Artificial Intelligence and Firm Resilience: Empirical Evidence from Natural Disaster Shocks
Miaozhe Han, Hongchuan Shen, Jing Wu, Xiaoquan Zhang · Information Systems Research · 25 Mar 2025
Where this sits in the debate
- Will AI actually boost productivity and growth? · its finding: it depends (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Abstract
Artificial intelligence (AI) has been increasingly deployed in business operations over the past decade, whereas direct evidence of its effectiveness in uncertain contexts is limited. Our work examines 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. Evidence suggests that AI generates resilience: An average firm that equips 2.4% of total jobs to be AI-related 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. Our analyses further 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.
About this publication
Leading field journal. A leading journal in economics or a neighbouring field (on the Tinbergen Institute’s A list or near the top of the RePEc ranking), or a top-rated computer-science venue.
Placed by: Leading field journal. How sources are ranked.
- Published
- 2025 (19 months ago)
- Citations
- 46 (OpenAlex)
Citations in context
46 citations — 85th percentile among the 33 indexed papers from 2025 in this collection. Citation counts favor older papers; this compares only same-year papers here, not the whole literature.
□ added to this collection in 2026.
Related by theme, not necessarily by method or conclusion.
- The Effects of Generative AI on High Skilled Work: Evidence from Three Field Experiments with Software Developers
SSRN · 2024 - GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
arXiv · 2023 - Extending “GPTs Are GPTs” to Firms
AEA Papers and Proceedings · 2025 - New Frontiers: The Origins and Content of New Work, 1940–2018
The Quarterly Journal of Economics · 2024
Overview assembled automatically from collected metadata. How this site is built.