IDEAS home Printed from https://ideas.repec.org/p/zbw/esconf/279146.html
   My bibliography  Save this paper

A hybrid SEM/ANN analysis to understand youtube video content's influence on university students' eLearning acceptance behavior

Author

Listed:
  • Phan Cong Thao Tien
  • Tran Thien Phuc
  • Nguyen Thi Hai Binh

Abstract

A hybrid analysis of Structural Equation Modeling (SEM), Artificial Neural Network (ANN), and Importance- Performance Map Analysis (IPMA) was used to examine how YouTube videos affect university students’ acceptance in Ho Chi Minh City (HCMC). Performance expectation was the most important component by both ANN and IPMA assessments, and theoretically, the presented model gave several explanations for the effect of individual determinants of desire to utilize eLearning from Internet services. The findings support earlier research showing performance and effort expectations strongly impact eLearning adoption. The report encouraged academics in HCMC to utilize YouTube. Respondents wanted to employ modern technologies in their teaching. UTAUT and TAM were used to discuss the findings.

Suggested Citation

  • 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.
    • Handle: RePEc:zbw:esconf:279146
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/279146/1/Hybrid-SEM/ANN-analysis.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Ashraf, Abdul R. & Thongpapanl Tek, Narongsak & Anwar, Ali & Lapa, Luciano & Venkatesh, Viswanath, 2021. "Perceived values and motivations influencing m-commerce use: A nine-country comparative study," International Journal of Information Management, Elsevier, vol. 59(C).
    3. Leong, Lai-Ying & Hew, Teck-Soon & Ooi, Keng-Boon & Wei, June, 2020. "Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach," International Journal of Information Management, Elsevier, vol. 51(C).
    Full references (including those not matched with items on IDEAS)

    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. Wang, Guoqiang & Tan, Garry Wei-Han & Yuan, Yunpeng & Ooi, Keng-Boon & Dwivedi, Yogesh K., 2022. "Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    2. Phan, Tien-Thao Cong & Dang, Tri-Quan & Nguyen, Luan-Thanh, 2023. "Consumer trust in social network sites in Vietnam: PLS-SEM-ANN analysis," EconStor Conference Papers 278766, ZBW - Leibniz Information Centre for Economics.
    3. Mahmud, Hasan & Islam, A.K.M. Najmul & Mitra, Ranjan Kumar, 2023. "What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    4. Lai-Ying Leong & Teck-Soon Hew & Keng-Boon Ooi & Bhimaraya Metri & Yogesh K. Dwivedi, 2023. "Extending the Theory of Planned Behavior in the Social Commerce Context: A Meta-Analytic SEM (MASEM) Approach," Information Systems Frontiers, Springer, vol. 25(5), pages 1847-1879, October.
    5. Babak Naysary & Mehdi Malekzadeh & Ruth Tacneng & Amine Tarazi, 2022. "Big data analytics application in multi-criteria decision making: the case of eWallet adoption," Working Papers hal-03632834, HAL.
    6. Talwar, Shalini & Srivastava, Shalini & Sakashita, Mototaka & Islam, Nazrul & Dhir, Amandeep, 2022. "Personality and travel intentions during and after the COVID-19 pandemic: An artificial neural network (ANN) approach," Journal of Business Research, Elsevier, vol. 142(C), pages 400-411.
    7. Nguyen, Nguyen-Hong & Nguyen, Luan-Thanh, 2023. "The impact of online shopping motivation on customer loyalty in Mobile Applications," MPRA Paper 119657, University Library of Munich, Germany, revised 02 Jan 2024.
    8. Tianyang Huang, 2023. "Using SOR framework to explore the driving factors of older adults smartphone use behavior," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    9. Ying Yu & Tung-Jung Sung, 2023. "A value-based view of the smart PSS adoption: a study of smart kitchen appliances," Service Business, Springer;Pan-Pacific Business Association, vol. 17(2), pages 499-527, June.
    10. Hemant Kumar Sah & Gyanendra Singh Sisodia & Gouher Ahmed & Aqila Rafiuddin & Naseem Abidi, 2023. "The Role of Energy Consumption and Economic Growth on Carbon Emission- Application of Artificial Neural Network," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 591-596, November.
    11. Taoufiq Dadouch & Bouchra Bennani & Malika Haoucha, 2023. "Consumer Acceptance of Mobile Shopping Apps, From Basic Apps to AI-Conversational Apps: A Literature Review," Post-Print hal-04194657, HAL.
    12. Pal, Shounak & Biswas, Baidyanath & Gupta, Rohit & Kumar, Ajay & Gupta, Shivam, 2023. "Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach," Journal of Business Research, Elsevier, vol. 156(C).
    13. Jaiswal, Deepak & Mohan, Ashutosh & Deshmukh, Arun Kumar, 2023. "Cash rich to cashless market: Segmentation and profiling of Fintech-led-Mobile payment users," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    14. Jacques Nel & Christo Boshoff, 2023. "Unraveling the link between status quo satisfaction and the rejection of digital-only banks," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(1), pages 189-207, March.
    15. Fu, Shihui & Xue, Kunkun & Yang, Mengya & Wang, Xiaona, 2023. "An exploratory study on users' resistance to mobile app updates: Using netnography and fsQCA," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    16. Abbasi, Ghazanfar Ali & Sandran, Thiviya & Ganesan, Yuvaraj & Iranmanesh, Mohammad, 2022. "Go cashless! Determinants of continuance intention to use E-wallet apps: A hybrid approach using PLS-SEM and fsQCA," Technology in Society, Elsevier, vol. 68(C).
    17. Nurul-Ain Abdul-Halim & Ali Vafaei-Zadeh & Haniruzila Hanifah & Ai Ping Teoh & Khaled Nawaser, 2022. "Understanding the determinants of e-wallet continuance usage intention in Malaysia," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3413-3439, October.
    18. de Blanes Sebastián, María García & Antonovica, Arta & Sarmiento Guede, José Ramón, 2023. "What are the leading factors for using Spanish peer-to-peer mobile payment platform Bizum? The applied analysis of the UTAUT2 model," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    19. Sutticherchart Juthatip & Rakthin Sirisuhk, 2023. "Determinants of digital wallet adoption and super app: A review and research model," Management & Marketing, Sciendo, vol. 18(3), pages 270-289, September.
    20. Giacomo Migliore & Ralf Wagner & Felipe Schneider Cechella & Francisco Liébana-Cabanillas, 2022. "Antecedents to the Adoption of Mobile Payment in China and Italy: an Integration of UTAUT2 and Innovation Resistance Theory," Information Systems Frontiers, Springer, vol. 24(6), pages 2099-2122, December.

    More about this item

    Keywords

    Social media; YouTube; Higher education; eLearning; Ho Chi Minh City’s students; TAM; UTAUT;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:zbw:esconf:279146. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zbwkide.html .

    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.