Patronage, only for happiness? – An analysis on Coexistence of Multiple Consumption Emotions
Bibliographic record
Abstract
Consumption emotion is the perceived feeling of customers when they use products or enjoy service. This feeling can exist for a long time in the minds of consumers and influence their purchase decisions. Consumption emotion falls into three categories, namely positive emotion, negative emotion and neutral emotion. During consuming process, customers may experience a variety of emotions. So it is natural to wonder how these emotions influence decision-making of customers when they coexist. Based on previous studies, this research proposes hypotheses and collects 756 valid data with questionnaire method applied. With employment of multiple linear regression models, hypotheses are verified to draw the final conclusions. Positive and neutral emotions can exert significantly positive effect on patronage intention, while negative emotion significantly affects patronage intention in a negative way. Meanwhile, negative influence on patronage intention is conspicuously reflected from interaction of positive and neutral emotions. Interaction of negative and neutral emotions does not generate obvious effect on patronage intention. However, positive effect on patronage intention is observed in interaction of positive and negative emotions. In different retail formats, interaction of consumption emotions exerts different influence on consumers. According to research results, in real business management, negative emotion of customers does not necessarily lead to worse consequence. Customer satisfaction can still be obtained if it is possible for them to experience positive emotion from other aspects.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".