Not So Averse After All: Behavioral Mechanisms Underlying the Use of Algorithms in Managerial Forecasting

Although algorithms potentially outperform human judgment in managerial decision-making and forecasting, practitioners frequently refrain from using them. Extant literature provides mixed evidence on algorithm aversion and suggests very different underlying behavioral mechanisms. We contribute to existing research in two ways. First, we replace the traditional experimental instrument of measuring algorithm aversion, which asks participants to abandon their personal forecast in favor of a second, usually superior algorithmic forecast, with a new approach, in which participants receive human-made and algorithmic information simultaneously to form their judgments. Second, we derive competing empirical implications from potential mechanisms, allowing for an evaluation of their relevance. Using two factorial survey experiments with a forecasting task, we find very limited algorithm aversion. Regarding mechanisms underlying algorithm aversion discussed in existing research, we find that selective attention is most relevant: Users are more likely to notice superior performance of a human than of an algorithm.