Research on the Influence of Firm’s Innovation Driven on New Product Innovation Performance
Bibliographic record
Abstract
New product innovation and R&D are important sources for firms to obtain competitive advantages, and market knowledge is the core element for firms to obtain new product innovation performance. However, it can be also found out that the relevant discussion upon innovation has been still limited to restricted theories and the developing empirical researching area by reviewing the literature. Based on knowledge-based theory, a questionnaire survey of 220 high-technology and internet firms in China was conducted to empirically analyze the relationship between innovation driven, potential absorptive capacity, and new product innovation performance. The study found that: the potential absorptive capacity mediates the relationship between market orientation and new product performance, technological opportunity and new product performance, and the potential absorptive capability positively adjusts the relationship between technological opportunities and realized absorptive capacity. It is possible to understand more clearly the process of firms acquiring and digesting information, transforming and mining knowledge to achieve new product innovation performance by analyzing the process of knowledge absorption and conversion.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".