From Organizational AI Capability to Employee Enactment in Public Sector Organizations
DOI:
https://doi.org/10.61194/ijss.v7i4.2790Keywords:
organizational ai capability, employee ai learning, employee unlearning, technology enactment, sustainable performanceAbstract
Artificial intelligence capability is increasingly viewed as an important organizational resource in public-sector digital transformation, yet the possession of AI-related resources does not necessarily translate into sustained employee-level use and performance. This article develops an accountability-bounded enactment framework explaining how organizational AI capability becomes consequential through employee adaptation and work practices. Using a theory-synthesis approach supported by structured thematic and targeted literature searches, the study integrates research on AI capability, organizational learning, employee unlearning, technology enactment, AI use, sustainable employee performance, and public-sector accountability. The framework generates three main theoretical insights. First, employee learning and unlearning are not treated merely as parallel adaptive mechanisms: employee unlearning is also proposed to condition whether newly developed AI competence becomes task-integrated use. Second, where sanctioned learning opportunities remain available, procedural rigidity is expected to constrain capability-driven unlearning more strongly than capability-driven learning. Third, accountability clarity strengthens the positive relationship between task-integrated AI use and employee sustainable performance, while not eliminating other potential costs of AI use such as cognitive burden, dependence, or technostress. These relationships are theoretically derived rather than empirically tested. The framework contributes a cross-level explanation of AI capability enactment in public-sector organizations by showing how competence development, routine revision, task-integrated AI use, and accountability conditions jointly shape whether organizational AI capability produces sustainable employee outcomes.
References
Albishri, N., Rai, J. S., Attri, R., Yaqub, M. Z., & Walsh, S. T. (2026). Breaking barriers: investigating generative AI adoption and organizational use. Journal of Enterprise Information Management, 39(1), 267–288. https://doi.org/10.1108/JEIM-01-2025-0010
Almheiri, H. M., Ahmad, S. Z., Khalid, K., & Ngah, A. H. (2025). Examining the impact of artificial intelligence capability on dynamic capabilities, organizational creativity and organization performance in public organizations. Journal of Systems and Information Technology, 27(1), 1–20. https://doi.org/10.1108/JSIT-10-2022-0239
Argyris, C., & Schön, D. A. (1978). Organizational Learning: A Theory of Action Perspective. Addison-Wesley Publishing Company.
Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice-Hall.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Bibi, M., Tan, T. G., & Yao, H. (2025). Exploring the Impact of AI Capabilities on Employee Well-Being: A Mediated Moderation Analysis. SAGE Open, 15(3). https://doi.org/10.1177/21582440251361981
Bozeman, B., & Feeney, M. K. (2011). Rules and Red Tape: A Prism for Public Administration Theory and Research (1st ed.). Routledge. https://doi.org/10.4324/9781315701059
Busuioc, M. (2021). Accountable Artificial Intelligence: Holding Algorithms to Account. Public Administration Review, 81(5), 825–836. https://doi.org/10.1111/puar.13293
Chen, Y. C., Ahn, M. J., & Wang, Y. F. (2023). Artificial Intelligence and Public Values: Value Impacts and Governance in the Public Sector. Sustainability, 15(6). https://doi.org/10.3390/su15064796
De Vos, A., Van der Heijden, B. I. J. M., & Akkermans, J. (2020). Sustainable careers: Towards a conceptual model. Journal of Vocational Behavior, 117, 103196. https://doi.org/10.1016/j.jvb.2018.06.011
Fountain, J. E. (2001). Building the Virtual State: Information Technology and Institutional Change. Brookings Institution Press. http://www.jstor.org/stable/10.7864/j.ctvcb59n3
Fu, F., Zha, W., & Zhou, Q. (2023). The Impact of Enterprise Digital Capability on Employee Sustainable Performance: From the Perspective of Employee Learning. Sustainability, 15(17), 12897. https://doi.org/10.3390/su151712897
Gui, C., Zhao, X., Ai, S., Ouyang, X., Deng, A., & Li, M. (2025). An augmented–depleted framework of AI awareness: Concept expansion and scale development. International Journal of Hospitality Management, 128. https://doi.org/10.1016/j.ijhm.2025.104164
Guo, S., Wu, Z., & Qin, C. (2026). How punishment avoidance motivation inhibits prohibitive voice behaviour: the role of blame risk and occupational reputation. Public Management Review, 1–24. https://doi.org/10.1080/14719037.2026.2638341
Haesevoets, T., Verschuere, B., & Roets, A. (2025). AI adoption in public administration: Perspectives of public sector managers and public sector non-managerial employees. Government Information Quarterly, 42(2), 102029. https://doi.org/10.1016/J.GIQ.2025.102029
Hansen, H. F., Lillesund, E., Mikalef, P., & Altwaijry, Ν. (2024). Understanding Artificial Intelligence Diffusion through an AI Capability Maturity Model. Information Systems Frontiers, 26(6), 2147–2163. https://doi.org/10.1007/s10796-024-10528-4
Hinterleitner, M. (2023). Blame Avoidance in Public Policy. In M. van Gerven, C. Rothmayr Allison, & K. Schubert (Eds.), Encyclopedia of Public Policy (pp. 1–5). Springer International Publishing. https://doi.org/10.1007/978-3-030-90434-0_44-1
Holmström, J., & Carroll, N. (2025). How organizations can innovate with generative AI. Business Horizons, 68(5), 559–573. https://doi.org/10.1016/j.bushor.2024.02.010
Howells, J., & Scholderer, J. (2016). Forget unlearning? How an empirically unwarranted concept from psychology was imported to flourish in management and organisation studies. Management Learning, 47(4), 443–463. https://doi.org/10.1177/1350507615624079
Jaakkola, E. (2020). Designing conceptual articles: four approaches. AMS Review, 10(1–2), 18–26. https://doi.org/10.1007/s13162-020-00161-0
Ji, T., de Jonge, J., Peeters, M. C. W., & Taris, T. W. (2021). Employee sustainable performance (E-super): Theoretical conceptualization, scale development, and psychometric properties. International Journal of Environmental Research and Public Health, 18(19). https://doi.org/10.3390/ijerph181910497
Koddebusch, M., Distel, B., Maria, M. Di, Brützke, P., & Becker, J. (2024). Getting Rid of It: An Unlearning Perspective on Digital Government Competences. In M. R. Johannessen, C. Csáki, L. Danneels, S. Hofmann, T. Lampoltshammer, P. Parycek, G. Schwabe, E. Tambouris, & J. Ubacht (Eds.), Electronic Participation (pp. 47–63). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-70804-6_4
Kulal, A., Rahiman, H. U., Suvarna, H., Abhishek, N., & Dinesh, S. (2024). Enhancing public service delivery efficiency: Exploring the impact of AI. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 100329. https://doi.org/10.1016/J.JOITMC.2024.100329
Kumar, S., & Mittal, S. (2024). Employee learning and skilling in AI embedded organizations – predictors and outcomes. Development and Learning in Organizations, 38(4), 11–15. https://doi.org/10.1108/DLO-11-2023-0249
Leyer, M., & Schneider, S. (2021). Decision augmentation and automation with artificial intelligence: Threat or opportunity for managers? Business Horizons, 64(5), 711–724. https://doi.org/10.1016/j.bushor.2021.02.026
MacInnis, D. J. (2011). A Framework for Conceptual Contributions in Marketing. Journal of Marketing, 75(4), 136–154. https://doi.org/10.1509/jmkg.75.4.136
Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3). https://doi.org/10.1016/j.im.2021.103434
Mikalef, P., Lemmer, K., Schaefer, C., Ylinen, M., Fjørtoft, S. O., Torvatn, H. Y., Gupta, M., & Niehaves, B. (2022). Enabling AI capabilities in government agencies: A study of determinants for European municipalities. Government Information Quarterly, 39(4). https://doi.org/10.1016/j.giq.2021.101596
Mikalef, P., Lemmer, K., Schaefer, C., Ylinen, M., Fjørtoft, S. O., Torvatn, H. Y., Gupta, M., & Niehaves, B. (2023). Examining how AI capabilities can foster organizational performance in public organizations. Government Information Quarterly, 40(2). https://doi.org/10.1016/j.giq.2022.101797
Orlikowski, W. J. (2000). Using Technology and Constituting Structures: A Practice Lens for Studying Technology in Organizations. Organization Science, 11(4), 404–428. http://www.jstor.org/stable/2640412
Peng, M. Y. P., & Yan, X. (2022). Exploring the Influence of Determinants on Behavior Intention to Use of Multiple Media Kiosks Through Technology Readiness and Acceptance Model. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.852394
Pentland, B. T., & Feldman, M. S. (2005). Organizational routines as a unit of analysis. Industrial and Corporate Change, 14(5), 793–815. https://doi.org/10.1093/icc/dth070
Qi, Z., Zhang, X., Ma, L., & Wang, G. (2026). Generative artificial intelligence use and employees’ digital performance. Management Decision, 64(9), 3536–3578. https://doi.org/10.1108/MD-09-2024-2060
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. The Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Tanaka, S. (2023). Effects of goal orientation and unlearning on individual exploration activities. Journal of Workplace Learning, 35(1), 57–74. https://doi.org/10.1108/JWL-02-2022-0022
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Tsang, E. W. K., & Zahra, S. A. (2008). Organizational unlearning. Human Relations, 61(10), 1435–1462. https://doi.org/10.1177/0018726708095710
van Noordt, C., & Tangi, L. (2023). The dynamics of AI capability and its influence on public value creation of AI within public administration. Government Information Quarterly, 40(4). https://doi.org/10.1016/j.giq.2023.101860
Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User Acceptance of Information Technology: Toward A Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Visser, M. (2017). Learning and unlearning: a conceptual note. Learning Organization, 24(1), 49–57. https://doi.org/10.1108/TLO-10-2016-0070
Wang, M., Lu, Y., & Li, W. (2025). Ambidextrous use of intelligent personal assistants: the role of individual unlearning. Internet Research, 1–20. https://doi.org/10.1108/INTR-07-2024-1099
Young, M. M., Bullock, J. B., & Lecy, J. D. (2019). Artificial Discretion as a Tool of Governance: A Framework for Understanding the Impact of Artificial Intelligence on Public Administration. Perspectives on Public Management and Governance, 2(4), 301–313. https://doi.org/10.1093/ppmgov/gvz014
Yuan, Q., & Chen, T. (2025). Holding AI-Based Systems Accountable in the Public Sector: A Systematic Review. Public Performance & Management Review, 48(6), 1389–1422. https://doi.org/10.1080/15309576.2025.2469784
Zhao, H., Zhang, X., Chen, P., Zhang, J., & Hu, A. (2026). Perceived blame risk and innovative behavior among Chinese grassroots civil servants: the roles of career calling and perceived organizational support. Frontiers in Psychology, 17, 1867276. https://doi.org/10.3389/fpsyg.2026.1867276
Zikri, M., & Prasojo, E. (2026). The Role of Digital Government Innovation in Driving Bureaucratic Reform and Improving Sustainable Public Service Governance. Ilomata International Journal of Social Science, 7(1), 193–208. https://doi.org/10.61194/ijss.v7i1.1942
Zirar, A., Ali, S. I., & Islam, N. (2023). Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda. Technovation, 124, 102747. https://doi.org/10.1016/J.TECHNOVATION.2023.102747
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