Modes of innovation in an emerging economy : a firm-level analysis from Mexico
Bibliographic record
Abstract
Firms that combine both the science, technology, and innovation (STI) and learning-by-doing, learning-by-using, and learning-by-interacting (DUI) modes of innovation are more likely to attain innovation outcomes than those employing either mode separately. Different studies across Europe and Canada support this proposition to different extents. However, the core of these studies has been carried out in advanced economies, inadvertently neglecting other relevant innovation milieus. This study examines the nuances of such innovation strategy in an emerging economy context. We explore differences and potential limitations in the existent literature. The analysis covers 9 628 Mexican firms with 10 or more employees. The results of the logit regressions suggest that a combined STI and DUI innovation approach yields better results in terms of product innovation. Contrary to the existing literature, our results point out that in an emerging economy context, the weight of DUI mode of innovation is larger on product innovation than the STI mode. Finally, DUI mode has a greater impact on process innovation than STI mode as well as the combination of STI and DUI; thus, showing that the benefits of combining STI and DUI are limited only to product innovation.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".