Online Self-Efficacy and Technostress as Predictors of Instructional Performance: A Convergent Mixed-Methods Study of Teachers in Online Education

Abstract

The rapid institutionalization of online education has intensified scholarly interest in the factors that determine teacher instructional performance in digital environments, yet the simultaneous predictive contributions of online self-efficacy and technostress to that performance remain empirically underspecified. This study investigated online self-efficacy and technostress as predictors of instructional performance among teachers engaged in online teaching, employing a convergent mixed-methods design in which quantitative and qualitative data were collected concurrently and triangulated at the analysis stage. The quantitative component utilized a structured survey administered to teachers, yielding descriptive and inferential statistical data. Descriptively, teachers reported high online self-efficacy, moderate technostress overall, and consistently high instructional performance. Multiple regression analysis revealed that online self-efficacy significantly and positively predicted instructional performance, while technostress did not achieve statistical significance. The model accounted for 15.8% of the variance in instructional performance. The qualitative component employed thematic analysis of interview data from teachers reflecting on their lived experiences during online instruction, yielding six emergent themes: blended delivery mode, learning management systems, coping with modern instructional demands, convenience and efficiency of online tools, access to resources and research, and technology's role in the learning process. Convergence between quantitative and qualitative findings confirmed that online self-efficacy—enabled through training, institutional support, and purposive technology adoption—constitutes the primary driver of instructional performance in online settings, while moderate technostress levels function as a manageable challenge rather than a performance-suppressing barrier when institutional supports are present. These findings carry specific implications for institutional professional development policy, pre-service teacher digital training, and the design of technology integration support systems.