Bibliographic record
Abstract
Innovation has become the defining challenge for global competitiveness. Traditional thinking about the management of innovation focuses almost exclusively on internal factors ? the capabilities and processes within companies for creating and commercializing technology. Although the importance of these factors is undeniable, the external environment for innovation is at least as important. For example, the United States has been an especially attractive environment for innovation in pharmaceuticals in the 1990s, while Sweden and Finland have seen extraordinary rates of innovation in wireless technology. Michael Porter, a leading thinker on competitiveness and Bishop William Lawrence University Professor at Harvard University, and Scott Stern, professor of management at the MIT Sloan School of Management, describe how managers can understand the role of location in innovation and evaluate the innovative capacity of both countries and regions. Using data from the Organization for Economic Cooperation and Development and emerging nations over the past quarter century, their findings show the striking degree to which location matters for successful innovation at the global technology frontier. Their analysis sheds light on why individual nations have registered sharp differences in innovative performance. The strong effect of location on innovation holds important implications for companies and creates a new broader agenda for innovation management. Choosing R&D location and managing relationships with outside organizations should not be driven by input costs, taxes, subsidies or even the wage rates for scientists and engineers, as they often are. Instead, R&D investments should flow preferentially to the locations with the greatest innovative capacity. Taking active steps to harness and extend locational advantages takes on equal weight with R&D process management. Locational advantages ? rooted in proprietary information flows, special relationships with local companies, and preferential access to local institutions ? are competitive advantages that are difficult for outsiders to overcome. They can help explain an apparent paradox of globalization: Ideas and technologies that can be accessed at a distance cannot serve as a foundation for competitive advantage. Effective management of locational advantages may ultimately prove more sustainable than simply implementing corporate best practices.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.009 |
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".