Evolving Trust in Robots: Specification Through Sequential and Comparative Meta-Analyses

ObjectiveThe objectives of this meta-analysis are to explore the presently available empirical findings on the antecedents of trust in robots and use this information to expand upon a previous meta-analytic review of the area. BackgroundHuman–robot interaction (HRI) represents an increasingly import...

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Bibliographic Details
Published in:Human factors 2020-06, Vol.63 (7)
Main Authors: Hancock, P. A., Kessler, Theresa T., Kaplan, Alexandra D., Brill, John C., Szalma, James L.
Format: Article
Language:English
Subjects:
Behavioral Sciences
Engineering
Psychology
Online Access:Get full text
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Summary:ObjectiveThe objectives of this meta-analysis are to explore the presently available empirical findings on the antecedents of trust in robots and use this information to expand upon a previous meta-analytic review of the area. BackgroundHuman–robot interaction (HRI) represents an increasingly important dimension of our everyday existence. Currently, the most important element of these interactions is proposed to be whether the human trusts the robot or not. We have identified three overarching categories that exert effects on the expression of trust. These consist of factors associated with (a) the human, (b) the robot, and (c) the context in which any specific HRI event occurs. MethodThe current body of literature was examined and all qualifying articles pertaining to trust in robots were included in the meta-analysis. A previous meta-analysis on HRI trust was used as the basis for this extended, updated, and evolving analysis. ResultsMultiple additional factors, which have now been demonstrated to significantly influence trust, were identified. The present results, expressed as points of difference and points of commonality between the current and previous analyses, are identified, explained, and cast in the setting of the emerging wave of HRI. ConclusionThe present meta-analysis expands upon previous work and validates the overarching categories of trust antecedent (human-related, robot-related, and contextual), as well as identifying the significant individual precursors to trust within each category. A new and updated model of these complex interactions is offered. ApplicationThe identified trust factors can be used in order to promote appropriate levels of trust in robots.
ISSN:0018-7208
1547-8181