2025年第89期(总第1130期)
演讲主题：When Ethics Meet Algorithms: Corporate Social Responsibility in Personalized Pricing and Network Effects
主讲人：卢立建 香港科技大学商公司助理教授
主持人：李建斌 供应链管理与系统工程系教授、副经理
活动时间：2025年12月3日（周三）09:00-11:00
活动地址: 管院大楼105教室
主讲人简介：
Professor Lu is an Assistant Professor at HKUST Business School. His primary research interests are data-driven decision-making with a focus on applications in e-commerce, pricing and revenue management, supply chain management, healthcare and service systems, finance-operations interface. His work has been published at journals such as Management Science, Manufacturing & Service Operations Management, Mathematics of Operations Research, Production and Operations Management. Prior to joining HKUST Business School, Professor Lu was Founder and CIO at UniQuant Capital, where he manages 3 billions quantitative long-short hedge funds; Executive Director at China Innovation Fund leading PE/VC investment in TMT area with 150 billions AUM; Vice President at Goldman Sachs Asset Management leading quantitative factor portfolio research and management with 15 billions USD AUM; Senior Research Scientist at Knight Capital Group, Amazon.com, and AppNexus.com.
活动简介：
As big data and advanced analytics become more prevalent, companies are increasingly adopting personalized pricing strategies. While these strategies can boost profitability, they also raise concerns about fairness and consumer privacy, often leading to reduced price transparency. This paper explores how corporate social responsibility (CSR) initiatives can reconcile the tension between profit-driven personalized pricing and consumer welfare in digital markets characterized by network effects and price (un)observability. We show that, without CSR, price discrimination can diminish consumer surplus and firm profits due to coordination failures among consumers stemming from network externalities and price opacity. Our findings reveal that CSR initiatives -- especially those that factor consumer surplus into pricing -- create a self-regulating mechanism that: (1) aligns consumer incentives, (2) enhances firm profits beyond standard discriminatory pricing, and (3) leads to Pareto improvements by increasing surplus for all market participants. We validate our findings across various contexts and illustrate their generalizability and robustness. Our findings offer managers a strategic framework for ethical pricing that strengthens competitive positioning in digital markets and provide valuable guidance for policymakers, suggesting that CSR incentives can effectively address concerns related to algorithmic fairness.
