主讲嘉宾：天普大学Guangwen Kong教授
时 间：2023年5月26日19:30
地 点：#腾讯会议：534-585-348
主 题：
Referral, Learning and Inventory Decisions
摘 要：
With the proliferation of digital social networks, businesses increasingly use referral programs to increase market exposure and sales. When customers refer a product to others they naturally disclose their purchase decisions. Thus the referral process introduces a social learning effect. We study the interaction between social learning and referral program structure and examine their impact on a firm's inventory decisions. We find that the presence of customers who lack knowledge of their own preferences introduces demand bias but social learning reduces this bias at the expense of increased demand variance. We characterize the optimal inventory levels for different numbers of referrals allowed by the firm and find that it is governed by the combination of market exposure effect and demand substitution effect. In a single referral program, the stock-out of one product can diminish the demand of the other product. In contrast, a multiple referral program allows a firm to achieve full market exposure but meanwhile increases the demand variance. Hence, the optimal referral program has to balance the trade-off between market exposure and demand variance, and thus allows either one or two referrals per customer.
主讲人简介：
Guangwen Kong received her Ph.D. degree in Operations Management from the University of Southern California in 2013. Her research studies emerging problems in sharing or on-demand platforms, service management and supply chain management using models, experiments, and data analysis, with a focus on behavior, incentive, and their implications to policies and operational decisions. She has published her papers in leading journals such as Management Science, Manufacturing &Service Operations Management, and Production and Operations Management.
Her research has been recognized by many research awards, including Management Science Best Paper Award in Operations Management (Winner, 2022), MSOM Service Management SIG Best Paper Award (Winner, 2021), POMS HOCM (Humanitarian Operations and Crisis Management) Best Paper Award (Runner-Up, 2021), Post-Pandemic Supply Chain and Healthcare Management Best Paper Award (Winner, 2021), DSI Best Problem-Driven Analytical Research Paper Award (Winner, 2021), CSAMSE Best Paper Award (Third Prize 2019, Honorable Mention 2017), ISCOM Best Paper Award (First Prize, 2019), INFORMS Service Science Cluster Best Paper Award (Finalist, 2018), INFORMS Service Science Cluster Student Best Paper Award (Finalist, Jingxuan Geng, 2020), POMS Supply Chain Management Best Student Paper Award (Finalist, 2013), POMS HK Best Student Paper Award (Finalist, Xiang Li, 2016), POMS CBOM (College of Behavioral Operations Management) Junior Scholar Award (Finalist, 2018) and The NET Institute Summer Grant Award (2018).
She is an associate editor of Naval Research of Logistics and an editorial review board member of Production and Operations Management. She is a co-editor of INFORMS Analytics Collections on Online Marketplaces. She has served as an NSF panelist in 2014 and as an INFORMS Doctoral Student Colloquium panelist in 2020 and 2021. She received the M&SOM Meritorious Service Award (2018, 2019, 2020, 2021) and Management Science Meritorious Service Award (2018, 2019).