Beyond Boundaries: A Machine Learning and Text Mining Approach to Mapping Corporate Entrepreneurship Research

The field of corporate entrepreneurship (CE) continues to evolve rapidly, yet existing research remains fragmented across regions, firm types, and sectors. Traditional literature review methodologies face significant limitations, including manual screening constraints, potential researcher bias, and time-consuming analysis processes. This study addresses these challenges by developing a machine learning-enhanced framework for comprehensive literature review and analysis in the field of corporate entrepreneurship. By extending and complementing previous systematic review approaches, our research introduces a reproducible and scalable methodology for exploring corporate entrepreneurship research trends. Leveraging text mining and machine learning algorithms, we provide an objective, dynamic approach to processing extensive academic databases, uncovering complex interconnections and emerging research patterns that conventional manual reviews might overlook. Our methodology aims to extend literature review practices in corporate entrepreneurship research, offering researchers and practitioners a more efficient, comprehensive, and adaptable approach to understanding the evolving landscape of corporate entrepreneurship scholarship.