Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/313795 
Year of Publication: 
2024
Citation: 
[Journal:] Journal of Behavioral Decision Making [ISSN:] 1099-0771 [Volume:] 37 [Issue:] 5 [Article No.:] e70001 [Publisher:] Wiley Periodicals, Inc. [Place:] Hoboken, NJ [Year:] 2024
Publisher: 
Wiley Periodicals, Inc., Hoboken, NJ
Abstract: 
Algorithms are capable of advising human decision‐makers in an increasing number of management accounting tasks such as business forecasts. Due to expected potential of these (intelligent) algorithms, there are growing research efforts to explore ways how to boost algorithmic advice usage in forecasting tasks. However, algorithmic advice can also be erroneous. Yet, the risk of using relatively bad advice is largely ignored in this research stream. Therefore, we conduct two online experiments to examine this risk of using relatively bad advice in a forecasting task. In Experiment 1, we examine the influence of performance feedback (revealing previous relative advice quality) and source of advice on advice usage in business forecasts. The results indicate that the provision of performance feedback increases subsequent advice usage but also the usage of subsequent relatively bad advice. In Experiment 2, we investigate whether advice representation, that is, displaying forecast intervals instead of a point estimate, helps to calibrate advice usage towards relative advice quality. The results suggest that advice representation might be a potential countermeasure to the usage of relatively bad advice. However, the effect of this antidote weakens when forecast intervals become less informative.
Subjects: 
confidence intervals
forecast
perfect automation schema
performance feedback
relatively bad advice
source of advice
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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