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Record W3169702599

Enhancing Organizational Performance: The Mediating Role of Human Resource Management

2018· article· en· W3169702599 on OpenAlexaff
Steven H. Appelbaum, Zakaria Oulbacha, Barbara T. Shapiro

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsConcordia University
Fundersnot available
KeywordsOrganizational behavior and human resourcesOrganizational performanceHuman resource managementKnowledge managementEmpirical researchOrganizational commitmentBusinessOrganizational effectivenessOrganizational behavior managementOrganizational learningOrder (exchange)PsychologyPublic relationsPolitical scienceComputer scienceEpistemology
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this article is to investigate the mediating role of human resource management to enhance organizational performance. A thorough review of scholarly articles and empirical evidence was conducted in order to bring to light the current and mediating role of human resource management in enhancing organizational performance. Even though the majority of scholars advocate that HRM practices have a direct and often positive impact on organizational performance, there is another stream of scholars who argue that HRM practices may have no impact, or at best an indirect effect on organizational performance. This article attempts to gather the obtained published empirical material, and clarify of the various views and findings formalized thus far including conclusions and recommendations for this critical issue.

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)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.004
GPT teacher head0.208
Teacher spread0.203 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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