Redefining digital competencies of managers in the Industry 5.0: evidence from the Brazilian automotive sector
Bibliographic record
Abstract
Disruptive technologies change production processes and redefine professional roles. Above the technological aspects and aligned with the human-centred challenge implied by Industry 5.0, managers play an integrative role applying organisation's technological knowledge for better results. However, the competencies of managers are little explored in industrial contexts undergoing technological transformation. Previous research has not indicated what competencies managers should focus according to their roles. Filling this gap, this research aims to assess and discuss competency’ profiles for top and middle managers facing the technological transformation. The Brazilian automotive sector, representative as a global productivity benchmark, is explored. A multicriteria analysis by Analytic Hierarchy Process (AHP) is applied to evaluate a structure of competencies. The sample is based on the judgments of 109 managers from 31 automotive companies, including 11 automakers and 20 direct suppliers. Focus group discussions are integrated to elucidate the results. Findings indicate motivational, managerial, technical and social competencies in different levels of relevance for top and middle managers from automakers and suppliers. This paper contributes to the literature by indicating how competency’ profiles progress according to the managerial role, productive context and technology implementation stages. This research provides guidelines implications for the strategic development of industrial managers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".