Attitude-Support Relationship in Gambling: A Modified Theory of Planned Behaviour
Bibliographic record
Abstract
In this study, we modified the theory of planned behaviour and proposed that attitude towards gambling explains and predicts gambling support. We explored whether locals and tourists support or oppose the gambling industry and the factors that led to support or opposition. This information is vital, as policy decisions are often made by taking into consideration public support. Using a structured questionnaire, we gathered data from 385 respondents from Goa, a popular tourist and gambling destination in India. Through structural equation modelling, we found that most of the variance (87%) in support of gambling was explained by attitude. Perceived benefits and risks explained the significant variance (58%) in attitude towards gambling, as indicated in the theory of planned behaviour. The coefficients were significant except for the path from social risk to attitude, which was removed from the final model. The direct path from benefits and risk to support were not significant. In addition, the path from personal risk to attitude was moderated by the respondent’s gambling behaviour. Although gamblers had a more positive attitude with increasing personal risk, non-gamblers had a more negative attitude with increasing personal risk. This finding confirms the risk-seeking behaviour of gamblers. Résumé Cet article modifie la théorie du comportement planifié et avance que l’attitude à l’égard des jeux de hasard explique et prédit l’appui donné à ces jeux. L’étude vise à déterminer si les habitants et les touristes appuient l’industrie des jeux de hasard ou s’ils s’y opposent, et à cerner les facteurs menant à un appui ou à une opposition. Il s’agit d’une information de grande importance, car les décisions d’orientation prennent souvent en considération l’appui du public. Des données ont été recueillies au moyen d’un questionnaire structuré auprès de 385 répondants de Goa, en Inde, une destination touristique et de jeu très prisée. À l’aide d’une modélisation par équation structurelle, la recherche a révélé que la variance (87 %) dans l’appui aux jeux de hasard s’explique en majeure partie par l’attitude. Comme le suggère la théorie du comportement planifié, les avantages et les risques perçus expliquent la variance importante (58 %) dans l’attitude à l’égard des jeux de hasard. Les coefficients sont significatifs, à l’exception de la piste causale entre le risque social et l’attitude, qui a été retirée du modèle final. Les pistes directes allant des avantages et des risques vers l’appui n’étaient pas significatives. Il est apparu que les comportements de jeu des répondants avaient un effet modérateur sur la piste allant du risque personnel vers l’attitude. L’attitude des joueurs était de plus en plus positive à mesure qu’augmentait le risque personnel, tandis que celle des non-joueurs était de plus en plus négative. Ce résultat confirme l’existence d’un comportement de recherche du risque chez les joueurs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".