IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v36y2025i4p2116-2133.html

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

Author

Listed:
  • Miaozhe Han

    (Department of Information Systems, Business Statistics and Operations Management, Business School, Hong Kong University of Science and Technology, Hong Kong)

  • Hongchuan Shen

    (Faculty of Business Administration, University of Macau, Macau, China)

  • Jing Wu

    (Department of Decisions, Operations and Technology, Business School, The Chinese University of Hong Kong, Hong Kong)

  • Xiaoquan (Michael) Zhang

    (Department of Decisions, Operations and Technology, Business School, The Chinese University of Hong Kong, Hong Kong)

Abstract

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 under-performing 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.

Suggested Citation

  • Miaozhe Han & Hongchuan Shen & Jing Wu & Xiaoquan (Michael) Zhang, 2025. "Artificial Intelligence and Firm Resilience: Empirical Evidence from Natural Disaster Shocks," Information Systems Research, INFORMS, vol. 36(4), pages 2116-2133, December.
  • Handle: RePEc:inm:orisre:v:36:y:2025:i:4:p:2116-2133
    DOI: 10.1287/isre.2022.0440
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.2022.0440
    Download Restriction: no

    File URL: https://libkey.io/10.1287/isre.2022.0440?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Thanh D. Huynh & Ying Xia, 2023. "Panic Selling When Disaster Strikes: Evidence in the Bond and Stock Markets," Management Science, INFORMS, vol. 69(12), pages 7448-7467, December.
    2. Prasanna Tambe & Lorin M. Hitt & Erik Brynjolfsson, 2012. "The Extroverted Firm: How External Information Practices Affect Innovation and Productivity," Management Science, INFORMS, vol. 58(5), pages 843-859, May.
    3. Erik Brynjolfsson & Wang Jin & Kristina McElheran, 2021. "The power of prediction: predictive analytics, workplace complements, and business performance," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 56(4), pages 217-239, October.
    4. Danny Miller & Peter H. Friesen, 1983. "Strategy‐making and environment: The third link," Strategic Management Journal, Wiley Blackwell, vol. 4(3), pages 221-235, July.
    5. Young Bong Chang & Vijay Gurbaxani, 2013. "An Empirical Analysis of Technical Efficiency: The Role of IT Intensity and Competition," Information Systems Research, INFORMS, vol. 24(3), pages 561-578, September.
    6. Sinan Aral & Erik Brynjolfsson & Marshall Van Alstyne, 2012. "Information, Technology, and Information Worker Productivity," Information Systems Research, INFORMS, vol. 23(3-part-2), pages 849-867, September.
    7. Daron Acemoglu & David Autor & Jonathon Hazell & Pascual Restrepo, 2022. "Artificial Intelligence and Jobs: Evidence from Online Vacancies," Journal of Labor Economics, University of Chicago Press, vol. 40(S1), pages 293-340.
    8. Klomp, Jeroen, 2014. "Financial fragility and natural disasters: An empirical analysis," Journal of Financial Stability, Elsevier, vol. 13(C), pages 180-192.
    9. Prasanna Tambe & Lorin M. Hitt, 2012. "The Productivity of Information Technology Investments: New Evidence from IT Labor Data," Information Systems Research, INFORMS, vol. 23(3-part-1), pages 599-617, September.
    10. Erik Brynjolfsson & Xiang Hui & Meng Liu, 2019. "Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform," Management Science, INFORMS, vol. 65(12), pages 5449-5460, December.
    11. Goldfarb, Avi & Taska, Bledi & Teodoridis, Florenta, 2023. "Could machine learning be a general purpose technology? A comparison of emerging technologies using data from online job postings," Research Policy, Elsevier, vol. 52(1).
    12. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    13. Brad Hershbein & Lisa B. Kahn, 2018. "Do Recessions Accelerate Routine-Biased Technological Change? Evidence from Vacancy Postings," American Economic Review, American Economic Association, vol. 108(7), pages 1737-1772, July.
    14. Francesco D’Acunto & Nagpurnanand Prabhala & Alberto G Rossi, 2019. "The Promises and Pitfalls of Robo-Advising," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1983-2020.
    15. Lorin M. Hitt, 1999. "Information Technology and Firm Boundaries: Evidence from Panel Data," Information Systems Research, INFORMS, vol. 10(2), pages 134-149, June.
    16. Edward Felten & Manav Raj & Robert Seamans, 2021. "Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses," Strategic Management Journal, Wiley Blackwell, vol. 42(12), pages 2195-2217, December.
    17. Landon Kleis & Paul Chwelos & Ronald V. Ramirez & Iain Cockburn, 2012. "Information Technology and Intangible Output: The Impact of IT Investment on Innovation Productivity," Information Systems Research, INFORMS, vol. 23(1), pages 42-59, March.
    18. Lynn Wu & Bowen Lou & Lorin Hitt, 2019. "Data Analytics Supports Decentralized Innovation," Management Science, INFORMS, vol. 65(10), pages 4863-4877, October.
    19. Babina, Tania & Fedyk, Anastassia & He, Alex & Hodson, James, 2024. "Artificial intelligence, firm growth, and product innovation," Journal of Financial Economics, Elsevier, vol. 151(C).
    20. John (Jianqiu) Bai & Erik Brynjolfsson & Wang Jin & Sebastian Steffen & Chi Wan, 2021. "Digital Resilience: How Work-From-Home Feasibility Affects Firm Performance," NBER Working Papers 28588, National Bureau of Economic Research, Inc.
    21. Zhuo (June) Cheng & Arun Rai & Feng Tian & Sean Xin Xu, 2021. "Social Learning in Information Technology Investment: The Role of Board Interlocks," Management Science, INFORMS, vol. 67(1), pages 547-576, January.
    22. Jian Chen & Jiaquan Yao & Qunzi Zhang & Xiaoneng Zhu, 2023. "Global Disaster Risk Matters," Management Science, INFORMS, vol. 69(1), pages 576-597, January.
    23. Sanjeev Dewan & Charles Shi & Vijay Gurbaxani, 2007. "Investigating the Risk-Return Relationship of Information Technology Investment: Firm-Level Empirical Analysis," Management Science, INFORMS, vol. 53(12), pages 1829-1842, December.
    24. Anindita Chakravarty & Rajdeep Grewal & V. Sambamurthy, 2013. "Information Technology Competencies, Organizational Agility, and Firm Performance: Enabling and Facilitating Roles," Information Systems Research, INFORMS, vol. 24(4), pages 976-997, December.
    25. Erik Brynjolfsson & Kristina McElheran, 2016. "The Rapid Adoption of Data-Driven Decision-Making," American Economic Review, American Economic Association, vol. 106(5), pages 133-139, May.
    26. David Deming & Lisa B. Kahn, 2018. "Skill Requirements across Firms and Labor Markets: Evidence from Job Postings for Professionals," Journal of Labor Economics, University of Chicago Press, vol. 36(S1), pages 337-369.
    27. Anandhi S. Bharadwaj & Sundar G. Bharadwaj & Benn R. Konsynski, 1999. "Information Technology Effects on Firm Performance as Measured by Tobin's q," Management Science, INFORMS, vol. 45(7), pages 1008-1024, July.
    28. Alekseeva, Liudmila & Azar, José & Giné, Mireia & Samila, Sampsa & Taska, Bledi, 2021. "The demand for AI skills in the labor market," Labour Economics, Elsevier, vol. 71(C).
    29. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    30. Prasanna Tambe, 2014. "Big Data Investment, Skills, and Firm Value," Management Science, INFORMS, vol. 60(6), pages 1452-1469, June.
    31. Timothy F. Bresnahan & Erik Brynjolfsson & Lorin M. Hitt, 2002. "Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(1), pages 339-376.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guo, Lihong & Pei, Huacheng & Liu, Yizi, 2025. "Artificial intelligence and corporate green innovation: Evidence from China," Research in International Business and Finance, Elsevier, vol. 79(C).
    2. Dong, Li & Liu, Junxian & Yang, Jinghan & Zhang, Xin, 2025. "The deployment of general large language models and corporate value: Evidence from the stock market," Finance Research Letters, Elsevier, vol. 86(PD).
    3. Jia, Fu & Hu, Shoufeng & Chen, Lujie, 2025. "Does supply chain visibility improve firm resilience: An organizational information processing theory perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 204(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kristina McElheran & J. Frank Li & Erik Brynjolfsson & Zachary Kroff & Emin Dinlersoz & Lucia Foster & Nikolas Zolas, 2024. "AI adoption in America: Who, what, and where," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 375-415, March.
    2. Flavio Calvino & Luca Fontanelli, 2025. "Decoding AI: Nine facts about how firms use artificial intelligence in France," LEM Papers Series 2025/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Prasanna B. Tambe, 2026. "Reskilling the Workforce for AI: Domain Expertise and Algorithmic Literacy," Management Science, INFORMS, vol. 72(1), pages 515-537, January.
    4. Ruyu Chen & Natarajan Balasubramanian & Chris Forman, 2024. "How does worker mobility affect business adoption of a new technology? The case of machine learning," Strategic Management Journal, Wiley Blackwell, vol. 45(8), pages 1510-1538, August.
    5. Flavio Calvino & Luca Fontanelli, 2026. "Decoding AI: an early look at how French firms use AI," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 16(1), pages 51-93, March.
    6. Chris Forman & Kristina McElheran, 2025. "Production Chain Organization in the Digital Age: Information Technology Use and Vertical Integration in U.S. Manufacturing," Management Science, INFORMS, vol. 71(2), pages 1027-1049, February.
    7. Babina, Tania & Fedyk, Anastassia & He, Alex & Hodson, James, 2024. "Artificial intelligence, firm growth, and product innovation," Journal of Financial Economics, Elsevier, vol. 151(C).
    8. Charles Hoffreumon & Chris Forman & Nicolas van Zeebroeck, 2024. "Make or buy your artificial intelligence? Complementarities in technology sourcing," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 452-479, March.
    9. Kristina McElheran & Mu-Jeung Yang & Zachary Kroff & Erik Brynjolfsson, 2025. "The Rise of Industrial AI in America: Microfoundations of the Productivity J-curve(s)," Working Papers 25-27, Center for Economic Studies, U.S. Census Bureau.
    10. Stefan Schweikl & Robert Obermaier, 2020. "Lessons from three decades of IT productivity research: towards a better understanding of IT-induced productivity effects," Management Review Quarterly, Springer, vol. 70(4), pages 461-507, November.
    11. Erdem Dogukan Yilmaz & Christian Peukert, 2024. "Who Benefits from AI? Project-Level Evidence on Labor Demand, Operations and Profitability," CESifo Working Paper Series 11321, CESifo.
    12. Lynn Wu & Lorin Hitt & Bowen Lou, 2020. "Data Analytics, Innovation, and Firm Productivity," Management Science, INFORMS, vol. 66(5), pages 2017-2039, May.
    13. Chun, Hyunbae & Shin, Donghan, 2025. "Beyond automation: The multifaceted impact of advanced digital technologies on employment dynamics," Research Policy, Elsevier, vol. 54(10).
    14. Mark Hellsten & Giuseppe Pulito & Sarah Schroeder, 2026. "Automation and the Changing Composition of Skill Demand," RFBerlin Discussion Paper Series 26122, ROCKWOOL Foundation Berlin (RFBerlin).
    15. Ciaschi, Matias & Falcone, Guillermo & Garganta, Santiago & Gasparini, Leonardo & Bertín, Octavio & Ramírez-Leira, Lucía, 2025. "The Potential Distributive Impact of AI-driven Labor Changes in Latin America," IDB Publications (Working Papers) 14253, Inter-American Development Bank.
    16. Tomáš Oleš, 2026. "In-demand skills: a shield against automation—evidence from online job vacancies," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 60(1), pages 1-56, December.
    17. Sunil Mithas & M. S. Krishnan & Claes Fornell, 2016. "Research Note—Information Technology, Customer Satisfaction, and Profit: Theory and Evidence," Information Systems Research, INFORMS, vol. 27(1), pages 166-181, March.
    18. Sunghun Chung & Animesh Animesh & Kunsoo Han & Alain Pinsonneault, 2019. "Software Patents and Firm Value: A Real Options Perspective on the Role of Innovation Orientation and Environmental Uncertainty," Information Systems Research, INFORMS, vol. 30(3), pages 1073-1097, September.
    19. Zhe Yuan & Yitong Wang & Tianshu Sun & Huilan Xu, 2025. "Democratizing Data Analytics Products to Drive SME Growth: A Natural Experiment," Manufacturing & Service Operations Management, INFORMS, vol. 27(4), pages 1053-1067, July.
    20. Lynn Wu & Bowen Lou & Lorin Hitt, 2019. "Data Analytics Supports Decentralized Innovation," Management Science, INFORMS, vol. 65(10), pages 4863-4877, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orisre:v:36:y:2025:i:4:p:2116-2133. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.