EDUCATIONAL IMPLICATIONS OF THE RELATIONSHIP BETWEEN PSYCHOLOGICAL WELL-BEING AND FEAR OF MISSING OUT AMONG UNIVERSITY STUDENTS USING SOCIAL MEDIA

Abstract

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.