Social Media and “FOMO-Work”: The Perceived Prevalence of Better Work Experiences
Bibliographic record
Abstract
Social media is commonly used by both organizations and individuals; yet, there is limited understanding of the psychological consequences of social media usage on employee attitudes and behaviors. In this paper, we examine the impact of social media intensity and its strong correlate, “fear of missing out” (FOMO), on employee work attitudes. We draw on Social Comparison Theory to understand how the unprecedented access to information about alternative work experiences on social media impacts job satisfaction and turnover intentions. We conceptualize a new construct, FOMO-work, as an individual’s intense concern for better work experiences. We examine the construct validity of FOMO-work including the predictive validity with various employee work attitudes. Then, we theorize a positive relationship between social media intensity and turnover intentions through FOMO¬-work and job satisfaction. A serial mediation model is tested with a sample of 138 employees at a Canadian consulting firm. The findings suggest that FOMO-work serves as a link between social media intensity and employee work attitudes. Our study contributes to better understanding the spillover effects of social media in the work context and the emerging research on social media in the workplace.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".