ArticleFrontiers in psychology2026
An integrated cognitive load-technology acceptance model for explaining behavioral intention to adopt Smart Physical Education Systems for extracurricular physical activity.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Smart Physical Education Systems are increasingly implemented in higher education institutions to promote undergraduates' physical activity. However, sustained engagement with these systems remains limited. Although the Technology Acceptance Model explains technology acceptance evaluations and Cognitive Load Theory emphasizes constraints associated with cognitive processing, insufficient attention has been paid to how the allocation of cognitive resources influences undergraduates' behavioral intention to adopt such systems. Integrating these perspectives may provide a more comprehensive understanding of the psychological mechanisms underlying technology adoption in digitally mediated exercise contexts. Methods: This study administered an electronic questionnaire survey to undergraduates from five higher education institutions that had implemented Smart Physical Education Systems, yielding 1,349 valid responses. Partial least squares structural equation modeling was employed to examine the structural relationships among the study variables. An integrated theoretical framework combining Cognitive Load Theory and the Technology Acceptance Model was adopted. The model comprised four technology acceptance constructs-perceived ease of use, perceived usefulness, attitude toward use, and behavioral intention-and three cognitive load dimensions-intrinsic, extraneous, and germane-to evaluate undergraduates' behavioral intention to adopt Smart Physical Education Systems for extracurricular physical activity. Results: The study showed that hypothesized relationships were supported. Perceived ease of use, perceived usefulness, and attitude toward use predicted behavioral intention, with perceived ease of use demonstrating the strongest effect. Germane cognitive load exerted significant effects on perceived ease of use and perceived usefulness and indirectly enhanced behavioral intention. Conversely, intrinsic and extraneous cognitive load exerted significant effects on behavioral intention, underscoring the importance of cognitive load management in undergraduates' adoption of Smart Physical Education Systems for extracurricular physical activity. Conclusion: Undergraduates' behavioral intention to adopt Smart Physical Education Systems for extracurricular physical activity is shaped not only by perceived usefulness and perceived ease of use but also by the allocation of cognitive resources during system interaction. The integrated Cognitive Load-Technology Acceptance Model framework advances understanding of technology adoption in smart physical education contexts and provides both theoretical insights and practical guidance for the design of effective Smart Physical Education Systems.
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