User Acceptance of Knowledge-Based System Recommendations: Explanations, Arguments, and Fit

Decision Support Systems, vol. 72(April), pp. 1-10 (doi: http://dx.doi.org/10.1016/j.dss.2015.02.005)

31 Pages Posted: 6 Apr 2015

See all articles by Justin Giboney

Justin Giboney

University of Arizona - Department of Management Information Systems

Susan Brown

University of Arizona - Eller College of Management

Paul Benjamin Lowry

Virginia Tech - Pamplin College of Business

Jay F. Nunamaker

University of Arizona - Center for the Management of Information (CMI)

Date Written: April 1, 2015

Abstract

Knowledge-based systems (KBS) can potentially enhance individual decision-making. Yet, recommendations from KBS continue to be met with resistance. This is particularly troubling in the context of deception detection (e.g., border control), in which humans are accurate only about half the time. In this study, we examine how the fit between KBS explanations and users’ internal explanations influences acceptance of KBS recommendations. We leverage cognitive fit theory (CFT) to explain why fit is important for user acceptance of KBS evaluations. We also compare the predictions of CFT to those of the person-environment fit (PEF) paradigm. The two theories make conflicting predictions about the outcomes of fit when it comes to KBS explanations. CFT predicts that explanations with a higher cognitive fit will have more influence and be evaluated faster whereas PEF predicts that individuals will take more time in evaluating explanations with greater fit. In our deception detection scenario, we find support for CFT in the sense that people are influenced more by cognitively fitting explanations, however PEF is supported in the sense that people take more time to evaluate the explanation.

Keywords: User acceptance, explanations, cognitive fit, recommendations

Suggested Citation

Giboney, Justin and Brown, Susan and Lowry, Paul Benjamin and Nunamaker, Jay F., User Acceptance of Knowledge-Based System Recommendations: Explanations, Arguments, and Fit (April 1, 2015). Decision Support Systems, vol. 72(April), pp. 1-10 (doi: http://dx.doi.org/10.1016/j.dss.2015.02.005), Available at SSRN: https://ssrn.com/abstract=2590069

Justin Giboney

University of Arizona - Department of Management Information Systems ( email )

AZ
United States

Susan Brown

University of Arizona - Eller College of Management

McClelland Hall
P.O. Box 210108
Tucson, AZ 85721-0108
United States

Paul Benjamin Lowry (Contact Author)

Virginia Tech - Pamplin College of Business ( email )

1016 Pamplin Hall
Blacksburg, VA 24061
United States

Jay F. Nunamaker

University of Arizona - Center for the Management of Information (CMI) ( email )

McClelland Hall 202
P.O. Box 210108
Tucson, AZ 85721-0108
United States

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