Examining Antecedents and Consequences of Gambling Passion: The Case of Gambling on Horse Races
Bibliographic record
Abstract
OBJECTIVE: This study investigated the antecedents and consequences of gambling passion using structural equation modeling to examine relationships among gambling motivation, passion, emotion, and behavioral intentions in the horse racing industry. METHODS: An onsite survey was conducted with 447 patrons at a horseracing park in South Korea. A confirmatory factor analysis showed that the Gambling Passion Scale was valid and reliable, resulting in two sub-scales: obsessive passion (OP) and harmonious passion (HP). RESULTS: Study results indicated that extrinsic motivation influenced OP whereas intrinsic motivation significantly affected HP. Furthermore, OP was correlated with negative emotion, whereas HP was related to positive emotion. Gamblers' satisfaction was found to be influenced positively by positive emotion and negatively by negative emotion. Finally, satisfaction appeared to affect gamblers' behavioral intentions. CONCLUSION: Study results echoed the notion of distinct and separate gambling motivations and passions among horse racing gamblers. Furthermore, results identified specific areas to which horse racing operators or policy makers should pay special attention in developing effective marketing strategies to promote responsible gambling.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".