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Record W2029824419 · doi:10.1108/00483480810850542

An examination of human resource management practices in Iranian public sector

2008· article· en· W2029824419 on OpenAlexaff
Hamid Yeganeh, Zhan Su

Bibliographic record

VenuePersonnel Review · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsExpatriatePublic sectorPerformance appraisalOriginalityHuman resource managementStaffingContext (archaeology)BusinessPrivate sectorValue (mathematics)Public relationsMarketingTraining and developmentHuman resourcesJob securitySeniorityQualitative researchPolitical scienceManagementSociologyEconomicsEconomic growthSocial science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.077
GPT teacher head0.289
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations106
Published2008
Admission routes1
Has abstractyes

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