报告题目：The Shadow of a Bright Past: AI Capability as a Moderator of Buyer AI Signals in Supply-Base Churn

报告所属学科：工商管理 

报告人：Lin Han（University of Exeter Business School）

报告时间：2026年8月6日 09:00-13:00

报告地点：经管学院604室

报告摘要：

Buyers increasingly publicize artificial intelligence (AI) initiatives, yet it remains unclear whether such signals stabilize or disrupt their supply bases. We conceptualize buyer AI commitment as an observable interfirm signal, AI-related statements in corporate communications that indicate a buyer's intent to invest in and deploy AI, and define supply-base churn as year-to-year instability in a buyer's supplier portfolio, reflecting suppliers' confidence in and attachment to the buyer through their decisions to remain or exit. Integrating signaling theory with an expectation-confirmation perspective, we predict that AI commitment reduces churn, but that its effect depends on the buyer's existing AI capability. We test these ideas using a 2010–2024 buyer-year panel that links FactSet Revere supply-chain relationships with LLM-scored AI commitment from earnings-call transcripts and patent-based AI capability from USPTO data. Fixed-effects results show that buyer AI commitment is associated with lower supply-base churn, primarily via fewer supplier exits and substitutions. However, this stabilizing effect is attenuated when buyer AI capability is high, consistent with expectation-confirmation: AI commitments from already capable buyers provide less incremental information to suppliers. Post hoc analyses further indicate that internal-operational AI commitments reduce churn, whereas supply-chain-facing AI commitments increase churn. These findings advance research on technology signaling and supply-base dynamics by explaining when AI signals retain suppliers—and when a buyer's strong technological past weakens the impact of new commitments.

报告人简介：

Dr Han Lin joined the Department of Management at the University of Exeter Business School in January 2023. Prior to this appointment, he served as a research fellow on various projects at both the University of Exeter and the University of Leeds.

Dr. Lin's research interests primarily encompass machine learning, entrepreneurship, financial economics, and applied econometrics. He is currently engaged in ongoing research endeavors focused on topics such as Public Economics, Agricultural Economics, Small and Medium-Sized Enterprises (SMEs), equity crowdfunding, machine learning-based forecasting, and international trade.

In addition to his research, Dr. Lin has a track record of instructing various modules in Mathematics, Economics, Econometrics, and Operations Analytics to students at both the undergraduate and master's levels. Additionally, he has showcased his capability in securing external funding, most notably by securing a research grant from the Eastern ARC Quantitative Social Science Research Fund for his project, China Emissions and Econometrics.