All That is Users Might Not be Gold: How Labeling Products as User Designed Backfires in the Context of Luxury Fashion Brands
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it