The DOI matches its Crossref record; 42 of the 43 references it cites were found in a registry or an index.
Persuasiveness of expert systems
Open accessAbstract
Expert system advice is not always evaluated by examining its contents. Users can be persuaded by expert system advice because they have certain beliefs about advice given by a computer. The experiment in this paper shows that subjects (n = 84) thought that, given the same argumentation, expert systems are more objective and rational than human advisers. Furthermore, subjects thought a problem was easier when advice on it was said to be given by an expert system while the advice was shown in production rule style. Such beliefs can influence expert system use.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“Reported behaviors include both a skepticism or distrust of algorithmic advice (algorithmic aversion)12–14 and more willingness to adhere to algorithmic advice over human advice (algorithmic appreciation)15,16.”
From Do as AI say: susceptibility in deployment of clinical decision-aids (2021) · cites this paper as [16]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
User Acceptance of Computer Technology: A Comparison of Two Theoretical Models
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989) · Management Science · doi:10.1287/mnsc.35.8.982 · Cited by 25,709
The Elaboration Likelihood Model of Persuasion
Petty, R. E., & Cacioppo, J. T. (1986) · Advances in Experimental Social Psychology · doi:10.1016/s0065-2601(08)60214-2 · Cited by 8,822
Cacioppo, J. T., & Petty, R. E. (1982) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.42.1.116 · Cited by 5,359
Communication and Persuasion: Central and Peripheral Routes to Attitude Change
Marquart, F., & Naderer, B. (2015) · Springer eBooks · doi:10.1007/978-3-658-09923-7_20 · Cited by 5,011
Davis, F. D. (1993) · International Journal of Man-Machine Studies · doi:10.1006/imms.1993.1022 · Cited by 4,754
The Efficient Assessment of Need for Cognition
Cacioppo, J. T., Petty, R. E., & Feng Kao, C. (1984) · Journal of Personality Assessment · doi:10.1207/s15327752jpa4803_13 · Cited by 2,913
The measurement of user information satisfaction
Ives, B., Olson, M. H., & Baroudi, J. J. (1983) · Communications of the ACM · doi:10.1145/358413.358430 · Cited by 1,860
Persuasion theory and research
O’Keefe, D. J. (1990) · Cited by 1,555
Explaining the Role of User Participation in Information System Use
Hartwick, J., & Barki, H. (1994) · Management Science · doi:10.1287/mnsc.40.4.440 · Cited by 1,540
Research works printed but not linked: 6
- [7]7. BODEN, M. 1990, Artificial intelligence and images of man, in R. Ennals and J. Gardin (eds), Interpretation in the Humanities: Perspectives from Artificial Intelligence (Cam- bridge: Cambridge University Press), 10-21.
- [17]17. DIJKSTRA, J. J. 1995, The influence of an expert system on the user's view: How to fool a lawyer. New Review of Applied Expert Systems, 1, 123 -138.
- [22]22. HOLLNAGEL, E. 1989, The reliability of expert systems-an inquiry of the background, in E. Hollnagel (ed.), The Reliability of Expert Systems (Sussex: Ellis Horwood), 14 - 32.
- [41]41. ROBEY, D. 1979, User attitude and management information system use. Academy of Management Journal, 22, 527-538.
- [50]50. WOGNUM, P. M. 1990, Explanation of Automated Reasoning: How and Why?, Ph.D. dissertation, University of Twente.
- [51]51. WORCHEL, S., COOPER, J. and GOETHALS, G. R. 1988, Under- standing Social Psychology (Chicago: Dorsey Press).
Cited by
Sorted by citations
Algorithm appreciation: People prefer algorithmic to human judgment
Logg, J. M., Minson, J. A., & Moore, D. A. (2019) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2018.12.005 · Cited by 2,061
The role of trust in automation reliance
Dzindolet, M. T., Peterson, S. A., Pomranky, R. A., Pierce, L. G., & Beck, H. P. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00038-7 · Cited by 1,276
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
(2002) · Cited by 770
Manipulating and Measuring Model Interpretability
Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Wortman Vaughan, J. W., & Wallach, H. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445315 · Cited by 728
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
Similarities and differences between human–human and human–automation trust: an integrative review
Madhavan, P., & Wiegmann, D. A. (2007) · Theoretical Issues in Ergonomics Science · doi:10.1080/14639220500337708 · Cited by 577
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
Do as AI say: susceptibility in deployment of clinical decision-aids
Gaube, S., Suresh, H., Raue, M., Merritt, A., Berkowitz, S. J., Lermer, E., Coughlin, J. F., Guttag, J. V., Colak, E., & Ghassemi, M. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00385-9 · Cited by 543
In bot we trust: A new methodology of chatbot performance measures
Przegalińska, A., Ciechanowski, L., Stróż, A., Gloor, P. A., & Mazurek, G. (2019) · Business Horizons · doi:10.1016/j.bushor.2019.08.005 · Cited by 355
Credibility: A multidisciplinary framework
Rieh, S. Y., & Danielson, D. R. (2007) · Annual Review of Information Science and Technology · doi:10.1002/aris.2007.1440410114 · Cited by 336
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
My Friends, Editors, Algorithms, and I
Thurman, N., Möller, J. E., Helberger, N., & Trilling, D. (2018) · Digital Journalism · doi:10.1080/21670811.2018.1493936 · Cited by 332
Watch Me Improve—Algorithm Aversion and Demonstrating the Ability to Learn
Berger, B., Adam, M., Rühr, A., & Benlian, A. (2020) · Business & Information Systems Engineering · doi:10.1007/s12599-020-00678-5 · Cited by 200
Towards Deeper Understanding of Persuasion in Software and Information Systems
B, H. O.-K. A., & Harjumaa, M. (2008) · ACHI ... the ... International Conference on Advances in Computer-Human Interactions · doi:10.1109/achi.2008.31 · Cited by 179
Keding, C., & Meissner, P. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120970 · Cited by 168
How to overcome algorithm aversion: Learning from mistakes
Reich, T., Kaju, A., & Maglio, S. J. (2022) · Journal of Consumer Psychology · doi:10.1002/jcpy.1313 · Cited by 156
Human–Human Reliance in the Context of Automation
Lyons, J. B., & Stokes, C. K. (2012) · Hum. Factors · doi:10.1177/0018720811427034 · Cited by 158
Inkpen, K., Chappidi, S., Mallari, K., Nushi, B., Ramesh, D., Michelucci, P., Mandava, V., Vepřek, L. H., & Quinn, G. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3534561 · Cited by 132
The 3 C's of anthropomorphism: Connection, comprehension, and competition
Yang, L. W., Aggarwal, P., & McGill, A. L. (2019) · Consumer Psychology Review · doi:10.1002/arcp.1054 · Cited by 141
Helberger, N., Araujo, T., & de Vreese, C. H. (2020) · Computer law & security review · doi:10.1016/j.clsr.2020.105456 · Cited by 142
Chugunova, M., & Sele, D. (2022) · Journal of Behavioral and Experimental Economics · doi:10.1016/j.socec.2022.101897 · Cited by 118
Hou, Y. T.-Y., & Jung, M. F. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479864 · Cited by 135
The World is not Enough: Trust in Cognitive Agents
de Visser, E. J., Krueger, F. W., McKnight, P. E., Scheid, S., Smith, M. A. B., Chalk, S., & Parasuraman, R. (2012) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181312561062 · Cited by 107
Kim, T., & Song, H. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2021.101595 · Cited by 101
Ischen, C., Araujo, T., van Noort, G., Voorveld, H. A. M., & Smit, E. G. (2020) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2020.1834297 · Cited by 115
AI in the hands of imperfect users
Kostick, K. M., & Gerke, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00737-z · Cited by 117
Bai, B., Dai, H., Zhang, D., Zhang, F., & Hu, H. (2020) · Manufacturing & Service Operations Management · doi:10.1287/msom.2022.1120 · Cited by 103
Anthropomorphizing information to enhance trust in autonomous vehicles
Niu, D., Terken, J., & Eggen, B. (2018) · Human Factors and Ergonomics in Manufacturing & Service Industries · doi:10.1002/hfm.20745 · Cited by 90
A Parsimonious Language Model of Social Media Credibility Across Disparate Events
Mitra, T., Wright, G. P., & Gilbert, É. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998351 · Cited by 80
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
Investigating Differences in Crowdsourced News Credibility Assessment
Bhuiyan, M. M., Zhang, A. X., Sehat, C. M., & Mitra, T. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415164 · Cited by 75
Job candidates’ reactions to AI-enabled job application processes
van Esch, P., Black, J. S., & Arli, D. (2021) · AI and Ethics · doi:10.1007/s43681-020-00025-0 · Cited by 72
Motivating, Influencing, and Persuading Users
Fogg, B. J., Danielson, D. R., & Cuellar, G. (2007) · Human factors and ergonomics · doi:10.1201/9781410615862.ch7 · Cited by 71
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Balagopalan, A., Zhang, H., Hamidieh, K., Hartvigsen, T., Rudzicz, F., & Ghassemi, M. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533179 · Cited by 98
Kulms, P., & Kopp, S. (2019) · Proceedings of Mensch und Computer 2019 · doi:10.1145/3340764.3340793 · Cited by 68
Lacroux, A., & Martin-Lacroux, C. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.895997 · Cited by 67
Believing in Analytics: Managers’ Adherence to Price Recommendations from a DSS
Caro, F., & Cuenca, A. S. D. T. (2023) · Manufacturing & Service Operations Management · doi:10.1287/msom.2022.1166 · Cited by 66
Wang, G., Guo, Y., Zhang, W., Xie, S., & Chen, Q. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2023.101803 · Cited by 75
Trust criteria for artificial intelligence in health: normative and epistemic considerations
Kostick, K. M., Lang, B. H., Smith, J. N., Hurley, M. E., & Blumenthal‐Barby, J. (2023) · Journal of Medical Ethics · doi:10.1136/jme-2023-109338 · Cited by 70
Yang, C., & Hu, J. (2021) · Journal of Brand Management · doi:10.1057/s41262-021-00261-7 · Cited by 67
Under Pressure: Examining Social Conformity With Computer and Robot Groups
Hertz, N., & Wiese, E. (2018) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720818788473 · Cited by 53
Effect of Human-AI Interaction on Detection of Malignant Lung Nodules on Chest Radiographs
Lee, J. H., Hong, H., Nam, G., Hwang, E. J., & Park, C. M. (2023) · Radiology · doi:10.1148/radiol.222976 · Cited by 53
Wojcieszak, M., Thakur, A., Gonçalves, J. F. F., Casas, A., Menchen-Trevino, E., & Boon, M. (2021) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmab006 · Cited by 60
Impact of Algorithmic Decision Making on Human Behavior: Evidence from Ultimatum Bargaining
Erlei, A., Nekdem, F., Meub, L., Anand, A., & Gadiraju, U. (2020) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v8i1.7462 · Cited by 52
Yoo, K., & Ulrike Gretzel, B. (2010) · Springer eBooks · doi:10.1007/978-0-387-85820-3_14 · Cited by 49
Tan, S.-M., & Liew, T. W. (2022) · Human Behavior and Emerging Technologies · doi:10.1155/2022/2501538 · Cited by 54
Algorithm aversion in delegated investing
Germann, M., & Merkle, C. (2022) · Journal of Business Economics · doi:10.1007/s11573-022-01121-9 · Cited by 57
Expert Systems for Forecasting
Collopy, F., Adya, M. P., & Armstrong, J. S. (2001) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-0-306-47630-3_14 · Cited by 43
Challenging presumed technological superiority when working with (artificial) colleagues
Rieger, T., Roesler, E., & Manzey, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-07808-x · Cited by 45
Using generative AI as decision-support tools: unraveling users’ trust and AI appreciation
Huynh, M.-T. (2024) · Journal of Decision System · doi:10.1080/12460125.2024.2428166 · Cited by 53
Behavior change support systems: the next frontier for web science
B, H. O.-K. A. (2010) · Cited by 40
Rafaeli, S., Dan-Gur, Y., & Barak, M. (2005) · International Journal of Distance Education Technologies · doi:10.4018/jdet.2005040103 · Cited by 39
Justice, trust, and moral judgements when personnel selection is supported by algorithms
Feldkamp, T., Langer, M., Wies, L., & König, C. J. (2023) · European Journal of Work and Organizational Psychology · doi:10.1080/1359432x.2023.2169140 · Cited by 48
Seeger, A.-M., & Heinzl, A. (2017) · Lecture notes in information systems and organisation · doi:10.1007/978-3-319-67431-5_15 · Cited by 35
Psychologically targeted persuasive advertising and product information in e-commerce
Saari, T., Ravaja, N., Laarni, J., Turpeinen, M., & Kallinen, K. (2004) · International Conference on Electronic Commerce · doi:10.1145/1052220.1052252 · Cited by 31
We and It: An Interdisciplinary Review of the Experimental Evidence on Human-Machine InteractionPreprint
Chugunova, M., & Sele, D. (2020) · SSRN Electronic Journal · doi:10.3929/ethz-b-000442053 · Cited by 30
Task-specific algorithm advice acceptance: A review and directions for future research
Kaufmann, E., Chacon, A., Kausel, E. E., Herrera, N., & Reyes, T. H. (2023) · Data and Information Management · doi:10.1016/j.dim.2023.100040 · Cited by 35
The (Im)perfect Automation Schema: Who Is Trusted More, Automated or Human Decision Support?
Rieger, T., Kugler, L., Manzey, D., & Roesler, E. (2023) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208231197347 · Cited by 30
Kim, T., & Song, H. (2022) · International Journal of Social Robotics · doi:10.1007/s12369-022-00951-5 · Cited by 30
Saragih, M., & Morrison, B. W. (2021) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2021.1990518 · Cited by 25
Mandl, S., Bretschneider, M., Meyer, S., Gesmann-Nuissl, D., Asbrock, F., Meyer, B. M., & Strobel, A. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.787970 · Cited by 25
Fenneman, A., Sickmann, J., Pitz, T., & Sanfey, A. G. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247084 · Cited by 31
FACTORS THAT AFFECT TRUST AND RELIANCE ON AN AUTOMATED AID
Sánchez, J. (2006) · UPT. Syiah Kuala University Library (Syiah Kuala University) · Cited by 24
Trusted Autonomy Under Uncertainty
Smithson, M. J. (2018) · Studies in systems, decision and control · doi:10.1007/978-3-319-64816-3_10 · Cited by 23
Wheatcroft, J. M., Jump, M., Breckell, A. L., & Adams-White, J. E. (2017) · Cogent Psychology · doi:10.1080/23311908.2017.1327628 · Cited by 22
The Effect of Advice Valence on the Perceived Credibility of Data Analytics
Chen, C. X., Hudgins, R., & Wright, W. F. (2021) · Journal of Management Accounting Research · doi:10.2308/jmar-2020-015 · Cited by 21
Human preferences toward algorithmic advice in a word association task
Bogert, E., Lauharatanahirun, N., & Schecter, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-18638-2 · Cited by 26
Lyons, H., Miller, T., & Velloso, E. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594041 · Cited by 26
Persuasion: An Analysis and Common Frame of Reference for IS Research
Slattery, P., Vidgen, R. T., & Finnegan, P. (2020) · Communications of the Association for Information Systems · doi:10.17705/1cais.04603 · Cited by 19
Claure, H., Kim, S., Kizilcec, R. F., & Jung, M. F. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107628 · Cited by 25
The nature, dimensionality, and effects of perceptions of community governance
Kayhan, V. O. (2014) · Information & Management · doi:10.1016/j.im.2014.10.004 · Cited by 19
Perceptions of AI engaging in human expression
Bower, A. H., & Mark Steyvers, B. (2021) · Scientific Reports · doi:10.1038/s41598-021-00426-z · Cited by 20
Mixing It Up: How Mixed Groups of Humans and Machines Modulate Conformity
Hertz, N., Shaw, T. H., de Visser, E., & Wiese, E. (2019) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343419869465 · Cited by 15
Calibrating workers’ trust in intelligent automated systems
Lucas, G., Becerik-Gerber, B., & Roll, S. C. (2024) · Patterns · doi:10.1016/j.patter.2024.101045 · Cited by 28
Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets
Normann, H., & Sternberg, M. (2022) · European Economic Review · doi:10.1016/j.euroecorev.2022.104347 · Cited by 17
Adaptive Automation: Building Flexibility into Human-Machine Systems☆
Dzindolet, M. T., Beck, H. P., & PIERCE, L. (2005) · Advances in human performance and cognitive engineering research · doi:10.1016/s1479-3601(05)06007-8 · Cited by 15
My Health Advisor is a Robot: Understanding Intentions to Adopt a Robotic Health Advisor
Kim, J., Merrill, K., Xu, K., & Collins, C. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2239559 · Cited by 17
Trust in self-driving vehicles is lower than in human drivers when both drive almost perfectly
Yokoi, R. (2024) · Transportation Research Part F: Traffic Psychology and Behaviour · doi:10.1016/j.trf.2024.03.019 · Cited by 10
Dong, X., Zhou, R., & Liao, J. (2024) · European Journal of Marketing · doi:10.1108/ejm-04-2023-0278 · Cited by 9
Rainey, C., Villikudathil, A. T., McConnell, J., Hughes, C., Bond, R., & McFadden, S. L. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000229 · Cited by 13
How does Value Similarity affect Human Reliance in AI-Assisted Ethical Decision Making?
Narayanan, S., Yu, G., Ho, C.-J., & Yin, M. (2023) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3600211.3604709 · Cited by 13
Lim, G., & Perrault, S. T. (2023) · Mobile communication in Asia · doi:10.1007/978-94-024-2225-2_11 · Cited by 13
AI or human: How the type of information to be disclosed alters customer service agent preferences
Dai, X., Zhang, L., Huang, Z., & Wang, L. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104621 · Cited by 12
Gain-loss separability in human- but not computer-based changes of mind
Lin, Y., Xu, P., Fan, J., Gu, R., & Luo, Y. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107712 · Cited by 15
User Perceptions of News Recommender Systems and Trust in Media Outlets: A Five-Country Study
Blassnig, S., Mitova, E., Strikovic, E., Urman, A., de Vreese, C. H., Hannák, A., & Esser, F. (2024) · Journalism Studies · doi:10.1080/1461670x.2024.2364628 · Cited by 26
Acharya, K. R. (2022) · Technical Communication: Journal of the Society for Technical Communication · doi:10.55177/tc885177 · Cited by 10
Ruled by robots: preference for algorithmic decision makers and perceptions of their choices
Chugunova, M., & Luhan, W. J. (2024) · Public Choice · doi:10.1007/s11127-024-01178-w · Cited by 12
Improving Human Deception Detection Using Algorithmic Feedback
Serra-García, M., & Uri Gneezy, B. (2025) · Management Science · doi:10.1287/mnsc.2023.02792 · Cited by 11
An expert system for the quantification of fault rates in construction fall accidents
Birgönül, M. T., Dikmen, İ., Budayan, C., & Demirel, T. (2015) · International Journal of Occupational Safety and Ergonomics · doi:10.1080/10803548.2015.1123516 · Cited by 10
Manipulation and the Ai Act: Large Language Model Chatbots and the Danger of MirrorsPreprint
Krook, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4719835 · Cited by 10
Talebi, A., Mukherjee, S., Gera, N., Kaur, K., & Das, G. (2025) · Journal of Product Innovation Management · doi:10.1111/jpim.12774 · Cited by 10
Beyond Algorithm Aversion in Human-Machine Decision-Making
Burton, J. W., Stein, M., & Jensen, T. B. (2023) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-031-30085-1_1 · Cited by 11
Emotional expressions facilitate human–human trust when using automation in high-risk situations
Feng, J., Sanchez, J., Sall, R. J., Lyons, J. B., & Nam, C. S. (2019) · Military Psychology · doi:10.1080/08995605.2019.1630227 · Cited by 9
Big Data and Trust in Public Policy Automation
Waggoner, P., Kennedy, R., Le, H., & Shiran, M. (2019) · Statistics Politics and Policy · doi:10.1515/spp-2019-0005 · Cited by 10
Steinke, F., Ingenhoff, A., & Fritsch, T. (2014) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2014.903789 · Cited by 9
Five principles for the intelligent use of AI in medical imaging
Colak, E., Moreland, R., & Ghassemi, M. (2021) · Intensive Care Medicine · doi:10.1007/s00134-020-06316-8 · Cited by 9
Human-Algorithm Interaction: Algorithmic Pricing in Hybrid Laboratory MarketsPreprint
Normann, H., & Sternberg, M. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4087304 · Cited by 9
Chatbots in education: Outperforming students but perceived as less trustworthy
Laun, M., Puderbach, L., Hirt, K., Wyss, E., Friemert, D., Hartmann, U., & Wolff, F. (2025) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2025.102373 · Cited by 11
Algorithms in selection decisions: Effective, but unappreciated
Rabinovitch, H., Budescu, D. V., & Bereby‐Meyer, Y. (2024) · Journal of Behavioral Decision Making · doi:10.1002/bdm.2368 · Cited by 10
Metadata
- Authors
- Dijkstra, J. J., Liebrand, W. B. G., & Timminga, E.
- Affiliations
- University of Groningen (NL)
- Venue
- Behaviour & Information Technology
- Publisher
- Informa UK Limited
- Published
- 1998-01
- Volume
- 17
- Issue
- 3
- Pages
- 155-163
- ISSN
- 0144-929X (ISSN-L)
- 1362-3001
- OpenAlex
- W1984763500
- Cited by
- 161
- References
- 43
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 16 · 2023: 19 · 2022: 21 · 2021: 18 · 2020: 11 · 2019: 8 · 2018: 7 · 2017: 4 · 2016: 2 · 2015: 2 · 2014: 2 · 2013: 1 · 2012: 2
- FWCI
- 2.08
- Type
- journal article
- Language
- en
- Open access
- green
- research.rug.nl
- Licence
- other-oa
- Locations
- Behaviour and Information Technology (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Topics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Social Sciences › Psychology › Social Psychology › Psychology of Social Influence
- Keywords
- expert systems, persuasiveness, user beliefs, perceived objectivity, production rules
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review