How to measure employees’ interests so as to be a more socially-responsible employer: A proposal of a new scale and its validation
Bibliographic record
Abstract
Research background: Many authors emphasize that successful human resource management (HRM) practices align with employees’ needs associated with the construct of employees’ interests. In particular, the importance of considering employees’ interests is emphasized in the process of shaping the architecture of Socially Responsible Human Resource Management (SR-HRM) systems. Purpose of the article: The aim of the article is to contribute to understanding employees’ interests by designing and validating a measure to recognize these interests. Methods: Through the use of literature sources and expert opinions, the authors developed a list of employee interests. Empirical data collected via the survey method in Poland was used to statistically verify the measurement scale. In particular, exploratory factor analysis and exploratory structural equation modelling were applied. Findings & value added: This article shows that it is important to create a comprehensive list of interests, as well as validate the research tool used. The newly developed scale has 22 items and five dimensions: support and development at the level of the enterprise, employee participation, support and development at the departmental level, employment security, working conditions and remuneration. It may be used in a variety of companies, as well as in complex research models, and developed further taking into consideration the context of other countries.
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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 | 0.031 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".