An experimental investigation of factors affecting consumers' perceptions of sales promotions
Bibliographic record
Abstract
This paper reports the results of an experimental study where four characteristics of premium‐based sales promotions were manipulated in the context of a computer purchase: the attractiveness of the premium, the extent to which it fits the product category, the reception delay of the premium, and the mention of its value. The results show that these factors had interactive effects on consumer reactions. Thus, although the attractiveness of the premium generally had a positive impact on consumer appreciation of the promotional offer, a promotion including an unattractive premium was nevertheless positively evaluated if the premium was a good fit to the product category. Sales promotions, including a premium that fits well the product category, were less likely to be perceived as manipulative. However, if the product‐premium fit was poor and the premium was not attractive, mentioning the value of the premium helped to reduce the perceptions of manipulation intent. It is concluded that more research is needed on this managerially relevant topic in light of the complex dynamics that appear to underlie the relationships between the characteristics of premium‐based promotions and consumer reactions.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".