Brand love and customer brand engagement for masstige: a cross-cultural perspective
Bibliographic record
Abstract
Purpose Masstige (mass-produced and affordable luxury) goods are receiving increasing literature-based attention. However, despite existing advances, insight into how different cultural backgrounds shape consumer perceptions, attitudes and behaviors toward masstige goods remains tenuous. Correspondingly, this study aims to examine the association of masstige luxury with customers’ love for and brand engagement with masstige products across cultures. Design/methodology/approach This study used a sample comprising 342 Indian and 354 Canadian masstige customers. Partial least squares structural equation modeling was used to analyze the data. Findings The results corroborate brand prestige and identification as key antecedents to customers’ love for masstige brands, which in turn impact their brand engagement. Surprisingly, the authors find that the effects of brand prestige and brand identification on brand love and customer brand engagement do not significantly differ between Indian and Canadian customers. However, the positive effect of brand identification and brand love on customer brand engagement is stronger for Indian customers than for Canadian customers. Research limitations/implications This study addresses an important literature-based gap in understanding how cultural backgrounds shape consumer perceptions of masstige brands. It offers key theoretical and practical implications for masstige marketing. Practical implications Identifying differential effects among Indian and Canadian customers provides a foundation for tailoring marketing approaches in the masstige sector. Originality/value This study addresses a critical literature-based gap in understanding how cultural backgrounds shape consumer perceptions of masstige brands, offering key theoretical and practical implications for masstige marketing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".