Affordable luxury consumption: an emerging market's perspective
Bibliographic record
Abstract
Purpose This study aims to focus on proposing and empirically validating a model that captures certain critical socio-psychological factors that nurture consumers' attitude towards affordable luxury brands in an emerging market context of India. Design/methodology/approach The data were collected via a cross-sectional questionnaire survey from 491 customers of different fashion accessory luxury products in India. The data were analyzed through structural equation modelling (SEM) using AMOS 23.0 SEM software. Findings The findings of this study reveal that conspicuousness, status consumption, brand name consciousness, need for uniqueness and hedonism positively affect consumer attitude towards affordable luxury, which consequently affects consumers' purchase intention. The findings further reveal that age acts as a moderator in driving consumers' neo-luxury consumption. Originality/value By uniting various socio-psychological factors with consumer attitude and purchase intention in a conceptual model, along with studying the moderating role of age, this study responds to the calls for further research regarding affordable luxury and offers a more granular understanding of specific consumer motivations that guide Indian consumers' affordable luxury consumption.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".