The role of sensory marketing and brand experience in building emotional attachment and brand loyalty in luxury retail stores
Bibliographic record
Abstract
Abstract Prior studies on the in‐store experience focus on the impact of store atmosphere. Sensory marketing and brand experience, on the other hand, have been found to be significant in providing a better consumer experience in recent studies. Thus, the goal of this paper is to broaden the scope of this study by examining the causal effect of sensory marketing cues and brand experience on emotional attachment, and subsequent brand loyalty in a luxury retail store setting. We also studied the moderating role of store image in the relationships. To this end, the three separate but related studies (Study 1, N = 409, Study 2, N = 294, Study 3, N = 139) were conducted. Study 1 shows that sensory marketing cues positively contribute to enhancing the luxury retail brand experiences. Both sensory marketing and brand experiences appeared promising in increasing emotional attachment and subsequent brand loyalty. Study 2 findings further suggest that sensory marketing cues and brand experience interact significantly with store image to improve consumers' emotional attachment with luxury brands. Study 3 replicated the pattern observed in Studies 1 and 2 and further suggested that consumers in the luxury store setting expressed stronger emotional attachment and brand loyalty than consumers in the nonluxury store environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".