讲座题目：ESG data processing in supply chains under spillover effects: counting on AI or Human
主讲嘉宾：李武
时间：2026年10月8日（星期四）上午9:00—11:00
地点：商学院116东方厅
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江南大学商学院
2026年9月23日
主讲嘉宾简介
Dr. Kevin W. Li is Professor of Management Science in the Odette School of Business at the University of Windsor, Ontario, Canada. Dr. Li was awarded two Invitation Fellowships by the Japan Society for the Promotion of Science (JSPS) in 2011 and 2015 as well as a Visiting Fellowship by the Centre of Advanced Studies at the University of Palermo in Italy in 2025. Dr. Li’s research spans from logistics and supply chain management to conflict resolution and decision modelling. His research has been supported by three individual Discovery Grants from the Natural Sciences and Engineering Research Council of Canada (NSERC). Dr. Li has coauthored 78 articles in international refereed journals such as Production and Operations Management, European Journal of Operational Research, IEEE Transactions on Systems, Man, and Cybernetics, Information Sciences, International Journal of Production Economics, International Journal of Production Research, Transportation Research, Part E, and Water Resources Research. These publications have been cited over 3500 times with an h-index of 34 as per the Web of Science. Dr. Li serves as an Associate Editor of Group Decision and Negotiation and Information Sciences as well as sits on the Editorial Boards of several other refereed journals.
讲座主要内容
Improving quality of environmental, social, and governance (ESG) information disclosure and containing ESG risks are notable challenges for firms. Crucially, the success of both initiatives fundamentally depends on a firm’s capability to process exploding volumes of ESG data. We develop a game-theoretical model involving a core firm and its partner to investigate how the core firm can process ESG data and promote ESG practices by counting on artificial intelligence (AI), humans, or human-AI collaboration under ESG spillover effects. We explicitly characterize how AI creates value through two distinct capabilities, automation and intelligence, and contrast these technological advantages with the human capacity to acquire and process private ESG information. Our analysis yields three main findings. First, AI adoption and ESG spillover intensity are mutually reinforcing: stronger spillover enhances the value of AI adoption for the core firm, while AI amplifies the impact of spillover intensity on the partner. Second, when human private information is at an intermediate level, AI adoption remains beneficial to the core firm despite its inferior ESG risk containment capability. Third, human-AI collaboration consistently outperforms human-only processing mode when the core firm retains majority human decision authority, while moderating AI intelligence under high spillover intensity mitigates the partner’s profit loss under AI-only processing mode. Moreover, the value of AI adoption is not inherently tied to the core firm’s position within the supply chain. The findings shed insights on when humans should collaborate with AI in managing ESG disclosure and risk containment.
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