EDUCATIONAL IMPLICATIONS OF THE RELATIONSHIP BETWEEN PSYCHOLOGICAL WELL-BEING AND FEAR OF MISSING OUT AMONG UNIVERSITY STUDENTS USING SOCIAL MEDIA - Alicia Wulan Cahyani; Prias Hayu Purbaning Tyas (CV. TOTUS TUUS, 2026)
Networked technologies have become integral to university life, simultaneously enabling collaboration and learning support while heightening social comparison and notification-driven checking. Within this ecology, Fear of Missing Out (FoMO) has been linked internationally to lower psychological well-being (PWB), yet evidence is scarce for Indonesian teacher-education students whose developmental tasks and relational demands may render specific facets of PWB especially sensitive. This study aimed to (a) describe FoMO and PWB levels among undergraduates in a teacher-education faculty and (b) test their association, providing context-specific, facet-aware evidence to inform student supports. Using a cross-sectional, quantitative correlational design, 168 FKIP Universitas Sanata Dharma students completed validated measures: ON-FoMO (post try-out 19 items) and Ryff’s PWB (post try-out 46 items). Analyses in SPSS included descriptive statistics, Kolmogorov–Smirnov normality checks, linearity tests, and two-tailed Pearson correlations (α = .05). Results showed FoMO was predominantly Low/Very Low (59.4%) with 29.0% Medium and 11.9% High/Very High, while PWB was mainly High/Very High (58.3%) with 38.7% Medium. FoMO correlated moderately and negatively with PWB, r = −0.420, p < .001 (95% CI [−0.537, −0.287]), implying r² ≈ 17.6% variance explained. We conclude that greater FoMO is meaningfully associated with lower eudaimonic functioning in this cohort. Implications include tiered, low-cost supports: universal digital self-regulation workshops (notification control, time-boxing), micro-interventions that reinforce purpose and self-acceptance (values–goals alignment, reflective journaling), and short “dose” trials of reduced daily social-media use paired with mood/sleep tracking. Future research should employ longitudinal or experimental designs to establish directionality, integrate behavioral usage logs (screen-time, notifications), examine platform-specific behaviors (passive vs. active use, time-of-day), and model additional covariates (e.g., socioeconomic status, practicum load) to clarify mechanisms and boundary conditions.