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Record W2785497986 · doi:10.6000/1929-7092.2017.06.48

The Impact of Human Resource Management Practices, Organizational Culture, Motivation and Knowledge Management on Job Performance with Leadership Style as Moderating Variable in the Jordanian Commercial Banks Sector

2018· article· en· W2785497986 on OpenAlexvenueno aff
Ghaith Abdul raheem raheem Ali Alsheikh, Enas Ali Theeb Alnawafleh, Mutia Sobihah Binti Abd Halim, Abdul Malek Bin A. Tambi

Bibliographic record

VenueJournal of Reviews on Global Economics · 2018
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessHuman resource managementModerationOrganizational cultureLeadership styleBusiness administrationManagement stylesHuman resourcesOrganizational commitmentKnowledge managementManagementPsychologyEconomicsSocial psychologyComputer science

Abstract

fetched live from OpenAlex

Abstract: In this paper, the effect of three human resource management practices namely, compensation and benefits, training and development, and performance appraisal and achievement was examined along with organizational culture, motivation and knowledge management on job performance among Jordanian banks. The construct of job performance was measured by the combination of task performance and organizational citizenship behavior. The study employed convenience sampling to which 30 questionnaires were distributed. The finding showed a significant impact of human resource management practices (compensation and benefits, training and development, and appraisal and achievement), organizational culture, motivation and knowledge management on job performance in the Jordanian banks. On the basis of the findings, the researcher provided recommendations for the banks in terms of strengthening the relationship between their knowledge management job performance. The study also provided implications to theory and practice based on the findings.Â

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

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

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 head0.053
GPT teacher head0.295
Teacher spread0.242 · 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.

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

Citations18
Published2018
Admission routes1
Has abstractyes

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