Knowledge Management Capability and Innovation Performance in Enterprise Digital Transformation: Evidence from Text Mining

Zheng Wu

Simen Owen Academic Proceedings Series, 2026, vol. 5, 459-469

Abstract: This study examines how knowledge management capability affects corporate innovation performance in the context of enterprise digital transformation. Drawing on knowledge-based theory and digital transformation research, the study constructs a firm-year panel dataset of U.S. listed companies from 2015 to 2024 by integrating open-source data from SEC EDGAR 10-K filings, SEC CompanyFacts, and USPTO PatentsView. To capture knowledge management capability, this study applies natural language processing and text mining methods to corporate annual report disclosures. Specifically, a text-based index is developed from four dimensions: knowledge acquisition, knowledge sharing, knowledge integration, and knowledge application. Digital transformation is also measured through textual indicators related to digital technologies and digital business practices. Patent-based indicators are used to measure corporate innovation performance. The empirical results show that knowledge management capability has a positive effect on innovation performance, and this effect is strengthened by digital transformation. Further analysis suggests that the effect is more pronounced in high-tech and R&D-intensive firms. This study contributes to the literature by linking knowledge management theory with natural language processing methods, providing evidence on how data-driven measurement can advance management research. The findings also suggest that firms should build digital and knowledge-based mechanisms to improve innovation outcomes.

Keywords: knowledge management; digital transformation; innovation performance; text mining; natural language processing (search for similar items in EconPapers)
Date: 2026
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