IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i24p7115-d297034.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/24/7115/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/24/7115/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shailesh Pandita & Surabhi Koul & Hari Govind Mishra, 2021. "Acceptance of Ride-sharing in India:Empirical Evidence from the UTAUT Model," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 20(2), pages 93-117, September.
    2. Weiwen Wang & Jian Gong & Ying Wang & Yang Shen, 2022. "The Causal Pathway of Rural Human Settlement, Livelihood Capital, and Agricultural Land Transfer Decision-Making: Is It Regional Consistency?," Land, MDPI, vol. 11(7), pages 1-24, July.
    3. Haibei Chen & Xianglian Zhao, 2023. "Use intention of green financial security intelligence service based on UTAUT," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 10709-10742, October.
    4. Syed Imran Zaman & Sharfuddin Ahmed Khan & Sahar Qabool & Himanshu Gupta, 2023. "How digitalization in banking improve service supply chain resilience of e-commerce sector? a technological adoption model approach," Operations Management Research, Springer, vol. 16(2), pages 904-930, June.
    5. Cristopher Siegfried Kopplin, 2021. "Two heads are better than one: matchmaking tools in coworking spaces," Review of Managerial Science, Springer, vol. 15(4), pages 1045-1069, May.
    6. Noor Irliana Mohd Rahim & Noorminshah A. Iahad & Ahmad Fadhil Yusof & Mohammed A. Al-Sharafi, 2022. "AI-Based Chatbots Adoption Model for Higher-Education Institutions: A Hybrid PLS-SEM-Neural Network Modelling Approach," Sustainability, MDPI, vol. 14(19), pages 1-22, October.
    7. Chia-Chien Hsu & Brian Sandford & Chia-Ju Ling & Ching-Torng Lin, 2021. "Can the Unified Theory of Acceptance and Use of Technology (UTAUT) Help Explain Subjective Well-Being in Senior Citizens due to Gateball Participation?," IJERPH, MDPI, vol. 18(17), pages 1-15, August.
    8. Shah Hussain & Muhammad Qasim Khan, 2023. "Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning," Annals of Data Science, Springer, vol. 10(3), pages 637-655, June.
    9. Kamoonpuri, Sana Zehra & Sengar, Anita, 2023. "Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    10. Shahbaz, Muhammad & Gao, Changyuan & Zhai, LiLi & Shahzad, Fakhar & Khan, Imran, 2021. "Environmental air pollution management system: Predicting user adoption behavior of big data analytics," Technology in Society, Elsevier, vol. 64(C).
    11. Yogesh K Dwivedi & Elvira Ismagilova & Prianka Sarker & Anand Jeyaraj & Yassine Jadil & Laurie Hughes, 2023. "A Meta-Analytic Structural Equation Model for Understanding Social Commerce Adoption," Information Systems Frontiers, Springer, vol. 25(4), pages 1421-1437, August.
    12. Arto O. Salonen & Annukka Tapani & Sami Suhonen, 2021. "Student Online Activity in Blended Learning: A Learning Analytics Perspective of Professional Teacher Education Studies in Finland," SAGE Open, , vol. 11(4), pages 21582440211, October.
    13. van de Sanden, Stephanie & Willems, Kim & Brengman, Malaika, 2022. "How customers motive attributions impact intentions to use an interactive kiosk in-store," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    14. Kapser, Sebastian & Abdelrahman, Mahmoud & Bernecker, Tobias, 2021. "Autonomous delivery vehicles to fight the spread of Covid-19 – How do men and women differ in their acceptance?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 183-198.
    15. Yogesh K. Dwivedi & Elvira Ismagilova & Nripendra P. Rana & Ramakrishnan Raman, 2023. "Social Media Adoption, Usage And Impact In Business-To-Business (B2B) Context: A State-Of-The-Art Literature Review," Information Systems Frontiers, Springer, vol. 25(3), pages 971-993, June.
    16. Pillai, Rajasshrie & Sivathanu, Brijesh & Dwivedi, Yogesh K., 2020. "Shopping intention at AI-powered automated retail stores (AIPARS)," Journal of Retailing and Consumer Services, Elsevier, vol. 57(C).
    17. Farah Alfanur & Yasuo Kadono, 2020. "Empirical Study Of Purchase Intention And Behavior Of E-Commerce Consumers In Indonesia," Malaysian E Commerce Journal (MECJ), Zibeline International Publishing, vol. 5(1), pages 20-28, December.
    18. Darja Topolšek & Dario Babić & Darko Babić & Tina Cvahte Ojsteršek, 2020. "Factors Influencing the Purchase Intention of Autonomous Cars," Sustainability, MDPI, vol. 12(24), pages 1-16, December.
    19. Shan Chen & Yuanzhao Ding, 2023. "A Machine Learning Approach to Predicting Academic Performance in Pennsylvania’s Schools," Social Sciences, MDPI, vol. 12(3), pages 1-13, February.
    20. Hailiang Wang & Jiaxin Zhang & Yan Luximon & Mingfu Qin & Ping Geng & Da Tao, 2022. "The Determinants of User Acceptance of Mobile Medical Platforms: An Investigation Integrating the TPB, TAM, and Patient-Centered Factors," IJERPH, MDPI, vol. 19(17), pages 1-17, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:24:p:7115-:d:297034. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.