Literature DB >> 32339928

A meta-analysis based modified unified theory of acceptance and use of technology (meta-UTAUT): a review of emerging literature.

Yogesh K Dwivedi1, Nripendra P Rana2, Kuttimani Tamilmani2, Ramakrishnan Raman3.   

Abstract

Over the past more than four decades, several theoretical models have been developed to understand the acceptance and use of information systems. Realising the dilemma in selecting the appropriate theoretical model to assess the acceptance and use of technology and considering the pattern of using the Unified Theory of Acceptance and Use of Technology (UTAUT), a modified version (meta-UTAUT) has been developed based on the synthesis of results from 162 existing studies. The aim of this article is to review the emerging literature on meta-UTAUT and offer some future research recommendations. The analysis suggests that studies have started citing the relationships suggested by meta-UTAUT and researchers have reviewed it alongside other alternative models while analysing acceptance and use of technology.
Copyright © 2020 Elsevier Ltd. All rights reserved.

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Year:  2020        PMID: 32339928     DOI: 10.1016/j.copsyc.2020.03.008

Source DB:  PubMed          Journal:  Curr Opin Psychol        ISSN: 2352-250X


  12 in total

1.  Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model.

Authors:  Ruchika Jain; Naval Garg; Shikha N Khera
Journal:  Front Psychol       Date:  2022-06-20

2.  Predicting the Mass Adoption of eDoctor Apps During COVID-19 in China Using Hybrid SEM-Neural Network Analysis.

Authors:  Qing Yang; Abdullah Al Mamun; Naeem Hayat; Mohd Fairuz Md Salleh; Anas A Salameh; Zafir Khan Mohamed Makhbul
Journal:  Front Public Health       Date:  2022-04-28

3.  Assessing Older Adults' Intentions to Use a Smartphone: Using the Meta-Unified Theory of the Acceptance and Use of Technology.

Authors:  Cheng-Chia Yang; Cheng-Lun Li; Te-Feng Yeh; Yu-Chia Chang
Journal:  Int J Environ Res Public Health       Date:  2022-04-28       Impact factor: 4.614

4.  Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model.

Authors:  Ángel F Villarejo-Ramos; Juan-Pedro Cabrera-Sánchez; Juan Lara-Rubio; Francisco Liébana-Cabanillas
Journal:  Front Psychol       Date:  2021-04-01

5.  Determinants of the behavioral intention to use a mobile nursing application by nurses in China.

Authors:  Minghao Pan; Wei Gao
Journal:  BMC Health Serv Res       Date:  2021-03-12       Impact factor: 2.655

6.  Comparing the Acceptance of Mobile Hypertension Apps for Disease Management Among Patients Versus Clinical Use Among Physicians: Cross-sectional Survey.

Authors:  Bernhard Breil; Christel Salewski; Jennifer Apolinário-Hagen
Journal:  JMIR Cardio       Date:  2022-01-06

7.  Barriers and Drivers Regarding the Use of Mobile Health Apps Among Patients With Type 2 Diabetes Mellitus in the Netherlands: Explanatory Sequential Design Study.

Authors:  Marloes Bults; Catharina Margaretha van Leersum; Theodorus Johannes Josef Olthuis; Robin Enya Marije Bekhuis; Marjolein Elisabeth Maria den Ouden
Journal:  JMIR Diabetes       Date:  2022-01-27

8.  Precursors of Openness to Provide Online Counseling: The Role of Future Thinking, Creativity, and Innovative Behavior of Future Online Therapists.

Authors:  Dorit Alt; Meyran Boniel-Nissim; Lior Naamati-Schneider; Adaya Meirovich
Journal:  Front Psychol       Date:  2022-04-01

9.  Nurses and the acceptance of innovations in technology-intensive contexts: the need for tailored management strategies.

Authors:  Chiara Barchielli; Cristina Marullo; Manila Bonciani; Milena Vainieri
Journal:  BMC Health Serv Res       Date:  2021-07-03       Impact factor: 2.655

10.  A mixed-method service evaluation of health information exchange in England: technology acceptance and barriers and facilitators to adoption.

Authors:  Fiona Watkinson; Kanika I Dharmayat; Nikolaos Mastellos
Journal:  BMC Health Serv Res       Date:  2021-07-25       Impact factor: 2.655

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