New Product Development Process Execution, Integration Mechanisms, Capabilities and Outcomes: Evidence from Chinese High‐Technology Ventures
Bibliographic record
Abstract
Abstract This study examines new product development (NPD) processes in high‐technology new product ventures in the emerging market context. Drawing upon the knowledge‐based view and the capability‐based view, we propose a model that characterizes relationships between NPD process execution stages and product competitive advantage, and accounts for the moderating effects of NPD integration mechanisms on these relationships. Our model also explains how pricing capabilities can become a liability that undermines how product advantage impacts new product performance. We test this framework within an emerging market context that has been notably absent from the literature. Our data are generated from 187 new product projects and a follow‐up of 83 projects, from Chinese high‐technology ventures. We identify important theoretical interdependencies within our structural model results. Specifically, marketing–technical integration positively moderates the relationship between product development and testing capability and commercialization capability, while new product implementation capability positively moderates the relationship of commercialization capability and product competitive advantage. Yet, penetration pricing capability negatively moderates the link between product competitive advantage and new product performance.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".