AI-Driven Sustainability Strategies: A Business Model for Emerging Economies
Keywords:
business sustainability, artificial intelligence, emerging economies, ESG, digital governance, adaptive models
Abstract
Business sustainability and artificial intelligence (AI) have emerged as strategic drivers of organizational transformation, especially in emerging economies. However, the effective adoption of AI in these contexts requires a systemic approach that integrates operational, organizational, and strategic dimensions. This article proposes an adaptive business sustainability model driven by AI, developed through a mixed-methods approach combining exploratory factor analysis, hierarchical clustering, multiple correspondence analysis, and qualitative coding from semi-structured interviews. The results revealed three core dimensions (sustainable productivity, ethical algorithmic governance, and smart environmental management), four technological adoption profiles, and seven key emerging variables. Based on these findings, the study presents a visual framework composed of three interconnected and feedback-driven levels, designed to guide the progressive implementation of AI in organizations seeking to align performance with ESG objectives and responsible digital transformation processes.Downloads
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References
Arenas-Marquez, F. J., Paternina-Arboleda, C. D., & García, A. (2021). AI in logistics: Optimization and sustainability perspectives. Journal of Cleaner Production, 289, 125636. https://doi.org/10.1016/j.jclepro.2021.125636
Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2021). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, 164, 105119. https://doi.org/10.1016/j.resconrec.2020.105119
Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2021). Industry 4.0 technologies assessment: A sustainability perspective. International Journal of Production Economics, 229, 107776. https://doi.org/10.1016/j.ijpe.2020.107776
Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability and Transparency, 149–159.
Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The next generation of responsible AI: A review and research agenda. International Journal of Information Management, 60, 102383. https://doi.org/10.1016/j.ijinfomgt.2021.102383
Del Río, P., Morales, I., & Sandoval, D. (2023). Artificial intelligence and sustainable innovation in Latin America: An institutional framework. Sustainable Futures, 5, 100120. https://doi.org/10.1016/j.sftr.2023.100120
Dwivedi, Y. K., Hughes, D. L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Ghobakhloo, M., Iranmanesh, M., Rezaei, S., & Maroufkhani, P. (2021). Industry 4.0, smart factory, and sustainable manufacturing: An integrative framework. European Journal of Operational Research, 297(2), 541–556. https://doi.org/10.1016/j.ejor.2021.05.048
Guszcza, J., Lewis, H., & Evans-Greenwood, P. (2020). Human-centered AI: The key to successful AI implementation. Deloitte Review, (27), 12–25.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. Technological Forecasting and Social Change, 152, 119899. https://doi.org/10.1016/j.techfore.2020.119899
Lee, J., Kao, H. A., & Yang, S. (2021). Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP, 16, 3–8. https://doi.org/10.1016/j.procir.2021.03.002
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. A. (2019). Investigating the effects of big data analytics capabilities on firm performance: The mediating role of dynamic capabilities. Information & Management, 56(8), 103207. https://doi.org/10.1016/j.im.2018.12.003
Moktadir, M. A., Rahman, T., Rahman, M. H., Ali, S. M., & Paul, S. K. (2020). Drivers to sustainable manufacturing practices and circular economy: A perspective of leather industries in Bangladesh. Journal of Cleaner Production, 251, 119737. https://doi.org/10.1016/j.jclepro.2019.119737
Nudurupati, S. S., Tebboune, S., & Hardman, G. (2021). Performance measurement and management in operations: Research gaps and new directions. International Journal of Operations & Production Management, 41(3), 260–303. https://doi.org/10.1108/IJOPM-12-2019-0777
Raj, A., Dwivedi, G., Sharma, A., & Tiwari, M. K. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. Technological Forecasting and Social Change, 159, 120153. https://doi.org/10.1016/j.techfore.2020.120153
Rodríguez, J., & Ceballos, S. (2022). Inteligencia artificial y sostenibilidad en América Latina: Avances y desafíos. Revista de Innovación y Sociedad, 10(2), 115–132.
Rodríguez, J., & Ceballos, S. (2022). Inteligencia artificial y sostenibilidad en América Latina: Avances y desafíos. Revista de Innovación y Sociedad, 10(2), 115–132.
Shamim, S., Cang, S., Yu, H., & Li, Y. (2022). Leadership behaviors, emotional intelligence, and digital transformation: A multilevel study. Technological Forecasting and Social Change, 174, 121273. https://doi.org/10.1016/j.techfore.2021.121273
Singh, R. K., Gupta, S., & Gunasekaran, A. (2022). AI adoption in SMEs for sustainable performance: A multistage model. Sustainable Production and Consumption, 29, 13–27. https://doi.org/10.1016/j.spc.2021.09.004
Sodhi, M. S., & Tang, C. S. (2022). Research opportunities in AI and supply chain management. International Journal of Production Research, 60(12), 3812–3822. https://doi.org/10.1080/00207543.2021.1988774
Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance and environmental sustainability. Technological Forecasting and Social Change, 158, 120118. https://doi.org/10.1016/j.techfore.2020.120118
Wamba, S. F., Kala Kamdjoug, J. R., Tchatchouang Wanko, C. E., & Fosso Wamba, S. (2021). Influence of artificial intelligence (AI) on firm performance and environmental sustainability. Technological Forecasting and Social Change, 158, 120118. https://doi.org/10.1016/j.techfore.2020.120118
Zhang, Y., Zhang, G., Liu, Y., & Xie, Y. (2022). Real-time energy optimization using deep reinforcement learning in intelligent manufacturing. Journal of Manufacturing Systems, 62, 393–404. https://doi.org/10.1016/j.jmsy.2021.11.004
Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2021). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, 164, 105119. https://doi.org/10.1016/j.resconrec.2020.105119
Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2021). Industry 4.0 technologies assessment: A sustainability perspective. International Journal of Production Economics, 229, 107776. https://doi.org/10.1016/j.ijpe.2020.107776
Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability and Transparency, 149–159.
Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The next generation of responsible AI: A review and research agenda. International Journal of Information Management, 60, 102383. https://doi.org/10.1016/j.ijinfomgt.2021.102383
Del Río, P., Morales, I., & Sandoval, D. (2023). Artificial intelligence and sustainable innovation in Latin America: An institutional framework. Sustainable Futures, 5, 100120. https://doi.org/10.1016/j.sftr.2023.100120
Dwivedi, Y. K., Hughes, D. L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Ghobakhloo, M., Iranmanesh, M., Rezaei, S., & Maroufkhani, P. (2021). Industry 4.0, smart factory, and sustainable manufacturing: An integrative framework. European Journal of Operational Research, 297(2), 541–556. https://doi.org/10.1016/j.ejor.2021.05.048
Guszcza, J., Lewis, H., & Evans-Greenwood, P. (2020). Human-centered AI: The key to successful AI implementation. Deloitte Review, (27), 12–25.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. Technological Forecasting and Social Change, 152, 119899. https://doi.org/10.1016/j.techfore.2020.119899
Lee, J., Kao, H. A., & Yang, S. (2021). Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP, 16, 3–8. https://doi.org/10.1016/j.procir.2021.03.002
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. A. (2019). Investigating the effects of big data analytics capabilities on firm performance: The mediating role of dynamic capabilities. Information & Management, 56(8), 103207. https://doi.org/10.1016/j.im.2018.12.003
Moktadir, M. A., Rahman, T., Rahman, M. H., Ali, S. M., & Paul, S. K. (2020). Drivers to sustainable manufacturing practices and circular economy: A perspective of leather industries in Bangladesh. Journal of Cleaner Production, 251, 119737. https://doi.org/10.1016/j.jclepro.2019.119737
Nudurupati, S. S., Tebboune, S., & Hardman, G. (2021). Performance measurement and management in operations: Research gaps and new directions. International Journal of Operations & Production Management, 41(3), 260–303. https://doi.org/10.1108/IJOPM-12-2019-0777
Raj, A., Dwivedi, G., Sharma, A., & Tiwari, M. K. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. Technological Forecasting and Social Change, 159, 120153. https://doi.org/10.1016/j.techfore.2020.120153
Rodríguez, J., & Ceballos, S. (2022). Inteligencia artificial y sostenibilidad en América Latina: Avances y desafíos. Revista de Innovación y Sociedad, 10(2), 115–132.
Rodríguez, J., & Ceballos, S. (2022). Inteligencia artificial y sostenibilidad en América Latina: Avances y desafíos. Revista de Innovación y Sociedad, 10(2), 115–132.
Shamim, S., Cang, S., Yu, H., & Li, Y. (2022). Leadership behaviors, emotional intelligence, and digital transformation: A multilevel study. Technological Forecasting and Social Change, 174, 121273. https://doi.org/10.1016/j.techfore.2021.121273
Singh, R. K., Gupta, S., & Gunasekaran, A. (2022). AI adoption in SMEs for sustainable performance: A multistage model. Sustainable Production and Consumption, 29, 13–27. https://doi.org/10.1016/j.spc.2021.09.004
Sodhi, M. S., & Tang, C. S. (2022). Research opportunities in AI and supply chain management. International Journal of Production Research, 60(12), 3812–3822. https://doi.org/10.1080/00207543.2021.1988774
Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance and environmental sustainability. Technological Forecasting and Social Change, 158, 120118. https://doi.org/10.1016/j.techfore.2020.120118
Wamba, S. F., Kala Kamdjoug, J. R., Tchatchouang Wanko, C. E., & Fosso Wamba, S. (2021). Influence of artificial intelligence (AI) on firm performance and environmental sustainability. Technological Forecasting and Social Change, 158, 120118. https://doi.org/10.1016/j.techfore.2020.120118
Zhang, Y., Zhang, G., Liu, Y., & Xie, Y. (2022). Real-time energy optimization using deep reinforcement learning in intelligent manufacturing. Journal of Manufacturing Systems, 62, 393–404. https://doi.org/10.1016/j.jmsy.2021.11.004
Published
2021-01-21
How to Cite
Arango, D. (2021). AI-Driven Sustainability Strategies: A Business Model for Emerging Economies. IJMSOR: International Journal of Management Science & Operation Research, 10(1), 22-38. Retrieved from https://ijmsoridi.com/index.php/ijmsor/article/view/133
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