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Predicting and Explaining the Acceptance of Social Video Platforms for Learning: The Case of Brazilian YouTube Users

Author

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  • Patricio Ramírez-Correa

    (School of Engineering, Universidad Católica del Norte, Larrondo, Coquimbo 1780000, Chile)

  • Ari Mariano-Melo

    (Department of Production Engineering, Universidade de Brasília, Campus Darcy Ribeiro Asa Norte, Brasília 04457, Brazil)

  • Jorge Alfaro-Pérez

    (School of Engineering, Universidad Católica del Norte, Larrondo, Coquimbo 1780000, Chile)

Abstract

This study aims to predict and explain the acceptance of social video platforms for learning. A research model is proposed that explains that the intention of using these platforms is based on the perception of performance, social influence, and hedonic motivation. To validate the model, 568 Brazilian YouTube users were surveyed. The data were analyzed with partial least squares structural equations modeling (PLS-SEM). In particular, the predictive power of the model was assessed using the PLSpredict procedure. The results of this study can help to understand and forecast the use of these platforms for learning in developing countries.

Suggested Citation

  • Patricio Ramírez-Correa & Ari Mariano-Melo & Jorge Alfaro-Pérez, 2019. "Predicting and Explaining the Acceptance of Social Video Platforms for Learning: The Case of Brazilian YouTube Users," Sustainability, MDPI, vol. 11(24), pages 1-11, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:24:p:7115-:d:297034
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    References listed on IDEAS

    as
    1. Patricio Ramírez-Correa & Elizabeth E. Grandón & Jorge Alfaro-Pérez & Giselle Painén-Aravena, 2019. "Personality Types as Moderators of the Acceptance of Information Technologies in Organizations: A Multi-Group Analysis in PLS-SEM," Sustainability, MDPI, vol. 11(14), pages 1-15, July.
    2. Yogesh K. Dwivedi & Nripendra P. Rana & Anand Jeyaraj & Marc Clement & Michael D. Williams, 2019. "Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model," Information Systems Frontiers, Springer, vol. 21(3), pages 719-734, June.
    3. Diego Buenaño-Fernández & David Gil & Sergio Luján-Mora, 2019. "Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study," Sustainability, MDPI, vol. 11(10), pages 1-18, May.
    4. Cherng-Jyh Yen & Chih-Hsiung Tu & Laura E. Sujo-Montes & Hoda Harati & Claudia R. Rodas, 2019. "Using Personal Learning Environment (PLE) Management to Support Digital Lifelong Learning," International Journal of Online Pedagogy and Course Design (IJOPCD), IGI Global, vol. 9(3), pages 13-31, July.
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    Cited by:

    1. Phan Cong Thao Tien & Tran Thien Phuc & Nguyen Thi Hai Binh, 2023. "A hybrid SEM/ANN analysis to understand youtube video content's influence on university students' eLearning acceptance behavior," EconStor Conference Papers 279146, ZBW - Leibniz Information Centre for Economics.

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