Debiasing behaviors in supply chain management: An experimental study

We design a decision-support tool that automates a key contract parameter to satisfy supply chain coordination conditions in buyback and revenue-sharing contracts to assist human decision-making. Specifically, after a supplier proposes a wholesale price, the decision-support tool determines a buyback price (revenue-sharing ratio) in the buyback case (revenue-sharing case) according to supply chain coordination requirements. Through a 2 × 2 human-to-human experiment design that varies the contract type (buyback vs. revenue-sharing) and decision-support condition (with vs. without), we compare how automating a contract parameter based on coordination requirements influences human decision biases and supply chain performance differently across two contracts. Leveraging behavioral model analysis, we find that automating buyback price mitigates the supplier’s bias of overweighting buyback costs under the buyback contract, thereby improving supply chain performance and promoting a fairer profit allocation between supply chain parties. However, automating revenue-sharing ratio does not improve supply chain performance under the revenue-sharing contract because it induces greater variability in retailer’s ordering decisions, leading to lower supply chain efficiency. Our findings suggest supply chain practitioners carefully implement such decision aids in practice.