Willingness to Pay for a Dating App: Psychological Correlates
Bibliographic record
Abstract
The smartphone dating app, Tinder, has become hugely popular in recent years. Although most people use a free version of the app, some pay for an augmented version to improve their experience. However, there is little evidence of the association between the willingness to pay for a dating app such as Tinder and users' psychological characteristics. This study thus aims to compare Tinder paying versus non-paying users in terms of their pattern of use, excessive use of Tinder, motives for using Tinder, impulsivity traits, depressive mood, and sociodemographic variables, as well as to examine which variables best predict group membership. A total of 1159 Tinder users participated in an online survey. Group comparisons indicated that payers were more frequently male, reported greater motives for using Tinder than non-payers, and differed in their pattern of use compared with non-payers. Impulsivity traits did not significantly differ between the two groups. Being male and reporting greater motives for Tinder use significantly predicted being a payer. These findings provide insights into the processes that stimulate users' greater consumption of online dating apps, such as reinforcement mechanisms and reward sensitivity.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".