Citation
Izani, M. and Abdul Razak, Aishah and Mohd Razak, Norainy and Asyrani, Akif (2026) Platform Ecosystems Matter: Differential Associations of TikTok, Instagram and Other Social-Media Networks with Student Well-Being and Academic Performance. In: 23rd International Learning and Technology Conference, L and T 2026, 14 April 2026 - 15 April 2026, Jeddah.|
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Abstract
Screen-time guidelines that treat all social-media platforms alike offer limited actionable insight for digitalwellness policy. Using a public synthetic dataset (N=705; 12 platforms; Adil Shamim, Kaggle, CC BY 4.0), this study illustrates a platform-ecosystem analytical framework. A MANOVA controlling for daily exposure, age, gender, and academic level identified a significant multivariate association between dominant platform and four psychosocial outcomes (addiction, mental health, sleep, conflict): Wilks’ Λ=0.439, F(44, 2626)=14.33, p<.001, η²≈0.09. Covariate-adjusted marginal means showed short-video and image-centric platforms (TikTok, Snapchat, Instagram) associated with substantially higher addiction scores than messenger or professional networks (≈2.6 scale points). Platform × ScreenTime-Tertile interactions were significant for all four outcomes (all p<.001), with heavy use (>5.5 h/day) selectively amplifying addiction associations within high-salience ecosystems. A chisquare analysis of academic disruption yielded χ²(11)=260.32, p<.001, Cramér’s V=0.608; logistic regression (five largest platforms) found TikTok users 4.42 times more likely to report academic disruption than Instagram users (95% CI: 1.81– 10.79). Because the dataset exhibits synthetic-data characteristics, findings are presented as proof-of-concept illustrations requiring replication with real longitudinal cohort data before informing policy.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Social-media ecosystem |
| Subjects: | Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science |
| Divisions: | Faculty of Business (FOB) |
| Depositing User: | Ms Rosnani Abd Wahab |
| Date Deposited: | 04 Aug 2026 03:46 |
| Last Modified: | 04 Aug 2026 03:46 |
| URII: | http://shdl.mmu.edu.my/id/eprint/16472 |
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