Does fear of missing out give satisfaction in purchasing based on social media content?
Bibliographic record
Abstract
The influence of social media in providing the information is undeniable in the current era. How the information is received is then processed as a helpful reference in deciding the purchase is always an exciting thing to discuss. Based on Self-Determination Theory (SDT), in this study, we examined how motivation arises in the form of fear if left behind or not involved in an excellent condition when consuming a product—through quantitative. Data approach collected from 231 sample people, then processed with Structural Equation Modeling using AMOS 23 through Confirmatory Factor Analysis and Model Fit Testing. The study results confirm that social media content can increase fear of missing out (FOMO), encouraging consumers to make purchases. The results of this study show that purchasing decisions influenced by FOMO can provide satisfaction for consumers. Situational Determinants, namely Perceived favorability and Self-relevance can significantly influence FOMO compared to personal traits (fears, worries, and anxieties). The results of this study are in line with Self Determination Theory, where the personal motivation of consumers in making purchases will encourage the creation of satisfaction.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".