Modeling students’ intentions to learn data science: Using an extended theory of planned behavior

Abstract

Academic and practitioner interest in data science has increased considerably. Yet scholarly understanding of what motivates students to learn data science is still limited. Drawing on the theory of planned behavior, we propose a research model to examine the determinants of behavioral intentions to learn data science. In the proposed research model, we also included constructs that are closely related to behavioral intentions. We used PLS-SEM to test the research hypotheses. The antecedents to behavioral intentions were found to explain 53% of variance in students’ behavioral intentions to learn data science. Among the constructs in the research model, the findings indicate that only attitude toward learning data science and perceived usefulness are positively related to behavioral intentions. The results also indicate that the influence of core constructs of the theory of planned behavior (e.g., subjective norm and perceived behavioral control) on behavioral intentions may not be as strong under certain circumstances. The findings contribute to an initial understanding of the drivers of students’ intentions to learn data science and open the door to new scholarship.

https://doi.org/10.34105/j.kmel.2024.16.029