Success Factors for Product Innovation in China’s Manufacturing Sector: Strategic Choice and Environment Constraints
Bibliographic record
Abstract
This study examines what factors contribute to firm innovation performance as a result of successful launch of new products in China. Rather than simply applying theories of product innovation often developed in the West, this study takes an indigenous perspective to explore what product strategies and which environment factors, defined by Chinese managers, contribute to the improved firm performance. With the data of Chinese firms from over 40 cities across the country, this study surveys more than 700 manufacturing firms that have introduced new products to the market. The result shows that while a defensive product strategy is negatively related to a firm’s patent application, a prospector strategy helps increase its market share in China. In addition, innovation policy and total R&D investment drive a firm to sell more products overseas and increase its new product sales across the globe. Local talent market can also help improve a firm’s patent application but often drive the firm to focus more on domestic markets. Implications of the results for theory and practice are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".