the impact of social media on students research paper pdf

Use of Social Media in Student Learning and Its Effect on Academic Performance

• First Online: 18 May 2021

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• G. D. T. D. Chandrasiri 3 &
• S. M. Samarasinghe 3  

Part of the book series: Future of Business and Finance ((FBF))

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With the advancement of the Internet, social media have become an integral part of our lives, impacting on every aspect of society, and especially in higher education. Thus, understanding the impact of social media on students’ academic performance is inevitable. Social media in higher education has been researched by many, but the impact on students’ academic performance has not been addressed sufficiently, particularly in Sri Lanka. Hence, the objective of this study is to examine the impact of social media on students’ academic performance. A comprehensive model has been formulated and validated using data collected from 320 undergraduates. The measurement model analysis provides adequate construct validity and reliability, and the structural model provides a good model fit. Of the ten hypotheses, nine are supported. The findings reveal that integrating social media in teaching and learning can assist in enhancing students’ academic performance.

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Acheaw, M. O., & Larson, A. G. (2015). Use of social media and its impact on academic performance of tertiary institution students: A study of students of Koforidua Polytechnic, Ghana. Journal of Education and Practice, 6 , 94–101.

Google Scholar  

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50 (2), 179–211.

Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management system. International Journal of Advanced Computer Science and Application, 5 (1), 143–155.

Article   Google Scholar  

Alnaser, F. M., Ghani, M., Rahi, S., & Abed, H. (2017). The impact of SERVQUAL model and subjective norms on Customer’s satisfaction and customer loyalty in Islamic banks: A cultural context. International Journal of Economics & Management Sciences, 6 (5), 455. https://doi.org/10.4172/2162-6359.1000455 .

Alqasa, K. M., Isa, F. M., Othman, N. S., & Zolait, A. H. S. (2014). The impact of students’ attitude and subjective norm on intention to use of banking systems. International Journal of Business Information Systems, 15 (1), 105–122.

Al-rahmi, W. M., & Othram, M. S. (2013). The Impact of Social Media use on Academic Performance among university students: A Pilot Study. Journal of Information Systems Research and Innovation , 1–10. At https://www.academia.edu/27575207/The_Impact_of_Social_Media_use_on_Academic_Performance_among_university_students_A_Pilot_Study

Al-rahmi, W. M., & Zeki, A. M. (2016). A model of using social media for collaborative learning to enhance learners’ performance on learning. Journal of King Saud University - Computer and Information Sciences, 29 (4), 526–535. https://doi.org/10.1016/j.jksuci.2016.09.002 .

Al-rahmi, W. M., Othman, M. S., & Yusuf, L. M. (2015). The role of social Media for Collaborative Learning to improve academic performance of students and researchers in Malaysian higher education. The International Review of Research in Open and Distributed Learning, 16 (4), 177–204.

Al-rahmi, W. M., Othman, M. S., & Musa, M. A. (2014). The improvement of students’ academic performance by using social media through collaborative learning in Malaysian higher education.  Asian Social Science , 10(8), 210.

Alshuaibi, M. S. I., Alshuaibi, A. S. I., Shamsudin, F. M., & Arshad, D. A. (2018). Use of social media, student engagement, and academic performance of business students in Malaysia. International Journal of Educational Management .

Asur, S., & Huberman, B. A. (2014). Predicting the Future with Social Media, (May). In 2010 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (Vol. 1, pp. 492–499). IEEE. https://doi.org/10.1109/WI-IAT.2010.63 .

Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative science quarterly , 421–458.

Bajpai, P. (2018). Analyzing effect of social media on academic performance of university graduates, (June). https://doi.org/10.1145/3234825.3234830 .

Bandura, A. (1998). Self-efficacy (reprint). In V. S. Ramachaudran (Ed.), Encyclopedia of human behaviour (Vol. 4, 1994) (pp. 71–81). New York: Academic.

Brooks, S. (2015). Computers in human behavior does personal social media usage affect efficiency and Well-being? Computers in Human Behavior, 46 , 26–37. https://doi.org/10.1016/j.chb.2014.12.053 .

Chen, C. (2013). The exploration on network behaviors by using the models of theory of planned behaviors (TPB), technology acceptance model (TAM) and C-TAM-TPB. African Journal of Business Management, 7 (30), 2976–2984. https://doi.org/10.5897/AJBM11.1966 .

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295 (2), 295–336.

Choi, G., & Chung, H. (2013). Applying the technology acceptance model to social networking sites (SNS): Impact of subjective norm and social capital on the acceptance of SNS. International Journal of Human-Computer Interaction, 29 , 619–628. https://doi.org/10.1080/10447318.2012.756333 .

Collins, E., & Hide, B. (2010). Use and relevance of Web 2.0 resources for researchers (pp. 271–289). At https://elpub.architexturez.net/system/files/pdf/119_elpub2010.content.pdf

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319. https://doi.org/10.2307/249008 .

DeLone, W.H., & McLean, E.R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3 (1), 60–95.

Delone, W., & McLean, E. (2003). The Delone and McLean model of information systems success : A ten-year update. Journal of Management Information Systems, 19 (4), 9–30. https://doi.org/10.1080/07421222.2003.11045748 .

Dumpit, D. Z., & Fernandez, C. J. (2017). Analysis of the use of social media in higher education institutions (HEIs) using the technology acceptance model. International Journal of Educational Technology in Higher Education, 14 (1), 5. https://doi.org/10.1186/s41239-017-0045-2 .

El-Badawy, T. A., & Hashem, Y. (2015). The impact of social media on the academic development of school students. International Journal of Business Administration, 6 (1), 46–52. https://doi.org/10.5430/ijba.v6n1p46 .

Ellen, P. S., & Ajzen, I. (1992). A comparison of the theory of planned behavior and the theory of reasoned action. Personality and Social Psychology Bulletin, 18 (1), 3–9. https://doi.org/10.1177/0146167292181001 .

Eom, S. B. (2012). Effects of LMS, self-efficacy, and self-regulated learning on LMS effectiveness in business education. Journal of International Education in Business, 5 (2), 129–144. https://doi.org/10.1108/18363261211281744 .

Eom, S. B. (2014). Understanding eLearners’ satisfaction with learning management systems. Bulletin of the IEEE Technical Committee on Learning Technology, 16 (2), 10–13.

Fan, W. S., Haung, Y. K., Hsu, H. C., & Chen, C. C. (2013). An analysis of the Blog-User’ attitude employing structural equation modeling combine TAM and TPB model, 414, 90–93. https://doi.org/10.4028/www.scientific.net/AMM.411-414.90 .

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research.

Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics.

González, M. R., Gasco, J., & Llopis, J. (2016). Facebook and academic performance : A positive outcome. The Anthroplogist, 23 (1-2), 59–67. https://doi.org/10.1080/09720073.2016.11891924 .

Gu, J., Lee, S., & Suh, Y. (2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications, 36 , 11605–11616. https://doi.org/10.1016/j.eswa.2009.03.024 .

Guo, L., Jian, J., & Tang, C. (2009). Understanding the psychological process underlying customer satisfaction and retention in a relational service. Journal of Business Research, 62 (11), 1152–1159. https://doi.org/10.1016/j.jbusres.2008.10.020 .

Hair, J. F. et al. (2009). Multivariate data analysis . New Delhi: Pearson Education.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19 (2), 139–151. https://doi.org/10.2753/MTP1069-6679190202 .

Hair, J. F., Hult, T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). SAGE Publications.

Henson, R. K. (2001). Understanding internal consistency reliability estimates: A conceptual primer on coefficient alpha. Measurement and evaluation in counseling and development, 34 (3), 177–189.

Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W.,... & Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational research methods, 17 (2), 182–209.

Hussain, Z. (2016). Leading to intention: The role of attitude in relation to technology acceptance model. Procedia Computer Science , 159–164.

Irianto, H. (2015). Consumers’ attitude and intention towards organic food purchase: An extension of theory of planned behavior in gender perspective. International Journal of Management, Economics and Social Sciences, 4 (1), 17–31.

Isaac, O., Aldholay, A., Abdullah, Z., & Ramayah, T. (2019). Online learning usage within Yemeni higher education: The role of compatibility and task-technology fit as mediating variables in the IS success model. Computers & Education, 58 (1), 113–129. https://doi.org/10.1016/j.compedu.2019.02.012 .

Junco, R. (2012). The relationship between frequency of Facebook use, participation in Facebook activities, and student engagemnet. Computers and Education, 58 (1), 162–171.

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53 (1), 59–68. https://doi.org/10.1016/j.bushor.2009.09.003

Khurana, N. (2015). The impact of social networking sites on the youth. Journal of Mass Communication Journalism, 5 (12), 10–13. https://doi.org/10.4172/2165-7912.1000285 .

Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® and academic performance . Centre for Learning Sciences and Technologies.

Kolan, B. J. (2018). Effect of social media on academic performance of students in Ghanaian universities: A case study of University of Ghana, Legon (February).

Lau, W. W. F. (2017). Computers in human behavior effects of social media usage and social media multitasking on the academic performance of university students. Computers in Human Behavior, 68 , 286–291. https://doi.org/10.1016/j.chb.2016.11.043 .

Lee, Y. C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30 . https://doi.org/10.1108/14684520610706406 .

Liao, C., Chen, J., & Yen, D. C. (2007). Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model. Computers in Human Behavior, 23(6), 2804-2822https:// doi.org/10.1016/j.chb.2006.05.006

Lu, Y., Zhou, T., & Wang, B. (2009). Computers in human behavior exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25 (1), 29–39. https://doi.org/10.1016/j.chb.2008.06.002 .

Melani, A., & Andrew, A. (2018). Social media and academic performance of undergraduate students (November 2017).

Michikyan, M., Subrahmanyam, K., & Dennis, J. (2015). Computers in human behavior Facebook use and academic performance among college students: A mixed-methods study with a multi-ethnic sample. Computers in Human Behavior, 45 , 265–272. https://doi.org/10.1016/j.chb.2014.12.033 .

Mingle, J., & Adams, M. (2015). Social media network participation and academic performance in senior high schools in Ghana.  Library Philosophy and Practice , 1.

Moghavvemi, S., Sulaiman, A., & Jaafar, N. I. (2018). The international journal of social media as a complementary learning tool for teaching and learning: The case of youtube. The International Journal of Management Education, 16 (August 2017), 37–42. https://doi.org/10.1016/j.ijme.2017.12.001 .

Nicholas, D., Farm, W., & Rowlands, I. (2014). Social media use in the research workflow (May). https://doi.org/10.3233/ISU-2011-0623 .

Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university student’s behavioral intention to use e-learning. Journal of Educational Technology & Society, 12 (3), 150–162. https://doi.org/10.1007/s00340-009-3513-0 .

Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned Behavio. MIS Quarterly, 30 (1), 115–143.

Pereira, F. A. D. M., Ramos, A. S. M., Gouvêa, M. A., & Da Costa, M. F. (2015). Satisfaction and continuous use intention of e-learning service in Brazilian public organizations. Computers in Human Behavior, 46 , 139–148. https://doi.org/10.1016/j.chb.2015.01.016 .

Phillips, B., & Shipps, B. (2012). Frequency of usage: the impact of technology acceptance factors versus social factors.  International Journal of Virtual Communities and Social Networking (IJVCSN), 4(2), 30-45.

Richter, N. F., Rudolf, S. R., Ringle, C. M., & Schlagel, C. (2016). A critical look at the use of SEM in international business research. International Marketing Review, 33 (3), 376–404. https://doi.org/10.1108/IMR-04-2014-0148 .

Rutherford, C. (2010). Using online social media to support preservice student engagement. MERLOT Journal of Online Learning and Teaching, 6 (4), 703–711.

Salloum, S. A., Maqableh, W., Mhamdi, C., & Kurdi, B. Al. (2018). Studying the Social Media Adoption by university students in the United Arab (December).

Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction , 4(4), 295–312, 4, 295–312.

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6 (2), 144–176.

Tenopir, C., Volentine, R., & King, D. W. (2013). Social media and scholarly reading. Information Review, online . https://doi.org/10.1108/OIR-04-2012-0062 .

Tess, P. A. (2013). The role of social media in higher education classes (real and virtual)–A literature review. Computers in human behavior, 29 (5), A60–A68.

Thuseethan, S., & Vasanthapriyan, S. (2015). Social media as a new trend in Sri Lankan digital journalism : A surveillance. Asian Social Science, 11 (10), 86. https://doi.org/10.5539/ass.v11n10p86 .

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11 (4), 342–365.

Venkatesh, V., & Davis, F. D. (2000). Studies linked references are available on JSTOR for this article : A theoretical extension of the technology acceptance model : Four longitudinal field studies. Management Science, 46 (2), 186–204.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view.  MIS quarterly , 425–478.

Wheeler, S., Yeomans, P., & Wheeler, D. (2008). The good, the bad and the wiki: Evaluating student-generated content for collaborative learning. British Journal of Educational Technology, 39 (6), 987–995. https://doi.org/10.1111/j.1467-8535.2007.00799.x .

Xinli, H. (2013). Effectiveness of information technology in reducing corruption in China. The Electronic Library, 33 (1), 52–64. https://doi.org/10.1108/EL-11-2012-0148 .

Yu, Y., Yi, W., Feng, Y., & Liu, J. (2018). Understanding the intention to use commercial bike-sharing systems: An integration of TAM and TPB . Hawaii International Conference on System Sciences.

Yunus, M. M., & Salehi, H. (2012). The effectiveness of Facebook groups on teaching and improving writing: Students’ perceptions. International journal of education and information Technologies, 1 (6), 87–96.

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Chandrasiri, G.D.T.D., Samarasinghe, S.M. (2021). Use of Social Media in Student Learning and Its Effect on Academic Performance. In: Dhiman, S., Samaratunge, R. (eds) New Horizons in Management, Leadership and Sustainability. Future of Business and Finance. Springer, Cham. https://doi.org/10.1007/978-3-030-62171-1_17

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Analysing the Impact of Social Media on Students’ Academic Performance: A Comparative Study of Extraversion and Introversion Personality

Sourabh sharma.

International Management Institute (IMI), Bhubaneswar, India

Ramesh Behl

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Complete data and material is available to support transparency.

The advent of technology in education has seen a revolutionary change in the teaching–learning process. Social media is one such invention which has a major impact on students’ academic performance. This research analyzed the impact of social media on the academic performance of extraversion and introversion personality students. Further, the comparative study between these two personalities will be analysed on education level (postgraduate and undergraduate) and gender (male and female). The research was initiated by identifying the factors of social media impacting students’ academic performance. Thereafter, the scale was developed, validated and tested for reliability in the Indian context. Data were collected from 408 students segregated into 202 males and 206 females. Two hundred and thirty-four students are enrolled in postgraduation courses, whereas 174 are registered in the undergraduate programme. One-way ANOVA has been employed to compare the extraversion and introversion students of different education levels and gender. A significant difference is identified between extraversion and introversion students for the impact of social media on their academic performance.

Introduction

Social Networking Sites (SNS) gained instant popularity just after the invention and expansion of the Internet. Today, these sites are used the most to communicate and spread the message. The population on these social networking sites (SNS) has increased exponentially. Social networking sites (SNS) in general are called social media (Boyd & Ellison, 2008 ). Social media (SM) is used extensively to share content, initiate discussion, promote businesses and gain advantages over traditional media. Technology plays a vital role to make SM more robust by reducing security threats and increasing reliability (Stergiou et al., 2018 ).

As of January 2022, more than 4.95 billion people are using the Internet worldwide, and around 4.62 billion are active SM users (Johnson, 2022 ). In India, the number of Internet users was 680 million by January 2022, and there were 487 million active social media users (Basuray, 2022 ). According to Statista Research Department ( 2022 ), in India, SM is dominated by two social media sites, i.e. YouTube and Facebook. YouTube has 467 million users followed by Facebook with 329 million users.

Although almost all age groups are using SM platforms to interact and communicate with their known community (Whiting & Williams, 2013 ), it has been found that social media sites are more popular among youngsters and specifically among students. They use SM for personal as well as academic activities extensively (Laura et al., 2017 ). Other than SM, from the last two years, several online platforms such as Microsoft Teams, Zoom and Google Meet are preferred to organize any kind of virtual meetings, webinars and online classes. These platforms were used worldwide to share and disseminate knowledge across the defined user community during the pandemic. Social media sites such as Facebook, YouTube, Instagram, WhatsApp and blogs are comparatively more open and used to communicate with public and/or private groups. Earlier these social media platforms were used only to connect with friends and family, but gradually these platforms became one of the essential learning tools for students (Park et al., 2009 ). To enhance the teaching–learning process, these social media sites are explored by all types of learning communities (Dzogbenuku et al., 2019 ). SM when used in academics has both advantages and disadvantages. Social media helps to improve academic performance, but it may also distract the students from studies and indulge them in other non-academic activities (Alshuaibi et al., 2018 ).

Here, it is important to understand that the personality traits of students, their education level and gender are critical constructs to determine academic performance. There are different personality traits of an individual such as openness, conscientiousness, extraversion and introversion, agreeableness and neuroticism (McCrae & Costa, 1987 ). This cross-functional research is an attempt to study the impact of social media on the academic performance of students while using extraversion and introversion personality traits, education levels and gender as moderating variables.

Literature Review

There has been a drastic change in the internet world due to the invention of social media sites in the last ten years. People of all age groups now share their stories, feelings, videos, pictures and all kinds of public stuff on social media platforms exponentially (Asur & Huberman, 2010 ). Youth, particularly from the age group of 16–24, embraced social media sites to connect with their friends and family, exchange information and showcase their social status (Boyd & Ellison, 2008 ). Social media sites have many advantages when used in academics. The fun element of social media sites always helps students to be connected with peers and teachers to gain knowledge (Amin et al., 2016 ). Social media also enhances the communication between teachers and students as this are no ambiguity and miscommunication from social media which eventually improves the academic performance of the students (Oueder & Abousaber, 2018 ).

When social media is used for educational purposes, it may improve academic performance, but some associated challenges also come along with it (Rithika & Selvaraj, 2013 ). If social media is incorporated into academics, students try to also use it for non-academic discussions (Arnold & Paulus, 2010 ). The primary reason for such distraction is its design as it is designed to be a social networking tool (Qiu et al., 2013 ). According to Englander et al. ( 2010 ), the usage of social media in academics has more disadvantages than advantages. Social media severely impacts the academic performance of a student. The addiction to social media is found more among the students of higher studies which ruins the academic excellence of an individual (Nalwa & Anand, 2003 ). Among the social media users, Facebook users’ academic performance was worse than the nonusers or users of any other social media network. Facebook was found to be the major distraction among students (Kirschner & Karpinski, 2010 ). However, other studies report contrary findings and argued that students benefited from chatting (Jain et al., 2012 ), as it improves their vocabulary and writing skills (Yunus & Salehi, 2012 ). Social media can be used either to excel in academics or to devastate academics. It all depends on the way it is used by the students. The good or bad use of social media in academics is the users’ decision because both the options are open to the students (Landry, 2014 ).

Kaplan and Haenlein ( 2010 ) defined social media as user-generated content shared on web 2.0. They have also classified social media into six categories:

• Social Networking Sites: Facebook, Twitter, LinkedIn and Instagram are the social networking sites where a user may create their profile and invite their friends to join. Users may communicate with each other by sharing common content.
• Blogging Sites: Blogging sites are individual web pages where users may communicate and share their knowledge with the audience.
• Content Communities and Groups: YouTube and Slideshare are examples of content communities where people may share media files such as pictures, audio and video and PPT presentations.
• Gaming Sites: Users may virtually participate and enjoy the virtual games.
• Virtual Worlds: During COVID-19, this type of social media was used the most. In the virtual world, users meet with each other at some decided virtual place and can do the pre-decided things together. For example, the teacher may decide on a virtual place of meeting, and students may connect there and continue their learning.
• Collaborative Content Sites: Wikipedia is an example of a collaborative content site. It permits many users to work on the same project. Users have all rights to edit and add the new content to the published project.

Massive open online courses (MOOCs) are in trend since 2020 due to the COVID-19 pandemic (Raja & Kallarakal, 2020 ). MOOCs courses are generally free, and anyone may enrol for them online. Many renowned institutions have their online courses on MOOCs platform which provides a flexible learning opportunity to the students. Students find them useful to enhance their knowledge base and also in career development. Many standalone universities have collaborated with the MOOCs platform and included these courses in their curriculum (Chen, 2013 ).

Security and privacy are the two major concerns associated with social media. Teachers are quite apprehensive in using social media for knowledge sharing due to the same concerns (Fedock et al., 2019 ). It was found that around 72% teachers were reluctant to use social media platforms due to integrity issues and around 63% teachers confirmed that security needs to be tightened before using social media in the classroom (Surface et al., 2014 ). Proper training on security and privacy, to use social media platforms in academics, is needed for  students and teachers (Bhatnagar & Pry, 2020 ).

The personality traits of a student also play a significant role in deciding the impact of social media on students’ academic performance. Personality is a dynamic organization which simplifies the way a person behaves in a situation (Phares, 1991 ). Human behaviour has further been described by many renowned researchers. According to Lubinski ( 2000 ), human behaviour may be divided into five factors, i.e. cognitive abilities, personality, social attitudes, psychological interests and psychopathology. These personality traits are very important characteristics of a human being and play a substantial role in work commitment (Macey & Schneider, 2008 ). Goldberg ( 1993 ) elaborated on five dimensions of personality which are commonly known as the Big Five personality traits. The traits are “openness vs. cautious”; “extraversion vs. introversion”; “agreeableness vs. rational”; “conscientiousness vs. careless”; and “neuroticism vs. resilient”.

It has been found that among all personality traits, the “extraversion vs. introversion” personality trait has a greater impact on students’ academic performance (Costa & McCrae, 1999 ). Extrovert students are outgoing, talkative and assertive (Chamorro et al., 2003 ). They are positive thinkers and comfortable working in a crowd. Introvert students are reserved and quiet. They prefer to be isolated and work in silos (Bidjerano & Dai, 2007 ). So, in the present study, we have considered only the “extraversion vs. introversion” personality trait. This study is going to analyse the impact of social media platforms on students’ academic performance by taking the personality trait of extraversion and introversion as moderating variables along with their education level and gender.

Research Gap

Past research by Choney ( 2010 ), Karpinski and Duberstein ( 2009 ), Khan ( 2009 ) and Kubey et al. ( 2001 ) was done mostly in developed countries to analyse the impact of social media on the students’ academic performance, effect of social media on adolescence, and addictiveness of social media in students. There are no published research studies where the impact of social media was studied on students’ academic performance by taking their personality traits, education level and gender all three together into consideration. So, in the present study, the impact of social media will be evaluated on students’ academic performance by taking their personality traits (extraversion and introversion), education level (undergraduate and postgraduate) and gender (male and female) as moderating variables.

Objectives of the Study

Based on the literature review and research gap, the following research objectives have been defined:

• To identify the elements of social media impacting student's academic performance and to develop a suitable scale
• To test the  validity and reliability of the scale
• To analyse the impact of social media on students’ academic performance using extraversion and introversion personality trait, education level and gender as moderating variables

Research Methodology

Sampling technique.

Convenience sampling was used for data collection. An online google form was floated to collect the responses from 408 male and female university students of undergraduation and postgraduation streams.

Objective 1 To identify the elements of social media impacting student's academic performance and to develop a suitable scale.

A structured questionnaire was employed to collect the responses from 408 students of undergraduate and postgraduate streams. The questionnaire was segregated into three sections. In section one, demographic details such as gender, age and education stream were defined. Section two contained the author’s self-developed 16-item scale related to the impact of social media on the academic performance of students. The third section had a standardized scale developed by John and Srivastava ( 1999 ) of the Big Five personality model.

Demographics

There were 408 respondents (students) of different education levels consisting of 202 males (49.5%) and 206 females (50.5%). Most of the respondents (87%) were from the age group of 17–25 years. 234 respondents (57.4) were enrolled on postgraduation courses, whereas 174 respondents (42.6) were registered in the undergraduate programme. The result further elaborates that WhatsApp with 88.6% and YouTube with 82.9% are the top two commonly used platforms followed by Instagram with 76.7% and Facebook with 62.3% of students. 65% of students stated that Google doc is a quite useful and important application in academics for document creation and information dissemination.

Validity and Reliability of Scale

Objective 2 Scale validity and reliability.

Exploratory factor analysis (EFA) and Cronbach’s alpha test were used to investigate construct validity and reliability, respectively.

The author’s self-designed scale of ‘social media impacting students’ academic performance’ consisting of 16 items was validated using exploratory factor analysis. The principle component method with varimax rotation was applied to decrease the multicollinearity within the items. The initial eigenvalue was set to be greater than 1.0 (Field, 2005 ). Kaiser–Meyer–Olkin (KMO) with 0.795 and Bartlett’s test of sphericity having significant values of 0.000 demonstrated the appropriateness of using exploratory factor analysis.

The result of exploratory factor analysis and Cronbach’s alpha is shown in Table ​ Table1. 1 . According to Sharma and Behl ( 2020 ), “High loading on the same factor and no substantial cross-loading confirms convergent and discriminant validity respectively”.

Exploratory factor analysis and Cronbach’s alpha for the self-developed scale of “Social media impact on academic performance”

The self-developed scale was segregated into four factors, namely “Accelerating Impact”, “Deteriorating Impact”, “Social Media Prospects” and “Social Media Challenges”.

The first factor, i.e. “Accelerating Impact”, contains items related to positive impact of social media on students’ academic performance. Items in this construct determine the social media contribution in the grade improvement, communication and knowledge sharing. The second factor “Deteriorating Impact” describes the items which have a negative influence of social media on students’ academic performance. Items such as addiction to social media and distraction from studies are an integral part of this factor. “Social Media Prospects” talk about the opportunities created by social media for students’ communities. The last factor “Social Media Challenges” deals with security and privacy issues created by social media sites and the threat of cyberbullying which is rampant in academics.

The personality trait of an individual always influences the social media usage pattern. Therefore, the impact of social media on the academic performance of students may also change with their personality traits. To measure the personality traits, the Big Five personality model was used. This model consists of five personality traits, i.e. “openness vs. cautious”; “extraversion vs. introversion”; “agreeableness vs. rational”; “conscientiousness vs. careless”; and “neuroticism vs. resilient”. To remain focussed on the scope of the study, only a single personality trait, i.e. “extraversion vs. introversion” with 6 items was considered for analysis. A reliability test of this existing scale using Cronbach’s alpha was conducted. Prior to the reliability test, reverse scoring applicable to the associated items was also calculated. Table ​ Table2 2 shows the reliability score, i.e. 0.829.

Cronbach’s alpha test for the scale of extraversion vs. introversion personality traits

Objective 3 To analyse the impact of social media on students’ academic performance using extraversion and introversion personality traits, education level and gender as moderating variables.

The research model shown in Fig.  1 helps in addressing the above objective.

Social media factors impacting academic performances of extraversion and introversion personality traits of students at different education levels and gender

As mentioned in Fig.  1 , four dependent factors (Accelerating Impact, Deteriorating Impact, Social Media Prospects and Social Media Challenges) were derived from EFA and used for analysing the impact of social media on the academic performance of students having extraversion and introversion personality traits at different education levels and gender.

Students having a greater average score (more than three on a scale of five) for all personality items mentioned in Table ​ Table2 2 are considered to be having extraversion personality or else introversion personality. From the valid dataset of 408 students, 226 students (55.4%) had extraversion personality trait and 182 (44.6%) had introversion personality trait. The one-way ANOVA analysis was employed to determine the impact of social media on academic performance for all three moderators, i.e. personality traits (Extraversion vs. Introversion), education levels (Undergraduate and Postgraduate) and gender (Male and Female). If the sig. value for the result is >  = 0.05, we may accept the null hypothesis, i.e. there is no significant difference between extraversion and introversion personality students for the moderators; otherwise, null hypothesis is rejected which means there is a significant difference for the moderators.

Table ​ Table3 3 shows the comparison of the accelerating impact of social media on the academic performance of all students having extraversion and introversion personality traits. It also shows a comparative analysis on education level and gender for these two personality traits of students. In the first comparison of extraversion and introversion students, the sig. value is 0.001, which indicates that there is a significant difference among extraversion and introversion students for the “Accelerating Impact” of social media on academic performance. Here, 3.781 is the mean value for introversion students which is higher than the mean value 3.495 of extraversion students. It clearly specifies that the accelerating impact of social media is more prominent in the students having introversion personality traits. Introversion students experienced social media as the best tool to express thoughts and improve academic grades. The result is also consistent with the previous studies where introvert students are perceived to use social media to improve their academic performance (Amichai-Hamburger et al., 2002 ; Voorn & Kommers, 2013 ). Further at the education level, there was a significant difference in postgraduate as well as undergraduate students for the accelerating impact of social media on the academic performance among students with extraversion and introversion, and introverts seem to get better use of social media. The gender-wise significant difference was also analysed between extraversion and introversion personalities. Female introversion students were found to gain more of an accelerating impact of social media on their academic performance.

One-way ANOVA: determining “Accelerating Impact” among extraversion and introversion personality traits students at different education levels and genders

Significant at the 0.05 level

Like Table ​ Table3, 3 , the first section of Table ​ Table4 4 compares the deteriorating impact of social media on the academic performance of all students having extraversion and introversion personality traits. Here, the sig. value 0.383 indicates no significant difference among extraversion and introversion students for the “Deteriorating Impact” of social media on academic performance. The mean values show the moderating deteriorating impact of social media on the academic performance of extraversion and introversion personality students. Unlimited use of social media due to the addiction is causing a distraction in academic performance, but the overall impact is not on the higher side. Further, at the education level, the sig. values 0.423 and 0.682 of postgraduate and undergraduate students, respectively, show no significant difference between extraversion and introversion students with respect to “Deteriorating Impact of Social Media Sites”. The mean values again represent the moderate impact. Gender-wise, male students have no difference between the two personality traits, but at the same time, female students have a significant difference in the deteriorating impact, and it is more on extroverted female students.

One-way ANOVA: Examining “Deteriorating Impact” among extraversion and introversion personality traits students at different education levels and genders

The significant value, i.e. 0.82, in Table ​ Table5 5 represents no significant difference between extraversion and introversion personality students for the social media prospects. The higher mean value of both personality students indicates that they are utilizing the opportunities of social media in the most appropriate manner. It seems that all the students are using social media for possible employment prospects, gaining knowledge by attending MOOCs courses and transferring knowledge among other classmates. At the education level, postgraduation students have no significant difference between extraversion and introversion for the social media prospects, but at the undergraduate level, there is a significant difference among both the personalities, and by looking at mean values, extroverted students gain more from the social media prospects. Gender-wise comparison of extraversion and introversion personality students found no significant difference in the social media prospects for male as well as female students.

One-way ANOVA: Examining “Social Media Prospects” among extraversion and introversion personality traits students at different education levels and genders

Table ​ Table6 6 shows the comparison of the social media challenges of all students having extraversion and introversion personality traits. It is also doing a comparative analysis on education level and gender for these two personality traits of students. All sig. values in Table ​ Table6 6 represent no significant difference between extraversion and introversion personality students for social media challenges. Even at the education level and gender-wise comparison of the two personalities, no significant difference is derived. The higher mean values indicate that the threat of cyberbullying, security and privacy is the main concern areas for extraversion and introversion personality students. Cyberbullying is seen to be more particularly among female students (Snell & Englander, 2010 ).

One-way ANOVA: Examining “Social Media Challenges” among extraversion and introversion personality traits students at different education levels and genders

The use of social media sites in academics is becoming popular among students and teachers. The improvement or deterioration in academic performance is influenced by the personality traits of an individual. This study has tried to analyse the impact of social media on the academic performance of extraversion and introversion personality students. This study has identified four factors of social media which have an impact on academic performance. These factors are: accelerating impact of social media; deteriorating impact of social media; social media prospects; and social media challenges.

Each of these factors has been used for comparative analysis of students having extraversion and introversion personality traits. Their education level and gender have also been used to understand the detailed impact between these two personality types. In the overall comparison, it has been discovered that both personalities (extraversion and introversion) have a significant difference for only one factor, i.e. “Accelerating Impact of Social Media Sites” where students with introversion benefited the most. At the education level, i.e. postgraduate and undergraduate, there was a significant difference between extraversion and introversion personalities for the first factor which is the accelerating impact of social media. Here, the introversion students were found to benefit in postgraduate as well as undergraduate courses. For the factors of deteriorating impact and social media challenges, there was no significant difference between extraversion and introversion personality type at the different education levels.

Surprisingly, for the first factor, i.e. the accelerating impact of social media, in gender-wise comparison, no significant difference was found between extraversion and introversion male students. Whereas a significant difference was found in female students. The same was the result for the second factor, i.e. deteriorating impact of social media of male and female students. For social media prospects and social media challenges, no significant difference was identified between extraversion and introversion students of any gender.

Findings and Implications

The personality trait of a student plays a vital role in analysing the impact of social media on their academic performance. The present study was designed to find the difference between extraversion and introversion personality types in students for four identified factors of social media and their impact on students’ academic performance. The education level and gender were also added to make it more comprehensive. The implications of this study are useful for institutions, students, teachers and policymakers.

This study will help the institutions to identify the right mix of social media based on the personality, education level and gender of the students. For example, technological challenges are faced by all students. It is important for the institutions to identify the challenges such as cyberbullying, security and privacy issues and accordingly frame the training sessions for all undergraduate and postgraduate students. These training sessions will help students with extraversion and introversion to come out from possible technological hassles and will create a healthy ecosystem (Okereke & Oghenetega, 2014 ).

Students will also benefit from this study as they will be conscious of the possible pros and cons that exist because of social media usage and its association with students’ academic performance. This learning may help students to enhance their academic performance with the right use of social media sites. The in-depth knowledge of all social media platforms and their association with academics should be elucidated to the students so that they may explore the social media opportunities in an optimum manner. Social media challenges also need to be made known to the students to improve upon and overcome with time (Boateng & Amankwaa, 2016 ).

Teachers are required to design the curriculum by understanding the learning style of students with extraversion and introversion personality type. Innovation and customization in teaching style are important for the holistic development of students and to satisfy the urge for academic requirements. Teachers should also guide the students about the adverse impacts of each social media platform, so that these can be minimized. Students should also be guided to reduce the time limit of using social media (Owusu-Acheaw & Larson, 2015 ).

Policymakers are also required to understand the challenges faced by the students while using social media in academics. All possible threats can be managed by defining and implementing transparent and proactive policies. As social media sites are open in nature, security and privacy are the two major concerns. The Government of India should take a strong stand to control all big social media companies so that they may fulfil the necessary compliances related to students’ security and privacy (Kumar & Pradhan, 2018 ).

The overall result of these comparisons gives a better insight and deep understanding of the significant differences between students with extraversion and introversion personality type towards different social media factors and their impact on students’ academic performance. Students’ behaviour according to their education level and gender for extraversion and introversion personalities has also been explored.

Limitation and Future Scope of Research

Due to COVID restrictions, a convenient sampling technique was used for data collection which may create some response biases where the students of introversion personality traits may have intentionally described themselves as extroversion personalities and vice versa. This study also creates scope for future research. In the Big Five personality model, there are four other personality traits which are not considered in the present study. There is an opportunity to also use cross-personality comparisons for the different social media parameters. The other demographic variables such as age and place may also be explored in future research.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dr. SS and Prof. RB. The first draft of the manuscript was written by Dr. SS, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

No funds, grants, or other support was received.

Availability of data and material

Declarations.

The authors declare that they have no conflict of interest.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Verbal informed consent was obtained from the participants.

Verbal consent is obtained for publication

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Sourabh Sharma, Email: ni.ude.hbimi@hbaruos .

Ramesh Behl, Email: ude.imi@lhebr .

• Alshuaibi M, Ibrahim S, Ibrahim AAS, Mohd F, Shamsudin DA, Arshad. Use of social media, student engagement, and academic performance of business students in Malaysia. International Journal of Educational Management. 2018; 32 (4):625–640. [ Google Scholar ]
• Amichai-Hamburger Y, Wainapel G, Fox S. "On the Internet no one knows I'm an introvert": Extroversion, neuroticism, and Internet interaction. Cyberpsychology & Behaviour. 2002; 5 (2):125–128. doi: 10.1089/109493102753770507. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Amin Z, Mansoor A, Hussain SR, Hashmat F. Impact of social media of student’s academic performance. International Journal of Business and Management Invention. 2016; 5 (4):22–29. [ Google Scholar ]
• Arnold N, Paulus T. Using a social networking site for experiential learning: Appropriating, lurking, modeling and community building. The Internet and Higher Education. 2010; 13 (4):188–196. doi: 10.1016/j.iheduc.2010.04.002. [ CrossRef ] [ Google Scholar ]
• Asur, S., & Huberman, B. A. (2010, August). Predicting the future with social media. In  2010 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology  (Vol. 1, pp. 492–499). IEEE.
• Basuray, T. (2022). Social media usage in India: Statistics & facts. https://www.statista.com/topics/5113/social-media-usage-in-india/#topicHeader__wrapper .
• Bhatnagar N, Pry M. Student attitudes, awareness, and perceptions of personal privacy and cybersecurity in the use of social media: An initial study. Information Systems Education Journal. 2020; 18 (1):48–58. [ Google Scholar ]
• Bidjerano T, Dai DY. The relationship between the big-five model of personality and self-regulated learning strategies. Learning and Individual Differences. 2007; 17 (1):69–81. doi: 10.1016/j.lindif.2007.02.001. [ CrossRef ] [ Google Scholar ]
• Boateng R, Amankwaa A. The impact of social media on student academic life in higher education. Global Journal of Human-Social Science. 2016; 16 (4):1–8. [ Google Scholar ]
• Boyd DM, Ellison NB. Social Network Sites: Definition, history and scholarship. Journal of Computer-Mediatied Communication. 2008; 13 :210–230. doi: 10.1111/j.1083-6101.2007.00393.x. [ CrossRef ] [ Google Scholar ]
• Chamorro Premuzic T, Furnham A. Personality predicts academic performance: Evidence from two longitudinal university samples. Journal of Research in Personality. 2003; 37 (4):319–338. doi: 10.1016/S0092-6566(02)00578-0. [ CrossRef ] [ Google Scholar ]
• Chen, J. (2013). Opportunities and challenges of MOOCs: perspectives from Asia.
• Choney, S. (2010) Facebook Use Can Lower Grades by 20 Percent, Study Says. http://www.msnbc.com/id/39038581/ns.technology_and_science-tech_and_gadgets/
• Costa PT, McCRAE RR. A five-factor theory of personality. The Five-Factor Model of Personality: Theoretical Perspectives. 1999; 2 :51–87. [ Google Scholar ]
• Dzogbenuku RK, George AK, Kumi DK. Social media and student performance: The moderating role of ICT knowledge. Journal of Information, Communication and Ethics in Society. 2019; 18 (2):197–219. doi: 10.1108/JICES-08-2019-0092. [ CrossRef ] [ Google Scholar ]
• Englander F, Terregrossa RA, Wang Z. Internet use among college students: Tool or toy? Educational Review. 2010; 62 (1):85–96. doi: 10.1080/00131910903519793. [ CrossRef ] [ Google Scholar ]
• Fedock BC, McCartney M, Neeley D. Online adjunct higher education teachers’ perceptions of using social media sites as instructional approaches. Journal of Research in Innovative Teaching & Learning. 2019; 12 (3):222–235. doi: 10.1108/JRIT-02-2018-0005. [ CrossRef ] [ Google Scholar ]
• Field AP. Discovering statistics using SPSS. 2. Sage Publications; 2005. [ Google Scholar ]
• Goldberg LR. The structure of phenotypic personality traits. American Psychologist. 1993; 48 (1):26–34. doi: 10.1037/0003-066X.48.1.26. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jain N, Verma A, Verma R, Tiwari P. Going social: The impact of social networking in promoting education. International Journal of Computer Science. 2012; 9 (1):483–485. [ Google Scholar ]
• John OP, Srivastava S. The Big Five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of Personality: Theory and Research. 1999; 2 (1999):102–138. [ Google Scholar ]
• Johnson, J. (2022). Worldwide digital population as of January 2022. https://www.statista.com/statistics/617136/digital-population-worldwide/ .
• Kaplan AM, Haenlein M. Users of the world, unite! The Challenges and Opportunities of Social Media. Business Horizons. 2010; 53 (1):59–68. doi: 10.1016/j.bushor.2009.09.003. [ CrossRef ] [ Google Scholar ]
• Karpinski, A. C., & Duberstein, A. (2009). A description of Facebook use and academic performance among undergraduate and graduate students. In  Annual Meeting of the American Educational Research Association, San Diego, CA  (pp. 5–10).
• Khan, U (2009). Facebook student underachieve in exams. Daily Telegraph. http://www.telegraph.co.uk/educationnews/5145243/Facebook-students-underachieve-in-exams.html
• Kirschner PA, Karpinski AC. Facebook® and academic performance. Computers in Human Behavior. 2010; 26 (6):1237–1245. doi: 10.1016/j.chb.2010.03.024. [ CrossRef ] [ Google Scholar ]
• Kubey RW, Lavin MJ, Barrows JR. Internet use and collegiate academic performance decrements: Early findings. Journal of Communication. 2001; 51 (2):366–382. doi: 10.1111/j.1460-2466.2001.tb02885.x. [ CrossRef ] [ Google Scholar ]
• Kumar, V., & Pradhan, P. (2018). Trust management issues in social-media marketing. In  Social Media Marketing: Breakthroughs in Research and Practice  (pp. 714–732). IGI Global.
• Landry, T. (2014). How social media has changed us: The good and the bad.  Retrieved from .
• Laura R, Jose B, Braojos J. From traditional education technologies to student satisfaction in Management education: A theory of the role of social media applications. Information & Management. 2017; 54 (8):1059–1071. doi: 10.1016/j.im.2017.06.002. [ CrossRef ] [ Google Scholar ]
• Lubinski D. Scientific and social significance of assessing individual differences:“Sinking shafts at a few critical points” Annual Review of Psychology. 2000; 51 (1):405–444. doi: 10.1146/annurev.psych.51.1.405. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Macey WH, Schneider B. The meaning of employee engagement. Industrial and Organizational Psychology. 2008; 1 :3–30. doi: 10.1111/j.1754-9434.2007.0002.x. [ CrossRef ] [ Google Scholar ]
• McCrae RR, Costa PT. Validation of the five-factor model of personality across instruments and observers. Journal of Personality and Social Psychology. 1987; 52 (1):81. doi: 10.1037/0022-3514.52.1.81. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nalwa K, Anand AP. Internet addiction in students: A cause of concern. Cyberpsychology & Behavior. 2003; 6 (6):653–656. doi: 10.1089/109493103322725441. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Okereke CE, Oghenetega LU. The impact of social media on the academic performance of university students in Nigeria. Journal of Education and Practice. 2014; 5 (33):21–24. [ Google Scholar ]
• Oueder M, Abousaber I. A Study on the Impact of Social Media Usage on Student Academic Performance: University of Tabuk an Example. American Academic Scientific Research Journal for Engineering, Technology, and Sciences. 2018; 40 (1):77–88. [ Google Scholar ]
• Owusu-Acheaw M, Larson AG. Use of social media and its impact on academic performance of tertiary institution students: A study of students of Koforidua Polytechnic, Ghana. Journal of Education and Practice. 2015; 6 (6):94–101. [ Google Scholar ]
• Park N, Kee KF, Valenzuela S. Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes. Cyberpsychology & Behavior. 2009; 12 (6):729–733. doi: 10.1089/cpb.2009.0003. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Phares EJ. Introduction to psychology. 3. Harper Collins; 1991. [ Google Scholar ]
• Qiu L, Lin H, Leung AKY. Cultural differences and switching of in-group sharing behavior between an American (Facebook) and a Chinese (Renren) social networking site. Journal of Cross-Cultural Psychology. 2013; 44 (1):106–121. doi: 10.1177/0022022111434597. [ CrossRef ] [ Google Scholar ]
• Raja, M. A. S., & Kallarakal, T. K. (2020). “COVID-19 and students perception about MOOCs” a case of Indian higher educational institutions.  Interactive Technology and Smart Education .
• Rithika M, Selvaraj S. Impact of social media on students’ academic performance. International Journal of Logistics & Supply Chain Management Perspectives. 2013; 2 (4):636–640. [ Google Scholar ]
• Sharma, S., & Behl, R. (2020). Strategic Alignment of Information Technology in Public and Private Organizations in India: A Comparative Study. Global Business Review , 1–18.
• Snell, P. A., & Englander, E. (2010). Cyberbullying victimization and behaviors among girls: Applying research findings in the field.  Journal of Social Sciences .
• Statista Research Department. (2022). Countries with the most Facebook users 2022. https://www.statista.com/statistics/268136/top-15-countries-based-on-number-of-facebook-users/
• Stergiou C, Psannis KE, Kim BG, Gupta B. Secure integration of IoT and cloud computing. Future Generation Computer Systems. 2018; 78 :964–975. doi: 10.1016/j.future.2016.11.031. [ CrossRef ] [ Google Scholar ]
• Surface JL, Stader D, Armenta A. Educator sexual misconduct and nondisclosure agreements: Policy guidance from missouri’s amy hestir student protection act. Educational Leadership Faculty Publications; 2014. [ Google Scholar ]
• Voorn RJ, Kommers PA. Social media and higher education: Introversion and collaborative learning from the student’s perspective. International Journal of Social Media and Interactive Learning Environments. 2013; 1 (1):59–73. doi: 10.1504/IJSMILE.2013.051650. [ CrossRef ] [ Google Scholar ]
• Whiting A, Williams D. Why people use social media: A uses and gratifications approach. Qualitative Market Research: An International Journal. 2013; 16 (4):362–369. doi: 10.1108/QMR-06-2013-0041. [ CrossRef ] [ Google Scholar ]
• Yunus M, Salehi H. The effectiveness of Facebook groups on teaching and improving writing: students’ perceptions. International Journal of Education and Information Technologies. 2012; 6 :87–96. [ Google Scholar ]

ORIGINAL RESEARCH article

High school students' social media use predicts school engagement and burnout: the moderating role of social media self-control.

• 1 Research Center for Digital Intelligence Strategy and Talent Development, Chongqing Technology and Business University, Chongqing, China
• 2 Research Center for Enterprise Management, Chongqing Technology and Business University, Chongqing, China
• 3 School of Business Administration, Chongqing Technology and Business University, Chongqing, China
• 4 Management School, Chongqing University of Technology, Chongqing, China

Students' social media use has quickly gained attention given the effect of considerable time spent on and widespread usage of social media on their development and success. The study aimed to examine whether high school students' social media use predicts more school engagement and less burnout for those who were more successful in controlling their social media use in goal-conflict situations. A sample of 107 Chinese high school students ( M age  = 19.21, SD age  = 1.85, 68% female) participated in an online survey. The results showed that social media self-control failure moderated the relationship between general social media use (rather than social media use intensity) and school engagement. A simple effect test revealed that more general social media use predicted higher school engagement for students who were more successful in controlling their social media use. However, no moderation effect was observed of social media self-control failure on the relationship between social media use intensity (or general social media use) and burnout. The results partially supported the study demands-resources model and indicated the potential benefits of controllable social media use on high school students’ engagement in the face of high academic demands.

1 Introduction

School engagement and burnout have been a significant research focus for educators and researchers for many years because they reflect the overall academic and psychological functioning of students ( 1 ). The demands-resources model has been widely used to examine the predictors of student engagement and burnout ( 2 – 5 ). Following this model, a number of studies on adolescents' engagement and burnout have been increasing to improve students' motivation toward study and to increase successful student achievement levels ( 3 , 6 ). However, only a few studies have focused on the possible role of social media use.

Students' social media use has quickly gained attention given the effect of considerable time spent on and widespread usage of social media on their development and success ( 7 , 8 ). Chinese high school students are known to have a high study load and academic demands in the face of the competitive National College Entrance Examination ( 5 ). This is particularly true for students in senior grades and return students (students who reattend classes after failing the college entrance examination). Social media use is popular in Chinese students, as its use can help to release pressure and fulfill several important gratifications of students (e.g., stay connected to social networks, organize and participate in relevant events and feel involved on campus) ( 9 ).

However, when using social media disturbs other school-related goals and tasks, many students fail to resist the temptation of social media use, which is also called “social media self-control failure” ( 10 ). Different from more extreme problematic (e.g., addictive or compulsive) use and more general dysfunction of self-control, social media self-control failure describes a momentary and intermittent self-control problem that many users experience daily ( 10 ). However, it was found to relate to negative consequences such as postponing school-related work and generating negative emotions (e.g., time pressure, guilt and academic strain) ( 10 – 12 ). The pros and cons of students' social media use stimulate a growing practitioner and academic interest in understanding its role in students' school performance and wellbeing. The main focus of the current literature has been on the maladaptive use of social media, such as addictive social media use ( 13 ). However, the positive impact of social media use on high school students' mental health and study performance is relatively ignored, taking into account self-control as an important boundary between social media use and the outcome measures.

We proposed that the balance of the social media effect depends on whether students can successfully control their social media behaviors when it conflicts with other goals. In other words, self-control may serve as a moderator that determines the effects of social media use on students' engagement and burnout. In addition to several review studies in media research that underlie the moderating role of self-control ( 14 – 16 ), no study has empirically examined this effect in high school students. Thus, using the demands-resources model as a guiding framework, the present study aimed to answer the question of whether social media-related self-control moderates the prediction of high school students' social media use on engagement and burnout.

1.1 Student engagement and burnout

The definition of engagement in the educational context varies by coverage and emphasis in different studies ( 17 , 18 ). Generally, scholars have concluded that student engagement is a positive state of vigor, dedication and absorption toward school and study-related activities ( 19 – 21 ). Engagement has a positive impact on several important aspects of school life, such as GPA ( 22 ) and life satisfaction ( 23 ).

Engagement focuses on students' mindset of motivation, which is considered a multidimensional construct ( 21 ). One often-used multidimensional model of student engagement is characterized by three facets that reflect psychological involvement in study-related activities: vigor, absorption and dedication ( 19 , 21 ). Vigor refers to the mental aliveness and behavioral endeavor to school activities and the persistence toward study tasks. Dedication reflects the aspect of identification in school, including inspiration, pride, and enthusiasm in academic learning; Absorption describes the deep concentration in tasks and activities of study ( 21 ). Based on the three-component model of engagement, Martin ( 19 ) developed a self-report engagement scale, which was validated in 12,237 high school students from 38 Australian high schools. Following this research, a Chinese version of the student engagement scale was validated in 2,330 high school students and showed excellent psychometric properties ( 24 ).

Student burnout refers to a state of mental and physical exhaustion toward school and the perception of inadequacy as a student ( 21 ). Burnout describes an extinction of motivation or incentive from unwanted results. It is reflected as deficient energetic resources and low dedication toward study ( 25 ). Burnout was found to predict poorer academic achievements in a meta-analysis of 29 studies ( 26 ). Moreover, it was also an indicator of school drop-out rate even from the data of Finland, known for equality-striving and high-quality educational system ( 27 ).

The components of burnout are commonly concluded to be three constructs that reflect the opposite attribute of engagement: emotional exhaustion, lack of personal accomplishment and cynicism ( 20 ). Emotional exhaustion describes the perception of weariness and fatigue toward study. Students suffering from burnout were chronically drained from their school tasks and devoted less energy to study. Reduced personal accomplishment refers to the loss of competence and achievement at school. Students often feel inadequate to be a student or unable to reach their study goals. Cynicism refers to a distant attitude toward study. Cynical students often express their hatred or indifference toward study ( 20 ). Based on the three components of student burnout, Wu et al. ( 28 ) established a Chinese version of the Student Burnout Inventory and validated its psychometric properties in 3,386 students from primary school to high school ( 28 ).

1.2 Demands-resources model and high school students' social media use

The demands-resources model provides a theoretical lens for understanding the effects of students' social media use on engagement and burnout ( 20 ). The demands-resources model originated from the job demands-resources model, which has been well used to explain occupational engagement and burnout ( 29 ). Based on the job demands-resources model and empirical evidence from educational studies, Salmela-Aro et al. ( 20 ) developed a study demands-resources model to explain the antecedents and development process of student engagement and burnout.

This model distinguishes two parallel but opposite factors that lead to school engagement and burnout: resources and demands. Resources are the physical, mental, and social aspects of study that help students achieve their study goals, reduce study demands or stimulate their personal growth ( 20 ). A typical resource is the feedback of students' performance obtained from school, which helps to evaluate the gap between current status and academic goals ( 2 ). Another core resource is emotional support from teachers ( 30 ) and significant others ( 31 ). Moreover, resources include students' personality factors that relate to self-autonomy. For instance, self-efficacy (i.e., feelings of competence in study due to the achievement of good results) was demonstrated to promote high school students' engagement ( 32 ). Self-control was also found to positively predict all three dimensions of engagement in undergraduate students ( 33 ).

In an early review, Heiberger & Harper ( 9 ) discussed the potential of Facebook use as a resource to increase college students' engagement. Based on the evaluation of practices in educational settings, they argued that Facebook is a useful tool for increasing social integration and the climate of commitment in college students. Facebook can be used for social interaction between students, faculty and staff members. It was also an effective application for programming initiatives, as well as information exchange for campus student issues ( 9 ). Overall, the use of Facebook was considered an opportunity and resource in promoting student involvement and engagement. Some empirical findings also supported the linkage between social media use and engagement. For instance, Mazer et al. ( 34 ) found that teachers' self-disclosure on Facebook was linked to higher levels of motivation in college students ( 34 ). Another investigation among undergraduate students' Facebook use and their engagement further revealed that only some Facebook activities (e.g., RSVP to events and viewing photos) were positively related to engagement. Other activities, such as playing games, posting photos, and Facebook chatting, were negatively related to engagement ( 35 ). The findings stress the necessity of distinguishing general social media usage and usage of different social media activities in predicting engagement.

Demand factors include physical, mental and organizational pressure that generate stress for students ( 20 ). According to the demands-recourses model, study demands, such as too much school work and too many courses, are the core indicators of burnout because these factors could generate mental and emotional burdens ( 20 ). When students experience depletion of energy without gaining sufficient returns, they are more likely to feel fatigue and exhaustion ( 4 ). Nevertheless, there are individual differences in perceiving the objective nature of the study environment. For example, when confronted with equal study load, some students perceive less stress, while others are easily overwhelmed. This is because individual factors such as autonomy and locus of self-control were identified to cope with stressors generated from demands ( 2 ).

Theoretically, social media can be used as a tool to help students recover from academic stress [e.g., ( 36 )], thus preventing students from burnout. However, previous studies have shown that social media use is also a main source of distraction ( 37 ). Social media was found to often distract people from study, work, sports, reading, sleep, family time, and house duties ( 38 , 39 ). This makes social media a possible risk factor for burnout. For high school students faced with high study demands, social media is an efficient tool for finding support from family and friends ( 40 ) and becoming entertained after study. However, this could motivate more social media usage and make students more easily distracted from pursuing other important goals ( 37 ). A study found that social media disrupted students from study-related activities, which created more time pressure ( 41 ). Another study found that social media use postponed school-related work, which may elevate students' academic stress ( 11 ). The evidence suggests that social media use could intensify study demands, which further predicts burnout.

1.3 The moderating role of social media self-control

As stated above, social media use can facilitate connection to study resources that will promote engagement and aggravate study demands that will lead to burnout. Thus, it is necessary to clarify the conditions under which social media use will help obtain resources and will create more study demands. We assumed that self-control plays an important role in leveraging this process. The literature on media use has identified self-control as an important moderator in the relationship between social media use and healthy outcomes such as wellbeing ( 14 ). It was argued that when social media use conflicts with other important goals (e.g., getting entertainment vs. reaching study goals), people need to exert self-control to regulate their social media behaviors ( 16 , 36 ). Self-control prioritizes more important goals over short-term media pleasure by eliciting negative emotions regarding social media use (e.g., guilt, time pressure, academic strain) ( 11 ) and inhibiting the impulsive enactment of social media behaviors ( 42 ). From the perspective of the demands-resources model, successful control over social media use can benefit study resources through more conscious use of social media in favor of academic goals. This can enhance students' efficacy toward study, such as actively using social media to obtain feedback from teachers and peers ( 43 ) and conducting distance learning ( 6 ), which promotes engagement.

In contrast, a lack of self-control over social media can increase the risk of burnout. This is because social media use may aggravate the stress and workload from study demands by creating more goal conflicts between excessive social media use and study goals ( 11 ). This also supports the idea regarding motivational interference. According to this theoretical framework, students often face conflicts between their motivation to learn and other alternative activities (e.g., talking to friends or watching TV). When the alternative activity is more emotional rewarding than concurrent learning goal, it can interfere with the ability to regulate one's learning ( 44 , 45 ). Similarly, social media use can be also seen as an attractive alternative activity that disrupts self-control in studying. Students with an impaired self-control are more likely to be drawn to social media activities that offer high emotional rewards. For instance, they may spend with more time browsing social networking sites or watching short videos instead of finishing school-related work. Leaving goals and tasks unfinished will cause competitive pressure ( 46 ) and create more strain toward studying ( 11 ), increasing the risk of burnout.

Evidence from empirical studies supported the moderating role of self-control between social media use and engagement and burnout. For instance, a study surveyed 210 social media users and found that a lack of impulse control in checking notifications on social media predicts more feelings of fatigue over social media use ( 47 ). A study among 634 college students found that using media during class predicted more burnout, particularly through a deficient level of self-control ( 48 ). An experimental study compared two groups of students who were allowed or not allowed to use Facebook messaging during class. The results showed that being unable to regulate social media multitasking reduced students' academic performance in class ( 49 ).

Taken together, being able to employ self-control to resist the temptation of social media in goal-conflict situations may serve as a moderator that alters the effect of students' social media use on engagement and burnout. Based on the demands-resources model and the above empirical evidence, we hypothesized that high school students' social media use will predict higher school engagement and lower burnout for those who are more successful (vs. less successful) in controlling their social media use in goal-conflict situations (see Figure 1 ). In addition to the independent variable and moderator, several control variables were also considered: self-control, depletion sensitivity, general social media use, and academic pressure. Self-control refers to a more general ability of impulse inhibition ( 50 ), while depletion sensitivity reflects the individual differences regarding the ratio of exhaustion under high cognitive load ( 51 ). The two measurements were found to be highly related to social media-induced self-control failure ( 10 ). We considered that the two constructs reflected a general ability students may exhibit in their social media use and study. Compared with self-reported educational status (e.g., exam scores) which could be biased due to inaccurate memory and social desirability, the measures could better reflect a trait-like aspect of individual differences in their school performance. Therefore, we estimated the moderation effect while controlling for the effects of general self-control capacity and depletion sensitivity. Academic pressure represents the subjective perception of study demands, and general social media use represents the time spent on and frequency of visit to social media. These two measures might vary among different high school students ( 43 ). To eliminate personal factors that may confuse the results, we controlled the above variables while testing the hypothesis.

Figure 1. Social media self-control failure moderates the relationship between social media use and school engagement and burnout.

The study was approved for exemption by the research ethics committee of the first author's university. Before data collection, we preregistered the research question, hypotheses, sample size, main variables to be collected and data exclusive criteria (see the preregistration file at https://aspredicted.org/wm2re.pdf ). The original data and analytical script were uploaded to Open Science Framework ( https://osf.io/kf783/ ).

2.1 Participants and procedure

Based on power analysis with a median effect (0.15) of the F -test, we aimed to collect 103 Chinese high school students. Participants were recruited through an online participant pool Credamo ( https://www.credamo.com ). Participants first provided consent by answering whether they were currently studying in high school and used social media and their agreement on participation. Then, they were instructed to answer questions about their social media use intensity, social media self-control failure, engagement, burnout and control variables (i.e., self-control, depletion sensitivity and academic pressure). Then, they provided demographic information. Each participant was paid 5 CNY for their participation.

The initial sample includes 110 participants. Following the preregistration, participants were excluded if they (1) did not use social media, (2) did not agree to participate, (3) completed the investigation faster than ±3 SD of the average completion time, (4) had missing values in the questionnaire, and (5) were identified as duplicated cases. The final sample included 107 participants aged between 18 and 25 ( M  = 19.21, SD  = 1.85), and 68% were female. Participants were mostly from the 3rd grade of senior high school (45%) and were mostly from the central districts of China (47%). The most commonly used social media was WeChat (96%) ( Table 1 ).

Table 1. Demographic information and most commonly used social media platforms.

2.2 Measurements

Social media use was measured by the 14-item Social Networking Activity Intensity Scale (SNAIS) ( 53 ). The SNAIS includes two subdimensions: social function use intensity and entertainment function use intensity, both of which reflect the leisure use of social media (i.e., keeping in contact with old friends, entertainment use and making new friends) in Chinese adolescents. Participants were asked: “How often have you performed the following online social networking activities in the last month?” Participants rated each of the social media activities (e.g., “Sent messages to friends on message board”, “Surfed entertainment/current news”) on a 6-point Likert scale (0 = never, 1 = few, 2 = occasional, 3 = sometimes, 4 = often, 5 = always). An overall average score including both dimensions was computed. A higher score indicates higher social networking use intensity. Confirmative factor analysis showed a 2-dimensional structure of the scale [χ 2  = 80.3, df  = 71, p  = .210, CFI = 0.97, TLI = 0.96, SRMR = 0.07, RSMEA = 0.04, 90% CI (0.00, 0.07)].

School engagement was measured by the Utrecht Learning Engagement Scale (UWES) ( 24 ). The revised UWES includes 9 items (e.g., “When I am studying, I forget everything else around me” and “I can continue for a very long time when I am studying”). Participants were asked to rate each item according to their agreement with the statement on a 7-point scale, 1 = never, 2 = rarely (once or twice a year and less), 3 = occasionally (once or twice a month and less), 4 = sometimes (2–4 times a month), 5 = often (about once a week), 6 = very often (2–4 times a week and more), 7 = always (about once a day). An average score was computed to represent high school students’ school engagement. A higher score indicates higher school engagement. The scale showed a unidimensional structure [χ 2  = 35.69, df  = 25, p  = .076, CFI = 0.97, TLI = 0.96, SRMR = 0.04, RSMEA = 0.06, 90% CI (0.00, 0.11)].

Burnout was measured with the 16-item Chinese Adolescent Student Burnout Inventory ( 28 ). The scale includes three dimensions: physical and mental exhaustion, study alienation and low accomplishment. The physical and mental exhaustion subscale measures exhaustion and tiredness due to study (“After a day of study, I felt very tired”). The study alienation subscale measures the passive attitude toward study (“I think that study is meaningless to me”). The low accomplishment subscale measures the diminished personal accomplishment regarding study (“I can often achieve my goals”, reverse scored). An averaged score was used to represent student burnout. A higher score indicates a higher tendency of burnout. The original 3-dimensional structure was confirmed in the present study [χ 2  = 125.06, df  = 97, p  < .05, CFI = 0.96, TLI = 0.95, SRMR = 0.07, RSMEA = 0.05, 90% CI (0.02, 0.08)].

Social media self-control failure was measured with the Social Media Self-Control Failure (SMSCF) scale ( 10 ). The Chinese version of the scale was validated in a sample of 2,400 university students ( 54 ). The scale includes 3 items measuring the extent to which people fail to control their social media use in goal-conflict situations. Participants were asked “How often do you give in to a desire to use social media even though your social media use at that particular moment: (1)…conflicts with other goals (for example: doing things for school/study/work or other tasks)? (2)…makes you use your time less efficiently? (3)…makes you delay other things you want or need to do?” Each item was rated on a 5-point scale (1 = Never, 5 = Always). An average score was computed to represent the extent of social media self-control failure. A higher score indicates a higher tendency to fail to control one's social media use in goal-conflict situations. Principal component analysis with oblimin rotation showed that the scale had a unidimensional structure with an eigenvalue of 2.18. The three items explained 72.8% of the variance.

General social media use was measured with four questions about the frequency of visits to social media and time spent on social media ( 37 ). As smartphone use is not allowed in some Chinese high schools during weekdays, participants reported their general social media use on weekdays and weekends: (1) “During weekdays/weekends, approximately how many minutes per day do you spend on social media?” (1 = 10 min or less, 2 = 11–30 min, 3 = 31–60 min, 4 = 1–2 h, 5 = 2–3 h, 6 = more than 3 h), (2) “During weekdays/weekends, how often do you visit social media?” (1 = Less than once a day, 2 = Once a day, 3 = 2–3 times a day, 4 = once an hour, 5 = 2–3 times an hour, 6 = More than 3 times an hour). Spearman's ρ between the four items was 0.33–0.46, all ps  < .001. We averaged the four items to represent overall social media use.

Self-control was measured by the 7-item Brief Self-Control Scale (BSCS) ( 55 ). The Chinese version of the scale was validated in a sample of 1,676 university students ( 56 ). Participants were asked to rate each item (“I am good at resisting temptations”, “I do certain things that are bad for me if they are fun”) on a 5-point Likert scale from 1 (not at all) to 5 (very much). An average score was computed. A higher score indicates a higher level of self-control capacity. The original 2-dimensional structure of the scale was confirmed in the present study [χ 2  = 11.06, df  = 12, p  = .524, CFI = 1.00, TLI = 1.01, SRMR = 0.04, RSMEA = 0.00, 90% CI (0.00, 0.09)].

Academic pressure was measured by the academic pressure subscale from the Mental Health Inventory of Middle-school students ( 57 ). The subscale includes 6 items measuring middle school students' perceived pressure related to the burden of study, fear of being questioned by teachers, aversion to homework, being nervous about exams and so forth (e.g., “I felt a heavy burden of study”, “I hate doing homework”). Participants rated each item on a 5-point scale (1 = never, 2 = slight, 3 = moderate, 4 = moderately severe, 5 = severe). An average score was computed. A higher score indicates a higher level of academic pressure. The original unidimensional structure of the scale was confirmed in the present study [χ 2  = 15.68, df  = 9, p  = .074, CFI = 0.97, TLI = 0.96, SRMR = 0.04, RSMEA = 0.08, 90% CI (0.00, 0.15)].

Depletion sensitivity was measured by the 11-item Depletion Sensitivity Scale [DSS; ( 51 )]. The scale assessed individual differences regarding how easily one's self-control resource is drained after a self-control demanding task (“After I have made a couple of difficult decisions, I will be mentally fatigued”, “I get mentally fatigued easily”). Participants rated each item on a 7-point scale ranging from 1 (totally disagree) to 7 (totally agree). An average score was computed. A higher score indicates a higher level of depletion sensitivity. The original unidimensional structure of the scale was confirmed in the present study [χ 2  = 57.80, df  = 43, p  = .065, CFI = 0.96, TLI = 0.95, SRMR = 0.05, RMSEA = 0.06, 90% CI (0.00, 0.09)].

2.3 Analytical strategy

Data analysis was conducted with jamovi (version 2.3) ( 58 ). We first conducted descriptive analysis of the key variables of the model. Then moderation analysis was conducted using bootstrap estimation (sample = 1,000). Using hierarchical multiple regression analysis, we further analyzed the moderation effect while controlling for the influence of several control variables based on their associations with the dependent variables. Moreover, gender was included in the model test as a control variable when examining the prediction of social media use to burnout, because female participants showed higher level of burnout than male participants ( t  = −2.37, df  = 105, p  = 0.020).

3.1 Descriptive results

The descriptive statistics and correlation matrix of the main variables and control variables are presented in Table 2 .

Table 2. Descriptive statistics and correlation matrix of the variables.

3.2 The moderating role of social media self-control failure on the relationship between social media use intensity and engagement

The results of moderation analysis showed no moderation effect of social media self-control failure on the relationship between social media use intensity and school engagement ( b  = 0.11, Z  = 0.63, p  = .527, 90% CI [−0.37, 0.31). However, the main effect of social media intensity and social media self-control failure was observed. Overall, students who had a higher intensity of social media use also showed higher school engagement [ b  = 0.34, Z  = 2.74, p  < .01, 90% CI (0.15, 0.63)]. Students who often fail to control their social media use in goal-conflict situations showed less school engagement [ b  = −0.59, Z  = −5.76, p  < .001, 90% CI (−0.78, −0.38)].

After controlling for general self-control capacity, depletion sensitivity, general social media use, age and grade, no interaction was found between social media use intensity and social media self-control failure. Neither a main effect of social media use intensity nor social media self-control failure was observed ( Table 3 ).

Table 3. Moderation effects of social media use intensity on the relationship between social media self-control failure and the outcome measures.

3.3 The moderating role of social media self-control failure on the relationship between social media use intensity and burnout

No moderation effect of social media self-control failure was found on the relationship between social media use intensity and student burnout [ b  = −0.00, Z  = −0.02, p  = .981, 90% CI (−0.26, 0.08)]. However, the main effect of social media self-control failure was observed. Overall, students who often failed to control their social media use in goal-conflict situations showed more burnout [ b  = 0.53, Z  = 7.07, p  < .001, 90% CI (0.38, 0.69)]. However, after controlling for academic pressure, general self-control capacity, depletion sensitivity, general social media use, age, gender and grade, the hierarchical multiple regression analysis showed no interaction or main effect of social media use intensity and social media self-control failure on burnout ( Table 3 ).

3.4 Exploratory analysis

We further analyzed the moderation of social media self-control failure on the relationship between general social media use and the outcome measures. This is because social media intensity describes the intensity of use in different social media activities. It would also be interesting to explore whether the general usage of social media could predict the two outcome measures at different levels of social media self-control failure. Thus, we used hierarchical multiple regression to examine whether general social media use predicts school engagement and burnout for students who had different levels of social media self-control failure while controlling for academic pressure, self-control, depletion sensitivity, social media use intensity, age and grade.

Regarding school engagement, the results showed an interaction between general social media use and social media self-control failure ( Table 4 ). A simple effect test showed that a higher level of general social media use predicted more engagement when the prediction was estimated at a lower level (−1 SD ) of social media self-control failure ( b  = −0.23, df  = 97, t  = −2.71, p  < .01). However, no significant prediction of general social media use on engagement was observed when the prediction was estimated at a higher level (+1 SD ) of social media self-control failure ( b  = 0.04, df  = 97, t  = 0.50, p  = .615). The main effect of general social media use and social media self-control failure was also found. Overall, students who used more social media in general or students who had a higher level of social media self-control failure reported a lower level of school engagement. Regarding burnout, neither an interaction between general social media use and social media self-control failure nor any main effect was found ( Table 4 ).

Table 4. Moderation effects of general social media use on the relationship between social media self-control failure and the outcome measures.

4 Discussion

The study aimed to answer the question of whether high school students' social media use will predict more school engagement and less burnout for those who were more successful in controlling their social media use. Based on the study demands-resources model, we tested the hypothesis through an online survey among Chinese high school students. To our knowledge, this is the first study to examine the boundary condition of self-control in the relationship between social media use and student engagement and burnout. It provides empirical evidence regarding the positive aspect of social media use on high school students' school performance, which was relatively understudied in the existing literature.

4.1 The moderating role of social media use on the relationship between social media self-control failure and engagement

As expected, for high school students who were more successful in controlling social media use in goal-conflict situations, their general social media use (rather than social media use intensity) predicted more engagement. This result supported our assumption that more controlled social media use will provide opportunities and resources for high school students to engage in academic learning, which, as a result, promote student engagement. The results also supported the study demands-resources model ( 20 ) and added to the literature regarding the role of social media use in high school students' engagement.

The results may also support the idea of motivational interference. It was argued that when students are confronted with a motivational conflict between their learning goals and alternative activities they find attractive, their self-control will be impaired by the interference of these competing activities ( 44 , 45 ). Our findings further indicated that such motivational conflict exists between students' important goals, including academic goals, and their use of social media. The use of social media platforms that elicit strong emotional rewards, such as online chatting and watching short videos, can disrupt students' motivation to learn. Nevertheless, students who were more successful in managing this motivational conflict were better able to resist the distractions of social media and maintain their focus on learning goals. This ability increases the potential for using social media as a tool to enhance engagement rather than leading to burnout.

It should be noted that we planned to use the Social Media Use Intensity scale to represent students' social media use because this measure delves into more specific activities on social media (e.g., social contact, entertainment, news reading, gaming) ( 53 ). However, no moderation or main effect was observed, whereas in exploratory analysis, general social media use (i.e., the time spent on and frequency of visits to social media) showed both moderation and main effect in predicting engagement. The reason could be that using a total score of the social media use intensity scale as an index of social media use may counteract the differences among various online activities in different persons across sample 1 . Future studies could separately examine the usage of different activities on social media when examining its effect on engagement.

Moreover, in the present study, the most popular social media platforms of high school students were WeChat (95%), QQ (89%) and TikTok (79%, Chinese name: Douyin). A prior study showed that TikTok was particularly attractive to Chinese students and was found to generate detrimental effects on adolescents' mental health such as depression and memory loss ( 59 ). The present study did not focus on specific social media platform in the analysis. However, it could be that for some social media platforms, the moderation effect of self-control on the association between social media usage and the outcome variables was more significant. Thus, future studies could further replicate the study regarding different social media platforms to better understand the impact of social media use on students' engagement and burnout. The results may contribute to establishing better guidance regarding high school students' social media use, especially for senior grade students and return students. We found that when students were able to regulate the conflict between social media use and study goals, more usage improved engagement at school. This may suggest that total abstinence from social media use may not be applicable for all students. A previous study showed that users who were abstinent from social media had a decline in wellbeing ( 60 ). Additionally, when people were not allowed to use social media for one day, they showed no difference in wellbeing compared to normal days ( 61 ). Our results add to the literature by showing that for high school students, the controllable use of social media can promote school engagement by facilitating connections to study resources. Thus, educators may consider encouraging controlled social media use in high school to increase engagement.

The findings from the research also indicate the importance of teachers finding effective ways to integrate social media into the classroom to enhance the study engagement of senior high school students. It is suggested that teachers focus on increasing students' ability to control their social media use. For example, by improving their digital self-efficacy, which refers to their confidence and ability to navigate and use digital platforms effectively ( 62 ), students will be better equipped to manage their social media usage and utilize it as a valuable tool for academic purposes, rather than a distraction. By becoming more proficient in using social media for educational purposes, students can establish a balance between recreational and educational activities on these platforms ( 62 ).

4.2 The moderating role of social media use on the relationship between social media self-control failure and burnout

Unexpectedly, high school students' social media use did not predict burnout regardless of how successful they were able to control their social media use. No main effect of the predictors was observed when considering the control variables. We hypothesized that high school students who were unable to control their social media use would be exposed more in goal-conflict situations between social media use and study goals. They might have a higher cognitive load in dealing with conflict dilemmas and suffer more from the time pressure and stress of unfinished tasks, which results in a higher level of burnout.

One of the reasons that we did not find support for the hypothesis could be that the association between social media use and burnout might be complex ( 63 ). Uncontrolled social media use could be an outcome rather than a predictor of burnout. It could also show a reinforcing influential pattern with burnout in the long run, which could not be observed with the current cross-sectional design. In a previous study, student burnout was found to predict sleep disturbance caused by social media ( 63 ). Another study demonstrated the mutual influences of students' excessive internet use and their burnout during a period of time ( 64 ). The findings suggest that burnout might be the predictor, or both predictor and outcome of uncontrolled social media use. Moreover, it was argued that burnout reflects a relatively long-term change in emotional state, which could fluctuate during a period of time ( 25 ). However, in the present study, we only examined the relationship between social media use and burnout at one time point. Thus, future research could use a longitudinal design to further test the association between students’ social media use and subsequent burnout or to explore their reciprocal relationships over time.

Another reason might be that we used a self-report measure to assess how successful students believed that they were able to control social media when it conflicts with other goals (i.e., Social Media Self-Control Failure scale). However, the explicit belief might not reflect people's actual behavioral tendency in real goal-conflict situations. It could be that social media use predicts less burnout only for students who not only had more successful belief in their self-control ability but also showed less implicit behavioral tendency toward social media. In support of this idea, a previous study found an inconsistency between social media users’ explicit self-report measure and implicit behavioral tendency toward social media in predicting excessive social media use ( 16 ). The study showed that an implicit attitude toward social media significantly predicted excessive social media, regardless of how successful people believed they could control social media use ( 16 ). Likewise, using an in-class design, another study found that whether or not a student believed that he or she could successfully control multitasking behaviors on computers, their social media usage constantly showed a negative effect on task performance ( 65 ). In the present study, a lack of implicit measurement regarding students' behavioral tendency toward social media limited the measurement to distinguish more successful (vs. less successful) social media users more precisely. Future studies could combine both explicit and implicit measures to study the impact of social media use on students' burnout.

Finally, we found that both general social media use and social media use intensity failed to predict burnout regardless of students' self-control level. The results were obtained while controlling for the effects of general self-control ability, the depletion sensitivity of self-control and perceived academic pressure. According to pairwise correlations, these control variables were highly correlated with burnout (all r  > 0.5). Meanwhile, burnout had a nonsignificant correlation with social media use intensity or only a small correlation with general social media use compared to its high correlation with social media self-control failure. This reveals that social media self-control and its related constructs (i.e., general self-control ability and depletion sensitivity) might be more significant than social media per se in explaining high school students' burnout. This idea was supported by a prior study that only found a significant correlation between social media self-control failure (rather than general social media use) and affective wellbeing [e.g., ( 37 )]. It could be that other alternative models might better explain the pathways from social media use to burnout, which is worth further exploration.

4.3 Limitations

Several limitations of the study should be noted. First, as an exploratory step, we collected a relatively small sample size to examine the effect of social media use on students' engagement and burnout. This is because the authors have conducted a prior study with similar variables based on a sample of 249 Chinese adolescents between 12 and 17 years old. The study demonstrated that students' academic performance was negatively associated with social media self-control failure, and the correlation index was significantly higher than its association with social media use. To further clarify the influential paths between social media use, social media self-control failure, and adolescents' school performance, we have conducted the current study based on the demands-resources model. In line with the pre-registration, we have also conducted the power analysis with a median effect size based on the correlation index of the prior study. However, the sample was mostly (88%) high school senior students (i.e., 3rd-grade students and returning students). Although this sample characteristic provides a novel perspective to look at students with particularly high study demands, it may also reduce the representativeness of high school students. Thus, any conclusion regarding the generalization of the results should be drawn with caution. Second, we used a cross-sectional design to study the moderation effect of self-control over social media use. The results only provide correlational results of the main variables. Caution should be taken when claiming any causal relationship between social media use and student engagement and burnout. Third, many Chinese high school students were resident students. Their social media use and school regulations regarding smartphone use might be different from regular students (e.g., resident students could be totally banned from smartphones during weekdays). Although we separately measured students' general social media use during weekends and weekends, the different statuses of residence may also affect the results, which should be considered in future studies. Last, given privacy concerns, we did not collect information regarding students' socioeconomic status (e.g., household income, educational level of parents). However, the potential impact of socioeconomic status on the results should also be take it into account.

5 Conclusion

Based on the demands-resources model, this study found that students who were more successful in controlling their social media use were more engaged in study. This implies that students who are able to effectively manage their time and restrict their social media usage when it disturbs other important goals are more likely to dedicate themselves to their schoolwork. However, the study did not find any significant correlation between social media use and burnout, regardless of how well a student could control their social media use. These findings suggest that acquiring successful self-control over social media usage could potentially enhance a student's engagement and commitment towards their studies. This, in turn, may significantly contribute to their academic achievements, especially for high school senior students. Replication of the analysis is necessary to further examine the generalization of the current findings in students with different grades and educational statuses. Also, the following studies are needed to establish a causal relationship between social media use, social media self-control, and high school students' engagement and burnout.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving humans were approved by Ethics Review Committee of Biomedical Studies at Chongqing Technology and Business University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

JD: Conceptualization, Data curation, Methodology, Software, Writing – original draft, Writing – review & editing. YW: Investigation, Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Chongqing Technology and Business University Research Start-up Fund (No. 2155021) supported data collection and publication of this study. Chongqing University of Technology Scientific Research Fund (No. 2017ZD61) supported publication of this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. ^ The original scale distinguished two groups of activities that relate to the entertainment function and social function of social media. When separately examining their prediction on engagement and burnout and the moderation effect of social media self-control failure, no conclusion was changed.

1. Salmela-Aro K, Upadyaya K. School engagement and school burnout profiles during high school—the role of socio-emotional skills. Eur J Dev Psychol . (2020) 17(6):943–64. doi: 10.1080/17405629.2020.1785860

Crossref Full Text | Google Scholar

2. Jagodics B, Nagy K, Szénási S, Varga R, Szabó É. School demands and resources as predictors of student burnout among high school students. Sch Ment Health . (2023) 15(1):90–104. doi: 10.1007/s12310-022-09534-1

3. Jagodics B, Szabó É. Student burnout in higher education: a demand-resource model approach. Trends Psychol . (2022) 31:757–76. doi: 10.1007/s43076-021-00137-4

4. Salmela-Aro K, Upadyaya K, Ronkainen I, Hietajärvi L. Study burnout and engagement during COVID-19 among university students: the role of demands, resources, and psychological needs. J Happiness Stud . (2022) 23(6):2685–702. doi: 10.1007/s10902-022-00518-1

PubMed Abstract | Crossref Full Text | Google Scholar

5. Teuber Z, Nussbeck FW, Wild E. School burnout among Chinese high school students: the role of teacher-student relationships and personal resources. Educ Psychol (Lond) . (2021) 41(8):985–1002. doi: 10.1080/01443410.2021.1917521

6. Harbour KE, Evanovich LL, Sweigart CA, Hughes LE. A brief review of effective teaching practices that maximize student engagement. Prevent Sch Fail . (2015) 59(1):5–13. doi: 10.1080/1045988X.2014.919136

7. Lu J, Hao Q, Jing M. Consuming, sharing, and creating content: how young students use new social media in and outside school. Comput Human Behav . (2016) 64:55–64. doi: 10.1016/j.chb.2016.06.019

8. Martin F, Wang C, Petty T, Wang W, Wilkins P. Middle school Students’ social media use. Educ Technol Soc . (2018) 21(1):213–24. Available online at: https://www.jstor.org/stable/26273881

Google Scholar

9. Heiberger G, Harper R. Have you facebooked astin lately? Using technology to increase student involvement. N Direct Stud Serv . (2008) 2008(124):19–35. doi: 10.1002/ss.293

10. Du J, van Koningsbruggen GM, Kerkhof P. A brief measure of social media self-control failure. Comput Hum Behav . (2018) 84:68–75. doi: 10.1016/j.chb.2018.02.002

11. Meier A, Reinecke L, Meltzer CE. “Facebocrastination”? predictors of using Facebook for procrastination and its effects on students’ well-being. Comput Hum Behav . (2016) 64:65–76. doi: 10.1016/j.chb.2016.06.011

12. Reinecke L, Meier A. Guilt and media use. In: Bulck J, editors. The International Encyclopedia of Media Psychology . 1st ed. Hoboken, NJ: Wiley (2020). p. 1–5. doi: 10.1002/9781119011071.iemp0183

13. Simsek A, Elciyar K, Kizilhan T. A comparative study on social media addiction of high school and university students. Contemp Educ Technol . (2019) 10(2):106–19. doi: 10.30935/cet.554452

14. Hofmann W, Reinecke L, Meier A. Of sweet temptations and bitter aftertaste: self-control as a moderator of the effects of media use on well-being. In: Reinecke L, Oliver MB, editors. The Routledge Handbook of Media Use and Well-Being: International Perspectives on Theory and Research on Positive Media Effects . New York, NY: Routledge (2017). p. 211–22. doi: 10.4324/9781315714752

15. Reinecke L, Meier A. Media entertainment as guilty pleasure?: the appraisal of media use, self-control, and entertainment (AMUSE) model. In: Vorderer P, Klimmt C, editors. The Oxford Handbook of Entertainment Theory . Oxford: Oxford University Press (2021). p. 203–30. doi: 10.1093/oxfordhb/9780190072216.013.12

16. Zahrai K, Veer E, Ballantine PW, Peter De Vries H. Conceptualizing self-control on problematic social media use. Australas Mark J . (2022) 30(1):74–89. doi: 10.1177/1839334921998866

17. Alrashidi O, Phan HP, Ngu BH. Academic engagement: an overview of its definitions, dimensions, and major conceptualisations. Int Educ Stud . (2016) 9(12):41–52. doi: 10.5539/ies.v9n12p41

18. Wang M-T, Hofkens TL. Beyond classroom academics: a school-wide and multi-contextual perspective on student engagement in school. Adolesc Res Rev . (2020) 5(4):419–33. doi: 10.1007/s40894-019-00115-z

19. Martin AJ. Examining a multidimensional model of student motivation and engagement using a construct validation approach. Br J Educ Psychol . (2007) 77(2):413–40. doi: 10.1348/000709906X118036

20. Salmela-Aro K, Upadyaya K, Vinni-Laakso J, Hietajärvi L. Adolescents’ longitudinal school engagement and burnout before and during COVID-19—the role of socio-emotional skills. J Res Adolesc . (2021) 31(3):796–807. doi: 10.1111/jora.12654

21. Schaufeli WB, Martínez IM, Pinto AM, Salanova M, Bakker AB. Burnout and engagement in university students: a cross-national study. J Cross Cult Psychol . (2002) 33(5):464–81. doi: 10.1177/0022022102033005003

22. Salanova M, Schaufeli W, Martínez I, Bresó E. How obstacles and facilitators predict academic performance: the mediating role of study burnout and engagement. Anxiety Stress Coping . (2010) 23(1):53–70. doi: 10.1080/10615800802609965

23. Heffner AL, Antaramian SP. The role of life satisfaction in predicting student engagement and achievement. J Happiness Stud . (2016) 17(4):1681–701. doi: 10.1007/s10902-015-9665-1

24. Chen W. Psychometric properties of the Utrecht learning engagement scale in high students: based on the Rasch model. J Guizhou Norm Univ . (2016) 34(5):110–4. doi: 10.16614/j.cnki.issn1004-5570.2016.05.020

25. Bakker AB, Demerouti E, Sanz-Vergel AI. Burnout and work engagement: the JD–R approach. Ann Rev Organ Psychol Organ Behav . (2014) 1(1):389–411. doi: 10.1146/annurev-orgpsych-031413-091235

26. Madigan DJ, Curran T. Does burnout affect academic achievement? A meta-analysis of over 100,000 students. Educ Psychol Rev . (2021) 33(2):387–405. doi: 10.1007/s10648-020-09533-1

27. Bask M, Salmela-Aro K. Burned out to drop out: exploring the relationship between school burnout and school dropout. Eur J Psychol Educ . (2013) 28(2):511–28. doi: 10.1007/s10212-012-0126-5

28. Wu Y, Dai X, Wen Z, Cui H. The development of adolescent student burnout inventory. Chin J Clin Psychol . (2010) 18:152–4. doi: 10.16128/j.cnki.1005-3611.2010.02.018

29. Demerouti E, Bakker AB, Nachreiner F, Schaufeli WB. The job demands-resources model of burnout. J Appl Psychol . (2001) 86(3):499–512. doi: 10.1037/0021-9010.86.3.499

30. Romano L, Angelini G, Consiglio P, Fiorilli C. Academic resilience and engagement in high school students: the mediating role of perceived teacher emotional support. Eur J Investig Health Psychol Educ . (2021) 11(2):334–44. doi: 10.3390/ejihpe11020025

31. Martin AJ, Dowson M. Interpersonal relationships, motivation, engagement, and achievement: yields for theory, current issues, and educational practice. Rev Educ Res . (2009) 79(1):327–65. doi: 10.3102/0034654308325583

32. Maricuțoiu LP, Sulea C. Evolution of self-efficacy, student engagement and student burnout during a semester. A multilevel structural equation modeling approach. Learn Individ Differ . (2019) 76:101785. doi: 10.1016/j.lindif.2019.101785

33. Kljajic K, Gaudreau P, Franche V. An investigation of the 2×2 model of perfectionism with burnout, engagement, self-regulation, and academic achievement. Learn Individ Differ . (2017) 57:103–13. doi: 10.1016/j.lindif.2017.06.004

34. Mazer JP, Murphy RE, Simonds CJ. I’ll see you on “Facebook”: the effects of computer-mediated teacher self-disclosure on student motivation, affective learning, and classroom climate. Commun Educ . (2007) 56(1):1–17. doi: 10.1080/03634520601009710

35. Junco R. The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Comput Educ . (2012) 58(1):162–71. doi: 10.1016/j.compedu.2011.08.004

36. Reinecke L, Rieger D. Media entertainment as a self-regulatory resource: the recovery and resilience in entertaining media use (R2EM) model. In: Vorderer P, Klimmt C, editors. The Oxford Handbook of Entertainment Theory . Oxford: Oxford University Press (2021). p. 754–79. doi: 10.1093/oxfordhb/9780190072216.013.39

37. Du J, Kerkhof P, Van Koningsbruggen GM. Predictors of social media self-control failure: immediate gratifications, habitual checking, ubiquity, and notifications. Cyberpsychol Behav Soc Netw . (2019) 22(7):477–85. doi: 10.1089/cyber.2018.0730

38. Hofmann W, Vohs KD, Baumeister RF. What people desire, feel conflicted about, and try to resist in everyday life. Psychol Sci . (2012) 23(6):582–8. doi: 10.1177/0956797612437426

39. Reinecke L, Hofmann W. Slacking off or winding down? An experience sampling study on the drivers and consequences of media use for recovery versus procrastination: slacking off or winding down? Hum Commun Res . (2016) 42(3):441–61. doi: 10.1111/hcre.12082

40. Cauberghe V, Van Wesenbeeck I, De Jans S, Hudders L, Ponnet K. How adolescents use social media to cope with feelings of loneliness and anxiety during COVID-19 lockdown. Cyberpsychol Behav Soc Netw . (2021) 24(4):250–7. doi: 10.1089/cyber.2020.0478

41. Siebers T, Beyens I, Pouwels JL, Valkenburg PM. Social media and distraction: an experience sampling study among adolescents. Media Psychol . (2022) 25(3):343–66. doi: 10.1080/15213269.2021.1959350

42. Du J, Van Koningsbruggen GM, Kerkhof P. Spontaneous approach reactions toward social media cues. Comput Human Behav . (2020) 103:101–8. doi: 10.1016/j.chb.2019.08.028

43. Lee J-S. The effects of the teacher–student relationship and academic press on student engagement and academic performance. Int J Educ Res . (2012) 53:330–40. doi: 10.1016/j.ijer.2012.04.006

44. Fries S, Dietz F, Schmid S. Motivational interference in learning: the impact of leisure alternatives on subsequent self-regulation. Contemp Educ Psychol . (2008) 33(2):119–33. doi: 10.1016/j.cedpsych.2007.10.001

45. Fries S, Dietz F. Learning in the face of temptation: the case of motivational interference. J Exp Educ . (2007) 76(1):93–112. doi: 10.3200/JEXE.76.1.93-112

46. Junco R, Cotten SR. Perceived academic effects of instant messaging use. Comput Educ . (2011) 56(2):370–8. doi: 10.1016/j.compedu.2010.08.020

47. Świątek AH, Szcześniak M, Aleksandrowicz B, Zaczkowska D, Wawer W, Ścisłowska M. Problematic smartphone use and social media fatigue: the mediating role of self-control. Psychol Res Behav Manag . (2023) 16:211–22. doi: 10.2147/PRBM.S389806

48. Li X, Liu D. The influence of technostress on cyberslacking of college students in technology-enhanced learning: mediating effects of deficient self-control and burnout. Int J Environ Res Public Health . (2022) 19(18):11800. doi: 10.3390/ijerph191811800

49. Demirbilek M, Talan T. The effect of social media multitasking on classroom performance. Act Learn High Educ . (2018) 19(2):117–29. doi: 10.1177/1469787417721382

50. Tangney JP, Baumeister RF, Boone AL. High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. J Pers . (2004) 72(2):271–324. doi: 10.1111/j.0022-3506.2004.00263.x

51. Salmon SJ, Adriaanse MA, De Vet E, Fennis BM, De Ridder DTD. When the going gets tough, who keeps going?” depletion sensitivity moderates the ego-depletion effect. Front Psychol . (2014) 5:647. doi: 10.3389/fpsyg.2014.00647

52. Simon K. Digital 2023: China. We Are Social. (2023). Available online at: https://datareportal.com/reports/digital-2023-china (accessed July 30, 2023).

53. Li J, Lau JTF, Mo PKH, Su X, Wu AMS, Tang J, et al. Validation of the social networking activity intensity scale among junior middle school students in China. PLoS One . (2016) 11(10):e0165695. doi: 10.1371/journal.pone.0165695

54. Chen B, Zheng X. Big five personality and social media self-control failure among college students: the role of fear of missing out. Appl Psychol . (2019) 25(2):161–8.

55. Morean ME, DeMartini KS, Leeman RF, Pearlson GD, Anticevic A, Krishnan-Sarin S, et al. Psychometrically improved, abbreviated versions of three classic measures of impulsivity and self-control. Psychol Assess . (2014) 26(3):1003–20. doi: 10.1037/pas0000003

56. Luo T, Cheng L, Qin L, Xiao S. Reliability and validity of Chinese version of brief self-control scale. Chin J Clin Psychol . (2021) 29(1):83–6. doi: 10.16128/j.cnki.1005-3611.2021.01.017

57. Peng A, Lan Y, Jia Y. Association between problematic smartphone use and mental health and the mediating effects of academic pressure among junior and senior high school students in Shanghai, China. Fudan Univ J Med Sci . (2022) 49(1):16–22. doi: 10.3969/j.issn.1672-8467.2022.01.003

58. The Jamovi Project. Jamovi (Version 2.3) [Computer software]. (2022). Available online at: https://www.jamovi.org (accessed July 30, 2023).

59. Sha P, Dong X. Research on adolescents regarding the indirect effect of depression, anxiety, and stress between TikTok use disorder and memory loss. Int J Environ Res Public Health . (2021) 18(16):8820. doi: 10.3390/ijerph18168820

60. Vally Z, D’Souza CG. Abstinence from social media use, subjective well-being, stress, and loneliness. Perspect Psychiatr Care . (2019) 55(4):752–9. doi: 10.1111/ppc.12431

61. Przybylski AK, Nguyen TT, Law W, Weinstein N. Does taking a short break from social media have a positive effect on well-being? Evidence from three preregistered field experiments. J Technol Behav Sci . (2021) 6(3):507–14. doi: 10.1007/s41347-020-00189-w

62. Ulfert-Blank A-S, Schmidt I. Assessing digital self-efficacy: review and scale development. Comput Educ . (2022) 191:104626. doi: 10.1016/j.compedu.2022.104626

63. Evers K, Chen S, Rothmann S, Dhir A, Pallesen S. Investigating the relation among disturbed sleep due to social media use, school burnout, and academic performance. J Adolesc . (2020) 84(1):156–64. doi: 10.1016/j.adolescence.2020.08.011

64. Salmela-Aro K, Upadyaya K, Hakkarainen K, Lonka K, Alho K. The dark side of internet use: two longitudinal studies of excessive internet use, depressive symptoms, school burnout and engagement among Finnish early and late adolescents. J Youth Adolesc . (2017) 46(2):343–57. doi: 10.1007/s10964-016-0494-2

65. Brooks S. Does personal social media usage affect efficiency and well-being? Comput Hum Behav . (2015) 46:26–37. doi: 10.1016/j.chb.2014.12.053

Keywords: social media, self-control, high school students, engagement, burnout

Citation: Du J and Wang Y (2024) High school students' social media use predicts school engagement and burnout: the moderating role of social media self-control. Front. Child Adolesc. Psychiatry 3 :1269606. doi: 10.3389/frcha.2024.1269606

Received: 30 July 2023; Accepted: 19 July 2024; Published: 13 August 2024.

Reviewed by:

© 2024 Du and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yu Wang, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.