Artificial Intelligence Capabilities and Their Influence on Supply Chain Resilience and Performance: Insights from Agri-Food Firms in an Emerging Economy

Document Type : Research Paper

Authors

1 Faculty of Business Management

2 Malaysia University of Science and Technology (MUST)

3 Nam Can Tho University, Can Tho, Vietnam

Abstract

Artificial Intelligence (AI) is emerging as a crucial tool to enhance supply chain performance and resilience in global agricultural supply chains, which are being disrupted by market volatility and climate change. This study examines how AI capabilities are used in agribusinesses in Vietnam's Mekong Delta, a growing industry. The study intends to examine how supply chain collaboration (SC), environmental uncertainty (EU), and AI technological compatibility (AT) affect Willingness to Adopt AI (WA), as well as how these factors affect Supply Chain Resilience (SR) and Supply Chain Performance (SP). Utilizing the Resource-Based View (RBV) and the Technology–Organization–Environment (TOE) framework, the study sent a questionnaire to 223 businesses, obtaining a 94% valid response rate. This study employs SmartPLS software to perform analysis based on the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The findings indicate that AT, SC, and EU are significant determinants of the readiness for WA, with T-Values of 4.822, 6.697, and 4.378, respectively. At the same time, WA has the strongest impact on SR (T-Value = 8.031) and also influences SP (T-Value = 4.482). The results emphasize the potential of AI to reduce disruptions and enhance operational efficiency, particularly in emerging markets. The study is constrained by its geographical focus on the Mekong Delta and the reliance on cross-sectional survey data, which limit generalizability and dynamic analysis. Future studies should broaden their focus and investigate certain AI technologies to enhance comprehension of AI applications inside agricultural supply chains.

Keywords

References

Aghababaei, A., Aghababaei, F., Pignitter, M., & Hadidi, M. (2025). Artificial Intelligence in agro-food systems: From farm to fork. Foods, 14(3), 411. https://doi.org/10.3390/foods14030411
Ahmad, A., Liew, A. X., Venturini, F., Kalogeras, A., Candiani, A., Di Benedetto, G., Ajibola, S., Cartujo, P., Romero, P., Lykoudi, A., De Grandis, M. M., Xouris, C., Lo Bianco, R., Doddy, I., Elegbede, I., D’Urso Labate, G. F., García del Moral, L. F., & Martos, V. (2024). AI can empower agriculture for Global Food Security: Challenges and prospects in developing nations. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1328530
Al Hadwer, A., Tavana, M., Gillis, D., & Rezania, D. (2021). A systematic review of organizational factors impacting cloud-based technology adoption using technology-organization-environment framework. Internet of Things, 15. https://doi.org/10.1016/j.iot.2021.100407
Bačiulienė, V., Bilan, Y., Navickas, V., & Civín, L. (2023). The aspects of artificial intelligence in different phases of the food value and Supply Chain. Foods, 12(8), 1654. https://doi.org/10.3390/foods12081654
Bahrami, M., Shokouhyar, S., & Seifian, A. (2022). Big data analytics capability and supply chain performance: the mediating roles of supply chain resilience and innovation. Modern Supply Chain Research and Applications, 4(1), 62-84. https://doi.org/10.1108/MSCRA-11-2021-0021
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108
Biazar, S. M., Golmohammadi, G., Nedhunuri, R. R., Shaghaghi, S., & Mohammadi, K. (2025). Artificial Intelligence in hydrology: Advancements in soil, water resource management, and Sustainable Development. Sustainability, 17(5), 2250. https://doi.org/10.3390/su17052250
Bran, F., Alexandru Bodislav, D., Gombos, S., & Petrică Angheluță, S. (2024). Artificial Intelligence for Sustainable Agribusiness: Innovations and challenges. European Journal of Sustainable Development, 13(3), 233. https://doi.org/10.14207/ejsd.2024.v13n3p233
Bromiley, P., & Rau, D. (2016). Operations management and the resource based view: Another view. Journal of operations management, 41, 95-106. https://doi.org/10.1016/j.jom.2015.11.003
Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2023). Artificial Intelligence in Supply Chain and Operations Management: A multiple case study research. International Journal of Production Research, 62(9), 3333–3360. https://doi.org/10.1080/00207543.2023.2232050
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), 465-492. https://doi.org/10.1007/s10479-022-04801-5
Chui, M., Hazan, E., Roberts, R., Singla, A., Smaje, K., Sukharevsky, A., Yee, L., & Zemmel, R. (2023). The economic potential of Generative AI: The Next Productivity Frontier. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
Chuyen, T. T., Trinh, P. T. X., & Huy T. T. (2024). Application of artificial intelligence in reverse logistics: A case study of seafood enterprises in the Mekong Delta. In 7.1 International Conference on Digital Innovation - Sustainability Through the Lens of Sustainable Development Goals (SDG), pp. 487–495.
Cicerelli, F., & Ravetti, C. (2024). Sustainability, resilience and innovation in industrial electronics: a case study of internal, supply chain and external complexity. Journal of Economic Interaction and Coordination, 19(2), 343-372. https://doi.org/10.1007/s11403-023-00396-7
Clulow, V., Barry, C., & Gerstman, J. (2007). The resource‐based view and value: the customer‐based view of the firm. Journal of European industrial training, 31(1), 19-35. https://doi.org/10.1108/03090590710721718
Cohen, J. W. (1988). Statistical power analysis for the behavioral sciences. Lawrence ErlbaumAssociates, Hillsdale, NJ. https://doi.org/10.4324/9780203771587
Cruz, L., & Ignacio, P. S. D. A. (2023). Application of blockchain disruptive technology in agri-food chains for sustainable development, a systematic review. International Journal of Supply and Operations Management, 10(4), 523-544. 10.22034/IJSOM.2023.110040.2837
Dai, J., Geng, R., Xu, D., Shangguan, W., & Shao, J. (2024). Unveiling the impact of the congruence between artificial intelligence and explorative learning on supply chain resilience. International Journal of Operations & Production Management, 45(2), 570-593. https://doi.org/10.1108/IJOPM-12-2023-0990
El Bhilat, E. M., El Jaouhari, A., & Hamidi, L. S. (2024). Assessing the influence of artificial intelligence on Agri-Food Supply Chain Performance: The mediating effect of distribution network efficiency. Technological Forecasting and Social Change, 200, 123149. https://doi.org/10.1016/j.techfore.2023.123149
El Jaouhari, A., & Hamidi, L. S. (2024). Assessing the influence of artificial intelligence on agri-food supply chain performance: the mediating effect of distribution network efficiency. Technological Forecasting and Social Change, 200. https://doi.org/10.1016/j.techfore.2023.123149
Feng, Y., Mei, D., & Zhao, H. (2023). Auction-based deep learning-driven smart agricultural supply chain mechanism. Applied Soft Computing, 149, 111009. https://doi.org/10.1016/j.asoc.2023.111009
Food Security Information Network (FSIN). (2024). Global Report on Food Crises 2024. https://www.wfp.org/publications/global-report-food-crises-grfc 
Gu, M., Yang, L., & Huo, B. (2021). The impact of information technology usage on supply chain resilience and performance: An ambidexterous view. International journal of production economics, 232. https://doi.org/10.1016/j.ijpe.2020.107956
Gupta, S., Modgil, S., Meissonier, R., & Dwivedi, Y. K. (2024). Artificial Intelligence and Information System Resilience to cope with supply chain disruption. IEEE Transactions on Engineering Management, 71, 10496–10506. https://doi.org/10.1109/tem.2021.3116770
Ha, D. H., Duc, P. N., Luong, T. H., Duc, T. T., Ngoc, T. T., Minh, T. N., & Minh, T. N. (2024). Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta. Applied Sciences, 14(15), 6763. https://doi.org/10.3390/app14156763
Hendriksen, C. (2023). Artificial Intelligence for Supply Chain Management: Disruptive innovation or innovative disruption? Journal of Supply Chain Management, 59(3), 65–76. https://doi.org/10.1111/jscm.12304
Huang, K., Wang, K., Lee, P. K. C., & Yeung, A. C. L. (2023). The impact of Industry 4.0 on supply chain capability and Supply Chain Resilience: A dynamic resource-based view. International Journal of Production Economics, 262, 108913. https://doi.org/10.1016/j.ijpe.2023.108913
Jackson, I., Ivanov, D., Dolgui, A., & Namdar, J. (2024). Generative Artificial Intelligence in Supply Chain and Operations Management: A capability-based framework for analysis and implementation. International Journal of Production Research, 62(17), 6120–6145. https://doi.org/10.1080/00207543.2024.2309309
Juan, S. J., Li, E. Y., & Hung, W. H. (2022). An integrated model of supply chain resilience and its impact on supply chain performance under disruption. The International Journal of Logistics Management, 33(1), 339-364. https://doi.org/10.1108/IJLM-03-2021-0174
Jung, J., Maeda, M., Chang, A., Bhandari, M., Ashapure, A., & Landivar-Bowles, J. (2021). The potential of remote sensing and artificial intelligence as tools to improve the resilience of Agriculture Production Systems. Current Opinion in Biotechnology, 70, 15–22. https://doi.org/10.1016/j.copbio.2020.09.003
Kalimuthu, T., Kalpana, P., Kuppusamy, S., & Raja Sreedharan, V. (2024). Intelligent decision-making framework for Agriculture Supply Chain in emerging economies: Research opportunities and challenges. Computers and Electronics in Agriculture, 219, 108766. https://doi.org/10.1016/j.compag.2024.108766
Khan, S. A., Sheikh, A. A., Shamsi, I. R., & Yu, Z. (2025). The implications of artificial intelligence for small and medium-sized enterprises’ sustainable development in the areas of blockchain technology, Supply Chain Resilience, and closed-loop supply chains. Sustainability, 17(1), 334. https://doi.org/10.3390/su17010334
Khiem, N. M., Takahashi, Y., Yasuma, H., Oanh, D. T. H., Hai, T. N., Ut, V. N., & Kimura, N. (2022). Use of GIS and machine learning to predict disease in shrimp farmed on the east coast of the Mekong Delta, Vietnam. Fisheries science, 1-13. https://doi.org/10.1007/s12562-021-01577-8
Kumawat, P. (2024). Impact of artificial intelligence in Building Supply Chain resiliency. International Journal of Supply Chain Management, 13(6), 10–20. https://doi.org/10.59160/ijscm.v13i6.6283
Lee, K. L., Azmi, N. A., Hanaysha, J. R., Alzoubi, H. M., & Alshurideh, M. T. (2022). The effect of digital supply chain on organizational performance: An empirical study in malaysia manufacturing industry. Uncertain Supply Chain Management, 10(2), 495–510. https://doi.org/10.5267/j.uscm.2021.12.002
Linh, T. (2025). The localities in the Mekong Delta region make a significant contribution to the socio-economic development of the entire country. The Ministry of Planning and Investment of Vietnam. Retrieved from https://www.mpi.gov.vn/portal/Pages/2025-1-3/Cac-dia-phuong-trong-vung-Dong-bang-song-Cuu-Long-pww99c.aspx
Liu, Yansui, Huang, X., & Liu, Y. (2024). Detection of long-term land use and ecosystem services dynamics in the Loess Hilly-Gully region based on Artificial Intelligence and multiple models. Journal of Cleaner Production, 447, 141560. https://doi.org/10.1016/j.jclepro.2024.141560
Liu, Yong, Wang, H., Zhang, H., & Liber, K. (2016). A comprehensive support vector machine-based classification model for Soil Quality Assessment. Soil and Tillage Research, 155, 19–26. https://doi.org/10.1016/j.still.2015.07.006
Liu, Z., Costa, C., & Wu, Y. (2024). Leveraging data-driven insights to enhance supplier performance and Supply Chain Resilience. World Journal of Innovation and Modern Technology, 7(5), 125–131. https://doi.org/10.53469/wjimt.2024.07(05).15
Lockett, A., & Thompson, S. (2001). The resource-based view and economics. Journal of management, 27(6), 723-754. https://doi.org/10.1016/S0149-2063(01)00121-0
Lockett, A., Thompson, S., & Morgenstern, U. (2009). The development of the resource‐based view of the firm: A critical appraisal. International journal of management reviews, 11(1), 9-28. https://doi.org/10.1111/j.1468-2370.2008.00252.x
Mahraz, M.-I., Benabbou, L., & Berrado, A. (2022). Machine Learning in Supply Chain Management: A Systematic Literature Review. International Journal of Supply and Operations Management, 9(4), 398–416. https://doi.org/10.22034/ijsom.2021.109189.2279
Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2022). IOT, Big Data, and artificial intelligence in agriculture and Food Industry. IEEE Internet of Things Journal, 9(9), 6305–6324. https://doi.org/10.1109/jiot.2020.2998584
Muhammed, D., Ahvar, E., Ahvar, S., Trocan, M., Montpetit, M.-J., & Ehsani, R. (2024). Artificial Intelligence of Things (AIOT) for Smart Agriculture: A Review of Architectures, technologies and solutions. Journal of Network and Computer Applications, 228, 103905. https://doi.org/10.1016/j.jnca.2024.103905
Nayal, K., Raut, R., Priyadarshinee, P., Narkhede, B. E., Kazancoglu, Y., & Narwane, V. (2021). Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the covid-19 pandemic. The International Journal of Logistics Management, 33(3), 744–772. https://doi.org/10.1108/ijlm-12-2020-0493
Nguyen, T. H., Le, X. C., & Vu, T. H. L. (2022). An extended technology-organization-environment (TOE) framework for online retailing utilization in digital transformation: Empirical evidence from Vietnam. Journal of Open Innovation: Technology, Market, and Complexity, 8(4). https://doi.org/10.3390/joitmc8040200
Nwamekwe, C. O., & Igbokwe, N. C. (2024). Supply Chain Risk Management: Leveraging AI for Risk Identification, mitigation, and Resilience Planning. International Journal of Industrial Engineering, Technology & Operations Management, 2(2), 41–51. https://doi.org/10.62157/ijietom.v2i2.38
Pandey, D. K., & Mishra, R. (2024). Towards sustainable agriculture: Harnessing AI for Global Food Security. Artificial Intelligence in Agriculture, 12, 72–84. https://doi.org/10.1016/j.aiia.2024.04.003
Patalas-Maliszewska, J., Szmołda, M., & Łosyk, H. (2024). Integrating artificial intelligence into the supply chain in order to enhance sustainable production—a systematic literature review. Sustainability, 16(16), 7110. https://doi.org/10.3390/su16167110
Peggy M.L. Ng., Lit, K. K., & Cheung, C. T. (2022). Remote work as a new normal? The technology-organization-environment (TOE) context. Technology in Society, 70. https://doi.org/10.1016/j.techsoc.2022.102022
Qader, G., Junaid, M., Abbas, Q., & Mubarik, M. S. (2022). Industry 4.0 enables supply chain resilience and supply chain performance. Technological Forecasting and Social Change, 185.  https://doi.org/10.1016/j.techfore.2022.122026
Rahbari, M., Arshadi Khamseh, A., & Mohammadi, M. (2024). A novel robust probabilistic chance constrained programming and strategic analysis for Agri-food closed-loop supply chain under pandemic crisis. Soft Computing, 28(2), 1179–1214. https://doi.org/10.1007/s00500-023-09156-y
Rahbari, M., Khamseh, A. A., & Mohammadi, M. (2025a). A multi-objective robust scenario-based stochastic chance constrained programming model for sustainable closed-loop agri-food supply chain. Computers & Chemical Engineering, 194. https://doi.org/10.1016/j.compchemeng.2024.108914
Rahbari, M., Tavakkoli-Moghaddam, R., Razavi Hajiagha, S. H., & Jafari, M. J. (2025b). Wheat Supply Chain Network Design: Lesson for Resilience and Sustainability in a Situation of War and Crisis. Journal of the Knowledge Economy, 1–45. https://doi.org/10.1007/s13132-025-02682-0
Riad, M., Naimi, M., & Okar, C. (2024). Enhancing supply chain resilience through artificial intelligence: Developing a comprehensive conceptual framework for AI implementation and supply chain optimization. Logistics, 8(4), 111. https://doi.org/10.3390/logistics8040111
Richey, R. G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial Intelligence in logistics and Supply Chain Management: A Primer and roadmap for research. Journal of Business Logistics, 44(4), 532–549. https://doi.org/10.1111/jbl.12364
Ritambara, Kaushal, S., & Shubham. (2024). Frontiers of Artificial Intelligence in agricultural sector: Trends and transformations. Journal of Scientific Research and Reports, 30(10), 970–980. https://doi.org/10.9734/jsrr/2024/v30i102518
Rugji, J., Erol, Z., Taşçı, F., Musa, L., Hamadani, A., Gündemir, M. G., Karalliu, E., & Siddiqui, S. A. (2024). Utilization of ai – reshaping the future of food safety, agriculture and Food Security – A critical review. Critical Reviews in Food Science and Nutrition, 1–45. https://doi.org/10.1080/10408398.2024.2430749
Sharifmousavi, M., Kayvanfar, V., & Baldacci, R. (2024). Distributed Artificial Intelligence Application in Agri-Food Supply Chains 4.0. Procedia Computer Science, 232, 211–220. https://doi.org/10.1016/j.procs.2024.01.021
Sharma, P., Gunasekaran, A., & Subramanian, G. (2024). Enhancing supply chain: Exploring and exploiting AI capabilities. Journal of Computer Information Systems, 1–15. https://doi.org/10.1080/08874417.2024.2386527
Singh, D., Sharma, A., Singh, R. K., & Rana, P. S. (2024). Augmenting supply chain resilience through AI and Big Data. Business Process Management Journal, 31(2), 631–657. https://doi.org/10.1108/bpmj-04-2024-0260
Singh, G., Sangeeta, K., Sharma, Y. K., Kothapalli, B., Bhanushali, M. M., Sanjeev, G., & Mircetic, D. (2025). Evaluating the Application of Blockchain for Electronics Supply Chain Traceability. In Recent Trends In Engineering and Science for Resource Optimization and Sustainable Development (pp. 10-13). CRC Press.
Singh, R. K., Modgil, S., & Shore, A. (2023). Building Artificial Intelligence Enabled Resilient Supply Chain: A multi-method approach. Journal of Enterprise Information Management, 37(2), 414–436. https://doi.org/10.1108/jeim-09-2022-0326
Tornatzky, L.G.; Fleischer, M.; Chakrabarti, A.K. (1990). Processes of Technological Innovation. Lexington Books: Blue Ridge Summit, PA, USA
Tseng, C. J., & Kiang, Y. J. (2025). Optimizing Supply Chain Sustainability through AI-Driven Policies and Integrator Facility. International Journal of Supply & Operations Management, 12(1). https://doi.org/10.22034/ijsom.2024.110137.2911
Vadlamudi, S. (2019). Agri-Food System and artificial intelligence: Reconsidering imperishability. Asian Journal of Applied Science and Engineering, 7(1), 33–42. https://doi.org/10.18034/ajase.v7i1.44
Vernier, C., Loeillet, D., Thomopoulos, R., & Macombe, C. (2021). Adoption of icts in Agri-Food Logistics: Potential and limitations for Supply Chain Sustainability. Sustainability, 13(12), 6702. https://doi.org/10.3390/su13126702
Vishwakarma, L. P., Singh, R. K., Mishra, R., & Venkatesh, M. (2024). Exploring the motivations behind artificial intelligence adoption for Building Resilient Supply Chains: A systematic literature review and future research agenda. Journal of Enterprise Information Management, 37(4), 1374–1398. https://doi.org/10.1108/jeim-11-2023-0606
Wang, M., & Pan, X. (2022). Drivers of Artificial Intelligence and their effects on Supply Chain Resilience and performance: An empirical analysis on an emerging market. Sustainability, 14(24), 16836. https://doi.org/10.3390/su142416836
Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2022). Artificial Intelligence and big data analytics for supply chain resilience: A systematic literature review. Annals of Operations Research, 327(2), 605–632. https://doi.org/10.1007/s10479-022-04983-y
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