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<p>This article presents a systemic and empirically grounded analysis of the epistemic community that sustains and expands the Unified Theory of Acceptance and Use of Technology, a foundational framework for understanding technology adoption across diverse sectors. Despite its widespread use, limited attention has been given to the scholars who shape its evolution and the extent to which their work contributes to sustainable development. To address this gap, the study applies a comprehensive bibliometric methodology using Web of Science data from 2003 to 2025, incorporating the laws of Price, Lotka, Zipf, and Hirsch, along with co-authorship, cross-citation, and keyword co-occurrence network analyses, visualized through VOSviewer. The results reveal exponential growth in publications, with 2278 articles authored by 6952 researchers. Among them, 31 stand out as central actors who combine high productivity, scientific impact, and thematic relevance. These scholars form distinct collaborative clusters, and their most influential contributions build directly upon the foundational work of Venkatesh and colleagues. Co‑citation analysis identifies two epistemic clusters—a methodological–instrumental pillar and a theoretical–foundational pillar—that structure the shared knowledge base of the UTAUT community. Thematic evolution shows a shift from general constructs of technology perception toward applied domains such as artificial intelligence, mobile banking, digital education, and service platforms. This research confirms the vitality of the model as a strategic framework for analyzing digital transformation and indicates a growing thematic alignment with the Sustainable Development Goals, particularly in education, health, and urban innovation, based on automated classification. By identifying key actors and mapping knowledge networks, the study offers a replicable methodology and practical insights for designing sustainable business models that are ethically grounded, context-aware, and capable of generating long-term value.</p>

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Mapping the UTAUT epistemic community and its alignment with sustainable digital transformation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月28日

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  • Gustavo Cruz-Martínez,
  • Miseldra Gil-Marín,
  • Nicolás Contreras-Barraza,
  • Martha Vidal-Sepúlveda,
  • Jose Carmelo Adsuar,
  • Alejandro Vega-Muñoz,
  • Guido Salazar-Sepúlveda

摘要

This article presents a systemic and empirically grounded analysis of the epistemic community that sustains and expands the Unified Theory of Acceptance and Use of Technology, a foundational framework for understanding technology adoption across diverse sectors. Despite its widespread use, limited attention has been given to the scholars who shape its evolution and the extent to which their work contributes to sustainable development. To address this gap, the study applies a comprehensive bibliometric methodology using Web of Science data from 2003 to 2025, incorporating the laws of Price, Lotka, Zipf, and Hirsch, along with co-authorship, cross-citation, and keyword co-occurrence network analyses, visualized through VOSviewer. The results reveal exponential growth in publications, with 2278 articles authored by 6952 researchers. Among them, 31 stand out as central actors who combine high productivity, scientific impact, and thematic relevance. These scholars form distinct collaborative clusters, and their most influential contributions build directly upon the foundational work of Venkatesh and colleagues. Co‑citation analysis identifies two epistemic clusters—a methodological–instrumental pillar and a theoretical–foundational pillar—that structure the shared knowledge base of the UTAUT community. Thematic evolution shows a shift from general constructs of technology perception toward applied domains such as artificial intelligence, mobile banking, digital education, and service platforms. This research confirms the vitality of the model as a strategic framework for analyzing digital transformation and indicates a growing thematic alignment with the Sustainable Development Goals, particularly in education, health, and urban innovation, based on automated classification. By identifying key actors and mapping knowledge networks, the study offers a replicable methodology and practical insights for designing sustainable business models that are ethically grounded, context-aware, and capable of generating long-term value.