Open innovation knowledge management in transition to market economy: integrating dynamic capability and institutional theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 it