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PERAN KETERLIBATAN KARYAWAN PADA HUBUNGAN REWARDS DAN INTERNAL COMMUNICATION DENGAN ORGANIZATIONAL CITIZENSHIP BEHAVIOUR

2018· article· en· W2953004755 on OpenAlexaff
I Made Dena Julio Mahendra Saputra, Putu Saroyini Piartrini

Bibliographic record

VenueE-Jurnal Manajemen Universitas Udayana · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEmployee Performance and Leadership
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsOrganizational citizenship behaviorInternal communicationsPsychologyCitizenshipSample (material)Data collectionSocial psychologyOrganizational communicationOrganizational commitmentBusinessBusiness administrationPublic relationsMarketingPolitical scienceSociology

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the effect of mediating employee involvement on rewards relationships and internal communication with organizational citizenship behavior. This research was conducted at Akana Boutique Hotel. The number of samples taken was 50 employees, using saturated sample techniques. Data collection is done through a questionnaire. The analysis technique used is simple linear regression. Based on the results of the analysis found that 1). Rewards and internal communication have a positive and significant effect on employee engagement, 2). Rewards, internal communication and employee involvement have a positive and significant effect on organizational citizenship behavior, 3). Employee involvement mediates the effects of rewards and internal communication on organizational citizenship behavior, the higher rewards, internal communication and employee involvement, the organizational citizenship behavior will also increase in Akana Boutique Hotel companies. Keywords: rewards, internal communication, employee involvement, organizational citizenship behavior

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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.020
GPT teacher head0.220
Teacher spread0.200 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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