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Record W3205413110 · doi:10.5539/ijef.v13n11p70

Influence of Motivational Human Resource Management Practices on Employee Role Behavior

2021· article· en· W3205413110 on OpenAlexvenueno aff
Chao Ling, Fuangfa Amponstira

Bibliographic record

VenueInternational Journal of Economics and Finance · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsnot available
Fundersnot available
KeywordsHuman resource managementEmbeddednessPsychologyKnowledge managementPerspective (graphical)Organizational behavior and human resourcesMultilevel modelExploratory factor analysisJob embeddednessHuman resourcesResource (disambiguation)Social psychologyManagementOrganizational commitmentComputer scienceSociologyEconomicsDevelopmental psychology

Abstract

fetched live from OpenAlex

Based on the perspective of social network, the study of motivational human resource management practice on employee behavior has far-reaching significance for theory and reality. This paper explores the relationship between the two dimensions of motivational human resource management practice and employee role behavior. Mainly use statistical software such as SPSS, AMOS, and HLM to conduct exploratory factor analysis, correlation analysis, regression analysis, and mediating effect analysis of data. It shows Motivational Human Resource Management Practices (MHRMP) is positively correlated with employee behavior; MHRMP is positively correlated with job embeddedness; Job embeddedness mediates the relationship between MHRMP and employee behavior. This paper adopts a cross-level research method, the research level is from the organizational level to the individual level. The discussion of motivational human resource management practice and employee individual behaviors can also provide references for the construction of cooperative human resource practices.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.318
Teacher spread0.287 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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