Structural Topic Modeling of Technology Acceptance Model Research (2021–2025)

This study analyses Technology Acceptance Model (TAM) research published between 2021 and 2025, employing Structural Topic Modeling (STM) to identify the field's principal thematic trends and their transformation over time. Article abstracts retrieved from the Scopus database were processed and modelled using the stm package in the R programming environment. Unlike conventional topic-modeling approaches, STM allows temporal and contextual covariates—such as publication year—to be integrated directly into the model, thereby enabling a deeper and more dynamic examination of the structural evolution of the TAM literature. The findings indicate that well-established theoretical frameworks, including TAM, UTAUT, TPB, and ECM, retain their central position within the literature, while the boundaries of inquiry have expanded markedly to encompass emerging technologies such as Metaverse, Generative AI, and blockchain. Research interest in AI-based systems and digital learning technologies has grown particularly rapidly during the 2024–2025 period. Overall, the TAM literature exhibits a dynamic transformation across both theoretical and methodological dimensions. In conclusion, STM emerges as a robust, flexible, and reliable analytical tool for investigating the thematic and temporal evolution of technology acceptance research.