Technological Advancements in Hospitality through Python-based Bibliometric Analysis

This paper presents a bibliometric analysis of academic literature at the intersection of technology and hospitality, using Python-based scripting and VOSviewer. A four-stage methodological framework is employed to identify trends, group research into thematic clusters, and explore key topics and their interconnections. First, co-occurrence analysis reveals seven clusters related to technological innovation, digital transformation, artificial intelligence, and post-COVID-19 strategies. Second, articles are assigned to clusters, and top keywords are extracted using Term Frequency–Inverse Document Frequency (TF-IDF), highlighting the thematic focus of each cluster. Third, Latent Dirichlet Allocation (LDA) identifies five dominant topics per cluster, offering insights into the evolving structure of hospitality research. Finally, a topic-based cluster interaction analysis reveals strong connections, particularly among clusters on digital transformation, sustainability, and technology adoption, while others, such as sustainability and customer satisfaction, appear more isolated. These findings suggest emerging or niche areas within the broader discourse. The study contributes methodologically by offering a replicable, data-driven framework for future bibliometric research and thematically by revealing the multidimensional impact of technology in hospitality. By integrating bibliometric techniques with established theories such as the Technology Acceptance Model and Diffusion of Innovation, this work supports a multidisciplinary approach to understanding technological shifts in hospitality scholarship. Keywords: Technology in Hospitality, Digital Transformation, Bibliometric Analysis, Artificial Intelligence in Tourism, Sustainability in Tourism, TAM, DOI.