AI-BASED INNOVATION MANAGEMENT: A BIBLIOMETRIC ANALYSIS
AI-BASED INNOVATION MANAGEMENT: A BIBLIOMETRIC ANALYSIS

Summary/Abstract: 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.

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