The Impact of Generative AI on Corporate Decision Making and Innovation Performance

Ernest Nirmala T.P.; Annisa Qurrota A'yun

doi:10.69725/jebi.v2i2.265

Authors

Ernest Nirmala T.P. Department of Management, Faculty of Vocational School, Universitas Diponegoro, Semarang, Indonesia.
Annisa Qurrota A'yun Department of Management, Faculty of Vocational School, Universitas Diponegoro, Semarang, Indonesia.

DOI:

https://doi.org/10.69725/jebi.v2i2.265

Keywords:

Generative AI, Organizational learning, Innovation performance, Knowledge management, Dynamic capabilities

Abstract

Purpose: This paper aims to illuminate the main predictors of innovation performance through exploring a set of direct i. e. generative AI adoption, data-driven decision making, knowledge management systems and leadership support and indirect paths testing an organizational learning mediating role suited for these relations.
Method: The research is a quantitative type with cross-sectional survey design. Structural equation modeling was used to test direct and mediated relationships in the proposed theoretical model.
Findings: The results reveal that the four antecedent factors significantly contribute to innovation performance, and organizational learning surface as a pivotal mediator. Precisely, organizational learning completely mediates the relationship between KM and innovation besides partially mediating the remaining three relationships implying its pivotal function of translating organizational inputs into attaining innovation.
Novelty: This study makes an original contribution by bringing together several theoretical perspectives to explain the sovereign role of organizational learning in connecting technological capital with innovation performance. The paper contributes to a line of research unifying the technological adoption, and organizational capabilities literatures.
Implications: The results indicate that companies need to accompany their investments in technology by a learning-oriented culture if they are to realise the potential of innovation. At the theoretical level, the study contributes by illuminating organisation learning as a key dynamic capability, which processes resource to create performance.

Downloads

Download data is not yet available.

Author Biographies

Ernest Nirmala T.P., Department of Management, Faculty of Vocational School, Universitas Diponegoro, Semarang, Indonesia.

Ernest Nirmala T.P. is affiliated with the Department of Management, Faculty of Vocational School, Diponegoro University, Semarang, Indonesia. Her academic focus lies in management and vocational education, with an interest in applied research that integrates business practices and education to support organizational and entrepreneurial development.
Annisa Qurrota A'yun, Department of Management, Faculty of Vocational School, Universitas Diponegoro, Semarang, Indonesia.

Annisa Qurrota A’yun is a lecturer in the Department of Management, Faculty of Vocational School, Diponegoro University, Semarang, Indonesia. Her scholarly interests include management, vocational-based education, and applied business practices, with research outputs recognized in international publications. She is registered with ORCID (0009-0001-4926-0693) and indexed in Scopus (Author ID: 57245851300), actively contributing to the academic community through teaching, research, and mentoring students in management and vocational studies.

References

Breen, A. (2017). The Innovation Engine: A Framework for Overcoming Cultural and Organizational Impediments to Innovation at Scale BT - Strategy and Communication for Innovation: Integrative Perspectives on Innovation in the Digital Economy (N. Pfeffermann & J. Gould (eds.); pp. 333–347). Springer International Publishing. https://doi.org/10.1007/978-3-319-49542-2_20 DOI: https://doi.org/10.1007/978-3-319-49542-2_20

Campbell, C., Sands, S., Whittaker, L., & Mavrommatis, A. (2025). The AI intelligence playbook: Decoding GenAI capabilities for strategic advantage. Business Horizons. https://doi.org/https://doi.org/10.1016/j.bushor.2025.08.004 DOI: https://doi.org/10.1016/j.bushor.2025.08.004

Channi, H. K., Kaur, A., & Kaur, S. (2025). AI-Driven Generative Design Redefines the Engineering Process. In Generative Artificial Intelligence in Finance (pp. 327–359). https://doi.org/https://doi.org/10.1002/9781394271078.ch17 DOI: https://doi.org/10.1002/9781394271078.ch17

Conboy, K., Mikalef, P., Dennehy, D., & Krogstie, J. (2020). Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda. European Journal of Operational Research, 281(3), 656–672. https://doi.org/https://doi.org/10.1016/j.ejor.2019.06.051 DOI: https://doi.org/10.1016/j.ejor.2019.06.051

Corvello, V. (2025). Generative AI and the future of innovation management: A human centered perspective and an agenda for future research. Journal of Open Innovation: Technology, Market, and Complexity, 11(1), 100456. https://doi.org/https://doi.org/10.1016/j.joitmc.2024.100456 DOI: https://doi.org/10.1016/j.joitmc.2024.100456

Cuozzo, B., Russo, G., Pascucci, F., & Fella, S. (2025). Knowledge management in a dynamic context in the digital era. European Journal of Innovation Management, 28(5), 2080–2100. https://doi.org/10.1108/EJIM-05-2024-0559 DOI: https://doi.org/10.1108/EJIM-05-2024-0559

Davison, R. M., & Ou, C. X. J. (2017). Digital work in a digitally challenged organization. Information & Management, 54(1), 129–137. https://doi.org/https://doi.org/10.1016/j.im.2016.05.005 DOI: https://doi.org/10.1016/j.im.2016.05.005

de Bruijn, H., Warnier, M., & Janssen, M. (2022). The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making. Government Information Quarterly, 39(2), 101666. https://doi.org/https://doi.org/10.1016/j.giq.2021.101666 DOI: https://doi.org/10.1016/j.giq.2021.101666

Del Giudice, M., Scuotto, V., Papa, A., Tarba, S. Y., Bresciani, S., & Warkentin, M. (2021). A Self-Tuning Model for Smart Manufacturing SMEs: Effects on Digital Innovation. Journal of Product Innovation Management, 38(1), 68–89. https://doi.org/https://doi.org/10.1111/jpim.12560 DOI: https://doi.org/10.1111/jpim.12560

Ebneyamini, S., & Bandarian, R. (2018). Explaining the role of technology in the dynamics of the players business models in the global oil playground. International Journal of Energy Sector Management, 13(3), 556–572. https://doi.org/10.1108/IJESM-09-2018-0004 DOI: https://doi.org/10.1108/IJESM-09-2018-0004

Graham, A. B., & Pizzo, V. G. (1996). A question of balance: Case studies in strategic knowledge management. European Management Journal, 14(4), 338–346. https://doi.org/https://doi.org/10.1016/0263-2373(96)00020-5 DOI: https://doi.org/10.1016/0263-2373(96)00020-5

Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024). Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cognitive Computation, 16(1), 45–74. https://doi.org/10.1007/s12559-023-10179-8 DOI: https://doi.org/10.1007/s12559-023-10179-8

Holmström, J., & Carroll, N. (2025). How organizations can innovate with generative AI. Business Horizons, 68(5), 559–573. https://doi.org/https://doi.org/10.1016/j.bushor.2024.02.010 DOI: https://doi.org/10.1016/j.bushor.2024.02.010

Kouzmin, Alexander, & Korac-Kakabadse, Nada. (2000). Mapping Institutional Impacts of Lean Communication in Lean Agencies: Information Technology Illiteracy and Leadership Failure. Administration & Society, 32(1), 29–69. https://doi.org/10.1177/00953990022019344 DOI: https://doi.org/10.1177/00953990022019344

Krakowski, S., Luger, J., & Raisch, S. (2023). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(6), 1425–1452. https://doi.org/https://doi.org/10.1002/smj.3387 DOI: https://doi.org/10.1002/smj.3387

Krawchuk, F. (2018). Design Thinking: How to Thrive in a VUCA World. In R. Elkington, M. van der Steege, J. L. Glick-Smith, & J. M. Breen (Eds.), Exceptional Leadership by Design: How Design in Great Organizations Produces Great Leadership (p. 0). Emerald Publishing Limited. https://doi.org/10.1108/978-1-78743-900-920181009 DOI: https://doi.org/10.1108/978-1-78743-900-920181009

Kumar, S., Lim, W. M., Sivarajah, U., & Kaur, J. (2023). Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis. Information Systems Frontiers, 25(2), 871–896. https://doi.org/10.1007/s10796-022-10279-0 DOI: https://doi.org/10.1007/s10796-022-10279-0

Lei, D., Hitt, M. A., & Bettis, R. (1996). Dynamic core competences through meta-learning and strategic context. Journal of Management, 22(4), 549–569. https://doi.org/https://doi.org/10.1016/S0149-2063(96)90024-0 DOI: https://doi.org/10.1177/014920639602200402

Maheshkar, C., Poulose, J., & Sharma, V. (2024). Data-Driven Decision Making in the VUCA Context: Harnessing Data for Informed Decisions BT - Data-Driven Decision Making (J. Poulose, V. Sharma, & C. Maheshkar (eds.); pp. 1–25). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-2902-9_1 DOI: https://doi.org/10.1007/978-981-97-2902-9_1

Mariani, C., & Mancini, M. (2025). Harnessing AI for value: examining the impact of AI capabilities and the mediating role of organizational agility on project value proposition. International Journal of Managing Projects in Business, 18(8), 112–143. https://doi.org/10.1108/IJMPB-03-2025-0068 DOI: https://doi.org/10.1108/IJMPB-03-2025-0068

Mikalef, P., Conboy, K., & Krogstie, J. (2021). Artificial intelligence as an enabler of B2B marketing: A dynamic capabilities micro-foundations approach. Industrial Marketing Management, 98, 80–92. https://doi.org/https://doi.org/10.1016/j.indmarman.2021.08.003 DOI: https://doi.org/10.1016/j.indmarman.2021.08.003

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), 103434. https://doi.org/https://doi.org/10.1016/j.im.2021.103434 DOI: https://doi.org/10.1016/j.im.2021.103434

O’Reilly, C. A., & Tushman, M. L. (2008). Ambidexterity as a dynamic capability: Resolving the innovator’s dilemma. Research in Organizational Behavior, 28, 185–206. https://doi.org/https://doi.org/10.1016/j.riob.2008.06.002 DOI: https://doi.org/10.1016/j.riob.2008.06.002

Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., & Krogstie, J. (2023). Toward AI Governance: Identifying Best Practices and Potential Barriers and Outcomes. Information Systems Frontiers, 25(1), 123–141. https://doi.org/10.1007/s10796-022-10251-y DOI: https://doi.org/10.1007/s10796-022-10251-y

Prasad Agrawal, K. (2025). Generative AI and the Intelligence Triad: An Epistemic Agility Approach. Journal of Computer Information Systems, 1–17. https://doi.org/10.1080/08874417.2025.2553158 DOI: https://doi.org/10.1080/08874417.2025.2553158

Serenko, A., & Bontis, N. (2021). Global ranking of knowledge management and intellectual capital academic journals: a 2021 update. Journal of Knowledge Management, 26(1), 126–145. https://doi.org/10.1108/JKM-11-2020-0814 DOI: https://doi.org/10.1108/JKM-11-2020-0814

Shafik, W. (2024). Generative AI for Social Good and Sustainable Development BT - Generative AI: Current Trends and Applications (K. Raza, N. Ahmad, & D. Singh (eds.); pp. 185–217). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-8460-8_10 DOI: https://doi.org/10.1007/978-981-97-8460-8_10

Stahl, B. C., Antoniou, J., Bhalla, N., Brooks, L., Jansen, P., Lindqvist, B., Kirichenko, A., Marchal, S., Rodrigues, R., Santiago, N., Warso, Z., & Wright, D. (2023). A systematic review of artificial intelligence impact assessments. Artificial Intelligence Review, 56(11), 12799–12831. https://doi.org/10.1007/s10462-023-10420-8 DOI: https://doi.org/10.1007/s10462-023-10420-8

Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49. https://doi.org/https://doi.org/10.1016/j.lrp.2017.06.007 DOI: https://doi.org/10.1016/j.lrp.2017.06.007

Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411 DOI: https://doi.org/10.1108/BPMJ-10-2019-0411

Wamba-Taguimdje, S.-L., Wamba, S. F., Kamdjoug, J. R. K., & Wanko, C. E. T. (2020). Impact of Artificial Intelligence on Firm Performance: Exploring the Mediating Effect of Process-Oriented Dynamic Capabilities BT - Digital Business Transformation (R. Agrifoglio, R. Lamboglia, D. Mancini, & F. Ricciardi (eds.); pp. 3–18). Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-47355-6_1

Wu, X., Duan, R., & Ni, J. (2024). Unveiling security, privacy, and ethical concerns of ChatGPT. Journal of Information and Intelligence, 2(2), 102–115. https://doi.org/https://doi.org/10.1016/j.jiixd.2023.10.007 DOI: https://doi.org/10.1016/j.jiixd.2023.10.007

Zhang, Q., Zuo, J., & Yang, S. (2025). Research on the impact of generative artificial intelligence (GenAI) on enterprise innovation performance: a knowledge management perspective. Journal of Knowledge Management, 29(7), 2238–2257. https://doi.org/10.1108/JKM-10-2024-1198 DOI: https://doi.org/10.1108/JKM-10-2024-1198

Zong, Z., & Guan, Y. (2025). AI-Driven Intelligent Data Analytics and Predictive Analysis in Industry 4.0: Transforming Knowledge, Innovation, and Efficiency. Journal of the Knowledge Economy, 16(1), 864–903. https://doi.org/10.1007/s13132-024-02001-z DOI: https://doi.org/10.1007/s13132-024-02001-z