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Record W2788966425 · doi:10.5430/bmr.v7n1p51

Adoption of Human Resources Management Policies for Practices: Harvard Model versus Religious Model

2018· article· en· W2788966425 on OpenAlexvenueno aff
Oginni Babalola, Erigbe Patience, Ojo Afolabi, ‘Sola Laosebikan, ‘Femi Ogunlusi

Bibliographic record

VenueBusiness and Management Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHonestyHuman resource managementCompetence (human resources)ContingencySincerityInterdependenceMoralityBusinessManagementSociologyMarketingPublic relationsEconomicsPolitical scienceLawEpistemologySocial science

Abstract

fetched live from OpenAlex

The paper set out to explore two different models of Human Resources Management as a policy for practice that will be adequate for adoption by any organisation. The Harvard and Religious models were the two models critically examined vis – a – vis their implications on the practice of Human Resources Management (HRM). It was revealed that Harvard model of HRM is a content model as it is contingent on specific core issues (work system, reward system, employees’ influence and flow of people) in human resources management while Religious model of HRM is a process model as it is based on identification of relationship among components units (management and employees) Harvard model of HRM as a policy is embedded in the organisation through congruence, commitment, cost effectiveness and competence and Religious model of HRM is anchored on value based ideology through morality, honesty, sincerity, fairness and integrity. The two models are practicable but Harvard model of HRM has no exception to a particular party in business organisation while Religious model of HRM is averse to development of trade union in organisation. Therefore, the adoption of the two models will make world of work conducive, however, Harvard model of HRM aligned more with the nature and belief of business. However, combination of the two models to give a contingency – hybrid model will make the workplace to be better than adopting one of the models.

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)0.001
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 score0.702
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.117
GPT teacher head0.367
Teacher spread0.250 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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