The DOI matches its Crossref record; 16 of the 17 references it cites were found in a registry or an index.
Timing Is Key for Robot Trust Repair
Open accessAbstract
Even the best robots will eventually make a mistake while performing their tasks. In our past experiments, we have found that even one mistake can cause a large loss in trust by human users. In this paper, we evaluate the effects of a robot apologizing for its mistake, promising to do better in the future, and providing additional reasons to trust it in a simulated office evacuation conducted in a virtual environment. In tests with 319 participants, we find that each of these techniques can be successful at repairing trust if they are used when the robot asks the human to trust it again, but are not successful when used immediately after the mistake. The implications of these results are discussed. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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In the citing paper
“In an emergency setting, where an apology right after violated trust has not recovered trust, an apology right before the next trust decision point has repaired trust.”
From Taxonomy of Trust-Relevant Failures and Mitigation Strategies (2020) · cites this paper as [63]
In the citing paper
“Promises lead to a better trust recovery than apologies, and in general, the message timing and exact content was shown to be crucial [63].”
From Taxonomy of Trust-Relevant Failures and Mitigation Strategies (2020) · cites this paper as [63]
Citations
Cites
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605 · cited 1×
“Our approach to trust is guided by research from psychology [15], human factors [5], and neuroscience[4].”
MethodEvaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk
Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012) · Political Analysis · doi:10.1093/pan/mpr057 · Cited by 4,008
Running experiments on Amazon Mechanical Turk
Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010) · Judgment and Decision Making · doi:10.1017/s1930297500002205 · Cited by 3,767
Getting to Know You: Reputation and Trust in a Two-Person Economic Exchange
King-Casas, B., Tomlin, D., Anen, C., Camerer, C. F., Quartz, S. R., & Montague, P. R. (2005) · Science · doi:10.1126/science.1108062 · Cited by 1,345
Psychological foundations of incentives
Fehr, E., & Falk, A. (2002) · European Economic Review · doi:10.1016/s0014-2921(01)00208-2 · Cited by 1,240
Kim, P. H., Dirks, K. T., Cooper, C. D., & Ferrin, D. L. (2005) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2005.07.002 · Cited by 577
Promises and lies: Restoring violated trust
Schweitzer, M. E., Hershey, J. C., & Bradlow, E. T. (2006) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2006.05.005 · Cited by 568
Psychological Foundations of Trust
Simpson, J. A. (2007) · Current Directions in Psychological Science · doi:10.1111/j.1467-8721.2007.00517.x · Cited by 541
Effect of Robot Performance on Human–Robot Trust in Time-Critical Situations
Robinette, P., Howard, A. M., & Wagner, A. R. (2017) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2017.2648849 · Cited by 205
Self-citationThe role of trust and relationships in human-robot social interaction
Wagner, A. R. (2009) · Cited by 50
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Self-citationWhen Robots Say No: Temporal Trust Recovery Through Explanation
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Perkins, R., Berkovich, B., & Robinette, P. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-2382-5_57
Self-citationWho’s Ready for Robots? Robot Acceptance Through the Lens of Workforce DiversityPreprint
Wu, S., Takizawa, R., Walzer, A. N., & Hall, D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7170932
Metadata
- Authors
- Robinette, P., Howard, A. M., & Wagner, A. R.
- Affiliations
- Georgia Institute of Technology (US)
- Georgia Tech Research Institute (US)
- Corresponding author
- Robinette, Paul
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2015
- Pages
- 574-583
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2398251251
- Cited by
- 121
- References
- 17
- Citations by year
- 2026: 2 · 2025: 17 · 2024: 14 · 2023: 19 · 2022: 15 · 2021: 16 · 2020: 15 · 2019: 9 · 2018: 7 · 2017: 6 · 2016: 1
- FWCI
- 8.21
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion, cc-by-nc)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- trust in robots, trust repair, human-robot interaction, virtual worlds
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