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Record W4391060464 · doi:10.5267/j.uscm.2023.11.019

The effectiveness of human resource management practices on increasing organizational performance and the mediating effect of employee engagement

2024· article· en· W4391060464 on OpenAlexvenueno aff
Asaad Alsakarneh, Hisham Ali Shatnawi, Wael Alhyasat, Fauzi Zowid, Ro’aa Adnan Mustafa Alrababah, Bilal Eneizan

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPerformance appraisalEmployee engagementBusinessHuman resource managementTraining and developmentOrganizational performanceHuman resourcesLoyaltyKnowledge managementEmployee researchCompensation (psychology)Performance managementEmployee resource groupsOrganizational behavior and human resourcesMarketingPublic relationsManagementPsychologyComputer science

Abstract

fetched live from OpenAlex

Enhancing employee loyalty to the company is essential to maximize corporate ability and achieve respective goals, as employees are the most valuable resource. Hence, managing human resources in organizations is key to achieving contemporary business success. The current study aims to assess the impact of human resource management practices (HRM) on organizational performance with employee engagement as a potential mediator. This study was conducted on Jordanian tourism projects. A total of 300 questionnaires were distributed with 237 returned. The data were analyzed through the partial least squares (PLS) software. Resultantly, performance appraisal and employee engagement significantly and positively impacted organizational performance. Recruitment and selection, training and development, compensation, and performance appraisal also significantly and positively impacted employee engagement. Employee engagement significantly mediated the impact of performance appraisal on recruitment, selection, and compensation with organizational performance.

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)0.009
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.885
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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 designTheoretical or conceptual
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

Citations16
Published2024
Admission routes1
Has abstractyes

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