Does the uptake of wagering inducements predict impulse betting on sport?
Bibliographic record
Abstract
Background and aims Marketing inducements for addictive products, such as wagering, can prompt impulse purchasing by triggering consumption reminders, urges, and cravings. Wagering inducements incentivize betting by providing bonus bets, money-back guarantees, deposits into betting accounts, and discounts. Their promotion during sporting events, push marketing efforts directed at consumers, and ease of uptake at the point-of-sale, may trigger betting on impulse. This study examined whether the uptake of wagering inducements predicted impulse betting on sport. Methods Australian sports bettors (N = 1,813) completed an online survey measuring their proportion of planned bets, impulse bets before match commencement, and impulse bets during play; frequency of using wagering inducements; and several psychological, behavioral, and demographic variables. Results More frequent users of wagering inducements had a greater tendency to place impulse in-play bets, which were also predicted by problem gambling, higher buying impulsiveness, higher frequency of watching sports, younger age, and higher educational status. Sports bettors with a greater tendency to place impulse bets before match commencement also tended to have higher buying impulsiveness and to be younger, but they used inducements less frequently, and tended to be female, less-educated and non-problem, moderate risk, or problem gamblers. Discussion and conclusions Uptake of wagering inducements appeared to be particularly effective in stimulating impulse in-play betting among problem gamblers and frequent sports viewers. These results suggest that a more cautious approach to the regulation of both in-play bets and wagering inducements may be required to better protect young adults from gambling problems and harm.
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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 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".