Loyalty Typologies and Consumer Choice Factors in the Online Sports-Betting Industry: An Explorative Study into the Italian Regulated Market
Bibliographic record
Abstract
The paper analyses the loyalty typologies and consumer choice factors in the regulated online sports-betting industry. From the methodological viewpoint an empirical investigation has been carried out through the administration of a questionnaire towards customers who make sports bets on online gambling sites in the Italian regulated market. We have found that, in keeping with consumers’ behaviour “regularities” which normally characterize frequently purchased consumer goods markets, the number of customers who are exclusively loyal to one gambling website is rather limited and that, therefore, online bettors mostly adopt a “multi-brand” buying behaviour. In the markets in which this behaviour pattern emerges, customer loyalty programs appear less efficacious in increasing market penetration of the companies with respect to the firms’ decisions on marketing mix-inputs that influence consumer choice of purchasing. The empirical investigation has therefore permitted us to single out the factors that influence the decision to bet at one online gambling site over another, and to show how the importance of these factors varies in relation to the different degree of the gamblers loyalty. The results are then applied to marketing strategies of the online gambling operators.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".