Human resources and Industry 4.0: an exploratory study in the Brazilian business context
Bibliographic record
Abstract
Purpose The present study aimed to evaluate how Brazilian companies from different sectors are developing human resources practices in the context of Industry 4.0 and which of these practices allows better differentiate of companies. Design/methodology/approach After a systematic literature review to identify the most important human resources practices in the context of Industry 4.0, a survey with professionals from human resources area of companies operating in Brazil was carried out. Data analysis was performed through frequency evaluation and CRITIC method (Criteria Importance Through Intercriteria Correlation). CRITIC method was used to identify the practices that best differentiate the studied companies. Findings The analysed companies are in different evolutionary stages regarding how human resources management practices are adapting to the Industry 4.0 context. Few companies have presented reliable results to better support the transition process. Practices related to evaluating employee performance in this context, estimating the needs of financial resources and time for the training required by Industry 4.0 and establishing systems to recognise talents among employees who already work for the company are the practices that best differentiate companies. Originality/value There are few studies on this topic for Brazilian context. The information presented in this article can be useful for professionals and researchers.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".