Digitalisation and emerging market enterprises’ global open innovation: Evidence from China
Bibliographic record
Abstract
This study investigates the effect of firm digitalisation on emerging markets enterprises’ innovation performance in a global open innovation environment setting. Adopting samples of listed manufacturing enterprises in China that participate in a global open innovation network by establishing overseas subsidiaries, we investigate how digitalisation impacts their innovation performance. Empirical results show that digitalisation can positively and significantly enhance enterprises’ innovation performance. The results remain consistent after implementing robustness tests and endogeneity correction. Economic mechanism analysis shows that digitalisation facilitates innovation in the global open innovation environment by lowering information asymmetry, enhancing absorptive capacity, and improving risk management ability. The relationship of digitalisation on emerging markets enterprises’ innovation is more prominent for collaborative innovation when the enterprise is located in regions with better intellectual property protection and digital infrastructure and when firms engage with a wider scope of international partners and collaborators. Overall, our study emphasises the role of digitalisation in emerging markets enterprises’ global open innovation and extends the theoretical and empirical understanding of enhancing technological innovation capabilities of enterprises through global open innovation and participating in international innovation collaboration networks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".