AI-Driven Supply Chain Resilience in the U.S. Hospitality Sector
DOI:
https://doi.org/10.51137/wrp.ijarbm.625Keywords:
Supply Chain Resilience, Dynamic Capabilities, Hospitality, Digital Transformation, Artificial IntelligenceAbstract
This study examines how Artificial Intelligence (AI) reshapes dynamic capabilities and enhances supply chain resilience within multi-tier hospitality supply networks in the United States. Anchored in Dynamic Capabilities Theory (DCT) and employing a qualitative multi-tier design, ten supply chain professionals across a luxury hotel, regional distributors, and a national supplier were interviewed. Data were analyzed abductively to examine how AI influences sensing, seizing, and transforming capabilities across interacting supply tiers. Findings reveal that AI disproportionately strengthens sensing through algorithmic forecasting and real-time analytics, while seizing capabilities develop moderately and transforming remains incremental. Capability development is uneven across supply tiers, with upstream distributors exhibiting stronger AI-enabled sensing than hotel actors. The study extends DCT into service-intensive hospitality supply ecosystems by reconceptualizing sensing as algorithmically embedded and introducing the concept of distributed capability asymmetry, demonstrating how uneven AI maturity across supply tiers shapes network-level resilience outcomes.
References
Adner, R., & Kapoor, R. (2010). Value creation in innovation ecosystems: How the structure of technological interdependence affects firm performance in new technology generations. Strategic Management Journal, 31(3), 306–333. https://doi.org/10.1002/smj.821
American Hotel & Lodging Association. (2023). State of the hotel industry report. AHLA.
Belhadi, A., Kamble, S. S., Gunasekaran, A., Mani, V., & Verma, S. (2021). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance. International Journal of Production Research, 59(14), 4481–4504. https://doi.org/10.1080/00207543.2021.1884833
Bode, C., & Macdonald, J. R. (2023). Stages of supply chain disruption response: Direct, indirect and network effects. Journal of Supply Chain Management, 59(1), 3–27.
Chowdhury, P., Paul, S. K., Kaisar, S., & Moktadir, M. A. (2023). COVID-19 pandemic related supply chain studies: A systematic review. Transportation Research Part E, 170, 102691.
Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., & Papadopoulos, T. (2023). Big data analytics and artificial intelligence pathway to operational resilience. International Journal of Production Research, 61(2), 523–543.
Dubois, A., & Gadde, L.-E. (2002). Systematic combining: An abductive approach to case research. Journal of Business Research, 55(7), 553–560. https://doi.org/10.1016/S0148-2963(00)00195-8
Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550. https://doi.org/10.5465/amr.1989.4308385
Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods, 16(1), 15–31. https://doi.org/10.1177/1094428112452151
Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903
Helfat, C. E., & Peteraf, M. A. (2023). Managerial cognitive capabilities and the microfoundations of dynamic capabilities. Strategic Management Journal, 44(1), 3–28.
Helfat, C. E., & Raubitschek, R. S. (2018). Dynamic and integrative capabilities for profiting from innovation in digital platform-based ecosystems. Research Policy, 47(8), 1391–1399. https://doi.org/10.1016/j.respol.2018.01.019
Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
Ivanov, D. (2021). Supply chain viability and the COVID-19 pandemic: A conceptual and formal generalisation of four major adaptation strategies. International Journal of Production Research, 59(12), 3535–3552. https://doi.org/10.1080/00207543.2020.1789675
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. International Journal of Production Research, 58(10), 2904–2915. https://doi.org/10.1080/00207543.2020.1750727
Ivanov, D., & Webster, C. (2019). Robots in hotel and tourism industry: Service automation versus service experience. In Robots, artificial intelligence, and service automation in travel, tourism and hospitality (pp. 55–69). Emerald Publishing.
Kamble, S. S., Gunasekaran, A., & Sharma, R. (2018). Analysis of the driving and dependence power of barriers to adopt Industry 4.0 in Indian manufacturing industry. Computers in Industry, 101, 107–119. https://doi.org/10.1016/j.compind.2018.06.004
Kapoor, R. (2023). Ecosystems and platform-based competition. Strategic Management Journal, 44(5), 1053–1076.
Kvale, S., & Brinkmann, S. (2009). InterViews: Learning the craft of qualitative research interviewing (2nd ed.). Sage.
Lee, A. S., & Baskerville, R. L. (1999). Generalizing generalizability in information systems research. Information Systems Research, 14(3), 221–243.
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.
Mandal, S. (2019). The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility. Information Technology & People, 32(2), 297–318. https://doi.org/10.1108/ITP-03-2018-0144
Min, H. (2010). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications, 13(1), 13–39. https://doi.org/10.1080/13675560902736537
Nambisan, S., Lyytinen, K., Majchrzak, A., & Song, M. (2022). Digital innovation management: Reinventing innovation management research in a digital world. MIS Quarterly, 46(1), 223–238.
Patton, M. Q. (2015). Qualitative research & evaluation methods (4th ed.). Sage.
Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management, 20(1), 124–143. https://doi.org/10.1108/09574090910954873
Queiroz, M. M., Ivanov, D., Dolgui, A., & Wamba, S. F. (2020). Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. International Journal of Production Economics, 232, 107145. https://doi.org/10.1016/j.ijpe.2020.107145
Sheffi, Y., & Rice, J. B. (2005). A supply chain view of the resilient enterprise. MIT Sloan Management Review, 47(1), 41–48.
Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics: Research and Applications, 9(1), 33–45. https://doi.org/10.1080/13675560500405584
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640
Teece, D. J., Peteraf, M., & Leih, S. (2016). Dynamic capabilities and organizational agility: Risk, uncertainty and strategy in the innovation economy. California Management Review, 58(4), 13–35. https://doi.org/10.1525/cmr.2016.58.4.13
Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001
Wieland, A., & Durach, C. F. (2021). Two perspectives on supply chain resilience. Journal of Business Logistics, 42(3), 315–322. https://doi.org/10.1111/jbl.12271
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage.
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Copyright (c) 2026 Salami Abdul Mohammed (Author)
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