Artificial Intelligence in Social Entrepreneurship Research: A Bibliometric Analysis of Emerging Themes, Intellectual Structures, And Future Research Directions

This study presents a systematic bibliometric analysis of research at the intersection of artificial intelligence (AI) and social entrepreneurship, drawing on 72 publications indexed in the Scopus database. The growing deployment of AI tools including machine learning, predictive analytics, and generative AI across social enterprise contexts has produced a rapidly expanding body of scholarly literature that spans management, information systems, engineering education, and sustainability science. Despite this growth, no structured mapping of the field's intellectual architecture has previously been conducted. Using VOSviewer, this study constructs keyword co-occurrence networks to systematically identify dominant research themes, thematic clusters, temporal evolution patterns, and research density distributions within this emerging interdisciplinary domain. The analysis identifies five major thematic clusters: AI for social innovation and decision-making, entrepreneurship education and engineering pedagogy, digitalization and economic and social effects, sustainable development and mission-driven entrepreneurship, and information systems and big data. The overlay visualization reveals that AI-related themes including generative AI, machine learning, agent-based modeling, and computational decision support represent the most recently active research directions in the dataset, confirming the frontier character of this intersection and signaling significant opportunities for future scholarship. In contrast, themes related to sustainable development, social enterprise, and economic effects occupy earlier temporal positions, indicating that sustainability oriented and structural perspectives have a longer standing presence in the literature. The density map confirms social entrepreneurship and sustainable development as the field's two primary intellectual anchors, with AI-technical clusters positioned at the periphery but gaining momentum. Citation analysis identifies Popkova and Sergi (2020), with 329 citations, as the most influential work in the dataset, followed by contributions deploying artificial neural networks and computational social science methods. These findings collectively provide a structured overview of the field's intellectual development, map the current state of the literature, and highlight specific priority areas including generative AI applications, cross-national empirical studies, and theory-building grounded in operating social enterprises for future inquiry.