Three Strikes and you are out!: The impacts of multiple human–robot trust violations and repairs on robot trustworthiness
Open accessAbstract
Robots like human co-workers can make mistakes violating a human’s trust in them. When mistakes happen, humans can see robots as less trustworthy which ultimately decreases their trust in them. Trust repair strategies can be employed to mitigate the negative impacts of these trust violations. Yet, it is not clear whether such strategies can fully repair trust nor how effective they are after repeated trust violations. To address these shortcomings, this study examined the impact of four distinct trust repair strategies: apologies, denials, explanations, and promises on overall trustworthiness and its sub-dimensions: ability, benevolence, and integrity after repeated trust violations. To accomplish this, a between-subjects experiment was conducted where participants worked with a robot co-worker to accomplish a task. The robot violated the participant’s trust and then provided a particular repair strategy. Results indicated that after repeated trust violations, none of the repair strategies ever fully repaired trustworthiness and two of its sub-dimensions: ability and integrity. In addition, after repeated interactions, apologies, explanations, and promises appeared to function similarly to one another, while denials were consistently the least effective at repairing trustworthiness and its sub-dimensions. In sum, this paper contributes to the literature on human–robot trust repair through both of these original findings.
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Robot Broken Promise? Repair strategies for mitigating loss of trust for repeated failures
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An EEG-network-metric based approach to real-time trust inference in human-autonomy teaming
Bales, G. L., Anderson, A. P., Clark, T. K., Dekarske, J., Joshi, S., & Kong, Z. (2025) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2025.1627483 · Cited by 7 · see citing works
Rethinking Trust Repair in Human-Robot Interaction
Esterwood, C. T. (2023) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3584931.3608919 · Cited by 6 · see citing works
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Interpersonal influence matters: Trust contagion and repair in human-human-AI team
Rojas, E., Hsu, D., Huang, J., & Li, M. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100194 · Cited by 4 · see citing works
Chung, H., Jiang, R., Shen, S., & Yang, X. J. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2526645 · Cited by 4 · see citing works
Drone Fail Me Now: How Drone Failures Affect Trust and Risk-Taking Decisions
Rahimzadagan, N., Vahs, M., Leite, I., & Stower, R. E. (2024) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3610978.3640609 · Cited by 4 · see citing works
Lane, S., Esterwood, C. T., Kulić, D., & Robinson, N. (2024) · IEEE RO-MAN · doi:10.1109/ro-man60168.2024.10731465 · Cited by 3 · see citing works
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Active interaction strategy generation for human-robot collaboration based on trust
Guo, Y., Yi, P., Wei, X., & Zhou, D. (2025) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-025-00198-7 · Cited by 3 · see citing works
Nagy, T.-N., Khavas, Z. R., Kotturu, M. R., Liefooghe, B., Robinette, P., & de Graaf, M. M. A. (2025) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3767729 · Cited by 3 · see citing works
Tang, L. R., & Bashir, M. N. (2025) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms65439.2025.11154364 · Cited by 3 · see citing works
Anthropomorphic Robots: Form Matters Especially in Case of Failure
Söhngen, Y. N., Alsagara, M. A.-A., & Prilla, M. (2025) · IEEE RO-MAN · doi:10.1109/ro-man63969.2025.11217603 · Cited by 3 · see citing works
Fan, W., Shao, B., Tang, J., Zhang, Y., & Zhao, Q. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2626746 · Cited by 3 · see citing works
Chen, N., & Zhang, X. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2676762 · Cited by 4 · see citing works
Promises and Trust Repair in UGVs
Esterwood, C. T., Ali, A., George, Z., Dubrow, S. R., Smereka, J. M., Riegner, K. L., Tilbury, D. M., & Robert, L. (2023) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/21695067231196235 · Cited by 3 · see citing works
Self-citationThe Impact of AI Trust Violation on Trustworthiness: An Empirical Study Based on AI Chatbots
Wu, L., Xiang, P., Chen, Y., Li, J., Cao, Z., & Qi, J. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-5803-6_27 · Cited by 3 · see citing works
Trustworthiness Assessment of an Adaptive and Explainable Robot in a Real Environment
Cantucci, F., Marini, M., & Falcone, R. (2025) · International Journal of Social Robotics · doi:10.1007/s12369-025-01257-y · Cited by 3 · see citing works
Predicting Trust Dynamics Type Using Seven Personal Characteristics
Chung, H., & Yang, X. J. (2024) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2025.3627578 · Cited by 3 · see citing works
Multidisciplinary perspectives on human‑AI team trust
Tielman, M. L., Bailey, M. E., Frattolillo, F., Jorge, C. C., Ulfert, A., & Meyer-Vitali, A. (2025) · Interaction Studies Social Behaviour and Communication in Biological and Artificial Systems · doi:10.1075/is.24048.tie · Cited by 4 · see citing works
Advanced Technologies for Industrial Applications
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Khan, H. M., Arboleda, S. A., & Raake, A. (2025) · ACM Symposium on Virtual Reality Software and Technology (VRST) · doi:10.1145/3756884.3766007 · Cited by 2 · see citing works
Zafari, S., Papagni, G., & Puthenkalam, J. (2025) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3765766.3769422 · Cited by 2 · see citing works
Cognitive Readiness for Human-AI Collaboration
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To Apologize or Not to Apologize? Trust Repair After Automated Vehicles’ Mistakes
Cai, Y., Chu, Y., Xu, Z., & Liu, P. (2025) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981251355535 · Cited by 2 · see citing works
Modeling Trust Dynamics in Robot-Assisted Delivery: Impact of Trust Repair Strategies
Mangalindan, D. H., Kandikonda, K., Rovira, E. M., & Srivastava, V. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127509 · Cited by 2 · see citing works
Older Adults’ Perceptions of Robots with Differing Intelligence in Social Multi-Robot Interactions
Giorgi, I., Rajapakse, S., Palomino, M. A., & Masala, G. L. (2025) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3769854 · Cited by 1 · see citing works
Trust and emotions in domestic robots: do gender preferences favor machine-like designs?
Morales, E. M. S., Aguiar, F., & Herreros, F. (2025) · AI & Society · doi:10.1007/s00146-025-02753-5 · Cited by 1 · see citing works
Charting the Ecosystem of Trust in Cat Royale Or What It Takes to Trust a Robot to Play with Cats
Benford, S. A., Barnard, P., Sharples, S., Webb, H., Mancini, C., Küçükyılmaz, A., Castle-Green, S. D., Schneiders, E., Ngo, V. Z. H., Chamberlain, A., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-2398-6_42 · Cited by 1 · see citing works
Mal, C., & Davies, G. (2026) · Corporate Reputation Review · doi:10.1057/s41299-026-00255-8 · Cited by 1 · see citing works
Guilty apology and trust repair in generative artificial intelligence: the role of mind perception
Xu, F., Yuan, Z., Jin, S., Niu, G., & Zhou, Z. (2026) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2026.103813 · Cited by 2 · see citing works
Kalluri, R. (2026) · Central European Journal of Operations Research · doi:10.1007/s10100-026-01028-8 · Cited by 1 · see citing works
Interactive and Explainable Robot Learning: A Comprehensive Review
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Error recovery in wearable robotic Co-Grasping: the role of human-led correction
Chang, E. Y., Torres, W. O., & Stuart, H. S. (2025) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1598296 · Cited by 1 · see citing works
Explainability in AI: Comparing Human-Like and System-Like Trust Repair Strategies
Konopka, B., & Wiesche, M. (2026) · Information Systems Frontiers · doi:10.1007/s10796-026-10751-1 · Cited by 1 · see citing works
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From Failure to Trust Repair: Expert Insights on Factors Influencing Trust in Collaborative Robots
Glawe, F., Vervier, L., Brauner, P., & Ziefle, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93982-2_3
The Role of Social Robots in Autonomous Public Transport
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Thomaschewski, L., Tausch, A., Berretta, S., Vogel, O., & Kluge, A. (2025) · Zeitschrift für wirtschaftlichen Fabrikbetrieb · doi:10.1515/zwf-2025-1113
Trust between humans and robots: Do people perceive that robots trust them?
Tsumura, T., & Yamada, S. (2025) · IEEE RO-MAN · doi:10.1109/ro-man63969.2025.11217751
Ham, S., & Maeng, K. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2594144
When Robots Say No: Temporal Trust Recovery Through Explanation
Webb, N., Huang, Z., Milivojević, S., Baber, C., & Hunt, E. R. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-2379-5_29
Explainable Artificial Intelligence in Robotics: A Review of Applications and Methods
Alıcıoğlu, G., & Sun, B. (2025) · International Conference on Machine Learning and Applications · doi:10.1109/icmla66185.2025.00151
BoP-X: A New XAI Approach to Explain Reinforcement Learning for Autonomous Driving
Alıcıoğlu, G., & Sun, B. (2025) · International Conference on Machine Learning and Applications · doi:10.1109/icmla66185.2025.00076
The Impact of Visually Grounded Descriptions on Acceptance of Robot Obstacle-Handling Behaviors
Bak, S., & Cha, S. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798376
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Mitigating psychological discomfort during automation error: A mixed methods research
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An Integrated Framework for Human-Machine Trust Research in the Era of Large Language Models
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“I Know You’re Capable, but Are You Willing?”: Allocating Tasks in Human–Machine Teams
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Apology as interactional governance in human-AI dialogue: Evidence from bidirectional apologising
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Beyond AI Errors and Apologies: How Performance Expectations Shape Human–AI TrustPreprint
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The Impact of a Third-Party on Trust of a Human-Robot Dyad
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Repairing trust in AI: Attribution, anthropomorphism, and the illusion of difference
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Beyond Text: Social Robots in a Multi-stage Trust Repair Trial
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Explanation Transparency and Trust Perception After Generative AI Failure: A Dual-Path Model
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When Explanations Shape Trust: Challenges for Embodied AI Systems
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Trust Disruption from LLM Hallucinations in Embodied vs. Text-Based Agents
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- Authors
- Esterwood, C., & Jr, L. P. R.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Esterwood, Connor
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2023-05
- Volume
- 142
- Pages
- 107658
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W4317468071
- Cited by
- 98
- References
- 82
- Citations by year
- 2026: 33 · 2025: 37 · 2024: 16 · 2023: 12
- FWCI
- 29.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers in Human Behavior (publishedVersion, cc-by-nc-nd)
- Deep Blue (University of Michigan) (submittedVersion)
- University of Michigan Library
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- trust in robots, trust repair, breach of trust, apology, denial, explanation, promise, ability, benevolence, integrity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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