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This dissertation examines the impact of business intelligence (BI) systems on decision-making in high-velocity environments, focusing on the balance between decision speed and quality. The research explores whether BI systems facilitate quicker and better decisions while addressing potential information overload and the relationship between system usage and decision quality. The findings aim to contribute to decision science literature and provide insights for organizations considering the implementation of BI systems.
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- 1. 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 Jr PhD Candidate Dissertation Committee Dr. Yufeng Tu Dr. Yajiong Xue Dr. William Kemple
- 2. Purpose 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.
- 3. Relevant 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 Quality Greater access to needed Information Leidner 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.
- 4. Research 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. 04/12/10 4
- 5. 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). 04/12/10
- 6. 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). 04/12/10
- 8. Variables Mapped to Survey Questions Variable Label (Type) Code Survey Question Number Source of Survey Item Decision Quality Dependent Variable DcnQu 6-11 12-15 Dooley and Fryxell, 1999 Paul, Saunders, and Haseman, 2005 Decision Speed DcnSp 19-21 Leidner and Elam, 1995 System Usage SysUs 16a-d, 18 17 Leidner & Elam, 2005 Iivari, 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
- 9. Research 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. 04/12/10
- 10. Job Level
- 11. Distribution of Major BI Systems
- 13. 04/12/10 Internal Consistency 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
- 14. 04/12/10 GFI=.72 Path Analysis
- 15. [email_address]
- 16. Backup Slides
- 17. Population and Sample 04/12/10 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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- 19. Research 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. 04/12/10
- 20. Variables and Hypotheses 04/12/10 Variables Hypotheses Decision Quality Dependent 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 Usage Independent 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 Availability Mediating Variable H8: Information availability is positively associated with decision speed. Information Quality Mediating Variable H9: The quality of the information is positively associated with decision effectiveness.