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Record W2768513104 · doi:10.5539/jsd.v10n6p51

Organizational Commitment and Rewards in Malaysia, with Comparison between University Graduates and Others

2017· article· en· W2768513104 on OpenAlexvenueno aff
Keisuke Kokubun

Bibliographic record

VenueJournal of Sustainable Development · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAutonomySocial psychologyCLARITYChinaMultilevel modelBusiness administrationBusinessDemographic economicsPolitical scienceEconomics

Abstract

fetched live from OpenAlex

This study investigates the relationship between rewards and organizational commitment (OC) of 12,076 employees who work for 32 Japanese manufacturing companies in Malaysia. Hierarchical regression analysis revealed that all of three types of reward are important for OC, in the order of intrinsic, social and extrinsic rewards. These findings suggest that the antecedents of OC in Malaysia are different from those in the West or other lower income Asian countries such as China. The comparison between University graduates and others showed that extrinsic and intrinsic rewards had stronger while social rewards had weaker influence on OC in university graduates than in others. In detail: satisfaction with personnel evaluation and autonomy were more strongly correlated with OC in university graduates while co-worker support and role clarity were more significantly correlated with OC in others; fatigue was negatively correlated with OC in university graduates while positively correlated with OC in others; other rewards, i.e., satisfaction with other treatments, supervisor support and training, were equally correlated with OC in university graduates and others. Discussions and implications concerning human resource management of Japanese companies in Malaysia are offered.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.223
Teacher spread0.209 · 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
Published2017
Admission routes1
Has abstractyes

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