HOW DO HIGH, MEDIUM, AND LOW TECH FIRMS INNOVATE? A SYSTEM OF INNOVATION (SI) APPROACH
Bibliographic record
Abstract
In the past decade, the innovation literature has mainly targeted high-tech (HT) sectors due to their higher return on investment and important role in building new societies and economies. While the HT sector is still of a leading importance, whether medium and low tech (LMT) sectors should be equivalently considered when analyzing long term economic growth, in both leading and catching up economies, is a fundamental question. This paper is our second milestone comparing HT and LMT sectors from an innovation perspective, using a National System of Innovation (NSI) approach. The general aim of this paper is to find the main principles that govern the difference between the two industrial segments (HT and LMT) while controlling for supranational boundaries. In order to measure the effect of NSI, countries are divided into two groups: leading and catching up economies. Our results suggest that, with respect to HT, leading economies can be considered as innovators, while catching up economies are the imitators. Furthermore, HT in leading economies relies on product modularity to outsource various components probably to firms in catching up economies. Catching ups are putting greater emphasis on universities to produce knowledge. In addition, firms in catching up economies benefit from high accessibility to funds in order to grow various industrial sectors, especially LMT. The role of institutions and governments with respect to regulatory policies, intellectual property protections are of high importance for firms in catching up economies, especially in LMT. As a result of those important steps, the various agents in catching up economies have achieved sustainable growth, notably in LMT. In contrast, the same growth is observed for HT for firms in leading economies. Our results suggest that catching up countries are strategizing for this sectoral evolution, renewal, and transformation process for both sectors, but with a stronger emphasis on LMT.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".