PREDICTORS OF INDUSTRY 4.0 TECHNOLOGIES AFFECTING LOGISTIC ENTERPRISES’ PERFORMANCE: INTERNATIONAL PERSPECTIVE FROM ECONOMIC LENS
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
This study examines the influence of the fourth industrial revolution on global and national economies by considering the case of Hungary, Canada and Poland. The research compares local logistic business to gain insight about the implementation of Industry 4.0 practices through exploring existing limited knowledge, preparing staff for challenges, implementation barriers, recognizing potentials and implications of Industry 4.0. Using mixed sampling strategies, we gathered data from 180 logistic enterprises (60 each in considered economies) and established the multi-predictors to investigate the relationship between Industry 4.0 technologies and performance of enterprises. Results revealed that all considered predictors are statistically significant in affecting the impact of Industry 4.0 technologies on the performance of enterprises in all three economies. However, the magnitude of impact differs to some extent. The authors propose recommendations for implications of Industry 4.0 technologies.
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 | 0.000 |
| 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