Determinants of perceived e-learning usefulness in higher education: A case of Thailand

  • Received October 4, 2022;
    Accepted November 5, 2022;
    Published November 15, 2022
  • Author(s)
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  • Article Info
    Volume 18 2022, Issue #4, pp. 86-96
  • Cited by
    6 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

Perceived e-learning usefulness as a marketing element has significantly affected student satisfaction, which results in a high propensity to continue using the current e-learning services with their universities. Therefore, this study aims to examine the effects of perceived risk, confirmation, and student motivation on perceived e-learning usefulness. This paper employed a convenience sampling technique to collect opinions from 689 university students at different universities (e.g., Thaksin University, Hatyai University, Prince of Songkla University, and Rajabhat University) around Thailand. Those students were actively using e-learning to access their education. After checking data validity, only 527 valid responses were analyzed through the path analysis method. According to empirical findings, confirmation significantly influenced student motivation, while perceived risk did not significantly impact student motivation. Finally, perceived e-learning usefulness was significantly influenced by confirmation, student motivation, and perceived risk. Furthermore, although these factors significantly influenced perceived e-learning usefulness, attitudes toward perceived e-learning usefulness relied mainly on the degree of confirmation, as this factor highlighted the most substantial effect on perceived e-learning usefulness. Moreover, perceived e-learning usefulness as a marketing element is a promising topic in the e-learning service sector, which requires future studies to examine to which extent the current study findings could apply to other groups of students or practitioners.

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    • Figure 1. Conceptual framework
    • Figure 2. Path analysis findings
    • Table 1. Reliability and convergent validity
    • Table 2. Discriminant validity
    • Table 3. Model fitness of path analysis
    • Table 4. Findings and hypotheses testing
    • Conceptualization
      Long Kim
    • Investigation
      Long Kim
    • Software
      Long Kim
    • Writing – original draft
      Long Kim
    • Data curation
      Pimlapas Pongsakornrungsilp
    • Methodology
      Pimlapas Pongsakornrungsilp
    • Supervision
      Pimlapas Pongsakornrungsilp, Siwarit Pongsakornrungsilp
    • Writing – review & editing
      Pimlapas Pongsakornrungsilp
    • Formal Analysis
      Siwarit Pongsakornrungsilp, Teerada Cattapan, Nuttaprachya Nantavisit
    • Project administration
      Siwarit Pongsakornrungsilp
    • Validation
      Siwarit Pongsakornrungsilp, Nuttaprachya Nantavisit
    • Funding acquisition
      Teerada Cattapan, Nuttaprachya Nantavisit
    • Resources
      Teerada Cattapan, Nuttaprachya Nantavisit
    • Visualization
      Teerada Cattapan