Drivers of Recreational Online Gambling Intentions: A UTAUT 2 Perspective, Enhancements, Results, and Implications
Bibliographic record
Abstract
Recreational gambling has become an accepted pursuit, and the advent of the Internet has rendered online gambling ubiquitous. However, the resultant rapid growth in online recreational gambling is not matched by an understanding of the drivers of customers’ intentions to gamble online. While this is potentially a fascinating aspect of consumer behavior, marketing scholars have shied away from giving online gambling much attention. This research seeks a better understanding of the drivers of recreational online gambling intentions among customers by applying the latest version of the Unified Theory of Acceptance and Technology—UTAUT 2, to customers in an online gambling context. It also proposes additional hypotheses that account for the role of anticipated enjoyment and perceived fairness. Data are collected from 593 casino customers of an online gambling firm and analyzed using PLS-SEM via Smart PLS. Results show that perceived fairness and anticipated enjoyment are significant drivers of online gambling intention, with perceived fairness being fully mediated by effort expectancy, anticipated enjoyment, and social influence. Shorn of drivers and moderators that are not significant, an online gambling intention model is proposed. Theoretical and managerial implications are discussed, limitations are noted, and areas for further research are suggested.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".