Bibliometric Mapping of Technology Acceptance in Artificial Intelligence, Machine Learning, and Neuronal Networks: A Comparative Analysis of WoS and Scopus (1999–2023)

Abstract

Background: This study presents a systematic bibliometric mapping and analysis on the acceptance of technology in the field of artificial intelligence (AI), machine learning (ML) and neuronal networks (NN), evaluating the evolution and research trends within this interdisciplinary field. Methods: Using data from the Web of Science (WoS) and Scopus databases, we identify important authors, institutions, and geographic distributions, highlighting key research areas and emerging themes. The analysis was performed using VOSviewer (v. 1. 6. 20) and RStudio (v. 4. 1. 3) with the Bibliometrix package. Results: Our analysis reveals a steady increase in scientific output between 1999 and 2023, with a notable acceleration in recent years, indicating a growing interest in how AI technologies are accepted in various domains. The research illuminates the central role of technology and AI acceptance models, as demonstrated by thematic and keyword analyses. The study reveals a pronounced focus on the technological facets of AI acceptance, alongside discernible gaps in research linking energy, climate mitigation, and sustainability. Differences in findings underline the characteristics of the WoS and Scopus databases. Conclusions: The findings argue for a diversified research agenda to overcome these identified gaps, fostering a more comprehensive understanding of technology acceptance in the age of AI. This research charts a course for future explorations within this critical interdisciplinary field.