Behavioral multi‐lever decision‐making: A study of consumer return policy, price, and inventory decisions

Consumer return policies have been long recognized and studied by operations management scholars as an important managerial lever in a retail environment. Yet, the behavioral aspects of return policy decision‐making and interaction of return policy decisions with other common operational decisions have not been investigated to date. We present a behavioral analysis of return policy decision‐making in a retail environment with aggregate demand and individual product valuation uncertainties. Leveraging a generalized newsvendor context, we conduct a randomized behavioral experiment to understand how individuals make each of the three key retail decisions—refund amount, price, and order quantity—and the causal effect of salvage value on these decisions. We find that decision‐makers exhibit behavioral regularities in making decisions across the three levers and they react to changes in the operating conditions in a boundedly rational manner, suggesting the use of heuristics. Based on behavioral regularities that we observe in our data—that is, responses, time‐dependent effects, and decision dependencies—we develop a process theory based on behavioral decision‐making tenets that offers a new direction with testable hypotheses for future research. The process theory describes a conditional decision‐making heuristic that leads to a propagation of decision errors across different levers as well as lever‐specific decision biases. Highlights We present a behavioral analysis of return policy decision‐making in a retail environment with aggregate demand and individual product valuation uncertainties. We find that decision‐makers exhibit behavioral regularities in making decisions across three levers and that they use a conditional decision heuristic that leads to propagation of errors across levers. Based on the observed regularities, we develop a process theory based on behavioral decision‐making tenets that offers new insights for how to aid in complex decision‐making environments.