Knowledge-based dynamic capabilities: a scientometric analysis of marriage between knowledge management and dynamic capabilities

Purpose Several manuscripts are adopting knowledge-based dynamic capabilities (KBDCs) as their main theoretical lens. However, these manuscripts lack consistent conceptualization and systematization of the construct. Consequently, the purpose of this study is to advance the understanding of KBDCs by clarifying the dominant concepts at the junction of knowledge management and dynamic capabilities domains, identifying which emerging themes are gaining traction with KBDCs scholars, demonstrating how the central thesis around KBDCs has evolved and explaining how can KBDCs scholars move towards finding a mutually agreed conceptualization of the field to advance empirical assessment. Design/methodology/approach The Clarivate Analytics Web of Science Core Collection database was used to extract 225 manuscripts that lie at the confluence of two promising management domains, namely, knowledge management and dynamic capabilities. A scientometric analysis including co-citation analysis, bibliographic coupling, keyword co-occurrence network analysis and text mining was conducted and integrated with a systematic review of results to facilitate an unstructured ontological discovery in the field of KBDCs. Findings The co-citation analysis produced three clusters of research at the junction of knowledge management and dynamic capabilities, whereas the bibliographic coupling divulged five themes of research that are gaining traction with KBDCs scholars. The systematic literature review helped to clarify each clusters’ content. While scientific mapping analysis explained how the central thesis around KBDCs has evolved, text mining and keyword analysis established how KBDCs emerge from the combination of knowledge management process capabilities and dynamic capabilities. Originality/value Minimal attention has been paid to systematizing the literature on KBDCs. Accordingly, KBDCs view has been investigated through complementary scientometric methods involving machine-based algorithms to allow for a more robust, structured, comprehensive and unbiased mapping of this emerging field of research.