The Relationship Between Market Orientation and Sustainable Innovation Performance Based on Dynamic Managerial Capability View of Management Decision Makers
Bibliographic record
Abstract
With the continuous development of science and technology, the innovation ability of enterprises is becoming more and more important, and its role has become an important factor in the development and growth of enterprises.This paper analyzes the relationship between dynamic management capability and firm innovation performance and concludes that market orientation has a significant degree of influence on firm innovation performance, which in turn shows an increase and then a decrease under market orientation.For this phenomenon, the empirical analysis shows that there is a positive relationship between market orientation and firm innovation performance, and a significant correlation between market orientation and firm innovation performance.The results of this study show that dynamic management capability has a positive impact on it.Therefore, when constructing the theoretical model of dynamic management capability, attention should be paid to maintaining the sustainability of the existing resource system of enterprises; at the same time, it should be adjusted at different levels according to the market orientation to improve the ability of enterprises to adapt to the external environment; in addition, attention should be paid to the unstable factors between market orientation and firm innovation that may lead to the intensification of the phenomenon of technology spillover.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".