Repairing Trust in Robots?: A Meta-analysis of HRI Trust Repair Studies with a No-Repair Condition

As robots become more integrated into various sectors, understanding human-robot interaction (HRI) dynamics, particularly trust repair, is crucial for successful collaboration. For this paper, the authors conducted a meta-analysis of 22 HRI trust repair studies with 3,763 participants to evaluate the effectiveness of strategies for restoring trust after breaches relative to offering no repair. The analysis identified three key findings: (1) strategies are differentially effective, showing limited success in restoring trustworthiness; (2) the overall impact on repairing trust is marginal, with a small effect size; and (3) apologies and explanations are the most effective strategies for trust repair. These insights enrich HRI literature by providing a comprehensive evaluation of trust repair mechanisms, offering valuable guidance for future research and practical improvements in human-robot collaboration.

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Published
2025-03-04
DOI
https://doi.org/10.1109/hri61500.2025.10974168
Citations
2
Primary Topic
Cognitive Functions and Memory
Type
article
Field-Weighted Citation Impact
4.24
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article

Repairing Trust in Robots?: A Meta-analysis of HRI Trust Repair Studies with a No-Repair Condition

Connor Esterwood, Lionel Robert
2 citations
Cognitive Functions and Memory
4.24
article

Repairing Trust in Robots?: A Meta-analysis of HRI Trust Repair Studies with a No-Repair Condition

Connor Esterwood, Lionel Robert
article en
2 citations

Abstract

As robots become more integrated into various sectors, understanding human-robot interaction (HRI) dynamics, particularly trust repair, is crucial for successful collaboration. For this paper, the authors conducted a meta-analysis of 22 HRI trust repair studies with 3,763 participants to evaluate the effectiveness of strategies for restoring trust after breaches relative to offering no repair. The analysis identified three key findings: (1) strategies are differentially effective, showing limited success in restoring trustworthiness; (2) the overall impact on repairing trust is marginal, with a small effect size; and (3) apologies and explanations are the most effective strategies for trust repair. These insights enrich HRI literature by providing a comprehensive evaluation of trust repair mechanisms, offering valuable guidance for future research and practical improvements in human-robot collaboration.

University of Michigan (US)
Decent work and economic growth
Openalex Percentile: Top 7%
Cognitive Functions and Memory
4.24
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Repairing Trust in Robots?: A Meta-analysis of HRI Trust Repair Studies with a No-Repair Condition — Connor Esterwood, Lionel Robert · (2025) | TGRS Research Map | TGRS