Impact of Task-Level Worker Specialization, Workload, and Product Personalization on Consumer Returns

• 成果类型: Article
• 署名作者: Cui, Hailong; Rajagopalan, Sampath; Ward, Amy R.
• 署名单位: University of Southern California; University of Chicago
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2019.0836
• 发表日期: 2021
• 页码: 346-366
• 关键词: production process; product personalization; returns; econometrics; marketing-operations interface

摘要

Problem definition: Are consumer product returns largely a function of retailers' return policies, or can manufacturers influence them through production characteristics and product attributes? How do operational levers under a manufacturer's control affect return rates? Academic/practical relevance: Consumer returns raise major questions for manufacturers and retailers because losses related to returns are substantial. However, levers commonly used to discourage returns, such as strict return policies, can dampen sales, which is a significant drawback. The literature on consumer returns has focused on retailer and purchase attributes but has ignored whether manufacturers can take a pro-active role to influence returns. Manufacturers and retailers have an interest in understanding how operational levers influence return rates. Methodology: This work attempts to answer questions about the influence of manufacturing on return rates by using detailed operational data from a leading U.S. manufacturer of automotive accessories. We study the impact of key operational levers on return rates by using a logistic regression model, while controlling for retailer return policies and numerous product, purchase, and consumer attributes. Results: We find that increased task-level worker specialization has a significant and U-shaped impact on return rates. Increased workload levels in production leads to a significant increase in return rates, whereas product personalization leads to lower return rates. Managerial implications: We provide an empirical estimate of the effect of operational variables on return rates that are useful in evaluating managerial trade-offs. Our findings suggest that allowing customers to personalize products can significantly reduce return rates.