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Record W3086706171 · doi:10.5267/j.ac.2020.8.018

Comparative significance of human resource management practices on banking financial performance with analytic hierarchy process

2020· article· en· W3086706171 on OpenAlexvenueno aff
Quang Linh Huynh, Thanh Thuy Nguyen Thi, Tan Khuong Huynh, Tuyet Anh Duong Thi, Thuy Lan Le Thi

Bibliographic record

VenueAccounting · 2020
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
FundersTrường Đại học Trà Vinh
KeywordsAnalytic hierarchy processHierarchyHuman resource managementRobustness (evolution)Process (computing)Rank (graph theory)Knowledge managementHuman resourcesComputer scienceProcess managementBusinessMathematicsManagementEconomicsOperations research

Abstract

fetched live from OpenAlex

Banking financial performance (BFP) has been recognized as a causation of human resource management (HRM). The causal linkages from HRM practices to BFP are different. Nevertheless, almost none of the studies has ranked and compared this difference among the practices of HRM in enhancing BFP. The current study applied an analytic hierarchy process to rank the relative importance of HRM practices. For the robustness of the results from the process of analytic hierarchy, the current work employed an analytic hierarchy process to reassess the relatively important levels of HRM practices on BFP as well. The findings are robust across both of the techniques. The practice of training and development (TT) plays the most critical part in BFP, followed by the practice of performance evaluation (PN) and the practice of reward system (RM) as the third most important. In contrast, the practice of recruitment and selection (RN) takes the least important position in BFP. Moreover, the findings also provide statistical evidence on the causal links from the practices of HRM to BFP. This work makes some contribution to how managers should decide on HRM practices in order to obtain the best possible BFP.

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)0.000
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 score0.148
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.278
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

Citations10
Published2020
Admission routes1
Has abstractyes

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