Effect of Industry 4.0 on the relationship between socio-technical practices and workers' performance
Bibliographic record
Abstract
Purpose This paper aims to examine the moderating effect of Industry 4.0 (I4.0) technologies on the relationship between socio-technical (ST) practices and workers' health, quality and productivity performance. Design/methodology/approach In this paper, 192 practitioners from different manufacturing firms adopting I4.0 technologies were surveyed, analyzed the collected data using multivariate techniques and discussed the results in light of ST theory. Findings Findings indicate that I4.0 moderates the relationship between ST practices and performance, to an extent and direction that varied according to the focus of the technologies and practices adopted. Originality/value The I4.0 movement has triggered changes in the work organization at unprecedented rates, impacting firms' social and technical aspects. This study bridges a gap in the literature concerning the integration of I4.0 technologies into manufacturing firms adopting ST practices, enabling the verification of the moderating effects on workers' performance. Although previous studies have investigated that relationship, the moderating effect of I4.0 on performance is still underexplored, characterizing an important contribution of this research.
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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 | 0.005 | 0.018 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".