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Cited by 46Forecasting Techniques and ApplicationsHuman-Automation Interaction and SafetyDecision-Making and Behavioral Economics

Antecedents and effects of trust in forecasting advice

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Goodwin, Paul, Sinan Gönül, M., Önkal, Dilek·2013·International Journal of Forecasting
DOI 10.1016/j.ijforecast.2012.08.001
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Crossref · 10.1016/j.ijforecast.2012.08.001

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  • Trust was measured as CADx prediction utilization, defined as an endoscopist's willingness to follow the CADx prediction when the endoscopist initially disagreed with the CADx prediction.

    “Trust was measured as CADx prediction utilization, defined as an endoscopist's willingness to follow the CADx prediction when the endoscopist initially disagreed with the CADx prediction 9,10”

    From Appropriate trust in artificial intelligence for the optical diagnosis of colorectal polyps: the role of human/artificial intelligence interaction (2024) · cites this paper as [9]

    The Literature review (p. 3) explicitly distinguishes stated trust from subsequent behavior rather than defining trust as willingness to follow advice. The paper measured trust with a five-point rating (section 4.3.1, p. 5) and studied adjustments to forecasting advice, not CADx predictions or endoscopists. (LLM, conf=0.87)

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  • Appropriate trust implies minimizing the negative performance outcomes through either overtrust or undertrust.

    “Hence, appropriate trust implies minimizing the negative performance outcomes through either overtrust or undertrust.8,9”

    From Appropriate trust in artificial intelligence for the optical diagnosis of colorectal polyps: the role of human/artificial intelligence interaction (2024) · cites this paper as [9]

    The performance results (sections 4.3.3, p. 7, and 5.2.3, p. 10) show that insufficient trust in reliable forecasting advice led participants to adjust forecasts and worsen their performance. The literature review (p. 3) also notes a danger of too much trust in systems that may give erroneous advice, but the paper does not establish negative performance outcomes from overtrust. The claim's two-sided performance assertion is therefore broader than the evidence. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Cites

Flagged first, then by citations

Research works printed but not linked: 26

  1. Bell, M. Z. (1985). Why expert systems fail. Journal of the Operational Research Society , 36 , 613-619.
  2. Bolger, F., & Harvey, N. (1993). Context-sensitive heuristics in statistical reasoning. The Quarterly Journal of Experimental Psychology Section A , 46 , 779-811.
  3. Chaudhuri, K., & Wu, Y. (2003). Random walk versus breaking trend in stock prices: evidence from emerging markets. Journal of Banking and Finance , 27 , 575-592.
  4. Fildes, R., Goodwin, P., Lawrence, M., & Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting , 25 , 3-23.
  5. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory and research . Reading, MA: Addison-Wesley Publishing Company.
  6. Franses, P. H., & Legerstee, R. (2010). Do experts' adjustments on model-based SKU-level forecasts improve forecast quality? Journal of Forecasting , 29 , 331-340.
  7. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly , 27 , 51-90.
  8. Gönül, M. S., Önkal, D., & Goodwin, P. (2009). Expectations, use and judgmental adjustment of external financial and economic forecasts: an empirical investigation. Journal of Forecasting , 28 , 19-37.
  9. Goodwin, P. (2000). Improving the voluntary integration of statistical forecasts and judgment. International Journal of Forecasting , 16 , 85-99.
  10. Goodwin, P., Önkal, D., & Thomson, M. (2010). Do forecasts expressed as prediction intervals improve production planning decisions? European Journal of Operational Research , 205 , 195-201.
  11. Gulati, R. (1995). Does familiarity breed trust? The implications of repeated ties for contractual choice in alliances. Academy of Management Journal , 38 , 85-112.
  12. Johnson-George, C., & Swap, W. (1982). Measure of specific interpersonal trust: construction and validation of a scale to assess trust in a specific other. Journal of Personal and Social Psychology , 43 , 1306-1317.
  13. Lim, J. S., & O'Connor, M. (1995). Judgemental adjustment of initial forecasts: its effectiveness and biases. Journal of Behavioral Decision Making , 8 , 149-168.
  14. Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integration model of organizational trust. Academy of Management Review , 20 , 709-734.
  15. McKnight, H., & Kacmar, C. (2006). Factors of information credibility for an internet advice site. In Proceedings of the 39th Hawaii International Conference on System Sciences .
  16. O'Connor, M., Remus, W., & Griggs, K. (1997). Going up going down: how good are people at forecasting trends and changes in trends? Journal of Forecasting , 16 , 165-176.
  17. Önkal, D., Goodwin, P., Thomson, M., Gönül, M. S., & Pollock, A. (2009). The relative influence of advice from human experts and statistical methods on forecast adjustments. Journal of Behavioral Decision Making , 22 , 390-409.
  18. Önkal, D., Lawrence, M., & Sayım, K. Z. (2011). Influence of differentiated roles on group forecasting accuracy. International Journal of Forecasting , 27 , 50-68.
  19. Remus, W. (1986). Graduate students as surrogates for managers in experiments on business decision making. Journal of Business Research , 14 , 19-25.
  20. Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all: a cross-discipline view of trust. Academy of Management Review , 23 , 393-404.
  21. Siegrist, M., Gutscher, H., & Earle, T. C. (2005). Perception of risk: the influence of general trust, and general confidence. Journal of Risk Research , 8 , 145-156.
  22. Urban, G. L., Amyx, C., & Lorenzon, A. (2009). Online trust: state of the art, newfrontiers, and research potential. Journal of Interactive Marketing , 23 , 179-190.
  23. Wang, W., & Benbasat, I. (2007). Recommendation agents for electronic commerce: effects of explanation facilities on trusting beliefs. Journal of Management Information Systems , 23 , 217-246.
  24. Wang, W., & Benbasat, I. (2005). Trust and adoption of online recommendation agents. Journal of the Association for Information Systems , 6 , 72-101.
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  26. Ye, L. R., & Johnson, P. E. (1995). The impact of explanation facilities on user acceptance of expert systems. MIS Quarterly , 19 , 157-172.

Cited by

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Authors
Goodwin, P., Sinan Gönül, M., & Önkal, D.
Affiliations
  • University of Bath (GB)
  • Bilkent University (TR)
Corresponding author
Goodwin, Paul
ORCID
Venue
International Journal of Forecasting
Publisher
Elsevier BV
Published
2013-04
Volume
29
Issue
2
Pages
354-366
ISSN
0169-2070 (ISSN-L)
DOI
10.1016/j.ijforecast.2012.08.001
OpenAlex
W1991039099
Cited by
46
References
1
Citations by year
2026: 2 · 2025: 8 · 2024: 7 · 2023: 5 · 2022: 5 · 2021: 4 · 2020: 2 · 2019: 1 · 2018: 2 · 2017: 2 · 2016: 4 · 2015: 3 · 2014: 1
FWCI
1.58
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Forecasting (publishedVersion)
  • Pure (University of Bath) (publishedVersion, other-oa)
  • Bilkent University Institutional Repository (Bilkent University) (submittedVersion)
  • The University of Bath Online Publications Store (The University of Bath) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • OpenMETU (Middle East Technical University) (submittedVersion, cc-by-nc-nd)
  • Northumbria Research Link (Northumbria University) (submittedVersion)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
Keywords
forecasting support systems, prediction intervals, forecast calibration, point forecasts, user trust

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