Open innovation knowledge management in transition to market economy: integrating dynamic capability and institutional theory
Bibliographic record
Abstract
This study provides a theoretical framework and empirical evidence to argue that a knowledge management process under the open innovation paradigm brings a viable solution for firms, especially those in transition economies, to acquire valuable knowledge-based dynamic capabilities to respond to environmental changes and achieve desirable organizational performance. These knowledge-based capabilities in turn enable firms to enhance their economic performance in terms of productivity and profitability. Dynamic capabilities act as an intermediary that bridges firms’ open innovation efforts and their economic realization. Local institutional quality plays an important moderating role in this process. Micro-sized firms have not consistently obtained the expected economic benefits from their open innovation efforts, which require more policy attention. For empirical evidence, we consider a comprehensive range of measures for open innovation and dynamic capabilities. Our proposed hypotheses are tested in a set of seemingly unrelated equations by combining two datasets from the Vietnam SME survey and the Provincial Competitiveness Index survey. As a robustness check, we estimate the performance equation applying fixed-effect regression and one-year lag structure.
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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 | 0.001 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".