Conceptual Framework for AI Enabled Learning and Sustainable Organizational Development

Anas Abudaqa; Itoimiham Noburu; Zafarya Arsean

doi:10.34306/itsdi.v8i1.749

Authors

Anas Abudaqa Abu Dhabi National Oil Company United Arab Emirates https://orcid.org/0000-0002-1375-0818
Itoimiham Noburu University Ryukyu Japan
Zafarya Arsean Ijiis Incorporation Singapore

DOI:

https://doi.org/10.34306/itsdi.v8i1.749

Keywords:

Artificial Intelligence, Learning Ecosystem, Digital Innovation Strategy, Sustainable Development, Technology Acceptance

Abstract

Artificial Intelligence (AI) offers opportunities to transform organizational learning and strengthen sustainable development. However, existing research often examines AI capability, organizational learning, digital innovation strategy, and sustainability separately. This study aims to develop an integrated conceptual framework explaining how AI-enabled organizational learning may contribute to sustainable organizational development. An integrative literature review was conducted using structured searches of Scopus, Web of Science, and ScienceDirect, supplemented by citation tracking through Google Scholar. Thirty-one scholarly sources were synthesized according to their theoretical constructs, explanatory mechanisms, and levels of analysis. The framework integrates the Resource-Based View, Dynamic Capability Theory, Technology-Organization-Environment framework, and technology acceptance theories. The study proposes an Artificial Intelligence-Enabled Learning Ecosystem (AILE) framework comprising four direct propositions linking AI Capability, AI-Enabled Learning Ecosystem, Digital Innovation Strategy, and Sustainable Organizational Development. Two additional propositions identify employee engagement and technology acceptance, alongside resource efficiency and AI governance, as moderating conditions. The framework also introduces organizational sustainability indicators, a resource cost-benefit assessment, and a four-stage implementation roadmap. The proposed framework offers a theoretical foundation for examining how AI-supported learning can contribute to organizational resilience, workforce development, and responsible digital innovation. Its potential relevance extends to SDGs 4, 8, and 9. As the framework remains conceptual, longitudinal and multilevel empirical studies are required to validate the proposed relationships and assess their practical effectiveness.

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References

Y. K. Dwivedi, L. Hughes, E. Ismagilova, G. Aarts, C. Coombs, T. Crick et al., “Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” International Journal of Information Management, vol. 57, p. 101994, 2021. [Online]. Available: https://doi.org/10.1016/j.ijinfomgt.2019.08.002

S.-L. Wamba-Taguimdje, S. Fosso Wamba, J. R. Kala Kamdjoug, and C. E. Tchatchouang Wanko, “Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects,” Business Process Management Journal, vol. 26, no. 7, pp. 1893–1924, 2020. [Online]. Available: https://doi.org/10.1108/BPMJ-10-2019-0411

L. Argote and E. Miron-Spektor, “Organizational learning: From experience to knowledge,” Organization Science, vol. 22, no. 5, pp. 1123–1137, 2011. [Online]. Available: https://doi.org/10.1287/orsc.1100.0621

P. Mikalef and M. Gupta, “Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance,” Information & Management, vol. 58, no. 3, p. 103434, 2021. [Online]. Available: https://doi.org/10.1016/j.im.2021.103434

G. Vial, “Understanding digital transformation: A review and a research agenda,” The Journal of Strategic Information Systems, vol. 28, no. 2, pp. 118–144, 2019. [Online]. Available: https://doi.org/10.1016/j.jsis.2019.01.003

J. J¨ohnk, M. Weißert, and K. Wyrtki, “Ready or not, AI comes—an interview study of organizational AI readiness factors,” Business & Information Systems Engineering, vol. 63, no. 1, pp. 5–20, 2021. [Online]. Available: https://doi.org/10.1007/s12599-020-00676-7

J. Holmstr¨om, “From AI to digital transformation: The AI readiness framework,” Business Horizons, vol. 65, no. 3, pp. 329–339, 2022. [Online]. Available: https://doi.org/10.1016/j.bushor.2021.03.006

A. Hanelt, R. Bohnsack, D. Marz, and C. Antunes Marante, “A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change,” Journal of Management Studies, vol. 58, no. 5, pp. 1159–1197, 2021. [Online]. Available: https://doi.org/10.1111/joms.12639

I. Guandalini, “Sustainability through digital transformation: A systematic literature review for research guidance,” Journal of Business Research, vol. 148, pp. 456–471, 2022. [Online]. Available: https://doi.org/10.1016/j.jbusres.2022.05.003

M. Feeney, T. Grohnert, W. Gijselaers, and P. Martens, “Organizations, learning, and sustainability: A cross-disciplinary review and research agenda,” Journal of Business Ethics, vol. 184, no. 1, pp. 217–235, 2023. [Online]. Available: https://doi.org/10.1007/s10551-022-05072-7

F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly, vol. 13, no. 3, pp. 319–340, 1989. [Online]. Available: https://doi.org/10.2307/249008

V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Quarterly, vol. 27, no. 3, pp. 425–478, 2003. [Online]. Available: https://doi.org/10.2307/30036540

I. Nonaka, “A dynamic theory of organizational knowledge creation,” Organization Science, vol. 5, no. 1, pp. 14–37, 1994. [Online]. Available: https://doi.org/10.1287/orsc.5.1.14

Y. K. Dwivedi, N. Kshetri, L. Hughes, E. L. Slade, A. Jeyaraj, A. K. Kar et al., “‘So what if ChatGPT wrote it?’ Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy,” International Journal of Information Management, vol. 71, p. 102642, 2023. [Online]. Available: https://doi.org/10.1016/j.ijinfomgt.2023.102642

U. Rahardja, F. P. Oganda, G. P. Cesna, and Y. M. Kareem, “Digital twin technology for smart campus management in higher education,” International Transactions on Education Technology (ITEE), vol. 5, no. 1, pp. 1–12, 2026. [Online]. Available: https://doi.org/10.33050/itee.v5i1.1119

F. Tajri M, O. F. Ngabito, D. Huldiansyah, A. N. Sofyan, M. B. Kurniawan, and S. A. Pratama, “Design and development of an augmented reality learning medium for the conservation of the endemic flora Balikpapan ginger (Etlingera balikpapanensis),” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 8, no. 1, pp. 1–11, 2026. [Online]. Available: https://doi.org/10.34306/itsdi.v8i1.728

M. Fahrurrozi, S. R. Djalilah, D. Yunitasari, M. Mohzana, and H. S. Butt, “Development of technopreneurship based e-modules on entrepreneurial learning,” Aptisi Transactions on Technopreneurship (ATT), vol. 8, no. 3, pp. 795–808, 2026. [Online]. Available: https://doi.org/10.34306/att.v8i3.680

K. S. R. Warner and M. W¨ager, “Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal,” Long Range Planning, vol. 52, no. 3, pp. 326–349, 2019. [Online]. Available: https://doi.org/10.1016/j.lrp.2018.12.001

S. Nambisan, M. Wright, and M. Feldman, “The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes,” Research Policy, vol. 48, no. 8, p. 103773, 2019. [Online]. Available: https://doi.org/10.1016/j.respol.2019.03.018

S. Bresciani, K.-H. Huarng, A. Malhotra, and A. Ferraris, “Digital transformation as a springboard for product, process and business model innovation,” Journal of Business Research, vol. 128, pp. 204–210, 2021. [Online]. Available: https://doi.org/10.1016/j.jbusres.2021.02.003

V. Venkatesh and H. Bala, “Technology acceptance model 3 and a research agenda on interventions,” Decision Sciences, vol. 39, no. 2, pp. 273–315, 2008. [Online]. Available: https://doi.org/10.1111/j.1540-5915.2008.00192.x

S. Oreg, “Resistance to change: Developing an individual differences measure,” Journal of Applied Psychology, vol. 88, no. 4, pp. 680–693, 2003. [Online]. Available: https://doi.org/10.1037/0021-9010.88.4.680

U. Udin, S. Udchachone, V. Zumitzavan, and N. W. Chien, “Examining adaptive leadership in business via literature review and bibliometric analysis,” Aptisi Transactions on Technopreneurship (ATT), vol. 8, no. 2, pp. 508–521, 2026. [Online]. Available: https://doi.org/10.34306/att.v8i2.753

E. Strubell, A. Ganesh, and A. McCallum, “Energy and policy considerations for deep learning in NLP,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 3645–3650. [Online]. Available: https://doi.org/10.18653/v1/P19-1355

E. Brynjolfsson, D. Rock, and C. Syverson, “The productivity J-curve: How intangibles complement general purpose technologies,” American Economic Journal: Macroeconomics, vol. 13, no. 1, pp. 333–372, 2021. [Online]. Available: https://doi.org/10.1257/mac.20180386

L. Floridi, J. Cowls, M. Beltrametti, R. Chatila, P. Chazerand, V. Dignum et al., “AI4People—an ethical framework for a good AI society: Opportunities, risks, principles, and recommendations,” Minds and Machines, vol. 28, no. 4, pp. 689–707, 2018. [Online]. Available: https://doi.org/10.1007/s11023-018-9482-5

D. J. Teece, “Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance,” Strategic Management Journal, vol. 28, no. 13, pp. 1319–1350, 2007. [Online]. Available: https://doi.org/10.1002/smj.640

J. Barney, “Firm resources and sustained competitive advantage,” Journal of Management, vol. 17, no. 1, pp. 99–120, 1991. [Online]. Available: https://doi.org/10.1177/014920639101700108

L. G. Tornatzky, M. Fleischer, and A. K. Chakrabarti, The Processes of Technological Innovation. Lexington, MA: Lexington Books, 1990.

E. Jaakkola, “Designing conceptual articles: Four approaches,” AMS Review, vol. 10, no. 1–2, pp. 18–26, 2020. [Online]. Available: https://doi.org/10.1007/s13162-020-00161-0

R. J. Torraco, “Writing integrative literature reviews: Guidelines and examples,” Human Resource Development Review, vol. 4, no. 3, pp. 356–367, 2005. [Online]. Available: https://doi.org/10.1177/1534484305278283

K. J. Preacher, M. J. Zyphur, and Z. Zhang, “A general multilevel SEM framework for assessing multilevel mediation,” Psychological Methods, vol. 15, no. 3, pp. 209–233, 2010. [Online]. Available: https://doi.org/10.1037/a0020141

C. J. M. Maas and J. J. Hox, “Sufficient sample sizes for multilevel modeling,” Methodology, vol. 1, no. 3, pp. 86–92, 2005. [Online]. Available: https://doi.org/10.1027/1614-2241.1.3.86

D. J. Teece, G. Pisano, and A. Shuen, “Dynamic capabilities and strategic management,” Strategic Management Journal, vol. 18, no. 7, pp. 509–533, 1997, [Online]. Available: https://doi.org/10.1002/(SICI)1097-0266(199708)18:7⟨509::AID-SMJ882⟩3.0.CO;2-Z.

K. M. Eisenhardt and J. A. Martin, “Dynamic capabilities: What are they?” Strategic Management Journal, vol. 21, no. 10–11, pp. 1105–1121, 2000, [Online]. Available: https://doi.org/10.1002/1097-0266(200010/11)21:10/11⟨1105::AID-SMJ133⟩3.0.CO;2-E.