When Social Media Can Be Bad for You: Community Feedback Stifles Consumer Creativity and Reduces Satisfaction with Self-Designed Products
Bibliographic record
Abstract
Enabling consumers to self-design unique products that match their idiosyncratic preferences is the key value driver of modern mass customization systems. These systems are increasingly becoming “social,” allowing for consumer-to-consumer interactions such as commenting on each other's self-designed products. The present research examines how receiving others' feedback on initial product configurations affects consumers' ultimate product designs and their satisfaction with these self-designed products. Evidence from a field study in a European car manufacturer's brand community and from two follow-up experiments reveals that receiving feedback from other community members on initial self-designs leads to less unique final self-designs, lower satisfaction with self-designed products, lower product usage frequency, and lower monetary product valuations. We provide evidence that the negative influence of feedback on consumers' satisfaction with self-designed products is mediated by an increase in decision uncertainty and perceived process complexity. The implications of socially enriched mass customization systems for both consumer welfare and seller profitability are discussed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".