Artificial Intelligence and Firm Resilience: Empirical Evidence from Natural Disaster Shocks

• 成果类型: Article; Early Access
• 署名作者: Han, Miaozhe; Shen, Hongchuan; Wu, Jing; Zhang, Xiaoquan (Michael)
• 署名单位: Hong Kong University of Science & Technology; University of Macau; Chinese University of Hong Kong
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2022.0440
• 发表日期: 2025
• 关键词: information-technology; supply chain; big data; LABOR; performance; INVESTMENT; IMPACT

摘要

Artificial intelligence (AI) has been increasingly deployed in business operations over the past decade. Although AI productivity in normal times has been extensively studied, direct evidence of its effectiveness in uncertain contexts is limited. Our work fills this gap by examining the contribution of AI to corporate resilience under natural disaster shocks, particularly concentrating on AI-using and goods-producing firms. We measure firm AI investment by the cumulative AI-relevant skills extracted from a comprehensive job posting database and firm resilience by the changes in corporate valuation in response to operational shocks induced by natural disasters. Using a pooled event study approach, we provide evidence that AI generates resilience: An average firm with 2.4% of its total job demands related to AI could approximately recover the full damage of disasters reflected in corporate valuation over a short event window. From the product function test, we find that resilience is attributable to the moderating effect of AI on the damaged input responsiveness under the volatile production environment. Further analyses reveal a pressing phenomenon: Although underperforming firms could benefit more from an additional unit of AI investment, the realized productivity is notably restrained due to a lack of complementary organizational designs. Our findings provide managerial implications regarding the interplay between environmental conditions and firm investments in both AI technology and complementary infrastructures.