Integrating Digital Technology and Resource Management Practices to Enhance Sustainable Production Systems: Evidence from Agro-Fisheries Value Chains
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Digital transformation has become a critical factor in enhancing the sustainability of production systems within agro-fisheries value chains facing efficiency and resource management challenges. This study aims to analyze the effect of digital technology adoption on resource management practices and its impact on sustainable production systems. A quantitative approach was employed using a survey of agro-fisheries actors and analyzed through structural equation modeling. The results indicate that digital technology adoption has a positive and significant effect on resource management practices and sustainable production systems. Furthermore, resource management practices partially mediate the relationship between digital technology adoption and production sustainability. These findings highlight that the effectiveness of digital transformation largely depends on the integration of technology with sound managerial practices. This study contributes to the understanding of how digital technology and resource management integration can drive sustainable production systems in agro-fisheries value chains.
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Adebayo, T. S., Kirikkaleli, D., & Oladipupo, S. D. (2024). Digital technologies, resource efficiency, and sustainable production in agri-food systems. Journal of Cleaner Production, 420, 140567. https://doi.org/10.1016/j.jclepro.2023.140567
Bag, S., Gupta, S., & Kumar, S. (2021). Industry 4.0 adoption and its impact on sustainable manufacturing performance. International Journal of Production Economics, 231, 107862. https://doi.org/10.1016/j.ijpe.2020.107862
Benitez, J., Ruiz, L., Castillo, A., & Llorens, J. (2022). How corporate social responsibility activities influence firm performance: The role of digital transformation. Technological Forecasting and Social Change, 174, 121274. https://doi.org/10.1016/j.techfore.2021.121274
Cheah, J. H., Memon, M. A., Richard, J. E., Ting, H., & Cham, T. H. (2024). Digital transformation and sustainability performance: A resource-based view. Business Strategy and the Environment, 33(2), 1042–1058. https://doi.org/10.1002/bse.3491
Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., & Papadopoulos, T. (2023). Digital transformation and sustainable supply chain performance: An integrated framework. International Journal of Production Economics, 252, 108580. https://doi.org/10.1016/j.ijpe.2022.108580
FAO. (2022). The state of world fisheries and aquaculture: Towards blue transformation. Food and Agriculture Organization of the United Nations.
Gupta, S., Modgil, S., & Gunasekaran, A. (2022). Big data analytics and sustainable supply chain management. Annals of Operations Research, 308(1–2), 343–370. https://doi.org/10.1007/s10479-020-03885-7
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2019). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage Publications.
Klerkx, L., Jakku, E., & Labarthe, P. (2020). A review of social science on digital agriculture. Agricultural Systems, 184, 102909. https://doi.org/10.1016/j.agsy.2020.102909
Kumar, A., Mangla, S. K., & Kumar, P. (2023). Sustainability-driven resource management in agri-food supply chains. Resources, Conservation and Recycling, 189, 106762. https://doi.org/10.1016/j.resconrec.2022.106762
Li, Y., & Zheng, S. (2025). Digital innovation, resource orchestration, and sustainable production performance. Journal of Business Research, 174, 114543. https://doi.org/10.1016/j.jbusres.2024.114543
Li, X., Chen, Y., & Wang, Y. (2023). Digital technology adoption and sustainable operations in food supply chains. Sustainability, 15(9), 7421. https://doi.org/10.3390/su15097421
Park, J., Kim, H., & Lee, S. (2024). Resource management capabilities and sustainability performance in agri-industries. Business Strategy and the Environment, 33(1), 221–235. https://doi.org/10.1002/bse.3369
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022). Partial least squares structural equation modeling. Handbook of Market Research, 587–632. https://doi.org/10.1007/978-3-319-57413-4_15
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2022). Big data in smart farming – A review. Agricultural Systems, 197, 103343. https://doi.org/10.1016/j.agsy.2021.103343