Analysing labour market indicators in entrepreneurship: an automated R framework for integrating bibliometric and narrative analysis

This study offers a comprehensive analysis of labour market indicators (LMIs) within the context of entrepreneurship, employing a hybrid methodology that combines systematic literature review (SLR), bibliometric techniques, and narrative content analysis. Drawing on a corpus of 242 peer-reviewed articles retrieved from Scopus and Web of Science. The research maps the rising of attention to the existing link between labour market dynamics and big data analytics. Utilizing the Biblioshiny interface in R, the study constructs co-citation and keyword co-occurrence networks, identifying thematic clusters and research trends from 2010 to 2025. Results reveal an expanding but fragmented field, with limited continuity among authors and underexplored dimensions in LMI evaluation. The analysis highlights three macro-themes: (i) technological innovation and its implications for labour markets, (ii) the evolving skill demands in terms of knowledge economy, and (iii) methodological approaches to labour policy evaluation. In particular, the study emphasizes the role of big data in mitigating informational asymmetries in employment matching and policy design. Additionally, it explores theoretical models related to skills mismatch, public policy effectiveness, and the impact of artificial intelligence on employment structures. The findings underscore the need for integrative frameworks that merge empirical rigor with conceptual depth, especially considering accelerating technological change and its socio-economic implications. This paper contributes to the literature by proposing a replicable methodological framework for mapping complex research fields, offering insights for scholars, policymakers, and practitioners concerned with labour market transformations in the digital age.