Determinants of Technology Acceptance: Two Model-Based Meta-Analytic Reviews

2020 ◽  
pp. 107769902095240 ◽  
Author(s):  
Guangchao Charles Feng ◽  
Xianglin Su ◽  
Zhiliang Lin ◽  
Yiru He ◽  
Nan Luo ◽  
...  
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Meta Analysis ◽  
Model Fit ◽  
Theoretical Frameworks ◽  
Model Based ◽  
Fit Testing ◽  
Central Interest ◽  
Acceptance Model ◽  
Analytical Approaches

Examining the determinants of technology acceptance has been a central interest across disciplines. The technology acceptance model (TAM) and its variants and extensions are the most popular theoretical frameworks in this line of research. Two model-based meta-analytical approaches, that is, meta-meta-analysis and conventional meta-analysis, are used to pool the correlations and to test the path relationships among the variables of the TAM. We find that the extended TAM, which we term the TAM Plus, prevails in the model fit testing and that the results of the pooled correlations and path coefficients estimated using the meta-meta-analysis and meta-analysis are generally consistent.

The Technology Acceptance Model

2008 ◽  
pp. 1088-1100 ◽  
Author(s):  
Qingxiong Ma ◽  
Liping Liu
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Empirical Studies ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Perceived Ease Of Use ◽  
Applied Model ◽  
Acceptance Model

The technology acceptance model proposes that perceived ease of use and perceived usefulness predict the acceptance of information technology. Since its inception, the model has been tested with various applications in tens of studies and has become the most widely applied model of user acceptance and usage. Nevertheless, the reported findings on the model are mixed in terms of statistical significance, direction, and magnitude. In this study, we conducted a meta-analysis based on 26 selected empirical studies in order to synthesize the empirical evidence. The results suggest that both the correlation between usefulness and acceptance, and that between usefulness and ease of use are somewhat strong. However, the relationship between ease of use and acceptance is weak, and its significance does not pass the fail-safe test.

scholarly journals The Effects of Technology Acceptance Model(TAM) in Sports Field: A Meta-analysis

2017 ◽  
Vol 28 (1) ◽  
pp. 81-90 ◽  
Author(s):  
배정섭 ◽  
원도연 ◽  
서광봉 ◽  
InSung Yeo ◽  
임범규
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Meta Analysis ◽  
Acceptance Model

scholarly journals Using the Technology Acceptance Model to Identify Factors That Predict Likelihood to Adopt Tele-Neurorehabilitation

2020 ◽  
Vol 11 ◽  
Author(s):  
Marlena Klaic ◽  
Mary P. Galea
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Computer Anxiety ◽  
New Technology ◽  
Health Sector ◽  
Theoretical Frameworks ◽  
Related Quality ◽  
Health Related ◽  
Human Decision ◽  
Acceptance Model

Tele-neurorehabilitation has the potential to reduce accessibility barriers and enhance patient outcomes through a more seamless continuum of care. A growing number of studies have found that tele-neurorehabilitation produces equivalent results to usual care for a variety of outcomes including activities of daily living and health related quality of life. Despite the potential of tele-neurorehabilitation, this model of care has failed to achieve mainstream adoption. Little is known about feasibility and acceptability of tele-neurorehabilitation and most published studies do not use a validated model to guide and evaluate implementation. The technology acceptance model (TAM) was developed 20 years ago and is one of the most widely used theoretical frameworks for predicting an individual's likelihood to adopt and use new technology. The TAM3 further built on the original model by incorporating additional elements from human decision making such as computer anxiety. In this perspective, we utilize the TAM3 to systematically map the findings from existing published studies, in order to explore the determinants of adoption of tele-neurorehabilitation by both stroke survivors and prescribing clinicians. We present evidence suggesting that computer self-efficacy and computer anxiety are significant predictors of an individual's likelihood to use tele-neurorehabilitation. Understanding what factors support or hinder uptake of tele-neurorehabilitation can assist in translatability and sustainable adoption of this technology. If we are to shift tele-neurorehabilitation from the research domain to become a mainstream health sector activity, key stakeholders must address the barriers that have consistently hindered adoption.

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