The Determinants of Repeat Purchase Intention for Luxury Brands among Generation Y Consumers in Malaysia
Bibliographic record
Abstract
Repeat purchasing has now become a critical factor for marketers, especially in the luxury goods market. Repeat purchasing not only saves costs (as opposed to attracting new customers), but increases sales as well. Both past and current researchers have been keen in investigating what drives consumers to repeat their purchase. The purpose of this academic research is to examine the relationship between Hedonic Value (HV), Satisfaction (S), Consumer Inertia (CI) and Product Attribute (PA) with the Repeat Purchase Intention (RPI) for luxury brands among Generation Y consumers in Malaysia. As such, eight luxury brands have been selected to investigate the consumer behaviour of consumers in Malaysia, in relation to the repeat purchase intention. This is a quantitative study that collected data from 134 respondents. Findings reveal that Hedonic Value and Satisfaction have positive and significant correlation with Repeat Purchase Intention, with Satisfaction being the strongest predictor of Repeat Purchase Intention. The findings can be used by marketers in Malaysia to aid them in creating marketing strategies to maintain their current customer base, as well as attract new customers to purchase their luxury brands within their target market. This study can also motivate current researchers to further investigate in the field of luxury brands, in an attempt to bridge the gap between luxury brands and Repeat Purchase Intention.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".