Developing New Innovation Models: Shifts in the Innovation Landscapes in Emerging Economies and Implications for Global R & D Management
Bibliographic record
Abstract
Over the past two decades, there has been a substantial shift in the global innovation landscape. Multinationals from developed economies are increasingly globalizing their R&D activities and are developing an "open innovation" model to source innovations from outside the firm, including from emerging economies such as those in Asia. In addition, emerging economy firms, which traditionally have played a secondary role in the global innovation landscape, have now begun to catch up in developing their own innovative capabilities. This study explores the implications of this new innovation landscape for CEOs of multinationals and emerging economy firms, as well as for international management scholars and educators. While the multinationals might appropriate rents from their existing capabilities and source new ones in emerging economies, they may be threatened by weak intellectual property rights regimes and unintended knowledge spillovers to local firms, creating potential competitors. Firms in the emerging economies can learn from and catch up with investing multinationals, but to do so they need to develop their own innovative capabilities and move from a process to a product focus and from imitation to innovation.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".