Cited by 76Impact of Technology on AdolescentsPerfectionism, Procrastination, Anxiety StudiesCyberloafing and Workplace Behavior

Profiles of Undergraduates’ Networks Addiction: Difference in Academic Procrastination and Performance

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Suárez-Perdomo, Arminda, Ruiz-Alfonso, Zuleica, Garcés-Delgado, Yaritza·2022·Computers & Education

Abstract

Social networks are a source of distraction in personal, academic, and professional tasks. In the university context, they can lead to academic procrastination behaviour and undermine students’ academic performance. The aims of this study are a) to identify profiles of students with similar values in excessive use, lack of control and obsession with social networks, and b) analyse whether there are significant differences in procrastination behaviours and academic performance of the identified profiles. A total of 1784 students from 24 Spanish universities took part in the study. The Social Network Addiction Questionnaire and the Spanish version of the Procrastination Assessment Scale-Student test were used. Profiles were identified through Latent Profile Analysis and differences between groups were analysed through the three-step Bolck-Croon-Hagenaars method. The results showed three profiles regarding the excessive use of social networks: low addiction, moderate addiction and high addiction. Significant differences were found between the three latent profiles in academic procrastination, showing that the higher the addiction to social networks, the higher the procrastination behaviour. However, there were no significant differences between the groups with respect to academic performance. In conclusion, although the addiction to social networks is related to procrastination behaviour, it is considered that they could be a useful resource in the initial training process of undergraduates.

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  • Strong associations exist between social media addiction and academic procrastination among university students.

    “Recent studies by Suárez-Perdomo et al. [11] and Li et al. [12] have confirmed strong associations between social media addiction and academic procrastination among university students”

    From Social Media Addiction and Procrastination in Peruvian University Students: Exploring the Role of Emotional Regulation and Age Moderation (2025) · cites this paper as Suárez-Perdomo et al. [11]

    The Preliminary Outcomes section reports significant correlations between every social-network-addiction factor and every procrastination factor. Section 3.4 also finds that procrastination increases across the low-, moderate-, and high-addiction profiles of university students. These findings support an association, but the cited passages do not establish that the associations are strong. (LLM, conf=0.82)

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Research works printed but not linked: 5

  1. Asparouhov, T., & Muthén, B. (2015). Auxiliary variables in mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary second model. In Mplus web notes (Vol. 21, pp. 1-22). https://bit.ly/2UhVJ0w.
  2. Ferrari, J. R. (2000). Procrastination and attention: Factor analysis of attention deficit, boredomne, intelligence, self-esteem, and task delay frequencies. Journal of Social Behavior & Personality, 15(5), 185-196. https://bit.ly/3u2TIll.
  3. Muthén, L. K., & Muthén, B. O. (2021). Mplus user's guide (8th ed.) https://bit.ly/363fRpW.
  4. Ndege, W., Mutavi, T., Kokonya, D., Nekesa, V., Musungu, B., Obondo, A., et al. (2015). Social networks and students performance in secondary schools: Lessonsforman open learning centre, Kenya. Journal of Education and Practice, 6(21), 171-178. https://bit.ly/3x6JiDn.
  5. Solomon, L. J., & Rothblum, E. D. (1994). Procrastination assessment scale-students (pass). In J. Fischer, & K. Corcoran (Eds.), Measures for clinical practice (pp. 446-452). The Free Press. https://bit.ly/3xabMfC.

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Authors
Suárez-Perdomo, A., Ruiz-Alfonso, Z., & Garcés-Delgado, Y.
Affiliations
Universidad de La Laguna (ES)
Venue
Computers & Education
Publisher
Elsevier BV
Published
2022-05
Volume
181
Pages
104459
ISSN
0360-1315 (ISSN-L)
OpenAlex
W4210777266
Cited by
66
References
60
Citations by year
2026: 14 · 2025: 13 · 2024: 15 · 2023: 16 · 2022: 8
FWCI
18.71
Type
journal article
Language
en
Open access
Locations
Computers & Education (publishedVersion, cc-by-nc-nd)
Funders
Ministerio de Ciencia e Innovación
Topics
  • Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
  • Social Sciences › Psychology › Clinical Psychology › Perfectionism, Procrastination, Anxiety Studies
  • Social Sciences › Demography › Cyberloafing and Workplace Behavior
Keywords
social network addiction, academic procrastination, academic performance, university students, excessive social media use, student distraction

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