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Record W3131046376 · doi:10.20381/ruor-24861

Evaluating the correlation between Innovation in HR practices and organizational performance using Shapley Value

2020· article· en· W3131046376 on OpenAlexaboutno aff
Mohammed El Mehdi Jeidane

Bibliographic record

VenueuO Research (University of Ottawa) · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicImpact of AI and Big Data on Business and Society
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)Shapley valueKnowledge managementBusinessIndustrial organizationComputer scienceEconomicsMicroeconomicsMathematicsStatistics

Abstract

fetched live from OpenAlex

Even though extensive research has been conducted on the potential that workers have on the productivity and profitability of a firm, not many scholars have based their findings and analysis with the use of the Shapley value tool. There is a possibility that Human Resource (HR) functions do have a positive impact on a firm's productivity, and the study aims at deducing both supporting and contrasting evidence concerning the same. The study below offers a detailed introduction of the status quo concerning Human Resource Management and offers a brief view of the papers and the objectives of the study. The literature review provides a detailed analysis of previous works building on the work of Huselid (1995) and others. The segment offers a preview of how other scholars felt and discovered the link between Human Resources practices and organizational performance. Most of the study is supportive of the idea that the Human Resources department and its practices correlate with the way a firm is performing, and it is mostly a positive one and that an improved Human Resources Management system would offer even better results. Little empirical data is availed by the review, which is why the paper details an investigation carried out in the field within the methodology and the data findings and analysis segment. The study collects data from about 160 participants, all working as employees or Human Resources officials in several companies within Canada. The findings are elaborated, and the discussions offer views based on the findings by the study. A conclusion offers a simplified summary of the research where Human Resources practices appear to correlate positively to organizational performance and, in some instances, negatively. The recommendations segment issues options of the steps the current Human Resources managers need to take to increase the efficiency of the department and the need for the installation of a strong and advanced innovatively and technologically Human Resources Management system. The study will also form a basis on which future researchers can base their studies.

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)0.015
metaresearch head score (Gemma)0.066
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 score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.066
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.634
GPT teacher head0.508
Teacher spread0.126 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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