Predicting Jobs, Shaping Economies: Bibliometric Insights into AI and Big Data in Workforce Demand Analysis

Abstract

The integration of Big Data and Artificial Intelligence (AI) is fundamentally transforming how labor markets are analyzed, predicted and managed. Despite significant advances in using these technologies for workforce analytics, the field suffers from several critical limitations: existing approaches predominantly rely on data from online job portals that may not capture informal employment sectors, current predictive models lack robustness in long-term forecasting under rapid economic transformations and cross-border data integration remains insufficiently addressed for comprehensive global analyses. Moreover, the field lacks a structured, quantitative assessment of scientific production that provides a comprehensive overview of research developments, with most existing studies being case-specific or focusing on narrow applications, leaving significant gaps in understanding the intellectual structure, key contributors and thematic evolution of this interdisciplinary domain. To address these research gaps, this study presents the first comprehensive bibliometric analysis of global scientific research examining the intersection of AI, Big Data and labor market prediction. Drawing on a systematic dataset of 276 publications from Scopus, Web of Science and OpenAlex databases spanning 2003 to 2025, this research employs advanced bibliometric techniques to map the intellectual landscape of this rapidly evolving field. Through a structured four-phase methodological framework incorporating performance analysis, science mapping and thematic evolution, the study identifies research trends, intellectual structures, influential contributors and emerging themes. The analysis reveals significant developments in predictive modeling, natural language processing, and hybrid AI approaches for recruitment forecasting and workforce analytics, while highlighting critical challenges posed by algorithmic bias and ethical considerations in AI-driven systems. Key contributions include: 1) the first systematic scientific mapping of the AI-Big Data-labor market intersection 2) identification of research gaps and future directions for long-term labor market prediction, 3) comprehensive analysis of institutional networks and collaborative patterns and 4) evidence-based recommendations for addressing data integration and model interpretability challenges. The findings offer actionable insights for researchers, policymakers and practitioners seeking to leverage intelligent systems to shape the future of work in the digital economy while addressing current methodological limitations.