Pengaruh Penggunaan Media Sosial terhadap Tingkat Depresi dan Prokrastinasi Akademik pada Mahasiswa

Authors

  • Mega Pratiwi Universitas Mulawarman
  • Ramaulvi Muhammad Akhyar Universitas Mulawarman
  • Celine Aloyshima Haris Universitas Mulawarman

DOI:

https://doi.org/10.24127/gdn.v16i2.16368

Keywords:

social media, depression, academic procrastination, college student, mediation model, psychological well-being

Abstract

Intensive social media use among university students has emerged as a global phenomenon closely correlated with declining psychological well-being and academic behavioral dysfunction. This study empirically examines the effect of social media use on depression levels and its subsequent impact on academic procrastination, positioning depression as a mediating variable. Adopting a quantitative explanatory design, the study sampled 58 undergraduate students from the Mathematics and Natural Sciences Education Department at Mulawarman University who were completing their final projects. Data were collected using three standardized instruments: the Social Media Use Integration Scale (SMUIS), Beck Depression Inventory-II (BDI-II), and Procrastination Assessment Scale for Students (PASS). Statistical analyses included validity, reliability, normality testing, multiple regression, and a mediation model based on the Baron & Kenny approach. Results indicated that social media use significantly predicted higher depression symptoms (b = 0.397, p < 0.001). Furthermore, depression fully mediated the relationship between social media use and academic procrastination (b = -0.103, p < 0.001), accounting for 78.9% of the variance in final-project delay behaviors. These findings confirm that digital platform engagement not only affects emotional well-being but also activates cognitive-affective mechanisms that drive academic task avoidance. Theoretical and practical implications are comprehensively discussed, including recommendations for cognitive-behavioral counseling interventions and digital literacy regulation within higher education settings.

References

Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report. American Psychologist, 73(1), 3–25. https://doi.org/10.1037/amp0000191

Ardelia, V. (2024). Adaptation and validation of the Social Media Use Integration Scale in Indonesian context: An exploratory and confirmatory factor analysis. Jurnal Psikologi, 51(1), 45–62. https://doi.org/10.22146/jpsi.78234

Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173

Beck, A. T., & Bredemeier, K. (2016). A 60-year evolution of cognitive theory and therapy. Perspectives on Psychological Science, 11(1), 16–20. https://doi.org/10.1177/1745691615600733

Creswell, J. W., & Guetterman, T. C. (2019). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (6th ed.). Pearson.

DeVellis, R. F. (2016). Scale development: Theory and applications (4th ed.). Sage Publications.

Eerde, W. van. (2021). Procrastination in academic settings: A meta-analytic review of antecedents, consequences, and interventions. Educational Psychology Review, 33(4), 1457–1489. https://doi.org/10.1007/s10648-021-09612-7

Erede, I., & Kuo, S. I. C. (2022). Digital media use and executive functioning in emerging adults: A longitudinal neurocognitive study. Computers in Human Behavior, 128, 107098. https://doi.org/10.1016/j.chb.2021.107098

Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage Publications.

Flora, D. B. (2020). Your scale development method is tired: Ordinary least squares factor analysis is not optimal for psychological research. Advances in Methods and Practices in Psychological Science, 3(3), 373–378. https://doi.org/10.1177/2515245920921009

Fowler, F. J. (2014). Survey research methods (5th ed.). Sage Publications.

Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological Science, 18(3), 233–239. https://doi.org/10.1111/j.1467-9280.2007.01882.x

Ginting, H., Näring, G., Van Der Veld, W. M., Srisayekti, W., & Becker, E. S. (2013). Validating the Beck Depression Inventory-II in Indonesia’s general population and coronary heart disease patients. International Journal of Clinical and Health Psychology, 13(3), 235–242. https://doi.org/10.1016/S1697-2600(13)70028-0

Gollwitzer, P. M., & Oettingen, G. (2019). Goal-directed behavior: The role of implementation intentions and mental contrasting. Current Directions in Psychological Science, 28(5), 493–499. https://doi.org/10.1177/0963721419858225

Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102–116. https://doi.org/10.1037/a0038889

Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). Guilford Press.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Huang, C. (2022). Time spent on social network sites and psychological well-being: A meta-analysis. Cyberpsychology, Behavior, and Social Networking, 25(4), 213–225. https://doi.org/10.1089/cyber.2021.0234

Jenkins-Guarnieri, M. A., Wright, S. L., & Johnson, L. D. (2013). Development and validation of a social media use integration scale. Psychology of Popular Media Culture, 2(1), 38–50. https://doi.org/10.1037/a0030274

Keles, B., McCrae, N., & Grealish, A. (2020). A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. International Journal of Adolescence and Youth, 25(1), 79–93. https://doi.org/10.1080/02673843.2019.1590851

Klingsieck, K. B. (2018). Procrastination in students’ academic life: The role of self-regulation and emotional regulation. Frontiers in Psychology, 9, 1245. https://doi.org/10.3389/fpsyg.2018.01245

Odgers, C. L., & Jensen, M. R. (2020). Annual research review: Adolescent mental health in the digital age: Facts, fears, and future directions. Journal of Child Psychology and Psychiatry, 61(3), 336–348. https://doi.org/10.1111/jcpp.13190

Preacher, K. J., & Kelley, K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16(2), 93–115. https://doi.org/10.1037/a0022658

Rozental, A., Forsell, E., & Carlbring, P. (2021). Procrastination, distress, and mental health across the life span: A longitudinal investigation. Clinical Psychology Review, 85, 102001. https://doi.org/10.1016/j.cpr.2021.102001

Sari, M., Rachman, H., Juli Astuti, N., Win Afgani, M., & Abdullah Siroj, R. (2022). Explanatory survey in quantitative descriptive research methods. Jurnal Pendidikan Sains Dan Komputer, 3(01), 10–16. https://doi.org/10.47709/jpsk.v3i01.1953

Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: Frequency and cognitive-behavioral correlates. Journal of Counseling Psychology, 31(4), 503–509. https://doi.org/10.1037/0022-0167.31.4.503

Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin, 133(1), 65–94. https://doi.org/10.1037/0033-2909.133.1.65

Steel, P., & Klingsieck, K. B. (2016). Academic procrastination: Psychological precursors and predictors. Journal of Clinical Psychology, 72(12), 1245–1258. https://doi.org/10.1002/jclp.22270

Steel, P., & Usher, A. (2018). The temporal motivation theory of procrastination: A meta-analytic synthesis. Motivation Science, 4(3), 225–245. https://doi.org/10.1037/mot0000089

Tourangeau, R., & Yan, T. (2020). Sensitive questions in surveys: A meta-analysis. Psychological Bulletin, 146(8), 671–702. https://doi.org/10.1037/bul0000245

Twenge, J. M., Martin, G. N., & Campbell, W. K. (2020). Decreases in psychological well-being among American adolescents after 2012 and links to screen time during the rise of smartphone technology. Emotion, 20(4), 722–733. https://doi.org/10.1037/emo0000724

Valkenburg, P. M., Meier, A., & Beyens, I. (2021). Social media use and well-being: A meta-analysis. Current Opinion in Psychology, 45, 101303. https://doi.org/10.1016/j.copsyc.2021.101303

Wang, Y., Li, X., & Chen, Z. (2020). The mediating role of depression in the relationship between social media use and academic procrastination among college students. Computers in Human Behavior, 112, 106458. https://doi.org/10.1016/j.chb.2020.106458

Wang, Y., Liu, Q., & Zhang, J. (2022). Group counseling intervention for academic procrastination and emotional regulation in university students: A randomized controlled trial. Journal of Counseling & Development, 100(2), 145–158. https://doi.org/10.1002/jcad.12415

Wang, Y.-P., & Gorenstein, C. (2018). Psychometric properties of the Beck Depression Inventory-II: A comprehensive review. Revista Brasileira de Psiquiatria, 40(1), 41–50. https://doi.org/10.1590/1516-4446-2017-2423

Wild, D., Grove, A., Martin, M., Eremenco, S., McElroy, S., Verjee-Lorenz, A., & Erikson, P. (2005). Principles of good practice for the translation and cultural adaptation process for patient-reported outcomes (PRO) measures: Report of the ISPOR task force. Value in Health, 8(2), 94–104. https://doi.org/10.1111/j.1524-4733.2005.04054.x

Published

2026-06-01

Issue

Section

ARTICLES