The Impact of Company Resources and Capabilities on Global New Product Program Performance
Bibliographic record
Abstract
Product innovation and the trend to globalization are two important and interrelated dimensions driving business today. In this article, the results of five published research articles on the topic of global new product development (NPD) are summarized to provide an integrated overview of the factors that impact global NPD program performance. The overall conceptual framework is based on three types of literature—NPD, globalization, and organization. The main theoretical approach for establishing relationships between factors is the dynamic capability/resource-based view. Accordingly, factors linked to outcome are seen as operating on different organizational levels, with more actionable initiatives or ‘capabilities’ largely mediating the softer and longer term background ‘resources’ of the firm. The analyses are based on a broad cross-industry sample of 467 firms (North America, Europe, B2B, goods/services). Three global NPD-related background resources (global innovation culture, resource commitment, and senior management involvement), labeled the ‘behavioral environment’ of the firm, are identified and shown to be linked to global NPD program performance via the mediated effect of four specific NPD capabilities (NPD process, strategy, team, and IT/communication). A qualitative synthesis of the findings is provided, along with recommended management initiatives with which firms can enhance their performance in the global NPD effort. Both sets of factors are found to be essential and highly interrelated, but it is the strength of the behavioral environment resources that distinguish the best performing firms, setting the stage for success in global NPD.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 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".