Towards a better understanding on mitigating algorithm aversion in forecasting: an experimental study - Markus Jung; Mischa Seiter (Journal of Management Control, 2021)
Forecasts serve as the basis for a wide range of managerial decisions. With the potential of new data sources and new techniques for data analysis, human forecasters are increasingly interacting with algorithms. Although algorithms can show better forecasting performance than humans, forecasters do not always accept these algorithms and instead show aversion to them. Algorithm aversion has become a widely known phenomenon. Drawing on the seminal study of Dietvorst et al. (J Exp Psychol Gen 144(1):114–126, 2015), we extend the evidence on algorithm aversion by introducing three environmental variables from the management accounting literature. We argue that time pressure, “do your best” goals, and forecasters' data input decision rights on the algorithms input mitigate algorithm aversion. To test our hypotheses, we conducted an experimental study with 1,840 participants overall. We found support for our hypothesis that time pressure mitigates algorithm aversion. We found evidence that the mitigation effect is based on forecasters' loss of confidence in their own forecast when they are under time pressure. We found no support for our hypothesis on “do your best” goals or forecasters' data input decision rights.
Keywords: 330; Algorithm aversion; Forecasting; Time pressure; Experimental study