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

Abstract

Introduction: In 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. Methods: The 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. Results: Among 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.