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Record W3021627675 · doi:10.5267/j.msl.2020.4.012

The effect of talent management on organizational performance improvement: The mediating role of organizational commitment

2020· article· en· W3021627675 on OpenAlexvenueno aff
Mohammad Fathi Almaaitah, Yousef Alsafadi, Shadi Altahat, Ahmad mohmad Yousfi

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsnot available
Fundersnot available
KeywordsOrganizational commitmentKnowledge managementBusinessPsychologyOrganizational behavior and human resourcesOrganizational learningProcess managementSocial psychologyComputer science

Abstract

fetched live from OpenAlex

This study investigates the effects of talent management (TM) of human resources on organizational performance improvement. The study is accomplished through hypothesizing the effect of talent management on organizational performance. Organizational commitment is theorized to be a mediating factor for this relationship. In addition, the model considers transformational leadership style as a potential moderating factor. Data was collected from 385 Jordanian hotel employees using questionnaires and then analyzed using structural equation modeling (SEM). The results demonstrate the positive impact of talent management (TM) on organizational performance, effective continuance and normative commitment. It is also shown that effective continuance and normative commitment played a mediating role. Finally, Transformational leadership style is proven to be a moderating variable with an effect on talent management and organizational performance. The findings show the significance of the role that organizational commitment plays in achieving human resources performance goals.

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)0.008
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.224
Teacher spread0.219 · 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

Citations61
Published2020
Admission routes1
Has abstractyes

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