Internal integration within human resource management subsystems
Bibliographic record
Abstract
Purpose The purpose of the study is to develop a deeper understanding of the construct “integration within the HRM subsystem”. The study attempts to shed light on the conceptual perspective, the characteristics of this construct as well as the meaning and the mechanisms of internal integration within a HRM subsystem. Design/methodology/approach The procedure involves three main steps: first data reduction followed by data display and conclusion drawing/verification. Semi‐structured, face‐to‐face interviews with 21 vice‐president HRM managers and senior managers were conducted. The average time of the interviews was 60 minutes. Findings The findings revealed a model composed of HRM infrastructure (HRM cooperative policy, integrative core competence, and integrative technological infrastructure), internal communication process (formal and informal) and integrating process (consistency of HRM practices at the subsystem and individual levels). The first two categories are related with the dependent category‐integrating process. Practical implications HRM subsystems should develop their integrative technological infrastructure so that they can have a wide‐ranging view about their activities. Also, informal mechanisms may enhance the integrating process, as well as the formal mechanisms. Thus, managers should support and encourage the informal climate, and facilitate especially on informal communication. Originality/value The findings suggest a new approach for analyzing the integration process within an organizational HR sub‐system. On the one hand, the continuity of integration demonstrates how each category may contribute to the integration process on a high level. On the other, the low level of each category illustrates the opposite side of integration.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".