Exploring college students’ continuance learning intention in data analysis technology courses: the moderating role of self-efficacy

Frontiers in Psychology

Psychology

Joseph Tan¹  Pinghao Ye²  Liqiong Liu² 

[1] DeGroote School of Business, McMaster University, Hamilton, ON, Canada;School of Business Administration, Wuhan Business University, Wuhan, China;

关键词: flow experience;    self-efficacy;    data analysis;    continuance learning intention;    moderating role;   

DOI  :  10.3389/fpsyg.2023.1241693

 received in 2023-06-17, accepted in 2023-09-28,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionIn today’s digital economy, data resources have gained strategic recognition. Enterprises view data analytic capabilities as a core organizational competitiveness. This study explored factors influencing college students’ continuance learning intention in data analysis technology courses to inform the role of self-efficacy on the relationship between interactivity and continuance learning intention.MethodsThe research model underpinning the study was based on the Stimulus-Organism-Response model and flow theory. The model was validated using SmartPLS. A total of 314 valid questionnaires were collected via the standard online survey approach.ResultsAmong internal factors, study results showed both cognitive interest and self-efficacy had significant positive effects on continuance learning intention. Also, cognitive interest had a significant positive effect on self-efficacy. Among external stimuli, content quality, software quality, and interactivity had significant positive effects on self-efficacy. Software quality did not have a significant effect on cognitive interest. Importantly, self-efficacy registered a significant moderating role on the relationship between interactivity and continuance learning intention.

【 授权许可】

Unknown   
Copyright © 2023 Liu, Ye and Tan.