Deciphering the underlying repercussions of cognitive overload on university students' fatigue, frustration and academic productivity : Implementation of stimulus–organism–response model

Abstract

Extant studies suggest that cognitive overload, as a nascent phenomenon, has become increasingly pervasive among university students, precipitating a multitude of detrimental consequences. Nevertheless, the adverse impacts of cognitive overload, particularly on Chinese higher education students, remain markedly underexplored in the extant literature. To improve academic understanding, this research combines quantitative data with the stimulus–organism–response (SOR) framework, constructing a robust theoretical model that elucidates the antecedents and sequelae of cognitive overload in relation to academic productivity in educational contexts. Research has suggested that several types of cognitive overload, including information, social, and system function overload, might contribute to university students' mobile SNS fatigue and frustration, which may detrimentally impact their academic productivity. This theoretical framework is empirically validated through rigorous statistical analyses derived from a sample of 660 Chinese university students who frequently utilized mobile social media. The findings underscore that these three cognitive overload forms significantly predict mobile SNS fatigue and frustration, subsequently impairing university students' academic productivity. Moreover, the relationships between three cognitive overload types and academic productivity are mediated by mobile SNS fatigue and frustration. Consequently, this study advances the theoretical and empirical discourse on cognitive overload by applying the SOR model, elucidating the underlying mechanisms by which cognitive overload impacts university students' academic productivity. These insights deepen the scholarly understanding of the adverse consequences associated with mobile social media usage from a cognitive overload perspective, furnishing empirically grounded recommendations for multiple stakeholders to implement targeted interventions aimed at ameliorating these detrimental effects.