Effects of Failure Types on Trust Repairs in Human–Robot Interactions

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초록

System performance is the central determinant of user trust in human–machine communication; however, performance failure is inevitable. This study develops a three-fold typology of performance failures (i.e., logic, semantic, and syntax) commonly observed in human-robot interactions based on the differences between the expected and actual outcomes. Herein, 1027 observations are collected from an online experiment to elucidate how the three types of failure and four repair methods (namely, internal attribution apology, external attribution apology, denial, and no repair) impact user trust while examining blame attributions as an underlying mechanism. The results reveal that despite some similarities, the interactions between trust violation types and repair methods differ in robot-to-human trust repair from those in human-to-human trust repair, which contradicts previous findings. Logic failures are found to be the most detrimental category of performance failures, and the internal-attribution apology is the optimal repair strategy. Notably, participants report greater levels of competence-based trust beliefs if they believe that the situation is jointly controlled by the human interactant and robot. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.

키워드

Blame attributions; Human–robot interactions; Performance failures; Trust repairs; INTEGRITY VIOLATIONS; ORGANIZATIONAL TRUST; IMPLICIT THEORIES; CUTOFF CRITERIA; ATTRIBUTION; COMPETENCE; AUTOMATION; APOLOGY; MODEL; INFORMATION
제목
Effects of Failure Types on Trust Repairs in Human–Robot Interactions
저자
Zhang, Xinyi; Lee, Sun Kyong; Maeng, Hoyoung; Hahn, Sowon
DOI
10.1007/s12369-023-01059-0
발행일
2023-10
유형
Article
저널명
International Journal of Social Robotics
권
15
호
9-10
페이지
1619 ~ 1635