On being loyal to a casino: The interactive influence of tier status and disordered gambling symptomatology on attitudinal and behavioral loyalty
Bibliographic record
Abstract
BACKGROUND AND AIMS: Casino loyalty programs are marketing strategies designed to foster attitudinal (i.e., identification and satisfaction) and behavioral (i.e., spending) loyalty among gamblers by offering rewards to members. Casino loyalty programs use a tier-based structure to segment members who spend more money into higher tiers, where they receive better rewards (compared to lower tiered members). Tier-based structures may encourage increased expenditure among patrons, especially among players living with a gambling disorder. The current work aimed to examine whether tier status and disordered gambling symptomatology interact to predict attitudinal and behavioral loyalty. METHODS: Study 1 used a cross-sectional design to examine whether tier status and disordered gambling symptomatology interact to predict self-reported loyalty among a sample of American casino loyalty program members (N = 396). In Study 2, archival player account data from Canadian casino loyalty program members (N = 649) were analyzed to examine whether tier status and disordered gambling symptomatology interact to predict objective measures of behavioral loyalty. RESULTS: The greatest effect of tier status on attitudinal and behavioral loyalty was observed among non-problem gamblers in the highest tiers. Tier status, however, did not influence loyalty among members high in disordered gambling symptomatology. DISCUSSION: Results suggest that once gambling has become problematic, loyalty programs may not influence player attitudes and behaviors. Non-problem gamblers may be particularly susceptible to the tiered structure of the programs. CONCLUSION: Non-problem gamblers may benefit from casino loyalty programs in the short-term but longitudinal research is needed to understand the long-term influence of membership.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".