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<p>The worldwide pandemic has heightened digital technology use in academic settings. This development has raised concerns about technology fatigue, necessitating further understanding of whether the digital stressors affect psychological academic outcomes. This study focuses on academic disengagement, drawing on the Stressor–Strain–Outcome (SSO) framework’s emphasis on behavioural withdrawal. Purposive sampling and snowball sampling were used to gather responses from students in higher education institutions. This study employed a comparative model analysis, beginning with a baseline model and progressively integrating additional paths for the competing models. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), this study evaluates and predicts the five competing models developed through distinct structural configurations to identify the most robust mechanism aligning with the SSO and Transactional Model of Stress and Coping framework. This study offers (i) methodological contribution by applying a rigorous stepwise PLS-SEM model comparison using Bayesian Information Criterion (BIC) and Akaike weights to identify the most predictive structure; (ii) theoretical novelty by extending the SSO framework through the integration of self-perception of academic performance as a cognitive-evaluative construct; and (iii) practical guidance for institutions and EdTech providers to design targeted strategies to reduce technostress and improve student engagement. Findings validate Model 2’s superior predictive capacity, confirming that compulsive technology use and information overload are positively associated with technology stressors. Results confirm that technostress drives academic disengagement and erodes students’ self-perception of academic performance. A crucial mediation path reveals that a decline in self-perception of academic performance intensifies academic disengagement. This research supports conceptualizing technostress as the critical psychological strain and mediator. The findings provide theoretical, practical, and social insights to inform the development of institutional interventions to alleviate digital strain, enhance self-efficacy, and promote students’ academic engagement in technology-driven settings.</p>

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Technology fatigue!? A comparative model analysis of technostress, academic self-perception and engagement in higher education settings

  • 期刊论文

  • 发表日期：

    2026年5月14日

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  • Mei Peng Low,
  • Weiline Wei Ling Chong,
  • Lei Mee Thien

摘要

The worldwide pandemic has heightened digital technology use in academic settings. This development has raised concerns about technology fatigue, necessitating further understanding of whether the digital stressors affect psychological academic outcomes. This study focuses on academic disengagement, drawing on the Stressor–Strain–Outcome (SSO) framework’s emphasis on behavioural withdrawal. Purposive sampling and snowball sampling were used to gather responses from students in higher education institutions. This study employed a comparative model analysis, beginning with a baseline model and progressively integrating additional paths for the competing models. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), this study evaluates and predicts the five competing models developed through distinct structural configurations to identify the most robust mechanism aligning with the SSO and Transactional Model of Stress and Coping framework. This study offers (i) methodological contribution by applying a rigorous stepwise PLS-SEM model comparison using Bayesian Information Criterion (BIC) and Akaike weights to identify the most predictive structure; (ii) theoretical novelty by extending the SSO framework through the integration of self-perception of academic performance as a cognitive-evaluative construct; and (iii) practical guidance for institutions and EdTech providers to design targeted strategies to reduce technostress and improve student engagement. Findings validate Model 2’s superior predictive capacity, confirming that compulsive technology use and information overload are positively associated with technology stressors. Results confirm that technostress drives academic disengagement and erodes students’ self-perception of academic performance. A crucial mediation path reveals that a decline in self-perception of academic performance intensifies academic disengagement. This research supports conceptualizing technostress as the critical psychological strain and mediator. The findings provide theoretical, practical, and social insights to inform the development of institutional interventions to alleviate digital strain, enhance self-efficacy, and promote students’ academic engagement in technology-driven settings.