Theoretical Perspectives on Digital Transformation and Practical Guidelines for Organizations and Policymakers in Advancing Sustainable Development within Regional Economic Contexts
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Abstract
This study explores how the adoption of Artificial Intelligence (AI) and Enterprise Resource Planning (ERP) systems influences Environment, Society, and Governance (ESG) development within a regional organization in Ubon Ratchathani Province, Thailand. Using a qualitative case study approach, data were collected through in-depth interviews, direct observation, and document analysis, and analyzed using thematic and matrix coding in NVivo. The results revealed that AI and ERP systems enhanced operational transparency, accountability, and efficiency through data centralization and process automation. Notably, digital adoption improved environmental monitoring, employee collaboration, and governance compliance, aligning technological transformation with sustainability outcomes. Leadership commitment and employee empowerment were identified as key mediators driving positive organizational culture and acceptance of digital systems. This study contributes to the theoretical understanding of digital transformation by demonstrating how AI and ERP systems can advance ESG principles in regional enterprises. The findings offer practical implications for policymakers and managers seeking to promote sustainable digital transformation in developing economies.
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References
Alzahrani, S. (2025). Assessing artificial intelligence and advanced analytics adoption: A technological, organizational, and environmental perspective. Edelweiss Applied Science and Technology, 9(7), 312–325. https://doi.org/10.55214/25768484.v9i7.8567
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Díaz-Arancibia, J., Hochstetter-Diez, J., Bustamante-Mora, A., Sepúlveda-Cuevas, S., Albayay, I., & Arango-López, J. (2024). Navigating Digital Transformation and Technology Adoption: A Literature Review from Small and Medium-Sized Enterprises in Developing Countries. Sustainability, 16(14), 5946. https://doi.org/10.3390/su16145946
Dittmar, F., Sposato, M., & Portillo, J.P.V. (2025). Bridging environmental wisdom and artificial intelligence: A transformation framework for sustainable organizations. Business Strategy & Development. 8, e70113. https://doi.org/10.1002/bsd2.70113
Guandalini, I. (2022). Sustainability through digital transformation: A systematic literature review for research guidance. Journal of Business Research, 148, 456–471. https://doi.org/10.1016/j.jbusres.2022.05.003
Mocanu, D., Preeda(Naum), D., State, O., & Țală, M. (2025). Digital transformation and sustainability: Mapping research gaps and thematic connections. Cactus Tourism Journal, 7(1), 45–62. https://doi.org/10.24818/cts/7/2025/1.07
Mutambik, I. (2024). Digital transformation as a driver of sustainability performance—A study from freight and logistics industry. Sustainability, 16(10), 4310. https://doi.org/10.3390/su16104310
Oncioiu, L., Mândricel, D.A., & Hojda, M.H. (2025). Artificial intelligence-enabled digital transformation in circular logistics: A structural equation model of organizational, technological, and environmental drivers. Logistics, 9(3), 102. https://doi.org/10.3390/logistics9030102
Prayoga Kasmo, A.B., Mor, S., Nugroho, L., Rohadi, M., & Purnama, A. (2025). Integrating corporate strategy with digitaltrans formation, cybersecurity, and sustainability. Jurnal Lemhannas RI, 13(1), 1062. https://doi.org/10.55960/jlri.v13i1.1062
Stroumpoulis, P.T., Kopanaki, E., & Chountalas, P.T. (2024). Enhancing sustainable supply chain management through digital transformation: A comparative case study analysis. Sustainability, 16(16), 6778. https://doi.org/10.3390/su16166778
Sun, A. (2024). Enterprise digital transformation and environmental performance: Based on the intermediary effect of green innovation. Journal of Cases on Information Technology, 26(2), 221–240. https://doi.org/10.4018/jcit.347665
