Simulation of Sales Promotions towards Buying Behavior among University Students
Bibliographic record
Abstract
The purpose of this study was to examine the influence of sales promotion on buying behavior among university students. Specifically, University Putra Malaysia (UPM) was chosen as study location. A total of 150 respondents were recruited using systematic random sampling technique. The data were collected using self-administrated questionnaires. This study found that there was no significant difference between gender and buying behavior (t = 1.569, p > 0.05). On the other hand, a there is a significant differences family monthly income and buying behavior (F = 2.597, p <= 0.05). There were significant relationship between attitude towards price discounts (r = 0.351, p <= 0.01), coupons (r = 0.392, p <= 0.01), free samples (r = 0.491, p <= 0.01) and “buy-one-get-one-free” (r = 0.456, p <= 0.01) with buying behavior. Results of Hierarchical Multiple Regression found that free samples and buy-one-get-one-free explained 28.7% variance in buying behaviour of the respondents. The findings of this study would help marketers to understand the types of promotion that significantly influence buying behaviour of the respondents. Hence, this could help marketers in their marketing planning to become more competitive and gain profit.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".