The Role of Marketing Intensity in Moderating CSR and Financial Performance in Luxury Fashion
Bibliographic record
Abstract
Of the studies which explore the CSR-FP relationship in luxury fashion, few utilize industry-specific, disaggregated CSR measures. Additionally, none have explored the role of marketing intensity (MI), the ratio of promotional expenses to sales, in moderating the CSR-FP relationship by linking CSR initiatives with luxury fashion consumers. Thus, this study aims to answer the question, “What is the disaggregated impact of CSR on the financial performance of luxury fashion brands?” The methodological approach of this study involved gathering the CSR and FP data of 12 luxury fashion brands from the Fashion Transparency Index and Capital IQ S&P 500 Database, respectively, constructing a cross-sectional panel dataset, and performing multivariable regression analysis. The significance of the “Traceability” and “Marketing Intensity * Traceability” terms in analysis implies that designer brands should allocate a greater proportion of marketing funds towards implementing traceability-oriented CSR initiatives in order to enhance FP. Additionally, the R2 values of each regression model improved when MI was included as a moderating variable, indicating that future research should incorporate MI in analysis. The findings of this research are limited by the FTI and Capital IQ S&P 500 databases and the parameters of the study. Therefore, future researchers should consider obtaining data from other sources and examining data over a longer time period. This study adds to the ongoing discussion of CSR in the luxury fashion industry by providing evidence to support the inclusion of MI in future analysis and informing the CSR strategies of luxury fashion brands.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".