讲座题目：Data or Insights? Platform Data Cooperation with Competing Manufacturers

讲座摘要：Online platforms accumulate massive amounts of consumer data that can be leveraged to support manufacturers’ product innovation. This paper examines how a platform should collaborate with two competing manufacturers through either data sharing or insight sharing. We develop a game-theoretic model in which the platform chooses whether to share data or insights mined from data, and manufacturers decide whether to use the shared data or insight for product innovation. We show that the platform prefers data sharing only when both data-mining cost and privacy cost are sufficiently low; otherwise, it prefers insight sharing. Under data sharing, manufacturers’ adoption decisions may be asymmetric, and a prisoner’s-dilemma outcome can arise when competition is weak and privacy cost is moderate. By contrast, insight sharing always yields symmetric adoption and does not harm manufacturers’ profits. We further find that insight sharing induces higher product innovation for high-commission products, whereas data sharing induces higher for low-commission products only when privacy cost is low and data-mining cost is high. In terms of welfare, both sharing models increase consumer surplus relative to no sharing, insight sharing improves social welfare, whereas data sharing may reduce social welfare. Moreover, when data-mining cost is sufficiently low, data sharing can generate higher consumer surplus and social welfare than insight sharing. These findings highlight the distinct roles of privacy, cost allocation, and competitive pressure in platform-based data cooperation.

主讲人简介：孙灿，中国科学技术大学科技商学院、管理学院特任研究员，加拿大阿尔伯塔大学商学院博士。主要研究兴趣包括信息系统经济、信息系统与运营管理交叉、数智产品管理等。在Production and Operations Management和Information Systems Research等期刊发表论文多篇。担任期刊Information Technology and Management副编辑，Production and Operations Management编辑审稿委员会成员。

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