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Exploring UTAUT2 factors affecting the acceptance of the Chaoxing network teaching platform by higher vocational students

Author

Listed:
  • Zhengyan Liu
  • Siti Nazuar Sailin
  • Sock Beei Yeap

Abstract

This study explores the factors influencing vocational students’ acceptance of the Chaoxing Network Teaching Platform (CNTP) in the post-pandemic era, with a focus on technology acceptance. A quantitative approach was employed, utilizing surveys, factor analysis, t-tests, ANOVA, regression analysis, and structural equation modeling (SEM) to examine the relationships between technology acceptance factors and self-regulated learning (SRL) among vocational students. The results indicate that Performance Expectancy (PE), Effort Expectancy (EE), Hedonic Motivation (HM), and Habit (HB) significantly impact students’ acceptance of CNTP. These findings highlight the importance of technology acceptance in fostering effective SRL in online learning environments. This study enriches the literature by identifying key determinants of online platform acceptance in vocational education, emphasizing the role of technology acceptance in supporting students’ SRL and digital learning success. This study highlights key factors influencing vocational students' acceptance of the Chaoxing Network Teaching Platform (CNTP) and its role in the digital transformation of vocational education. Based on the findings, online learning platforms should enhance user experience through better design, usability, and engagement. Vocational education can implement training programs to foster self-regulated learning habits and long-term platform use. Policymakers should incorporate technology acceptance factors into strategies that support the effective and sustainable integration of online learning in vocational education.

Suggested Citation

  • Zhengyan Liu & Siti Nazuar Sailin & Sock Beei Yeap, 2025. "Exploring UTAUT2 factors affecting the acceptance of the Chaoxing network teaching platform by higher vocational students," Edelweiss Applied Science and Technology, Learning Gate, vol. 9(3), pages 2457-2469.
  • Handle: RePEc:ajp:edwast:v:9:y:2025:i:3:p:2457-2469:id:5819
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