Assessment of Readiness of Croatian Companies to Introduce I4.0 Technologies
Bibliographic record
Abstract
The main topic of this paper is to estimate the possibility and inclination of Croatian companies towards technology and innovation as well as to analyze advantages, limitations and risks involved with this significant technological leap. We analyzed 7147 Croatian business entities operating in different industries in this paper. The starting point in this research is to identify subjects, which could be users of I4.0 or its elements, based on the similarity of indicators with indicators of a sample of 58 identified I4.0 companies. We developed a machine-learning model by using the eXtreme Gradient Boosting algorithm (XGBoost) for this purpose, an approach that has not been used in any similar research. This research shows that the main difference between I4.0 and traditional industry is mostly observable in significantly better business performance of investment indicators, cost efficiency, technical equipment and market competitiveness. We identified 141 companies (1.97% of total analyzed sample) as potential users of I4.0, which makes up around 27% of total assets of the analyzed sample and around 26% of revenues.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".