TY - JOUR
T1 - User acceptance of knowledge-based system recommendations
T2 - Explanations, arguments, and fit
AU - Giboney, Justin Scott
AU - Brown, Susan A.
AU - Lowry, Paul Benjamin
AU - Nunamaker, Jay F.
N1 - Funding Information:
This research was supported by the US Department of Homeland Security through the National Center for Border Security and Immigration (Grant # 2008-ST-061-BS0002 ). However, any opinions, findings, and conclusions or recommendations herein are those of the authors and do not necessarily reflect views of the US Department of Homeland Security. The views, opinions, and/or findings in this report are those of the authors.
Publisher Copyright:
©2015 Elsevier B.V. All rights reserved.
PY - 2015/4
Y1 - 2015/4
N2 - 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.
AB - 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.
KW - Cognitive fit
KW - Explanations
KW - Recommendations
KW - User acceptance
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U2 - 10.1016/j.dss.2015.02.005
DO - 10.1016/j.dss.2015.02.005
M3 - Article
AN - SCOPUS:84923288269
SN - 0167-9236
VL - 72
SP - 1
EP - 10
JO - Decision Support Systems
JF - Decision Support Systems
ER -