Innovation systems: a bibliometric review of the contemporary research employing natural language processing

Abstract

Innovation systems research has seen rapid evolution, expanding in both depth and scope. The increasing frequency of contributions has added complexity, posing challenges for scholars and practitioners in management and policy. This paper employs advanced data-driven techniques, including natural language processing and machine learning, to map and analyze contemporary research. Focusing on studies between 2001 and 2023, the investigation introduces a novel clustering of sub-topics, tracking their dynamics and evolution over time. It highlights a growing emphasis on National Innovation Systems, Institutional Cooperation Networks, and Frameworks and Process Modelling. Although European research remains dominant, contributions from emerging economies are steadily increasing, underscoring the global importance of innovation. By highlighting key concepts, influential works, and mapping recent shifts in the field, this paper offers a focused analysis of the current state of innovation systems research, prioritizing developments from the last two decades and shedding light on emerging areas of attention.