The Performance Impact of Content and Process in Product Innovation Charters
Bibliographic record
Abstract
The significance of product innovation charters (PICs) cannot be overemphasized, as they provide understanding and a tool for setting organizational goals, charting strategic direction, and allocating resources for new product portfolios. In a unique way, a PIC represents a sort of mission statement mutation for new products. With the backdrop of strategy formulation and product innovation literatures, this article investigates the impact of both content specificity within PICs and satisfaction with the PIC formulation process on new product performance in North American corporations. A survey was undertaken among executives knowledgeable about their organization's new product development process. The respondents included chief executive officers, vice presidents, directors, and managers. The findings demonstrate that significant differences exist both in PIC content specificity and process satisfaction between highly innovative and low innovative firms. The study also shows that PIC specificity in terms of the factors mission content and strategic directives positively influences new product performance. Further, the study demonstrates that satisfaction with the process of formulating PICs plays a positive and powerful mediating role in the PIC specificity–performance relationship. The results suggest that product innovation charters, like their mission statement cousins, may be of more value than most managers realize. The study shows that achieving a state of organizational satisfaction with a PIC's formulation process is critical for obtaining better new product performance. Directions for future research also are suggested.
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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 | 0.008 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".