Human resources management 4.0: Literature review and trends
Bibliographic record
Abstract
Digitalization across a range of industry and service sectors is transforming the workplace and human resources. The adoption of disruptive technologies associated with the Fourth Industrial Revolution, also known as Industry 4.0, is reshaping the way people work, learn, lead, manage, recruit, and interact with each other. The aim of this study is to contribute to the theoretical development of human resource management (HRM) in the context of Industry 4.0, promoting directions for the sector and HRM professionals, organizations, and the workforce that are required to face the challenges of Industry 4.0. To achieve this objective, this study performs a systematic literature review and content analysis of 93 papers from 75 journals. The main results of the research show that digital trends resulting from Industry 4.0 affect the field of HRM in 13 different themes, promoting trends and challenges for HRM, the workforce, and organizations. This paper seeks to promote insights for studies on the interference of digitalization in HR for the evolution of the digital age, as well as for companies that want to become more productive, human, and digital.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.036 | 0.050 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".