Value-Based Segmentation of U.S. Luxury Consumers: Conceptual Replication and Model Validation
Bibliographic record
Abstract
The primary objective of this study was to understand the U.S. consumers’ luxury value perceptions using Luxury Value Perception (LVP) model (Wiedmann, Hennigs & Siebel, 2009). The study replicated the procedure used in Wiedmann et al.’s (2009) article to validate the dimensions of the LVP model in the U.S. context. Data were collected using an online survey through Amazon Mechanical Turk. The findings revealed the applicability of the LVP model in the U.S. context and revealed interesting differences in luxury value perceptions among U.S. and German consumers. This study advances theory as it is the first to validate the latent luxury value construct as influenced by individual, social and functional luxury value perceptions in the U.S. context. The LVP model helped identify luxury value drivers of U.S. consumers and cluster them in homogenous segments. These findings may potentially help U.S. luxury brand marketers to know the needs and values of different customer segments, ultimately helping them to develop effective brand positioning strategies in a competitive marketplace.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".