An examination of human resource management practices in Iranian public sector
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze HRM practices in Iran in view of underlying cultural, political and economic factors. Design/methodology/approach The paper is organized in three major parts. The first part deals with HRM concept and Iranian social context. The second part presents methodology and data analysis. The third part discusses results and illustrates HRM practices in Iranian organizations. The study involves in‐depth interviews with four Iranian managers and data collected from 82 respondents through Likert‐type questionnaires (n=82, rate of response=44 per cent). Findings The findings in the paper shed light on the main HRM functions in the Iranian public sector. Staffing is marked by pervasiveness of networking, entitlement, compliance with Islamic/revolutionary criteria and high job security. Compensation is described by features such as fixed pay, ascription/seniority‐based reward, and hierarchical pay structure. Training and development programs are found to be unplanned and spontaneous. Finally, the paper shows that the appraisal function receives little attention and tends to be based on subjective and behavioral criteria. Research limitations/implications The paper shows that the study is limited in terms of HRM functions, sector and sample size. Further research may make comparison between large/state‐owned and small/private organizations. Practical implications The findings in the paper might be valuable for MNEs, NGOs, international negotiators, expatriate managers, investors and those who are concerned with this part of the world. Originality/value The paper presents a convenient approach in assessing HRM variations. The combination of qualitative and quantitative data provides a thick description of HRM enriched by secondary data and previous research. Given some commonalities between Iran and other developing countries, the findings might be of potential interest in comparative studies dealing with management transferability.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".