The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Effects of Information Source, Pedigree, and Reliability on Operator Interaction With Decision Support Systems
Abstract
Objective: Two experiments are described that examined operators' perceptions of decision aids. Background: Research has suggested certain biases against automation that influence human interaction with automation. We differentiated preconceived biases from post hoc biases and examined their effects on advice acceptance. Method: In Study 1 we examined operators' trust in and perceived reliability of humans versus automation of varying pedigree (expert vs. novice), based on written descriptions of these advisers prior to operators' interacting with these advisers. In Study 2 we examined participants' post hoc trust in, perceived reliability of, and dependence on these advisers after their objective experience of advisers' reliability (90% vs. 70%) in a luggage-screening task. Results: In Study 1 measures of perceived reliability indicated that automation was perceived as more reliable than humans across pedigrees. Measures of trust indicated that automated “novices” were trusted more than human “novices”; human “experts” were trusted more than automated “experts.” In Study 2, perceived reliability varied as a function of pedigree, whereas subjective trust was always higher for automation than for humans. Advice acceptance from novice automation was always higher than from novice humans. However, when advisers were 70% reliable, errors generated by expert automation led to a drop in compliance/ reliance on expert automation relative to expert humans. Conclusion: Preconceived expectations of automation influence the use of these aids in actual tasks. Application: The results provide a reference point for deriving indices of “optimal” user interaction with decision aids and for developing frameworks of trust in decision support systems.
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“Research has suggested that the principles of trust in interpersonal relations cannot be directly applicable to human-to-machine trust, as technology lacks volition and moral agency (e.g., Madhavan and Wiegmann 2007;McKnight et al. 2011).”
From Artificial Intelligence in Crisis Communication: Investigating Source Effects and Matched Crisis Responses on Stakeholder Perceptions (2026) · cites this paper as Madhavan and Wiegmann 2007
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I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290 · cited 1×
“The earliest attempt at distinguishing operator trust in and use of different types of DSS goes back to the research of Turing (1950), who found that, when the source of a piece of diagnostic information was masked, human judges were largely unable to distinguish between a human adviser and a…”
BackgroundTrust 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×
“Lee and See (2004) pointed out that the primary reason trust in humans differs from trust in machines is that the latter lacks “intentionality” or traits such as loyalty, benevolence, and values that are critical to the development of trust in a human partner.”
BackgroundHumans and automation: Use, misuse, disuse, abuse
Parasuraman, R., & Riley, V. (1997) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872097778543886 · Cited by 4,554
Machines and Mindlessness: Social Responses to Computers
Nass, C., & Moon, Y. (2000) · Journal of Social Issues · doi:10.1111/0022-4537.00153 · Cited by 4,022 · cited 1×
“Much of the data supporting these assumptions has come from the Computers Are Social Actors (CASA) studies (Nass & Moon, 2000; Nass, Moon, Fogg, Reeves, & Dryer, 1995), which have demonstrated that social rules guiding human-human interaction may apply equally to human-computer interaction, with…”
BackgroundRempel, J. K., Holmes, J. G., & Zanna, M. P. (1985) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.49.1.95 · Cited by 3,146
Foundations for an Empirically Determined Scale of Trust in Automated Systems
Jian, J.-Y., Bisantz, A. M., & Drury, C. G. (2000) · International Journal of Cognitive Ergonomics · doi:10.1207/s15327566ijce0401_04 · Cited by 1,799
MUIR, B. M., & MORAY, N. (1996) · Ergonomics · doi:10.1080/00140139608964474 · Cited by 993
Ten challenges for making automation a ‘team player’ in joint human-agent activity
Klein, G., Woods, D. D., Bradshaw, J. M., Hoffman, R. R., & Feltovich, P. J. (2004) · IEEE Intelligent Systems · doi:10.1109/mis.2004.74 · Cited by 590
The Perceived Utility of Human and Automated Aids in a Visual Detection Task
Dzindolet, M. T., Pierce, L. G., Beck, H. P., & Dawe, L. A. (2002) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/0018720024494856 · Cited by 386 · cited 3×
“…(e.g., Dzindolet, Pierce, Beck, & Dawe, 2002; Dzindolet et al., 2001; Lerch et al., 1997) and the effects of framing on automation trust (e.g., Dzindolet et al., 2002; Lacson, Wiegmann, & Madhavan, 2003), we hypothesized trust in automation to be higher than in humans when portrayed as…”
BackgroundInfluentialAutomation failures on tasks easily performed by operators undermine trust in automated aids
Madhavan, P., Wiegmann, D. A., & Lacson, F. C. (2006) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872006777724408 · Cited by 268
Self-citation
Research works printed but not linked: 19
- 10.1518/001872098779649265 · DOI
- Bowers, C.A., Oser, R.A., Salas, E. & Cannon-Bowers, J.A. (1996). Team performance in automated systems. In R. Parasuraman & M. Mouloua (Eds.), Automation and human performance: Theory and applications (pp. 243-263). Mahwah, NJ: Erlbaum.
- 10.1080/014492999118832 · DOI
- 10.1207/S15327876MP1303_2 · DOI
- Klein, G., 2004, IEEE Intelligent Systems, 4, 1541
- Lacson, F.C., Paper presented at the 111th Annual Meeting of the American Psychological Association
- 10.1006/ijhc.1994.1007 · DOI · Paper page
- 10.1518/hfes.46.1.50.30392 · doi:10.1518/hfes.46.1.50.30392 · Paper page
- Lerch, F.J., Prietula, M.J. & Kulik, C.T. (1997). The Turing effect: The nature of trust in expert system advice. In P. J. Feltovich & K. M. Ford (Eds.), Expertise in context: Human and machine (pp. 417-448). Cambridge, MA : MIT Press.
- 10.1037/1076-898X.6.2.104 · DOI
- 10.1518/0018720054679489 · DOI
- 10.1080/14639220500337708 · DOI · Paper page
- 10.1518/hfes.46.2.196.37335 · DOI
- 10.1006/ijhc.1996.0073 · DOI · Paper page
- 10.1006/ijhc.1995.1042 · DOI · Paper page
- Reeves, B., 1996, The media equation: How people treat computers, television, and new media like real people and places
- 10.1037/e577392012-059 · doi:10.1037/e577392012-059
- Sheridan, T.B., 2002, Humans and automation: System design and research issues
- 10.1177/009365000027006001 · DOI · Paper page
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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
Lee, M. K. (2018) · Big Data & Society · doi:10.1177/2053951718756684 · Cited by 1,338
Automation bias: a systematic review of frequency, effect mediators, and mitigators
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000089 · Cited by 1,162
In AI we trust? Perceptions about automated decision-making by artificial intelligence
Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020) · AI & SOCIETY · doi:10.1007/s00146-019-00931-w · Cited by 1,038
Trust in AI and Its Role in the Acceptance of AI Technologies
Choung, H., David, P., & Ross, A. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2050543 · Cited by 963
“Other studies have noted that human users have a distinctive view on interacting with technologies, and those works have suggested that the principles of trust in interpersonal relations cannot be directly applicable to human-to-machine trust (e.g., Madhavan & Wiegmann, 2007).”
BackgroundSchaefer, K. E., Chen, J. Y. C., Szalma, J. L., & Hancock, P. A. (2016) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720816634228 · Cited by 768
Almost human: Anthropomorphism increases trust resilience in cognitive agents.
de Visser, E. J., Monfort, S. S., McKendrick, R., Smith, M. A. B., McKnight, P. E., Krueger, F., & Parasuraman, R. (2016) · Journal of Experimental Psychology: Applied · doi:10.1037/xap0000092 · Cited by 614
What influences algorithmic decision-making? A systematic literature review on algorithm aversion
Mahmud, H., Islam, A. K. M. N., Ahmed, S. I., & Smolander, K. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121390 · Cited by 601
Metrics for explainable AI: challenges and prospects
Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2018) · arXiv · doi:10.48550/arxiv.1812.04608 · Cited by 518
Trust and reliance on AI — An experimental study on the extent and costs of overreliance on AI
Klingbeil, A., Grützner, C., & Schreck, P. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108352 · Cited by 450
Explanations in Autonomous Driving: A Survey
Omeiza, D., Webb, H., Jirotka, M., & Kunze, L. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3122865 · Cited by 355
Understanding algorithm aversion: When is advice from automation discounted?
Prahl, A., & Van Swol, L. (2017) · Journal of Forecasting · doi:10.1002/for.2464 · Cited by 308
Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1096257 · Cited by 272
Affective Processes in Human–Automation Interactions
Merritt, S. M. (2011) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720811411912 · Cited by 260
Zhang (2021) · doi:10.1145/3432945 · Cited by 225
Evaluating Effects of User Experience and System Transparency on Trust in Automation
Yang, X. J., Unhelkar, V. V., Li, K., & Shah, J. A. (2017) · Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/2909824.3020230 · Cited by 208
Keding, C., & Meissner, P. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120970 · Cited by 168
Learning From the Slips of Others: Neural Correlates of Trust in Automated Agents
de Visser, E. J., Beatty, P. J., Estepp, J. R., Kohn, S., Abubshait, A., Fedota, J. R., & McDonald, C. G. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00309 · Cited by 73
The Next Generation of Medical Decision Support: A Roadmap Toward Transparent Expert Companions
Bruckert, S., Finzel, B., & Schmid, U. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.507973 · Cited by 66
Do Humans Trust Advice More if it Comes from AI?: An Analysis of Human-AI Interactions
Vodrahalli, K., Daneshjou, R., Gerstenberg, T., & Zou, J. (2022) · Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3514094.3534150 · Cited by 63
Effects of trust, self-confidence, and feedback on the use of decision automation
Wiczorek, R., & Meyer, J. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00519 · Cited by 46
Understanding the Impact of Time Pressure and Automation Support in a Visual Search Task
Rieger, T., & Manzey, D. (2024) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208221111236 · Cited by 40
Bobko, P., Hirshfield, L., Eloy, L., Spencer, C., Doherty, E., Driscoll, J., & Obolsky, H. (2023) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2022.2086644 · Cited by 33
The Perception of Automation Reliability and Acceptance of Automated Advice
Hutchinson, J., Strickland, L., Farrell, S., & Loft, S. (2023) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208211062985 · Cited by 32
Continuous Calibration of Trust in Automated Systems
Merritt, S. M., Huber, K., LaChapell-Unnerstall, J., & Lee, D. (2014) · doi:10.21236/ada606748 · Cited by 20
Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions
Vodrahalli, K., Daneshjou, R., Gerstenberg, T., & Zou, J. (2021) · arXiv · doi:10.48550/arxiv.2107.07015 · Cited by 16
Freel, A., Pias, S. B. H., Šabanović, S., & Kapadia, A. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732187 · Cited by 11
Lu, Y., & Sarter, N. (2019) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181319631345 · Cited by 5
Schneider, E., Kyriakopoulos, V., Seo, Y., Hong, N., Lee, M., & Lim, J. S. (2026) · Corporate Reputation Review · doi:10.1057/s41299-026-00254-9 · Cited by 4
Metadata
- Authors
- Madhavan, P., & Wiegmann, D. A.
- Venue
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Publisher
- SAGE Publications
- Published
- 2007-10
- Volume
- 49
- Issue
- 5
- Pages
- 773-785
- ISSN
- 0018-7208
- 1547-8181
- Cited by
- 157
- References
- 10
- Language
- en
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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