Deciphering factors that make a narcissistically loved salon brand
Bibliographic record
Abstract
Purpose This research is conducted in the context of beauty salons in India, to investigate how enhanced perceived acceptance in interpersonal relationships through consuming beauty salon services can generate narcissistic brand love among consumers via the mediation of brand happiness. It also investigates the moderating impact of consumer's anxious interpersonal attachment style and cynicism on the relationship between perceived salon brand-interpersonal acceptance goal congruence and salon brand happiness. Design/methodology/approach To test the hypothesized relationships, a survey was conducted among 225 regular consumers of beauty salon brands. The data were analyzed using Hayes' (2017) process macro in SPSS. Findings The results suggest that perceived goal congruence between beauty salon brand-interpersonal acceptance positively influences brand happiness, which in turn predicts consumer's narcissistic brand love. Consumer's anxious interpersonal attachment style positively moderates the effect of brand-interpersonal acceptance goal congruence on brand happiness, while cynicism negatively moderates the path. Originality/value Value of the study lies in extending interpersonal acceptance and rejection (IPAR) theory to the domain of consumer–salon brand relationship, to posit that if salon brands satisfy consumers' interpersonal acceptance goals, there is a potential for such happy consumers to love the salon brand, albeit narcissistically.
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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 | 0.000 |
| 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) | 0.005 | 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".