ON THE EFFECTS OF A FIRM�S SIZE, A SECTOR�S TECHNOLOGY INTENSIVENESS, AND PROPENSITY TO INNOVATION ON THE ADOPTION OF ADVANCED TECHNOLOGIES: THE CASE OF MANUFACTURING SMEs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the increasing growth of global competition, the adoption of advanced technologies has become a great challenge for manufacturing firms, especially SMEs that are in a disadvantageous context (comparatively to larger firms) in terms of resources. Various factors affect the adoption of a variety of technologies in these SMEs. This article aims to explain the effect of size, innovation, and sector of activity on the adoption of a variety of these advanced technologies. The statistical exercise conducted in this paper is based on a sample of 248 manufacturing firms. The findings of this study reveal that the adoption of a variety of advanced technologies is positively influenced by the SME’s size, the technological intensiveness of the industry where it operates, and finally, its innovation capacity.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it