Mediating Effect of the Adoption of Industry 4.0 Technologies on the Relationship between Job Involvement and Job Performance of Millennials
Bibliographic record
Abstract
Despite recent interest in Industry 4.0, little is known about the relationship between job involvement and job performance of millennial workers in companies. The present study addresses this knowledge gap by exploring the mediation of the adoption of Industry 4.0 technologies (IND) between job involvement (INV) and job performance (PRF). Data was collected from 241 employees of large Canadian companies. The structural equation model was used to test the mediation effect of IND and the relationship between INV and PRF. Results based on this model (SEM) revealed differences by gender. It was found that in men, INV was positively related to PRF and that in women, INV was positively related to IND, although it was also evident that millennial employees showed egalitarian gender attitudes by strongly perceiving IND positively with PRF. Furthermore, IND fully measured the relationship between INV and PRF in manufacturing firms but not in service firms. Years of work experience was also found to affect the mediation effect of IND between INV and PRF, while it was not significant for education level. This study also highlights demographic criteria such as the age, income, and status of millennial employees. Implications of these findings are discussed, and useful insights are provided on new I4.0 approaches that improve industrial processes. This research contributes to developing the Theory of Planned Behaviour and proposes that managers use current continuous improvement approaches, human-centred and consistent with new I4.0 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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".