Teknoloji Kabul Modeli Araştırmalarının Yapısal Konu Modellemesi (2021–2025)

Öz

Bu çalışma, 2021 ile 2025 yılları arasında yayımlanan Teknoloji Kabul Modeli (TAM) araştırmalarını, alandaki temel tematik eğilimleri ve bunların zaman içindeki dönüşümünü belirlemek amacıyla Yapısal Konu Modellemesi (Structural Topic Modeling – STM) yöntemiyle analiz etmektedir. Araştırma kapsamında Scopus veritabanından elde edilen makale özetleri, R programlama dilindeki stm paketi kullanılarak işlenmiş ve modellenmiştir. Geleneksel konu modelleme yaklaşımlarından farklı olarak STM, yayın yılı gibi zamansal ve bağlamsal değişkenlerin modele doğrudan entegre edilmesine olanak tanıyarak, TAM literatürünün yapısal evriminin daha derinlemesine ve dinamik biçimde incelenmesini mümkün kılmaktadır. Elde edilen bulgular, TAM, UTAUT, TPB ve ECM gibi köklü teorik modellerin literatürdeki merkezi konumlarını sürdürdüklerini; ancak araştırma sınırlarının Metaverse, üretken yapay zekâ (Generative AI) ve blok zinciri (blockchain) gibi yeni ve gelişmekte olan teknolojilerle belirgin biçimde genişlediğini ortaya koymaktadır. Yapay zekâ tabanlı sistemlere ve dijital öğrenme teknolojilerine yönelik araştırma ilgisinin, özellikle 2024–2025 döneminde hızla arttığı gözlemlenmektedir. Genel değerlendirmede, TAM literatürünün hem teorik hem de metodolojik boyutlarda dinamik bir dönüşüm sergilediği görülmektedir. Sonuç olarak STM, teknoloji kabulü araştırmalarının tematik ve zamansal evrimini incelemek için güçlü, esnek ve güvenilir bir analitik araç olarak öne çıkmaktadır.

Anahtar Kelimeler

• Teknoloji Kabul Modeli
• Yapısal Konu Modellemesi
• Yapay Zekâ
• Dijital Dönüşüm
• Metin Madenciliği

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

Öz

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.

Anahtar Kelimeler

• Technology Acceptance Model
• Structural Topic Modeling
• Artificial Intelligence
• Digital Transformation
• Text Mining

Kaynakça

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