ISSN: 2222-6990
Open access
The rapid advancement of artificial intelligence (AI) is fundamentally reshaping how organisations manage innovation, sustain competitiveness, and respond to dynamic market environments. As AI capabilities continue to evolve, understanding their implications for innovation management has become increasingly critical for organisations seeking long-term value creation and sustainable development. Despite a growing body of research on AI-driven innovation, existing studies are largely fragmented and focus on specific applications, offering limited holistic insight into the broader evolution and future trajectory of AI capabilities within innovation management. To address this gap, this study aims to provide a comprehensive analysis of past, current, and emerging trends in AI capabilities and innovation management. Using a systematic literature network analysis (SLNA) approach, the study integrates a systematic literature review (SLR) and bibliometric analysis based on Scopus-indexed publications. The SLR follows a structured three-step process comprising study scoping, database selection, and defined selection and evaluation criteria, while bibliometric techniques are employed to examine research performance and intellectual linkages through performance analysis and science-mapping methods. The findings identify key publication trends, influential journals, leading publications, and dominant country contributions, with sustainability-oriented research emerging as a central theme and China identified as the leading contributor to scholarly output in this domain. By synthesising the intellectual structure and evolution of the field, this study offers actionable insights for researchers, practitioners, and policymakers, supporting informed decision-making on how AI capabilities can be effectively leveraged to foster innovation, enhance organisational competitiveness, and promote sustainable innovation ecosystems.
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment. International Journal of Production Economics, 182, 113–131.
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report EBSE-2007-01.
Ismagilova, E., Dwivedi, Y. K., Slade, E., & Williams, M. D. (2017). Electronic Word of Mouth (eWOM) in the Marketing Context: A State of the Art Analysis and Future Directions. SpringerBriefs in Business.
Chowdhury, S. N., Faruque, M. O., Sharmin, S., Talukder, T., Al Mahmud, M. A., Dastagir, G., & Akter, S. (2024). The Impact of Social Media Marketing on Consumer Behavior: A Study of the Fashion Retail Industry. Open Journal of Business and Management, 12, 1666–1699.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296.
Ismagilova, E., Dwivedi, Y. K., Slade, E., & Williams, M. D. (2020). “The Effect of Electronic Word-of-Mouth Communications on Consumer Behaviour” — a research article revisiting eWOM effects.
Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Dey, B. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting & Social Change, 170, Article 120880.
Colicchia, C., & Strozzi, F. (2012). Supply chain risk management: A new methodology for a systematic literature review. Supply Chain Management: An International Journal, 17(4), 403–418.
Denyer, D., & Tranfield, D. (2009). Producing a systematic review. In D. A. Buchanan & A. Bryman (Eds.), The SAGE Handbook of Organizational Research Methods (pp. 671–689). Sage Publications.
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57, Article 103169.
Mariani, M., Machado, I., & Nambisan, S. (2022). Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda. Journal of Business Research, 155, Article 113364.
Plekhanov, D. (2023). Digital transformation: A systematic review and research agenda. Technological Forecasting & Social Change, 191, Article 122395.
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502–517.
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. In Digital Business Transformation (pp. 3–18). Springer International Publishing.
Marshad, A. H. M., Ramli, R., & Mahmod, A. (2025). Rise of Artificial Intelligence Capabilities in Innovation Management: A Bibliometric Analysis. International Journal of Academic Research in Business and Social Sciences, 15(12), 2217-2236.
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