Upper Echelons Theory and Artificial Intelligence: Bibliometric Mapping of an Emerging Research Field (2017–2025)

Abstract

This research article examines the scientific literature on the relationship between Upper Echelons Theory (UET) and artificial intelligence (AI) through a bibliometric analysis. In a context where AI technologies are transforming decision-making processes and organizational practices, an increasing number of studies draw on UET to analyse how top executives influence the adoption and strategic use of these technologies. To the best of our knowledge, no review has yet provided a targeted bibliometric mapping of the intersection between UET and AI. The aim of this study is to analyse the scientific production in order to understand its main dynamics, identify the most influential actors, and highlight the dominant themes that structure this body of literature.

The analysed corpus consists of 176 documents indexed in the Web of Science database and published between 2017 and 2025. The analysis was conducted using the Bibliometrix package in RStudio and the Biblioshiny interface, drawing on performance analysis (productivity and impact of authors, journals and countries), as well as co‑occurrence and co‑citation network analyses. The results show strong growth in research on UET and AI, with an annual growth rate of 29.15% and a peak of 77 publications in 2025 (43% of the corpus). China leads in terms of publication volume, while several European and North American countries exhibit higher average citations per article. The intellectual structure of the field is organized around three main themes: the adoption of AI and executive governance; digital transformation, strategy and organizational performance; and executive values, cognitive biases and responsible AI. By providing a systematic mapping of this field, the study clarifies its conceptual and intellectual architecture and points to several avenues for future research.

Classification JEL : M12, M15

Paper type : Theoretical Research