What People Want From Their Professionals: Attitudes Toward Decision‐making Strategies
Abstract
Attitudes toward four types of decision‐making strategies—clinical/fully rational, clinical/heuristic, actuarial/fully rational, and actuarial/heuristic—were examined across three studies. In Study 1, undergraduate students were split randomly between legal and medical decision‐making scenarios and asked to rate each strategy in terms of the following: (i) preference; (ii) accuracy; (iii) fairness; (iv) ethicalness; and (v) its perceived similarity to the strategies used by actual legal and medical professionals to make decisions. Studies 2 and 3 extended Study 1 by using a more relevant scenario and a community sample, respectively. Across the three studies, the clinical/fully rational strategy tended to be rated the highest across all attitudinal judgments, whereas the actuarial/heuristic strategy tended to receive the lowest ratings. Considering the two strategy‐differentiating factors separately, clinically based strategies tended to be rated higher than actuarially based strategies, and fully rational strategies were always rated higher than heuristic‐based strategies. The potential implications of the results for professionals' and those affected by their decisions are discussed. Copyright © 2011 John Wiley & Sons, Ltd.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Patients view medical AI as performing more poorly than comparable human providers.
“as performing more poorly than comparable human providers 12”
From Understanding, explaining, and utilizing medical artificial intelligence (2021) · cites this paper as [12]
Patients are reluctant to use medical AI.
“patients are reluctant to utilize medical AI [10][11][12] .”
From Understanding, explaining, and utilizing medical artificial intelligence (2021) · cites this paper as [12]
Cites
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810 · see citing works
A BEHAVIORAL MODEL OF RATIONAL CHOICE
Simon, H. A. (1955) · The Quarterly Journal of Economics · doi:10.2307/1884852 · Cited by 15,877 · see citing works
Judgment under Uncertainty: Heuristics and Biases
(1982) · doi:10.1017/cbo9780511809477 · Cited by 8,240 · see citing works
On the psychology of prediction.
Kahneman, D., & Tversky, A. (1973) · Psychological Review · doi:10.1037/h0034747 · Cited by 6,202 · cited 1× · see citing works
“…rational versus heuristic debate can arguably be traced back to studies showing peoples’ inability to meet the standards of fully rational decision making,1 the hallmark of optimal decision making (e.g., Edwards & Fasolo, 2001; Kahneman & Tversky, 1973; Miljkovic, 2005; Phillips & Edwards, 1966).”
BackgroundThe theory of decision making.
Edwards, W. (1954) · Psychological Bulletin · doi:10.1037/h0053870 · Cited by 2,710 · see citing works
“Clinical versus Actuarial Judgment.”
Dawes, R. M., Faust, D., & Meehl, P. E. (1989) · Science · doi:10.1126/science.2648573 · Cited by 2,298 · see citing works
(2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511808098 · Cited by 2,214 · see citing works
Clinical versus statistical prediction: A theoretical analysis and a review of the evidence.
Meehl, P. E. (1954) · University of Minnesota Press eBooks · doi:10.1037/11281-000 · Cited by 2,187 · see citing works
Clinical versus mechanical prediction: a meta-analysis
Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. (2000) · Psychological Assessment · doi:10.1037/1040-3590.12.1.19 · Cited by 1,640 · see citing works
Conservatism in a simple probability inference task.
Phillips, L. D., & Edwards, W. (1966) · Journal of Experimental Psychology · doi:10.1037/h0023653 · Cited by 878 · see citing works
Measurement and prediction, clinical and statistical.
Sawyer, J. (1966) · Psychological Bulletin · doi:10.1037/h0023624 · Cited by 594 · see citing works
Why we still use our heads instead of formulas: Toward an integrative approach.
Kleinmuntz, B. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.107.3.296 · Cited by 437 · see citing works
Causes and effects of my disturbing little book
Meehl, P. E. (1986) · Journal of Personality Assessment · doi:10.1207/s15327752jpa5003_6 · Cited by 410 · see citing works
Bounding rationality to the world
Todd, P. M., & Gigerenzer, G. (2003) · Journal of Economic Psychology · doi:10.1016/s0167-4870(02)00200-3 · Cited by 353 · see citing works
Diagnosticians vs. diagnostic signs: The diagnosis of psychosis vs. neurosis from the MMPI.
Goldberg, L. R. (1965) · The Psychological Monographs · doi:10.1037/h0093885 · Cited by 342 · see citing works
The relative influence of advice from human experts and statistical methods on forecast adjustments
Önkal, D., Goodwin, P., Thomson, M. E., Gonul, M. S., & Pollock, A. C. (2009) · Journal of Behavioral Decision Making · doi:10.1002/bdm.637 · Cited by 331 · see citing works
Psychological Models of Professional Decision Making
Dhami, M. K. (2003) · Psychological Science · doi:10.1111/1467-9280.01438 · Cited by 251 · see citing works
Promberger, M., & Baron, J. (2006) · Journal of Behavioral Decision Making · doi:10.1002/bdm.542 · Cited by 242 · see citing works
Edwards, W., & Fasolo, B. (2001) · Annual Review of Psychology · doi:10.1146/annurev.psych.52.1.581 · Cited by 123 · see citing works
Fast and frugal versus regression models of human judgement
Dhami, M. K., & Harries, C. (2001) · Thinking & Reasoning · doi:10.1080/13546780042000019 · Cited by 103 · see citing works
Patients Derogate Physicians Who Use a Computer-Assisted Diagnostic Aid
Arkes, H. R., Shaffer, V. A., & Medow, M. A. (2007) · Medical Decision Making · doi:10.1177/0272989x06297391 · Cited by 103 · see citing works
Redding, R. E., Floyd, M. Y., & Hawk, G. L. (2001) · Behavioral Sciences & the Law · doi:10.1002/bsl.455 · Cited by 73 · see citing works
Applying the acute ischemic heart disease predictive instrument.
Corey, G. A., & Merenstein, J. H. (1987) · PubMed · Cited by 49 · see citing works
Miljković, D. (2005) · The Journal of Socio-Economics · doi:10.1016/j.socec.2003.12.031 · Cited by 46 · see citing works
Clinical versus actuarial prediction: a review of the literature.
Marchese, M. C. (1992) · PubMed · Cited by 43 · see citing works
The statistical viewpoint in vocational selection.
Freyd, M. (1925) · Journal of Applied Psychology · doi:10.1037/h0074663 · Cited by 43 · see citing works
The clinical viewpoint in vocational selection.
Viteles, M. S. (1925) · Journal of Applied Psychology · doi:10.1037/h0071305 · Cited by 40 · see citing works
Mathematics and the Moral Sciences: The Rise And Fall of the Probability of Judgments, 1785 – 1840
Daston, L. (1981) · Springer eBooks · doi:10.1007/978-94-009-8414-1_17 · Cited by 29 · see citing works
Fast and Frugal Models of Clinical Judgment in Novice and Expert Physicians
Kee, F., Jenkins, J. G., McIlwaine, S., Patterson, C. C., Harper, S., & Shields, M. (2003) · Medical Decision Making · doi:10.1177/0272989x03256004 · Cited by 26 · see citing works
Medow, M. A., Arkes, H. R., & Shaffer, V. A. (2010) · Journal of General Internal Medicine · doi:10.1007/s11606-010-1251-y · Cited by 12 · see citing works
Modelling Police Officers’ Judgements of the Veracity of Suicide Notes
Snook, B., & Mercer, J. (2010) · Canadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale · doi:10.3138/cjccj.52.1.79 · Cited by 7 · see citing works
Self-citationPhysicians' Diagnoses Compared with Aiaorithmic Differentiation of Causes of Jaundice
Boom, R. A., Chavez-Oest, J., González, C. N., Cantú, M. A. Q., Rivero, F., Reyes, A. S., Aguilar, E., & Santamaria, J. (1988) · Medical Decision Making · doi:10.1177/0272989x8800800305 · Cited by 7 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Algorithm aversion: People erroneously avoid algorithms after seeing them err.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015) · Journal of Experimental Psychology: General · doi:10.1037/xge0000033 · Cited by 3,232 · see citing works
Resistance to Medical Artificial Intelligence
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019) · Journal of Consumer Research · doi:10.1093/jcr/ucz013 · Cited by 2,015 · see citing works
Task-Dependent Algorithm Aversion
Castelo, N., Bos, M. W., & Lehmann, D. R. (2019) · Springer eBooks · doi:10.1007/978-3-031-88067-4_21 · Cited by 1,596 · see citing works
Forecasting: theory and practice
Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Taieb, S. B., Bergmeir, C., Bessa, R., Bijak, J., Boylan, J., et al. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.11.001 · Cited by 630 · see citing works
A systematic review of algorithm aversion in augmented decision making
Burton, J. W., Stein, M., & Jensen, T. B. (2020) · Journal of Behavioral Decision Making · doi:10.1002/bdm.2155 · Cited by 814 · see citing works
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 · see citing works
Understanding, explaining, and utilizing medical artificial intelligence
Cadario, R., Longoni, C., & Morewedge, C. K. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01146-0 · Cited by 342 · see citing works
Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions
Luo, X., Qin, M. S., Fang, Z., & Qu, Z. (2020) · Journal of Marketing · doi:10.1177/0022242920956676 · Cited by 338 · see citing works
Human-machine collaboration in managerial decision making
Haesevoets, T., De Cremer, D., Dierckx, K., & Van Hiel, A. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106730 · Cited by 280 · see citing works
Man versus machine: Complex estimates and auditor reliance on artificial intelligence
COMMERFORD, B. P., DENNIS, S. A., JOE, J. R., & ULLA, J. W. (2022) · Journal of Accounting Research · doi:10.1111/1475-679x.12407 · Cited by 270 · see citing works
Thumbs Up or Down: Consumer Reactions to Decisions by Algorithms Versus Humans
Williams, G. Y., Lim, S., Puntoni, S., & van Osselaer, S. M. J. (2021) · Journal of Marketing Research · doi:10.1177/00222437211070016 · Cited by 196 · see citing works
Behavioural evidence for a transparency–efficiency tradeoff in human–machine cooperation
Ishowo-Oloko, F., Bonnefon, J.-F., Soroye, Z., Crandall, J., Rahwan, I., & Rahwan, T. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0113-5 · Cited by 178 · see citing works
Sohn, K., Sung, E., Koo, G., & Kwon, O. (2020) · International Journal of Retail & Distribution Management · doi:10.1108/ijrdm-03-2020-0091 · Cited by 141 · see citing works
An Integrative Perspective on Algorithm Aversion and Appreciation in Decision-Making
Jussupow, E., Benbasat, I., & Heinzl, A. (2024) · MIS Quarterly · doi:10.25300/misq/2024/18512 · Cited by 131 · see citing works
Banker, S., & Khetani, S. (2019) · Journal of Public Policy & Marketing · doi:10.1177/0743915619858057 · Cited by 120 · see citing works
Artificial intelligence or human: when and why consumers prefer AI recommendations
Jin, F., & Zhang, X. (2023) · Information Technology and People · doi:10.1108/itp-01-2023-0022 · Cited by 89 · see citing works
Mahmud, H., Islam, A. K. M. N., & Mitra, R. K. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122641 · Cited by 98 · see citing works
A survey of human judgement and quantitative forecasting methods
Zellner, M., Abbas, A. E., Budescu, D., & Galstyan, A. (2021) · Royal Society Open Science · doi:10.1098/rsos.201187 · Cited by 63 · see citing works
Consumer reactions to professionals who use decision aids
Palmeira, M. M., & Spassova, G. (2015) · European Journal of Marketing · doi:10.1108/ejm-07-2013-0390 · Cited by 43 · see citing works
Marocco, S., Barbieri, B., & Talamo, A. (2024) · AI · doi:10.3390/ai5040123 · Cited by 50 · see citing works
Zehnle, M., Hildebrand, C., & Valenzuela, A. (2025) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2025.02.005 · Cited by 45 · see citing works
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 41 · see citing works
Yu, M. C., & Kuncel, N. R. (2020) · Personnel Assessment and Decisions · doi:10.25035/pad.2020.02.002 · Cited by 37 · see citing works
Zhou, Y., Shi, Y., Lu, W., & Wan, F. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.866124 · Cited by 35 · see citing works
The AI‐augmented crowd: How human crowdvoters adopt AI (or not)
Freisinger, E., Unfried, M., & Schneider, S. (2023) · Journal of Product Innovation Management · doi:10.1111/jpim.12708 · Cited by 30 · see citing works
Downen, T., Kim, S., & Lee, L. S. (2023) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2023.100664 · Cited by 30 · see citing works
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 28 · see citing works
Nolan, K., Carter, N., & Dalal, D. (2016) · Personnel Assessment and Decisions · doi:10.25035/pad.2016.004 · Cited by 28 · see citing works
A Tutorial on Mechanical Decision-Making for Personnel and Educational Selection
Meijer, R. R., Neumann, M., Hemker, B. T., & Niessen, A. S. M. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.03002 · Cited by 28 · see citing works
CONTROL ISSUES: HOW PROVIDING INPUT AFFECTS AUDITORS' RELIANCE ON ARTIFICIAL INTELLIGENCE
Commerford, B. P., Eilifsen, A., Hatfield, R. C., Holmstrom, K. M., & Kinserdal, F. (2024) · Contemporary Accounting Research · doi:10.1111/1911-3846.12974 · Cited by 34 · see citing works
Le, H., Shen, Y., Li, Z., Xia, M., Tang, L., Li, X., Jia, J., Wang, Q., Gašević, D., & Fan, Y. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13614 · Cited by 38 · see citing works
A longitudinal approach for understanding algorithm use
Chacon, A., Kausel, E. E., & Reyes, T. H. (2022) · Journal of Behavioral Decision Making · doi:10.1002/bdm.2275 · Cited by 28 · see citing works
Saragih, M., & Morrison, B. W. (2021) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2021.1990518 · Cited by 25 · see citing works
Other-serving bias in advice-taking: When advisors receive more credit than blame
Palmeira, M. M., Spassova, G., & Keh, H. T. (2015) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2015.06.001 · Cited by 26 · see citing works
Rebitschek, F. G., Gigerenzer, G., & Wagner, G. G. (2021) · Scientific Reports · doi:10.1038/s41598-021-99802-y · Cited by 25 · see citing works
Acceptance of Medical Treatment Regimens Provided by AI vs. Human
Wu, J., Xu, L., Yu, F., & Peng, K. (2021) · Applied Sciences · doi:10.3390/app12010110 · Cited by 27 · see citing works
Cai, Z., He, H., Huo, W., & Xu, X. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2291616 · Cited by 26 · see citing works
A Classification of Decision Automation and Delegation in Digital Investment Management Systems
Rühr, A., Streich, D. J., Berger, B., & Heß, T. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.174 · Cited by 25 · see citing works
Yue, Z., Tuk, M. A., & Klesse, A.-K. (2024) · Journal of the Association for Consumer Research · doi:10.1086/730710 · Cited by 22 · see citing works
Design and implementation of an intelligent tutoring system in the view of learner autonomy
Le, H., & Jia, J. (2022) · Interactive Technology and Smart Education · doi:10.1108/itse-12-2021-0210 · Cited by 21 · see citing works
Consumers' promotion focus mitigates the negative effects of chatbots on purchase likelihood
Khan, H., Sararueangpong, P., Mathmann, F., & Wang, D. (2023) · Journal of Consumer Behaviour · doi:10.1002/cb.2289 · Cited by 21 · see citing works
Assessing AI receptivity through a persuasion knowledge lens
Watson, J., Valsesia, F., & Segal, S. (2024) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2024.101834 · Cited by 21 · see citing works
Preventing algorithm aversion: People are willing to use algorithms with a learning label
Chacon, A., Kausel, E. E., Reyes, T. H., & Trautmann, S. T. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.115032 · Cited by 21 · see citing works
The effect of medical artificial intelligence innovation locus on consumer adoption of new products
Huang, X., Wu, X., Cao, X., & Wu, J. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122902 · Cited by 20 · see citing works
The autonomy‐validity dilemma in mechanical prediction procedures: The quest for a compromise
Neumann, M., Niessen, A. S. M., Tendeiro, J. N., & Meijer, R. R. (2021) · Journal of Behavioral Decision Making · doi:10.1002/bdm.2270 · Cited by 15 · see citing works
Moriuchi, E., Berbary, C. M., & Easton, C. J. (2023) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-022-00298-8 · Cited by 14 · see citing works
Improving Human Sequential Decision Making with Reinforcement Learning
Bastani, H., Bastani, O., & Sinchaisri, W. P. (2025) · Management Science · doi:10.1287/mnsc.2022.02455 · Cited by 15 · see citing works
Wang, H., Sun, Q., Gu, L. C., Lai, K., & He, L. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.1006173 · Cited by 14 · see citing works
On the decision-making framework for policing: A proposal for improving police decision-making
Halford, E. (2024) · International journal of law, crime and justice · doi:10.1016/j.ijlcj.2024.100702 · Cited by 11 · see citing works
Do IT students prefer doctors who use IT?
Wolf, J. R. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.03.020 · Cited by 11 · see citing works
Jain, R. M., Garg, N., & Khera, S. N. (2025) · Journal of Organizational Change Management · doi:10.1108/jocm-09-2023-0397 · Cited by 10 · see citing works
Till Tech Do Us Part: Betrayal Aversion and Its Role in Algorithm Use
Kormylo, C., Adjerid, I., Ball, S. B., & Dogan, C. (2025) · Management Science · doi:10.1287/mnsc.2022.03510 · Cited by 10 · see citing works
Nurek, M., & Kostopoulou, O. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad019 · Cited by 10 · see citing works
Testing the value of expert insight: Comparing local versus general expert judgment models
Yu, M. C., & Kuncel, N. R. (2021) · International Journal of Selection and Assessment · doi:10.1111/ijsa.12356 · Cited by 10 · see citing works
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 9 · see citing works
Overcome Medical Algorithm Aversion: Conditional Joint Effect of Direct and Indirect Information
Zhao, Y., & Xiao, C. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2344146 · Cited by 10 · see citing works
Aidoo-Anderson, A., Polychronakis, Y. E., Sapountzis, S., & Kelly, S. (2025) · International Journal of Production Research · doi:10.1080/00207543.2025.2508335 · Cited by 8 · see citing works
Enhancing the Integrity of the Sentencing Process through the Use of Artificial Intelligence
Bagaric, M., & Hunter, D. (2022) · doi:10.1093/oso/9780197539538.003.0007 · Cited by 8 · see citing works
Mahadeen, T., Galanakis, K., Samara, E. T., & Kilintzis, P. (2020) · Journal of the Knowledge Economy · doi:10.1007/s13132-020-00688-4 · Cited by 8 · see citing works
Bouwer, A. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-9268-0_4 · Cited by 8 · see citing works
Content Analysis of Judges' Sentiments Toward Artificial Intelligence Risk Assessment Tools
Fine, A., Le, S., & Miller, M. K. (2023) · Criminology Criminal Justice Law & Society · doi:10.54555/ccjls.8169.84869 · Cited by 7 · see citing works
Li, Z., & Shi, Z. (2026) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2025.2584725 · Cited by 9 · see citing works
Comparing discriminatory behavior against AI and humans
Zhuang, M., Deschrijver, E., Ramsey, R., & Turel, O. (2025) · Scientific Reports · doi:10.1038/s41598-025-94631-9 · Cited by 5 · see citing works
Predicting the future with humans and AI
Mellers, B. A., Lu, L., & McCoy, J. P. (2022) · Consumer Psychology Review · doi:10.1002/arcp.1089 · Cited by 5 · see citing works
Luther, K., Snook, B., MacDonald, S., & Barron, T. (2014) · Journal of Police and Criminal Psychology · doi:10.1007/s11896-014-9147-0 · Cited by 5 · see citing works
Self-citationTransforming technology-mediated health-care services through strategic sense-giving
Daskalopoulou, A., Jefferies, J. G., & Skandalis, A. (2020) · Journal of Services Marketing · doi:10.1108/jsm-11-2019-0452 · Cited by 5 · see citing works
Brown, V. L., Dennis, S. A., Dickins, D. E., Higgs, J. L., & Schaefer, T. J. (2019) · Current Issues in Auditing · doi:10.2308/ciia-52493 · Cited by 5 · see citing works
Innate Reasoning and Critical Incident Decision-Making
Bryant, R. P. (2019) · Palgrave Macmillan UK eBooks · doi:10.1057/978-1-349-95847-4_4 · Cited by 4 · see citing works
Content Analysis of Judges’ Sentiments Toward Artificial Intelligence Risk Assessment Tools
Fine, A., Le, S., & Miller, M. K. (2024) · Актуальные проблемы экономики и права · doi:10.21202/2782-2923.2024.1.246-263 · Cited by 4 · see citing works
Das, P., & Das, S. (2024) · Vision The Journal of Business Perspective · doi:10.1177/09722629241291731 · Cited by 5 · see citing works
Using conventional framing to offset bias against algorithmic errors
Tariq, H., Fugelsang, J. A., & Koehler, D. J. (2025) · Judgment and Decision Making · doi:10.1017/jdm.2025.8 · Cited by 3 · see citing works
Investigating lay evaluations of models
Kane, P. B., & Broomell, S. B. (2021) · Thinking & Reasoning · doi:10.1080/13546783.2021.1999327 · Cited by 3 · see citing works
Decision tree modeling in R software to aid clinical decision making
Toth, E. G., Gibbs, D., Moczygemba, J., & McLeod, A. J., Jr. (2021) · Health and Technology · doi:10.1007/s12553-021-00542-w · Cited by 3 · see citing works
Kipp, P. C., Olvera, R. M., Robertson, J. C., & Vinson, J. M. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3775740 · Cited by 3 · see citing works
Fu, L., Dai, Y. H., & Jia, W. (2025) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2025.2605123 · Cited by 2 · see citing works
Toward Behavioral AI: Cognitive Factors Underlying the Public Psychology of Artificial Intelligence
Mukherjee, S., Senapati, D., & Mahajan, I. (2023) · Springer eBooks · doi:10.1007/978-981-99-3966-4_1 · Cited by 3 · see citing works
Sinclair, S. (2023) · Scandinavian Journal of Work and Organizational Psychology · doi:10.16993/sjwop.183 · Cited by 3 · see citing works
Brown, V. L., Dickins, D. E., Hermanson, D. R., Higgs, J. L., Jenkins, J. G., Nolder, C. J., Schaefer, T. J., & Smith, K. W. (2019) · Current Issues in Auditing · doi:10.2308/ciia-52603 · Cited by 2 · see citing works
Bonner, B. L., Meikle, N. L., Bain, K., & Shannahan, D. B. (2018) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190630188.013.24 · Cited by 2 · see citing works
Yu, M. C. (2018) · University of Minnesota Digital Conservancy (University of Minnesota) · Cited by 2 · see citing works
Kim, K.-E. (2023) · Behavioral Sciences · doi:10.3390/bs13110938 · Cited by 1 · see citing works
Ochmann, J., Zilker, S., Michels, L., Tiefenbeck, V., Matzner, M., & Laumer, S. (2025) · ACM SIGMIS Database the DATABASE for Advances in Information Systems · doi:10.1145/3715966.3715970 · Cited by 1 · see citing works
Le, H., Jia, J., & Chen, W. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2660851 · Cited by 1 · see citing works
A Model “Critical Decision-Making Model”, Case-Study of Ghana, West Africa
Norman, I. D. (2023) · Advances in Applied Sociology · doi:10.4236/aasoci.2023.134020 · Cited by 1 · see citing works
Goto, T., Kano, K., & Shiose, T. (2023) · Frontiers in Education · doi:10.3389/feduc.2023.1107341 · Cited by 4 · see citing works
Understanding and Utilizing Medical Artificial IntelligencePreprint
Cadario, R., Longoni, C., & Morewedge, C. K. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3675363 · Cited by 1 · see citing works
The Effects of the Use of Artificial Intelligence and Task Complexity on Auditor LiabilityPreprint
Cui, J., & Robertson, J. C. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4038554 · Cited by 1 · see citing works
The Autonomy-validity Dilemma in Mechanical Prediction Procedures: The Quest for a CompromisePreprint
Neumann, M., Niessen, A. S. M., Tendeiro, J. N., & Meijer, R. R. (2021) · doi:10.31234/osf.io/6cnxd
Research on the impact of corporate governance on enterprise bankruptcy risk
Zhang</p>, <. (2023) · Academic Journal of Business & Management · doi:10.25236/ajbm.2023.051202
Downen, T., Kim, S., & Lee, L. S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5363415
Perceived AI Use and Hiring Penalties: Experimental EvidencePreprint
Wang, S. Q., & Qiu, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5675322
Behzad, T., Gurney, N., Wang, N., & Pynadath, D. V. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13187-4_8
Wang, L., Wang, Y., Li, X., Zhang, J., & Zhao, Y. (2026) · Information Technology and People · doi:10.1108/itp-10-2024-1272
Fallgruver og lærdommer når revisorer bruker KI og dataanalyser
Li, X., & Kinserdal, F. (2026) · Praktisk økonomi & ledelse · doi:10.18261/pol.42.2.6
Are farmers algorithm-averse? The case of decision support tools in crop management
Massfeller, A., Hermann, D., Leyens, A., & Storm, H. (2026) · Food Policy · doi:10.1016/j.foodpol.2026.103193
- Authors
- Eastwood, J., Snook, B., & Luther, K.
- Affiliations
- Memorial University of Newfoundland (CA)
- Corresponding author
- Snook, Brent
- Venue
- Journal of Behavioral Decision Making
- Publisher
- Wiley
- Published
- 2011-06-15
- Volume
- 25
- Issue
- 5
- Pages
- 458-468
- ISSN
- 0894-3257 (ISSN-L)
- 1099-0771
- OpenAlex
- W2150479185
- Cited by
- 97
- References
- 32
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 10 · 2023: 20 · 2022: 9 · 2021: 15 · 2020: 8 · 2019: 9 · 2018: 2 · 2016: 1 · 2015: 2 · 2014: 3
- FWCI
- 0.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Behavioral Decision Making (publishedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Social Sciences › Decision Sciences › Management Science and Operations Research › Leadership, Behavior, and Decision-Making Studies
- Keywords
- decision strategies, clinical decision-making, heuristic decision making, legal decision-making, perceived fairness, perceived accuracy
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
