Examining Behavioral Biases in Discretionary Pricing: Evidence from Field and Lab Experiments

• 成果类型: Article
• 署名作者: Liang, Xinyu; Wang, Yixin (Iris); Li, Jun
• 署名单位: University of Michigan System; University of Michigan; University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478241248747
• 发表日期: 2024
• 页码: 1335-1353
• 关键词: Behavioral operations management; retail pricing; field experiment; lab experiment

摘要

In theory, discretionary pricing enhances company performance by enabling managers to incorporate local information. However, in practice, managers may be prone to behavioral biases that can lead to suboptimal decisions. This paper investigates the effectiveness of discretionary pricing and behavioral elements on pricing decisions via field and lab experiments. Collaborating with a pharmacy chain retailer, we first analyzed a field experiment that delegated pricing authority to store managers. We find that managers began engaging in discretionary pricing after two months of experiment implementation and tended to raise the prices of high-priced drugs, resulting in significant sales and revenue losses. This effect was particularly prominent among less-experienced managers and in low-competition stores. We further designed a set of lab experiments to generalize our field observations and explore possible behavioral drivers. We requested lab participants to make pricing decisions under different information display scenarios and compared the average price adjustments between the direct display and click-to-view designs. We observe that participants also tended to raise product prices, and the magnitude of elevation was higher in the click-to-view group where the demand function information is less salient and accessible. Follow-up lab experiments support our field observations, suggesting that experience through repeated decisions and real-time feedback could alleviate the bias effect. Additionally, participants focused on high-priced products when given a list of items for discretionary pricing. Our results highlight the need to consider behavioral biases in human-algorithm collaborations and provide practical insights for improving information provision and training in discretionary settings.