AI-BASED INNOVATION MANAGEMENT: A BIBLIOMETRIC ANALYSIS

Innovation management is widely recognized as a critical driver of organizational performance, competitiveness, and long-term growth. The advent of

advanced digital technologies, particularly artificial intelligence (AI), has transformed how organizations approach innovation, positioning AI not

merely as a supportive tool but as a strategic enabler of knowledge creation, product development, and business model innovation. Despite the

increasing relevance of AI in innovation management, current research remains fragmented, focusing largely on specific applications such as machine

learning for product design, predictive analytics for strategic planning, or natural language processing in knowledge management. Consequently, there

is a lack of comprehensive understanding that integrates technological, managerial, and societal dimensions, limiting both theoretical advancement

and practical guidance for organizations aiming to leverage AI for innovation.

This study addresses this gap through a systematic bibliometric analysis of the AI-based innovation management literature. Using Scopus as the

primary data source, publications up to August 31, 2025, were analyzed to map the field’s evolution, thematic clusters, key contributors, and

emerging trends. The analysis employed performance indicators including publication counts, citation patterns, h-index metrics, and science mapping

methods such as keyword co-occurrence and co-authorship networks. Visualization was performed using VOSviewer to identify thematic clusters and

interrelationships.

Results indicate that the field has undergone two distinct phases. The formative phase (1993–2015) was characterized by sporadic publications and

limited academic visibility, while the growth phase (post-2018) exhibits exponential increases in both publications and citations, reaching a peak of

86 publications and 2,175 citations in 2025. The bibliometric mapping identified eight major thematic clusters: (1) Generative AI, NLP, and

innovation practices; (2) Industrial innovation, risk, and strategic management; (3) Artificial intelligence, technology management, and foresight; (4)

Research methods, organizational change, and knowledge work; (5) Decision-making, design, and innovation processes; (6) Sustainability, risk, and

societal impacts of technology; (7) Open innovation, SMEs, and technology-driven entrepreneurship; and (8) Digital transformation, global

competitiveness, and management practices. These clusters illustrate the field’s interdisciplinary nature, bridging technical, managerial, and societal

perspectives. Key topics such as generative AI, digital transformation, and sustainability reflect emerging priorities for both research and practice.

The study underscores ongoing gaps and opportunities in the literature, including the need for integrative frameworks that combine technological

capabilities, managerial practices, and societal considerations. Furthermore, context-specific research in emerging economies and empirical studies

assessing AI adoption across sectors are limited but necessary for advancing both theory and practice. Overall, AI-based innovation management has

evolved into a rapidly expanding, influential research field that functions as a transformative force, shaping organizational knowledge creation,

strategic foresight, and sustainable competitiveness in the digital economy.