To Collect or to Purchase? Collaboration Between Manufacturer and E-Commerce Platform on Customization

Recently, an increasing number of manufacturers have started adopting innovative customer-to-manufacturer (C2M) customization alongside traditional make-to-order (MTO) customization. In this context, manufacturers could collect market data independently or purchase data-driven insights from e-commerce platforms to proactively tailor products. However, firms’ crucial decision regarding the data acquisition way is unclear and still being debated in practice. In this regard, although the information systems (IS) literature has studied the benefits of acquiring external insights, firms’ choice of data acquisition way and the interplay between the two customization models have not been rigorously analyzed despite their practical importance. Hence, our study fills this critical gap by exploring different data acquisition ways in the context of hybrid customization. Our results show that even when the manufacturer could obtain more informative market data under platform-initiated C2M, the manufacturer should still prefer collecting market data independently rather than purchasing data-driven insights. This happens when the marginal cost of data acquisition is high for both the manufacturer and the e-commerce platform, and customers have a stronger bias against the tailored product. This is because these two factors would cause a decrease in data value, diminishing the e-commerce platform’s role in data collection. We also find that even when the e-commerce platform’s data-driven insights could lead to a higher customization level, which generates more commission revenue, the e-commerce platform should not always provide insight support. Moreover, manufacturer-collected C2M may sometimes lead to a higher effort level of acquiring informative data compared with platform-initiated C2M. Further, interestingly, even when the effort level is lower, manufacturer-collected C2M may occasionally result in more customer welfare. These results provide valuable guidelines to manufacturers on choosing an appropriate data acquisition way, to e-commerce platforms on offering data-driven insights, and to policymakers on improving customer welfare. These results also contribute to the emerging literature in IS and related disciplines on data-driven production models.

History: Hsing Kenneth Cheng, Senior Editor; Yifan Dou, Associate Editor.

Funding: This work was supported by the National Natural Science Foundation of China [Grants 72188101, 72331004, 72471075, and 72501087], the Fundamental Research Funds for the Central Universities [Grant JZ2023YQTD0075], the Base of Introducing Talents of Discipline to Universities for Optimization and Decision-Making in the Manufacturing Process of Complex Product (111 projects) [Grant B17014], and the Projects of International Cooperation and Exchanges the National Natural Science Foundation of China [Grant W2411063]. S. Kumar thanks Temple Center for International Business Education and Research for partially supporting this research.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2024.1266.