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Record W3034674477 · doi:10.5267/j.msl.2020.6.019

Human resource management practices and person-organization fit towards nurses’ job satisfaction

2020· article· en· W3034674477 on OpenAlexvenueno aff
Saleh Amarneh, Rajendran Muthuveloo

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsJob satisfactionHuman resource managementPsychologyKnowledge managementBusinessHuman resourcesApplied psychologySocial psychologyManagementComputer science

Abstract

fetched live from OpenAlex

This empirical research aims to investigate the relationship between Human Resource Management (HRM) practices, Person-Organization (P-O) fit and Job Satisfaction (JS) from the nurses’ perspective working in Private Hospitals in Jordan. This study examines the individual effect of HRM practices i.e. Recruitment and Selection (RS); Training and Development (TD); and Performance Appraisal (PA) on P-O fit and JS. In addition, it also examines the mediating effect of P-O fit on the relationship between HRM practices and JS. Total of 274 responses were collected directly using structured questionnaire via convenience sampling technique. The result was analyzed with PLS-SEM. Findings indicate that RS and PA were found to have significant effect on P-O fit. TD did not have any significant effect on P-O fit. In addition, P-O fit also had significant effect on nurse’s JS. In terms of mediating effect, P-O fit only mediated the relationship between RS and PA toward JS. However, there was no mediation effect on the relationship between TD and JS. This study may provide an initial blueprint for further investigation of other theoretical nurses’ job satisfaction and turnover intention related models. Finally, the study findings work as evidence about the new realization among the Jordanian organizations about the importance of adaption the 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.262
Teacher spread0.234 · 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 teacher head, not a consensus.

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

Citations20
Published2020
Admission routes1
Has abstractyes

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