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TUI University College of Business Administration
TUI University College of Business Administration
BALANCING DECISION SPEED AND DECISION QUALITY: ASSESSING THE IMPACT OF BUSINESS INTELLIGENCE
SYSTEMS IN HIGH VELOCITY ENVIRONMENTS
Criston W Cox JrPhD Candidate
Dissertation CommitteeDr. Yufeng Tu
Dr. Yajiong XueDr. William Kemple
Purpose of ResearchPurpose of Research
• The purpose of this research effort is to determine if:
• the output of the BI System sufficiently balances information quality, quantity, and availability
• delivers the right information, to the right people, at the right time
• enabling quality decisions in high velocity environments.
Relevant Literature and flow to the DVRelevant Literature and flow to the DV
High Velocity Environment Faster decision = better performance
Type of information most needed in high velocity environments is real time information. This need drives
the decision to implement a real time Business Intelligence System
Enough info to make decision without information overload. Measured
by Number of Alternatives
Jacoby, Russo, Malhotra, Gallupe, et al.
(accuracy) Haubl and Trifts, 2000; Sharda, Barr, and McDonald, 1988;
BI System Usage Frequency and Length of Use
depends on three primary effects of information (which affect user satisfaction)
Eisenhardt and Bourgeois, 1989 Bogner and Barr, 2000; Judge and Miller, 1995, Baum and Wally, 2003
Effectiveness of the BI System to provide timely, accurate , and
relevant information (R3) / Impact on OODA Loop
Bryant, 2006; Negash, 2004, Nicolas, 2004; Stalk and Hout, 1990
Impact on Decision QualityGreater access to needed InformationLeidner and Elam (1995)
Reduced cognitive effort(Todd and Benbaset, 2000)
Information Quality
Information Quantity
Information Availability
Sawy and Majchrzak, 2004; Eisenhardt and Bourgeois, 1989
Delone and McLean, 1992; Leidner and Elam, 1995; Huber, 1990; Jones and Straub, 2006
multicolinearity Oreilly, 1982 found significant associations among both information
quality and availability of information sources, and the frequency of their use.
Research QuestionsResearch Questions
Research Question 1:
Do BI Systems enable faster and better decisions in High Velocity Environments, or are decision speed and decision quality inversely related?
Eisenhardt and Bourgeois, 1989; Bogner and Barr, 2000; Judge and Miller, 1995; Baum and Wally, 2003.
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Research Questions (continued)Research Questions (continued)
Research Question 2:
What is the relationship between usage of a BI system and the quality of decisions made in a High Velocity Environments?
(Delone and McLean, 2005; Burton-Jones and Straub, 2006; Baroudi, Olson, and Ives, 1986; Straub, Limayem, and Karahanna-Evaristo, 1995).
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Research Questions (continued)Research Questions (continued)
Research Question 3:
What affect do BI systems have on information overload in High Velocity Environments?
(Keller and Staelin, 1987; Jacoby, 1974; Malhotra, 1982; Russo, 1974).
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Conceptual ModelConceptual Model
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Variables Mapped to Survey QuestionsVariables Mapped to Survey Questions
Variable Label (Type) Code Survey Question Number
Source of Survey Item
Decision QualityDependent Variable
DcnQu 6-1112-15
Dooley and Fryxell, 1999Paul, Saunders, and Haseman, 2005
Decision Speed DcnSp 19-21 Leidner and Elam, 1995
System Usage SysUs 16a-d, 1817
Leidner & Elam, 2005Iivari, 2005
Information Overload InfoOv 31-35 O’Reilly, 1980
Information Availability InfoAv 28-30 Leidner and Elam, 1995
Information Quality InfoQu 22-27 Iivari, 2005
Research Design and MethodologyResearch Design and Methodology
• To empirically measure effect size of associations between the variables, constructs will be tested using Structural Equation Modeling (SEM).
• Survey is hosted on Zoomerang.com. Participants are solicited using Linked-In User Groups.
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Job Level
Distribution of Major BI Systems
Organization Size
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VARIABLE CRONBACH'S ALPHA
SYSTEM USAGE 0.65
INFORMATION OVERLOAD 0.73
INFORMATION QUALITY 0.89
INFORMATION AVAILABILITY 0.78
DECISION SPEED 0.75
DECISION QUALITY 0.82
Internal Consistency
SysUs
InfoQu
DcnQu
DcnSp
InfoOv
InfoAv
-.09 -.04
.58
.34
.30
.32
.42
.55
e1
1
.38
e2
1
.66
e3
.55
e4
1
.16
e5
1
-.05
.50
e6
1 1
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GFI=.72
Path Analysis
cwcox@tuiu.educwcox@tuiu.edu
Backup SlidesBackup Slides
Population and SamplePopulation and Sample
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•The population under study are those who employ Business Intelligence Systems to aid in rapid decision making in High Velocity Environments.
•Research indicates an adequate sample size for CFA based SEMs is 150 (Ding, Velicer, and Harlow (1995); Anderson and Gerbring, 1998); Muthen and Muthen, 2002).
Based on the recommendations of previous SEM research, the sample size desired for this study is 300, with a minimum acceptance of 150.
–It is estimated that a minimum of 1500 surveys must be send to yield the desired sample size of 300.
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Research ContributionsResearch Contributions
This study has both theoretical and practical implications.
• First, from the theoretical perspective, this study contributes to IS and Decision Science literature by pulling topics from each together into a cohesive set of dependent and influencing relationships. Decision theory is a mature area of research that has shaped the development of decision support systems. Understanding the value of decision support systems as they grow and evolve with technological advances is important to the continued development of information systems sciences. .
• Second, this study enhances our understanding about the value of BI Systems as a decision aid, which may prove beneficial to organizations considering adoption and investment in a BI System. The outcome of this research will extend the knowledge of BIS within the information systems community.
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Variables and HypothesesVariables and Hypotheses
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Variables Hypotheses
Decision QualityDependent Variable
Decision quality is the Dependent Variable
Decision Speed H1: When enabled with a Business Intelligence System, Decision quality is positively associated to decision speed.
System UsageIndependent Variable
H2: Higher BI system usage is positively associated to greater information availability.H3: Higher BI System Usage reduces information overload.H4: Higher BI System Usage is positively related to information quality.
Information Overload Mediating Variable
H5: BI aided groups will consider a greater number of simultaneous alternatives than non BI aided groups.H6: The number of alternatives is inversely related to the decision speed. H7: The number of alternatives is positively related to the decision quality.
Information AvailabilityMediating Variable
H8: Information availability is positively associated with decision speed.
Information QualityMediating Variable
H9: The quality of the information is positively associated with decision effectiveness.