Performance of Global New Product Development Programs: A Resource‐Based View
Bibliographic record
Abstract
Gaining a competitive edge in today's turbulent business environment calls for a commitment by firms to two highly interrelated strategies: globalization and new product development (NPD). Although much research has focused on how companies achieve NPD success, little of this deals with NPD in the global setting. The authors use resource‐based theory (RBT)—a model emphasizing the resources and capabilities of the firm as primary determinants of competitive advantage—to explain how companies involved in international NPD realize superior performance. The capabilities RBT model is used to test how firms achieve superior performance by deploying organizational capabilities to take advantage of key organizational resources relevant for developing new products for global markets. Specifically, the study evaluates (1) organizational NPD resources (i.e., the firm's global innovation culture, attitude to resource commitment, top‐management involvement, and NPD process formality); (2) NPD process capabilities or routines for identifying and exploiting new product opportunities (i.e., global knowledge integration, NPD homework activities, and launch preparation); and (3) global NPD program performance. Based on data from 387 global NPD programs (North America and Europe, business‐to‐business), a structural model testing for the hypothesized mediation effects of NPD process capabilities on organizational NPD resources was largely supported. The findings indicate that all four resources considered relevant for effective deployment of global NPD process capabilities play a significant role. Specifically, a positive attitude toward resource commitment as well as NPD process formality is essential for the effective deployment of the three NPD process routines linked to achieving superior global NPD program performance; a strong global innovation culture is needed for ensuring effective global knowledge integration; and top‐management involvement plays a key role in deploying both knowledge integration and launch preparation. Of the three NPD process capabilities, global knowledge integration is the most important, whereas homework and launch preparation also play a significant role in bringing about global NPD program success. Tests for partial mediation suggest that too much process formality may be negative and that top‐management involvement requires careful focus.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".