Assessing the OECD Countries’ Industry 4.0 Maturity from Sustainable Development Goals’ Perspective: An Integrated PCA and DEA Approach
Bibliographic record
Abstract
Industry 4.0 (I4.0) technologies and relevant research initiatives have been at the focal point of sustainable industrial development initiatives. Adoption of these technologies require a maturity level to create sustainable economic, social, and environmental benefits to society. In this study, we investigated the I4.0 maturity in OECD countries. A two-phase methodology is proposed: principal component analysis (PCA) and data envelopment analysis (DEA). The main contribution of the study to the state-of-art is a statistically reliable analytical framework which yields I4.0 maturity score from relevant United Nations Sustainable Development Goals’ perspectives. Results indicate that the proposed two-phase method significantly reduces the potential multi-collinearity impacts on I4.0 maturity performance. Moreover, USA, Sweden, Finland, and Switzerland were found to the on the efficiency frontier in terms of I4.0 maturity whereas Turkey, Chile, Latvia, and Mexico were found to be in the lowest ranks which need substantial policy implementation to increase their digitalization efforts.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".