Technology acceptance model: a literature review from 1986 to 2013

By Nikola Marangunic, Andrina Granic. Universal Access in the Information Society, 2014

2,130 citations recorded by the source corpus.

Subfields: Information Systems and Management, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/e76223ae615bbcb44f3c2d3c99e316fe580e5777

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Citations in the corpus

The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education (2019, methodology): “…particularly due to its transferability to various contexts and samples, its potential to explain variance in the intention to use or the use of technology, and its simplicity of speci ﬁ cation within structural equation modeling frameworks (e.g., King & He, 2006; Maranguni ć & Grani ć , 2015).”
Faculty Perceptions about Teaching Online: Exploring the Literature Using the Technology Acceptance Model as an Organizing Framework (2017, methodology): “The updated model continued to be used by researchers in different fields; in fact, Marangunić and Granić (2015) concluded, after a review of 85 publications using the TAM model, that the “TAM has evolved to become the key model in understanding the predictors of human behavior toward potential…”
Expanding the Technology Acceptance Model with the Inclusion of Trust, Social Influence, and Health Valuation to Determine the Predictors of German Users’ Willingness to Continue using a Fitness App: A Structural Equation Modeling Approach (2018, positive): “Studies into technology adoption have considerably relied on Davis ’ TAM in understanding the impact of two relevant technology features, namely perceived ease of use and perceived usefulness, on people ’ s willingness to adopt a specific technology (Marangunic & Granic, 2015).”
Augmented reality in support of Industry 4.0 - Implementation challenges and success factors (2019, background): “This is highly appropriate for end-user applications [85].”
Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024, methodology): “In addition to these factors, the model also suggests that external factors like social influence and facilitating conditions can influence the acceptance and use of a new technology [57, 59].”