Examining the Influence of Store Environment in Hedonic and Utilitarian Shopping
Bibliographic record
Abstract
Much of the literature on the attractiveness and pleasantness of retail stores has focused on the critical influence of store atmosphere or ambient attributes, which influence customer satisfaction and store choice. However, little is known about the environmental cues that influence customers’ satisfaction in different shopping contexts. In this context, the present research aims to answer the following questions: “Are the store atmospheric variables equally relevant in hedonic and utilitarian shopping?”; and further: “Does the influence of store environment on customer satisfaction vary depending on the type of shopping?”. For this purpose an empirical research is developed through PLS Structural Equation Modeling (PLS-SEM) based on data obtained from hedonic (n = 210) and utilitarian (n = 267) shopping contexts. Results indicate that customers perceive differently store atmospherics in utilitarian and in hedonic shopping. More precisely, findings report that customer satisfaction is driven by internal ambient and merchandise layout in hedonic shopping contexts; while the external ambient and the merchandise layout are major atmospheric cues in utilitarian shopping. Interestingly, store crowding does not influence customers’ satisfaction. This study provides a deeper understanding into the specific store attributes that influence customer satisfaction, which could be used by retailers to differentiate themselves from competitors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".