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Record W3129494176 · doi:10.5267/j.msl.2021.2.007

The effect of high-performance work systems according to AMO model on HRM performance outcomes: An analytical study on the banking sector

2021· article· en· W3129494176 on OpenAlexvenueno aff
Fatimah Mahdy, Asia Yagoub Alhadi

Bibliographic record

VenueManagement Science Letters · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersKing Khalid University
KeywordsHuman resource managementWork (physics)Work systemsHuman resourcesBusinessKnowledge managementHuman resource management systemSample (material)Resource management (computing)Resource (disambiguation)Operations managementManagementComputer scienceEngineeringEconomics

Abstract

fetched live from OpenAlex

This research paper aims to study the impact of high-performance work systems (HPWS) according to the AMO includes three human resource management practices which are (Ability-enhancing human resource management practices, motivation-enhancing human resource management practices, and opportunities -enhancing human resource management practices). The authors select a random sample of 400 individuals from 10 banks in Saudi Arabia, Egypt, Sudan to prove the validity of the hypotheses; The study found a set of results and one of them indicate that there is a significant positive relationship among high-performance work systems according to the AMO model on the results of human resource 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 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)0.004
metaresearch head score (Gemma)0.010
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 score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.021
GPT teacher head0.243
Teacher spread0.223 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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