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

Hancock, P. A.; Kessler, Theresa T.; Kaplan, Alexandra D.; Brill, John C.; Szalma, James L.

doi:10.1177/0018720820922080

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

Journal Article · Wed Jun 10 00:00:00 EDT 2020 · Human Factors

DOI:https://doi.org/10.1177/0018720820922080· OSTI ID:1983024

^[1]; Kessler, Theresa T. ^[2]; Kaplan, Alexandra D. ^[2]; Brill, John C. ^[3]; Szalma, James L. ^[2]

University of Central Florida, Orlando, USA; Institute for Simulation and Training, University of Central Florida, Orlando, USA
University of Central Florida, Orlando, USA
United States Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, Ohio, USA

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.

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Research Organization:: Oak Ridge Inst. for Science and Education (ORISE), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: SC0014664

OSTI ID:: 1983024

Journal Information:: Human Factors, Vol. 63, Issue 7; ISSN 0018-7208

Publisher:: SAGE

Country of Publication:: United States

Language:: English

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