Risk-averse algorithmic support and inventory management

Abstract

We study how managers allocate resources in response to algorithmic recommendations that are programmed with specific levels of risk aversion. Using the anchoring and adjustment heuristic, we derive our predictions and test them in a series of multi-item newsvendor experiments. We find that highly risk-averse algorithmic recommendations have a strong and persistent influence on order decisions, even after the recommendations are no longer available. Furthermore, we show that these effects are similar regardless of factors such as source of advice (i.e., human vs. algorithm) and decision autonomy (i.e., whether the algorithm is externally assigned or chosen by the subjects themselves). Finally, we disentangle the effect of risk attitude from that of anchor distance and find that subjects selectively adjust their order decisions by relying more on algorithmic advice that contrasts with their inherent risk preferences. Our findings suggest that organizations can strategically utilize risk-averse algorithmic tools to improve inventory decisions while preserving managerial autonomy.