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虞義敏副教授学术报告会
作者:发布时间:2023-06-06

题目:Behavior-Based Pricing in Two-Sided Platforms: The Role of Network Effects

时间:2023年6月6日 14:00-15:30

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邀请人:李勇祥 副教授(工业工程与管理系)

 

Biography

Yimin Yu is an associate professor of Management Sciences at City University of Hong Kong. He received his Ph.D. degree from University of Minnesota, Twin Cities. His research focuses on supply chain management and service operations. This includes topics on the optimal design of inventory-production systems, incentive issues in supply chains, and pricing strategies. His research work has been published in leading business journals, including Manufacturing & Service Operations Management, Marketing Science, Operations Research, and Production and Operations Management. His current research projects explore how incomplete information affects business strategies, including the robustness of microeconomic models in the presence of model uncertainty and data-driven decision-making.

 

Abstract

Behavior-based pricing (BBP) is a pricing strategy used by firms to differentiate between past and new customers based on their purchase history data. Two-sided platforms commonly adopt BBP, but previous research has not considered the network effects. In this study, we use a duopoly two-period model to investigate the role of BBP in two-sided platforms, which connect the customer and developer sides. The developers can be either single-homing or multiple-homing. We consider the cross-side network effects between customers and developers, and our results show that customer-side BBP can increase platform profits when developers are multi-homing. This is in contrast to the traditional understanding of BBP's impact on profitability. The driving force behind this result is the cross-side network effects that reduce competition among customers in the first period. However, when developers are single-homing, BBP does not have any positive effect on platform profits. Our results shed light on the profitability of BBP on two-sided platforms and the role of cross-side network effects.

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