Promoting crowdsourced new products: Competing co‐contributor attractiveness, similarity, and persuasion knowledge processes
Bibliographic record
Abstract
Abstract Crowdsourcing has become an increasingly popular way for marketers to conceive of and design new products. Promoting these with “designed by consumers” claims has proven highly effective in boosting innovation appeal and market success. However, beyond the appeal of the perceived similarity between co‐contributors and customers (Dahl et al., 2015), little is known about the effectiveness of different communication strategies for crowdsourced products. Central to the current investigation is the crucial creative strategy decision about whether to show the co‐contributor in the advertisements and the persuasive role of the co‐contributor's level of physical attractiveness. The use of attractive sources is highly prevalent in standard advertisements and is known to have reliable positive effects in persuading consumers (Mello et al., 2020). In contrast, our research suggests that showing attractive co‐contributors in advertisements for crowdsourced products undermines their unique appeal and can even backfire. Through a series of qualitative and experimental studies, we found that this effect results from two mechanisms: (1) negative persuasion knowledge, where consumers question whether the attractive source is the genuine co‐contributor, and (2) disruption of the similarity appeal that typically makes crowdsourced products well‐received. These findings not only advance our understanding of the effectiveness of “designed by consumers” claims but also contribute significantly to the broader communication and persuasion literature. Importantly, our findings provide managers with actionable strategies for maximizing the commercial success of crowdsourced products.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".