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Complexity And Familiarity With Computer Assistance When Making Ill-Structured Business Decisions

Author

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  • DAVID L. MCLAIN

    (School of Management, State University of New York Institute of Technology, Utica, NY 13504-3050, USA)

  • RAMON J. ALDAG

    (School of Business, University of Wisconsin, 975 University Avenue, Madison, WI 53706, USA)

Abstract

Using high-level tasks typical of managerial decisions, this experimental study examined the influence of computer assistance on solving ill-structured problems. Of specific interest were the influences of complex and simple assistance on decision performance and decision maker attitudes. Our findings suggest that performance and user attitudes can be enhanced by technology that provides clear and simple instruction in good problem-solving practices. However, when that technology adds a complex array of technical options and features, the assistance may fail to improve or/and may even diminish performance and damage user attitudes. Familiarization with such complex technology may not improve these outcomes. The findings regarding the relationship between assistance complexity and decision performance are consistent with those of studies that suggest complexity has a curvilinear influence on performance. When considering the application of technological assistance to ill-structured decision tasks, the complexity of the assistance should not be ignored.

Suggested Citation

  • David L. Mclain & Ramon J. Aldag, 2009. "Complexity And Familiarity With Computer Assistance When Making Ill-Structured Business Decisions," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(03), pages 407-426.
    • Handle: RePEc:wsi:ijitdm:v:08:y:2009:i:03:n:s0219622009003491
    DOI: 10.1142/S0219622009003491
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    Cited by:

    1. Chi-Wen Chen & Marios Koufaris, 2015. "The impact of decision support system features on user overconfidence and risky behavior," European Journal of Information Systems, Taylor & Francis Journals, vol. 24(6), pages 607-623, November.
    2. Keding, Christoph & Meissner, Philip, 2021. "Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    3. Gert-Jan Vreede & Robert O. Briggs & Triparna Vreede, 2022. "Exploring a Convergence Technique on Ideation Artifacts in Crowdsourcing," Information Systems Frontiers, Springer, vol. 24(3), pages 1041-1054, June.

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