The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Evaluating Effects of User Experience and System Transparency on Trust in Automation
Open accessAbstract
Existing research assessing human operators' trust in automation and robots has primarily examined trust as a steady-state variable, with little emphasis on the evolution of trust over time. With the goal of addressing this research gap, we present a study exploring the dynamic nature of trust. We defined trust of entirety as a measure that accounts for trust across a human's entire interactive experience with automation, and first identified alternatives to quantify it using real-time measurements of trust. Second, we provided a novel model that attempts to explain how trust of entirety evolves as a user interacts repeatedly with automation. Lastly, we investigated the effects of automation transparency on momentary changes of trust. Our results indicated that trust of entirety is better quantified by the average measure of "area under the trust curve" than the traditional post-experiment trust measure. In addition, we found that trust of entirety evolves and eventually stabilizes as an operator repeatedly interacts with a technology. Finally, we observed that a higher level of automation transparency may mitigate the "cry wolf" effect -- wherein human operators begin to reject an automated system due to repeated false alarms.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In addition, some studies also employed linear trust meters [137], or visual analog scales [139].”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [139]
In the citing paper
“Some authors work with correlations to get a better sense of perceived trustworthiness and system capabilities [5,12,24,39,66,67,81,94,130,139,142].Here the larger the correlation between perceived trustworthiness and system capabilities, the better the calibration.”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [139]
In the citing paper
“With few exceptions, we have little understanding of how trust strengthens or decays due to moment-to-moment interactions with automation”
From Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation (2023) · cites this paper as Yang et al., 2017
In the citing paper
“trust is a dynamic variable that can change over time”
From Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation (2023) · cites this paper as Yang et al., 2017
In the citing paper
“Critically, the opaqueness of the AI's internal computational processes may decrease calibrated reliance on the system [63 , 64] .”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [64]
In the citing paper
“Such measures include, but are not limited to measuring end-users' decision time [19,62], how many times end-users requested AI recommendation [52], and how many times they agreed with AI recommendations [7,23,58].”
From Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust (2023) · cites this paper as [58]
In the citing paper
“Most of the studies investigated how to maintain appropriate trust calibration by continuously presenting system information to prevent over-trust or under-trust.”
From Adaptive trust calibration for human-AI collaboration (2020) · cites this paper as [13]
In the citing paper
“Several studies [13, 23, 26] have demonstrated that reliance behavior can be explained by the relationship between a user’s trust in the agents and the user’s self-confidence.”
From Adaptive trust calibration for human-AI collaboration (2020) · cites this paper as [13]
Citations
Cites
Trust in Automation: Designing for Appropriate Reliance
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Hoff, K. A., & Bashir, M. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814547570 · Cited by 2,817
A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction
Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y. C., de Visser, E. J., & Parasuraman, R. (2011) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720811417254 · Cited by 2,051 · cited 2×
“Research has indicated that the calibration between operators’ trust and an automated/robotic technology’s actual ability is often imperfect [9, 10].”
BackgroundMemory: A contribution to experimental psychology.
Ebbinghaus, H. (1913) · doi:10.1037/10011-000 · Cited by 1,645
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Trust, self-confidence, and operators' adaptation to automation
Lee, J. D., & Moray, N. (1994) · International Journal of Human-Computer Studies · doi:10.1006/ijhc.1994.1007 · Cited by 932
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Cited by
Sorted by citations
Items 201–209 of 209
The Role of Transparency in Takeover Information for Trust Calibration in Automated Driving Systems
Liu, X., Wan, L., & Ge, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29459-3_2
Behavior Ranking Systems in Online Multiplayer Video Games: An Analysis & Design Space
Vrettis, P., Pfau, J., & Frommel, J. (2026) · International Conference on the Foundations of Digital Games (FDG) · doi:10.1145/3815598.3815647
Qin, Y., Prugsanapan, P., Leary, S., Shih, K. Y., Kong, Z., Anderson, A. P., & Clark, T. K. (2026) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2026.1810115
Chen, X., Liu, Y., Ma, Y., Zhang, X., Chen, Y., Wang, Z., Zhang, W., Tian, Y., Gao, Z., & Shen, M. (2026) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208261477486
Modeling Faculty Adoption and Effectiveness of AI-Based Educational Systems in Engineering Education
Belmonte, Z. J. A., Tibo, H., Prasetyo, Y. T., Esberto, E. M., & Cruz, T. D. D. (2025) · International Conference on Digital Technology in Education (ICDTE) · doi:10.1145/3796159.3796190
Kim, J., & Yang, X. J. (2026) · Human Factors · doi:10.1177/00187208261491447
Self-citationKim, J. Y., Lester, C. A., & Yang, X. J. (2026) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813261498756
Self-citationWang, Y., Zhu, X., Hao, X., & Wang, X. (2026) · Journal of Hospitality and Tourism Insights · doi:10.1108/jhti-02-2026-0221
Metadata
- Authors
- Yang, X. J., Unhelkar, V. V., Li, K., & Shah, J. A.
- Affiliations
- University of Michigan (US)
- Massachusetts Institute of Technology (US)
- Venue
- Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction
- Publisher
- ACM
- Published
- 2017-03-06
- Pages
- 408-416
- OpenAlex
- W2594678573
- Cited by
- 208
- References
- 36
- Citations by year
- 2026: 13 · 2025: 26 · 2024: 41 · 2023: 29 · 2022: 22 · 2021: 25 · 2020: 23 · 2019: 18 · 2018: 10 · 2017: 1
- FWCI
- 23.05
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- ACM/IEEE International Conference on Human-Robot Interaction (HRI) (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- Singapore University of Technology and Design
- Topics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- trust in automation, automation transparency, user experience, trust dynamics, false alarms, human-automation interaction, trust measurement
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