Facilitating and Rewarding Creativity during New Product Development
Bibliographic record
Abstract
In an effort to improve creativity in the new product development process, many firms offer incentive programs, creativity training programs, or both. However, creativity continues to be a construct that is not well understood in marketing, and little research has examined the joint influence of such initiatives on creative outcomes. As a result, there is considerable variance in the way firms approach these issues. A qualitative study of 20 firms indicates that 15 offered some type of incentive program, whereas only 7 engaged in creativity training (a subset of the firms used both). Given that previous research has consistently found that extrinsic rewards offered in isolation actually undermine the creative process (by reducing intrinsic motivation), it seems that many firms may be unwittingly hampering their own creative efforts. However, two experiments demonstrate that the effect of rewards can be made positive if offered in conjunction with appropriate training. Specifically, product creativity was highest when the monetary reward was paired with a dedicated creative training technique. The training alters the influence of the reward such that it reinforces, rather than undermines, intrinsic motivation. Managers can improve the effectiveness of their creative efforts by leveraging the use of incentives and training in combination.
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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 | 0.022 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".