This research aims to identify the factors influencing AI adoption in supply chain to resolve supply chain disruptions. It is undeniable that the adoption of AI in the supply chain could be essential to resolve supply chain disruptions. Supply chain disruptions could be defined as unexpected events such as earthquakes, the COVID-19 pandemic, and the Suez Canal crisis. To promote the adoption of AI in supply chain management and leverage its benefits, it is important to investigate the factors influencing AI adoption in supply chain to resolve supply chain disruptions. This research explores the multifaceted dynamics of adopting artificial intelligence (AI) in supply chain management through the lens of the Technology-Organization-Environment (TOE) framework. Drawing from a comprehensive review of existing literature, the study identifies critical factors influencing AI adoption across three key contexts: technological, organizational, and environmental. Within the technological context, compatibility and complexity emerge as pivotal factors, facilitating seamless integration of AI with existing systems while addressing implementation challenges through comprehensive training and user-friendly interfaces. Organizational factors, particularly top management support, are found to play a decisive role in driving AI initiatives by ensuring strategic alignment, resource allocation, and fostering an innovation-friendly culture. The study underscores the significance of governmental regulations and support mechanisms in creating an enabling environment for AI adoption, enhancing clarity, reducing compliance risks, and incentivizing investment in AI technologies. Managerial implications highlight the importance of organizational readiness, cultural alignment, and strategic planning in leveraging AI to enhance supply chain resilience and competitive advantage. By strategically navigating these factors, organizations can optimize AI adoption efforts, thereby enhancing operational efficiencies, decision-making capabilities, and overall supply chain performance. In conclusion, this research contributes a nuanced understanding of AI adoption in supply chain management, offering actionable insights for businesses to effectively integrate AI technologies and navigate the complexities of the digital transformation era.
Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2020). Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03620-w.
Badghish, S., & Soomro, Y. A. (2024). Artificial Intelligence Adoption by SMEs to Achieve Sustainable Business Performance: Application of Technology–Organization–Environment Framework. Sustainability (Switzerland) , 16(5).
https://doi.org/10.3390/su16051864
Baral, M. M., Chittipaka, V., Pal, S. K., Mukherjee, S., & Shyam, H. S. (2023). Investigating the factors of blockchain technology influencing food retail supply chain management: a study using TOE framework. Statistics in Transition New Series, 24(5). https://doi.org/10.59170/stattrans-2023-067
Bialas, C., Bechtsis, D., Aivazidou, E., Achillas, C., & Aidonis, D. (2023). A Holistic View on the Adoption and Cost-Effectiveness of Technology-Driven Supply Chain Management Practices in Healthcare. Sustainability (Switzerland), 15(6).
https://doi.org/10.3390/su15065541
Charles, V., Emrouznejad, A., & Gherman, T. (2023). A critical analysis of the integration of blockchain and artificial intelligence for supply chain. Annals of Operation Research/Annals of Operations Research, 327(1), 7–47.
https://doi.org/10.1007/s10479-023-05169-w
Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting and Social Change, 170.
https://doi.org/10.1016/j.techfore.2021.120880
Chittipaka, V., Kumar, S., Sivarajah, U., Bowden, J. L. H., & Baral, M. M. (2023). Blockchain Technology for Supply Chains operating in emerging markets: an empirical examination of technology-organization-environment (TOE) framework. Annals of Operations Research, 327(1). https://doi.org/10.1007/s10479-022-04801-5
Chiu, C.-Y., Chen, S., & Chen, C.-L. (2017). An integrated perspective of TOE framework and innovation diffusion in broadband mobile applications adoption by enterprises. International Journal of Management, Economics and Social Sciences (IJMESS), 6(1).
Cohen, M. C., & Tang, C. S. (2024, February 16). The role of AI in developing Resilient Supply Chains: Gjia. Georgetown Journal of International Affairs.
https://gjia.georgetown.edu/2024/02/05/the-role-of-ai-in-developing-resilient-supply-chains/#:~:text=In%20addition%20to%20real%2Dtime,shipping%20delays%2C%20among%20other%20issues.
Dash, R., McMurtrey, M., Rebman, C., & Kar, U. K. (2019). Application of artificial intelligence in automation of supply chain management. Journal of Strategic Innovation and Sustainability, 14(3). https://doi.org/10.33423/jsis.v14i3.2105
Dora, M., Kumar, A., Mangla, S. K., Pant, A., & Kamal, M. M. (2022). Critical success factors influencing artificial intelligence adoption in food supply chains. International Journal of Production Research, 60(14). https://doi.org/10.1080/00207543.2021.1959665
Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., Roubaud, D., & Foropon, C. (2019). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59(1), 110–128. https://doi.org/10.1080/00207543.2019.1582820
Elluru, S., Gupta, H., Kaur, H., & Singh, S. P. (2017). Proactive and reactive models for disaster resilient supply chain. Annals of Operation Research/Annals of Operations Research, 283(1–2), 199–224. https://doi.org/10.1007/s10479-017-2681-2
Farah, M. a. B., Ukwandu, E., Hindy, H., Brosset, D., Bures, M., Andonovic, I., & Bellekens, X. (2022). Cyber security in the Maritime industry: A Systematic survey of recent advances and future trends. Information, 13(1), 22. https://doi.org/10.3390/info13010022
Fu, H. P., Chang, T. H., Lin, S. W., Teng, Y. H., & Huang, Y. Z. (2023). Evaluation and adoption of artificial intelligence in the retail industry. International Journal of Retail and Distribution Management, 51(6). https://doi.org/10.1108/IJRDM-12-2021-0610
Guan, W., Ding, W., Zhang, B., Verny, J., & Hao, R. (2023). Do supply chain related factors enhance the prediction accuracy of blockchain adoption? A machine learning approach. Technological Forecasting and Social Change, 192.
https://doi.org/10.1016/j.techfore.2023.122552
Ineak. (2023, September 11). Case study: Toyota’s response to the 2011 earthquake and Tsunami. Toyota Management System. https://www.ineak.com/case-study-toyotas-response-to-the-2011-earthquake-and-tsunami/#:~:text=Immediate%20Impact%20of%20the%20Disaster&text=Toyota%2C%20like%20many%20Japanese%20automakers,production%20of%20over%20150%2C000%20vehicles.
Issa, H., Jabbouri, R., & Palmer, M. (2022). An artificial intelligence (AI)-readiness and adoption framework for AgriTech firms. Technological Forecasting and Social Change, 182, 121874.
Kalaitzi, D., & Tsolakis, N. (2022). Supply chain analytics adoption: Determinants and impacts on organisational performance and competitive advantage. International Journal of Production Economics, 248. https://doi.org/10.1016/j.ijpe.2022.108466
Kersting, K., & Meyer, U. (2018). From big data to big artificial intelligence? KI. Künstliche Intelligenz/KI - Künstliche Intelligenz, 32(1), 3–8. https://doi.org/10.1007/s13218-017-0523-7
Kumar S, V., & Sheshadri, K. (2019). Applications of Artificial Intelligence in Academic Libraries. International Journal of Computer Sciences and Engineering, 7(16). https://doi.org/10.26438/ijcse
Kumar, A., Mani, V., Jain, V., Gupta, H., & Venkatesh, V. G. (2023). Managing healthcare supply chain through artificial intelligence (AI): A study of critical success factors. Computers and Industrial Engineering, 175. https://doi.org/10.1016/j.cie.2022.108815
Merhi, M. I., & Harfouche, A. (2023). Enablers of artificial intelligence adoption and implementation in production systems. International Journal of Production Research. https://doi.org/10.1080/00207543.2023.2167014
Molopa, T. (2023). Factors affecting the adoption of artificial intelligence (AI) in the supply chain and logistics lndustry [Dissertation, University of the Western Cape]. https://etd.uwc.ac.za/handle/11394/10606
Moosavi, J., Fathollahi-Fard, A. M., & Dulebenets, M. A. (2022, June 1). Supply chain disruption during the COVID-19 pandemic: Recognizing potential disruption management strategies. International journal of disaster risk reduction : IJDRR.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9027543/
Mukherjee, S., Nagariya, R., Mathiyazhagan, K., Baral, M. M., Pavithra, M. R., & Appolloni, A. (2024). Artificial intelligence-based reverse logistics for improving circular economy performance: a developing country perspective. International Journal of Logistics Management. https://doi.org/10.1108/IJLM-03-2023-0102
Notteboom, T., Rodrigue, J.-P., & Pallis, A. (2024, April 29). Blockage of the Suez Canal, March 2021: Port Economics, management and policy. Port Economics, Management and Policy | A comprehensive analysis of the port industry.
https://porteconomicsmanagement.org/pemp/contents/part6/port-resilience/suez-canal-blockage-2021/
Paul, S. K., Riaz, S., & Das, S. (2022). Adoption of Artificial Intelligence in Supply Chain Risk Management: An Indian Perspective. Journal of Global Information Management, 30(8), 1–18. https://doi.org/10.4018/jgim.307569
Reim, W., Åström, J., & Eriksson, O. (2020). Implementation of Artificial Intelligence (AI): A Roadmap for Business model innovation. AI, 1(2), 180–191.
https://doi.org/10.3390/ai1020011
Shahzad, K., Zhang, Q., Ashfaq, M., Zafar, A. U., & Ahmad, B. (2024). Pre- to post-adoption of blockchain technology in supply chain management: Influencing factors and the role of firm size. Technological Forecasting and Social Change, 198.
https://doi.org/10.1016/j.techfore.2023.122989
Sharabati, A. A. A., Awawdeh, H. Z., Sabra, S., Shehadeh, H. K., Allahham, M., & Ali, A. (2024). The role of artificial intelligence on digital supply chain in industrial companies mediating effect of operational efficiency. Uncertain Supply Chain Management, 12(3), 1867–1878. https://doi.org/10.5267/j.uscm.2024.2.016
Supply Chain Disruption. Arena. (2024, May 22).
https://www.arenasolutions.com/resources/glossary/supply-chain-disruption/#:~:text=A%20supply%20chain%20disruption%20is,%2C%20regional%20conflicts%2C%20and%20pandemics.
wael AL-khatib, A. (2023). Drivers of generative artificial intelligence to fostering exploitative and exploratory innovation: A TOE framework. Technology in Society, 75. https://doi.org/10.1016/j.techsoc.2023.102403
Zhong, Y., & Moon, H. C. (2023). Investigating the Impact of Industry 4.0 Technology through TOE-Based Innovation Model. Systems, 11(6).
https://doi.org/10.3390/systems11060277
