Investigating the Impacts of Individual Traits and Product Characteristics on Customer Evaluation of Sweepstakes
Bibliographic record
Abstract
This study investigates the effects of individual traits and products characteristics on customer evaluation of sweepstakes promoted by retail firms. We assume that customer’s preference toward sweepstakes is determined by four primary attributes: implementation term, entry condition, prize size, and winning odds. The importance customers attach to these attributes are proposed to be influenced by the extent of rationality, regret, and store loyalty. Further, we explore how the evaluations are moderated by the type of product category (goods vs. service) and product involvement (low vs. high). The results from a conjoint analysis and a multivariate regression analysis applied to ordered-preference data show that rationality and behavioral loyalty have significant effects on the importance attached to implementation term, prize size, and winning odds. Further, the results also reveal that attitudinal loyalty play a significant role in the evaluation of low involvement products, while rationality and behavioral loyalty appear to be influential for high involvement products. These results provide new insights into the interplays among sweepstakes attributes, individual traits, and products characteristics as well as managerial implications for retailers developing a loyalty program strategy.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".