Effects of social media overload on academic performance: a stressor–strain–outcome perspective

ABSTRACT

As an emerging phenomenon, social media overload has become prevalent among students and led to significant negative consequences. Based on the stressor–strain–outcome model, this study argues that three kinds of overload (information, communication, and social overloads) can influence two psychological strains (technostress and exhaustion) among students and thus affect their behavioral outcome (academic performance). The model is empirically tested through an online survey of 249 Chinese university students who use social media. Results indicate that the three types of social media overload are significant stressors that create technostress, but solely information overload significantly influence exhaustion. In addition, technostress and exhaustion likewise exhibit negative influences on the academic performance of university students.

KEYWORDS:

• Social media
• Overload
• Stressor–Strain–Outcome model
• Academic performance

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes on contributors

Chenling Shi is Master student of School of Management at University of Science and Technology of China. Her current research mainly focuses on social media.

Lingling Yu is Ph.D. student of School of Management at University of Science and Technology of China. Her current research mainly focuses on mobile payment, social media, and technology addiction.

Nan Wang is Professor at Business School of Beijing Technology and Business University. She received her Ph.D. at Beijing University of Aeronautics and Astronautics. Her research focuses on innovation and entrepreneurship, internet user behavior, enterprise business model and strategic management.

Bayi Cheng is Professor of School of Management at Hefei University of Technology. He received his Ph.D. at University of Science and Technology of China. His current research focuses on personalized manufacturing, car sharing and big data decision-making.

Xiongfei Cao is Associate Professor of Information Systems at Hefei University of Technology. He received his Ph.D. in Information Systems at City University of Hong Kong and Ph.D. in Management Science and Engineering at University of Science and Technology of China. His current research focuses on knowledge management, social media and IT addiction.

Additional information

Funding

This work was supported by the China Scholarship Council: [Grant Number 201706340065], State-owned Assets Collaborative Innovation Center “Research on innovation management of state-owned enterprises”: [Grant Number BTBUGZGL201902], and National Natural Science Foundation of China: [Grant Number 71602006].