Appraisal and Positive Emotion Effect on Consumer Impulse Buying Behaviour Using Mobile Discount
Bibliographic record
Abstract
This article investigates the influence of appraisal and positive emotions on consumers’ impulse buying behaviour concerning the use of mobile discount sales promotion techniques. Grounded on both attribution and cognitive appraisal theory, hypotheses were devised based on the relationship between sales discounts to positive psychological responses, resulting in impulse purchases. A closed-ended survey involving 251 consumers who had recently made purchases utilising discount promotions was conducted. The study reveals that consumers’ impulse buying behaviour is positively affected by appraisal attributes, specifically price–quality perception, goal interest and goal importance, associated with discount sales promotions. Moreover, positive emotions are identified as a mediating factor in the relationship between these appraisal attributes and impulse buying behaviour. As the scope of the findings is limited to the mobile discount sales promotion technique, various possibilities for further exploration and expansion of these findings are discussed. Insights derived from this study offer a targeted focus and resource prioritisation in terms of theoretical and managerial implications.
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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 | 0.000 | 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) | 0.002 | 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".