Mesuared as the Level of Activities (Model
IVb) .................................................................................................................................. 145
Figure 31: Residual Plot for Effectiveness Mesuared as the Level of Collaboration
(Model IVa) ..................................................................................................................... 148
Figure 32: Normal Plot for Effectiveness Mesuared as the Level of Collaboration (Model
IVa) .................................................................................................................................. 148
Figure 33: Residual Plot for Effectiveness Mesuared as the Level of Collaboration
(Model IVb) ..................................................................................................................... 149
Figure 34: Normal Plot for Effectiveness Mesuared as the Level of Collaboration (Model
IVb) .................................................................................................................................. 149
Figure 35: Effectiveness Models’ Explanatory Power ............................................................ 151
Figure 36: Variations in the Effectiveness Measures (Model VIa) ......................................... 159
Figure 37: Variations in the Effectiveness Measures (Model VIb) ......................................... 159
Figure 38: Inter-action effect of Technology and Degree Centrality on Effectiveness as
Measured by the Level of Activities ................................................................................ 166
Figure 39: Inter-action effect of Technology and Degree Centrality on Effectiveness as
Measured by the Level of Collaboration ......................................................................... 166
Figure 40: Inter-action effect of Technology and Network Density on Effectiveness as
Measured by the Level of Activities ................................................................................ 166
Figure 41: Inter-action effect of Technology and Network Density on Effectiveness as
Measured by the Level of Collaboration ......................................................................... 166
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LIST OF TABLES
Table 1: Principles of Humanitarian Information Management and Exchange. ..................... 20
Table 2: Summary of Inter-Organizational Coordination/Collaboration Challenges .............. 24
Table 3: Models of Organizational Effectiveness ................................................................... 30
Table 4: Inter-organizational Network Effectiveness in the Nonprofit Sector ........................ 40
Table 5: Summary of Hypotheses ........................................................................................... 64
Table 6: Surveys’ Participation .............................................................................................. 75
Table 7: Perceived Network Effectiveness Index Table ......................................................... 122
Table 8: Choosing Effectiveness Measures: Illustrative Quotes from the Interview .............. 125
Table 9: Network Effectiveness (Objective measures) ........................................................... 126
Table 10: Network Effectiveness Ranking .............................................................................. 127
Table 11: Network Density ..................................................................................................... 128
Table 12: Cliques Characteristics Project Network ................................................................. 129
Table 13: Cliques Characteristics Advice Network ................................................................ 129
Table 14: Clique Characteristics: Minimum Set Size of Five ................................................. 130
Table 15: Clique Overlap ....................................................................................................... 131
Table 16: Multidimensional Clique Overlap ........................................................................... 133
Table 17: Summary of Hypotheses Testing at Network Level of Analysis ............................ 135
Table 18: Organizational Effectiveness Variables .................................................................. 141
Table 19: Descriptive Statistics and Correlations ................................................................... 142
Table 20: Regression Analysis on Effectiveness Measured as the Level of Activities ........... 146
Table 21: Regression Analysis on Effectiveness Measured as the Level of Collaboration .... 150
Table 22: Summary of Hypotheses Testing at Organizational Level of Analysis................... 157
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ACKNOWLEDGEMENTS
I owe a debt of gratitude to the members of my dissertation committee, colleagues, friends, and
family for their guidance, support and encouragement throughout my PhD journey. My
dissertation committee members have been crucial to my training. I would like to express my
deepest appreciation to my adviser and committee chair, Dr. Carleen Maitland, for supporting this
work over time and distance and for the countless hours of patient mentoring. It has been a great
privilege to have had the opportunity to be her mentee.
I would like to thank my committee members, Dr. Andrea Tapia, for her unwavering support
throughout my PhD journey; for believing in me and constantly reminding me that a person can
always do more than he thinks he can, and then supporting me in my efforts; Dr. Lynette Kvasny,
for being available whenever I needed her assistance; for her insights in blending qualitative and
quantitative data analysis, and Dr. Wenpin Tsai, for introducing me to the world of social network
analysis; for helping me define and organize my thoughts, rounding me up when I needed focus
and for always challenging me while fully supporting me. I feel very fortunate and blessed to
have received the attention and guidance of such a team of mentors.
I have been lucky to be surrounded by wonderful friends throughout my PhD program. I would
like to thank all my fellow graduate students who have been my companions and co-learners on
this journey for their gifts of insights and their constant support. I am also honored to have been a
member of COHORT, a NSF sponsored research project. COHORT provided the financial
support to this research. COHORT also introduced me to the UNOCHA Global Symposium
community. Surveys and interviews with representatives of organization members of this
community were crucial to my research, and I am grateful to those participants for their time.
To my close and extended family, I owe thanks beyond measure. I am grateful for their prayers
and encouragement and for tolerating me through all this time of academic self-absorption when I
was not able to be the father, son, brother, uncle or friend that I would have wanted to be.
Heartiest thanks to my children Laetitia, Frank-Eric and Hermann for being so supportive and for
helping me transcribing my interviews. Above all, I want to thank my wife and best friend
Madeleine, for her love and support; for believing in me with such intensity as to make it almost
impossible for me to not believe in myself even in the most difficult moments along this journey.
1
1 INTRODUCTION
In recent years, as the number of man-made and natural disasters has risen, so has the
range of challenges faced by humanitarian organizations (Saab et al, 2008; Ngamassi et
al, 2011). One of these challenges is the management of humanitarian information.
Effective information sharing is becoming increasingly important to the humanitarian
relief sector. Humanitarian organizations need a large variety of information, such as
population displacement, relief expertise, disaster situation, availability and movement of
relief supplies, and meteorological satellite images or maps (Zhang et al., 2002). The
need for effective humanitarian information exchange is not just for supporting
emergency response operations but more importantly for enhancing the capacity of the
international community to respond to disasters before and after they occur.
Humanitarian organizations must therefore contend with the production, retrieval,
processing, validation, consumption, and distribution of humanitarian information.
Criteria for success include the information’s relevance to decision-makers, timeliness
and accuracy.
Researchers have identified numerous humanitarian information management related
problems, including the quality and timeliness of information (e.g., De Bruijn, 2006;
Fisher & Kingma, 2001), unpredictability of required information (Longstaff, 2005),
unwillingness to share (Ngamassi et al, 2011), and mismatch in location, information
overload, misinterpretation of information (Bui et al., 2000; Saab et al., 2008). Moreover,
in addition to the challenges specific to the humanitarian context humanitarian
organizations are also challenged by what are recognized as problems facing most
organizations (see Galbraith, 1977). In an attempt to mitigate these challenges,
organizations in the nonprofit sector including the humanitarian field are increasingly
forming inter-organizational networks such as coalitions, alliances, partnerships, and
coordination bodies, both within and across the sector (Guo & Acar, 2005; Stephenson,
2005; 2006; Arya & Lin, 2007). Though an accurate census of these networks does not
exist in the literature, several studies offer some insight into their growing presence (Guo
& Acar, 2005; Feiock & Andrew 2006; Jang & Feiock, 2007; Arya & Lin, 2007).
2
1.1 Problem Definition
Massive international response to humanitarian crises such as the South Asian Tsunami
in 2004, the Hurricane Katrina in 2005 and the Haiti earthquake in 2010 highlights the
importance of humanitarian inter-organizational collaboration networks, especially in
information management and exchange. Though, in recent years, humanitarian
information management has considerably improved due to significant development in
humanitarian information management principles and systems (Van de Walle et al.,
2009), humanitarian information sharing continues to challenge the international
community (Maiers et al, 2005; Wentz, 2006; Maitland et al., 2009; Bharosa et al., 2010,
Tapia et al., 2010). In the humanitarian relief field, the number of inter-organizational
networks has significantly increased with the rise in number and complexity of
humanitarian disasters of the past few decades (Stephenson, 2005; 2006; Ngamassi et al.,
2011). The effectiveness of these networks in disaster response is still to be determined.
Despite more than a decade old call for better understanding of the effectiveness of inter-
organizational networks in the nonprofit context (see O’Toole, 1997; Provan & Milward
1995), to date limited work has been done (Provan et al., 2007).
The appeal for assessing inter-organizational network effectiveness in the nonprofit
context, and especially in the humanitarian relief field, appears to stem from several
different perspectives among which, the following four seem to be the most important.
First, apart from establishing the value of networking for a member of the network,
evaluating the entire network has become increasingly important for all the stakeholders
who share an interest in systematic efforts of network (Sydow & Milward, 2003).
Second, evaluating the effectiveness of humanitarian inter-organizational networks is
critical for understanding whether networks are effective in meeting the goals of the
network as a whole, those of the individual network members and more importantly
especially in the humanitarian relief field, the extent to which the needs of the affected
people have been met. Third, establishing the level of network effectiveness is also
important for member organizations and those whose policies and funding support the
network. Ideally, an effective inter-organizational collaboration network would enhance
the quality of service provided to its clients and optimize use of resource by reducing
3
redundancies. Finally, given the high failure rates reported by network researchers in both
the for-profit and the nonprofit sectors, organizations are often overly optimistic about
the benefits of network participation (Barringer & Harrison, 2000, p. 368). A thorough
evaluation of networks could contribute to a more realistic attitude towards inter-
organizational networking (Sydow & Milward, 2003).
Assessing the effectiveness of inter-organizational networks is however a daunting task
(Alter & Hage, 1993; Provan & Milward, 1995; Sydow & Windeler, 1998; Provan et al.,
2007). There is no consensus on the criteria of measuring effectiveness among
researchers and no clear classification of the different levels of effectiveness. Assessing
effectiveness at the network level is more complex than at the organizational level due to
the involvement of multiple heterogeneous organizations in a network (Provan &
Milward, 1995; Sydow & Windeler, 1998). Moreover, networks use multiple
organizations to produce one or more pieces of a single service, thus making their
evaluation more complex than that of a single organization. Network effectiveness issues
are also problematic because networks usually have multiple stakeholders and it may be
harder to satisfy all of them (Provan & Milward, 1995; Sydow & Windeler, 1998; Sydow
& Milward, 2003).
1.2 Previous Studies
Notwithstanding the difficulties of assessing the effectiveness of inter-organizational
networks, there have been some attempts. Researchers have done some conceptual
studies on inter-organizational network effectiveness (Alter & Hage, 1993; Provan &
Milward, 2001; Sydow & Windeler, 1998; Staber & Sydow 2002). For example Alter &
Hage (1993) propose a “performance gap” model to assess inter-organizational network
effectiveness. Performance gap is defined as the difference between the current situation
and the idealized standard. According to Alter & Hage, effectiveness is usually best
measured by the expectation of what is a reasonable outcome, given the context and the
barriers to goal achievement. They argue that effectiveness is achieved when goals are
4
met within the context of technological and resource constraints, given certain levels of
internal conflict and pressure from external constituencies.
Moscovice et al., (1995) offer a network typology, a framework for assessing network
performance, and examples of measurable performance indicators. Provan & Milward
(1995) develop a model of inter-organizational network effectiveness through a
comparative study of four community mental health networks. They investigated the
relationship between the structure and context of mental health networks and their
effectiveness. In this study, effectiveness measures were linked to “enhanced client
wellbeing”. In addition to structure and context, Provan & Milward (2001) examined
network effectiveness at different levels. These three levels are (i) community, (ii)
network, and (iii) organization / participant. The paper argues that
organization/participant and network-level effectiveness criteria can be satisfied by
focusing on community-level effectiveness goals. Weech-Maldonado et al., (2003) build
upon Provan & Milward’s (2001) network effectiveness framework and Gamm’s (1996)
accountability framework to develop a “stakeholder accountability approach” in
assessing network effectiveness. The authors use this approach to evaluate the
effectiveness of community health partnerships.
Sydow & Windeler (1998) define inter-organizational network effectiveness as viable
and acceptable outcome and practices. They argue that network effectiveness form a
structurationist perspective and, is more than embedded in social interactions and
structures. It is rather social in character. Building upon Sydow & Windeler (1998),
Staber & Sydow (2002) propose the concept of adaptive capacity as an appropriate
approach to assess organizational and inter-organizational network effectiveness in
highly volatile and complex environments such as the case in the humanitarian assistance
sector. They define adaptive capacity as the ability of organizations or networks to cope
with unknown future circumstances. Organizations / networks with adaptive capacity can
reconfigure themselves quickly in changing environments.
5
These different conceptual models for assessing network effectiveness found in the
literature often borrowed from the four models traditionally used to study organizational
effectiveness. They include (i) the Goal Model (Parson 1964; Price, 1971; Cameron &
Whetten 1981), (ii) the System Resource Model (Yuchtman & Seashore, 1967; Price,
1971), (iii) the Internal Processing Model (Alter & Hage, 1993; Lee, 2006) and (iv) the
Multiple Constituencies’ Model (D’Aunno, 1992; Zammuto, 1984; Sowa et al., 2004).
All these models for assessing organizational/network effectiveness have been criticized
in the literature for their respective shortcomings.
Previous research has also identified important antecedents of inter-organizational
network effectiveness (Provan & Milward, 1995; Moscovice, et al., 1995; Wright et al.,
1995; Provan & Sebastian, 1998; Schumaker, 2003; Lemieux-Charles et al., 2005). For
example, several authors (e.g., Provan & Milward, 1995; Moscovice, et al., 1995; Wright
et al., 1995; Provan & Sebastian, 1998) highlight the importance of integration of
network members to network effectiveness. Provan & Sebastian (1998) argued that
achieving integration across an entire network of organizations is difficult. Their findings
also suggest that to be the most effective, clique integration must be intensive, involving
multiple and overlapping relationships both with and across organizations that compose
the core of a network. Similarly, the diversity of network membership is also deemed
relevant by several authors (e.g., Moscovice, et al., 1995; Schumaker, 2003). Schumaker
(2003) for example found that effectiveness is influenced by external and internal factors
that are operationalized through external control, technology, structure, and operational
process variables. Other important effectiveness predictors include the degree of
multiplexity in the network, revenue sources, and the duration of the network.
While several predictors of network effectiveness have been identified throughout the
literature, as well as conceptual models provided, limited research has used these models
or any other approach to empirically analyze the possible antecedents of network
effectiveness, particularly for humanitarian inter-organizational networks.
6
1.3 Motivation of the Study
The motivations of this study are theoretical and practical. Theoretically, my research is
motivated by the growing literature that highlights the increasing number of inter-
organizational networks in the nonprofit sector including in the humanitarian field and
stresses the need for better understanding of the effectiveness of these networks. Despite
more than a decade old call, to date limited work has been done (Provan et al., 2007). The
few existing studies have been conducted in the public health sector. In the literature,
there is virtually no study that investigates the effectiveness of inter-organizational
network in the humanitarian relief field.
The practical motivations of my research are related to three main issues including (i) the
need for better humanitarian information, (ii) the critical role of information technology
disaster response and (iii) the growing number disaster victims. First, effective
humanitarian response depends highly on the quality and timeliness of information. The
faster humanitarian organizations are able to collect, analyze and disseminate
information, the more effective the response becomes and the more lives are potentially
saved. In humanitarian relief operations, organizations deal with information that are by
nature multi-sector, multi-dimensional, multi-source, and non-standardized. Though
humanitarian information management has improved in recent years, some constraints
(such as funding, tools and technical skills) continue to handicap information from
becoming a core component of humanitarian relief operations (Wentz, 2006). More
effective inter-organizational networks would help to mitigate these constraints.
Second, information technology (IT) has also been shown to play a critical role in
mitigating the informational related issues for inter-organizational humanitarian response
(Comfort, 1990; Graves, 2004; Comfort & Kapucu, 2006; Moss & Townsend, 2006).
According to Lee & Whang (2000), the advances in information technology have
significantly facilitated inter-organizational information sharing. High use of technology
can result in the achievement of high levels of information sharing. However, the
literature also shows that information technology has hindered inter-organizational
collaboration (e.g., Bui et al., 2000; Junglas & Ives, 2007; Miller et al., 2005; Saab et al.,
7
2008). Inter-organizational collaboration issues related to technology include technical
interoperability, semantic interoperability, non-matching data formats, different
presentation forms, and heterogeneous systems. More research is needed to further
explore the solutions to these problems.
Third and lastly, the numbers of humanitarian natural disasters and the people affected by
these disasters have increased over recent years. According to the IFRC, (2005) the
average annual number of humanitarian disasters during 2000-2004 was 55% higher than
during 1995-1999. The number of people affected by humanitarian disasters has
continued to grow (ISDR, 2006). This growing trend in the number and impact of
humanitarian disasters and the high scale of international response efforts have brought
growing attention to the need for effective and efficient humanitarian disaster response
operations. More effective inter-organizational networks would help to meet this need.
1.4 Research Objectives
The objective of my research is to develop a theory that provides insight into inter-
organizational information management and exchange relationships in the field of
humanitarian relief. To this end, I combine two theoretical lenses including Social
Network and Resource Based View to assess effectiveness at two levels, organizational
and network levels. Network structural characteristics (density, centrality, clique and
clique overlap) have been found to have implications on performance/effectiveness
(Ahuja & Carley, 1999; Tsai & Ghoshal, 1998; Tsai, 2000; Nohria & Garcia-Pont, 1991;
Wasserman & Faust, 1994; Kilduff & Tsai, 2006; Provan et al., 2007). Similarly, the
embeddedness of organizations in networks of external relationships with other
organizations holds significant implications for organization performance / effectiveness
(Granovetter, 1985; Uzzi, 1996; 1997; 1999; Gulati et al., 2000). Resource Base View
(RBV) explains performance/effectiveness exclusively through internal resources
(Barney, 1991; Prahalad & Hamel, 1990; Barnett et al., 1994). As mentioned earlier,
information technology (IT) has also been shown to play a critical role in mitigating the
8
informational related issues for inter-organizational humanitarian response (Comfort,
1990; Graves, 2004; Comfort & Kapucu, 2006; Moss & Townsend, 2006).
I studied multidimensional networks of collaborative relationships among humanitarian
organizations that are members of a community of interest in humanitarian information
management. Communities of interest, as defined by Arias & Fischer (2000), are groups
from different backgrounds coming together to solve a particular problem of common
concern. According to Arias & Fischer (2000), members of communities of interest need
to learn to communicate with and learn from others who have a different perspective and
perhaps a different vocabulary for describing their ideas and establish a common ground
and a shared understanding. The goal of the community of interest studied in this
research is to foster collaboration on humanitarian information management projects and
to disseminate best practices of information exchange. This community also aims to (i)
sensitize its members on the critical aspects of humanitarian information management
preparedness, (ii) facilitate headquarter-field partnerships and (iii) advocate for more
funding from donors for humanitarian information management related projects. My
study focuses on members which actively participate in the activities in the community
and have developed collaborative relationships with other members within the
community. Also, the multidimensional networks that I investigated are not directly
involved in disaster assistance. They lay groundwork at the headquarter level, for
collaborative humanitarian disaster response.
Multidimensional networks refer to networks examined at more than one level, with more
than one set of nodes and more than one type of link (Lee, 2008). Understanding
networks in the field of humanitarian relief can be enhanced by considering the content of
relationships that exist among organizations. Katz & Anheier (2005) identify the major
types of relationships among stakeholders (e.g. nongovernmental organizations,
international nongovernmental organizations, and international governmental
organizations) in responding to humanitarian disasters. They include information
exchange, project collaboration, participation in meetings and forums, or joint
9
membership in advocacy coalitions. My study is concerned with two types of
collaborative relationships, namely projects and advice.
In the field of humanitarian relief, inter-organizational networks can be classified into
two types: those oriented to project implementation and those oriented to information-
sharing (Lee, 2008). The purpose of the implementation network is to implement
humanitarian relief projects. Implementation networks are activity-focused, project-
based networks which rely on partnerships to draw on resources such as funding, and
skills from various partners (Unwin, 2005). This type of network applies more to the field
of humanitarian relief since projects are more often implemented by numerous project
partners. Knowledge-sharing networks on the other hand are often formed through
affiliation to common events, such as global and regional committees, forums,
conferences, and publication activities (Katz & Anheier, 2005). These networks enable
organizations to be informed of their partner’s and community’s activities as a whole.
The networks investigated in this dissertation can be considered as a hybrid between
these two types of networks (implementation network and knowledge-sharing networks).
Humanitarian relief activities often involve nonprofit and for-profit organizations. There
is a vast body of literature that compares nonprofit and for-profit organizations from
different perspectives (for example, see Vladeck, 1988; Moore, 2000) including (i)
revenue sources, (ii) goals, and (iii) stakeholders. With regards to revenue sources, for-
profit organizations draw their revenue mainly from customers who pay for goods and
services while nonprofit organizations get their revenues from people and organizations
that expect no economic benefits in return (Moore, 2000). Concerning the goals, for-
profit and nonprofit organizations differ in that while for-profit organizations seek to
make profits and provide financial returns to their shareholders (Boland & Fowler, 2000)
nonprofit organizations try to achieve their social purpose and mission (Moore, 2000).
For-profit and nonprofit organizations are also distinguishable in terms of their
stakeholder characteristics. For-profit organizations have a privileged interest group that
is clearly defined and homogenous with respect to interests (Speckbacher, 2003). This
10
privileged group owns the business. On the other hand, nonprofits serve a multitude of
constituencies whose goals and needs may be heterogeneous (Speckbacher, 2003).
Most of the limited studies on nonprofit inter-organizational network effectiveness have
examined networks in which an administrative organization serves as a governing
authority (Provan & Milward, 2001). Most of these previous studies also focused on
networks of collocated organizations and explored one type of collaborative relationship.
Moreover almost all of these studies are in the domain of public health service delivery.
Studies on inter-organizational network effectiveness in the domain of humanitarian
relief are virtually nonexistent. Recent research in this domain provides a new context for
the study of inter-organizational effectiveness (Maitland & Tapia, 2007a; 2007b; 2008;
Maitland et al., 2008; 2009). These networks are formed and maintained with support
from foundations and multilateral donors (e.g. Gates Foundation, European Commission
Directorate General for Humanitarian Aid (ECHO)), including funding for meetings,
administration, report generation, and research to define the barriers to coordination.
Despite the recognized need for and support of such entities by the humanitarian relief
community there is little systematic analysis of their effectiveness, in other words the
extent to which they meet the goals the network and the donors set out to achieve. My
study is conducted in this new context.
1.5 Research Design
In this study, I use a mixed methods research design (Tashakkori & Teddlie, 2003) to
explore effectiveness of multidimensional inter-organizational networks of collaborative
relationships among humanitarian organizations, members of the Global Symposium. I
investigate how organizational characteristics and network structure properties influence
effectiveness. I explore effectiveness at two levels of analysis, organizational and
network. At the network level, I conduct a clique analysis using Provan & Sebastian’s
(1998) framework for evaluating public-sector organizational networks, to determine the
extent to which this framework explains network effectiveness in the humanitarian relief
context. At the organizational level, I use multiple regression analysis method. I combine
11
two theoretical lenses including Social Network and Resource Based View to discuss my
findings and develop my theory. Network effectiveness was assessed using three
different criteria including one subjective criteria – perceived network effectiveness and
two objectives criteria – number of organization funded projects measuring the level of
activities and number of organization funding partners measuring the level of
collaboration.
I collected data through multiple sources including surveys, interviews and online
database search. A survey instrument that also contains network-related questions was
my main data collection source. I conducted a series of three surveys during October
2007, May 2008 and July 2009. I conducted during the period of September to December
2009, 19 personal phone based semi-structured interviews with representative of
organizations members of the Global Symposium. My intent was to supplement the
quantitative survey data with a more detailed description and explanation of activities in
the Global Symposium community. My third and last data source was the ReleifWeb
Financial Tracking Service (FTS). FTS is an online database which records all reported
international humanitarian financial assistance (Office for the Coordination of
Humanitarian Affairs (OCHA), 2010). I collected data related to the amount of funding
raised, the number of funded projects and the number of funding partners of
organizations member of the Global Symposium community. I used the UCINET
software (Borgatti et al., 1999) to analyze network data.
My research questions are formulated as follows:
RQ#1: To what extent do network structural characteristics explain effectiveness
in humanitarian inter-organizational collaboration networks?
i. How accordingly does Provan & Sebastian model of network
effectiveness explain network effectiveness in the international context of
inter-organizational collaboration in the humanitarian field?
RQ#2: How accurately does a linear combination of organizational internal
attributes and network structural properties explain effectiveness at organizational
level in humanitarian inter-organizational collaboration networks?
12
i. To what extent do resources internal to organizations and especially
information technology explain effectiveness?
ii. To what extent do ego-net properties explain network effectiveness?
iii. To what extent do network level structural characteristics (e.g. density)
explain effectiveness?
iv. To what extent does the interaction of information technology and
network structural characteristics impact organizational effectiveness?
1.6 Key Findings
1.6.1 Network Level
Consistent with those of Provan & Sebastian (1998) my findings suggest that at the
network level of analysis, an inter-organizational network in the field of humanitarian
relief is more effective when it is more integrated at the subnet level (clique) and
displaying higher level of multiplexity. My study however makes one significant
additions to Provan & Sebastian model. Unlike Provan & Sebastian, in my study, I used
three different measures of network effectiveness (one subjective and two objectives).
Using these effectiveness measures allowed me to find consistent ranking pattern for each
of the six network structural characteristics I used. It is important here to note that Provan
& Sebastian’s study which is the foundation of my study, matched two out of the six
network structural characteristics. Moreover, my findings suggest that the subjective and
objective forms of network effectiveness are better explained by different network
structural attributes. Whereas subjective network effectiveness is better explained by the
number of cliques and clique membership, objective network effectiveness is better
explained by the multifaceted nature of inter-organizational relationships as measured by
clique overlap and multiplexity. These findings highlight the importance of multiple
criteria for assessing network effectiveness. In a nutshell, my research extends Provan &
Sebastian’s model.
1.6.2 Organizational Level
At the organizational level, I found that effectiveness can be accurately explained by a
linear combination of organizational internal attributes and network structural properties.
13
Regarding the resources internal to organizations, my findings suggest that information
technology was an important determinant of effectiveness. My research also highlighted
the importance of ego-net level attributes such as degree centrality and bridging structural
holes in collaborative networks of organizations in the humanitarian relief sector.
Moreover, my study also revealed that network level attributes had some implications on
organizational effectiveness. My findings suggested that the density score in
humanitarian inter-organizational networks may be detrimental for explaining
effectiveness. Overall my results were for the most part consistent with those of the two
similar studies (Zaheer & Bell, 2005; Arya & Lin, 2007). However, none of those two
previous similar studies had explored the network level attributes. My study therefore
extends the Resource Based View theory by adding network level attributes as predictor
of organizational effectiveness.
1.7 Organization of Dissertation
My dissertation begins with a background chapter which provides some information on
the humanitarian relief context in which the study is conducted. I then continue with an
overview of relevant literature on organizational and network effectiveness. This
literature review is followed by a theoretical framework chapter in which I develop my
research models and hypotheses. The theoretical framework chapter is followed by the
methods chapter which (i) provides detailed explanation on my research design; (ii)
describes the methods of data collection which consists of preliminary a series of three
surveys, semi-structured interviews, and database search; (iii) depicts the analyses that
were done on the data which includes a description of the variables used in the analyses
conducted; and (iv) provides an overview of the limitations of this research. The methods
chapter is followed by a findings chapter. This chapter begins with a discussion on my
three criteria for measuring effectiveness. The chapter then provides an analysis of
network effectiveness at two levels, organizational and network levels. Finally, a
concluding chapter is presented. This chapter discusses the implications from the
findings. Limitations of the study and future research directions are suggested.
14
2 CONTEXT OF THE STUDY
2.1 Introduction
This chapter provides some background information on the humanitarian relief context in
which the study is conducted. It focuses especially on humanitarian information
management and exchange related issues.
2.2 Humanitarian Relief
The term “humanitarian” has a wide range of different interpretations. This term is
however generally associated with actions and operations that seek to alleviate human
suffering the face of crises as diverse as armed conflicts, epidemics, famine and natural
disasters. These crises often occur in fragile environments characterized by low incomes,
sparse infrastructure and in some cases low levels of information technology skills.
Humanitarian relief efforts are complex responses to emergent situations where the facts
and challenges on the ground can change rapidly.
The international community has been increasingly putting more efforts into disaster
mitigation and humanitarian assistance (Zhang et al., 2002, UNOCHA, 2002; 2007a,
2007b). For example, a joint multi-agency exercise, combining civil, military as well as
the United Nations organizations, was carried out in the Asia/Pacific region in 2000. This
exercise aimed at establishing a forum to exchange relevant information between
humanitarian organizations and the military, delivering a coordinated response to a
population in crisis, and documenting the implementation and output of combined
activities (Zhang et al., 2002). Another example of the international community effort to
better response to disaster relief is the launch in 2002, of the Global Symposium by the
United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA, 2002;
2007a; 2007b). The goal of the Global Symposium is to foster collaboration on
humanitarian information management projects and to disseminate best practices of
information exchange.
15
The Federal Emergency Management Agency (http://www.fema.gov) defines three
phases of the disaster relief process. They include the pre-crisis phase, the crisis phase
and the post crisis phase. The pre-crisis phase is concerning which gathering and
updating disaster data as well as monitoring disaster-related information sources for early
warning purposes. The crisis phase is concerned which information management and
exchange among humanitarian organizations, disseminations of demands, and
coordination of assistance. The post-crisis phase is concerned with summarizing the
lessons learned and suggesting recommendation for better disaster preparedness.
Information needs vary in different phases of disaster relief and also vary for different
stakeholders. Humanitarian organizations engage in two broad types of activities
including relief activities and development activities. Relief activities consist of assisting
to victims of large-scale emergencies. These short-term activities focus on providing
goods and services to minimize immediate risks to human health and survival.
Development activities are longer-term assistance, focusing on community self-
sufficiency and sustainability. These activities include establishing permanent and
reliable transportation, healthcare, housing, and food.
Humanitarian organizations increasingly need to look outside their own boundaries and
engage into a significant level of inter-organizational alliances. Through partnerships,
humanitarian organizations can successfully take on issues that would be beyond the
scope of any single organization.
2.3 Humanitarian Inter-organizational Coordination
Despite the variety of academic perspectives from which research on coordination and
inter-organizational coordination is approached (e.g. Comfort & Kapucu, 2006;
Crowston, 1994; Grandori, 1997; Lewis & Talalayevsky, 2004; Mulford & Rogers, 1982;
Mulford, 1984; Thompson, 1967; Van de Ven et al., 1976; Whetten & Rogers, 1982), a
common theme across all of them is that coordination requires the sharing of information,
resources and responsibilities to achieve a common goal.
16
In the particular realm of NGO coordination, initiatives are seen as a solution to
duplication of efforts in assistance projects, badly planned and implemented relief efforts,
and the lack of knowledge among humanitarian organizations on the actual situation in
which they operate. These initiatives entail developing strategies, determining
objectives, planning, sharing information, the division of roles and responsibilities, and
mobilizing resources. They are also concerned with synchronizing the mandates, roles
and activities of the various stakeholders and actors at higher organizational levels. In a
nutshell, NGO coordination is intended to ensure that priorities are clearly defined,
resources more efficiently utilized and duplication of effort minimized; the ultimate goal
being to provide coherent, effective and timely assistance to those in need (Harpviken et
al., 2001).
Coordination among NGOs, as well as between NGOs and other humanitarian actors,
takes place at different levels. Harpviken et al., (2001) identify these levels as
international, national, regional and local. At the international level, the formulation of
policy, general guiding principles and strategies are of concern. At the national level,
coordination typically revolves around program development and policy articulation. At
this level, local groups are typically less involved, while United Nations agencies,
government departments and NGOs representatives assume a central role. Coordination
at the local level usually takes place between representatives from NGOs, United Nations
agencies, and local communities. It is at the local level where humanitarian priorities can
be most readily identified and articulated. Figure 1 below depicts these different levels of
coordination, within which inter-organizational relationships may vary, depending on the
level of coordination pursued. My study focuses on coordination at the international
level.
17
Local level
National level
International level
Type of Coord.
Project/Program
coordination
Coordinated
activities
Actors:
IGOs,
NGOs,
main offices
CBs, UN
agencies,
UN
Type of Coord.
Program/ policy
coordination
(Standards)
Actors:
IGOs,
NGOs,
HQ,
Int.CBs,
UN,
Donors
Type of Coord.
Policy and norms
Actors:
IGOs,
NGOs,
CBs, UN
agencies,
UN, Donors
IGOs = Inter-governmental Organizations
CBs = Coordination Bodies
UN = United Nations
HQ = headquarters
Figure 1: Level of NGOs coordination
Source: Author adaptation from Harpviken et al., (2001)
Inter-organizational Coordination Forms: Identifying and classifying the various forms of
inter-organizational coordination has been a subject of research in both the for- and non-
profit domains. Research on for-profit organizations has identified two general structures
of coordination (Malone, 1987; Thompson et al., 1991). The first is a hierarchical
coordination structure, characterized by long-lasting relationships with fixed rules of
behavior and clear authoritative relationships. Put simply, one organization has control
over the other(s). The second is a “market” coordination structure, in which all
organizations are fully autonomous and make decisions in their own interest.
In the non-profit domain, research has similarly identified multiple structures (Donini &
Niland, 1999). The first is "coordination by command," in which the lead NGO has
authority to pursue coordination through the use of carrots or sticks and possesses strong
leadership abilities. In such a situation, a central authority has the power to define the
agenda, instigate preferences and enforce sanctions. Power can come in the form of
control of information or resources, but also the institutionalized legal means, through
which preferences might be implemented. The second form is "coordination by
18
consensus". In this form, organizations develop agreed-upon guidelines and standards to
achieve similar goals, and there is no authority to enforce compliance. The last form,
"coordination by default" describes ad-hoc coordination in which a division of labor is
generally the only exchange of information among actors. Obstacles to inter-
organizational coordination may vary depending on these various forms of coordination.
Alternatively, research on coordination structures in the humanitarian sector finds that
structure within NGOs themselves. Enjorlas (2008) argues that collectively NGOs on
their own serve as coordination structures. Due to the nature of their individual
governance structures, they reinforce the norm of reciprocity; making possible the
pooling of resources and, because of these features, thereby facilitate collective action
oriented toward public or mutual interest as well as advocacy. Moreover, this nonprofit
governance structure is also compatible with other types of coordination mechanisms,
and thus NGOs are able to operate in complex environments, mobilizing resources from
market operations, governmental subsidies, or from reciprocity (Enjorlas, 2008).
2.4 Humanitarian Information Management and Exchange
My research explored inter-organizational networks in the Global Symposium, a
community of interest in humanitarian information management and exchange
spearheaded by the United Nations Office for the Coordination of Humanitarian Affairs
(UNOCHA). UNOCHA initiated a Global Symposium in recognition of the centrality of
information management to effective and timely response to humanitarian disasters.
Timely and accurate information is recognized as integral to humanitarian action in both
natural disasters and complex emergencies. The international community's ability to
collect, analyze, disseminate, and act on key information is fundamental to an effective
response. Better information, leading to improved responses, directly benefits affected
populations. Over time, improved assessment of impacts and responses through better
data collection and management contributes to a more complete global database on
disaster impacts, leading to better risk assessment and prevention and preparedness
activities.
19
The goal of the Global Symposium is to foster collaboration on humanitarian information
management projects and to disseminate best practices of information exchange. This
community also aims to (i) sensitize its members on the critical aspects of humanitarian
information management preparedness, (ii) facilitate headquarter-field partnerships and
(iii) advocate for more funding from donors for humanitarian information management
related projects. My study focuses on members which actively participate in the activities
in the community and have developed collaborative relationships with other members
within the community.
The Global Symposium held a series of conferences and workshops, organized by
UNOCHA. The series began in 2002 as a meeting of humanitarian information
management professionals and was followed by a series of regional meetings intended to
bring humanitarian information management principles (Table 1) and best practices to a
wider range of humanitarian organizations and in particular bring together practitioners in
the field, as opposed to only headquarters staff. The second meeting of the Global
Symposium was held in October 2007 and included three days of working group
meetings, designed to update the principles and best practices and identify an agenda for
further development of humanitarian information management (HIM).
Principle Description
Accessibility Humanitarian information and data should be made accessible to all humanitarian actors by applying easy-to-use formats and by translating information into common or local languages when necessary. Information and data for humanitarian purposes should be made widely available through a variety of online and offline distribution channels including the media.
Inclusiveness
Information management and exchange should be based on a system of collaboration, partnership and sharing with a high degree of participation and ownership by multiple stakeholders, especially representatives of the affected population.
Inter-operability All sharable data and information should be made available in formats that can be easily retrieved, shared and used by humanitarian organizations.
Accountability Users must be able to evaluate the reliability and credibility of data and information by knowing its source. Information providers should be responsible to their partners and stakeholders for the content they publish and disseminate.
Verifiability Information should be accurate, consistent and based on sound methodologies, validated by external sources, and analyzed within the proper contextual framework.
20
Principle Description
Relevance Information should be practical, flexible, responsive, and driven by operational needs in support of decision-making throughout all phases of a crisis.
Objectivity Information managers should consult a variety of sources when collecting and analyzing information so as to provide varied and balanced perspectives for addressing problems and recommending solutions.
Humanity Information should never be used to distort, to mislead or to cause harm to affected or at-risk populations and should respect the dignity of victims.
Timeliness Humanitarian information should be collected, analyzed and disseminated efficiently, and must be kept current.
Sustainability Humanitarian information and data should be preserved, cataloged and archived, so that it can be retrieved for future use, such as for preparedness, analysis, lessons learned and evaluation.
Table 1: Principles of Humanitarian Information Management and Exchange.
The Geneva 2002 meeting was followed by a series of regional workshops in Bangkok
(2003), Panama (2005) and Nairobi (2006). While the issues confronting the
humanitarian community are global in scope, there are regional differences in both the
types of problems as well as the appropriate solutions. Each workshop focused on
information initiatives and tools in their regional context, each region with its different
vulnerabilities and response capacities. The goals of these workshops were to (i) bring
together regional information management professionals in order to strengthen the
professional community of practice, (ii) discuss the principles and best practices in
information management, especially those which have been developed at the regional
level, and (iii) deepen understanding of the regional issues and priorities that will help
build a plan for improving information exchange in the region. The recommendations
from these workshops reinforced the need for attention to the promotion of standards,
user requirements, quality of information, appropriate responses, tools and technology,
and strong partnerships.
2.5 Humanitarian Collaboration and Coordination Challenges
Research on barriers to inter-organizational coordination and collaboration has been
undertaken in both general organizational contexts (e.g. Burbridge & Nightingale, 1989;
Comfort, 1990; Comfort & Kapucu, 2006; Crowston, 1997; De Bruijn, 2006; Faraj &
21
Xiao, 2006; Quarantelli, 1982; Thompson, 1967), as well as among organizations in the
nonprofit context (e.g. Bennett, 1995; Bui et al., 2000; Foster-Fishman et al., 2001; Saab
et al., 2008; Uvin, 1999; Van Brabant, 1999). After an analysis of the literature,
Ngamassi et al., (2011) found a fairly consistent set of eight coordination and
collaboration barriers (Table 2). They include (i) bureaucratic and turf-protection, (ii)
divergent goals and conflicting interests, (iii) resource dependency, (iv) coordination
cost, (v) information and communication issues, (vi) assessing and planning joint
activities, (vii) competition for resources, and (viii) emergency response time.
Bureaucratic barriers and turf-protection refer to the desire to maintain autonomy and
thus avoid having individuals in other organizations interfere within one's own
organization. Burbridge & Nightingale (1989) note a common fear among organizations
is that coordination may somehow result in a take-over or a loss of decision-making
autonomy. Furthermore, the discipline of coordination can limit maneuverability, and
hence poses a major challenge (Uvin, 1999). Coordination may be perceived as
increasing bureaucracy, generating institutional resistance among bureaucratically
burdened NGOs (Van Brabant, 1999).
A common problem in inter-organizational collaboration is that divergent goals or an
over-emphasis on individual organizational goals as opposed to those of beneficiaries
may lead to conflicting interests (Bennett, 1995; Bui et al., 2000; Quarantelli, 1982; Saab
et al., 2008; Van Brabant, 1999). Goal conflicts occur when a party seeks divergent or
incompatible ends. Further, divergent goals may also lead to an exacerbation of turf
issues or other coordination problems (Bui et al., 2000).
Resource dependency is both a motivation for and barrier to coordination (Crowston,
1997; Dawes et al., 2004; Thompson, 1967). Interdependencies, whether of the pooled,
sequential or reciprocal type, require coordination (Thompson, 1967). However, at the
same time they can create problems for coordination and constrain the efficiency of task
performance (Crowston, 1997). One of these problems is the associated cost of
22
coordination, as to be effective it is time and staff intensive and the benefits must
outweigh these costs (Aldrich, 1972; Bennett, 1995; Van Brabant, 1999).
Coordination cost is yet another barrier that hampers coordination among organizations.
Inter-organizational coordination is believed to limit an organization because scarce
resources and energy have to be invested in the maintenance of relationships with other
organizations. Negotiation of resources allocation can lead to difficult bargaining among
parties engaged in coordinated activities. Usually, organizations find it difficult to
allocate scare resources (Bui et al., 2000). Aldrich (1972) argued that it is costly for
organizations to initiate and/or maintain linkages with other organizations. For example,
the costs can be seen as in term of additional staff-time necessary to attend a joint board
of directors’ meeting; or the additional funds necessary to participate in joint database.
According to Uvin (1999), the high cost in time and money that effective co-ordination
entails constitute one of the major barriers to inter-organization coordination.
Another frequently encountered barrier is related to the availability and the quality of
information. This is usually due to the inconsistency in data collection and management
across organizations and to the mismatch between the informational demands and
supplies (De Bruijn, 2006; Fisher & Kingma, 2001). According to Bui, et al., (2000),
there are varying levels of mistrust, misrepresentation of facts, and incomplete
information exchange among organizations. Further, the high level of uncertainty in
humanitarian operations likely requires greater amounts of information to be processed
among decision makers (Galbraith, 1977).
General assessment and planning of joint activities can lead to disagreement about the
means and the ends of a coordinated activity (Bui, et al., 2000). Situations tend to worsen
when organizations are unsure of their role, and act independently, without consulting or
coordinating with others. Joint activities must also confront problems of understanding,
which emanate from the fact that participants in inter-organizational relationships are
accustomed to different structures, cultures, functional capabilities, cognitive frames,
terminologies, and management styles and philosophies (Vlaar et al., 2006).
23
In addition to the resources related to coordination itself, competition for scarce resources
in general may inhibit the initiation of inter-organizational coordination generally (Uvin,
1999; Van Brabant, 1999). Given the increasing numbers of NGOs, combined with
decreasing overseas development assistance budgets, competition for funding between
organizations is heating up (Salm, 1999; Van Brabant, 1999).
Finally, response time is considered yet another obstacle to coordination among
organization. Coordination is often perceived as increasing response time especially in
case of emergency. According to Van Brabant (1999), there is the fear that the
coordination effort will cause delays in providing relief. Comfort (1990) observed that
coordination activities generated delays in response in the four events she analyzed.
Thus, inter-organizational coordination between international humanitarian NGOs will
seek to share information, resources and responsibilities that through more efficient use
of resources and minimization of duplicate activities will provide effective and timely
assistance to those in need (Harpviken et al., 2001). This coordination can occur at
multiple levels and may be carried out through one of several forms, including command,
consensus or default. Whatever the form, it must contend with a wide range of
challenges.
Barriers Issues Authors
Bureaucratic and turf protection
Desire to maintain autonomy and thus avoid having individuals in other organizations interfere within one's own organization
Burbridge and Nightingale (1989) (Uvin, 1999). (Van Brabant, 1999).
Divergent goals and Conflicting interests
Divergent goals or an over-emphasis on individual organizational goals
Bennett 1995; Bui et al, 2000; Quarantelli, 1982; Saab et al, 2008; Van Brabant, 1999.
Resource dependency Interdependencies require coordination but at the same time they can create problems for coordination and hamper performance.
Crowston, 1997; Dawes et al., 2004; Thompson 1967). Aldrich 1972; Bennett, 1995; Van Brabant 1999
24
Barriers Issues Authors
Coordination cost Scarce resources have to be invested in the maintenance of relationships with other organizations.
Bui et al, 2000; Aldrich,1972; Uvin, 1999
Information and communication issues,
Information availability and accessibility,
Information quality,
Information Sharing
Information system quality,
Standards and interoperability
Systems integration
De Bruijn, 2006; Fisher & Kingma, 2001; Bui, et al 2000; Galbraith, 1977.
Assessing and planning joint activities
Disagreement about the means and the ends of a coordinated activity
Bui, et al, 2000; Vlaar et al., 2006
Competition for resources
Competition for scarce resources may inhibit the initiation of inter-organizational coordination
Uvin, 1999; Van Brabant, 1999; Salm, 1999.
Emergency response time
Coordination is often perceived as increasing response time especially in case of emergency
Van Brabant, 1999; Comfort, 1990.
Table 2: Summary of Inter-Organizational Coordination/Collaboration Challenges
25
3 REVIEW OF RELEVANT LITERATURE
3.1 Introduction
As discussed in the introductory chapter, this study is situated in the broader context of
research on inter-organizational networks in the non-profit sector. I investigate the
organizational attributes and network structural characteristics that explain effectiveness.
In this chapter, I review the relevant literature. The chapter is made up of two sections.
The first (Section 3.2) is related to effectiveness at the organizational level of analysis
while the second (Section 3.3) is concerned with effectiveness at the network level of
analysis.
3.2 Organizational effectiveness
3.2.1 Defining Organizational Effectiveness
Although researchers have devoted considerable amount of time investigating
organizational effectiveness, the construct remains elusive. In the literature, there is a
wide range of definitions to this construct (for a review, see Goodman et. al, 1977; Cho,
2007). There is no consensus on the criteria of measuring effectiveness among
researchers (Quinn & Rohrbaugh, 1983; Scott, 1992). Moreover, debates still exit about
the primary factors that constitute organizational effectiveness (Goodman et al., 1977;
Rainey & Steinbauer, 1999) and about the validity of measuring the construct (Goodman
et al., 1983; Steers, 1975). In addition, there is no single theory of organizational
effectiveness (Goodman et al., 1983), rather each paradigm of organizational behavior
generates its own model or criterion of effectiveness (D’Aunno, 1992).
In the literature, there is a wide range of definition to the concept of organizational
effectiveness (for a review, see Goodman et Al., 1977; Cho, 2007). There is no consensus
on the criteria of measuring effectiveness among researchers and no clear classification of
the different levels of effectiveness. For Goodman et al., (1977) organizational
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effectiveness is measured in terms of the organization's ability to satisfy constraints and
meet organizational goal. Reviewing the literature on organizational effectiveness,
Cameron (1986a; 1986b) describes the concept of effectiveness as theory-bound,
multidimensional, interest-driven, and paradoxical in nature.
3.2.2 Models of Organizational Effectiveness
A variety of different models of organizational effectiveness have been used however,
four major models dominate the literature. They include the goal model, the systems-
resource model, internal processing model, and the multiple constituencies’ model.
Below, I briefly review these different models.
3.2.2.1 Goal Model
The problem of organizational effectiveness has traditionally been studied by means of
the goal approach (Parson 1964; Price, 1971; Cameron & Whetten 1981). The
distinguishing characteristic of the goal model is that it defines effectiveness in terms of
the degree of goal achievement. The greater the degree to which an organization
achieves its goals the greater is its effectiveness. The goal model approach to
organizational effectiveness assumes that organizations are designed to achieve certain
goals, both formally specified and implicit (Perrow 1965; Sowa et al., 2004). The model
also assumes that organizations have goals that are clearly defined and easily measurable
and that data relevant to those measures can be collected, processed and applied in a
timely and appropriate manner (Herman & Renz, 2004a; 2004b). The model views
organizations as a rational set of arrangements oriented toward achieving a goal.
Yuchtman & Seashore (1967) distinguish two components of the Goal Model approach to
organizational effectiveness. The first component is the "prescribed goal approach".
According to the authors, this component focuses on the formal charter of the
organization, or in some category of its personnel as the most valid source of information
27
concerning organizational goals. The second component is the "derived goal approach".
In this component, the researcher derives the goal of the organization from his/her theory.
The Goal Model of organizational effectiveness is suitable for those organizations where
activity is shaped by a focus on output (Cameron & Whetten 1981), and organizational
effectiveness is generally operationalized in term of productivity or efficiency (Scheid &
Greenley, 1997). Organizational effectiveness in organizations with clearly defined and
easily measurable goals may be assessed using the goal model (Cameron & Whetten,
1983).
The main criticism to the Goal Model of organizational effectiveness consistently
identified in the literature especially by the adherents of the System Resource Model, has
been that its proponents have not developed measures of effectiveness which can be used
to study many types of organizations. Adherents of the System Resource approach to
organizational effectiveness make two criticisms of the goal approach (see Price, 1971).
First, they say that the goal approach has provided no means to identify organizational
goals; second, they say that the goal approach uses society, not the organization, as the
basis for the evaluation of effectiveness. The absence of general measures is serious
because it hinders the development of theory. The existence of general measures
promotes measurement standardization; measurement standardization, in turn, facilitates
comparison; and comparison, in turn, furthers the development of theory.
3.2.2.2 System Resource Model
The System Resource Model defines effectiveness, not with respect to the degree of goal-
achievement, but in terms of the ability of the organization to exploit its environment in
the acquisition of scarce and valued resources (Yuchtman & Seashore, 1967; Price,
1971). In this model, organizational effectiveness is the degree to which an organization
can preserve its internal integration, adapt to the environment and therefore survive
(Scheid & Greenley, 1997). Organizational effectiveness is positively related to the
ability of the organization to exploit its environment. According to Sowa et al., (2004),
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in the system resource model of organizational effectiveness, the inputs into an
organization are more important than their outputs because an organization’s ability to
maintain sufficient resources for survival is the most important indicator of effectiveness.
Steers (1975) found that the most common utilized systems criteria of organizational
effectiveness were organizational adaption and flexibility. Cameron & Whetten (1981)
see systems resource models as best fitting organizations where formalization is low or
when environmental turbulence (uncertainty and complexity) is high, and, hence, system
effectiveness precedes and is a prerequisite for goal effectiveness.
The System Resource Model of organizational effectiveness has also been criticized.
Price (1971) outlined three criticisms of this approach. First, he states that the idea of
"optimization" is an important component of effectiveness as conceptualized the
proponents of the systems approach and yet, according to the author, these same scholars
show little concern for trying to measure optimization. Second, Price argues that the
systems oriented researchers have expressed the need for general measures of
effectiveness, but none have developed these general measures that they claim to be so
necessary. Finally, Price believes that the frame of reference used in the analysis process
by the system researchers is somewhat confused. According to the author, the confusion
centers around the difference between a multidimensional approach to effectiveness with
multiple measures of effectiveness, and a multidimensional approach with multiple
measures of a series of different analytical concepts.
3.2.2.3 Internal Processing Model
The Internal Processing Model conceptualizes organizational effectiveness as the absence
of internal strain and a smooth internal functioning of organizations / networks (Lee,
2006). For Alter & Hage (1993), much of the existing government and foundation
sponsored inter-organizational systems has adopted the internal processing model. They
believe that the choice of this model has been based on the assumption that the outcomes
of the system, the product or service, will be of higher quality if the system functions
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smoothly and with a minimum of conflict. There are currently several attempts to
evaluate service delivery using the internal processing model (Lee, 2006).
3.2.2.4 Multiple Constituencies Model
The Multiple Constituencies’ Model defines organizational effectiveness as the ability of
organizations to satisfy key strategic constituencies in their environment (D’Aunno,
1992; Zammuto, 1984; Sowa et al., 2004). This approach to organizational effectiveness
began to emerge when researchers focused less on the assessment criteria of abstract
dimensions and more on the concrete expression of stakeholders’ expectation (Connolly
et al., 1980; Zammuto, 1984). The model recognizes that an organization comprises
multiple stakeholders or constituents who are likely to use different criteria to evaluate its
effectiveness (Herman & Renz, 1998). Effective organizations are viewed as those which
had accurate information about the expectation of strategically critical constituents and
adapted internal organizational activities, goals, and values to match those expectations
(Scheid & Greenley,1997). In the Multiple Constituency Model, the emphasis is on the
organizations’ ability to satisfy (or adapt to) divergent preferences. The Multiple
Constituency Model conceives of differing groups of stakeholders, such as clients or
customers, board members, staff, volunteers, and funders, as probably having different
goals and requires that researchers recognize the potential differences in their interests
(Herman & Renz, 1998).
The Multiple Constituencies’ Model of organizational effectiveness spawns a large
number of research (Whetten, 1978; Cameron, 1978; Tsui, 1990). According to Cameron
& Whetten, (1983), researchers using this approach encountered four difficult
methodological challenges including (i) When asked individual stakeholders have
difficulty explaining their personal expectations for an organization; (ii) a stakeholder’s
expectations change sometime dramatically, over the time; (iii) a variety of contradictory
expectations are almost always pursued simultaneously in an organization and (iv) The
expectations of strategic constituencies frequently are unrelated, or negatively related, to
their overall judgments of an organization’s effectiveness.
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I summarize in Table 3 below, the four model traditionally used to study organizational
effectiveness
Model Definition When Useful? Criticisms
An organization is effective to the extent that
The model is most preferred when
Goal model It accomplishes its stated goals Goals are clear, consensual, time-bound, measurable
No means to identify network goals; Absence of general measures for effectiveness.
System resource model
It acquires needed resources A clear connection exits between inputs and performance
Little concern for trying to measure optimization, a big component of effectiveness; No general measures of effectiveness; Confusion centers around the difference between a multidimensional approach to effectiveness with multiple measures of effectiveness, and a multidimensional approach with multiple measures of a series of different analytical concepts.
Internal processing model
It has an absence of internal strain with smooth internal functioning
A clear connection exits between organizational processes and performance
Strategic constituencies model
All strategic constituencies are at least minimally satisfied
Constituencies have powerful influence on the organization, and it has to respond to demands
A stakeholder’s expectations change sometime dramatically, over the time; A variety of contradictory expectations are almost always pursued simultaneously in a network Expectations of strategic constituencies frequently are unrelated, or negatively related, to their overall judgments of an organization’s effectiveness.
Table 3: Models of Organizational Effectiveness
Source: Author Adaptation from Cameron (1986)
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3.3 Inter-organizational Network effectiveness
3.3.1 Defining Network Effectiveness
The concept of inter-organizational network effectiveness is discussed at length in the
literature. Much of these discussions highlight the difficulties of defining and assessing
network effectiveness (Alter & Hage, 1993; Provan & Milward, 1995; Sydow &
Windeler, 1998). For example, Sydow & Windeler (1998) argue that since establishing
a shared understanding of effectiveness is already difficult for a single organization with
a clearly identifiable center and a rather stable boundary, it is even more likely to be
puzzling for inter-organizational networks with several centers and more blurred
boundaries. They say that what exactly counts as effective and which particular
evaluating practices are really used, depends upon these stakeholders and their diverse
interests. For Provan & Milward (1995), assessing inter-organizational network
effectiveness is more complex than organizational effectiveness due to the involvement
of multiple organizations in a network. Given that networks use multiple organizations to
produce one or more pieces of a single service, making their evaluation in order to assess
their effectiveness becomes more complex than that of a single organization.
Alter & Hage (1993), identifies two other reasons why it is difficult to conceptualize
effectiveness of network systems. First, according to the paper, inter-organizational
networks go through phases, each phase having a set of developmental tasks that must be
accomplished before the next phased can be successfully entered. Evaluation of the
network must be phase-specific, or expectations will be inappropriately high. The second
area of concern identified by Alter & Hage (1993), when assessing inter-organizational
effectiveness is the level of analysis. In network systems, even if it is possible to specify
system level goals and objectives, one is faced with deciding the level at which the data
will be collected. This is difficult because the production process is a hierarchy of cause
and effect in a cybernetic process, with change occurring at different levels, and the
outcomes at each level acting as determinants for the next set of outcomes.
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With all these difficulties in conceptualizing inter-organizational network effectiveness,
researchers have come out with wide varieties of the concept. For Goodman et. Al.,
(1977), inter-organizational effectiveness should be conceptualized as an outcome, but
measured relative to the constraints that exit in the system. According to the authors, the
expectation of what is a reasonable outcome, given the context and the barriers to goal
achievement, is the best measure of effectiveness. Alter & Hage (1993) define
effectiveness in inter-organizational network as the perception among administrators and
workers that their collective effort is achieving what it was intended to achieve, that it
works smoothly and that it is reasonably productive. According to Sydow & Windeler
(1998), inter-organizational network effectiveness is an outcome and as a medium of
inter-organizational practices. According to the paper, “network effectiveness can be
understood as the viability and acceptability of inter-organizational practices and
outcomes in the light of system requirements and powerful stakeholders, both of which
are, of course, subject to change in the course of time”.
All these definitions highlight as I mentioned earlier, the complexity of the concept of
inter-organizational network effectiveness as it encompasses many different perspectives.
Drawing from these different perspectives and trying to be consistent with the concept of
effectiveness at the organizational level, in my study, I conceptualized network
effectiveness as a multidimensional concept measured as the level of activities and as the
level of collaboration. In this research, the most effective network or organization is the
one that displays the highest level of humanitarian activities or the highest level of
collaboration.
The daunting difficulties in determining a clear definition and the criteria for assessing
inter-organizational effectiveness may explain the limited number of studies on inter-
organizational network effectiveness in general, and especially in the nonprofit sector.
Notwithstanding these difficulties they have been attempts to investigate inter-
organizational network effectiveness. Reviewing the literature, I found five conceptual
studies on approaches to assess inter-organizational network effectiveness. In the
following subsections I first review these different approaches ; secondly I briefly
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present the empirical work in the nonprofit sector using one of these approaches and
finally I discuss the main issues I identified in this body of literature on organizational
and network effectiveness.
3.3.2 Model of Network Effectiveness
3.3.2.1 Performance Gap Model
Alter & Hage, (1993) proposed a “performance gap” model to assess inter-organizational
network effectiveness. Performance gap is defined as the difference between the current
situation and the idealized standard. According to Alter & Hage, effectiveness is usually
best measured by the expectation of what is a reasonable outcome, given the context and
the barriers to goal achievement. They argue that effectiveness is achieved when goals
are met within the context of technological and resource constraints, given certain levels
of internal conflict and pressure from external constituencies. Alter & Hage, (1993)
identified and discussed factors associated with high level of performance gap. These
factors were grouped into the following five categories: (i) environmental controls –
vertical dependency, autonomy, involuntary status-; (ii) technological characteristics –
task scope, task uncertainty, task intensity, task duration, task volume-; (iii) structural
characteristics – centrality, size, complexity, differentiation, connectivity- (iv)
administrative decision making – impersonal methods, personal methods, groups
methods- ; and (v) task integration –sequential pattern, reciprocal pattern, team pattern-.
3.3.2.2 Provan & Milward Model
Provan & Milward (1995) developed a model of inter-organizational network
effectiveness through a comparative study of four community mental health networks.
They investigated the relationship between the structure and context of mental health
networks and their effectiveness. Findings from this research suggest that network
effectiveness could be explained by various structural and contextual factors such as
network integration, system stability and environmental resource munificence. Provan
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& Sebastian (1998), further developed the model, focusing on clique and clique overlap
in the networks. Their findings suggest that achieving integration across an entire
network of organizations is difficult. Their theory is that most effective networks are
those that are integrated at clique or sub-network level.
Network Structure
Centralized integration
Direct, non fragmented
external control
Network Effectiveness
Network Context
System stability
High resource munificence
Figure 2. A Preliminary model of network effectiveness
Source : Provan & Milward (1995)
3.3.2.3 Principles Agents Model
Provan & Milward (2001) propose another approach to assess inter-organizational
network effectiveness based on the Principles Agents theory. In addition to structure and
context, Provan & Milward (2001) examined network effectiveness at different levels.
These three levels are (i) community, (ii) network, and (iii) organization/participant. The
paper argues that organization/participant and network-level effectiveness criteria can be
satisfied by focusing on community-level effectiveness goals. Weech-Maldonado et al.,
(2003) build upon Provan & Milward’s (2001) network effectiveness framework and
Gamm’s (1998) accountability framework to develop a “stakeholder accountability
approach” in assessing network effectiveness. The stakeholder accountability approach
posits that with each level of analysis (community, network, organizational/participant)
there are different effectiveness criteria reflecting the needs of the various stakeholders.
The authors use this approach to evaluate the effectiveness of community health
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partnerships. Figure 3 below depicts the relationships between network effectiveness at
different levels of network analysis and influence by key stakeholders.
Network-level
effectiveness
Community-level
effectiveness
Agents
Organization/
participant-level
effectiveness
Key Stakeholders
Principals
Clients
Figure 3. Relationships between network effectiveness at different levels of network
analysis and influence by key stakeholders
Source: Provan & Milward (2001)
3.3.2.4 Stucturalist Perspective Model
Sydow & Windeler (1998) define inter-organizational network effectiveness as viable
and acceptable outcome and practices. They argue that network effectiveness form a
structurationist perspective, is more than embedded in social interactions and structures,
it is social in character. They discuss the concept of inter-organizational network
effectiveness in the light of Giddens’ (1984) duality of structure. For Sydow & Windeler
the meaning of the criteria to assess network effectiveness is not simply given, but
necessarily interpreted and ascribed (signification). Moreover, these criteria are always
interest-related and value-laden (legitimation). And finally, they are powerfully (re-)
produced by individual and collective agents (domination).
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Sydow & Windeler (1998) identify two levels of analysis of network effectiveness
including (i) the level of the individual network firm and (ii) the level of the total inter-
organizational network. On the level of individual network organizations, they argue that
network effectiveness results from that part of the network effect which a particular
network firm is able to appropriate and eventually to represent in its accounts. In this
sense, network effectiveness contributes to organizational effectiveness. On the level of
the total inter-organizational network, network effectiveness depends upon the
effectiveness of all single network firms and upon the augmentation of resources to be
achieved by the differentiation and integration of the entire network (Sydow & Windeler,
1998). For Sydow & Windeler, network effectiveness on this level of analysis usually
evades conventional calculating and accounting practices by taking the efficacy of
network structures into account.
3.3.2.5 Adaptive Capacity Model
Building upon Sydow & Windeler (1998), Staber & Sydow (2002) propose the concept
of adaptive capacity as an appropriate approach to assess organizational and inter-
organizational network effectiveness in highly volatile and complex environments such
as the case in the humanitarian assistance sector. They define adaptive capacity as the
ability of organizations or networks to cope with unknown future circumstances.
Organizations / networks with high adaptive capacity can reconfigure themselves quickly
in changing environments and consequently are more effective. They argue that adaptive
capacity should thus be viewed in relative and dynamic terms. That is, organizations /
networks have adaptive capacity when learning takes place at a rate faster than the rate of
change in the conditions that require dismantling old routines and creating new ones.
Using Giddens’s structuration theory (Giddens, 1984), Staber & Sydow (2002) discuss
multiplexity, redundancy, and loose coupling as important structural dimensions of
adaptive capacity.
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These different conceptual models for assessing network effectiveness found in the
literature most of the time borrowed from the four models traditionally used to study
organizational effectiveness that I discussed earlier.
3.3.3 Predictors of Network Effectiveness in Nonprofit
Previous research has also identified important predictors of inter-organizational network
effectiveness (Provan & Milward, 1995; Moscovice, et al., 1995; Wright et al., 1995;
Provan & Sebastian, 1998; Schumaker, 2003; Lemieux-Charles et al., 2005). These
predictors could be grouped into two categories, structural and relational. For example,
several authors (e.g., Provan & Milward, 1995; Moscovice, et al., 1995; Wright et al.,
1995; Provan & Sebastian, 1998) highlight the importance of integration of network
members to network effectiveness.
Provan & Sebastian (1998) argued that achieving integration across an entire network of
organizations is difficult. Their findings also suggest that to be most effective, clique
integration must be intensive, involving multiple and overlapping relationships both with
and across organizations that compose the core of a network. Lerch et al., (2006)
investigated the relationships between formal cluster governance and actual networks of
relationships and between multi-dimensional network integration and innovation
activities. The paper applies a multi-level analysis that distinguishes the cluster level
from network and clique levels and accounts for the recursive interplay between
structural properties of these levels and how agents refer to them in inter-organizational
inter-actions. The paper used longitudinal data which allow for studying network
dynamics. Their results were consistent with those of Provan & Sebastian (1998). They
found that multiplex over-lapping cliques provide not only for a fair amount of network
integration, but also a social context conducive for turning complex knowledge of
research organizations into marketable products.
Lemieux-Charles et al., (2005) examined the effectiveness of four community-based,
nonprofit dementia care networks located in Ottawa, Toronto, Hamilton, and the Niagara
38
region. The research focused on the evolution, structure, and processes of the networks
and on how these networks served the needs of care recipients and caregivers who were
using community-based or ambulatory care services provided by acute-care agencies.
Though the authors studied each network as a whole, they also examined the
relationships that existed among groups of agencies within them. The types of
relationships examined were based on activities related to administrative functions and
service delivery functions. Findings of the study suggest that members perceived higher
administrative and service delivery effectiveness when network members shared multiple
ties with members of different groups within the network as opposed to the sharing of ties
across the network. The centralization of network structure was also found to be related
to the perception of service delivery effectiveness.
Morehead (2008) provides insight into the correlates of effectiveness for a type of health
network, vertically integrated rural health networks. The study uses Provan & Milward’s
(2001) framework for evaluating the effectiveness of public-sector organizational
networks to analyze the effectiveness of twenty three rural health networks. One-to-one
interviews, questionnaires, and archives were used to collect data on the networks
sampled. Findings of the study revealed a few significant predictors for the effectiveness
of vertically integrated rural health networks. Financing was found to be the most
important predictor, as it was significant at both the community and network levels. Both
cohesiveness and the number of problems in the rural environment were also found to be
significant predictors but only at the network level. No significant predictors were found
at the organizational level; however, organizational and network-level effectiveness were
found to be strongly correlated with each other. Overall, networks were found to be more
favorable about their effectiveness at the network and organizational levels.
Similarly, the diversity of network membership is also deemed relevant by several
authors (e.g., Moscovice, et al., 1995; 1996; Schumaker, 2003). Schumaker (2003) for
example found that effectiveness is influenced by external and internal factors that are
39
operationalized through external control, technology, structure, and operational process
variables. In Table 4 below, I present a summary of these studies.
Authors Issues Measures of effectiveness Findings/outcomes
Provan & Milward (1995)
Develop a theory to assess network effectiveness
Perception of solving problems Building social capital Decrease service duplication Improve coordination Goal commitment
Networks are more effective when network integration is centralized, external fiscal control by the state is non-fragmented and direct, resources are sufficient, and the overall system is secure
Moscovice et al., (1995)
Develop an approach to study vertically integrated rural health networks
Benefits and costs of health care provision to network’s clients
Questions for further research
Grusky (1995)
Assess networks effectiveness of mental health care delivery networks
Service quality Coverage Comprehensiveness Coordination
The longer key inter-organizational network agency directors have served the more likely the care system was perceived as effective. The more powerful the lead agency relative to other organizations in the network the more likely the system was perceived as effective.
Provan & Sebastian (1998)
Explore the use of clique analysis for explaining network effectiveness.
client outcomes Effectiveness was negatively related to the integration of full networks. In contrast, effectiveness was positively related to integration among small cliques of agencies when these cliques had overlapping links through both reciprocated referrals and case coordination.
Provan & Milward (2001)
Develop a framework to assess network effectiveness at three levels of analysis (community, network, and organization/participant)
Network membership growth Range of service provided Absence of service duplication Relationship strength (multiplexity) Creation and maintenance of network administrative organization (NAO) Integration/coordination of services Cost of network maintenance Member commitment to network goals
A framework with different effectiveness criteria depending on the level of analysis
Schumaker (2003)
Assess networks outcomes of rural health care delivery networks
Gap between best possible and actual practice
Effectiveness increased with network connectivity, decision making methods, and pattern of service delivery. Centrality and network size decrease together where there is little reliance on vertical sources of funds.
Weech-Maldonado et al., (2003)
Develop an approach to assess network effectiveness (stakeholder accountability approach)
Perceived benefit to the various stakeholders of the network
Use the approach to evaluate the effectiveness of community health partnerships
Lemieux-Charles et al., (2005)
Assess the effectiveness of community-based networks
Facilitate sharing Provide opportunity for share program Facilitate administrative information exchange
Perceived effectiveness increased with multiplexed ties among members of different groups within the network. Perceived effectiveness related to the centralization of network structure.
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Authors Issues Measures of effectiveness Findings/outcomes
Lerch et al., (2006)
Study the emergence and overlap of organizational cliques in an optics/photonics cluster in Berlin-Brandenburg.
The paper applies a multi-level analysis that distinguishes the cluster level from network and clique levels and accounts for the recursive interplay between structural properties of these levels and how agents refer to them in inter-organizational inter-actions. The paper used longitudinal data which allow for studying network dynamics.
Arya & Lin (2007)
Assess the impact of organization characteristics and network structure characteristics on collaboration outcomes
Ability to obtain funding Ability to enhance reputation Ability to meet clients’ needs
High-status organizations are able to derive critical resources from network involvement
Morehead (2008)
Assess networks effectiveness of rural health care delivery networks
Perceived benefit Number or organizations added Number of service provided Existence of NAO
Financing was found to be the most important predictor, of network effectiveness
Table 4: Inter-organizational Network Effectiveness in the Nonprofit Sector
3.4 Issues Identified in the Literature on Effectiveness
After reviewing the literature on effectiveness at organizational and network level, one
general observation is that while several studies have investigated this concept, and
several other have provided conceptual models to assess effectiveness, limited research
has used these models to empirically analyze the possible antecedents of effectiveness,
particularly for humanitarian inter-organizational networks.
I also observed that each of the four models of organizational effectiveness had a
specific focused perspective of effectiveness. For instance, in the Goal Model -
effectiveness is the ability to excel at one or more output goals - the focus is on the output
of the organizations. The System Resource Model - effectiveness is the ability to acquire
scarce and valued resources from the environment -focuses on the input. Concerning the
Internal Process Approach - effectiveness is the ability to excel at internal efficiency,
coordination, motivation, and employee satisfaction- the focus is on the transformation of
input to output. However, all these different focuses had one thing in common. They all
assess effectiveness based mostly on resources internal to organizational. Not much
attention is paid to external resources.
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Concerning the inter-organizational network effectiveness, I made the following three
observations. Firstly, in almost all of the various inter-organizational network
effectiveness models the focus was at the whole network level of analysis. Using these
models, it would be difficult to conduct organizational level of analysis. Findings from
empirical work (e.g. Stuart et al., 1999; McEvily & Zaheer, 1999; Stuart, 2000;
Rothaermel, 2001) suggest that inter-organizational relationships play a significant role in
shaping the effectiveness of an organization.
Secondly, the vast majority of studies related to inter-organizational network
effectiveness in the nonprofit field are conducted in the public health sector. Moreover, in
most of them, the level of analysis is either community or network. To my knowledge,
only two studies in the specific field of humanitarian assistance investigate humanitarian
inter-organizational network effectiveness. Those papers are Stephenson (2005) and
Stephenson (2006). Stephenson (2005) identifies some of the reasons for the problems
of inter-organizational coordination faced by humanitarian organizations and suggests
ways to address these problems in order to have more effective humanitarian inter-
organizational networks. Stephenson (2006) contributes to the debate in the humanitarian
community about how to make humanitarian assistance more effective. The author argues
that the problem of power and authority in the environment of humanitarian assistance,
best conceived as an inter-organizational social network, must reconceived.
Thirdly and more importantly, using social network theories in the study of
organizational performance, social network researchers have focused on the
organization’s ego network, which encompasses the focal organization (ego), its set of
partners (alters), and their connecting relationships (Wasserman & Faust, 1994). For
example, by counting the number of alliance partners and measuring structural
equivalence, patent counts, and relative scope, Baum et al., (2000) found that the
composition of alliance networks explains differences in organizational performance.
Ahuja (2000) examined the effects of direct ties, indirect ties, and structural holes on
innovation output. Arya & Lin (2007) found that nonprofit organizations that provide a
broad range of services enhance their effectiveness from collaboration in terms of
42
resource gains. Findings of the study also suggest that high-status organizations are able
to derive critical resources from network involvement.
Other social network studies have explored the effect of network structural characteristics
such as centrality, network density, and clique structure on network-level performance /
effectiveness in terms of outcomes (Provan & Milward, 1995; Provan & Sebastian, 1998;
Lerch et al., 2006). For example, the findings from Provan & Sebastian (1998) suggest
that the most effective networks are those that are integrated at clique or sub-network
level. Their findings also suggest that to be most effective, clique integration must be
intensive, involving multiple and overlapping relationships both with and across
organizations that compose the core of a network. Social network researchers have also
shown that strong ties differ from weak ties in terms of their effect on organizational
performance (Rowley et al., 2000).
However, many of these studies have focused on analysis only at the dyadic or the
network level. There is little research in the inter-organizational networks literature
about organizational-level characteristics that can explain whether or not organizations
can enhance their performance from their network positions. Some studies have looked at
the relationships between organizations’ network ties and these organizations’
performance (e.g., Powell et al., 1999; Stuart, 2000; Lee et al., 2001; Almeida et al.,
2003), but none of these studies have explicitly investigated the relationships between
individual organizational characteristics, ego-net characteristics and network structural
characteristics as antecedents of effectiveness. My dissertation intends to contribute to
reduce this gap in the literature.
I investigate inter-organizational network effectiveness in the humanitarian field. I study
a community of interest in humanitarian information management and exchange. Using a
mixed methods research design, I explored the relationships between the structural
properties of network and network effectiveness in humanitarian information exchange.
Network effectiveness was assessed using three different criteria including one subjective
criteria – Perceived network effectiveness and two objectives criteria – number of funded
43
projects and number of funding partners. My investigation is conducted at two different
levels of analysis, network and organizational levels.
Provan & Sebastian (1998) found that network-level effectiveness can be explained by
intensive integration through network cliques. Building upon this work, I explored
networks of international heterogeneous and geographically dispersed organizations
engaged in humanitarian assistance and disaster relief. I sought to understand the extent
to which Provan & Sebastian’s Model would explain effectiveness in this context. In
addition, unlike Provan & Sebastian who assessed network effectiveness using one
subjective criteria (Patient outcomes), in my study, I explored three effectiveness criteria
including one subjective and two objectives.
At the organizational level, I combined two theoretical lenses including Social Network
and Resource Based View. Network structural characteristics (density, centrality, clique
and clique overlap) have been found to have implications on performance/effectiveness
(Ahuja & Carley, 1999; Tsai & Ghoshal, 1998; Tsai, 2000; Nohria & Garcia-Pont, 1991;
Wasserman & Faust, 1994; Kilduff & Tsai, 2006; Provan et al., 2007). Similarly, the
embeddedness of organizations in networks of external relationships with other
organizations holds significant implications for organization performance/effectiveness
(Granovetter, 1985; Uzzi, 1996; 1997; 1999; Gulati et al., 2000). Resource Base View
(RBV) explains performance / effectiveness exclusively through internal resources.
(Barney, 1991; Prahalad & Hamel, 1990; Barnett at al., 1994). As mentioned earlier,
information technology (IT) has also been shown to play a critical role in mitigating the
informational related issues for inter-organizational humanitarian response (Comfort,
1990; Graves, 2004; Comfort & Kapucu, 2006; Moss & Townsend, 2006).
By using this combined theoretical approach, I intend to contribute to address some of the
shortcomings of the traditional models of assessing organizational effectiveness. As
discussed earlier, all these models have been criticized in the literature. For example one
main criticism to the Goal Model of organizational effectiveness has been that the model
does not provide measures of effectiveness which can be used to study many types of
44
organizations. Concerning the System Resource Model, it is said that there is confusion
around the difference between a multidimensional approach to effectiveness with
multiple measures of effectiveness, and a multidimensional approach with multiple
measures of a series of different analytical concepts (Price, 1971). One of the criticisms
to the Multiple Constituencies Model is that expectations of stakeholders can change
sometime dramatically over the time. Moreover, a variety of contradictory expectations
are almost always pursued simultaneously in a network (Price, 1971).
In this study, I also intend to further extend the RVB by taking into consideration the
characteristics of the networks in which organizations are embedded. Unlike most RBV
studies that conceptualize organizations as atomistic profit-seeking entities, my study
views organizations as resource-sharing entities embedded in a web of complex inter-
organizational relationships. Reviewing the literature, I found two similar studies
including Zaheer & Bell (2005) and Arya & Lin (2007). The first was conducted in the
for-profit sector and the later in the nonprofit. None of the two studies explored network
level variables.
Zaheer & Bell (2005) investigated how innovative capabilities—both those internal to
organizations and those they access through their networks—influence the performance
of Canadian mutual fund companies. Their proposition was that organizations occupying
better network structures may be better able to exploit their internal capabilities and
therefor enhance their performance. They found that organization’s innovative
capabilities and its network structure both enhance organization performance. Their
findings also suggested that innovative organizations that bridged structural holes got a
further performance boost.
Arya & Lin (2007) extend the resource-based view perspective to a network of nonprofit
organizations by investigating the roles of organizational characteristics, partner
attributes, and network structures on organizational ability to acquire monetary and
nonmonetary resources through collaborations. The study views organizations as
resource-sharing entities that are embedded in complex network relations. The results of
this research suggested that nonprofit organizations that provided a broad range of
45
services enhanced their effectiveness from collaboration in terms of resource gains.
Findings of the study also suggested that high-status organizations were able to derive
critical resources from network involvement.
My study differs from these two on the following points: Firstly, both studies are
conducted on organizations that are geographically collocated (one country for Zaheer &
Bell (2005) and one city for Arya & Lin (2007). In my study I investigate organizations
that are geographically distant. Secondly, organizations investigated in both studies focus
on one specific type of activities. Zaheer & Bell (2005) studied mutual fund companies
and Arya & Lin (2007) examined organizations providing HIV/AIDS services. My study
explored organizations engaged in humanitarian assistance. In the humanitarian relief
field there is a wide range of needs requiring various types of services (e.g. provide
shelter, food, water, sanitation, coordination). Relief activities often involve
heterogeneous organizations including both for-profit and nonprofit.
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4 THEORETICAL FRAMEWORK
4.1 Introduction
In the humanitarian relief field, an integrated framework is required to understanding
effectiveness at organizational and network levels. I propose in this study, a framework
that incorporates three theoretical lenses including Social Network theories and Resource
Based View. In the subsection below, I provide a brief discussion of each of these four
theoretical approaches, followed by the description of my research model and
hypotheses.
4.2 Social Network Theories
Across the literature, the use of the term theory in relation to social networks varies
considerably. The phrase “social network theory” is often used interchangeably with
“social network analysis”. I thought that it was fundamental in my study to clarify
upfront, the distinction between social network theory and social network analysis. Social
network theories seek to explain the functioning of networks and the relationships that
interconnect network members. Social network analysis is the methodology used to
investigate network behavior. I elaborate more on social network analysis in the
Methodology chapter of this study.
A number of terms common to social network theories and social network analysis forms
the terminology of network research. They include:
• Network: an interconnected system
• Node/actor/social entity: “discrete individual, corporate or collective social units”
(Wasserman & Faust, 1999, p. 17)
• Ties: the relationship connection between pairs of nodes/actors/entities:
o Content: the resource shared, delivered or exchanged
47
o Directed/Asymmetrical: content flows in one direction
o Reciprocal/Symmetrical: content flows in both directions
o Undirected: physically proximate but no exchange, or the exchange is not
considered relevant to the research question
o Strong: close association, based on the research context
o Weak: distant association, based on the research context
• Structural properties:
o Size: the number of logically possible relationships; the reach or extent of
a network that describes the amount of information an actor will have
access to
o Density: the extent to which members are connected to all other members
o Degree: the number of connections from an actor to others, outgoing and
incoming
o Centralization: the extent to which a set of actors are organized around a
central point(s)
o Distance: the number of connections between actors and the number of
ways available to connect two actors
o Clusters: subgroups of highly interconnected actors
o Cliques: fully interconnected clusters
• Network positions:
o Prominence: a network position of distinction that describes the position
of an individual in a network as opposed to centralization that measures
the configuration of the network as a whole
o Brokerage: those network positions that provide bridging opportunities to
other networks; where entrepreneurial opportunities exist
o Equivalence: categorizing actors who have the same profile of relations
across all other actors in the network.
The application of social network theories to the study of groups and group dynamics has
its roots in the 1930s and the formulation of sociometry (Moreno, 1934). Social network
theories investigate the patterns of relationships among network members and the
48
structural network attributes (Wasserman & Faust, 1994). Social network theories suggest
that the patterns and implications of relationships demonstrate specific behavioral
principles and properties: “network theories require specification in terms of patterns of
relations, characterizing a group or social system as a whole” (Wasserman & Faust, p.
22). Understanding of these characteristics can help explain the functioning of networks.
Social network research explores form or relationships such as social groups, cliques,
social cohesion, roles, positions, dominance, social exchange, reciprocity. Important
contributions in the social network literature include the notion of structural holes (Burt,
1992); the distinction between weak and strong ties (Granovetter, 1973); the degree
centrality measures of network positions (Bonacich, 1987; Freeman, 1979; Ibarra, 1993;
Podolny, 1993); the measures of network density and social capital (Burt, 1992, 1997;
Coleman, 1988; 1990); the measures of network cohesion (Coleman et al., 1957); and the
concept of structural equivalence (Burt, 1987).
A large body of literature applies social network theories to the study of inter-
organizational networks and performance / effectiveness. Social network research
criticizes theories that seek to explain performance / effectiveness solely on the basis of
unilateral profit-seeking behavior in a resource-based or competition-oriented
environment (Granovetter, 1985; Gulati, 1995; Nohria, 1992). Instead, social network
researchers analyze inter-organizational relationship structures and examine the impact of
network-level structural and relational characteristics on organizational performance /
effectiveness.
Using social network theories in the study of organizational performance, social network
researchers have focused on the organization’s ego network, which encompasses the
focal organization (ego), its set of partners (alters), and their connecting relationships
(Wasserman & Faust, 1994). For example, by counting the number of alliance partners
and measuring structural equivalence, patent counts, and relative scope, Baum et al.,
(2000) found that the composition of alliance networks explains differences in
organizational performance. Ahuja (2000) examined the effects of direct ties, indirect
49
ties, and structural holes on innovation output. Arya & Lin (2007) found that nonprofit
organizations that provide a broad range of services enhance their effectiveness from
collaboration in terms of resource gains. Findings of the study also suggest that high-
status organizations are able to derive critical resources from network involvement.
Other social network studies have explored the effect of network structural characteristics
such as centrality, network density, and clique structure on network-level performance /
effectiveness in terms of outcomes (Provan & Milward, 1995; Provan & Sebastian, 1998;
Lerch et al., 2006). For example, the findings from Provan & Sebastian (1998) suggest
that the most effective networks are those that are integrated at clique or sub-network
level. Their findings also suggest that to be most effective, clique integration must be
intensive, involving multiple and overlapping relationships both with and across
organizations that compose the core of a network. Social network researchers have also
shown that strong ties differ from weak ties in terms of their effect on organizational
performance (Rowley et al., 2000).
4.3 Resource Based View
Resource Based View (RBV) theory conceptualizes organizations as heterogeneous
entities consisting of bundles of idiosyncratic resources (Penrose, 1959; Rumelt, 1984 ;
Wernerfelt, 1984). The Resource-Based View theory posits that organizations possess
resources, a subset of which enable them to achieve competitive advantage, and a subset
of those that lead to superior long-term performance. Barney (1991) identifies two
preconditions for competitive advantage including (i) resource heterogeneity and (ii)
imperfect mobility.
According to Barney (1991), resource heterogeneity requires that not all organizations
possess the same amount and kinds of resources. Imperfect mobility involves resources
that are non-tradable or less valuable to users other than the organization that owns them
(Peteraf, 1993). Generally, the organization is said to possess a set of resources that can
produce a positive, neutral, or negative impact on its overall competitive advantage. This
50
impact depends on two characteristics of each resource: its value and its rarity (Barney,
1991). In addition, the firm’s competitive advantage is influenced by interactions,
combinations, and complementarities across internal resources of the firm (Amit &
Schoemaker, 1993). The competitive advantage of the firm can be understood as a
function of the combined value and rarity of all firm resources and resource interactions.
4.4 Working Definitions
4.4.1 Effectiveness
As discussed earlier, effectiveness is a multidimensional concept that is especially
challenging to measure in humanitarian assistance and disaster relief which often involve
a large variety of stakeholders with diverse goals and for which outputs are not easily
operationalized. In my study, effectiveness has three dimensions. In the first dimension, I
measure effectiveness in term of level of activities in humanitarian assistance. The most
effective organization/network is the most active. For this dimension, I use the number of
funded projects as measure of effectiveness. In the second dimension, I measure
effectiveness in term of level of collaboration in humanitarian assistance. The most
effective organization/network is the one the most in collaborative activities. For this
dimension, I use the number of funding partners as measure of effectiveness. The third
and last dimension is perceptual. The most effective organization/network is the one that
displays the highest perception of effectiveness. In this research, I use indifferently the
term effectiveness and performance.
4.4.2 Network
In my research, the term network is used to describe multiple-organizational relations
involving multiple nodes of interactions. A network is group of organizations which, on a
voluntary basis, exchange information and undertake joint activities and which organize
themselves in such a way that their individual autonomy remains intact. In this definition
important points are that the relationship must be voluntary, that these are mutual or
51
reciprocal activities, and that belonging to the network does not affect autonomy and
independence of the members.
Multidimensional networks refer to networks examined at more than one level, with more
than one set of nodes and more than one type of link (Lee, 2008). Understanding
networks in the field of humanitarian relief can be enhanced by considering the content of
relationships that exist among organizations. Katz & Anheier (2005) identify the major
types of relationships among stakeholders (e.g. nongovernmental organizations,
international nongovernmental organizations, and international governmental
organizations) in responding to humanitarian disasters. They include information
exchange, project collaboration, participation in meetings and forums, or joint
membership in advocacy coalitions. My study will be concerned with two types of
collaborative relationships, namely projects and advice.
In the field of humanitarian relief, inter-organizational networks can be classified into
two types: those oriented to project implementation and those oriented to information-
sharing (Lee, 2008). The purpose of the implementation network is to implement
humanitarian relief projects. Implementation networks are activity-focused, project-
based networks which rely on partnerships to draw on resources such as funding, and
skills from various partners (Unwin, 2005). This type of network applies more to the field
of humanitarian relief since projects are more often implemented by numerous project
partners. Knowledge-sharing networks on the other hand are often formed through
affiliation to common events, such as global and regional committees, forums,
conferences, and publication activities (Katz & Anheier, 2005). These networks enable
organizations to be informed of their partner’s and community’s activities as a whole.
The networks investigated in this dissertation can be considered as a hybrid between
these two types of networks (implementation network and knowledge-sharing networks).
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4.4.3 Level of Analysis of Network Effectiveness
Irrespective of the purpose of a particular inter-organizational network, its effectiveness
can be conceptualized on at least three levels: the network, the individual organizational
level, and the beneficiary level. One of the main challenges to inter-organizational
network researchers is that the criteria for assessing and evaluating network effectiveness
will vary depending on the level being considered (Provan & Milward, 2001; Lemieux-
Charles et Al., 2005). I review below the criteria used for assessing network effectiveness
for the three levels in nonprofit sector.
4.4.3.1 Network Level
At the network level, network effectiveness is perceived as outcomes resulting from the
functioning of the network as a whole and whose benefits accrue to all members,
although not necessarily equally (Sydow & Windeler 1998; Provan & Milward, 2001).
Criteria for measuring network effectiveness at network level include improve
coordination, network membership growth, decrease service duplication, range of
service provided, absence of service duplication, relationship strength (multiplexity),
member commitment to network goals (Provan & Milward, 1995 ; Sydow & Windeler,
1998; Provan & Milward, 2001). The network level sees outcomes of network activity as
being beyond what each individual actor in the network can achieve alone and thus being
conditioned by variables at a higher level, beyond agency in the network, and include
structural factors.
4.4.3.2 Organizational Level
The second level of network effectiveness is organizational. It includes outcomes that
arise from the functioning of the network as a whole but generate specific benefits to
individual members. According to Sydow & Windeler (1998), at organizational level,
network effectiveness results from the part of the network effect which a particular
network member is able to appropriate and eventually to represent in it accounts.
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Whereas an overall network may be highly effective, individual members may be less so,
and benefits may be spread unevenly across the members. Examples of benefits to
individual organizations include increased opportunities for (i) building social capital,
(ii) sharing knowledge and information, (iii) obtaining resources including funding, and
(iv) better meeting clients’ needs (Provan & Milward, 2001) . Benefits that accrue to
individual organization may be determined by a host of factors, including the structure of
the network, the strength of relationship (multiplexity) and the demographics of the
individual organization.
4.4.3.3 Beneficiary Level
Network effectiveness can also be assessed from the point of view of the beneficiary of
the network activities. Criteria for assessing network effectiveness at the beneficiary level
include: service quality, perception of solving problems, range of services provided,
coverage, comprehensiveness (Provan & Milward, 2001). A highly effective network
from the perspective of its member organizations may have low levels of beneficiary
satisfaction. Hence, achieving network effectiveness at both the network and the
customer level requires communication. For example, research on networks of health
care providers found that each single network actor alone (healthcare provider in this
case) does not render the end user (the patient) the full service of “overall well-being”;
instead, this is done by a network of healthcare providers does (Provan & Milward,
1995).
In this study, the focus is on the first and second levels of network effectiveness. At the
network level, I seek to understand how network structure properties including network
density, centralization, connectedness, multiplex clique and clique overlaps impact
network effectiveness. I explore ego-net characteristics at organizational level.
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4.5 Research Models and Hypotheses
In this section, I develop two research models, one concerning the network level of
analysis and the second, related to the organizational level. I also present my hypotheses
for both research models.
4.5.1 Network Characteristics and Effectiveness
Exploring the influence of network structure on actors has long attracted the attention of
network researchers (Ahuja & Carley, 1999; Tsai & Ghoshal, 1998; Tsai, 2000; Nahapiet
& Ghoshal, 1998, McEvily & Zaheer, 1999; Nohria & Garcia-Pont, 1991; Wasserman &
Faust, 1994; Kilduff & Tsai, 2006; Provan et al., 2007). Many of the studies that have
explored the implications of network structures have focused in particular on centrality,
structural holes, density, and the existence of sub-networks or cliques. In my study, I also
investigate these characteristics.
4.5.1.1 Centrality
Researchers have studied different measures of centrality, including betweenness, degree,
and closeness. The literature on the three centrality measures (e.g. Freeman, 1979;
Wasserman & Faust, 1994; Kilduff & Tsai, 2006) suggests that each affects the network
in different ways. Degree centrality measures capture the actors with the most ties to
other actors in the network. The higher an actor’s degree centrality value the more direct
links that actor has with other actors in the network (Wasserman & Faust, 1994; Kilduff
& Tsai, 2006). The degree centrality of a network member may be seen as an indicator of
its potential communication activity (Freeman, 1979). Eigenvector centrality measures
the strength of a network member’s relationships to other members of the network and
the centrality of those other members (Faust, 1997). Flow betweenness centrality
measures the degree to which certain members of the network may be more central or
exercise greater influence due to their location on the paths between various
organizations (Wasserman & Faust, 1999; Faust, 1997). In this study, I use the degree
centrality measure.
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In the literature, centrally located network members are assumed to be important, while
peripheral network members are powerless (Knoke, 1990; Wasserman & Faust, 1994;
Stevenson & Greenberg, 2000; Kilduff & Tsai, 2006). The centrally located members are
enabled by their position to accomplish their purposes, but the peripheral members are
constrained by their position to powerlessness (Stevenson & Greenberg, 2000). Centrally
located network members, are likely to have advantages of information and resources
compared with those on the periphery since information and other resources are assumed
to flow more within the centrally located positions of a network (Knoke, 1990). Most
network researchers assume that peripheral network members are somehow
disadvantaged as compared with the centrally located members. However, peripheral
members, for some reasons, may want to stay in peripheral positions. For example,
peripheral actors have minimal obligations to others.
In the nonprofit context, findings form Galaskiewicz et al., (2006) suggest that
organizational centrality in inter-organizational networks may be more beneficial to
certain types of nonprofit organizations (public charitable nonprofits that rely on
donations), but may be less for others (public charitable nonprofits that depend on fees
and sales). It is also more likely that centrally located nonprofit organizations will be
closely monitored by partners as well as various funding agencies. This can further limit
the centrally located organization’s ability to devote all its resources to meet its goals. In
this study, I hypothesize that greater degree centrality increases organization
effectiveness measured both as the level of collaboration as well as the level of activities.
Hypothesis HO#1: Greater centrality increases organization effectiveness.
4.5.1.2 Structural Holes
Structural holes theory (Burt, 1992; Wasserman & Faust, 1994; Kilduff & Tsai, 2006)
highlights the benefits associated with making contacts that offer links to additional
resources without the costs associated with having more contacts than needed. According
to Burt (2000) an organization can obtain important performance advantages when
exploiting relationships to partners that do not maintain direct ties among one another.
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The absence of direct relationships among a organization’s partners (the presence of
structural holes) indicates that these partners are located in different parts of a network,
that they are connected to heterogeneous sources of information, and that their invitations
to interact present the focal organization with access to diverse opportunities (McEvily &
Zaheer, 1999). Network researchers have investigated the effect of structural hole on
network members. Members bridging structural holes have been frequently shown to
perform better than other members of the network (e.g., Finlay & Coverdill, 2000;
Hargadon & Sutton, 1997), whereas other studies have shown negative performance
effects of firms’ maintaining positions in open networks (e.g., Ahuja, 2000; Dyer &
Nobeoka, 2000). In this study, I hypothesize that organizations that bridge structural
holes will be well positioned to enhance their effectiveness measured both as the level of
collaboration as well as the level of activities.
Hypothesis HO#2: Bridging of structural holes increases organization
effectiveness.
4.5.1.3 Density
Density describes the overall communication links in a network and thus represents how
information flows among organizations. Kilduff & Tsai (2006) define density as the
number of links between members of the network compared to the maximum possible
number of links that could exist in the network. Researchers have used the concept of
density in a number of inter-organizational network studies and in various contexts (e.g.
Provan & Sebastian, 1998; Krackhardt, 1999; Sparrowe et al., 2001; Reagans &
Zuckerman, 2001). Findings from some of these studies were intuitive. For example,
Venkatraman & Lee (2004) found that the density of links in an inter-organizational
network tends to increase over time. Another example of intuitive result is Brown &
Ashman (1996). Findings from this study suggest that dense networks of local
organizations indicate high levels of social capital. Some other studies produced
counter-intuitive results. For instance, Provan & Sebastian (1998) study of the networks
of mental health agencies operating in three cities showed that the city with the lowest
network-wide density of ties among agencies had the highest effectiveness, whereas the
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city with the highest density of ties among its agencies had the lowest effectiveness. Here
I make the following two hypotheses:
Hypothesis HN#1: Network effectiveness increases with network density.
Hypothesis HO#3: Organization effectiveness increases with the density of the
network it which it belongs.
4.5.1.4 Cliques
A network clique consists of actors who all are interconnected but have no common links
with anyone else in the network (Wasserman & Faust, 1994; Kilduff & Tsai, 2006). In an
inter-organizational network, cliques may form on the basis of shared demographic
characteristics (Mehra et al., 1998). Cliques can also be created based on the provision of
a certain set of services (Morrissey et al., 1994; Provan & Sebastian, 1998). Studies on
cliques in inter-organizational networks have found that they can play important roles in
the creation of positive outcomes (Provan & Sebastian, 1998; Lerch et al., 2006). Provan
& Sebastian (1998) for example found that network effectiveness can be explained
through the intensive integration via network cliques. My hypotheses here are that:
Hypothesis HN#2: Network effectiveness increases with the number of cliques in
the network.
Hypothesis HN#3: Network effectiveness increases with the number of
organizations in cliques.
Hypothesis HO#4: Organization effectiveness increases with the number of
cliques to which it belongs.
4.5.1.5 Overlapping Clique
Clique overlap refers to the extent to which members of a clique interact with members
of other cliques (Kilduff & Tsai, 2006). Grouping network members into cliques is
important to understanding how the network as a whole is likely to behave. For example,
when cliques overlap it can be expected that conflict between them is less likely than
when they do not overlap. Also, when cliques overlap, resources can be mobilized and
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shared effectively across the entire network; when they do not overlap, resource sharing
may occur in one clique and not occur in others. In Provan & Sebastian (1998), clique
overlap was a measure of mental health system integration. Findings from this study
suggests that agencies involved in cliques related to sharing of information and referrals
represent stronger clique overlap relationships and these might be tied to outcomes.
Provan & Sebastian posited that to be effective, clique integration must be intensive,
involving multiple and overlapping links both within and across the organizations that
compose the core of the network. Researchers have argued that the high levels of
communication or inter-organizational activity occurring within cliques is a form of
coordination (Bolland & Wilson 1994). In this study, I hypothesize that the higher the
level of overlapping clique, the greater the network effectiveness.
Hypothesis HN#4: Network effectiveness increases with the level of overlapping
clique in the network.
4.5.1.6 Multiplexity
Two organizations have multiplex ties if they are connected in more than one type of
relationships (Scott, 1991; Provan & Milward, 2001; Kenis & Knoke, 2002; Kilduff &
Tsai, 2006). For instance, two organizations that are project partners and also maintain
advice relationship are connected with a multiplex tie. In an inter-organizational
network, multiplex ties would allow each organization to exchange several different types
of resources with any other organization in the network rather than exchange only a
single type of resource.
Multiplexity can be measured at the individual network member level and at the level of
the whole network. A high degree of multiplexity of a member indicates high
embeddedness of the member in a network and signifies less liability to disruption of
single relationships. A member with a large number of multiplex relations is expected to
have a high potential of mobilizing different resources and information through these
relations. On the other hand, such a member is subject to a high level of social control. At
the network level, the degree of multiplexity specifies the overlap between the different
relation-specific networks. For evaluating network effectiveness, multiplexity can be a
59
particularly useful measure (Provan & Milward 2001). Effective networks might have a
majority of network members connected through two or more different types of
relationships. In this case, multiplexity will be high, reflecting commitments among
network members to one another through multiple activities. In this study, my hypothesis
is that Network effectiveness increases with its level of multiplexity.
Hypothesis HN#5: Network effectiveness increases with the level of multiplexity in
the network.
Hypothesis HN#6: Network effectiveness increases with the level of identical
cliques in the network.
4.5.2 Organizational Characteristics and Effectiveness
4.5.2.1 Organization Size
A general assumption in organizational theory is that organizations that are large or have
many constituents have more inter-organizational relationships compared with small
organizations (Blau & Schoenherr, 1971; Knoke & Wood, 1981). Large organizations are
apt to have more funds, larger facilities, larger and more diversified staffs, more clients,
more visibility and prestige, and better connections with community center power. These
resources make it highly likely that large organizations will be the object of large
dependency relationships initiated from smaller organizations (Lincoln & McBride,
1985). Size of an organization has also been recognized as an important determinant of
an organization's role in social service systems. According to Banaszak-Holl et al., (1998)
large organizations are often situated at the center of the delivery system. They have the
largest client populations and are responsible for referring large numbers of clients,
distributing funds to other agencies, and playing an important role in planning activities.
For Graddy & Chen (2006), large organizations are presumably better able to absorb the
costs of developing and sustaining partnerships. These transactions costs are considerable
and include the administration and coordination of the partnership function, partner
search costs, and the costs associated with negotiating and monitoring the terms of the
contract. Large organizations are expected to have sufficient budgets and staff to support
the development of effective networks (Graddy & Chen, 2006). Small organizations,
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however, may have greater need to form partnerships, as they are more likely to lack
requisite resources to meet contractual requirements. Large organizations, in contrast, are
more likely to have the internal capacity to deliver required services and thus have less
need for external collaboration. If an organization is able to provide most of their clients’
services internally, it has less incentive to form partnerships. Thus, size is associated with
differences in both the ability and the need to form partnerships. These effects may offset
each other, obscuring the role of organizational size in this relationship. I hypothesize
that the size of an organization is positively associated with its effectiveness
Hypothesis HO#5: The size of an organization is positively associated with its
effectiveness.
4.5.2.2 Range of Services Provided
The range of services a humanitarian organization provides (food, shelter, water,
sanitation, medical care, information services (media/coordination), IT infrastructure
and/or applications) is an important resource dimension on which organizations/
networks vary. Organizations delivering many different services are able to meet clients
with diverse needs. The power and autonomy of these organizations in the network are
likely to be high, for they are less dependent on the services of other organizations
(Kobrin & Klein, 1980). It is difficult to conceive of a plausible argument relating
diversity per se to homopholous interaction. This is particularly true of specialized
organizations. Those delivering the same services might find mutual support in
interaction, but equally specialized organizations performing quite different functions are
not apt to see themselves as like-minded peers. My hypothesis for this study is that the
range of service provided by an organization is positively associated with its
effectiveness.
Hypothesis HO#6: The range of service provided by an organization is positively
associated with its effectiveness.
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4.5.2.3 Information Technology
Information Technology (IT) “is a general term that describes any technology that helps
to produce, manipulate process, store, communicate, and/or disseminate information”
William & Sawyar (2005). Research has shown that information technology contributes
to the improvement of organizational performance (Mukhopadhyay et al., 1995;
Brynjolfsson & Hitt, 1996; Kohli & Devaraj 2003; Melville et al., 2004). For example,
Melville et al., (2004) found that information technology is valuable to organizations,
offering a wide range of potential benefits ranging from flexibility and quality
improvement to cost reduction and productivity enhancement. Previous research has also
revealed that the dimensions and extent of information technology value depend on a
variety of factors, such as the type of information technology, management practices, and
organizational structure (Dewan & Kraemer 2000; Cooper et al. 2000). Research has
also shown that the use information technology may have a positive impact on inter-
organizational collaboration and coordination (Malone & Crowston, 1994).
In the specific domain of humanitarian assistance, a rich body of literature points to the
critical role information technology plays in complex inter-organizational disaster
response plans (Comfort, 1993; Comfort et al., 2001; Moss & Townsend, 2006). Wentz
(2006) presents current knowledge and best practices in creating a collaborative, civil-
military, information environment to support data collection, communications,
collaboration, and information-sharing needs in disaster situations and complex
emergencies. Comfort (1993) identifies three main roles of information technology in
managing humanitarian disaster including. According to the author, information
technology enables disaster managers to create an interactive network that facilitates
communication and focuses attention on the same problem at the same time. The second
role identified by Comfort (1993) is that information technology allows the
representation of information in graphic form, thus simplifying complex data and
increasing the speed and accuracy of communication. Thirdly, information technology
enables and facilitates the development of a database for a given community which stores
relevant information about the community and its population and assists managers in
quickly formulating alternative solutions for assistance.
62
Additionally, the convergence of information and communication technologies, the
growth of the Internet including the mobile Internet, and the advent of social media also
termed social software are providing a significant contribution to the international
community ability to collaborate more efficiently in disaster relief. Suter et al., (2005)
defines social software “as a tool for augmenting human social and collaborative abilities,
as a medium for facilitating social connection and information interchange, and as an
ecology for enabling a 'system of people, practices, values, and technologies in a
particular local environment'" (p. 48). Examining the impact that these new technologies
are having on organizations, researchers on public relations have claimed that the
development of blogging and other aspects of the social media has significantly
empowered a wide variety of strategic organization stakeholders by giving them dynamic
opportunities that many are using to communicate more effectively (Wright & Hinson,
2006; 2007; 2008). Research on new social software have also highlighted the important
role that these technology play in humanitarian assistance and disaster relief (Palen et al.,
2007a; 2007b; 2007c; Sutton et al., 2008; Hughes et al., 2008; Liu et al., 2008 ; Vieweg
et al., 2008). Humanitarian organizations have been exploring these technologies as a
way of maximizing the effectiveness of their responses to emergent disasters and
enhancing the delivery of humanitarian relief to affected communities around the globe
(Zhang et al., 2002; Van de Walle et al., 2009).
Based on their functionality, social software is grouped into three major categories
including communication, collaboration and community (Clearinghouse, 2008a; 2008b;
2008c). In this study, I make the following hypotheses on the relationships between
information technology related variable and organizational effectiveness:
Hypothesis HO#7: The greater the variety of communication media available in
an organization, the higher its effectiveness.
Hypothesis HO#8: The greater the variety of collaboration social software
available in an organization, the higher its effectiveness.
Hypothesis HO#9: The greater the variety of community social software available
in an organization, the higher its effectiveness.
63
The following two hypotheses are meant to assess the impact of the inter-action of
information technology and network structural characteristics on organizational
effectiveness.
Hypothesis HO#10: Organizations that possess a wide variety of communication
media will benefit more from high network degree centrality to enhance their
effectiveness than those that do not.
Hypothesis HO#10: Organizations that possess a wide variety of communication
media will benefit more from high network density to enhance their effectiveness
than those that do not.
In the table below (Table 5) I summarize the hypotheses formulated for this research.
Number Hypothesis Level of analysis
Hypothesis HO#1 Greater centrality increases organization effectiveness Organization
Hypothesis HO#2 Bridging of structural holes increases organization effectiveness Organization
Hypothesis HN#1 Network effectiveness increases with network density Network
Hypothesis HO#3 Organization effectiveness increases with the density of the network it which it belongs
Organization
Hypothesis HN#2 Network effectiveness increases with the number of cliques in the network
Network
Hypothesis HN#3 Network effectiveness increases with the number of organizations in cliques
Network
Hypothesis HO#4 Organization effectiveness increases with the number of distinct cliques to which it belongs
Organization
Hypothesis HN#4 Network effectiveness increases with the level of overlapping clique in the network
Network
Hypothesis HN#5 Network effectiveness increases with the level of multiplexity in the network
Network
Hypothesis HN#6 Network effectiveness increases with the level of identical cliques in the network
Network
Hypothesis HO#5 The size of an organization is positively associated with its effectiveness
Organization
Hypothesis HO#6 The range of service provided by an organization is positively associated with its effectiveness
Organization
Hypothesis HO#7 The greater the variety of communication media available in an organization, the higher its effectiveness
Organization
Hypothesis HO#8 The greater the variety of collaboration social software available in an organization, the higher its effectiveness
Organization
Hypothesis HO#9 The greater the variety of community social software available in an organization, the higher its effectiveness
Organization
64
Hypothesis HO#10 Organizations that possess a wide variety of communication media will benefit more from high network degree centrality to enhance their effectiveness than those that do not.
Organization
Hypothesis HO#11 Organizations that possess a wide variety of communication media will benefit more from high network density to enhance their effectiveness than those that do not.
Organization
Table 5: Summary of Hypotheses
The two figures (Figure 4 and Figure 5) below, depict my research models.
NETWORK
CHARACTERISTICS
Perceived effectiveness
Number of funded projects
Number of funding partners
EFFECTIVENESS
Network Attributes
Density (+)
Clique (+)
Org in Clique (+)
Clique overlaps (+)
Multiplexity (+)
Identical clique (+)
Figure 4: Research Model for Network Level of Analysis
INTERNAL
CHARACTERISTICS
Number of funded projects
Number of funding partners
EXTERNAL
CHARACTERISTICSEFFECTIVENESS
Organization Attributes
Size (+)
Service provided (+)
Communication media (+)
Collaboration social media (+)
Community social media (+)
Ego-net Attributes
Degree centrality (+)
Structural hole (+)
Number of clique (+)
Network Attributes
Density (+)
Figure 5: Research Model for Organizatioanl Level of Analysis
65
5 METHODOLOGY
5.1 Introduction
This chapter presents the research design and method that I used in my study.
5.2 Research Design
I use a mixed methods research design (Tashakkori & Teddlie, 2003) to explore
multidimensional inter-organizational networks of collaborative relationships among
humanitarian organizations that are members of the Global Symposium. Using mixed
method allowed me to leverage on both the quantitative and the qualitative research
techniques.
Quantitative researchers use postpositivist propositions for developing knowledge, such
as hypotheses and questions, reduction to specific variables, and the test of theories.
Numerical data and statistics are their main instrument (Charles & Mertler, 2002). They
isolate variables and causally relates them to determine the magnitude and frequency of
relationships. In addition, they determine themselves which variables to investigate and
chooses instruments, which will yield highly reliable and valid scores (Ivankova et al.,
2006). Contrary to quantitative research, qualitative research is interpretive or
constructive. It is “an inquiry process of understanding” where the investigator develops
a “complex, holistic picture, analyzes words, reports detailed views of informants, and
conducts the study in a natural setting” (Creswell, 1998, p. 15). In qualitative research,
the investigator makes knowledge claims based on the constructivist (Guba & Lincoln,
1989) or advocacy/participatory (Mertens, 2003) perspectives. In this approach, data is
collected from those immersed in everyday life of the setting in which the study is framed
(Ivankova, et al., 2006). Data analysis is based on the values that these participants
perceive for their world. According to Miller (2000), qualitative research produces an
understanding of the problem based on multiple contextual factors.
66
Combining both quantitative and qualitative in a mixed methods approach, the
investigators develop and build the knowledge on pragmatic grounds (Creswell, 2003;
Maxcy, 2003). They choose approaches, as well as variables and units of analysis, which
are most appropriate for finding an answer to their research question (Tashakkori &
Teddlie, 1998). A major tenet of pragmatism is that quantitative and qualitative methods
are compatible. Thus, both numerical and text data, collected sequentially or
concurrently, can help better understand the research problem (Ivankova, et al., 2006).
Mixed methods involve collecting, analyzing and combining both quantitative and
qualitative data within a single study. According to Creswell (2002), mixed methods help
to understand a research problem more completely. Another argument for mixed methods
is that neither quantitative nor qualitative methods are sufficient by themselves to capture
the details of the situation, such as a complex issue information management and
exchange among organizations engaged in disaster relief. When the quantitative and
qualitative methods are combined, they complement each other and allow for more
complete analysis (Green et al., 1989, Tashakkori & Teddlie, 1998).
While designing my research, I considered the following three important issues: priority,
implementation, and integration (Creswell et al., 2003). Priority refers to which method,
either quantitative or qualitative, is given more emphasis in the study. Implementation
refers to whether the quantitative and qualitative data collection and analysis comes in
sequence or in chronological stages, one following another, or in parallel or concurrently.
Integration refers to the phase in the research process where the mixing or connecting of
quantitative and qualitative data occurs. Quantitative method had the priority in my study.
I first collected quantitative data through a series of three surveys and then conducted
interviews to collect qualitative data. I integrated the qualitative and quantitative data in
the analysis phase. The research took place over a two-year period, encompassing
numerous interactions with various network members and feedback with the research
participants. Data analysis involved multiple levels of social network analysis, statistical
analysis and a combination of inductive and deductive content analysis techniques.
67
5.3 Research Participants
My research explored inter-organizational networks in the Global Symposium, a
community of interest in humanitarian information management and exchange
spearheaded by the United Nations Office for the Coordination of Humanitarian Affairs
(UNOCHA). The research participants were representative of organizations member of
the Global Symposium who attended to at least one of the five Global Symposium
meetings. UNOCHA provided me with the list of all the attendees of the various Global
Symposium meetings. They were almost all high ranked senior staff (e.g., CEO, CIO, IT
Director) in their organizations.
As stated earlier, the Global Symposium is a community of interest in humanitarian
information management spearheaded by UNOCHA. A community of interest as defined
by Arias & Fischer (2000), is made up of individuals from different backgrounds that
come together to solve a particular problem of common concern. The Global Symposium
began its activities in 2002 as a meeting of humanitarian information management
professionals. This community of interest is made up of about 300 information
technology (IT) and information management (IM) professionals from roughly 120
international and national organizations in the field of humanitarian assistance. The goals
of the Global Symposium include (i) to foster collaboration among members on
humanitarian information management related projects, (ii) to disseminate best practices
of information exchange, (iii) to sensitize its members on the critical aspect of
humanitarian information management preparedness and (iv) to facilitate headquarter-
field partnerships and to advocate for more funding from donors for humanitarian
information management related projects.
For both theoretical and empirical reasons, I subdivided the Global Symposium
community into different sub-networks. I identified three sub-networks including the
non-governmental organizations (NGO) subnet, the United Nations agencies (UNA)
subnet, and the governmental organization (GO) subnet. Separating members of a
network into subnets and analyzing how they overlap can be an important means for
68
understanding how the network as a whole is likely to facilitate or constrain certain
actions of these members (Giddens 1984; Sydow & Windeler 1998). Although the
members of the Global Symposium are all interested in humanitarian relief and especially
humanitarian information management and exchange, they theoretical differ on a good
number of characteristics including their missions / goals, their sources of funding and
their mode of governance. The three subnets were also identified based on UNOCHA
categories of organization in the humanitarian relief field. I briefly describe below, the
general characteristics of the organizations member of each of the three sub-networks.
a) NGO
NGOs are “private organizations that pursue activities to relieve suffering, promote the
interests of the poor, protect the environment, provide basic social services, or undertake
community development” (World Bank, 2000). One of the long-established activities of
these organizations is to provide humanitarian assistance. NGOs engage in two broad
types of activities including relief activities and development activities. Relief activities
consist of assisting to victims of natural or manmade disasters. Relief NGOS frequently
specialize in one or more of the five activities that are commonly understood to compose
the relief discipline: food distribution, shelter, water, sanitation and medical care.
Development activities are longer-term assistance, focusing on community self-
sufficiency and sustainability. These activities include establishing permanent and
reliable transportation, healthcare, housing, and food. NGOs’ resources come primarily
from private sources and major donor government contributions. NGOS are governed by
boards of directors that tend to reflect the particular culture, history and mandates of the
organizations concerned. In my study, the NGO subnet was made up of 72 organizations.
b) UNA
The United Nations (UN) plays a vital role in humanitarian assistance. For this endeavor,
the institution operates several major organizations among which five are such visible
players in most complex humanitarian emergencies that describing their functions and
mandates will describe most if not all of the operational work of the entire UN system in
relief operations. They are the World Food Program, the Office of the United Nations
69
High Commissioner for Refugees, the United Nations Children's Fund (UNICEF) and the
United Nations Development Program (UNDP) and the UN Office for the Coordination
of Humanitarian Affairs (UNOCHA). The UN World Food Program functions as the
food aid agency of the UN system, providing a central coordinating role in developing
crop production estimates, food aid requirements and logistics planning for major relief
operations. UNICEF'S special mandate is to focus on the relief and development needs of
women and children, which has made it the focal point among the UN agencies for
emergency medical interventions, mass inoculation campaigns for children, water and
sanitation programs and therapeutic feeding programs for severely malnourished children
in emergencies. UNDP technically has the mandate to manage UN emergency operations
in the field while UNOCHA is charged with the coordination and synchronization of
United Nations humanitarian efforts. Each of these UN organizations depends for funding
on the goodwill of member governments and/or the broader populations of those nations.
In my study, the UNA subnet comprised 25 agencies.
c) GO
Governmental organizations are owned by governments. Governmental organizations
work to achieve the goals set by the government. These goals are often set for political
reasons. The managers of these organizations are appointed by the government. The
government also provides the necessary resources to these organizations. In my study, the
GO subnet was made up of 53 organizations.
Subdividing the Global Symposium community in sub-networks also had an empirical
justification. Using social network block model I found that these sub-networks
presented diversified patterns of inter-organizational relationships. The level of inter-
organizational relationships (measured as network density) ranged from 0.076 to 0.193
for project collaboration dimension and from 0.025 to 0.074 for the advice dimension.
The United Nations agencies sub-network displayed was the most strongly
interconnected on both dimensions followed respectively by the non-governmental
organizations subnet and lastly the governmental organization subnet. On the project
collaboration dimension for example, approximately twenty percent (19.30%) of all the
possible project collaboration relationships between the organizations in the United
70
Nations agencies sub-network were actually found to exit. In contrast, only about eight
percent (7.6%) of all possible linkages between organizations in the governmental
organizations sub-network were found to exist. On the advice dimension, these
percentages were respectively (7.42%) for the United Nations agencies sub-network,
(2.92%) for the non-governmental organizations subnet and (2.52%) for the
governmental organizations subnet.
GO 0.071
0.193
UNA
NGO
0.0780.076
0.1
360.
128
Figure 6: Global Symposium Project Collaboration Sub-Networks
GO 0.0218
0.0742
UNA
NGO
0.02920.0252
0.0
3110.
0336
Figure 7: Global Symposium Advice Sub-Networks
71
An examination of the level of interaction cross the three subnets also shown a significant
discrepancy for both project collaboration and advice dimensions of relationships. Figure
6 and Figure 7 above depict these differences.
Understanding networks in the field of humanitarian relief can also be enhanced by
considering the different type of relationships that exist among organizations. In my
study, I investigate two types of inter-organizational relationships in the community. I
study the relationship on inter-organizational collaboration on humanitarian project
among members of the Global Symposium and the advice relationship.
5.4 Data Collection Instruments
I used multiple instruments to collect data including surveys, interviews and online database
search. Collecting data by different methods from different sources produces a wider scope of
coverage and may result in a fuller picture of the phenomena under study than would be achieved
otherwise (Bonoma, 1985). The data captures both the whole network and the individual
organizational perspectives on inter-organizational humanitarian information exchange
relationships among members of the Global Symposium community.
5.4.1 Survey
A survey instrument that also contains network-related questions was my main data
collection instrument (See Appendix B). I conducted a series of three surveys (October
2007, May 2008 and July 2009) and used two different types of survey instrument. The
first survey was paper based and the two subsequent were web based. The electronic
form of the survey instrument was designed based on the quality criteria identified in
(Wright, 2005). It had a simple layout using a straightforward navigation strategy. I kept
graphics and color to a minimum in other to minimize the downloading time. I used the
Survey Monkey software to develop the survey. I chose this software mainly because I had
earlier used it in several research projects with my adviser. I had developed extensive
experience and a high skill set in the use of that software.
72
On the survey instrument, an introduction page including an informed consent preceded the
survey questions. The purpose of the survey introduction page was multiple. First, I wanted
to create a trusting relationship with survey participants by repeating the survey purpose
already explained in the invitation letter. Second, the introduction page was intended to offer
a non-financial incentive – a report of the results, and to guarantee confidentiality and
privacy of research participants. Third, I wanted to provide a third party guarantee of the
survey’s authenticity and credibility by stating the University’s Institutional Review Board
(IRB) approval. The informed consent asked participants to give their permission for the
survey.
Following the introduction page, came the survey questions. Though they were not
completely identical cross surveys, there were significant overlaps especially with
regards to inter-organizational relationship questions. In general, the questions included
the following four categories: (i) respondent’s organization information; (ii) issues on
humanitarian information management and exchange in the Global Symposium
community; (iii) Global Symposium community collaborative benefits and effectiveness,
and (iv) the community inter-organizational networks. For questions concerning the
inter-organizational network, survey participants were provided with the list of members
of the Global Symposium community and were asked to identify (i) those with which
they had collaborated on humanitarian projects and (ii) those with which they had advice
relationships.
Most of the questions were structured using a five point Likert scale (Likert, 1932). For
every question or statement, I provided respondents with five choices representing the
degree of agreement on the question. For the network question, the survey instrument
included the list of all the organizations member of the Global Symposium. With regards
to conducting the Surveys, the paper based survey was administered during the 2007 Global
Symposium meeting. Survey questionnaires were handed to participants. They had to
compete it and turned back by the end of the conference. With regards to the electronic
surveys, the survey invitation was sent through direct email to each participant. The
invitation was a shortened version of the survey introduction page. After reading the
invitation, online community members ignored the post or self-selected to take the survey by
73
clicking on the survey URL. Two follow-up “reminder” invitations were sent approximately
one week apart to the participants. All inquiry email, whether sent as a reply to the invitation
was responded to as soon as I got the mail.
Both the electronic and paper forms of survey instrument have advantages and
drawbacks. By using a combination of the two forms, my intention was to leverage on
their advantages to limit the impact of their disadvantages on my research. As compared
to paper based surveys, electronic surveys present many advantages. They provide a way
to conduct studies when it is impractical or financially unfeasible to access certain
targeted populations (Couper, 2000; Sheehan & Hoy, 1999; Weible & Wallace, 1998)
and they are very cost effective as the costs per response decrease as sample size
increases (Watt, 1999). Electronic surveys also provide potentially quicker response time
with wider magnitude of coverage (Wright, 2005). Online surveys may also save time by
allowing researchers to collect data while they work on other tasks (Llieva et al., 2002) .
Moreover, electronic surveys are easy to edit.
One of the most significant weaknesses of electronic surveys is related to the access
issue. Electronic surveys’ population and sample are limited to those with access to
computer and online network. This weakness will not hamper my research since my
targeted population is made up of information professionals that are computer literate and
that have access to the Internet. Another important drawback of electronic survey is
related to sampling (Andrews et al., 2003; Howard et al., 2001). More often, relatively
little is known about the characteristics of people in online communities, aside from some
basic demographic variables, and even this information may be questionable (Dillman,
2000; Stanton, 1998). Once more this weakness was not an issue in my research since
my target was the whole community.
5.4.2 Interviews
Semi-structured Interview was the second data collection instrument that I used (See
Appendix C for the interview guide). My intent was to supplement the quantitative survey
data with a more detailed description and explanation of activities in the Global Symposium
74
community. Using semi-structured interviews allowed me to follow the same basic lines
of inquiry, which makes it more structured than the informal conversational interview,
but also leaves room for the interviewer to explore the topic while keeping focus on a
particular subject (Patton, 2002; Mason, 1996). Semi-structured interviews also allowed
the participants to explore the responses to both surveys and discuss the humanitarian
information exchange activity in the Global Symposium community. Maintaining
flexibility with the interview questions was important to eliciting the most useful
responses (Schensul & LeCompte, 1999).
The interviews focused various aspects related to the effectiveness of inter-organization
humanitarian information exchange networks in the Global Symposium community. The
majority of questions were taken directly from the survey projects with the intent to
maintain a semblance for comparison between the surveys and interviews. Questions
were then modified into open-ended, semi-structured format. Some questions were also
added during interview, to address areas not covered by or areas requiring further detail
than in the initial survey.
5.4.3 Database Search
Our third data source was the ReleifWeb Financial Tracking Service (FTS) . FTS is an
online database which records all reported international humanitarian financial assistance
(Office for the Coordination of Humanitarian Affairs (OCHA), 2010). I collected data
related to the amount of funding raised, the number of funded projects and the number of
funding partners of organizations member of the Global Symposium+5 community. In
the humanitarian relief literature, data from the FTS database has been used in a number
of academic work and reports to donors (e.g. Torrente, 2004; Walker et al., 2005; Amin
& Goldstein, 2008; VanDeWalle & Turoff, 2008; Tomaszewski & Czaran, 2009).
75
5.5 Data Collection
5.5.1 Survey Data
I conducted a series of three surveys (October 2007, May 2008 and July 2009) among
organizations/agencies members of Global Symposium+5. The first survey was paper
based, administered during the Geneva 2007 meeting of the Global Symposium+5. The
two subsequent surveys were web based, administered in May 2008 and July 2009 to all
those who attended either of both of the Global Symposium+5 meetings (Geneva, 2002;
Geneva, 2007) or were at one of the regional conferences (Bangkok, 2003; Panama,
2005; Nairobi, 2006). The UNOCHA provided us the list of participants to these various
meetings. The majority of respondents considered themselves to be managers, working in
the field of information management, and located at headquarters. Table 6 below
presents organizations’ participation in the various surveys.
Network
Number of
Organizations
surveyed
Number of responses
October
2007
May
2008
July
2009
Government
Organizations 37 7 11 10
Non-Governmental
Organizations 48 17 19 13
United Nations Agencies 24 12 10 10
Private sector 10 2 3 3
Total 119 38 43 36
Response rate 31.93% 36.13% 30.25%
Table 6: Surveys’ Participation
The coding of network data began by assigning unique identifiers to each organization
member of the Global Symposium community. The community is made up of
approximately one hundred and twenty (120) members. Codes ranged from OGR001 to
ORG120. An organization’s code was unique cross the three surveys. Since in my
research I was not interested in the dynamics of the networks, I merged into a single
dataset the data from the three surveys. Combining data collected at different point in
time is common practice in the literature (e.g., Hartley, 1958; Jorgenson et al., 1982;
76
Bound et al., 1986; Arellano, 1992). I merged the data after conducting a Quadratic
Assignment Procedure (QAP) analysis. The QAP procedure (Krackhardt, 1988) is
principally used to test the correlation between networks. Using this procedure, I found
no statistical significant correlation among the three sets of survey data.
Organizations were then grouped into three networks including (i) governmental
organizations, (ii) nongovernmental organizations, and (iii) United Nations
organizations/agencies. These networks were identified based on the UNOCHA
categories
The next stage of data analysis began with the construction of binary adjacency matrices.
These matrices represent the linkage between organizations in the relationships
measured, with a 1 entered in a cell to indicate the presence of a tie between actors or a 0
entered if there is no tie. For example, in the case of the project collaboration
relationship, a one indicates a survey participant from organization identified another
organization as partner in a collaborative humanitarian project. The matrices were
asymmetric because one participant identifying an organization as partner does not
necessarily equal a reverse identification. The ties between organizations are directed ties
going from a source to a receiver and in the matrix, the rows representing the origin of
the directed ties, and the columns the targets (Wasserman & Faust, 1994).
As I mentioned earlier, I studied multidimensional networks. Organizations members of
the Global Symposium interact on several dimensions. My study focused on two
dimensions including (i) humanitarian information management project collaboration,
and (ii) advice seeking/receiving. I considered a project collaboration linkage to exit
between two organizations if a survey participant from either of the two organizations
reported that the two organizations had collaborated on a humanitarian information
management project. Similarly, an advice linkage exited if a survey respondent from
either of the two organizations reported that one organization had provided or had
received advice from the other. On the networks diagrams presented below (Figure 6, 7
77
and 8) the blue links represent the project collaboration relationships while the black
links represent the advice relationships.
Figure 8: United Nations Agencies Network
Structure
Figure 9: Non-Governmental
Organizations Network Structure
Figure 10: Governmental Organizations Network Structure
5.5.2 Interview Data
I started the interviews at the end of the third survey. Interviewing spanned over a period
of four months (September to December 2009). Participants to the interview were
recruited through the survey instrument. The survey included a question asking
participants if they were willing to be interviewed. I registered twenty five (25) positive
answers out of the eighty six (86) respondents of the survey. Due to certain
contingencies and scheduling constraints I ended up conducting nineteen (19) interviews.
78
The interviews were conducted over the phone. Each interview lasted between three
quarter and one and half hours. Prior to each interview, the participant consent was asked
for tape recording. All the interviewees gave their consent to be tape recorded. After the
interviews, in the privacy of my office, I transcribed manually all the recordings and
came out with document of approximately two hundred (200) pages. I reviewed each
interview, checked the spelling, adjusted the sentence structure (if needed), and printed
the interviews. I took great care not to change the intent of the participant or the integrity
of the interview. After the transcription, I sent each interview to the interviewee for
participant checking. I updated the transcripts with the feedback from the interviewees.
Because of the position of the interviewees in their organizations, the level of active
participation in humanitarian information exchange collaborative projects, responses
were accepted as credible accounts of comprehensive knowledge of organization
collaborative activities.
5.5.3 Database Data
I collected the data related to the number of funded projects and funding partners from
the ReleifWeb Financial Tracking Service, a UNOCHA web based database which
records all reported international humanitarian financial assistance (OCHA, 2010). The
the ReleifWeb Financial Tracking Service was implemented and launched in 1999. In the
humanitarian relief literature, data from the ReleifWeb database has been used in a
number of academic work and reports to donors (e.g. Torrente, 2004; Walker et al.,
2005; Amin & Goldstein, 2008; VanDeWalle & Turoff, 2008; Tomaszewski & Czaran,
2009). In order to increase the validity of data and the number of cases, I collected data
for a period of ten years (1999-2009). I realized that no data was available in this
database for organizations of the for-profit network. This network would be ranked in the
study using only the perceived network effectiveness criteria.
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5.6 Data Analysis Techniques
I used a combination of social network and statistical analyses techniques in my study.
5.6.1 Social Network Techniques
Reasons for Using Social Network Techniques
Social network analysis is appropriate for my research for the following four reasons.
First, Social network analysis is a powerful and relatively new research tool which has
developed popularity in recent years (Kilduff & Tsai, 2006; Quatman, 2008). The
network perspective offers some unique advantages to the research process. According to
Quatman (2008), network approaches allow for example for: (1) a concrete vitality for
several difficult-to-define constructs; (2) simultaneous analysis of multiple levels of
relational data thus providing some fluidity between micro-, meso-, and macro- linkages;
and (3) a unique integration of quantitative, qualitative, and graphical data producing an
intuitive, thorough, and rich analysis of phenomena .
Second, social network studies cover a wide range of research contexts. The utility and
applicability of social network analysis is very broad and has been embraced by
researchers in a number of fields (Kilduff & Tsai, 2006; Quatman, 2008). Several papers
provide quite extensive reviews and a variety of contextual examples of the uses of social
network analysis for research purposes such as: (i) Brass et al., (2004) and Parkhe et al.,
(2006) for management and organizational behavior topics; and (ii) Provan & Milward
(1995), Lemieux-Charles et Al. (2005), and Arya & Lin (2007) for health service
delivery.
Third, in a network approach, actors can be characterized by any type of entity embedded
within a larger system of entities (Granovetter, 1985; Wasserman & Faust, 1994; Kilduff
& Tsai, 2006). In the social sciences, the entities of interest are often individual people or
groups of people acting as a unit. In a network approach, researchers also have the
freedom to operationalize the relationships of interest between the actors. For example, a
80
researcher might explore friendship links between employees in an organization or
resource exchange links between organizations in a market.
Fourth, social network approaches also allow researchers to investigate several different
attributes of relational ties between actors (Wasserman & Faust, 1994; Kilduff & Tsai,
2006). Instead of simply considering whether or not a tie is present, a researcher can
examine additional implications from network configurations. For example, a network
investigation can incorporate such things as the intensity (often measured by strength or
frequency of interaction) and direction of ties (often used to represent the direction of
effect). In addition, a single set of network members can also be used to examine the
multiplexity of ties between members in the network. As discussed earlier, the
multiplexity of a tie refers to the extent to which two network members are linked
together by more than one relationship. Moreover, the attributes of the ties (for example
directionality, intensity, and multiplexity) do not have to be considered mutually
exclusively. In a nutshell, a network can be examined from any and all of these
perspectives simultaneously.
Social Network Analysis
In network analysis I first considered measurement of the basic network structural
properties. They included size, density, connectedness and centralization. This allowed
for consideration of whole network behavior as well as an understanding of individuals in
the networks. At the network level, the size of the network is an important consideration
for the potential reach or number of logically possible relationships for the number of
actors in the network. Network size at the individual level considers a number of factors
in relation to the number of adjacent actors.
As I mentioned earlier, the density of a network indicates to the number of recorded links
between network members in proportion to the number of all possible links within a
network. Density calculations illustrate the degree to which a network realizes its
potential, assuming that the optimum is a fully saturated network where everyone
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contacts everyone else. The organizational context is important in assessing the desired
density. With the humanitarian information management and exchange, high density in
project collaboration may indicate a tightly group who consult frequently to resolve
issues. On the other hand, a high density in advice may indicate many organizations
struggling with how to handle problems.
At the whole network level, another consideration is centralization. Centralization
measures the extent to which a network or group is organized around its central point
(Freeman, 1979). The arrangement of actors in the network affects how quickly and
easily information can be distributed among all the actors (Freeman, 1979; Wasserman &
Faust 1994; Haythornthwaite, 1996). Centralization is a measure of integration or
cohesion of the group. A centralized network may reflect an uneven distribution of
knowledge such that knowledge is concentrated in the focal points of the network. In
addition to matrix calculation, the sociograms illustrating these calculations are
particularly useful for viewing the different networks.
To consider network structural influence on individual actors and identify the variety of
network roles within the various networks, a number of adjacency calculations on the
direct connections from one member to another demonstrated the degree to which an
actor sends or receives information. For graph theorists, there exits four types of network
nodes including (i) isolate, (ii) transmitter, (iii) receiver and (iv) carrier. An isolate
neither sends nor receives information; a transmitter sends information; a receiver
receives information; and a carrier both sends and receives information (Wasserman &
Faust, 1994, p. 128). The outdegree calculated as the sum of connections an actor has to
others, is often used to measure an actor’s influence. In-degree links refer to the number
of actors sending information to the actor in question. Network members that receive a
lot of information may be more powerful, suffer from information overload, or hold a
position of prestige (Hanneman & Riddle, 2005, p. 43). Network members neither
sending nor receiving information either withhold information or fail to contribute to a
network. In this study, I did not take into consideration the isolates. I excluded all the
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isolated network members from my analysis. I considered the rest (those that had at least
one connection) as carriers.
The presence or absence of subgroups within the social network structures was a prime
consideration for the analysis of the inter-organizational collaboration activities among
members of the Global Symposium community. In some networks, a sub-group forms
when only two actors have a tie to each other. In others, groups of actors demonstrate
more ties to each other than to the other members of a network. This type of sub-group is
termed a clique, defined by Hanneman & Riddle (2005) as “some number of actors (more
than two, usually three is used) who have all possible ties present among themselves” (p.
80). This definition may restrict the concept’s application in many social networks. As a
result, an extension is the concept of the n-clique, where n is the maximum path length at
which members of the clique are considered connected. This extension “is much closer to
people’s everyday understanding of the word clique” (Scott, 1991, p. 115).
The impact of subsets within a social network may depend on the degree to which they
are connected. The examination of bridges, or critical ties between two actors, extends
from the consideration of small groups in a larger network. An actor who provides the
connection or critical tie to another group of actors performs “the liaison role of
connecting two otherwise disconnected cliques” (Kilduff & Tsai, 2003, p. 28). This role
becomes important in considerations of what happens if the connection drops, and the
value of maintaining or continuing to invest in the relationship.
Finally, the construction of sociograms to demonstrate visually some of the properties
across the networks assisted the analysis by highlighting numerous features for
consideration.
Unit of Analysis in Social Network
In network analysis there are four units of analysis that are frequently used. They include
dyads, triads, egocentric networks and whole networks (Wasserman & Faust, 1994). In
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my study, I am concerned with two units of analysis, the egocentric network and the
whole network. The egocentric network level has been used primarily to study network of
individuals. For example, Laumann (1973) analyzed friendship networks among urban
men; Granovetter (1974) studied how information about job is transmitted; and Minor
(1983) examined personal relationships among former heroin addicts. One problem with
the egocentric network level analysis when applied to organizational networks is that it
carries a connotation of individual relations, with a psychoanalytic orientation. For this
reason, I will refer to the unit of analysis as organizational-centered networks, implying a
focus on organizations as opposed to individuals.
This level of analysis consists of each organization along with all other organizations
with which it has a relationship. Generally, this unit of analysis is used to examine
attributes and characteristics of the relationships which exist between each organization
and all other organizations in its organizational-centered network. Each organizational-
centered network can be described by the number, magnitude, type and other
characteristics of it linkages with others in the network (Knoke & Kuklinski, 1982;
Streeter, 1989).
The network-level of analysis has also been intensively used in inter-organizational
network research. At the network-level of analysis, researchers look at the composition of
the networks (e.g., network size, network heterogeneity, mean frequency of contact) and
the structure of these networks (e.g., density of links among alters). According to
Wellman & Frank (2000), such analyses seek to understand how the properties of
networks affect what happens in them and to them. Provan et al., (2007) provide an
extensive and comprehensive review of inter-organizational network research conducted
at the network level of analysis. According to this review, research at network level has
mainly been conceptual, anecdotal, or based on single, descriptive case studies performed
at one point in time. Also most of the research reviewed by Provan et al., (2007) was
done in the health sector.
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5.6.2 Content Analysis
Content analysis “is a well-established set of techniques for making inferences from text
about source, content, or receivers of information” (Schamber, 2000, p. 735). Organizing
and properly coding data is critical to content analysis. Coding is the process of combing
the data for themes, ideas and categories and then marking similar passages of text with a
code label. Coding the data makes it easier to search the data, to make comparisons and
to identify any patterns that require further investigation. The process of coding is an
iterative and cyclical process of constant discovery. Seidel (1998) developed a model
(figure 9) to explain the basic process of qualitative data analysis. The model consists of
three parts: Noticing, Collecting, and Thinking about interesting things. These parts are
interlinked and cyclical. For example while thinking about things you notice further
things and collect them. Noticing interesting things in the data and assigning ‘codes’ to
them, based on topic or theme, potentially breaks the data into fragments. Codes which
have been applied to the data then act as sorting and collection devices.
Figure 11: Qualitative data analysis coding process (Seidel, 1998)
In my research, I coded the transcribed interviews both deductively and inductively
(Epstein & Martin, 2004). In the deductive coding approach, the codes are developed
before data collection. I developed my set of codes based on my research questions.
Usually the deductive approach is used when researchers may be seeking to test existing
theories or, as it is the case in my research, expand on them.
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During the coding process I also let some inductive codes emerge from the data. The
inductive approach reflects frequently reported patterns used in qualitative data analysis.
Inductive coding begins with close readings of text and consideration of the multiple
meanings that are inherent in the text. The researcher then identifies text segments that
contain meaning units, and creates a label for a new category into which the text segment
is assigned. Additional text segments are added to the category where they are relevant.
At some stage the researcher may develop an initial description of meaning of category
and by the writing of a memo about the category (e.g., associations, links and
implications). The category may also be linked to other categories in various
relationships such as: a network, a hierarchy of categories or a causal sequence. Coding
inductively, researchers are likely to create new codes, they therefore need to go back and
check the units of data they coded previous to creating this code. This is to check if there
is any more data that should be coded at the newly created node. The diagram below
shows how I applied new codes to previously coded data.
While organizing and coding qualitative data, it is important to carefully read and
recognize data prior to the coding process. Mason (1996) suggests there are three main
epistemological reading schemes; literal, interpretive and reflexive. In literal reading, the
researcher is interested in the literal form of the data, whether it is the content, structure,
style, and layout. Researchers do not make interpretation of the data. They look at the
data as it is presented. Most qualitative researchers argue that a literal version of reading
data could not yield desirable results for this kind of data organization as it might direct
our attention from the whole to details and style. In interpretive reading, researchers look
beyond literal form of the data, and try to get to the underlined or implied meanings.
Interpretive reading brings the researcher's own opinions into play. In reflexive readings,
the researcher will look at him/herself as part of the data they generated, and will seek to
explore his or her role and perspective in data generation and data interpretation. The
interpretive and reflexive reading puts emphasis on construction and documentation of
the meaning of data rather than the literal structure of it. In my research, I used the
interpretative reading scheme.
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Mason (1996) also highlights two different methods for coding qualitative data including
(i) cross-sectional or categorical coding and non-cross-sectional or contextual coding.
Cross-sectional coding consists to consistently code the whole data set according to some
sets of common principles in a very systematic way. The simplest form of cross-sectional
coding is serial coding, which is to insert subheadings at relevant points in the text data.
Some of the advantages of this method are that (i) it makes sorting and retrieval easier (ii)
it gives a holistic view or understanding of the data set (iii) it can index the locations of
interpretive, conceptual and theoretic themes within the data, and (iv) it can provide
analytic handles of different parts of the data set for cross-comparison. According to
Mason (1996), there are three main limitations to the cross-sectional coding approach. Its
categories might be too broad to be very useful. Second, a section of text is likely to be
related to more than one concept, thus serial coding might be inappropriate. Third, serial
coding is unlikely to work well if the data is not of a uniform layout. Cross-sectional
indexing can be done very easily, if the data is mostly textual information. For instance it
might not cater to some relevant comparisons across categories. In addition, it tends to be
less useful for interview transcripts, particularly when the interview is either semi-
structured or unstructured.
Non-cross-sectional coding, on the other hand, relates to a totally different idea. The
researcher using this method reads over the data set and constructs a different lens for
each document by examining the documents individually. The principal advantage
behind this approach is that it involves the evaluation of each document in its entirety
inclusive of the context of the data generation. Since this approach helps the researcher
build a case out of each examination, it is also referred to as the case study method of
organizing data. According to Mason (1996), one of the driving reasons to perform non-
cross-sectional data organization is related to the fact that the researcher can identify and
analyze deeply the ideas inherent in each document and how these ideas are interwoven.
In my research, I used the cross-sectional coding method.
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5.6.3 Statistical Analysis
In my study, I used correlation and multiple regression for statistical analysis. Multiple
regression is a statistical technique that allows to predict the value of a dependent
variable on the basis of the values of several independent variables. Multiple regressions
and multiple correlations deals with the relationship of one variable compared with a
number of other variables. In this research, the multiple regression and multiple
correlation was used to compare different predictors of organizational effectiveness in
humanitarian information management networks.
5.7 Methodological Issues
The issues of external validity involve the degree to which the results of the research
study apply to other communities. These issues can occur at different levels including the
level of theories and methods used as well as the level of the findings. At the level of
theories at methods, threats to external validity occur if inappropriate concepts,
instruments, or methods are applied to a research study. At the level of findings, external
validity concerns the extent to which the results of the study hold true for similar
populations. My literature review, the concepts and methods that I used were appropriate
and indicate no threat by the external validity concerns to my study.
External reliability relates to the ability of other independent researchers to “discover the
same phenomena or generate the same constructs as an original researcher if they did
studies in the same or similar settings” (Schensul, Schensul, & LeCompte, p. 275).
Clearly, identifying the research steps and detailing the analysis process and
interpretation of results elevates external reliability. I believe that it is not possible to
duplicate the research setting and results of my study. However, other researchers can
duplicate the research process.
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5.7.1 Social Network Analysis Issues
Validity
According to Wasserman & Faust (1994), construct validity is the extent to which the
questions really assess what they purport to measure. Wasserman & Faust state that “very
little research on the construct validity of measures of network concepts has been
conducted” (p. 58). However, they do acknowledge that “the construct validity of social
network measures can be studied by examining how these measure behave in a range of
theoretical propositions” (p. 58). The construction of questions (both survey and
interview) relied on validated questions I had used on numerous occasions (Cross &
Parker, 2004) in the humanitarian field, to “uncover important network relationships” (p.
147) of information exchange and collaboration.
Measurement Errors
A discrepancy between what is measured and the “true” value of a concept constitutes
measurement error (Wasserman & Faust, 2004, p. 59). In my research, the response rate
of the three surveys that I conducted had an impact on the measurement of network
structural properties, reciprocity in particular. Higher response rate would have been
better. One way to counter threats to internal validity and measurement error is to use
member checking. Presenting the data results to the members of the Global Symposium
community improved the degree to which the responses obtained reflected their
perception of network interactions. The data acquired through interviews provided
additional confirmation.
Reliability
Assessing reliability of measures of sociometric data relates to the success of achieving
the same estimates from repeated measurements. In the words of Wasserman & Faust
(1994) “this assumption is likely to be inappropriate for social network properties, since
social phenomena cannot be assumed to remain in stasis over any but the shortest spans
of time” (p. 58). In this study, sociometric data collection relied on three datasets
collected at three different points in time. Admittedly, this introduces an element of error.
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5.7.2 Content Analysis Issues
Validity
Validity concerns the extent to which instruments are accurate and dependable, and the
degrees to which the research results make sense to the people studied and generalize to
other similar populations (Schensul & LeCompte). In my research, content validity
reflects the high correlation between what users described in their own words and
examples of those expressions in the research literature. The frequency and redundancy
of these descriptions supports the degree to which the research results apply to the people
studied.
Reliability
The issue of reliability concerns whether another researcher using the same methods can
replicate the research results. Internal reliability for content analysis relates to the match
between the constructs identified and the data sets that generated the constructs. One way
to increase the reliability of these matches is for at least one other researcher to review
the data sets and the constructs to see if there is agreement on the matches generated. To
accomplish this, one colleague with content analysis experience reviewed a sample of
three interview transcripts. Krippendorff’s alpha (Krippendorff, 2004) was the basis of
the test for inter-coder reliability. In this case, the observed disagreement divided by the
expected disagreement produced a calculation of desired agreement, with 84% agreement
meeting the minimum goal of 80% acknowledged as appropriate for exploratory studies
(p. 242). Confirming the coding reliability increased the confidence in applying the
results.
5.8 Summary
In this study, I used a mixed methods research design to investigate the relationships
between structural properties of inter-organizational networks and network effectiveness
among organizations members of a community of interest in humanitarian information
management and exchange. Data collection included surveys, individual semi-structured
interviews, and database search. The process of triangulation encompassed in this
research design provided the means to corroborate findings and extend the results beyond
this research setting. The next chapter presents the research results.
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6 ANALYSIS
6.1 Introduction
In this chapter I present the findings of my investigations. The chapter contains four main
sections. In the first section, I analyze the qualitative data collected through interviews. In
section two, I present my three criteria for assessing effectiveness. As discussed in the
research model, they are my dependent variables. The third section presents my findings
related to the network level of analysis. In this third section, I present my analysis of the
relationships between the structural properties (density, clique, clique overlap and
multiplexity) and network effectiveness. Finally in the fourth and last section, I present
my findings related to organizational level effectiveness.
6.2 Qualitative Data Analysis
As discussed in the method section, in my research, I collected data through multiple
sources. In this section, I analyze the qualitative data gathered through interviews. I
conducted a total of nineteen semi-structured interviews among members of the Global
Symposium. I coded the data using deductive and inductive methods. The five deductive
code categories that I used were guided by my research questions and the literature. They
included (i) network benefit, (ii) network effectiveness, (iii) collaboration factors, (iv)
barriers to collaboration and (v) measures of network effectiveness. Three inductive code
categories emerged from the data including (i) from advice to collaboration, (ii) network
scope and (iii) network audience.
6.2.1 Deductive codes
6.2.1.1 Network benefit
One of the objectives of the interview was to assess the benefit of the Global Symposium
to individual organizations as well as to the community as a whole. It is important to
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recall that membership to the Global Symposium network is voluntary. If members do
not benefit from being part of the network, they will more likely cease to participate in
the activities of the network, and if the feeling is widespread, the network will cease to
function. Recognizing the benefits that members receive from network membership is
therefore a crucial tool to assess how well the network is functioning. Based on the data
that I collected, numerous benefits were perceived to be associated with the Global
Symposium inter-organizational network.
Benefit to individual organization
I asked to the interview participants, what benefits their organization had gained from
being a member of the Global Symposium. About eighty five percent (84.21%) of the
interviewees answered this question. Some of the most commonly cited benefits of the
network to individual organization members included: increased access to humanitarian
information; expertise and financial resources; solidarity and support; and increased
networking. Another important perceived benefit reported was increased credibility of the
Global Symposium members. One subject expressed this benefit in the following term:
Subject#18: The greatest as I said, was meeting with various people from all over, networking and then of course it was informal relations but strangely enough you could think that this networking will be closer with the working group which I was part, but it was not. At the time I made those networks it was at the closing session in fact.
Another subject said:
Subject#1: There are people and entities we met that we are now discussing with and sharing information with sharing ideas with and you know just keep in touch at an informal level. I think that is very good for us.
Benefit to the whole community
The second aspect of the question on benefits was related to the contributions of the
Global Symposium to the community. I asked the interview participants about how the
Global Symposium benefited the humanitarian community as a whole. Approximately
fifty eight percent (57.89%) of the interviewees answered this question. Among those that
responded, the vast majority expressed highly positive opinions on the Global
Symposium’s contributions to the community. Especially, they believed the Global
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Symposium benefited the community in two major aspects including (i) promoting the
use of humanitarian information management principles and dissemination of best
practices; (ii) fostering collaboration on humanitarian information management projects. I
provide below, some illustrative quotes from interview data.
Subject#8: I think the development of the principles was useful. I think that just the working groups to address certain issues was useful although I would be the first to admit there has been not any real organized follow up. But I think documenting the information management principles and the actual document itself that came out of the symposium I think was useful in terms of the issues of humanitarian information management. Subject#17: It was a very very important networking opportunity, and you know in some respect it was very cutting edge. The only thing I find very disappointing was the lack of invitation to some key players and so, the one in particular. Subject#5: I think one of the great benefits is actually making like minded people and you realize that we are all confronting the same problems, so I think that firstly is one big positive aspect. I think the second, is that there was a lot of networking going on that was actually quite crucial.
However, for some interviewees, the benefit of the Global Symposium was limited to
certain linguistic regions. They said the English speaking members of the community
were those that benefited the most.
The impact of the Global Symposium for this region [Spanish] is very very low, I would say or almost invisible. I am not sure if we would benefit from the Global Symposium in that sense. No unfortunately not. Subject#16
Across the interviews, the discussion on the benefits of the Global Symposium to
individual organizations on the one hand and to the whole community on the other hand
was done with almost the same intensity (Figure 12). The former represented
approximately fifty eight percent (57.81%) of occurrences of benefits discussed in all the
interviews combined while the later represented about forty two eight percent (42.19%).
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Figure 12: Network Benefit Code’s Coverage
Figure 13: Aggregated Benefit Cross Network
Aggregated data per network (Figure 13) shows that cross networks, the Global
Symposium was perceived to benefit more to individual organizations than to the
community as a whole. In the network of NGOs the proportion is almost seventy percent
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(70.00%) and thirty percent (30.00%). In the two other networks, the proportion is
approximately sixty percent (60.00%) and forty percent (40.00%).
6.2.1.2 Network effectiveness
Another objective of the interview was to assess the perception of the participants about
the effectiveness of the Global Symposium as a network of organizations engaged in
humanitarian information management and exchange. Though I often used open
questions during the interviews, in this case, I recalled some of the main objectives of the
Global Symposium to the interviewees. I then asked them to comment on whether or not
the Global Symposium was effective in the pursuit of these objectives. I coded the data
collected using four categories. They included (i) resource availability, (ii) internal
processing characteristics, (iii) goal achievement, and (iv) multiple constituencies’
satisfaction. These categories came from the literature on organizational and network
effectiveness (Parson 1964; Yuchtman and Seashore, 1967; Price, 1971; Cameron and
Whetten 1981; Conlon, D’Aunno, 1992; Zammuto, 1984; Sowa et al., 2004). Figure 14
depicts the coverage of these codes.
Resource availability
In this code category, effectiveness was defined in terms of the ability of the organization
/ network to acquire resources necessary to it survival. The greater the ability of the
organization / network to acquire needed resources, the greater its effectiveness. Only
about thirty two percent (31.58%) of the interviewees discussed the effectiveness of the
Global Symposium in term of resource availability. The majority of these subjects had a
negative opinion about the ability of the Global Symposium to make resources more
available to its members. For example, Subject#2 talked about the “unrealistic”
objectives of the Global Symposium.
Subject#2: How could you possibly do that? I mean these objectives are ummm let us be realistic. How are we going to help organizations get more resources? Subject#5: I think a lot of the organization including ours, have simply not got the time or context to seek the necessary funding and resources.
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Internal processing characteristics
In this category, effectiveness was conceptualized as the absence of internal strain and a
smooth internal functioning of organizations / networks. Approximately fifty eight
percent (57.89%) of the interviewees discussed the effectiveness of the Global
Symposium with regards to its internal processing characteristics. Once more, the
majority of these subjects expressed a negative opinion. The two majors grievances most
frequently reported included the relatively big size of the Global Symposium and the lack
of a clear and concise definition of the objectives of the event. I provide below, some
illustrative quotes from interview data.
Subject#1: you know you can only coordinate it if people, institutes want to be coordinated. And to do that you need a certain trust, it has to be a two way things, you cannot just come waving the coordination flag and expect everyone to lineup nicely. Subject#12: we spent so long figuring out what we were supposed to be talking about that we never got to the details. Subject#14: Well I think that we need to look at what is the purpose? What are you trying to achieve? and I would certainly not go in any kind of precooked formula which will reflects the earlier symposiums, on come up with a list of those and answer these questions. Subject#2: any type of meetings and workshops where you are gathering various organizations and numerous people I think it’s important to clarify terms and terminologies and I am not sure if this happened there. Subject#2: It’s always very difficult globally to bring people together. So I would say, first try to do it within a country or a region instead of trying to do it globally.
Another negative view expressed on the internal processing characteristics of the Global
Symposium was related to the lack of follow-up activities. A good number of interview
participants noted that the fact that there was no rigorous planning of follow-up activities
significantly compromised the effectiveness of the Global Symposium.
Subject#1: I don’t see any specific follow up or activities. Subject#11: It would have been good to have even virtually, not necessary another event, but if there was some follow up, it would have been good.
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Subject#9: But I think we need follow up. Follow up in particular at the regional level; follow up at the country level. How they helped? And what are the achievements at the regional level.
Goal achievement
In the goal achievement category, effectiveness was defined as the extent to which
organization / network’s goal was achieved. The greater the degree to which an
organization / network achieves its goals the greater its effectiveness. During the
interviews, I recalled to the participants some of the main goals of the Global
Symposium. They included: (i) promote the use of humanitarian information
management principles; (ii) disseminate best practices of humanitarian information
management; (iii) improve the community’s preparedness in humanitarian information
management; (iv) help organizations/agencies acquire resources; (v) improve the level of
professionalization in the field of humanitarian information management (vi) foster
collaboration on humanitarian information management projects (vii) facilitate sharing
of expertise among organizations/agencies; (viii) promote humanitarian information
sharing; (ix) strengthen relationships between organizations/agencies; (x) increase
awareness of humanitarian information systems and (xi) improve humanitarian
information quality.
Approximately eight five percent (84.21%) of the interviewees discussed the
effectiveness of the Global Symposium with regards to achieving its goals. For most of
these respondents, the Global Symposium was effective in the pursuit of its goals. More
specifically, as illustrated in the following quotes from the interview data, the Global
Symposium was reported to be very effective in strengthening relationships between
organizations/agencies, in promoting the use of humanitarian information management
principles and in promoting humanitarian information sharing.
Subject#11: So I would say the Global Symposium was one of other events which promoted interactions among different partners. Subject#15: I think the event was mostly successful in coming to agreement among the various actors on certain standards for use of information in humanitarian response. So I
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think the report was useful and the input that all the groups provided into that report was useful. Subject#19: the global symposium has actually been very important to us, as group of organizations that has been working on this effort. Because originally, this working group wasn’t on the agenda, but we requested that it be put on the agenda, to recognize the various information that affect the population. Subject#2: I mean I am talking specifically about one the positive things that came out with which is the ability to meet others in the field and in particular in bringing together the private and NGOs the UN communities which was good. Subject#8: I would say that there have been more information related projects and initiatives in the last two years and so I mean I think it encouraged information related projects.
However, for some interviewees, the impact of the Global Symposium was limited to a
single event (e.g Subject#15:). According to these participants, this was mainly due to
the lack of follow-up activities.
Subject#15: I think they disseminated [information on humanitarian information management best practices] but mostly to the conference participants. I don’t know how much this was disseminated beyond the conference.
Constituencies’ satisfaction
For constituencies’ satisfaction, effectiveness was defined the ability of network to satisfy
key multiple stakeholders. In the case of the Global Symposium, some of the main
stakeholders include the individual organizations, the governments, the United Nations,
the victims of humanitarian disasters and the international community. My study included
only members of the Global Symposium. They could be grouped into the following four
categories: NGOs, governmental organizations, private organizations and United Nations
organizations. I did not for example interview any victim of the humanitarian disaster.
Approximately eight five percent (84.21%) of the interviewees discussed the
effectiveness of the Global Symposium with regards to constituencies’ satisfaction.
Almost all of them expressed a mixed feeling about the effectiveness of the Global
Symposium in satisfying its multiple stakeholders.
Subject#8: I think my organization has used the symposium to a high degree. I would say the broader community has lessen so. I mean at a scale of 1 to 10, I think that the HIU
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has used and benefited from the symposium probably of a 7 or 8, and the entire humanitarian community at a 4 or 5. Subject#15: Well I think the event was mostly successful in coming to agreement among the various actors on certain standards for use of information in humanitarian response. So I think the report was useful and the input that all the groups provided into that report was useful.
Figure 14: Network Effectiveness Code’s Coverage
Across the interviews, the intensity of the discussion on network effectiveness varied
significantly depending on the code category (Figure 15).
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Figure 15: Network Effectiveness Code’s Loudness
Exploring my data cross networks (Figure 16), I found almost a similar pattern in the
ranking of the loudness of the different categories of network effectiveness. In all the
networks, the category “goal achieved” was ranked first followed respectively by
“internal processing”, “constituencies’ satisfaction” and then “resource availability”. The
pattern of the percentage of discussion of the different categories was also similar. They
were over fifty percent (50.00%) for the “goal achieved” category, approximately twenty
two percent (22.00%) for the “internal processing” category; approximately fifteen
percent (15.00%) for the “constituencies’ satisfaction” category; lest than ten percent
(10.00%) for the “resource availability” category.
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Figure 16: Network Effectiveness Code’s Loudness Cross Network
6.2.1.3 Collaboration factors
While conducting the interviews, I was also interested in assessing the participants’
opinion about factors for collaboration among members of the Global Symposium. I
present below the major factors that emerged from the interviews. A total of seven factors
were found including mandate/goals, skills, trust/reputation, funding, size, geographical
proximity/language and processes. The identification of these factors was guided by the
literature.
Mandate /Goals
One of the most frequently cited factors for inter-organizational collaboration on
humanitarian information management and exchange was related to the similarity in the
mandate and goals of the organizations wishing to work together. About eighty five
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(84.21%) of the interviewees discussed the similarity of mandate / goals as being an
important motive for their organization to engage into collaboration with another
organization. I provide below, some illustrative quotes from the interview, expressing this
point of view:
Subject#9: We have to have a common work plan in order to work together. Subject#17: What it takes for us to collaborate is just a kind of share objective.
The issue of mandate and goals as driving factor for inter-organizational collaboration
was not only cited by the greatest percentage of participants, this factor was also among
those that were the most intensively discussed. It represented approximately twenty nine
percent (28.38%) of occurrences of collaboration factors discussed in all the interviews
combined (Figure 19).
Figure 17. Factor’s Coverage
Skills
The skills set of the potential collaboration partners was the second most reported factor
for inter-organizational collaboration among the members of the Global Symposium.
Approximately seven four percent (73.68%) of the participants reported that in deciding
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to engage into collaboration, their organization would highly consider the type of skills of
the potential partners (Figure 17). The issue of skills as factor for collaboration was
discussed in two different perspectives. The majority of participants, who mentioned this
issue, discussed it in term of availability of complementarity set of skills in the potential
partner organizations. Their organization would collaborate with another organization if
the later possessed a set of skills that they lacked. For example, participant number two
(Subject#2) said:
Subject#2: Both have to be able to bring to the table their competitive advantage. You can’t have two organizations that do the same thing. So you need different skills set from any of the organizations.
Other participants discussed the issue of skills in term of high quality and competency. In
deciding to engage into collaboration their organizations would consider the quality and
competency of the skills available to the potential partner organizations.
Subject#7: We think about the quality of what that agency does and the quality of what that agency is known to do. Subject#17: We are trying to be a service provider to those organizations. So I guess we are trying to provide a competency. But we also have interest in the ability of these other organizations to develop new competencies.
Similarly to the issue of mandate and goals, the discussion of skills as factor of
collaboration was also very intense. This factor also represented approximately thirty
percent (29.73%) of occurrences of collaboration factors in all the interviews combined
(Figure 19).
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Figure 18. Factor’s Loudness
Trust / Reputation
The third most discussed factor for inter-organizational collaboration among the members
of the Global Symposium was trust and reputation. Approximately forty eight percent
(47.37%) of the interview participants mentioned that they will more likely not get into
collaborative activities with an organization that they do not trust or an organization that
has a poor reputation (Figure 17). Below, I illustrate this point of view with quotes from
subject number five and subject number eight.
Subject#5: I thing on the one hand getting the quality information which is credible and I think the emphasis has to be on the word credible because there is no good having information which is bad because people see through that very very quickly and you can lose your credibility very quickly. Subject#8: we are looking for partners that have a good reputation that provide value added to what we can provide.
This third most reported factor of collaboration was also very intensely discussed. It
represented roughly seventeen percent (16.22%) of occurrences of collaboration factors
in all the interviews combined (Figure 18).
Geographical Proximity / Language
Geographical proximity was the fourth most reported factor for inter-organizational
collaboration among the members of the Global Symposium. Approximately thirty seven
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percent (36.84%) of the interview participants mentioned proximity and especially the
language as an important factor that drives collaboration (Figure 17).
Subject#16: I think we are gaining popularity among humanitarian here because of the Spanish.[….] Spanish is a really an important and imperative thing if you want to enter here. English is a secondary one and really a secondary one. So that is why I think we are gaining popularity among humanitarian here because of the Spanish. Subject#18: if people are approaching us, I would say that first that because we are French and people that approach us usually are also French NGOs.
The intensity of discussion around the geographical proximity and language as factor for
collaboration was relatively low as to compare with others factors. This factor accounted
only for roughly eleven percent (10.81%) of occurrences of collaboration factors in all
the interviews combined (Figure 18).
Size
The fifth most discussed factor for inter-organizational collaboration among the members
of the Global Symposium was the size of the potential partners. Approximately sixteen
percent (15.79%) of the interview participants reported that they consider the size of the
organizations that approach them to seek for collaboration (Figure 17). Participant
number one for example reported that:
Subject#1: We tend to work I guess it is natural, we tend to work better with the smaller entity that seem to be more flexible, more users oriented than big entities, be they national entity or the private companies or of course UN entities.
The issue of size was also among the factors the least intensively discussed. It accounted
only for roughly seven percent (6.76%) of occurrences of collaboration factors in all the
interviews combined (Figure 18).
Funding
Funding was ranked sixth most reported factor for inter-organizational collaboration
among the members of the Global Symposium. Approximately sixteen percent (15.79%)
of the interview participants reported that they look at the funding possibilities available
at the potential partners before deciding to engage into collaboration (Figure 17).
Subject#8: we all provide any funding for you know we contribute to our work, and they contribute to their work. So you know they need to be sort of self-sufficient.
Surprisingly, the issue of funding as factor of collaboration was among the factors the
least intensively discussed. This factor represented only approximately eight percent
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(5.41%) of occurrences of collaboration factors in all the interviews combined (Figure
18).
Processes
The seventh and last reported factor for inter-organizational collaboration among the
members of the Global Symposium was the processes. Nearly eleven percent (10.53%) of
the interview participants reported that they engage into collaboration with partners that
follow clearly predefined processes (Figure 17). This was especially important for
collaborating with donors organizations.
Subject#14: We have very well defined process for people, partners contacting us.
The issue of processes the least intensively discussed. It accounted for less than three
percent (2.7%) of occurrences of collaboration factors in all the interviews combined
(Figure 18).
Borrowing from the framework developed by Ngamassi et al., (2011) to analyze factors
that hinder inter-organization coordination and collaboration among humanitarian
organizations, the seven factors identified in this study could be grouped into the
following three categories: organizational, structural and behavioral (Figure 19). The
organizational category would include factors related to the mandate/ goals and the
processes. The factors in the structural category would be skills, funding, size and
geographical proximity. The last category, behavioral, would include factor related to
trust and reputation. This categorization allows to have another perspective of the
influential drivers of inter-organizational collaboration in the humanitarian relief field.
Figure 19 below, depicts the aggregated loudness of factor per category.
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Figure 19: Loudness of Collaboration Factors Grouped per Category
It is important to note that nearly half of the reasons to collaborate are structural. This is
important because these structural factors are the ones most likely to be supported and
affected by the use of information technologies. The use of this framework helps to
highlight the fact information technologies have important potential to influence inter-
organizational collaboration relationships among humanitarian organizations.
Analyzing cross networks (Figure 20), the loudness of the different collaboration factors
that emerged from the interviews I made the following two observations. First, there was
a similar pattern in the ranking of the different factors of collaboration based on the
number of their occurrences. The structural factors were the most reported cross networks
followed respectively by organizational and lastly the behavioral factors. Second, I found
a wide discrepancy in the loudness of the different factors, cross networks. For example,
in the network of the United Nations agencies the discussion around the structural factors
represented approximately sixty seven percent (66.67) of the discussion related on
collaboration factor in that network. This proportion was fifty (50%) for the network of
Governmental organizations and just about forty two percent (41.67) for the network of
non-governmental organizations. Another important discrepancy was observed on the
collaboration factors grouped in the behavioral category. Discussion around this category
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represented less than ten percent (9.52%) in the network of the United Nations agencies
and approximately twenty percent in the two other networks.
Figure 20: Loudness of Collaboration Factors Cross Network
Information Technologies
During the interviews, I also asked participants to give their opinion specifically on the
implications of information technologies on inter-organizational collaboration among
members of the Global Symposium. Approximately half (47.37%) of the interviewees
shared their opinion on this issue. I registered a wide range of diverse point of views.
Some participants, roughly thirty one percent (30.77%) of those that answered the
question, had a very positive opinion about the implications and especially the catalytic
role that information technologies play in fostering humanitarian inter-organizational
collaboration. The vast majority (69.23%) however, expressed mixed feelings.
For the participants that had positive opinions, information technologies served as an
important catalyst for inter-organizational collaboration in the Global Symposium
community. They argued that if without information technologies, effective simple
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communication is difficult, collaboration would be even harder. Participant number five
for example reported that:
Subject#5: I think that information technology is extremely important because we basically need to communicate to all these different communities in as many different ways as possible.
They also believed that the use of information technologies is instrumental in quickly
gather analyze and disseminate humanitarian information leading to effective disaster
response. Below, I illustrate this point of view with quotes from three participants,
number six, seven and eleven.
Subject#6: You cannot do it without information technology. Gathering information, managing information, analyzing information, distributing information, really you cannot do all this without information technology. So I think the question is kind of obvious. Subject#7: Information technology essentially supports what we do. It helps in sharing information, mainly transporting information around, maintaining our communication. Subject#11: I think the information technology is key of cause, because without proper systems in place, you will not be able to do that.
The participants who expressed mixed feelings about the role of information technologies
as catalyst in inter-organizational collaboration believed that taken alone, information
technologies would not lead to better / more collaboration. They gave a number of
reasons that could be grouped into two main categories. The first category of reasons was
related to the information technologies infrastructure. Participants argued that more often,
organizations in the field do not have the necessary technology tools either because they
were destroyed by the disaster or because they did not even exist in the first place. They
also talked about the discrepancy in term of infrastructure between organizations based in
developed countries and those in the developing countries. They argued that people in
developed countries often enjoy latest technologies but the realty in developing countries,
scenes of most humanitarian disasters is quite different. Participant number twelve for
example reported that:
Subject#12: when you get out on the fields you see that the most basic important tool is paper map and a pencil. And I think we have got to really recognize that fact. […]You know we do this information technology that we love where they follow the latest systems and the fastest processor and stuff like that and we really like to paddle
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ourselves on the back on what we are able to do here in Washington DC. And then you get out on the fields and everyone is using paper maps and a pencil.
Finally they talked about the fast pace of change in technology which makes it difficult
for organizations to have and especially keep the technical staff that possesses adequate
knowledge to make use of these new technologies.
Subject#6: as the technology changes, it is hard to find the people that have skills that are up to date.
The second category of reasons concerned the management of information. Participants
believed that without proper standard for humanitarian information exchange, the
technology will be of no effective use.
Subject#5: I think yes, continue to explore all the new technologies that are available but at the same time realize that in the end what it really comes down to is quality information and information that is based on facts and that’s credible but people actually belief in. So I think we should not be allowed to be measured by technology if the content is not there. Subject#14: One is developing some basic standards, and some basic platforms for information exchange.
They also believed that the humanitarian field needs better processes and well trained
staff in order to make good use of the technology.
Subject#8: I think there are certain organizations who think that technology can solve all the problems, so they don’t have a proper appreciation and understanding of the information management challenges and obstacles, but at the same time there is probably some information, people who are very skeptical about technology and do not sort of realize the value that it has.
6.2.1.4 Collaboration barriers
The interview included questions about barriers to inter-organizational collaboration. I
asked the interview participants to identify the major barriers to inter-organizational
collaboration among members of the Global Symposium on humanitarian related
projects. The lack of leadership, extensive bureaucracy, the lack humanitarian
information standard, the lack sharing spirit the lack of skills and the lack of resources are
some of the most frequently reported barriers to inter-organizational collaboration among
members of the Global Symposium. I coded these barriers using three categories
including (i) structural, (ii) behavioral and (iii) mandated. These categories were once
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more borrowed from the framework developed by Ngamassi et al., (2011) to analyze
factors that hinder inter-organization coordination and collaboration among humanitarian
organizations. As discussed by Ngamassi et al., (2011), this higher order analytical
structure that organizes these collaboration barriers into three larger categories is
appropriate to both the context of humanitarian relief as well as to collaboration among
organizations engaged in humanitarian information management and exchanged. I
present below, these barriers in order of their intensity as discussed by interview
participants.
Structural
Collaboration barriers in the structural category included barriers such as extensive
bureaucracy, lack of humanitarian information standard, problems of communications /
language, size of organization, lack of tools (IT/IM) for collaboration, geographical
distance, lack of technical skills, lack of resources and lack of leadership. This category
represented the most frequently reported barriers to collaboration. All of the participants
to the interview identified at least one collaboration barrier that fell into the structural
category. In other to refine my investigation and pay more attention to IT and IM related
barriers, I distinguished the following three subcategories of structural barriers.
Information Management (IM) related
Approximately eighty five percent (84.21%) of the interview participants talked about
challenges to inter-organizational collaboration related to information management.
Issues such as information quality, information standards and information security were
frequently reported.
Subject#5: I thing on the one hand getting the quality information which is credible and I think the emphasis has to be on the word credible because there is no good having information which is bad because people see through that very very quickly and you can lose your credibility very quickly. Subject#14: For instance, information security, you know, that is becoming more and more of a concern. It used to be that you could pretty much share information freely, but now it is not more the case. [….] Subject#7: there are things like not sharing security information because you think it is so important to you.
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Another major barrier to inter-organizational collaboration related to information
management that emerged from the data concerns the language.
Subject#16: Spanish is a really an important and imperative thing if you want to enter here. English is a secondary one and really a secondary one. So that is why I think we are gaining popularity among humanitarian here because of the Spanish. But unfortunately we are not gaining worldwide reach or even in headquarters because we do not have too many Spanish readers so they do not see this importance. Subject#18: I would say that first that because we are French and people that approach us usually are also French NGOs […] as I have mentioned in your second survey who have establish relationship with the Groupe URD and that is because we are French.
Information management related barriers to inter-organizational collaboration were also
the most intensively discussed. It represented approximately forty percent (40%) of
occurrences of collaboration barriers discussed in all the interviews combined.
Information Technology (IT) related
Approximately seventy four percent (73.68%) of the interview participants discussed
challenges to inter-organizational collaboration related to information technology. They
talked for example about some technology tools that are not wide spread and are used
only specific organizations.
Subject#2: And in the same way these organizations are all doing information management and a lot of these organizations have these tools which they only know about within that organization. And so we talk about the community but in reality there isn’t much of a community.
They also talked about lack of IT skills
Subject#8: I think there are certain organizations who think that technology can solve all the problems, so they don’t have a proper appreciation and understanding of the information management challenges and obstacles, but at the same time there is probably some information, people who are very skeptical about technology and do not realize the value that it has.
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Barriers to inter-organizational collaboration related to information technology
represented approximately thirty three (33%) of occurrences of collaboration barriers
discussed in all the interviews combined.
Other structural barriers
I grouped in this category all other structural barriers that were identified and that were
neither IT nor IM related. Approximately fifty eight percent (57.89%) of the interview
participants discussed challenges to inter-organizational collaboration that fell into this
category. The most frequently reported barrier in this category was the lack of
humanitarian dedicated staff and also the competition for funding.
Subject#18: In fact the main challenge is human resources that are dedicated and that have time to do the work. Because why? Most of the people that were there were note really specialist or were not fully dedicated to the job of information management. […] you cannot ask someone to share information if it is not his job. You cannot ask someone to produce a map with the right standard with the right quality if it is not his job. Subject#5: Unfortunately what is happening now is that there are too many organizations running after the money, running after they think what others want, and not running after the real needs. Subject#16: I would say that the most difficult part ummmm. I use a word here “humanware”. [….] In my opinion, and considering my experience in Africa, in Asia this is where most of the information management systems are struggling to survive or to go ahead, to move forward to achieve their objectives. Subject#11: Very often we have a situation that the information in available and everything but who is able then to present it, to analyze it, to prioritize it, and all of these, that is for me the role in information management, or information management for doing that, I think for me one of the key issues.
These other structural barriers represented approximately twenty seven percent (27%) of
occurrences of collaboration barriers discussed in all the interviews combined (Figure
21).
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Figure 21: Break Down of Structural Barriers
Behavioral
Inter-organizational collaboration barriers in the behavioral category included barriers
such as lack of sharing spirit, lack of trust, lack of incentives. They were the second most
frequently reported barriers to collaboration. Approximately forty seven percent
(47.37%) of the interview participants identified at least one collaboration barrier that fell
into the behavioral category (Figure 21). I present below some illustrative quotes drawn
from the interview data.
Subject#12: There is a big problem with information sharing. But that you know that’s the problem of the world. I do not know if that is a very specific problem with these organizations. Subject#13: I think the main challenge here is that the idea of sharing formation has always been said in many areas. It is usually always said yeah it is good to share but you do not sometime see concrete platforms or formalities on how to share this information. It is not formalize. It is always thought as an objective but never formalize.
Behavioral barriers represented approximately sixteen percent (16.10%) of occurrences
of collaboration barriers discussed in all the interviews combined.
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Mandated
Collaboration barriers in the mandated category included barriers such as conflict of goal,
conflict of interest and lack of leadership. This category of barriers was the third and last
most frequently reported barriers to collaboration. Approximately twenty one percent
(21.05%) of the interview participants identified at least one collaboration barrier that fell
into the mandated category (Figure 21). I present below some illustrative quotes drawn
from the interview data.
Subject#9: I mean the different administrative work of organization is very difficult and it varies from organization to organization. There is no commonality among organizations. Subject#5: I think the problem is getting the decision making of all the organizations to actually understand what the issues are and have to understand that they have the responsibility. Subject#9: I think there is the issue of contingency plan. Contingency plan is very important, contingency fund. Because by the time funding happens it might be already too late. I know that in some countries they already have it at the governmental level but where we work where the government is very weak or even nonexistent you do not have contingency plan. So it depends on international actors to provide that type contingency fund that can be utilized during emergency.
Inter-organization collaboration barriers the mandated category represented
approximately five percent (5.08%) of occurrences of collaboration barriers discussed in
all the interviews combined.
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Figure 22: Loudness of Barriers to Collaboration Grouped per Category
Summing up, the most significant barriers to inter-organizational collaboration among
organizations in the humanitarian relief field are structural (Figure 22 above).
Exploring the intensity of the different collaboration barriers cross networks, I made the
following observation (Figure 23): Behavioral barriers to inter-organizational
collaboration were less discussed in the network of non-governmental organizations
(approximately ten percent – 10.26%) than in the two other networks where this
percentage was almost double (21.88% for the network of Governmental organizations
and 17.39% for the network of the United Nations agencies).
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Figure 23: Loudness of Barriers to Collaboration Cross Network
6.2.1.5 Measures of effectiveness
Effectiveness is a multidimensional concept that is especially challenging to measure in
humanitarian assistance and disaster relief which often involve a large variety of
stakeholders with diverse goals and for which outputs are not easily operationalized. One
other objective of my interview was to get the opinion of the member of the Global
Symposium of what would make an appropriate metric for measuring network
effectiveness in their community. About sixty nine percent (68.42%) of the interviewees
answered this question. Guided by the literature, I coded the data in the following four
categories, range of activities, level of coordination, level of collaboration and
availability of resource including funding. The intensity of discussion was almost evenly
distributed cross these categories. I provide below some illustrative quotes for each of the
categories.
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Range of activities
Subject#1: I think if we were to be an effective symposium, one thing would be add new activities that could be implemented within a reasonable timeframe. [….]Research activity, research projects; or even operation activities that could be implemented within a reasonable short time following direct results you know, or direct resources or direct resolution from the symposium. Subject#18: I think for me, I would look at the number of implemented projects.
Level of coordination
Subject#17: I think it would definitely be knowledge exchange. I think at this point, I would want to be able to read, learn about things that I didn’t know anything about or features about things that I have just heard about, and then and those things that I would assume would be facilitate and lead to collaboration. […] So the first step of that would be sufficient information sharing. An then one of things that I would expect to see is the consolidation of this and a lot less competition and a lot more synergy. So one of the eventual outcomes is that we will just become a lot more collaborative and a lot more of these systems talk to each other and a lot more links to one another, and we don’t do as much replication of efforts. Subject#4: organization inter-operate although they are competing for donors whom they appeals, I would seek to analyze the attitude of all those organizations, to see to what extent they compete in the market and to what extent they understand what synergy mean.
Level of collaboration
Subject#10: I think you need to look at the level of coordination and funding. How much of funding have organizations successfully secured to work in this area? The extent to which there are working with other partners or coordinating. Subject#17: I think it would definitely be knowledge exchange. I think at this point, I would want to be able to read, learn about things that I didn’t know anything about or features about things that I have just heard about, and then and those things that I would assume would be facilitate and lead to collaboration. […] So the first step of that would be sufficient information sharing. An then one of things that I would expect to see is the consolidation of this and a lot less competition and a lot more synergy. So one of the eventual outcomes is that we will just become a lot more collaborative and a lot more of these systems talk to each other and a lot more links to one another, and we don’t do as much replication of efforts. Subject#4: organization inter-operate although they are competing for donors whom they appeals, I would seek to analyze the attitude of all those organizations, to see to what extent they compete in the market and to what extent they understand what synergy mean.
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Funding and other resources
Subject#10: I think you need to look at the level of coordination and funding. How much of funding have organizations successfully secured to work in this area? The extent to which there are working with other partners or coordinating. Subject#11:it is probably easier to use the money which has been given because that will at least express a certain level of satisfaction of what we are doing. Because we are funded by volunteering contribution from donors. So at least I would say that if we get a lot of money for one of the other projects that at least indicate the level of satisfaction from our stakeholders. So maybe that is a better one. Subject#3: I think another way which is being relatively successful is the way you have the agencies to have high number of technical people in them. Especially when they come from a professional background where you can do humanitarian practices across agencies where people really know how to improve the competency or work.
6.2.2 Inductive codes
The inductive coding process of my interview data yielded three set of codes that I
believe would help to shed more light in inter-organizational collaboration in the Global
Symposium community and consequently to better understand the effectiveness of this
community in providing disaster assistance. These three code categories included (i)
from advice to collaboration, (ii) the scope of the Global Symposium community and (iii)
the audience / stakeholders.
6.2.2.1 From advice to project collaboration
The first inductive code was related to the connection between advice and project
collaboration relationships. My data highlighted the fact that in the Global Symposium
community, organizations that are linked through advice relationship would in a long run
collaborate in humanitarian relief project.
Subject#1: yes I would say so, I mean it is not humongous impact, but it is an important element as well. There are people and entities we met that we are now discussing with and sharing information with sharing ideas with and you know just keep in touch at an informal level. I think that is very good for us. Also because the UN being UN some
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institutions are more difficult to approach officially or let say institutionally. While if you have this more ad hock loose network where you could exchange without it become very formal that is very useful. Subject#8: I mean you know, I have been in this community for a long time and I have a lot of informal contacts with people asking for advice or asking for you know and you it sort of this informal, this sort of one on one type of thing.
6.2.2.2 Network Scope
The second inductive code that emerged from the data concerned the scope of the Global
Symposium. Some interview participants reported that the large size of the Global
Symposium would more likely negatively impact its effectiveness. Other participants also
highlighted the fact that the “ambitious objectives” of the Global Symposium would
undermine its effectiveness.
Subject#2: I would say, first try to do it within a country or a region instead of trying to do it globally. Because then you have a smaller community and those community are much more important, the regional community or the national community. Subject#2: Tried to strive for much less ambitious objectives and discuss some of the core issues within each of those sub sectors if you will or the sectors. Subject#5: there should be smaller groups that held very very specifically with mixed of media communication people and these organizations perhaps have small groups that meets for one day but in a highly intensive manner, and really look at the issues maybe to review what happened in the last symposium, but reviewed this in a very very pragmatic manner and a very outspoken critical manner as well. Subject#11: May be a smaller group, because it was rather a large event, so maybe if you could do it regionally, let say one in Latin America, and another one in Africa or central Africa, west Africa, maybe that would be more effective, because you would have fewer participants. On the other hand it would not necessary always get the global perspective, so then you would not have everybody in one place western countries, eastern countries, developing, developed countries and whatever. May be a mixed you know a regional approach is not too bad. It can also be as an advantage. Subject#12: I think it needs to be longer basically. […] We spent so long figuring out what we were supposed to be talking about that we never got to the details. And I remember I think we were only in that room for like you know a day or less than a day or something like that. […] And these are big difficult issues and I think you need to spend a lot longer on them rather of assuming that you are going to come up with answers in a few hours especially when you have so many different organizations at the table.
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Subject#12: I think it would be a very very very good idea for just you knows the UN organizations themselves to get together. And not try and bring on all those non-governmental NGO types. Because you know the UN needs to kind of figure out its own game and how it wants to interact with the rest of us. Subject#16: In this region, it seems that this type of global agreement or these international events are not having strong impact this is my perception at this point.
6.2.2.3 Network Audience
The third and last inductive code that emerged from the data was related to the audience
of the Global Symposium. Interview participants would have liked to have a more
diversified audience especially people from the field of disaster reduction.
Subject#1: For the other external partners or external participants, it would be nice to see their contributions are really recognized, but for them it would be less important but for us it would be a major boost because it would be an overall recognition of our work and what we have been doing since 2001, but there were some forces in OCHA that for personal reasons did not want to see that. Subject#5: One of the things that we were suggesting, is that when you have a symposium like this, it doesn’t help just to have media or information people talking among themselves. We need to mainstream. We need to bring in the heads, in fact the very senior people within these organizations; whether it is the UN agencies such as FAO or UNHCR. They should not be communication people, they should be operation people. Subject#5: I think that what need to be done is that the major organizations and the senior operation people needs to be invited and included, and also may even see in them. They should say, look unless you are serious about his, then you shouldn’t be in the business. But if you are serious about helping refugees, displaced people, you name it, then you have another responsibility to communicate with these people. Subject#10: As I said these thing needs to be mainstream at all levels of the disaster response rather than be treated as a separate area of expertise or data collection. Subject#17: I thought the symposium would have been extremely relevant to national platforms for disaster prevention or disaster reduction what even they are calling them. And I know that ISDR was part of the planning process, but I think that, as far as I know, the part of ISDR that was most internally involved in the planning process was the Information Management Division and they did not involve the rest of ISDR and so nobody realized that this was a tremendous opportunity to bring some very specifically qualified people from national disaster reduction platforms. And you know there is probably a hundred of those. And you know rather than… usually it’s important to
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participate in things and we never get to actually get the people together who do the information systems work. That would have been an incredible addition to the audience. Subject#12: I think it would be a very very very good idea for just you know the UN organizations themselves to get together. And not try and bring on all those non-governmental NGO types. Because you know the UN needs to kind of figure out its own game and how it wants to interact with the rest of us.
6.3 Effectiveness Measures
As I stated in the previous chapter, the measurement of effectiveness has always been a
nagging and unsolved problem for inter-organizational network researchers. There is no
consensus on the criteria of measuring effectiveness among researchers. Prior research
has used wide varieties of measures (see Table 4). These measures include the perception
of solving problems, decreased service duplication, improved coordination (Provan &
Milward,1995); service quality (Grusky, 1995); and perceived benefit to various
stakeholders of the network (Weech-Maldonado et al., 2003). In my research, I use three
different measures of network effectiveness including one subjective (perceived network
effectiveness) and two objectives measures (number of funded projects and number of
funding partners). The number of funded projects measures effectiveness in term of level
of activities in humanitarian assistance while the number of funding partners measures
effectiveness in term of level of collaboration.
6.3.1 Perceived Network Effectiveness
I used as one criteria of effectiveness, the perception of the members of the Global
Symposium community about the effectiveness of their humanitarian information
management and exchange network. As discussed earlier, subjective measures of
effectiveness have been widely used in previous research. I also chose this measure in
order to take into account the context of my study. My survey instrument included a five
point Likert scales question that asked respondents about their perception of the
effectiveness of the network on the following items: (i) dissemination of best practices
and humanitarian information principles; (ii) accessibility to resources; (iii) community
development; and (iv) knowledge and information exchange. These items were drawn
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from my prior research and also from the literature. Network effectiveness ranged from 1
(strongly disagree) to 5 (strongly agree). Cronbach’s alpha coefficient for this four-item
scale was .83.
For each of the three networks investigated, I computed the mean score of the responses
on each of the items. The results are presented in Table 7 below. I also conducted an
independent-samples t test to evaluate the differences among the three networks on these
items. I found that there was not a statistically significant difference.
Perceived Effectiveness
Network
Governmental
Organizations
(n= 12)
Non-Governmental
Organizations
(n= 17)
United Nations
Agencies
(n= 11)
Dissemination of best practices
and information principles
M
(SD)
2.14
0.98
2.27
0.64
2.45
1.00
Accessibility to resources M
(SD)
3.08
1.44
2.82
1.29
2.18
0.60
Community development M
(SD)
2.30
1.09
2.59
0.80
2.06
0.49
Knowledge and information
exchange
M
(SD)
2.17
0.98
2.40
0.87
1.93
0.69
Mean score 2.42 2.52 2.16
Table 7: Perceived Network Effectiveness Index Table
My first general observation was that the overall patterns of results within each of the
three networks (governmental organizations/agencies – GO; non-governmental
organizations – NGO; and United Nations agencies – UNA) were similar. The main
contrasting difference observed concerned UNA. This network registered the lowest
score on the following three items: accessibility to resources (UNA score: 2.18; mean
score: 2.70), community development (UNA score: 2.06; mean score: 2.35), and
knowledge and information exchange (UNA score: 1.93; mean score: 2.21). Conversely,
perceptions of respondents of UNA revealed this network is more effective in the
dissemination of best practices and humanitarian information management principles.
UNA displayed the highest score on this (UNA score: 2.45; mean score: 2.27). When
considering all the different survey items on which network effectiveness was measured,
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accessibility to resources came first in two out of the three networks including GO (AR
score: 3.08; mean score: 2.42) and NGO (AR score: 2.82; mean score: 2.25).
In order to reflect the overall network level of perceived effectiveness, I developed an
index by averaged the four factor scores generated for each network to produce a single
mean item score for that network. Based on this mean score, NGO displayed the highest
level of network effectiveness follow respectively by GO, and UNA in the last position.
6.3.2 Level of Activities and Level of Collaboration
The two objective measures for assessing effectiveness that I used were respectively the
number of funded projects and the number of funding partners in humanitarian relief. I
used the number of funded projects as proxi measure for the level of activities while the
number of funding partners was the measure of the level of collaboration. These indices
are important performance factors in humanitarian disaster assistance. Both measures are
related to the concept of social capital. Social capital refers broadly to characteristics of
social structure that function as a resource for individuals and groups. Putman (1993)
defines social capital as the “features of social organization, such as trust, norms and
networks that can improve the efficiency of the society by facilitating coordinated
actions” (P. 167). Social capital can be interpreted as combining a structural component
consisting of involvement in voluntary associations and a cultural component consisting
of norms, values and trust. In my study, I used this interpretation of social capital. The
structural component of social capital was measured by the level of collaboration while
the cultural component was measured by the level of activities. As I discussed earlier, I
considered that greater level of activities and or greater level of collaboration was
associated with higher level of effectiveness. One outcome of voluntary interaction
among members in a community is the development of social trust that facilitates
collective social action toward achieving common social goals. The level of collaboration
in a community is therefore a function of interaction among members via their social
networks. As the level of collaboration increases, so does the effectiveness of the
community in achieving its goals. Thus communities with vibrant communication
networks are likely to display higher level of effectiveness.
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Moreover, I considered the opinion of research participants to choose these two measures
for my study. For instance, during interviews, I asked participants about what in their
opinion would be an appropriate measure that could be used to assess network
effectiveness in their community. More than half of the interviewees (53%) answered
this question. Not surprisingly, I registered a diversified range of responses.
Summarizing, approximately nine (9) different criteria for assessing network
effectiveness emerged from the interviews. They included (i) the range of activities
provided by the network, (ii) the level of preparedness in of the Global Symposium
community in responding to humanitarian disaster especially with regards to information
management and exchange, (iii) the level of coordination in the network, (iv) the level of
collaboration among members (v) the availability and access to funding (vi) the timely
response to crises especially with regards to information sharing (vii) the level of use of
best practices (viii) the availability and access to resources especially technology tools
and technical staff, and finally (ix) the level of attendance to Global Symposium events.
I present in the table below (Table 8) some illustrative quotes from the interview data
concerning these effectiveness measures.
Effectiveness measure
Illustrative quotes
Range of activities
I think if we were to be an effective symposium, one thing would be add new activities that could be implemented within a reasonable timeframe. [….]Research activity, research projects; or even operation activities that could be implemented within a reasonable short time following direct results you know, or direct resources or direct resolution from the symposium. (Subject#1)
I think for me, I would look at the number of implemented projects (Subject#18)
Level of Coordination
I think it would definitely be knowledge exchange. I think at this point, I would want to be able to read, learn about things that I didn’t know anything about or features about things that I have just heard about, and then and those things that I would assume would be facilitate and lead to collaboration. […] So the first step of that would be sufficient information sharing. An then one of things that I would expect to see is the consolidation of this and a lot less competition and a lot more synergy. So one of the eventual outcomes is that we will just become a lot more collaborative and a lot more of these systems talk to each other and a lot more links to one another, and we don’t do as much replication of efforts. (Subject#17)
organization inter-operate although they are competing for donors whom they appeals, I would seek to analyze the attitude of all those organizations, to see to what extent they compete in the market and to what extent they understand what synergy mean. (Subject#4)
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Effectiveness measure
Illustrative quotes
Level of collaboration
I think you need to look at the level of coordination and funding. How much of funding have organizations successfully secured to work in this area? The extent to which there are working with other partners or coordinating. ( Subject#10)
I think it would definitely be knowledge exchange. I think at this point, I would want to be able to read, learn about things that I didn’t know anything about or features about things that I have just heard about, and then and those things that I would assume would be facilitate and lead to collaboration. […] So the first step of that would be sufficient information sharing. An then one of things that I would expect to see is the consolidation of this and a lot less competition and a lot more synergy. So one of the eventual outcomes is that we will just become a lot more collaborative and a lot more of these systems talk to each other and a lot more links to one another, and we don’t do as much replication of efforts. (Subject#17)
organization inter-operate although they are competing for donors whom they appeals, I would seek to analyze the attitude of all those organizations, to see to what extent they compete in the market and to what extent they understand what synergy mean. Subject#4
Funding and other resources
I think you need to look at the level of coordination and funding. How much of funding have organizations successfully secured to work in this area? The extent to which there are working with other partners or coordinating. (Subject#10)
[…]it is probably easier to use the money which has been given because that will at least express a certain level of satisfaction of what we are doing. Because we are funded by volunteering contribution from donors. So at least I would say that if we get a lot of money for one of the other projects that at least indicate the level of satisfaction from our stakeholders. So maybe that is a better one. (Subject#11) I think another way which is being relatively successful is the way you have the agencies to have high number of technical people in them. Especially when they come from a professional background where you can do humanitarian practices across agencies where people really know how to improve the competency or work.(Subject#3)
Table 8: Choosing Effectiveness Measures: Illustrative Quotes from the Interview
It is by analyzing and trying to synthesize this wide range of diverse opinions on
effectiveness measures and by considering the findings of my previous research in this
community (Ngamassi et al, 2010) that I chose to use the number of funding partners and
the numbers of funding projects. As mentioned earlier, I collected the data related to the
number of funded projects and funding partners from the ReleifWeb Financial Tracking
Service, a UNOCHA web based database which records all reported international
humanitarian financial assistance. The the ReleifWeb Financial Tracking Service was
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implemented and launched in 1999. In the humanitarian relief literature, data from the
ReleifWeb database has been used in a number of academic work and reports to donors
(e.g. Torrente, 2004; Walker et al., 2005; Amin & Goldstein, 2008; VanDeWalle &
Turoff, 2008; Tomaszewski & Czaran, 2009). In order to increase the validity of data and
the number of cases, I collected data for a period of ten years (1999-2009).
I started the analysis of this data by conducting a paired-sampled t test to evaluate the
differences between the numbers of funded projects and funding partners in the whole
community. The results indicated that the mean of the number of funded projects (M =
253.02, SD = 523.98) was significantly greater than the mean of the number of funding
partners (M = 20.13, SD = 37.10), t(55) = -3.50, p < .001. The 95% confidence interval
for the mean difference between the two numbers rating was -366.20 to -99.59.
I continued the analysis by computing for each of the three networks, the mean score of
the number of funded projects and funding partners. The results (Table 9 below) will be
used to compare and rank the networks. I then conducted an independent-samples t test
to assess the differences among the three networks on these two items. Concerning the
number of funded projects, I found that there was a statistically significant difference
between the GO and UNA [t(25) = -1.874, p = .07] on the one hand, and on the other
between NGO and UNA [t(39) = -2.239, p < .05]. The difference between GO and NGO
was not statistically significant [t(42) = -.231, p = .818]. With regards to the number of
funding partners, there was a significant difference only between NGO and UNA [t(39)
= -2.470, p < .05].
Effectiveness Measure
Networks
Governmental Organizations
(n= 15)
Non-Governmental Organizations
(n= 29)
United Nations
Agencies (n= 12)
Total
(n=56)
Number of funded projects (from 1999 to 2009)
M (SD)
141.47 346.82
166.48 337.48
601.58 872.07
253.02 523.98
Number of funding partners (from 1999 to 2009)
M (SD)
19.20 60.15
14.62 20.82
34.58 29.37
20.13 37.10
Table 9: Network Effectiveness (Objective measures)
Note: (i) Data collected form ReliefWeb Financial Tracking Service; (ii) data were not available for for-profit organizations.
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Notwithstanding the fact that not all the networks presented a statistically significant
difference on both effectiveness measures, I used the two criteria for ranking the
networks. When using the number of funded projects as measure for effectiveness, I
found that the most effective network was UNA followed respectively by NGO and GO.
UNA was also found to be the most effective network when using the number of funding
partners as measure for effectiveness. With this measure, GO came second and NGO last.
A preliminary observation of these results (see Table 10) is that, as one could anticipate,
that network effectiveness varies depending of the effectiveness measure. For instance,
based on the mean score of perceived network effectiveness, the United Nations agencies
is the least effective network of the three networks in our study. This same network is the
most effective when network effectiveness is measured either by the number of funded
projects or by the number of funding partners.
Network
Criteria
GO Governmental Organizations
NGO Non-Governmental
Organizations
UNA United Nations
Agencies
Perceived effectiveness 2nd 1st 3rd
Number of funded projects 3rd 2nd 1st
Number of funding partners 2nd 3rd 1st
Table 10: Network Effectiveness Ranking
6.4 Network Structural Characteristics and Effectiveness
I present in this section the structural characteristics of the three multi-dimensional inter-
organizational collaboration networks (GO, NGO, and UNA) that I studied.
6.4.1 Density
I started the analysis of network structural characteristics by computing the overall level
of network integration in all three networks. I used the density score to measure the level
of integration of the network. This practice is common in inter-organizational network
research (Provan et al., 2007; Arya & Lin, 2007). I used UCINET (1991) to compute the
density scores. In overall, the level of integration was low in all the three networks I
studied. Density scores ranged from roughly six percent (6.27%) to approximately
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nineteen percent (18.33%) (Table 4). GO displayed the lowest network wide integration
(6.27%). This means that GO had less organizations involved in inter-organizational
relationships (project collaboration and advice) than the other networks. GO was
followed respectively by NGO (6.77%), and UNA (18.33%).
Network
Relationship
GO Governmental Organizations
NGO Non-Governmental
Organizations
UNA United Nations
Agencies
Project collaboration 0.0762 0.0779 0.1933
Advice 0.0493 0.0575 0.1433
Multidimensional 0.0627 0.0677 0.1833
Ranking 3rd 2nd 1st
Table 11: Network Density
With this ranking of the three networks, my hypothesis (HN#1) that network
effectiveness increases with network density found support when effectiveness was
measured as the of level of activities (number of funded projects).
6.4.2 Clique
My first step in clique and clique overlap analyses was to determine the minimum set size of a
clique. Apart from the study done by Provan & Sebastian (1998), there is no research in the
literature that reports clique overlap analysis. Given this lack of information, I set the minimum
clique size based on the data I had. Similarly to the Provan & Sebastian (1998) study, I assumed
in my study that that greater and more intensive integration within and across cliques would mean
higher level of effectiveness. In a first step I determined the clique size in all two dimensions that
could be compared across the different networks. I began by generating lists of three, four, and so
on actor cliques in all three networks. Tables 11 and 12 below present the general
characteristics of cliques in the three networks and for the two dimensions I investigated.
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Project Network GO Governmental Organizations
NGO Non-Governmental
Organizations
UNA United Nations
Agencies
Number of cliques 40 72 8
Min Size 3 3 3
Max Size 5 5 8
Average 3.825 4.014 5.375
Stddev 0.958 0.702 1.847
Clique members 35/53 55/72 14/25
Table 12: Cliques Characteristics Project Network
Advice Network GO
Governmental Organizations
NGO Non-Governmental
Organizations
UNA United Nations
Agencies
Number of cliques 20 57 8
Min Size 3 3 3
Max Size 5 5 7
Average 3.4006 3.6667 5.1250
Stddev 0.6806 0.6362 1.6421
Clique members 23/53 40/72 12/25
Table 13: Cliques Characteristics Advice Network
I also noted that the six actor cliques did not yield the possibility to compare cliques and
clique overlap in all dimensions, in the rest of the three networks. Consequently, I set the
minimum clique set size at five, even though there are larger cliques present in the
networks (especially in UNA). Table 7 below presents the number of cliques and the
number of organizations in cliques for each of the networks. These results were obtained
by calculating the number of cliques with five or more organizations. I then calculated
the total number of organizations in each network involved in one or more of these
cliques. The number of cliques on the multidimensional row was generated for each
network by summing the results of the two dimensions (projects collaboration and
advice) and subtracting the total number of identical cliques. I used the same method to
calculate the number of organizations on the multidimensional row.
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Clique Characteristics: Minimum Set Size of Five
Clique Characteristics
GO Governmental Organizations
NGO Non-Governmental
Organizations
UNA United Nations
Agencies
Number of Cliques
Project collaboration 15 18 5
Advice 2 5 5
Multidimensional 17 22 8
Ranking 2nd
1st
3rd
Number of agencies in clique
Project collaboration 14 24 11
Advice 6 9 10
Multidimensional 15 26 11
Ranking 2nd
1st
3rd
Table 14: Clique Characteristics: Minimum Set Size of Five
When analyzing these results, I made two observations. First, I found that that across the
different networks investigated, NGO was the most integrated as measured by the
number of cliques and the number of organizations in cliques. This finding may indicate
a sort of cluster environment in which organizations knew each another and interacted
frequently through collaborative projects and/or advice. The second observation was that,
at the multidimensional level, there was a similar ranking pattern of the networks using
the number of cliques or the number of organizations in cliques. For both ranking criteria,
NGO was first (22 cliques and 26 organizations in cliques), followed respectively by GO
(17 cliques and 19 organizations in cliques) and finally UNA (8 cliques and 12
organizations in cliques). Exploring individual dimensions, the same ranking pattern held
for projects collaboration relationships. The ranking was different with regards to the
advice relationships. On this dimension, UNA was first both in term of number of cliques
and the number of organizations in cliques followed respectively by NGO and GO.
With these findings, my two propositions, HN#2 - network effectiveness increases with
the number of cliques in the network and HN#3 - network effectiveness increases with
the number of organizations in cliques found support when I used perceived effectiveness
as measured network effectiveness. These propositions were not supported when using
the other measures of network effectiveness.
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6.4.3 Clique Overlap
One-dimensional clique overlap analysis explores one single type of inter-organizational
relationship at a time. I calculated clique overlap in several ways using Provan &
Sebastian’s (1998) procedure. I counted the number of times organizations in a particular
relational type of clique appeared in at least n (n being a cut off number) cliques of that
type and divided the result by divided by the total number of organizations in cliques.
The few previous studies in the literature that used this procedure (e.g. Provan &
Sebastian, 1998; Lemieux-Charles et Al., 2005) set the cut number at 50%. Unlike these
studies, I explored four different levels (low medium and high) of clique overlap using
respectively 25%, 40%, 50% and 75% as cut off number (See table 8). Low overlap
would indicate that the members of these cliques interact intensively among themselves
but only little across different cliques. In the contrast, in a network with high clique
overlap, many clique members would also belong to other cliques. This would lead to a
highly integrated core of organizations spanning multiple cliques.
Clique Characteristics: One-dimensional Clique Overlap
Clique Characteristics
GO Governmental Organizations
NGO Non-Governmental
Organizations
UNA United Nations
Agencies
75%
Project collaboration 3/14 = 21.43% 2/24 = 8.33% 3/11 = 27.27%
Advice 4/6 = 66.66% 3/9 = 33.33% 4/10 = 40.00%
50%
Project collaboration 3/14 = 21.43% 3/24 = 12.5% 7/11 = 63.63%
Advice 6/6 = 100% 4/9 = 44.44% 6/10 = 60.00%
40%
Project collaboration 5/14 = 35.71% 4/24 = 16.66% 10/11 = 90.90%
Advice 6/6 = 100% 7/9 = 77.77% 9/10 = 90.00%
25%
Project collaboration 8/14 = 55.14% 5/24 = 20.83% 11/11 = 100%
Advice 6/6 = 100% 9/9 = 100% 10/10 = 100%
Ranking 2nd 3rd 1st
Table 15: Clique Overlap
After a preliminary analysis of the results, I chose to use the lower level (25%) of cliques
overlap in this study. The reason for my choice was threefold. First, the lower level
(25%) of cliques overlap presented in overall, the highest percentage of cliques overlap
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across networks and across the two dimensions of inter-organizational relationships
investigated. Second, I observed that the different networks maintained the same ranking
irrespective of the level of clique overlap. On the projects collaboration dimension, UNA
came first followed by GO and then NGO. On the advice dimension the order was GO,
UNA and NGO for three levels of overlap (40%, 50% and 75%). At 25% clique overlap
was similar (100%) in all the networks. This meant that working at this level the
differences in clique overlap among the networks at multidimensional level could be
assessed just using the projects collaboration relationships. Analyzing these results the
projects collaboration relationships results, I observed a very high discrepancy in cliques
overlap scores cross networks. These scores ranged from roughly twenty one percent
(20.83%) to hundred percent (100.00%). UNA displayed the highest score (100.00%)
followed by GO (55.14%) and finally NGO (20.83%). With this ranking of the three
networks, my hypothesis (HN#4) that network effectiveness increases with the level of
overlapping clique in the network found support when effectiveness was measured as the
of level of collaboration (number of funding partners).
6.4.4 Multiplexity
As discussed earlier, in this study, multiplexity indicates the level overlap between the
two different dimensions of networks. I measured multiplexity as the extent to which
organizations belonged to cliques in more than one relational dimension. I computed
multiplexity as the percentage of organizations that were members in cliques in both
advice and projects collaboration dimensions. I also explored clique identical overlap. I
calculated the degree of identical overlap as the percentage of cliques in the advice
dimensions exactly matching (or completely embedded in) cliques in the projects
collaboration dimension.
Table 9 below presents the results of my investigations. I found a high discrepancy in
both the multiplexity and the identical clique overlap scores. With regards to
multiplexity, scores ranged from approximately seventeen percent (66.66%) to ninety
percent (100.00%). UNA displayed the highest multiplexity scores (100.00%) followed
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by NGO (77.77%) and finally GO (66.66%). Concerning identical cliques overlap, UNA
was ranked first with a score of forty percent (40.00%) followed by NGO (20.00%) and
GO (0%).
Clique Characteristics: Multidimensional clique overlap
Relationship overlap
GO Governmental Organizations
NGO Non-Governmental
Organizations
UNA United Nations
Agencies
Multiplexity 4/6=66.66% 7/9 = 77.77% 10/10 = 100.00%
Identical 0/2 = 0% 1/5 = 20.00% 2/5 = 40.00%
Table 16: Multidimensional Clique Overlap
With this ranking of the three networks, my two propositions HN#5 (network
effectiveness increases with the level of multiplexity in the network) and HN#6 ( network
effectiveness increases with the level of identical cliques in the network in the network)
found support when effectiveness was measured as the of level of activities.
As a final way of exploring my finding regarding clique structure and overlap, I
generated a graphical representation of the clique structure for each of the three networks.
Using the NetDraw function of UCINET (1991), I developed graphics of all clique
members for each network. Figures 24, 25, and 26 present these graphics. I used three
different types of line each representing one the type of inter-organizational relationship.
Organizations that were member of a clique in the Project Collaboration relationship
were linked by dotted lines. Members of a clique in the Advice relationship were linked
by dashed lines. Clique overlap, in which both types of relationships occur among clique
members, was represented by a thick solid line. This line linked each pair of
organizations for which overlap existed. Examining these graphics, it was clear that there
were important differences in the overlap structures of cliques for each network.
Link overlap among clique members in UNA (Figure 24) was substantial. All of the of
the eleven clique-member organizations, maintained at least one multiplex relationship
(both project collaboration and advice) with another clique member. More than thirty five
percent (36.33%) of organizations were connected exclusively through multiplex ties.
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Examining the effectiveness of these organizations, I found that they were among those
that displayed the highest number of funded projects. For example, Org188 had 2777
funded projects as compared to 601, the average number in the network. NGO (Figure 25
had many more organizations (twenty six) in cliques than the UNA. Similarly to UNA
organizations, organizations in NGO also maintained at least one multiplex relationship
with another organization. In this network, only less than four percent (3.83%) of the
organizations had exclusively a multiplex relation. It was also found that these
organizations were among the most effective in the network in term of number of funded
projects. An examination of the last graphic (GO) depicted in Figure 26, also revealed
that many organizations were involved in cliques. In this network however, the level of
link overlap among clique member was lower than in the two previous networks.
Approximately seven percent (6.66%) of the organizations maintained only one type of
relationship with other organizations.
Figure 24: United Nations Agencies Clique
Structure
Figure 25: Non-Governmental
Organizations Clique Structure
135
Figure 26: Governmental Organizations Clique Structure
Summing up, I found that overlap in inter-organizational relationships across cliques
appeared to be important for explaining network effectiveness. Moreover, the specific
composition of these overlapping cliques was also important, particularly when the
cliques involved organizations, like the leading humanitarian organizations (e.g. org188),
that may be critical to overall network success.
In Table 17 below, I summarize the network level hypotheses. For each hypothesis, I
indicate the measure of effectiveness for which I found support.
Number Hypothesis Supported
Perceived Effectiveness
Level of Activities
Level of Collaboration
HN# 1 Network effectiveness increases with the density
Yes
HN# 2 Network effectiveness increases with the number of cliques in the network
Yes
HN# 3 Network effectiveness increases with the number of organizations in cliques
Yes
HN# 4 Network effectiveness increases with the level of overlapping clique in the network
Yes
HN# 5 Network effectiveness increases with the level of multiplexity in the network
Yes
HN# 6 Network effectiveness increases with the level of identical cliques in the network
Yes
Table 17: Summary of Hypotheses Testing at Network Level of Analysis
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6.4.5 Discussion
I studied the relationship between network structural characteristics and network
effectiveness in multidimensional networks of collaborative relationships among
humanitarian organizations. I used three different measures for network effectiveness
including one subjective criteria – perceived network effectiveness and two objective
criteria – number of funded projects measuring the level of activities and number of
funding partners measuring the level of collaboration. It is important here to note that in
Provan & Sebastian (1998) study which forms the foundation of this work, the authors
used only one measure for network effectiveness.
Based on the perceived network effectiveness index, NGO displayed the highest level of
network effectiveness followed respectively by GO and UNA in the last position. When
using this measure, findings from my study suggested that network effectiveness was
driven by two network structural properties including the number of network cliques and
the number of members in cliques. This result meant that in a network, inter-
organizational interactions among humanitarian organizations through multiple
relationships would be more effective when only a small number of closely connected
sub-groups of organizations are involved. This finding is consistent with the one of some
previous studies on inter-organizational network effectiveness (e.g. Provan & Sebastian,
1998; Lemieux-Charles et Al., 2005). These studies found that clique structures played
important role in the creation of positive network outcomes.
When using the level of activities as effectiveness measure, my findings suggest that
network effectiveness is driven by two other network structural characteristics including
network density and the level of multidimensional identical clique overlap in the
network. As discussed earlier, the density of a network is the number of links between
members of the network compared to the maximum possible number of links that could
exist in the network (Kilduff & Tsai, 2006). Multidimensional identical overlap degree,
in my study, is the percentage of advice cliques exactly matching project collaboration
cliques. The ranking of the three networks studied based on the level of activities
matched their ranking based on the density and on the degree of multidimensional
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identical clique overlap. UNA was found to be the most effective network. UNA was
followed by NGO and then GO.
Finally, when using the level of collaboration as effectiveness measure, I found that
network effectiveness is driven by the level of one dimensional and multidimensional
(multiplexity) clique overlap in the network. Based on the level of collaboration, the
ranking of the networks investigated matched their ranking based on the level of one
dimensional and multidimensional clique overlap. UNA was found to be the most
effective network. UNA was followed by GO and then NGO.
One first general observation of these findings is that, as one could anticipate, network
effectiveness varies depending of the effectiveness measure. For instance, based on the
mean score of perceived network effectiveness, the United Nations agencies is the least
effective network of the three networks in our study. This same network is the most
effective when network effectiveness is measured either by the level of activities (number
of funded projects) or by the level of collaboration (number of funding partners). These
findings corroborate with the literature on inter-organizational network effectiveness
which highlights the existence of a wide range of definitions and criteria for network
effectiveness (Alter & Hage, 1993; Provan & Milward, 1995; Sydow & Windeler, 1998;
Provan et al., 2007).
Though it would be risky to generalize about research results from a sample of only three
networks in a single area of humanitarian information exchange, my study contributes to
the literature on inter-organizational humanitarian networks in a number of ways. Firstly,
building on Provan & Sebastian (1998), my study further highlights the need to consider
network analyses in smaller substructures than what has been done previously. Large
scale integration across an entire network of organizations is difficult to achieve and is
probably not a very efficient way of organizing (Provan & Sebastian, 1998). For
instance, in the field of humanitarian relief field, disaster response often involves
heterogeneous organizations, both for-profit and nonprofit, with a wide range of different
characteristics. In this field, achieving effective inter-organizational collaboration is more
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challenging especially with regards to information management and exchange (Ngamassi
et al, 2011; Maitland et al., 2009). As the findings of my research suggest, it is more
appropriate to assess network effectiveness in smaller substructures such as subnets or
cliques. These findings derived from quantitative analysis corroborated with results from
qualitative data. For instance, some interview participants reported that the large size of
the Global Symposium would more likely negatively impact its effectiveness. I provide
below, illustrative quotes from Subjects #2, 5 and 11.
Subject#2: I would say, first try to do it within a country or a region instead of trying to do it globally. Because then you have a smaller community and those community are much more important, the regional community or the national community. Subject#5: there should be smaller groups that held very very specifically with mixed of media communication people and these organizations perhaps have small groups that meets for one day but in a highly intensive manner, and really look at the issues maybe to review what happened in the last symposium, but reviewed this in a very very pragmatic manner and a very outspoken critical manner as well. Subject#11: May be a smaller group, because it was rather a large event, so maybe if you could do it regionally, let say one in Latin America, and another one in Africa or central Africa, west Africa, maybe that would be more effective, because you would have fewer participants.
Secondly, my research extends Provan & Sebastian’s model in the humanitarian relief
field. My research offers some evidence that similarly to the public health service
delivery sector, network effectiveness can be explained by intensive integration and
network cliques in the humanitarian relief field. My data supported the idea that
differences in effectiveness across networks could be better understood by focusing on
cliques and the overlap among cliques of multiple relationships among humanitarian
organizations. My study would help to do the clique analysis or to search for closely
connected and cohesive subgroups. Additionally, my work can help to design efficient
inter-organizational network structures in the humanitarian relief sector. For example, by
increasing the level of clique overlap (one dimensional or multidimensional) in inter-
organizational humanitarian networks, network designers should expect a higher level of
inter-organizational collaboration.
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Thirdly, by empirically testing Provan & Sebastian (1998) conceptual framework for
assessing network effectiveness, my study contributes to further research in inter-
organizational collaboration in the humanitarian relief field. During my investigations, I
realized the importance of understanding the different type of relationships that exist
among humanitarian organizations. I found out that the relationships were significantly
complex especially when considering their motives. As mentioned earlier, disaster
response often involves heterogeneous organizations with a wide range of different goal
and need which render collaboration very challenging. In my study for example, when
asked about their reasons for getting into a relationship, my study subjects provided a
wide range of different reasons. Network designers need to examine more closely the
nature of relationships in which humanitarian organizations are engaged and the self-
reinforcing dynamic of overlapping groups.
Fourthly, my research also highlights the need to explore network effectiveness using a
set of different measures. The majority of existing work on network effectiveness,
including that of Provan and Sebastian (1998) was conducted using one measure. As
mentioned earlier, Provan & Sebastian used client outcomes, a subjective measure, to
assess network effectiveness. Moreover, in most cases, the effectiveness measure was not
selected with input from the various network members. In my study, I used input from
network members to determine the three measures of effectiveness. Using a set of three
different measures for network effectiveness allowed me to find consistent ranking
pattern for each of the six network structural characteristics studied. Moreover, my
findings suggest that the subjective and objective forms of network effectiveness are
better explained by different network structural attributes. Whereas subjective network
effectiveness is better explained by the number of cliques and clique membership,
objective network effectiveness is better explained by the multifaceted nature of inter-
organizational relationships as measured by clique overlap and multiplexity. My study
serves as an example of effectiveness being measured with multiple criteria. In a nut
shell, my work extends in the humanitarian relief field, Provan & Sebastian (1998)’s
model of inter-organizational network effectiveness.
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Lastly, my research also has implications for network theories. For many organization
theorists, the study of both inter-organizational and intra-organizational networks has
primarily been an exercise in analysis and methods (Salancik, 1995). Building upon
Provan & Sebastian (1998), my study further develops an alternative method for network
analysis and contributes to building network theories by examining and explaining how
network structural properties including network density, cliques and overlapping cliques,
might promote the interests of network members and that of the community as a whole.
6.5 Ego-Net Characteristics and Effectiveness
I used the multiple linear regression method to investigate effectiveness at organizational
level. The independent variables, nine in total, were grouped into the following three
categories: organization, ego-network and network. These independents variables are
described in Table 18 below. I also used two interaction variables with the purpose to
assess the combined impact of technology and network characteristics on organizational
effectiveness. I conducted the regression analysis on two different measures of
organizational effectiveness, the dependent variable. The first measure was the level of
activities, measured as the number of funded projects and the second was the level of
collaboration, measured as the number of funding partners. In order to examine
separately the influence of each category of the independent variables on the dependent
variable, I developed four models as described below.
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Variable level Variable Definition
Organization
Size
Size of the organization
Service Range of services provided by the organization
Com_Med Varieties of Communication media (e.g. Internet, Website)
Coll_SM Varieties of Collaboration social software (e.g. wiki, shared
db)
Cty_SM Varieties of Community social software (e.g. Facebook)
Ego-network
Centrality Degree centrality of the organization in the network
Bridge Structural hole value of the organization in the network
cliques Number of distinct cliques to which organization is a member
Network
Density Density of the network to which organization is a member
Interaction
Com_Med x Density Interaction between communication media and density
Com_Med x Centrality Interaction between communication media and centrality
Table 18: Organizational Effectiveness Variables
6.5.1 Models Building
As a first step in the model building process, I examined my data to check for consistency
and eventual errors of data due to data manipulation. This examination led to the
identification of one outlier in the data. Before proceeding to the next step of the analysis
I removed the outlier.
The next step in the model building process was to compute some basic statistics and to
check the correlation between the variables. Table 19 reports the descriptive statistics and
correlations between the variables. One preliminary observation was that all the
correlations between the independent variables and the dependent variables were
positive. This was an indication that organizations that have higher number on these
variables would tend to display higher level of effectiveness.
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M SD 1 2 3 4 5 6 7 8 9 1 11 12
Size Service .483** Com_Med .106 .176 Coll_SM -.0.56 -.016 .674** Cty_SM .142 .183 .691** .753** Clique .221 .088 .384** .261* .312* Centrality .200 .057 .46** .365** .384** .875** Bridge .108 -.016 .383** .328** .262* .407** .601** Density .283 -.081 -.022 -.047 -.122 .256 .214 .094 Com_Medxcentrlity .179 .079 .565** .396** .406** .888** .958** .524** .215 Com_Medxdensity .231 .022 .668** .427** .376** .526** .524** .379** .636** .613** Funding_Parners .211 .030 .259* .152 .272** .350** .646** .253 .203 .608** .351** Funded_Projects .264* .107 .393** .209 .274** .627** .699** .336* .351 .770** .643** .724**
Note: N = 56
** correlation is significant at the 0.01 level (2-tailed)
* correlation is significant at the 0.05 level (2-tailed)
Table 19: Descriptive Statistics and Correlations
As mentioned earlier, I built four models to investigate the independent and interaction
effects of the organization and network level variables on organizational effectiveness. I
built these various models using a linear combination of the dependents variables. I had
checked the non-linear approach and found no improvement in fit of the models. In
Model I, my baseline model, I modeled effectiveness as a function of the variables of the
organization category. This model shows the regression results for the effects of
organizational characteristics only, on effectiveness. In Model II, I added the independent
variables of the ego-net category. Model III included the independent variables of the
network category. Finally, in Model IV (the full model) I used all the independent
variables including the interaction terms. I build separately a full model for each of the
two interaction terms. I examined all the models for collinearity issues. I used the
variation inflation factor (VIF) for this endeavor. VIF is a technique commonly used for
collinearity diagnostic. A general rule is that the VIF should not exceed 10 (Belsey,
1991). The VIF values for the variables in all the four models were not higher than 8. I
concluded therefore that collinearity was not an important issue. I present below the four
models that I developed for each of the two measures of organizational effectiveness.
6.5.1.1 Effectiveness measured as Level of Activities
I developed the first set of multiple linear regression models, using the level of activities
as dependent variable. As mentioned earlier, the number of funded projects of an
organization was used as a proxy measure for the level of its activities.
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Model I
In the first model, I used only the organizational internal characteristics as independent
variables to predict effectiveness. The linear combination of these variables was
significantly related to effectiveness, F (5, 50) = 2.7, p < .05. The multiple correlation
coefficient was .461, and the adjusted R2 was 0.134. This value for R
2 was an indication
that approximately 13% of the variance in organizational effectiveness can be accounted
for by the linear combination of organizational internal characteristics. In this model, two
predictors were found to significantly contribute in explaining organizational
effectiveness. They included the size of the organization (β = 0.259; p < .05) and the
communication media available in the organization (β = 0.418; p < .05).
Model II
To build this model, I added in Model I the independent variables of the ego-net
category. The multiple regression model of the eight predictors also yielded a significant
linear relation with the effectiveness, F(8, 47) = 6.75, p < .001. The multiple correlation
coefficient was .731 and the adjusted R2 was 0.456. This value for R
2 was an indication
that approximately 46% of the variance in organizational effectiveness can be accounted
for by the linear combination of organizational internal resources and the organization
ego net characteristics. In this model, only one predictor – the organization centrality
degree – was found to significantly contribute in the model (β = 0.768; p < .001).
Model III
Model III shown the regression results for the effects of organizational internal
characteristics, organizational ego-net properties and organizational network structural
properties on effectiveness. This model had nine independents variables. The linear
combination of these predictors was also significantly related to effectiveness, F (9, 46) =
6.83, p < .001. The multiple correlation coefficient of this integrated model was .756. The
adjusted R2 for this model indicated that the model explained approximately 49% of the
variance in the organizational effectiveness. In this model, two predictors were found to
significantly contribute in the model. They included the organization centrality degree (β
= 0.763; p < .005) and the network density (β = 0.219; p < .1).
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Model IV
As mentioned earlier, for each of the two interaction terms, I built a separate full model.
The interaction term in the first full model (Model IVa) was the combination of
communication media and degree centrality (ego-net category variable). In the second,
Model IVb the interaction term was the combination of communication media and
network density (network category variable).
Model IVa
The linear combination of all the predictors was significantly related to the effectiveness,
F(10, 45) = 10.71, p < .001. The multiple correlation coefficient of this integrated model
was .839. The adjusted R2 for this full model indicated that the model explained
approximately 64% of the variance in the organizational effectiveness. In this model,
three predictors were found to significantly contribute in the model. They included the
number of cliques (β = -0.414; p < .05) the network density (β = 0.196; p < .05) and the
interaction term (β = 1.736; p < .001). Figure 27 and Figure 28 below depict the normal
probability plot and the plot of residuals. They provide an indication of the normal
distribution of the data and the residuals produced by the model.
Figure 27:Residual Plot for Effectiveness Mesuared
as the Level of Activities (Model IVa)
Figure 28: Normal Plot for Effectiveness Mesuared
as the Level of Activities (Model IVa)
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Model IVb
The linear combination of all the predictors was significantly related to the effectiveness,
F (10, 45) = 9.85, p < .001. The multiple correlation coefficient of this integrated model
was .828. The adjusted R2 for this full model indicated that the model explained
approximately 62% of the variance in the organizational effectiveness. In this model, five
predictors were found to significantly contribute in the model. They included the
communication media available in the organization (β = -0.462; p < .05), the degree
centrality (β = 0.869; p < .001) the network density (β = -0.403; p < .05), bridging
structural hole (β = -0.207; p < .05) and the interaction term (β = 1.013; p < .001).
Figure 29 and Figure 30 below depict the normal probability plot and the plot of
residuals. They provide an indication of the normal distribution of the data and the
residuals produced by the model.
Figure 29: Residual Plot for Effectiveness Mesuared
as the Level of Activities (Model IVb)
Figure 30: Normal Plot for Effectiveness Mesuared
as the Level of Activities (Model IVb)
In Table 20 below I provide a summary of the four models. A brief analysis of the table
shows that, cross model, none of the independent variables was consistently found to be
an important predictor of effectiveness.
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Variable Model I Model II Model III Model IV
A (Centrality) B (Density)
β t β t β t β t β t Organization Size .259 1.78† .128 1.08 .041 0.33 .089 0.86 .059 0.56 Service -.097 -0.66 -.029 -0.24 .023 0.19 .011 0.11 .063 0.62 Com. Media .418 2.25* .213 1.38 .209 1.40 -.185 -1.20 -.462 -2.20* Coll. Media -.081 -0.38 -.117 -0.69 -.151 -0.91 -.146 -1.05 -.153 -1.07 Cty. Media .027 0.13 -.037 -0.22 .037 0.22 .172 1.18 .075 0.51 Ego-network Centrality .768 3.04** .763 3.11** -.584 -1.61 .869 4.07** Bridge -.155 -1.16 -.147 -1.14 -.009 -0.08 -.207 -1.83* Clique -.047 -0.22 -.100 -0.47 -.414 -2.16* -.303 -1.59 Network Density .219 2.00† .196 2.12* -.403 -2.23* Interaction ComMxCentrality 1.736 4.49** ComMxDensity 1.013 4.05**
N 56 56 56 56 56 P 0.031 0.000 0.000 0.000 0.000 Adjusted R2 .134 .456 .488 .638 .617
† p < .1
* p < .05
** p < .01
Standardized coefficient and t statistics are reported.
Table 20: Regression Analysis on Effectiveness Measured as the Level of Activities
6.5.1.2 Effectiveness measured as Level of Collaboration
Building this second set of models, I proceeded the same way as for the previous set. The
only difference here was that I used the level of collaboration as the dependent variable.
Model I
The linear combination of independent variables was somewhat significantly related to
effectiveness, F (5, 50) = 1.605, p = .176. The multiple correlation coefficient was .372,
and the adjusted R2 was 0.052. This value for R
2 was an indication that only about 5% of
the variance in organizational effectiveness can be accounted for by the linear
combination of organizational internal characteristics. In this model, none of the
predictors was found to significantly contribute in explaining effectiveness.
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Model II
The multiple regression model of the eight predictors also yielded a significant linear
relation with the effectiveness, F(8, 47) = 19.06, p < .001. The multiple correlation
coefficient was .874 and the adjusted R2 was 0.724. This value for R
2 was an indication
that approximately 72% of the variance in organizational effectiveness can be accounted
for by the linear combination of organizational internal characteristics and the
organization ego net characteristics. In this model, four predictors were found to
significantly contribute in the model including one in the organizational category
(collaboration social media) and all the three ego-net category variables (degree
centrality, clique-count and bridging structural hole). The statistical parameters of these
predictors in the model are as follow: collaboration social software (β = -0.257; p < .05);
degree centrality (β = 1.913; p < .001); clique-count (β = -1.183; p < .001); bridging
structural hole (β = -0.410; p < .001). The community social software was fond to
contribute somewhat significantly (β = 0.176; p = .155)
Model III
The linear combination of these predictors was also significantly related to effectiveness,
F(9, 46) = 17.82, p < .001. The multiple correlation coefficient of this integrated model
was .882. The adjusted R2 for this model (0.734) indicated that the model explained
approximately 73% of the variance in the organizational effectiveness. The five
predictors that were found to significantly contribute in the previous model (Model II)
also significantly contribute to this model. The statistical parameters of these predictors
in the model are as follow: collaboration social software (β = -0.276; p < .05);
community social software (β = 0.219; p < .1); degree centrality (β = 1.910; p < .001);
clique-count (β = -1.214; p < .001); bridging structural hole (β = -0.406; p < .001). The
network density was found to contribute somewhat significantly (β = 0.129; p = .122).
Model IVa
The linear combination of all the predictors was significantly related to the effectiveness,
F (10, 45) = 17.63, p < .001. The multiple correlation coefficient of this integrated model
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was .893. The adjusted R2 for this full model (0.751) indicated that the model explained
approximately 75% of the variance in the organizational effectiveness. The five
predictors that were found to significantly contribute in the previous model (Model III)
also significantly contribute to this model. The statistical parameters of these predictors
in the model are as follow: collaboration social software (β = -0.274; p < .05);
community social software (β = 0.271; p < .05); degree centrality (β = 1.392; p < .001);
clique-count (β = -1.335; p < .001); bridging structural hole (β = -0.352; p < .001) and
the interaction term (β = 0.667; p < .05). I also found in this model that the network
density contribute somewhat significantly (β = 0.129; p = .122).
Figure 31 and Figure 32 below depict the normal probability plot and the plot of
residuals. They provide an indication of the normal distribution of the data and the
residuals produced by the model.
Figure 31: Residual Plot for Effectiveness Mesuared
as the Level of Collaboration (Model IVa)
Figure 32: Normal Plot for Effectiveness
Mesuared as the Level of Collaboration (Model
IVa)
Model IVb
The linear combination of all the predictors was significantly related to the effectiveness,
F(10, 45) = 18.15, p < .001. The multiple correlation coefficient of this integrated model
was .895. The adjusted R2 for this full model (0.757) indicated that the model explained
approximately 76% of the variance in the organizational effectiveness. In addition to the
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interaction term, the five predictors that were found to significantly contribute in Model
III also significantly contribute to this model. The statistical parameters of these
predictors in the model are as follow: collaboration social software (β = -0.277; p < .05);
community social software (β = 0.237; p < .05); degree centrality (β = 1.959; p < .001);
clique-count (β = -1.307; p < .001); bridging structural hole (β = -0.433; p < .001) and
the interaction term (β = 0.665; p < .05).
Figure 33: Residual Plot for Effectiveness Mesuared
as the Level of Collaboration (Model IVb)
Figure 34: Normal Plot for Effectiveness
Mesuared as the Level of Collaboration (Model
IVb)
In Table 21 below I provide a summary of the four models. Unlike in the previous set of
models where none of the independent variables was consistently found cross model to
be an important predictor of effectiveness, in this set, I found six variables. They include
two variables of the organization category (collaboration social software and community
social media) all the three variables of the ego-net category (degree centrality, bridging
structural hole, number of cliques) and the only network category variable (network
density). The relationship between these variables and effectiveness was also
consistently either positive or negative across models. The two interaction terms were
also found to significantly contribute to explain effectiveness.
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Variable Model I Model II Model III Model IV
A (Centrality) B (Density)
β t β t β t β t β t Organization Size .223 1.46 .133 1.58 .082 0.92 .101 1.17 .091 1.07 Service -.162 -1.05 -.089 -1.06 -.059 -0.69 -.063 -0.77 -.040 -0.50 Com. Media .195 1.01 .042 0.39 .040 0.37 -.111 -0.87 -.268 -1.60 Coll. Media -.165 -0.74 -.257 -2.12* -.276 -2.31* -.274 -2.37* -.277 -2.43* Cty. Media .259 1.16 .176 1.45† .219 1.79† .271 2.24* .237 2.02* Ego-network Centrality 1.913 10.64** 1.910 10.81** 1.392 4.61** 1.959 11.52** Bridge -.410 -4.31** -.406 -4.34** -.352 -3.75** -.433 -4.81** Clique -1.183 -7.62** -1.214 -7.89** -1.335 -8.37** -.1307 -8.59** Network Density .129 1.62† .120 1.56† -.157 -1.09 Interaction ComMxCentrality .667 2.08* ComMxDensity .465 2.34*
N 56 56 56 56 56 P 0.176 0.000 0.000 0.000 0.000 Adjusted R2 0.052 0724 0.734 0.751 0.757
† p < .1
* p < .05
** p < .01
Standardized coefficient and t statistics are reported.
Table 21: Regression Analysis on Effectiveness Measured as the Level of Collaboration
Comparing the two sets of models, I made a number of observations. First, when
examining the models within each of the two effectiveness measures, I found that the
linear combination of the independent variables used in the model was significantly
related to effectiveness. When using the level of collaboration as effectiveness measure,
the explanatory power (Adjusted R2) of the models gradually increased ranging from
approximately 5% for the baseline model to nearly 76% for the full model. In the full
model, the variables in the organization category accounted for approximately 5.2% of
the variance while those of the ego-net category explained 67.2%. The network category
variable accounted for 1% of the variance while the interaction term explained 1.7%
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(ModelIVa) and 2.3% (ModelIVb). Similarly, when using the level of activities as
effectiveness measure, the explanatory power of the models also gradually increased. Its
value ranged from approximately 13% for the baseline model to nearly 64% for the full
model. In the full model, the variables in the organization category accounted for
approximately 13.4% of the variance while those of the ego-net category explained
32.2%. The network category variable accounted for 3.2% of the variance while the
interaction term explained 15% (ModelIVa) and 12.9% (ModelIVb). Figure 35 below
highlights the contribution of each category of variables in explaining effectiveness.
Figure 35: Effectiveness Models’ Explanatory Power
Second, when examining the models between the two effectiveness measures, I found a
discrepancy in the explanatory power of the full model. When using the level of
collaboration as effectiveness measure, the linear combination of my independent
variables in the full model, explained more than 75% of the variances (75.1% for
ModelIVa and 75.7% ModelIVb). This value was less than 64% when effectiveness was
measured as the level of activities. In both cases, ego-net category variables made the
highest contribution, 67.2% and 32.2% respectively.
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6.5.2 Hypotheses Testing
6.5.2.1 Main Effects
Hypothesis HO#1
I argued in hypothesis HO#1 that greater centrality increases organizational effectiveness
both in terms of level activities as well as the level of collaboration. I found support to
this hypothesis in almost all the models in which centrality score was used as a predictor
and for both measures of organizational effectiveness. When using the level of activities
as effectiveness measure, I obtained the following statistics in the full model (Model IVb)
β = 0.869; p < .005. When using the level of collaboration as effectiveness measure, the
statistics were the following: Model IVa (β = 1.392; p < .005); Model IVb (β = 1.959; p <
.005). The level of significance was relatively high with this measure of effectiveness
than with the previous one. This finding is consistent with most previous research that
explored the influence of network position and especially the degree centrality on
outcome such as performance and effectiveness (Knoke, 1990; Wasserman & Faust,
1994; Stevenson & Greenberg, 2000; Kilduff & Tsai, 2006).
Hypothesis HO#2
Hypothesis HO#2 concerned bridging structural hole in a network. My proposition was
that organizations will enhance their effectiveness by bridging structural holes both in
terms of level activities as well as the level of collaboration. When using the level of
activities as effectiveness measure, I found that bridging structural hole was an important
predictor of effectiveness only in Model IVb (β = -0.207; p < .05). This variable showed
no significance in in Model IVa. Bridging structural hole was found to be an important
predictor of effectiveness when using the level of collaboration as effectiveness measure.
I obtained the following statistics: Model IVa (β = -0.352; p < .005); Model IVb (β = -
0.433; p < .005). But contrary to my proposition, my findings rather showed a negative
relationship between bridging structural hole and effectiveness. This result is not an
isolated case. For instance, while several previous studies have shown that organizations
improve their performance as a result of bridging structural holes (e.g., Hargadon &
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Sutton, 1997; Finlay & Coverdill, 2000), other studies have shown negative performance
effects of bridging structural holes (e.g., Ahuja, 2000; Dyer & Nobeoka, 2000). This
result may be due to the high heterogeneity of the humanitarian organizations concerned
in this study.
Hypothesis HO#3
My Hypothesis HO#3 was related to the importance of the density of the network in
predicting network effectiveness. I proposed that organization will benefit from high-
density networks to enhance their effectiveness both in terms of level activities as well as
the level of collaboration. When using the level of activities as effectiveness measure, I
found that network density was an important determinant of effectiveness. Both Model
IVa (β = 0.196; p < .05) and Model IVb (β = -0.403; p < .05) yielded statistically
significant evidence that supported this hypothesis. The negative sign on the β coefficient
in Model IVb results from adding the interaction term (communication media X network
density) in the model. This hypothesis was somewhat supported in Model IVa (β =
0.120; p < .1) when using the level of collaboration as effectiveness measure. The main
effect of network density was not statistically significant in Model IVb.
Hypothesis HO#4
My proposition in Hypothesis HO#4 was that the effectiveness of an organization will
increase both in terms of level activities as well as the level of collaboration, with the
number of the distinct cliques to which it belongs. This hypothesis was not supported by
my findings. In both measures of effectiveness the number of cliques was found to be an
important predictor of effectiveness, but contrary to my proposition, the final models
rather showed a negative relationship. When using the level of activities as effectiveness
measure, I obtained the following statistics: Model IVa (β = -0.414; p < .05); Model IVb
(β = -0.303; p = .119). When using the level of collaboration as effectiveness measure,
the statistics were as follow: Model IVa (β = -1.335; p < .005); Model IVb (β = -0.1307;
p < .005).
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Hypothesis HO#5
I argued in Hypothesis HO#5 that the size of an organization will be positively associated
with its effectiveness both in terms of level activities as well as the level of collaboration.
I found no support to this hypothesis. When using the level of activities as effectiveness
measure, I obtained the following statistics: Model IVa (β = 0.089; p = .395); Model IVb
(β = 0.059; p = .579). When using the level of collaboration as effectiveness measure,
the statistics were the following: Model IVa (β = 0.101; p = .249); Model IVb (β =
0.091; p = .291).
Hypothesis HO#6
In Hypothesis HO#6, I proposed that the range of service provided by an organization
will be positively associated with its effectiveness both in terms of level activities as well
as the level of collaboration. This hypothesis was also not supported in any of the models.
The range of service provided by an organization was not found to be an important
deterrent of organizational effectiveness. When using the level of activities as
effectiveness measure, I obtained the following statistics: Model IVa (β = 0.011; p =
.913); Model IVb (β = 0.063; p = .540). When using the level of collaboration as
effectiveness measure, the statistics were the following: Model IVa (β = -0.063; p =
.442); Model IVb (β = -0.040; p = .622). This result may suggest that in inter-
organizational network for humanitarian information management, organizational
effectiveness measured both in terms of level activities as well as the level of
collaboration is driven by other factors regardless of the number of services an
organization provides.
Hypothesis HO#7
Hypothesis HO#7 was related to my proposition that the greater the variety of
communication media available in an organization the higher its effectiveness both in
terms of level activities as well as the level of collaboration. When using the level of
activities as effectiveness measure, Model I yielded statistically significant evidence that
supported this hypothesis (β = 0.418; p < .05). Communication media was also found to
significantly contribute to explain effectiveness in one of the full models (Model IVb).
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When using the level of collaboration as effectiveness measure, none of the models
produced statistically significant evidence and thus failed to support the hypothesis.
Hypothesis HO#8
I proposed in hypothesis HO#8 that the greater the variety of collaboration social
software available in an organization the higher its effectiveness both in terms of level
activities as well as the level of collaboration. When using the level of activities as
effectiveness measure, none of the models produced statistically significant evidence and
thus failed to support the hypothesis. This hypothesis was supported when using the level
of collaboration as effectiveness measure. The full models showed that the range of
collaboration social software available in an organization was an important predictor for
effectiveness. I obtained the following statistics: Model IVa, (β = -0.274; p < .05);
Model IVb, (β = -0.277; p < .05). But contrary to my proposition, the final model rather
showed a negative relationship between the range of collaboration social software and
effectiveness.
Hypothesis HO#9
I hypothesize in HO#9 that the greater the variety of community social software available
in an organization the higher its effectiveness both in terms of level activities as well as
the level of collaboration. When using the level of activities as effectiveness measure,
similarly to the previous hypothesis, none of the models produced statistically significant
evidence and thus failed to support the hypothesis. When using the level of collaboration
as effectiveness measure, I found support to the hypothesis. The full models yielded
statistically significant evidence that the range of community social software was an
important predictor of organizational effectiveness (Model IVa, β = 0.271; p < .05; and
Model IVb, β = 0.237; p < .05).
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6.5.2.2 Information Technology Interaction Effects
Hypothesis HO#10
In hypothesis HO10, I proposed that organizations that possess a wide variety of
communication media will benefit more from high network degree centrality to enhance
their effectiveness than those that do not. I found support to this hypothesis for both
measures of organizational effectiveness. When using the level of activities as
effectiveness measure, I obtained the following statistics: Model IV (β = 1.736; p <
.005). When using the level of collaboration as effectiveness measure, the statistics were
the following: Model IV (β = 0.667; p < .05).
Hypothesis HO#11
In hypothesis HO11, I proposed that Organizations that possess wide varieties of
communication media will benefit more from high network density to enhance their
effectiveness than those that do not. I found support to this hypothesis for both measures
of organizational effectiveness. When using the level of activities as effectiveness
measure, I obtained the following statistics: Model IV (β = 1.013; p < .005). When
using the level of collaboration as effectiveness measure, the statistics were the
following: Model IV (β = 0.465; p < .05).
In Table 22 below, I summarize the results of the hypotheses testing for both measures of
effectiveness. For each hypothesis, I indicate (with ‘S’) whether the independent variable
in the hypothesis was found to be an important predictor of effectiveness. I also indicate
if the hypothesis was support (with ‘SS’).
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Number Hypothesis Significant & Supported
Level of Activities
Level of Collaboration
HO# 1 Greater centrality increases organization effectiveness SS SS
HO# 2 Greater bridging of structural holes increases effectiveness S
HO# 3 Organization effectiveness increases with the density of the network it which it belongs
SS SS
HO# 4 Organization effectiveness increases with the number of distinct cliques to which it belongs
S S
HO# 5 The size of an organization is positively associated with its effectiveness
HO# 6 The range of service provided by an organization is positively associated with its effectiveness
HO# 7 The greater the varieties of communication media available in an organization, the higher its effectiveness
S
HO# 8 The greater the varieties of collaboration social software available in an organization, the higher its effectiveness
S
HO# 9 The greater the varieties of community social software available in an organization, the higher its effectiveness
SS
HO# 10 Organizations that possess wide varieties of communication media will benefit more from high network degree centrality to enhance their effectiveness than those that do not.
SS SS
HO# 11 Organizations that possess wide varieties of communication media will benefit more from high network density to enhance their effectiveness than those that do not.
SS SS
Table 22: Summary of Hypotheses Testing at Organizational Level of Analysis
6.5.3 Discussion
I begin this discussion section by restating that previous studies that used the theoretical
lenses of Resource Based View primarily focused on characteristics internal to the
organizations to predict effectiveness and performance. Most of these studies were
conducted in the for-profit sector, conceptualizing organizations as atomistic profit-
seeking entities (Arya & Lin, 2007). Subsequent research on organization performance
and effectiveness highlighted the importance to view organizations as embedded in a web
of inter-organizational relationships which may serve both as resources themselves and as
mediums for accessing external resources (Granovolta, 1985; Baum & Dutton, 1996;
Portes, 1998; Gulati et al., 2000; Shipilov, 2006). On the other hand, most studies that
apply social network approach to explore effectiveness focused on the characteristics of
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network structure, without paying much attention to the attributes of the organizations
that comprise the network. In my study, I draw on both the RBV and the social network
theories to investigate how organizations’ attributes combined with network structural
characteristics influence organizational effectiveness. I especially explore the influence of
information technology on effectiveness. I discuss below the findings of my
investigations with regards to (i) the measures of organizational effectiveness (ii) the
relationship between organization internal characteristics and organizational
effectiveness (ii) the relationship between ego-network characteristics and
organizational effectiveness (iii) the relationship between network structural
characteristics and organizational effectiveness and (iv) the Catalytic role of Information
Technology on organizational effectiveness.
Measure of organizational effectiveness
The findings from my investigations suggest that in networks of organizations engaged in
humanitarian information management, organizational effectiveness would be better
assess using the level of collaboration. When using the level of collaboration as
dependent variable in a regression model, the linear combination of the independent
variables explained almost 76% of the variances. This proportion was less than 64%
when effectiveness was measured as the level of activities. Figures 36 and 37 below
depict these variations. Figure 36 presents the variation for the case where the interaction
term is combination of communication media and degree centrality; while for Figure 37
the interaction term is communication media and network density.
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Figure 36: Variations in the Effectiveness Measures (Model VIa)
Figure 37: Variations in the Effectiveness Measures (Model VIb)
Organization internal characteristics and organizational effectiveness
My research also showed the importance of considering the characteristics internal to
organizations when explaining effectiveness. As discussed in the analysis section, when
using only the organizational internal resources as independent variables to predict
effectiveness, the regression model showed that the linear combination of these variables
was significantly related to effectiveness. Taken alone, organizations internal
characteristics explained only approximately 5% of the variances in organizational
effectiveness when using the level of collaboration but this percentage was much higher
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(over 13%) when using the level of activities. This finding was consistent with previous
studies that used the Resource Based View to assess organizational effectiveness (e.g.
Zaheer & Bell, 2005; Arya & Lin, 2007).
Moreover, the findings of my investigation suggest that among humanitarian
organizations engaged in information management and exchange, information technology
would be one of the most important internal characteristics that would more accurately
predict effectiveness. Previous research has shown an increase in the adoption and use of
information technology in general, for disaster relief among humanitarian organizations
(Comfort, 1993; Quarantelli, 1997). For these organizations, information technology
plays a vital role in disasters relief. Research has also shown that the use information
technology may have a positive impact on inter-organizational collaboration and
coordination (Malone & Crowston, 1994). Studies have also highlighted the importance
of the use of social software in humanitarian disaster relief and crisis management (Palen
et al., 2007a; Palen et al., 2007b; Palen et al., 2007c; Sutton et al., 2008; Vieweg et al.,
2008; Hughes et al., 2008; Lui et al., 2008). Although most of these studies investigated
the use of social software at the individual user level of analysis, the positive impact of
these tools for disaster relief could easily be extrapolated at other levels of analysis
including the organizational level and the network level.
In my study, all the three information technology related variables were in some ways
found to significantly contribute to explain organizational effectiveness. However, not all
these information technology related variables were found to be positively related to
organizational effectiveness as I hypothesized. For instance, while wide varieties of
community social software were found to be positively associated with effectiveness, my
findings rather suggest a negative relationship between collaboration social software (e.g.
wiki, shared database) and effectiveness.
These contrasting results obtained from my statistical analysis concerning the importance
of information technology to humanitarian organizations were somewhat similar to those
obtained from the qualitative data gathered through interviews. As mentioned earlier,
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interview participants were asked to give their opinion specifically on the implications of
information technologies on inter-organizational collaboration among members of the
Global Symposium and the contribution of these technologies in helping to meet the
organizational goal. Approximately seventy percent (68.42%) of the interviewees shared
their opinion on this issue. I registered a wide range of diverse point of views. Some
participants, roughly thirty one percent (30.77%) of those that answered the question, had
a very positive opinion about the implications and especially the catalytic role that
information technologies play in fostering humanitarian inter-organizational
collaboration. The vast majority (69.23%) however, expressed mixed feelings.
For the participants that had positive opinions, information technologies served as an
important catalyst for inter-organizational collaboration in the Global Symposium
community. They argued that if without information technologies effective simple
communication is difficult, collaboration would be even harder. Participant number five
for example reported that:
Subject#5: I think that information technology is extremely important because we basically need to communicate to all these different communities in as many different ways as possible.
They also believed that the use of information technologies is instrumental in quickly
gather analyze and disseminate humanitarian information leading to effective disaster
response. Below, we illustrate this point of view with quotes from three participants,
number six, seven and eleven.
Subject#6: You cannot do it without information technology. Gathering information, managing information, analyzing information, distributing information, really you cannot do all this without information technology. So I think the question is kind of obvious. Subject#7: Information technology essentially supports what we do. It helps in sharing information, mainly transporting information around, maintaining our communication. Subject#11: I think the information technology is key of cause, because without proper systems in place, you will not be able to do that.
Most of these participants who had a very positive opinion about the important
implications of information technologies in fostering collaboration among humanitarian
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organizations also believed that information technologies were instrumental for their
organizations in meeting their goals.
The participants who expressed mixed feelings about the role of information technologies
in inter-organizational collaboration gave a number of reasons that could be grouped into
two main categories. The first category of reasons was related to the information
technologies infrastructure. Participants argued that more often, organizations in the field
do not have the necessary technology tools either because they were destroyed by the
disaster or because they did not even exist in the first place. They also talked about the
discrepancy in term of infrastructure between organizations based in developed countries
and those in the developing countries. They argued that people in developed countries
often enjoy latest technologies but the realty in developing countries, scenes of most
humanitarian disasters is quite different. Participant number twelve for example reported
that:
Subject#12: when you get out on the fields you see that the most basic important tool is paper map and a pencil. And I think we have got to really recognize that fact. […]You know we do this information technology that we love where they follow the latest systems and the fastest processor and stuff like that and we really like to paddle ourselves on the back on what we are able to do here in Washington DC. And then you get out on the fields and everyone is using paper maps and a pencil.
Finally they talked about the fast pace of change in technology which makes it difficult
for organizations to have and especially keep the technical staff that possesses adequate
knowledge to make use of these new technologies.
Subject#6: as the technology changes, it is hard to find the people that have skills that are up to date.
The second category of reasons concerned the management of information. Participants
believed that without proper standard for humanitarian information exchange, the
technology will be of no effective use.
Subject#5: I think yes, continue to explore all the new technologies that are available but at the same time realize that in the end what it really comes down to is quality information and information that is based on facts and that’s credible but people actually belief in. So I think we should not be allowed to be measured by technology if the content is not there.
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Subject#14: One is developing some basic standards, and some basic platforms for information exchange.
They also believed that the humanitarian field needs better processes and well trained
staff in order to make good use of the technology.
Subject#8: I think there are certain organizations who think that technology can solve all the problems, so they don’t have a proper appreciation and understanding of the information management challenges and obstacles, but at the same time there is probably some information, people who are very skeptical about technology and do not sort of realize the value that it has.
Ego-network characteristics and organizational effectiveness
My investigations highlighted the significant impact that ego-network characteristics
have on organizational effectiveness. I explored how humanitarian organizations would
benefit from better ego-network characteristics (e.g. degree centrality, and bridging
structural holes, numbers of cliques). When using the level of collaboration as
effectiveness measure, ego-network related variables accounted for approximately 67%
of the explanatory power of the full regression model. This proportion was about 32%
when using the level of activities as effectiveness measure. These findings corroborated
with the view of organizations as embeddedneed in a web of relationships that provide
opportunities and values (Gulati, 1999). Network characteristics can be understood as
external resources embedded in organizations’ networks. According to Gulati et al.,
(2000), the embeddedness of organizations in networks holds significant implications for
organization performance.
The degree centrality was found to be the most important predictor of effectiveness. This
variable was consistently found cross models and cross effectiveness measures to be
significantly and positively related to organizational effectiveness. As mentioned earlier,
this finding is consistent with most previous research that explored the influence of
network position and especially the degree centrality on outcome such as performance
and effectiveness (Knoke, 1990; Wasserman & Faust, 1994; Stevenson & Greenberg,
2000; Kilduff & Tsai, 2006).
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Bridging network structural hole was another ego-network category variable found to be
an important determinant of organizational effectiveness. This finding supported the
direct effect of network structure, in the form of access to structural holes, on
effectiveness. My results thus confirmed prior research by Burt (1992), McEvily &
Zaheer (1999), and others that access to structural holes influences organizational
performance. However, contrary to my proposition that organizations will enhance their
effectiveness by bridging structural my findings rather suggested a negative relationship
between bridging structural hole and effectiveness. This finding was not consistent with
those of Zaheer & Bell (2005) and Arya & Lin (2007). Both similar studies found a
positive effect of bridging structural holes on organizational effectiveness. As mentioned
earlier, one explanation of this result may be due to the high heterogeneity of the
organizations that I investigated. In the humanitarian relief field and especially in
humanitarian information management and exchange, maintaining non redundant may be
very costly to humanitarian organizations. Some previous studies have also shown
negative performance effects of bridging structural holes (e.g., Ahuja, 2000; Dyer &
Nobeoka, 2000).
In my study, the number of cliques another ego-network category variable explored. This
variable was also found to be significantly and negatively related to organizational
effectiveness measured both as the level of collaboration as well as the level of
collaboration. This finding was one of the most surprising of my investigations. In my
knowledge, no previous study had explored the relationship between the number of
cliques and organizational effectiveness. However, given some previous research on
effectiveness conducted at network level (e.g. Provan & Sebastian, 1998) and theoretical
reasoning, this was an unexpected result. I was expecting at the organizational level, a
similar the positive relationship that exists between the number of cliques and network
effectiveness. One possible reason for this finding may the fact that there may have been
a high level of overlap in the cliques. Using distinct cliques may have probably yielded
more meaningful results.
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Whole network characteristics and organizational effectiveness
The density of the network was the only network category attribute that was in the study.
This variable was found to be an important predictor of effectiveness. I found that high-
density networks benefited more to organizations than low density networks. This
evidence makes a significant contribution to similar previous studies such as Zaheer &
Bell (2005) and Arya & Lin (2007) that emphasized an integrative approach in exploring
organizational effectiveness. None of these two previous similar studies had examined
the impact of network category variable on organizational effectiveness. Moreover, by
showing that organizations perform better when they occupy a better network position,
my study contribute to demonstrate the value of including external resources, or the
ability of an organization to exploit a favorable network structural position (Gnyawali &
Madhavan, 2001; Gulati, 1999).
Catalytic role of Information Technology on organizational effectiveness
The most important contribution of my study is related to the catalytic role of information
technology on organizational effectiveness in humanitarian inter-organizational networks.
For instance, my findings suggest that organizations that possess a wide variety of
different types of communication media (e.g. internet - available to the majority of staff-,
website – regularly updated-, blogs, etc…) will benefit more from high network degree
centrality to enhance their effectiveness than those that do not. These organizations will
benefit more from high network density than those that do not possess these technologies.
These findings are illustrated by the interaction plots presented in Figure 38-41 below. To
generate these plots I first grouped the organizations investigated in two categories based
on the number of different types of commination media. This number ranged from 1 to 5.
Organizations grouped in the first category had 1 or 2 different types of commination
media. This category is represented on the interaction plots as “low variety of
communication media”. Organizations grouped in the second category were those that
had more than 2 different types of commination media. This category is represented on
the interaction plots as “high variety of communication media”. Secondly, for each
category, I generated a scattered plot (with the fitted line option checked) using
respectively each of the two effectiveness measures (level of activities and level of
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collaboration) as dependent variable and the degree centrality then network density as
independent variable.
Degree centralityLow
Low
High
High
Lev
el o
f act
ivit
ies Wide variety of communication media
(y= 11.72x -6.4)
Low variety of communication media
(y= 8.75x + 26.30)
Degree centralityLow
Low
High
High
Lev
el o
f co
lla
bo
rati
on
Wide variety of communication media
(y= 0.795x + 1.74)
Low variety of communication media
(y= 0.345x + 7.90)
Figure 38: Inter-action effect of Technology and
Degree Centrality on Effectiveness as Measured by
the Level of Activities
Figure 39: Inter-action effect of Technology and
Degree Centrality on Effectiveness as Measured by
the Level of Collaboration
Network DensityLow
Low
High
High
Lev
el o
f act
ivit
ies
Wide variety of communication media
(y= 7871x - 279.2)
Low variety of communication media
(y= 997.4x + 19.17)
Network DensityLow
Low
High
High
Lev
el o
f co
lla
bo
rati
on
Wide variety of communication media
(y= 249.9x + 8.26)
Low variety of communication media
(y= 102.8x + 1.67)
Figure 40: Inter-action effect of Technology and
Network Density on Effectiveness as Measured by
the Level of Activities
Figure 41: Inter-action effect of Technology and
Network Density on Effectiveness as Measured by
the Level of Collaboration
An examination of these interaction plots highlights the significant boost of
communication media on the effectiveness of organizations that are centrally located in
the humanitarian information management networks. As mentioned earlier, these plots
also show that organizations that possess a wide variety of different types of
communication media will benefit more when they belong to high network density than
when they are member of loosely connected networks.
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7 CONCLUSIONS AND DIRECTIONS FOR FUTURE RESEARCH
7.1 Introduction
In this chapter, I provide a summary of the key findings of my research. I also briefly
discuss the contributions of my work to the literature on organizational and inter-
organizational network effectiveness. This discussion is followed by some limitations of
the research and the future directions that I envisioned.
7.2 Summary of the Literature
Previous research on organizational and inter-organizational network effectiveness has
provided some empirical and conceptual approaches for assessing effectiveness.
However, little research has explored the antecedents of effectiveness for humanitarian
inter-organizational networks. The review of the literature on organizational effectiveness
highlights the difficulty in assessing effectiveness. There is no consensus on the criteria
of measuring effectiveness among researchers and no clear classification of the different
levels of effectiveness. Moreover, there are various approaches to view effectiveness
such as the Goal Model (ii) the System Resource Model (iii) the Internal Processing
Model and (iv) the Multiple Constituencies’ Model. Concerning inter-organizational
networks in the nonprofit sector, they have been more than a decade long clarion call for
a better understanding of their effectiveness (O’Toole, 1997; Provan and Milward 1995).
To date limited work has been done (Provan et al., 2007). Additionally, the few studies
that have investigated the effectiveness inter-organizational networks in the nonprofit
sector have also used a wide range of effectiveness measures and have almost all been
conducted in the public health delivery services. Furthermore, for some authors (e.g.
Stephenson, 2005; 2006; Van de Walle et al., 2009) not much is known about network
effectiveness for humanitarian inter-organizational networks and especially with regards
to humanitarian information management and exchange (Van de Walle et al., 2009). My
research thus intended to provide answers to the following research questions:
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RQ#1(Network level of analysis): To what extent do network structural characteristics
explain effectiveness in humanitarian inter-organizational collaboration networks? More
specifically, how does Provan & Sebastian model of network effectiveness explain
network effectiveness in the international context of inter-organizational collaboration in
the humanitarian field?
RQ#2(organizational level of analysis): How accurately does a linear combination of
organizational internal attributes and network structural properties explain effectiveness
at organizational level in humanitarian inter-organizational collaboration networks? (i)
To what extent do resources internal to organizations and especially information
technology explain effectiveness? (ii) To what extent do ego-net properties explain
network effectiveness? (iii) To what extent do network level structural characteristics
(e.g. density) explain effectiveness? (iv)To what extent does the interaction of
information technology and network structural characteristics impact organizational
effectiveness?
7.3 Key findings
In this dissertation, I investigated how organizational characteristics and network
structural properties influence effectiveness in humanitarian inter-organizational
networks. I explored effectiveness at two levels of analysis, organizational and network.
I used three different criteria to assess effectiveness including (i) perceived network
effectiveness (ii) level of activities and (iii) level of collaboration. To answer my
research questions, I used a multi-method approach that applies social network analytic
techniques in combination with statistical analyses (correlation and regression) and
content analysis to analyze data collected through multiple sources including a web-based
survey and semi-structured interviews and database search.
RQ#1: Relationship between network structural characteristics and network effectiveness
To answer this question, I conducted a clique analysis using Provan & Sebastian’s (1998)
framework. One first general observation of my findings was that network effectiveness
varies depending of the effectiveness measure. These findings corroborate those of most
research on inter-organizational network effectiveness which highlight the existence of a
169
wide range of definitions and criteria for network effectiveness (Alter & Hage, 1993;
Provan & Milward, 1995; Sydow & Windeler, 1998; Provan et al., 2007). Consistent
with those of Provan & Sebastian my findings suggest that at the network level of
analysis, an inter-organizational network in the field of humanitarian relief is more
effective when it is more integrated at the subnet level (clique) and displays higher level
of multiplexity. My study however makes one significant addition to Provan & Sebastian
model. Unlike Provan & Sebastian, in my study, I used three different measures of
network effectiveness (one subjective and two objectives). Using these effectiveness
measures allowed me to find consistent ranking pattern for each of the six network
structural characteristics used in my work. It is important to note that Provan &
Sebastian’s study which forms the foundation of my study, matched two out of the six
network structural characteristics. This study found a match in ranking only among
multiplexity and identical clique overlap and effectiveness.
Moreover, my findings suggest that the subjective and objective forms of network
effectiveness are better explained by different network structural attributes. Whereas
subjective network effectiveness is better explained by the number of cliques and clique
membership, objective network effectiveness is better explained by the multifaceted
nature of inter-organizational relationships as measured by clique overlap and
multiplexity. These findings highlight the importance of multiple criteria for assessing
network effectiveness. Finally, comparing the three measures of effectiveness that I used
in my study, my findings suggest that the level of activities is the best. This measure
matched three out of the six network structural characteristics investigated.
At the network level, the findings of my investigations could be summarized as follow:
Finding #1: In inter-organizational humanitarian information management networks,
network effectiveness will be better explained by network structural characteristics when
assessed at subnet levels.
Finding #2: In inter-organizational humanitarian information management networks, the
level of effectiveness will likely be higher in networks that are more dense and cohesive
at subnet levels.
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Finding #3: Given the high heterogeneity and differentiation among humanitarian
organizations, network effectiveness in humanitarian information management, will more
accurately be explained by multiplexity and clique overlap.
RQ#2: Relationship between organization internal characteristics, network structural
properties and organizational effectiveness.
In addition to investigating effectiveness at the network level, in this work, I also studied
organizational effectiveness. I took several steps to explore organizational level
effectiveness. I used two measures of effectiveness including the level of activities
measured as the number of funded project and the level of collaboration measured as the
number of funding partners. For each of these effectiveness measures, I built a set of
four consecutive multiple linear regression models. In the baseline model, I modeled
effectiveness as a function of the variables of the organization category. I then gradually
added variables from ego-network category (Model II), network category (Model III) and
two inter-action terms (Model IVa and Model IVb). Overall, my findings suggest that in
humanitarian inter-organizational networks, organizational effectiveness can be
accurately explained by a linear combination of organizational internal attributes and
network structural properties.
RQ#2a: Relationship between organization internal characteristics and especially
information technology and organizational effectiveness.
My research also showed the importance of considering the characteristics internal to
organizations when explaining effectiveness. Taken alone, organizational internal
characteristics accounted for over 13% of the variances in organizational effectiveness
when I used the level of activities as effectiveness measure. The regression model
showed that the linear combination of organizational internal characteristics was
significantly related to effectiveness for both effectiveness measures. Moreover, my
findings suggested that among humanitarian organizations engaged in information
management and exchange, information technology would be one of the most important
determinants of effectiveness. In my study, all the three information technology related
variables that I used, were found to significantly contribute to explain organizational
171
effectiveness. However, not all these variables were found to be positively related to
organizational effectiveness as I hypothesized. For instance, while the availability of a
wide variety of community social software was found to be positively associated with
effectiveness, my findings rather suggest a negative relationship between collaboration
social software (e.g. wiki, shared database) and effectiveness. These contrasting results
obtained from my statistical analysis concerning the importance of information
technology to humanitarian organizations are somewhat similar to those obtained from
the qualitative data gathered through interviews.
RQ#2b: Relationship between ego-network characteristics and organizational
effectiveness.
Exploring the relationship between ego-network characteristics and organizational
effectiveness I also got some interesting results. My findings suggested that ego-network
characteristics have a significant impact on organizational effectiveness. Taken alone,
ego-network variables accounted for approximately 67% of the variances in
organizational effectiveness when I used the level of collaboration as effectiveness
measure. This proportion was about 32% when using the level of activities as
effectiveness measure. Among the ego-network variables, the degree centrality was
found to be the most important predictor of effectiveness. This variable was consistently
found cross models and cross effectiveness measures to be significantly and positively
related to organizational effectiveness. Bridging network structural hole was another ego-
network category variable found to be an important determinant of organizational
effectiveness. However, contrary to my proposition that organizations will enhance their
effectiveness by bridging structural my findings rather suggested a negative relationship
between bridging structural hole and effectiveness. This result may be due to the high
heterogeneity of the organizations that I investigated. In the humanitarian relief field and
especially in humanitarian information management and exchange, maintaining non
redundant may be very costly to humanitarian organizations. The number of cliques
another ego-network category variable explored was also found to be significantly and
negatively related to organizational effectiveness measured both as the level of
collaboration as well as the level of collaboration. This finding was one of the most
172
surprising of my investigations. In my knowledge, no previous study had explored the
relationship between the number of cliques and organizational effectiveness. However,
given some previous research on effectiveness conducted at network level (e.g. Provan &
Sebastian, 1998) and theoretical reasoning, this was an unexpected result. One possible
reason for this finding may the fact that there may have been a high level of overlap in
the cliques. Using distinct cliques may have probably yielded more meaningful results.
RQ#2c: Relationship between network characteristics and organizational effectiveness.
Investigating the relationship between network structural characteristics and
organizational effectiveness was one of the peculiarities of this study. None of the two
previous similar studies (Zaheer & Bell, 2005; Arya & Lin, 2007) had examined the
impact of network category variable on organizational effectiveness. The density of the
network, the only network category variable that was in the study was found to be an
important predictor of effectiveness. Taken alone, network density accounted for
approximately 3.2% of the variances in organizational effectiveness when I used the level
of activities as effectiveness measure. For both measures of effectiveness, my findings
suggested that high-density networks benefited more to organizations than low density
networks.
RQ#2d: Impact of inter-action between information technology and network structural
characteristics on organizational effectiveness.
Examining the impact on organizational effectiveness of the inter-action between
information technology and network structural characteristics in humanitarian
information management networks was another important peculiarity of my research.
My findings suggested that organizations that possess a wide variety of communication
media (e.g. internet - available to the majority of staff-, website – regularly updated-,
blogs, etc…) will benefit more from high network degree centrality to enhance their
effectiveness than those that do not. These organizations will benefit more from high
network density than those that do not possess these technologies.
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At the organizational level of analysis, the findings of my investigations could be
summarized as follows:
Finding #1: In inter-organizational networks for humanitarian information
management, centrally located organizations will more likely display higher level
of effectiveness than those situated at the peripheral.
Finding #2: Organizations that are member of dense and cohesive humanitarian
information management networks will more likely display higher level of
effectiveness than loosely connected networks.
Finding #3: In humanitarian information management networks, organizations
that possess wide varieties of communication media will benefit more from high
network degree centrality to enhance their effectiveness than those that do not.
Finding #4: Other things being equal, in humanitarian information management
networks, organizations that possess wide varieties of communication media will
benefit more from high network density to enhance their effectiveness than those
that do not.
Summing up, my investigations confirmed the proposition that organizational
effectiveness is affected by different organizational and network attributes in
humanitarian information management networks. More broadly, my findings on the one
hand pointed to a need in inter-organizational social network studies to go beyond a
structuralist view and take into consideration the characteristics of individual
organizations, as predicted by the RBV, in assessing effectiveness. On the other hand, my
study highlighted the fact that organizational level network studies that tend to overlook
resources internal to organizations may be missing a large source of variance in
effectiveness. Finally my study highlighted the important role of communication media
in organizational effectiveness among humanitarian organizations.
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7.4 Contributions
Network Effectiveness
My research extents to the humanitarian relief field, Milward & Provan’s (1998)
framework for evaluating public-sector organizational networks. My research contributes
to the literature on network effectiveness in a number of ways. First, my findings
confirmed some of the results of previous research and especially those of Provan &
Sebastian (1998) which showed that most effective networks are those that are integrated
at clique level. Specifically, my findings confirmed the importance of network structural
characteristics such as integration and cohesion to network effectiveness measure.
Moreover, building on Provan & Sebastian (1998), my study further highlighted the need
to consider network effectiveness analyses in smaller substructures instead the whole
network as has usually been the case.
Secondly, my research highlighted the need to explore network effectiveness using a set
of different measures. The majority of existing work on network effectiveness, including
that of Provan and Sebastian (1998) was conducted using one measure. Moreover, in
most cases, the effectiveness measure was not selected with input from the various
network members. In my study, I used input from network members to determine the
three measures of effectiveness. Using a set of three different measures for network
effectiveness allowed me to find consistent ranking pattern for each of the six network
structural characteristics. Moreover, my findings suggested that the subjective and
objective forms of network effectiveness are better explained by different network
structural attributes. Whereas subjective network effectiveness is better explained by the
number of cliques and clique membership, objective network effectiveness is better
explained by the multifaceted nature of inter-organizational relationships as measured by
clique overlap and multiplexity. My study serves as an example of effectiveness being
measured with multiple criteria. In a nut shell, my work builds on various models of
effectiveness already present within the literature on inter-organizational effectiveness to
provide a multidimensional model for evaluation effectiveness in the nonprofit
humanitarian field.
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Lastly, my research also has implications for social network theories. For many
organization theorists, the study of both inter-organizational and intra-organizational
networks has primarily been an exercise in analysis and methods (Salancik, 1995).
Building upon Provan & Sebastian (1998), my study further develops an alternative
method for network analysis and contributes to building network theories by examining
and explaining how network structural properties including network density, cliques and
overlapping cliques, might promote the interests of network members and that of the
community as a whole.
Characteristics of successful inter-organizational networks
My investigations have helped to identify the following four main characteristics that
seem to be common among organization members of successful networks. These
characteristics include (i) their ability to share, (ii) their ability to contribute, (iii) their
commitment to networking and (iv) the level of their embeddedness through multiplex
ties in the network.
1. Sharing spirit: Organization members of a network must “dare to share” (ICCO
2004). They need to be open, willing and able to learn from each other. In my research,
the lack of sharing spirit was consistently reported as one of the biggest problems that
undermines network effectiveness.
Subject#13: I think the main challenge here is that the idea of sharing formation has always been said in many areas. It is usually always said yeah it is good to share but you do not sometime see concrete platforms or formalities on how to share this information. It is not formalize. It is always thought as an objective but never formalize.
Network members must feel confident enough about what they do and the information
they possess that they are willing to share with others. There must therefore exist an
atmosphere of openness among members and potential members which allows them to
admit mistakes and to learn from them. Networks cannot flourish without this trust. A
network can help to develop sharing spirit among its members by creating an open
environment in which people are willing to analyze and learn from both their successes
176
and their mistakes. Networks and partnerships are more likely to become effective when
they are founded by members that share a history of working together, that know each
other and have relationships characterized by mutual trust. This suggests that networks
may have a longer incubation and startup period before they can reach the stage of
maximum effectiveness.
2. Capacity to contribute: Organization members of a network must have the capacity
to contribute especially in terms of skills, access and time/money available. In my study,
the ability of a potential network partner to contribute was reported to be one of the main
collaboration factors.
Subject#2: Both [organizations] have to be able to bring to the table their competitive advantage. You can’t have two organizations that do the same thing. So you need different skills set from any of the organizations. Subject#7: We think about the quality of what that agency does and the quality of what that agency is known to do.
In order to foster inter-organizational coordination/collaboration there must be space for
learning, reflection and interaction. Also, it is paramount that senior leaders of
organization provide support to network by emphasizing the importance of networking.
They must also encourage the involvement of staff in the activities of the network.
Moreover, all network members must have equal access to any technology that the
network uses so that certain groups are not marginalized.
3. Commitment: Organization members of a network must be committed to the
networking activities. They must consider the priorities of the network their own. They
must also be motivated by self-interest because networking is a potential added-value to
their daily work. Commitment will be strong if members see the network as adding value
to their work, and if the priorities of the network match their own. According to ICCO
(2004) incentive grants are of little value in enticing members. I agree with this author in
his contention that funding should not be the reason that a NGO joins a network. For
instance, he suggests that a golden rule for success may be to let a network start from its
own resources with the idea that initial self-reliance builds commitment (ICCO 2004).
The author also mentions, however, that this does not mean networks do not need funding
177
for the activities they would like to undertake. Networks need funding for example to
help support a facilitator, coordinator, or staff of some sort that is able to spend the time
required to nurture relationships and in order to keep the group together. It is important
that careful attention is given to these aspects when funding is initially proposed.
Another important condition is that the initiators of networks are enough committed to
overcome the organizational and establishment phase, which takes a lot of effort, while
often working for not immediately seen results with little money.
4. Multiplexity: Organization members of a network must strive to keep multiple type of
connection with other members. Multiplexity can be measured at the individual network
member level and at the level of the whole network. A high degree of multiplexity of a
member indicates high embeddedness of the member in a network and signifies less
liability to disruption of single relationships. A member with a large number of multiplex
relations is expected to have a high potential of mobilizing different resources and
information through these relations. On the other hand, such a member is subject to a
high level of social control. At the network level, the degree of multiplexity specifies the
overlap between the different relation-specific networks. For evaluating network
effectiveness, multiplexity can be a particularly useful measure (Provan & Milward
2001). Effective networks might have a majority of network members connected through
two or more different types of relationships. In this case, multiplexity will be high,
reflecting commitments among network members to one another through multiple
activities.
Organizational Effectiveness
Concerning the literature on organizational effectiveness, my study illustrates the
importance of both internal organizational characteristics as well characteristics external
to organizations, for effectiveness. My findings confirm the extended Resource Based
View perspective of organizational effectiveness. More specifically, my analysis of the
relationships of the various determinants of effectiveness illustrated that variables from
all the three categories (organization, ego-network and whole network) are found to be
178
important predictors for organizational effectiveness. One of the most significant
contributions of my research to organizational effectiveness literature and especially to
the resource based view perspective concerns including whole network category variables
in assessing organizational effectiveness. None of the two previous similar studies
(Zaheer & Bell, 2005; Arya & Lin, 2007) had examined the impact of network category
variable on organizational effectiveness. By showing that organizations enhance their
effectiveness when they occupy a better network position, my research contributes to
demonstrate the value of including external resources, or the ability of an organization to
exploit a favorable network structural position (Gnyawali & Madhavan, 2001; Gulati,
1999). Another important contribution of my study is that it extends the Resource based
view perspective in the nonprofit sector and especially in the humanitarian relief field.
Most of the previous studies that draw on the RBV examine effectiveness in for-profit
network contexts. By applying the RBV to a collaborative nonprofit context as opposed
to a competitive for-profit context, my research shows that internal and external resources
allow some organizations to enhance their capabilities by collaborating with others.
Humanitarian Inter-organizational Network Effectiveness
With regards to humanitarian inter-organizational network effectiveness, my research
offers some evidence that similarly to the public health service delivery sector; network
effectiveness can be explained by intensive integration and network cliques. My data
supports the idea that differences in effectiveness across networks could be better
understood by focusing on cliques and the overlap among cliques of multiple
relationships among humanitarian organizations. My study would help to do the clique
analysis or to search for closely connected and cohesive subgroups. Additionally, my
work can help to design efficient inter-organizational network structures in the
humanitarian relief sector. For example, by increasing the level of clique overlap (one
dimensional or multidimensional) in inter-organizational humanitarian networks, network
designers should expect a higher level of inter-organizational collaboration.
179
Information Technology and Humanitarian Organizational Effectiveness
The most important contribution of my study is related to the catalytic role of information
technology on organizational effectiveness in humanitarian inter-organizational networks.
For instance, my findings suggest that organizations that possess a wide variety of
communication media will benefit more from better network positions (e.g. high network
degree centrality, high network density) to enhance their effectiveness than those that do
not possess these technologies. The Resource Based View perspective of organizational
effectiveness tends to focus more on organization’s internal resources, while
downplaying those available to the organization from external sources. On the other
hand, network researchers tend to focus attention on the value of the network structure,
without considering the capabilities of the organizations tied together by the network. My
study highlights the importance of fusing these two streams of research, and considering
simultaneously the inner capabilities of the organizations in a network together with
capabilities they derive from the structure of the network that binds them together.
7.5 Limitations and Directions for Future Research
My study has some theoretical, methodological and practical limitations that suggest a
number of directions for future research.
Theoretical Limitations
Theoretically, the implications of relating organization characteristics, ego-network
characteristics and network structure, to organizational and network effectiveness need
further investigation. Provan & Sebastian’s (1998) study on the relationship between
network structure and network effectiveness applied to inter-organizational networks of
mental health delivery. Their research provides suggestions for application to other fields.
Building upon Provan & Sebastian, this study investigated networks in the humanitarian
relief field. These networks were not defined based on sound theoretical perspectives but
just on the categories provided by UNOCHA. Studies on better defined networks would
help to further understand network effectiveness in the humanitarian relief field.
180
Moreover, my study further illustrated the need to consider network effectiveness
analyses in smaller substructures instead the whole network as has usually been the case.
It would be interesting however, to investigate what network configuration enables
humanitarian organizations to operate at optimum and effectiveness while meeting their
individual goals. This may include factoring in the impact of organizations internal
resources and especially the information technology over time as new organizations join
the network and/or new information technology tools are used in humanitarian
information management and exchange. In this way, the predictive nature of network
structure may complement assessment of organizational effectiveness and extend to ways
to support humanitarian information management and exchange.
Finally, to investigate further and identify better measures of network effectiveness,
future research should consider exploring similar network operations in other non-profit
sector activities.
Methodological Limitations
Methodologically, the first limitation is related to the source of information. Much of the
organizational and network data that I analyzed was provided by individuals. The
position of these individuals in their organization may not allow them to always have the
complete and accurate information about the organization especially with regards to
inter-organizational relationships. For instance, network structures were generated based
on information provided by these individuals. They meant to demonstrate the existence
of a relationship based on the data collected. They represent neither the totality nor the
absence of relationships. Future research may envision a much better source of data that
is less subjective.
Secondly, the most obvious and probably the most serious shortcoming of the research at
its network level of analysis is the small number of networks. My study involved only
three networks. At the network level of analysis, this can create an important problem
with regards to generalizing the research findings. Moreover, it is certainly possible that
181
the network effectiveness measures used, which were tied to individual organizations, did
not accurately reflect network effectiveness at each network. Future research should
consider a much bigger network sample size.
Thirdly, while in my research design at the organizational level of analysis I included a
number of variables that have been neglected in previous research (e.g. whole network
category variables), there are still others that could be added. These clearly include tie
multiplexity, but perhaps also a better understanding of the processes through which
effectiveness comes about, such as communication flows across the ties and the content
of ties. Future research could consider examining closely communication and other flows
passing through the network (Gnyawali & Madhavan, 2001).
Fourthly, another limitation of my study is related to alter characteristics. Much previous
research has highlighted the important impact that alter characteristics have on
organizational effectiveness (Arya & Lin, 2007). In my research design, I did not include
any of these characteristics. Future research in the humanitarian relief field may consider
building models that would help to assess the effect of alter capabilities organizational
effectiveness. For example, current literature suggests that the exchange of humanitarian
information between organizations is highly challenging. Understanding how and why
beneficial network structure captures alter organization capabilities may help to better
understand the inter-organizational humanitarian information management and exchange.
Lastly, research on information and knowledge exchange has illustrated the importance
of proximity and co-location (Almeida & Kogut, 1997; Tallman et al., 2004) as an
enabler in the transmission of tacit knowledge. Future research may consider including
geographical propinquity as a further element that could affect the efficacy of
humanitarian information exchange and influence effectiveness.
Practical Limitations
The opportunity to apply social network tools to explore networks of humanitarian
organizations engaged in information management and exchange provides a practical
182
benefit to the international community in considering this approach to encourage
collaboration in responding to humanitarian disasters. The network diagrams depicting
project collaboration and advice relationships among humanitarian organizations
demonstrate the reach accomplished in humanitarian information management and
exchange. Linking these network structures as well as organizational characteristics to
effectiveness highlighted the determinants of effectiveness among organizations in the
humanitarian relief field. This provides the opportunity for humanitarian organizations
and especially UNOCHA to consider weaknesses and strengths of the Global Symposium
to increase network effectiveness in managing and exchanging humanitarian information.
Identifying the information technology tools that the Global Symposium community
needs for better humanitarian information exchanged would be an extension of this study
worthy of consideration.
183
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APPENDIX
Appendix A: Inform Consent Form for Social Science Research
The Pennsylvania State University
Title of Project:
Inter-organizational decision making and organization design for improved ICT
coordination in disaster relief
Principal Investigators: Dr. Carleen Maitland
College of Information Sciences and Technology
102 J IST Bldg.
University Park, PA 16802
(814) 863-0460; [email protected]
Dr. Andrea Tapia
College of Information Sciences and Technology
329 G IST Bldg.
University Park, PA 16802
(814) 865-1524; [email protected]
1. Purpose of the Study: We are conducting research concerning the flow of
information and information sharing among relief organizations. In particular, we
seek to understand the ways in organizational characteristics (e.g. organizational
structure, size, goals, and resources) and inter-organizational network structural
and relational properties influence the ability of humanitarian organizations to
make decisions that will enable them to effectively collaborate during disaster
relief as well as development.
2. Procedures to be followed: You will be asked to participate in an online survey.
3. Duration: The survey is likely to take between 30 and 40 minutes to be
completed.
4. Benefits: There are no direct benefits to you as a participant in this study.
However, with the data collected from this study we hope to improve information
flows and sharing among relief organizations and eventually get help to those that
need it more efficiently.
5. Statement of Confidentiality: Your participation in this research is confidential.
Only Drs. Maitland and Tapia and their assistants, will know your identity. Each
participant will be assigned a number. The data will be stored and secured in a
secure computer located in Dr Maitland’s office (Room 102J, IST Building) on
the Penn State University Campus. Your confidentiality will be kept to the degree
200
permitted by the technology used. No guarantees can be made regarding the
interception of data sent via the Internet by any third parties. The data will be
destroyed in December 2012 (3 years following the end of the project). In the
event of a publication or presentation resulting from the research, no personally
identifiable information will be shared.
6. Right to Ask Questions: Please contact Dr. Carleen Maitland or Dr. Andrea
Tapia at 814-865-1524 or 814-863-0460 with questions or concerns about this
study.
7. Voluntary Participation: Your decision to be in this research is voluntary. You
can stop at any time. You do not have to answer any questions you do not want to
answer. Refusal to take part in or withdrawing from this study will involve no
penalty or loss of benefits you would receive otherwise.
8. You must be 18 years of age or older to take part in this research study. If
you agree to take part in this research study and the information outlined above,
please sign your name and indicate the date below.
9. Completion and submission of the survey is considered your implied consent
to participate in this study. Please print this form for your records.
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Appendix B: Letter-Email sent to potential Survey participants
Hello ____,
We are two professors from the Pennsylvania State University, USA. We are studying
how organizations which provide humanitarian relief coordinate around information
management projects. In particular, we seek to understand the ways in organizational
characteristics and inter-organizational network structural and relational properties
influence the ability of humanitarian organizations to make decisions that will enable
them to effectively collaborate during disaster relief as well as development.
In October 2007 the Global Symposium +5 was held in Geneva. We are writing to you
because we believe you also attended this event or previous related symposia. We would
like to ask for your help in participating in our short online survey.
The survey is likely to take between 30 to 40 minutes. Everything you say will be kept
confidential and Drs. Maitland and Tapia will not share your information or response
with anyone or store it with any personally identifying information. You decision to
participate in the survey is voluntary. You do not have to participate or answer any
questions you do not want to answer. You must be 18 years of age or older to participate.
More information about this survey can be found in our research consent form.
If you agree to participate, please visit our online survey at www.
Thank you!
Drs. Carleen Maitland and Andrea Tapia
College of Information Sciences and Technology
The Pennsylvania State University
University Park, PA, USA
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Appendix D: Interview Guide
Interview appointment mail
Dear ,
Thank you again for responding to our follow up survey on the Global Symposium+5 and
for accepting to take part in a phone interview. The main purpose of this interview is to
share some of your personal experiences and perspectives on the Global Symposium+5.
I am writing to set up an appointment for this interview which I anticipate will take no
more than one hour. Please email me a time slot you will be available for the interview.
Interviewing can be as early as next Monday October 25, 2009.
Best regards,
Louis-Marie
Interview Guide
Greetings
Informed consent (to get the verbal consent to participate to the interview)
Do you agree to be interviewed?
Do you mind if we record you for reference in our research? In reference we will
maintain anonymity.
Your organization
o We would like to hear about your specific experience at the Global
Symposium (GS).
o Did your organization participate in any working groups?
o What do you see as the goals of the GS and the extent to which the GS is
effectively meeting these goals?
o Some more general goals of the GS were
Policy making
Agenda setting
Networking
What do you feel the GS was effective at, for your organization?
o In your opinion, what are the major barriers to collaboration in the Global
Symposium community?
o What do you see as the most important challenges to your organization to
fully participate in the Global Symposium community activities?
o Can you tell us about some contacts you made at the GS and if (and how)
that led to project partnerships?
o Can you tell us if the project you realized with the people you met at the
symposium were already in your to do list?
227
o How do you think your organization size impact your ability to work with
other people? Do you feel like other organizations want to work with you
because you are small or they do not want to work with you because you
are small? (Similar question for: mission, region of focus, Head quarter
location, IT infrastructure).
o What is the most important benefit that your organization has gained from
being member of the Global Symposium?
o What factors (organizational, project) do you consider in selecting a
partner for collaboration
o Overall, how successful are collaborative projects resulting from your
participation to the GS?
o What relationships would say exist between IT and IM?
o In your organization is IT used just as a tool or is it part of your
Organization competency?
o Talking about IT, where would you say your organization is a Consumer,
a Producer or an Implementer?
o What role if any, play Information Technology in fostering collaboration
among organization?
o What type of social media is available in your organization?
o In your opinion, how useful are these new social media in humanitarian
information management and exchange?
Effectiveness of the Global Symposium Community
o How would you measure the effectiveness of the Global Symposium
Community? (Easy access to humanitarian information, extend of
information sharing, number of collaborative projects, easy access to
funding, level of satisfaction of different stakeholders)?
o Based on these measures, in your opinion, how effective has been the
Global Symposium?
o How do you think organizational characteristics (e.g. size, age, missions
etc..) of the Global Symposium members impact the effectiveness of
collaboration in the community?
o Does the advice/communication relationship with another org help to
establish project collaborations?
o Does higher frequency of interaction reflect stronger relationship among
organizations?
Project collaboration formation
o What triggers collaboration partnerships?
o Do larger organizations often have more projects on their agenda/to-do
lists?
o Will your organization's evaluation of a potential collaborative project be
influenced by your acquaintance?
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Vita
Louis-Marie Ngamassi Tchouakeu is a doctoral student in the College of Information
Sciences and Technology at Penn State University. He holds a Master’s degree in
Computer Information Systems from Pace University – New York and B.S. in Economics
from the University of Yaoundé - Cameroon. His research interests include information
systems (IS) and information technology (IT) development in the international context.
Fundamentally, he is interested in the use of information and communication
technologies in coordination and collaboration between organizations, which provide
humanitarian relief, and development services. Prior to joining Penn State University he
worked as a program staff at the UN Economic Commission for Africa (UNECA) in
Addis Ababa, Ethiopia and has over a decade of experience in IT administration at the
University of Dschang - Cameroon.
Louis-Marie’s work has appeared in the following journals: International Journal of
Intelligent Control and Systems (IJICS); International Journal of Society Systems Science
(IJSSS); International Journal of Information Systems and Social Change (IJISSC); He
has also presented his work at different conferences including : the iConference, the
Americas Conference on Information Systems (AMCIS), the Biennial Conference of the
International Telecommunications Society (ITS); the International Information Systems
for Crisis Response and Management (ISCRAM) Conference; the World Congress on
Social Simulation (WCSS) and the Research Conference on Communication, Information
and Internet Policy (TPRC).
Louis-Marie is a former Fulbright and a former United Nations Fulbright Fellow. He is
also recipient of numerous scholarships and awards from organizations such as USAID
and the French Agency for Technical and Cultural Cooperation (ACCT).