Employee human resource management values: validation of a new concept and scale
Bibliographic record
Abstract
Purpose: Although human resource management (HRM) practices all seek to support and improve organizational functioning, the value ascribed to various HRM practices differs greatly among employees. Drawing on an exhaustive measure of HRM practices, this study proposed a new conceptualization and measure of HRM values, the HRM Values Scale (HRM-VS). Design/methodology/approach: To examine the psychometric properties of scores obtained on this new measure, we rely on a sample of 979 employees occupying a variety of jobs within various private and public organizations. Findings: Through the comparison of confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM) solutions, our results supported a nine-factor structure of participants' responses to the HRM-VS and the measurement invariance of this solution across male and female employees. Specifically, they support that the HRM-VS items adequately capture core HRM values underlying independent HRM practices. Criterion-related validity was evidenced with respect to employees' ratings of intrinsic and extrinsic job satisfaction. Research implications: The HRM-VS appears to represent a promising tool for research and intervention seeking to account for individual differences in the relative importance of various HRM practices, in order to devise more effective HRM systems. Practical implications: This new concise but complete measure could help better guide organizations in tailoring their strategic HRM. Originality/value: This study introduces HRM values as a valid concept that characterizes what employees desire or consider to be important in relation to HRM practices.
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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".