Successfully Communicating a Cocreated Innovation
Bibliographic record
Abstract
Despite the growing popularity of cocreation approaches to innovation, the bottom-line results of these efforts continue to frustrate many firms. Marketing communications are one important tool in stimulating consumer adoption, yet marketers to date have not taken advantage of a unique phenomenon associated with many cocreated innovations: the presence of a genesis story in the words of the creator, which can be combined in different ways with traditional marketing messaging. Using mixed methods, the authors demonstrate a crossover effect in which a “mismatch” of the fundamental motivations behind authentic creation narratives and traditional persuasive messages enhances adoption of the cocreated innovation. This effect is mediated by potential adopters’ self-referencing of their own stories about similar experiences or consumption episodes. Furthermore, the effect of a motivation mismatch strategy is attenuated for expert consumers. Finally, this motivation mismatch strategy triggers “takeoff” of cocreated innovations. This research offers substantial implications for research on cocreated innovation, narrative persuasion, and firm-generated and user-generated communication. It provides managers specific guidance on enhancing the success of cocreation programs through an integrated communications strategy.
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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 | 0.005 | 0.026 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".