The effects of shopping environment on consumption emotions, perceived values and behavioral intentions
Bibliographic record
Abstract
The main objective of this study is to develop and to test a comprehensive model that investigates the effect of shopping environment on consumption emotion, perceived value and behavioral intentions in tourism setting.The proposed model specifies the effect of environment perceptions on consumption emotions (pleasure and arousal), hedonic and utilitarian value, which in turn emotions and values affect tourist's satisfaction and behavioral intentions.Data were collected through tourists who visited a tourist city by using cluster random sampling method.A total of 410 questionnaires were used for data analysis.Structural equations modeling (SEM) by using LISREL was performed to empirically test the relationships between the constructs of this research .Results show that environment has a positive and significant influence on pleasure and arousal.However, the effect of environment perceptions on behavioral intentions was not significant.In addition, results indicate that pleasure and arousal have positive and significant effects on tourist's values.Findings also indicate that hedonic and utilitarian values had direct effect on customer satisfaction and the effect of satisfaction on behavioral intention was positive and significant.Finally, it suggests that service providers should focus on components of environment in a way that contributes positively in creating positive emotions in customers, which in turn consumption emotions enhance perceived value and positive behavioral intentions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".