The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Using judgment to select and adjust forecasts from statistical models
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“Proposition 6d. The greater the ability of an algorithm to perform its intended task (purpose or process) in a manner consistent with user expectations (predictability) , the higher the probability of building trust in AI algorithms over time.”
From Why do users trust algorithms? A review and conceptualization of initial trust and trust over time (2022) · cites this paper as De Baets & Harvey, 2020
Citations
Cites
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810 · cited 2×
“This would have led people to place too much value on their initial judgment and to adjust too little towards the advice provided to them (Tversky & Kahneman, 1974).”
The case for motivated reasoning.
Kunda, Z. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.108.3.480 · Cited by 9,022
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
Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust
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
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018) · Management Science · doi:10.1287/mnsc.2016.2643 · Cited by 1,230
Bonaccio, S., & Dalal, R. S. (2006) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2006.07.001 · Cited by 1,011
Trust, Confidence, and Expertise in a Judge-Advisor System
Sniezek, J. A., & Van Swol, L. M. (2001) · Organizational Behavior and Human Decision Processes · doi:10.1006/obhd.2000.2926 · Cited by 469
Summary knowledge of results for skill acquisition: Support for the guidance hypothesis.
Schmidt, R. A., Young, D. E., Swinnen, S., & Shapiro, D. C. (1989) · Journal of Experimental Psychology: Learning, Memory, and Cognition · doi:10.1037/0278-7393.15.2.352 · Cited by 407
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 1×
“For example, humans exaggerate the errors made by automated machines, even when such errors occur only very rarely (Dzindolet, Pierce, Beck, & Dawe, 2002; Hoff & Bashir, 2015).”
BackgroundUnderstanding 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 · cited 1×
“Prahl and Van Swol (2017) work supports this: they found that people’s use of advice dropped significantly more after they received bad advice from an algorithm than bad advice from a human judge.”
Background
Research works printed but not linked: 50
- Balzer, 1989, The effects of cognitive feedback on performance, Psychological Bulletin, 106, 410, 10.1037/0033-2909.106.3.410 · DOI
- Bendoly, 2016, Fit, bias, and enacted sensemaking in data visualization: Frameworks for continuous development in operations and supply chain management analytics, Journal of Business Logistics, 37, 6, 10.1111/jbl.12113 · DOI
- Bendoly, 2010, Bodies of knowledge for research in behavioral operations, Production and Operations Management, 19, 434, 10.1111/j.1937-5956.2009.01108.x · DOI
- Björkman, 1972, Feedforward and feedback as determiners of knowledge and policy: Notes on a neglected issue, Scandinavian Journal of Psychology, 13, 152, 10.1111/j.1467-9450.1972.tb00063.x · DOI
- Bolger, 1993, Context-sensitive heuristics in statistical reasoning, Quarterly Journal of Experimental Psychology, Section A. Human Experimental Psychology, 46, 779, 10.1080/14640749308401039 · DOI
- Feng, B., & MacGeorge, E.L. (.2010). The influences of message and source factors on advice outcomes communication research, 37(4), 553–575. · DOI
- Fifić, 2014, Are two interviewers better than one, Journal of Business Research, 67, 1771, 10.1016/j.jbusres.2014.03.003 · DOI
- Fildes, 2007, Against your better judgment? How organizations can improve their use of management judgment in forecasting, Interfaces, 37, 570, 10.1287/inte.1070.0309 · DOI
- Fildes, 2009, Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning, International Journal of Forecasting, 25, 3, 10.1016/j.ijforecast.2008.11.010 · DOI
- Fildes, 2015, Improving forecast quality in practice, Foresight: The International Journal of Applied Forecasting, Winter, 5
- Fischer, 1999, Combining forecasts: What information do judges need to outperform the simple average?, International Journal of Forecasting, 15, 227, 10.1016/S0169-2070(98)00073-9 · DOI
- Gardner, 1995, The effect of different forms of advice on the control of a simulated complex system, Applied Cognitive Psychology, 9, S55, 10.1002/acp.2350090706 · DOI
- Gefen, 2003, Trust and TAM in online shopping: An integrated model, MIS Quarterly, 27, 51, 10.2307/30036519 · DOI · Paper page
- Goodwin, 1993, Improving judgmental time series forecasting: A review of the guidance provided by research, International Journal of Forecasting, 9, 147, 10.1016/0169-2070(93)90001-4 · DOI
- Grove, 2005, Clinical versus statistical prediction: The contribution of Paul E. Meehl, Journal of Clinical Psychology, 61, 1233, 10.1002/jclp.20179 · DOI
- Grove, 2000, Clinical versus mechanical prediction: A meta-analysis, Psychological Assessment, 12, 19, 10.1037/1040-3590.12.1.19 · DOI · Paper page
- Han, 2018, Brain imaging and forecasting: Insights from judgmental model selection, Omega, 87, 1
- Harries, 2000, Measuring doctors' self‐insight into their treatment decisions. applied cognitive psychology, The Official Journal of the Society for Applied Research in Memory and Cognition, 14, 455
- Harvey, 2019, Commentary: Algorithmic aversion and judgmental wisdom, Foresight: The International Journal of Applied Forecasting, 54, 13
- Harvey, 1996, Graphs versus tables: Effects of data presentation format on judgemental forecasting, International Journal of Forecasting, 12, 119, 10.1016/0169-2070(95)00634-6 · DOI
- Harvey, 1997, Taking advice: Accepting help, improving judgment, and sharing responsibility, Organizational Behavior & Human Decision Processes, 70, 117, 10.1006/obhd.1997.2697 · DOI
- Keeffe, 2005, Provider response to computer-based care suggestions for chronic heart failure, Medical Care, 43, 461, 10.1097/01.mlr.0000160378.53326.f3 · DOI
- Klayman, 1988, On the how and why (not) of learning from outcomes, Advances in psychology, 54, 115, 10.1016/S0166-4115(08)62172-X · DOI
- Lawrence, 1985, An examination of the accuracy of judgmental extrapolation of time series, International Journal of Forecasting, 1, 25, 10.1016/S0169-2070(85)80068-6 · DOI
- Lawrence, 2002, Influence of user participation on DSS use and decision accuracy, Omega: The International Journal of Management Science, 30, 381, 10.1016/S0305-0483(02)00048-8 · DOI
- Lawrence, 2006, Judgmental forecasting: A review of progress over the last 25years, International Journal of Forecasting, 22, 493, 10.1016/j.ijforecast.2006.03.007 · DOI
- Lawrence, 1989, Factors affecting judgmental forecasts and confidence intervals, Organizational Behavior & Human Decision Processes, 43, 172, 10.1016/0749-5978(89)90049-6 · DOI
- Leitner, 2011, Experiments on forecasting behavior with several sources of information: A review of the literature, European Journal of Operational Research, 213, 459, 10.1016/j.ejor.2011.01.006 · DOI
- Lim, 1995, Judgmental adjustment of initial forecasts: Its effectiveness and biases, Journal of Behavioral Decision Making, 8, 149, 10.1002/bdm.3960080302 · DOI
- Madhavan, 2007, Similarities and differences between human-human and human-automation trust: An integrative review, Theoretical Issues in Ergonomics Science, 8, 277, 10.1080/14639220500337708 · DOI · Paper page
- Makridakis, 2000, The M3-Competition: Results, conclusions and implications, International Journal of Forecasting, 16, 451, 10.1016/S0169-2070(00)00057-1 · DOI
- O'Connor, 1993, Judgemental forecasting in times of change, International Journal of Forecasting, 9, 163, 10.1016/0169-2070(93)90002-5 · DOI
- Önkal, 2009, The relative influence of advice from human experts and statistical methods on forecast adjustments, Journal of Behavioral Decision Making, 22, 390, 10.1002/bdm.637 · DOI
- Önkal, 2011, Influence of differentiated roles on group forecasting accuracy, International Journal of Forecasting, 27, 50, 10.1016/j.ijforecast.2010.03.001 · DOI
- Önkal, 1994, Evaluating probabilistic forecasts of stock prices in a developing stock market, European Journal of Operational Research, 74, 350, 10.1016/0377-2217(94)90102-3 · DOI
- Paravastu, 2014, Human trust in other humans, automation, robots, and cognitive agents: Neural correlates and design implications, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 58, 340, 10.1177/1541931214581070 · DOI
- Perera, 2019, The human factor in supply chain forecasting: A systematic review, European Journal of Operational Research, 274, 574, 10.1016/j.ejor.2018.10.028 · DOI
- Petropoulos, 2019, Judgmental model selection, Foresight: The International Journal of Applied Forecasting, 54, 4
- Petropoulos, 2018, Judgmental selection of forecasting models, Journal of Operations Management, 60, 34, 10.1016/j.jom.2018.05.005 · DOI
- Phillips, 1966, Conservatism in a simple probability inference task, Journal of Experimental Psychology: General, 72, 346, 10.1037/h0023653 · DOI
- Sanders, 1997, The impact of task properties feedback on time series judgmental forecasting tasks, Omega, International Journal of Management Science, 25, 135, 10.1016/S0305-0483(96)00057-6 · DOI
- Soll, 2009, Strategies for revising judgment: How (and how well) people use others’ opinions, Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 780
- Stanislaw, 1999, Calculation of signal detection theory measures, Behavioral Research Methods, Instruments and Computers, 31, 137, 10.3758/BF03207704 · DOI · Paper page
- Theocharis, 2019, The influence of graphical format on judgmental forecasting accuracy: Lines versus points, Futures & Foresight Science, 1, e7, 10.1002/ffo2.7 · DOI
- Weinstein, 1996, Unrealistic optimism: Present and future, Journal of Social and Clinical Psychology, 15, 1, 10.1521/jscp.1996.15.1.1 · DOI
- Weller, 2012, Supply chain forecasting: Best practices & benchmarking study, Lancaster Centre for Forecasting Technical report
- White, 2006, The meta-analysis of clinical judgment project: Fifty-six years of accumulated research on clinical versus statistical prediction, The Counseling Psychologist, 34, 341, 10.1177/0011000005285875 · DOI · Paper page
- Willemain, 1989, Graphical adjustment of statistical forecasts, International Journal of Forecasting, 5, 179, 10.1016/0169-2070(89)90085-X · DOI
- Yaniv, 2004, The benefit of additional opinions, Current Directions in Psychological Science, 13, 75, 10.1111/j.0963-7214.2004.00278.x · DOI
- Yates, 1991, Probabilistic forecasts of stock prices and earnings: The hazards of nascent expertise, Organizational Behavior & Human Decision Processes, 40, 60, 10.1016/0749-5978(91)90042-R · DOI
Cited by
Sorted by citations
Why do users trust algorithms? A review and conceptualization of initial trust and trust over time
Cabiddu, F., Moi, L., Patriotta, G., & Allen, D. G. (2022) · European Management Journal · doi:10.1016/j.emj.2022.06.001 · Cited by 138
Metadata
- Authors
- De Baets, S., & Harvey, N.
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 284
- Issue
- 3
- Pages
- 882-895
- ISSN
- 0377-2217
- Cited by
- 44
- References
- 10
- Language
- en
- Funders
- Fonds Wetenschappelijk Onderzoek
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Using judgment to select and adjust forecasts from statistical models. https://app.cochat.ai/papers/10.1016/j.ejor.2020.01.028
- The paper
- De Baets, S., & Harvey, N. (2020). Using judgment to select and adjust forecasts from statistical models. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2020.01.028