From Automation to Personalization: A Bibliometric Analysis of Chatbot Adoption in Banking

Abstract

This bibliometric analysis explores the evolving landscape of chatbot adoption in the banking sector using data sourced from the Scopus database. The study employed advanced bibliometric tools such as Biblioshiny, VOSviewer, and CiteSpace to map the intellectual structure, research trends, and influential contributors in the field. Analysis of scientific output over the years reveals a sharp increase in publications post-2020, indicating rising scholarly interest in banking chatbots. Influential researchers and high-impact journals such as Tolga Cekic and the Lecture Notes in Networks and Systems were identified as key knowledge sources. The global distribution of research showed strong contributions from technologically advanced economies, with notable international collaborations visualized through timezone-based network maps. Co-citation analysis of authors and journals highlighted the foundational and interdisciplinary nature of chatbot research. Bibliographic coupling and keyword co-occurrence patterns underscored the thematic depth of current studies. Thematic mapping revealed well-developed motor themes like NLP and ChatGPT, while basic themes such as AI and customer satisfaction remained underexplored in terms of maturity. Trend topic analysis demonstrated a shift from technological adoption toward user experience and satisfaction in recent years. Overall, the findings illuminate existing research gaps and offer practical implications for future studies aiming to optimize chatbot implementation in banking services.