All That is Users Might Not be Gold: How Labeling Products as User Designed Backfires in the Context of Luxury Fashion Brands
Bibliographic record
Abstract
An emerging literature stream posits that drawing on users rather than internal designers in new product creation may benefit firms because the resulting products effectively satisfy consumer needs. Four studies conducted in the context of the luxury fashion industry uncover an important conceptual boundary condition of this positive user-design effect. Contrary to extant research, the results show that being “close” to users does not help but rather harms luxury fashion brands. Specifically, the authors find that user design backfires because consumer demand for a given luxury fashion brand collection is reduced if the collection is labeled as user (vs. company) designed. The results further reveal the underlying rationale for this reversal: user-designed luxury products are perceived to be lower in quality and fail to signal high status, which results in a loss of agentic feelings for the consumer. The authors explore several strategies luxury brands can pursue to overcome this negative user-design effect. Finally, they find that negative outcomes of user design are attenuated for luxury fashion products that are not used for status signaling—that is, product categories of a luxury brand that are characterized by lower status relevance for the consumer.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".