Artificial Intelligence Adoption and Organisational Performance: The Mediating Role of Knowledge Management Capability
Keywords:
Artificial Intelligence Adoption, Knowledge Management Capability, Organisational Performance, Resource-Based View, Knowledge-Based View, Structural Equation ModellingAbstract
Artificial intelligence (AI) has become an important organisational capability that supports decision-making, service innovation, process redesign, and operational efficiency across industries. Despite increasing organisational investment in AI technologies, evidence on how AI translates into superior organisational performance remains inconclusive. Drawing on the Resource-Based View (RBV) and the Knowledge-Based View (KBV), this study proposes that Knowledge Management Capability (KMC) explains how AI adoption contributes to organisational performance. It suggests that implementing AI has a positive effect on network performance, particularly by strengthening KMC, namely knowledge acquisition, sharing, storage, and use. The study develops and validates a mediation model to investigate the relationships among AI adoption, KMC, and organisational performance across operational, financial, and innovation dimensions. For managers and/or professionals in digitally transforming organisations, a positivist, deductive, and quantitative cross-sectional survey design is specified. AI adoption positively influences organisational performance; AI adoption positively influences KMC; KMC positively influences organisational performance, which AI-Performance partially mediates due to AI; and KMC does not have any causality. This study contributes to the literature by integrating RBV and KBV to explain the mechanism through which AI adoption improves organisational performance. It further demonstrates that knowledge management capability functions as a critical organisational capability linking AI-enabled resources with operational, financial, and innovation outcomes.
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References
Ågerfalk, P. J. (2020). Artificial intelligence as digital agency. European Journal of Information Systems, 29(1), 1–8. https://doi.org/10.1080/0960085X.2020.1721947
Abdul Wahab, M. D., & Radmehr, M. (2024). The impact of AI assimilation on firm performance in small and medium-sized enterprises: A moderated multi-mediation model. Heliyon, 10(8), Article e29580. https://doi.org/10.1016/j.heliyon.2024.e29580
Aichouche, R., Chergui, K., Brika, S. K. M., El Mezher, M., Musa, A., & Laamari, A. (2022). Exploring the relationship between organizational culture types and knowledge management processes: A meta-analytic path analysis. Frontiers in Psychology, 13, Article 856234. https://doi.org/10.3389/fpsyg.2022.856234
Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107–136. https://doi.org/10.2307/3250961
Andreeva, T., & Kianto, A. (2012). Does knowledge management really matter? Linking knowledge management practices, competitiveness and economic performance. Journal of Knowledge Management, 16(4), 617–636. https://doi.org/10.1108/13673271211246185
Badghish, S., & Soomro, Y. A. (2024). Artificial intelligence adoption by SMEs to achieve sustainable business performance: Application of technology–organization–environment framework. Sustainability, 16(5), Article 1864. https://doi.org/10.3390/su16051864
Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163, Article 120420. https://doi.org/10.1016/j.techfore.2020.120420
Baird, A., & Maruping, L. M. (2021). The next generation of research on IS use: A theoretical framework of delegation to and from agentic IS artifacts. MIS Quarterly, 45(1), 315–341.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Benbya, H., Pachidi, S., & Jarvenpaa, S. L. (2021). Artificial intelligence in organizations: Implications for information systems research. MIS Quarterly, 45(3), 1–24.
Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Managing artificial intelligence. MIS Quarterly, 45(3), 1433–1450.
Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance: An empirical investigation. MIS Quarterly, 24(1), 169–196. https://doi.org/10.2307/3250983
Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482.
Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021a). The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, 205–219. https://doi.org/10.1016/j.indmarman.2021.07.013
Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021b). Understanding AI adoption in manufacturing and production firms using an integrated TAM–TOE model. Technological Forecasting and Social Change, 170, Article 120880. https://doi.org/10.1016/j.techfore.2021.120880
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553
Donate, M. J., & de Pablo, J. D. S. (2015). The role of knowledge-oriented leadership in knowledge management practices and innovation. Journal of Business Research, 68(2), 360–370. https://doi.org/10.1016/j.jbusres.2014.06.022
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J. S., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., 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, Article 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., ... Wright, R. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI. International Journal of Information Management, 71, Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709–1734. https://doi.org/10.1007/s10796-021-10186-w
Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005
Ferreira, J., Cardim, S., & Coelho, A. (2021). Dynamic capabilities and mediating effects of innovation on the competitive advantage and firm’s performance: The moderating role of organizational learning capability. Journal of the Knowledge Economy, 12, 620–644. https://doi.org/10.1007/s13132-020-00655-z
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185–214. https://doi.org/10.1080/07421222.2001.11045669
Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(S2), 109–122. https://doi.org/10.1002/smj.4250171110
Gürlek, M., & Çemberci, M. (2020). Understanding the relationships among knowledge-oriented leadership, knowledge management capacity, innovation performance and organizational performance: A serial mediation analysis. Kybernetes, 49(11), 2819–2846. https://doi.org/10.1108/K-09-2019-0632
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE.
Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. (2021). A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. Journal of Management Studies, 58(5), 1159–1197. https://doi.org/10.1111/joms.12639
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8
Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). Guilford Press.
Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science, 3(3), 383–397. https://doi.org/10.1287/orsc.3.3.383
Kraus, S., Durst, S., Ferreira, J. J., Veiga, P., Kailer, N., & Weinmann, A. (2022). Digital transformation in business and management research: An overview of the current status quo. International Journal of Information Management, 63, Article 102466. https://doi.org/10.1016/j.ijinfomgt.2021.102466
Lei, H., Khamkhoutlavong, M., & Le, P. B. (2021). Fostering exploitative and exploratory innovation through HRM practices and knowledge management capability: The moderating effect of knowledge-centered culture. Journal of Knowledge Management, 25(8), 1926–1946. https://doi.org/10.1108/JKM-07-2020-0505
Li, N., Yan, Y., Yang, Y., & Gu, A. (2022). Artificial intelligence capability and organizational creativity: The role of knowledge sharing and organizational cohesion. Frontiers in Psychology, 13, Article 845277. https://doi.org/10.3389/fpsyg.2022.845277
Liu, G., Kianto, A., & Tsui, E. (2023). Knowledge management technologies and organizational performance: A meta-analytic study. Industrial Management & Data Systems, 123(2), 386–408. https://doi.org/10.1108/IMDS-02-2022-0121
Mahboub, R. M., & Ghanem, A. E. M. (2024). The mediating role of knowledge management practices and balanced scorecard in the association between artificial intelligence and organization performance in MENA region commercial banks. Cogent Business & Management, 11(1), Article 2404484. https://doi.org/10.1080/23311975.2024.2404484
Malik, A., Froese, F. J., & Sharma, P. (2020). Role of HRM in knowledge integration: Towards a conceptual framework. Journal of Business Research, 109, 524–535.
Mikalef, P., Conboy, K., Lundström, J. E., & Popovič, A. (2022). Thinking responsibly about responsible AI and “the dark side” of AI. European Journal of Information Systems, 31(3), 257–268. https://doi.org/10.1080/0960085X.2022.2026621
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), Article103434. 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), Article 101596. https://doi.org/10.1016/j.giq.2021.101596
Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: A systematic literature review and research agenda. Information Systems and e-Business Management, 16, 547–578. https://doi.org/10.1007/s10257-017-0362-y
Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14–37. https://doi.org/10.1287/orsc.5.1.14
Obeso, M., Hernández-Linares, R., López-Fernández, M. C., & Serrano-Bedia, A. M. (2020). Knowledge management processes and organizational performance: The mediating role of organizational learning. Journal of Knowledge Management, 24(8), 1859–1880. https://doi.org/10.1108/JKM-10-2019-0553
Ouakouak, M. L., AlBuloushi, N., Ouedraogo, N., & Sawalha, N. (2021). Knowledge sharing as a give-and-take practice: The role of the knowledge receiver in the knowledge-sharing process. Journal of Knowledge Management, 25(8), 2043–2066.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Richard, P. J., Devinney, T. M., Yip, G. S., & Johnson, G. (2009). Measuring organizational performance: Towards methodological best practice. Journal of Management, 35(3), 718–804. https://doi.org/10.1177/0149206308330560
Sahoo, S., Kumar, S., Donthu, N., & Singh, R. K. (2024). Artificial intelligence capabilities, open innovation, and business performance: Empirical insights from multinational B2B companies. Industrial Marketing Management, 117, 28–41. https://doi.org/10.1016/j.indmarman.2023.12.008
Shabbir, M. Q., & Gardezi, S. B. W. (2020). Application of big data analytics and organizational performance: The mediating role of knowledge management practices. Journal of Big Data, 7, Article 47. https://doi.org/10.1186/s40537-020-00317-6
Singh, S., Darwish, T. K., & Potočnik, K. (2016). Measuring organizational performance: A case for subjective measures. British Journal of Management, 27(1), 214–224. https://doi.org/10.1111/1467-8551.12126
Singh, S., Chatterjee, S., & Mariani, M. (2024). Applications of generative AI and future organizational performance: The mediating role of explorative and exploitative innovation and the moderating role of ethical dilemmas and environmental dynamism. Technovation, 133, Article 103021. https://doi.org/10.1016/j.technovation.2024.103021
Sjödin, D., Parida, V., Palmié, M., & Wincent, J. (2021). How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops. Journal of Business Research, 134, 574–587. https://doi.org/10.1016/j.jbusres.2021.05.009
Soomro, R. B., Al-Rahmi, W. M., Dahri, N. A., Aldaijy, A., Yahaya, N., Al-Adwan, A. S., Alkhalaf, S., & Cruz, A. P. S. (2025). A SEM–ANN analysis to examine impact of artificial intelligence technologies on sustainable performance in the context of SMEs. Scientific Reports, 15, Article 9191. https://doi.org/10.1038/s41598-025-86464-3
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%3C509::AID-SMJ882%3E3.0.CO;2-Z
Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
Wamba-Taguimdje, S.-L., Wamba, S. F., Kamdjoug, J. R. K., & Wanko, C. E. T. (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
Wang, S., & Noe, R. A. (2010). Knowledge sharing: A review and directions for future research. Human Resource Management Review, 20(2), 115–131. https://doi.org/10.1016/j.hrmr.2009.10.001
Wessel, L., Baiyere, A., Ologeanu-Taddei, R., Cha, J., & Blegind Jensen, T. (2021). Unpacking the difference between digital transformation and IT-enabled organizational transformation. Journal of the Association for Information Systems, 22(1), 102–129. https://doi.org/10.17705/1jais.00655
Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. Academy of Management Review, 27(2), 185–203. https://doi.org/10.5465/amr.2002.6587995.
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