Pay-to-Win Gaming and its Interrelation with Gambling: Findings from a Representative Population Sample
Bibliographic record
Abstract
Pay-to-Win gaming describes a common type of video game design in which players can pay to advance in the game. The frequency and value of payments is unlimited, and payments are linked to players' competitiveness or progress in the game, which can potentially facilitate problematic behavioral patterns, similar to those known from gambling. Our analyses focus on assessing similarities and differences between Pay-to-Win and different forms of gambling. Based on a survey among 46,136 German adult internet users, this study presents the demographic and socio-economic profile of (1) Pay-to-Win gamers who make purchases in such games, (2) heavy users who conduct daily payments, and (3) gamers who are also gamblers. Motives for making payments were assessed and participation, frequency and spending in gambling by Pay-to-Win gamers are presented. To assess the similarity of Pay-to-Win gaming and gambling, we tested whether Pay-to-Win participation, frequency of payments and problematic gaming behavior are predictors for gambling and cross-tested the opposite effects of gambling on Pay-to-Win. We find that Pay-to-Win gamers are a distinct consumer group with considerable attraction to gambling. High engagement and problematic behavior in one game form affects (over)involvement in the other. Common ground for Pay-to-Win gaming and gambling is the facilitation of recurring payments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".