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Forecasting another's enjoyment versus giving the right answer: Trust, shared values, task effects, and confidence in improving the acceptance of advice

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  • Van Swol, Lyn M.
Abstract
In two experiments, participants received advice from another participant on a task either with a correct answer (intellective tasks) or without a correct answer (judgmental task), in which the participant had to make a forecast. In both experiments, the level of trust in the advisor and a perception of the advisor having similar values were important predictors of the acceptance of advice for a judgmental, taste forecast task, whereas advisor confidence was a more important predictor of the acceptance of advice on the intellective task. In Experiment 2, the face-to-face interactions between the decision-maker and the advisor were videotaped and coded. Advisors provided more information to decision-makers for the taste forecast than for the intellective task. Further, whether the advisor provided information to supplement their recommendation or not was a significant predictor of the acceptance of advice on the taste forecast, but not on the intellective task. The results are discussed in the context of previous research on advice, which has predominately used intellective tasks.

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  • Van Swol, Lyn M., 2011. "Forecasting another's enjoyment versus giving the right answer: Trust, shared values, task effects, and confidence in improving the acceptance of advice," International Journal of Forecasting, Elsevier, vol. 27(1), pages 103-120, January.
    • Handle: RePEc:eee:intfor:v:27:y::i:1:p:103-120
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    References listed on IDEAS

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    Cited by:

    1. Mesbah, Neda & Tauchert, Christoph & Buxmann, Peter, 2021. "Whose Advice Counts More – Man or Machine? An Experimental Investigation of AI-based Advice Utilization," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124796, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Jaeseob Lim & Sang-Hun Lee, 2020. "Utility and use of accuracy cues in social learning of crowd preferences," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-25, October.
    3. Eksoz, Can & Mansouri, S. Afshin & Bourlakis, Michael, 2014. "Collaborative forecasting in the food supply chain: A conceptual framework," International Journal of Production Economics, Elsevier, vol. 158(C), pages 120-135.
    4. Philipp Ecken & Richard Pibernik, 2016. "Hit or Miss: What Leads Experts to Take Advice for Long-Term Judgments?," Management Science, INFORMS, vol. 62(7), pages 2002-2021, July.
    5. Önkal, Dilek & Sinan Gönül, M. & Goodwin, Paul & Thomson, Mary & Öz, Esra, 2017. "Evaluating expert advice in forecasting: Users’ reactions to presumed vs. experienced credibility," International Journal of Forecasting, Elsevier, vol. 33(1), pages 280-297.
    6. Wright, George & Rowe, Gene, 2011. "Group-based judgmental forecasting: An integration of extant knowledge and the development of priorities for a new research agenda," International Journal of Forecasting, Elsevier, vol. 27(1), pages 1-13, January.
    7. repec:eee:intfor:v:27:y:2011:i:1:p:1-13 is not listed on IDEAS
    8. Sah, Sunita & Moore, Don A. & MacCoun, Robert J., 2013. "Cheap talk and credibility: The consequences of confidence and accuracy on advisor credibility and persuasiveness," Organizational Behavior and Human Decision Processes, Elsevier, vol. 121(2), pages 246-255.

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