成果速递：IOM实验室探究新产品组合多样性与 B2B 平台表现的关系

Title: New Product Repertoire Diversity and Performance on the B2B Platforms: The Moderating Role of Platform Big Data Analytics

Authors: Wei Wang; Yuting Wang; Tianyi Ma; Hefu Liu

Journal: IEEE Transactions on Engineering Management

URL: https://doi.org/10.1109/TEM.2025.3639664

abstract: 

Confronted with intense competition on B2B platforms, manufacturers are increasingly diversifying their new product offerings. However, enhancing the performance of diverse new products can be challenging, particularly in a big data environment where large, varied, and rapidly changing data are difficult to manage. To address this challenge, this study draws on competitive repertoire theory to investigate the relationship between new product repertoire diversity and performance, as well as the moderating role of platform big data analytics. Using a unique dataset of 11,818 repertoire-week observations encompassing 117,751 new products from 1688.com, we conducted fixed-effects analyses and a series of robustness checks. The results show that both new product repertoire separation and variety have inverted U-shaped effects on repertoire performance, whereas disparity shows no significant effect. Furthermore, platform big data analytics strengthens the inverted U-shaped effect of variety on repertoire performance. This research contributes to the diversification literature by conceptualizing three dimensions of new product repertoire diversity, uncovering their distinct performance implications, and revealing the boundary conditions associated with platform big data analytics. These findings offer actionable insights for manufacturers on optimizing new product repertoire diversity and leveraging platform big data analytics to maximize performance outcomes.