The Innovation Effect of User Design: Exploring Consumers’ Innovation Perceptions of Firms Selling Products Designed by Users
Bibliographic record
Abstract
The authors study consumer perceptions of firms that sell products designed by users. In contrast with the traditional design mode, in which professional designers employed by firms handle the design task, common design by users involves the firm's user community in creating new product designs for the broader consumer market. In the course of four studies, the authors find that common design by users does not decrease but actually enhances consumers’ perceptions of a firm's innovation ability. This “innovation effect of user design” leads to positive outcomes with respect to purchase intentions, willingness to pay, and consumers’ willingness to recommend the firm to others. The authors identify four defining characteristics of common design by users that underlie this innovation inference; namely, the number of consumers, the diversity of their background, the lack of company constraints, and the fact that consumer designers actually use the designed product all contribute in building positive perceptions. Finally, the authors identify consumer familiarity with user innovation and the design task's complexity as important moderators that create boundary conditions for the innovation effect of user design.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".