The Importance of Dream in Advertising: Luxury Versus Mass Market
Bibliographic record
Abstract
Luxury companies typically follow managerial approaches that differ from those of mass market companies and, in particular, their marketing strategies are based on opposite tactics. For instance, luxury companies commonly use imagery rather than text in their print advertising as a way of allowing customers to assign their own personal meanings to the message, thus fulfilling their desire to dream. Indeed, in this current era of information proliferation, today’s consumers are increasingly less willing to process advertising information they receive as text. In this study we explore luxury communication by analyzing some luxury brands’ print advertisement and showing how luxury companies mainly communicate through images instead of text, thus creating appealing advertisements. On the basis of those results and some literature insights, we formulate some managerial propositions that mass market companies may use to start developing dream-evoking communication in order to appeal to modern consumers. In particular, we present mass market managers with suggestions about how to employ the luxury model to make their communications more aspirational than rational through imagery rather than text.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".