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. 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).
    3. 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).
    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. Khondaker Sazzadul Karim & Mohammad Ekramol Islam & Abdullah Mohammed Ibrahim & Shin-Hung Pan & Md. Mominur Rahman, 2023. "Online Marketing Trends and Purchasing Intent Advances in Customer Satisfaction through PLS-SEM and ANN Approach," Advances in Decision Sciences, Asia University, Taiwan, vol. 27(4), pages 24-54, December.
    3. Samadhiya, Ashutosh & Yadav, Sanjeev & Kumar, Anil & Majumdar, Abhijit & Luthra, Sunil & Garza-Reyes, Jose Arturo & Upadhyay, Arvind, 2023. "The influence of artificial intelligence techniques on disruption management: Does supply chain dynamism matter?," Technology in Society, Elsevier, vol. 75(C).
    4. Arghya Ray & Muskan Jain & Lan Ma & Khalid Hussain Alhamzi & Ananya Ray & Long She, 2024. "The impact of personality traits, barriers and gamification on Gen X continuance intention for mobile credit bill payment apps," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 29(3), pages 1154-1174, September.
    5. Hiran, Kamal Kant & Dadhich, Manish, 2024. "Predicting the core determinants of cloud-edge computing adoption (CECA) for sustainable development in the higher education institutions of Africa: A high order SEM-ANN analytical approach," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    6. 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.
    7. Yanyan Zhang & Tat-Huei Cham & Chuen Khee Pek & Choi-Meng Leong, 2024. "Is face and information availability important in green purchasing among young consumers?," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    8. Pinochet, Luis Hernan Contreras & de Gois, Fernanda Silva & Pardim, Vanessa Itacaramby & Onusic, Luciana Massaro, 2024. "Experimental study on the effect of adopting humanized and non-humanized chatbots on the factors measure the intensity of the user's perceived trust in the Yellow September campaign," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    9. Nadjim Mkedder & Fatma Zeynep Özata, 2024. "I will buy virtual goods if I like them: a hybrid PLS-SEM-artificial neural network (ANN) analytical approach," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(1), pages 42-70, March.
    10. 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).
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Murugan, Manivel & Marisamynathan, Sankaran, 2024. "Policy analysis for sustainable EV charging facility adoption using SEM-ANN approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
    17. Bilal, Muhammad & Zhang, Yunfeng & Cai, Shukai & Akram, Umair & Halibas, Alrence, 2024. "Artificial intelligence is the magic wand making customer-centric a reality! An investigation into the relationship between consumer purchase intention and consumer engagement through affective attach," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    18. 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.
    19. 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.
    20. Siyal, Abdul Waheed & Chen, Hongzhuan & Jamal Shah, Syed & Shahzad, Fakhar & Bano, Shaher, 2024. "Customization at a glance: Investigating consumer experiences in mobile commerce applications," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).

    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.