Individual differences, environmental scanning, innovation framing, and champion behavior: key predictors of project performance
Bibliographic record
Abstract
Although increasing evidence points to the importance of champions for keeping product innovation ideas alive and thriving, little is known about how champions identify potential product innovation ideas, how they present these ideas to gain much needed support from key stakeholders, and their impact on innovation project performance over time. Jane M. Howell and Christine M. Shea address this knowledge gap by using measures of individual differences, environmental scanning, innovation framing and champion behavior to predict the performance of 47 product innovation projects. Champion behavior was defined as expressing confidence in the innovation, involving and motivating others to support the innovation, and persisting under adversity. Interviews with 47 champions were conducted to collect information about the innovation projects and the champions' tendency to frame the innovation as an opportunity or threat. Survey data were obtained from three sources: 47 champions provided information on their personal characteristics (locus of control and breadth of interest) and activities (environmental scanning), 47 division managers subjectively assessed project performance at two points in time, and 237 innovation team members rated the frequency of champion behavior. The results revealed that an internal locus of control orientation was positively related to framing the innovation as an opportunity, and breadth of interest was positively related to environmental scanning. Environmental scanning of documents and framing the innovation as a threat was negatively related to champion behavior, while environmental scanning through people was positively related to champion behavior. Champion behavior positively predicted project performance over a one‐year interval. Overall, the findings suggest that in scanning the environment for new ideas, the most effective source of information is the champion's personal network of people inside and outside the organization. Also, the simple labeling of an idea as a threat appears to diminish a champion's perceived influence and erode credibility in promoting an innovation. From the perspective of division managers, champions make a positive contribution to project performance over time, reinforcing the crucial role that champions play in new product development process.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".