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An Experimental Investigation of the Impact of Computer Based Decision Aids on Decision Making Strategies

Author

Listed:
  • Peter Todd

    (School of Business, Queen's University, Kingston, Ontario, Canada K7L 3N6)

  • Izak Benbasat

    (Faculty of Commerce, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Y8)

Abstract

Although Decision Support Systems (DSSs) have been in use since the early seventies, there is as yet no strong theoretical base for predicting how a DSS will influence decision making. Furthermore, the findings of various empirical studies on the outcomes of DSS use are often contradictory. Consequently, there is a need in the Decision Support Systems field for theories or explanatory models to formulate hypotheses, to conduct research in a directed, parsimonious manner and to interpret findings in a coherent way. This will assist both academics and practitioners interested in the use of information systems to support managerial workers. This paper proposes the use of a cognitive effort model of decision making to explain decision maker behavior when assisted by a DSS. The central proposition is that specific features can be incorporated within a DSS that will alter the effort required to implement a particular strategy, and thus influence strategy selection by the decision maker. This was investigated in a series of three experimental studies which examined the influence of computer based decision aids on decision making strategies. In the three experiments, subjects were given different degrees of support to deal with various components of cognitive effort (processing effort, memory effort and information tracking effort) associated with the strategies applicable to preferential choice problems. The results show that decision makers tend to adapt their strategy selection to the type of decision aids available in such a way as to reduce effort. These results suggest that the assumption that decision makers use a DSS exclusively to maximize decision quality is open to question. DSS studies which consider the joint effects of effort and quality, or control one while manipulating the other, are more likely to provide consistent and interpretable results.

Suggested Citation

  • Peter Todd & Izak Benbasat, 1991. "An Experimental Investigation of the Impact of Computer Based Decision Aids on Decision Making Strategies," Information Systems Research, INFORMS, vol. 2(2), pages 87-115, June.
  • Handle: RePEc:inm:orisre:v:2:y:1991:i:2:p:87-115
    DOI: 10.1287/isre.2.2.87
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    Citations

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    Cited by:

    1. Meißner, Martin & Oppewal, Harmen & Huber, Joel, 2020. "Surprising adaptivity to set size changes in multi-attribute repeated choice tasks," Journal of Business Research, Elsevier, vol. 111(C), pages 163-175.
    2. Proserpio, Luigi & Magni, Massimo, 2012. "Teaching without the teacher? Building a learning environment through computer simulations," International Journal of Information Management, Elsevier, vol. 32(2), pages 99-105.
    3. Amar Gupta & Igor Crk & Rajdeep Bondade, 2011. "Leveraging temporal and spatial separations with the 24-hour knowledge factory paradigm," Information Systems Frontiers, Springer, vol. 13(3), pages 397-405, July.
    4. Adi Katz & Dov Te’eni, 2007. "The Contingent Impact of Contextualization on Computer-Mediated Collaboration," Organization Science, INFORMS, vol. 18(2), pages 261-279, April.
    5. O'Keefe, Robert M., 2016. "Experimental behavioural research in operational research: What we know and what we might come to know," European Journal of Operational Research, Elsevier, vol. 249(3), pages 899-907.
    6. O. Alan Tidwell & Paul Gallimore, 2014. "The influence of a decision support tool on real estate valuations," Journal of Property Research, Taylor & Francis Journals, vol. 31(1), pages 45-63, March.
    7. Kai H. Lim & Izak Benbasat & Lawrence M. Ward, 2000. "The Role of Multimedia in Changing First Impression Bias," Information Systems Research, INFORMS, vol. 11(2), pages 115-136, June.
    8. Paul John Steinbart & Mark J. Keith & Jeffry Babb, 2016. "Examining the Continuance of Secure Behavior: A Longitudinal Field Study of Mobile Device Authentication," Information Systems Research, INFORMS, vol. 27(2), pages 219-239, June.
    9. Maryam Alavi & George M. Marakas & Youngjin Yoo, 2002. "A Comparative Study of Distributed Learning Environments on Learning Outcomes," Information Systems Research, INFORMS, vol. 13(4), pages 404-415, December.
    10. Ekaterina Jussupow & Kai Spohrer & Armin Heinzl & Joshua Gawlitza, 2021. "Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence," Information Systems Research, INFORMS, vol. 32(3), pages 713-735, September.
    11. Gudigantala, Naveen & Song, Jaeki & Jones, Donald, 2011. "User satisfaction with Web-based DSS: The role of cognitive antecedents," International Journal of Information Management, Elsevier, vol. 31(4), pages 327-338.
    12. Josef Frysak & Edward W. N. Bernroider & Konradin Maier, 2017. "An Effort Feedback Perspective on Persuasive Decision Aids for Multi-Attribute Decision-Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 161-181, January.
    13. W-N Xiang, 1996. "Making Better, Quicker, and Wiser Decisions with a Decision Facilitating and Advising System," Environment and Planning B, , vol. 23(4), pages 401-419, August.
    14. Viswanath Venkatesh, 2000. "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, INFORMS, vol. 11(4), pages 342-365, December.
    15. Young Eun Lee & Izak Benbasat, 2011. "Research Note ---The Influence of Trade-off Difficulty Caused by Preference Elicitation Methods on User Acceptance of Recommendation Agents Across Loss and Gain Conditions," Information Systems Research, INFORMS, vol. 22(4), pages 867-884, December.
    16. Wynne W. Chin & Barbara L. Marcolin & Peter R. Newsted, 2003. "A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study," Information Systems Research, INFORMS, vol. 14(2), pages 189-217, June.

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