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Record W4226313397 · doi:10.5267/j.ijdns.2022.3.006

The effect of electronic human resources practices on employee satisfaction in private hospitals

2022· article· en· W4226313397 on OpenAlexvenueno aff
Fatima Lahcen Yachou Aityassine, Abedalsttar Mustafa Yousef Alsayaha, Mahmoud Mohammad Al-Ajlouni

Bibliographic record

VenueInternational Journal of Data and Network Science · 2022
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsIBMHuman resourcesSample (material)Human resource managementJob satisfactionTest (biology)BusinessPath analysis (statistics)PopulationKnowledge managementPsychologyManagementMedicineComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

The objective of the study was to determine the impact of electronic human resources practices on Employees Satisfaction in private hospitals in Jordan. The dimensions of electronic human resources practices are (e-recruitment, e-training, e- performance evaluation, e-rewards system, and e-communication). Data were analyzed using IBM SPSS and AMOS software. The population of the study involves all the physicians working in private hospitals in Jordan. Data were primarily gathered through self-reported questionnaires created by Google Forms which were distributed to a purposive sample of physicians via email. To achieve the objectives of the study and test hypotheses, the researcher used SPSS and path analysis. The study results showed that there is a statistically significant impact of electronic human resources practices on employee’s satisfaction. Considering study finding, the researcher recommends decision makers to provide the largest possible investment in modern technology, and to subscribe to databases that qualify doctors to practice electronic human resource management dimensions that were mentioned in the study and ensuring doctors’ job stability.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0030.001
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 head0.013
GPT teacher head0.303
Teacher spread0.291 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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