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PENGARUH IKLIM ORGANISASI TERHADAP KEPUASAN KERJA YANG BERDAMPAK PADA LOYALITAS KARYAWAN PT TELKOM AKSES WILAYAH TELKOM SURABAYA UTARA

2023· article· en· W4385224685 on OpenAlexaff
Tri Jatmiko, Handy Aribowo, Iswati Iswati

Bibliographic record

VenueJURNAL EKSEKUTIF · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsLoyaltyBusiness administrationBusinessJob satisfactionPopulationOrganisation climatePsychologyMarketingPolitical sciencePublic relationsMedicineEnvironmental healthSocial psychology

Abstract

fetched live from OpenAlex

This study aims to analyze and determine the effect of Organizational Climate and Job Satisfaction on Employee Loyalty of PT Telkom Access Telkom North Surabaya Region. This study uses quantitative research with a population of all employees of PT Telkom Access Telkom North Surabaya. Sampling is in accordance with Ferdinand's opinion, so that the research sample obtained is 120 respondents. Using multiple linear regression analysis with t test as a hypothesis test. The results showed that Organizational climate had a significant effect on Employee Satisfaction at PT Telkom Access Telkom North Surabaya with a magnitude of 0.433 or 43.3% with a positive direction of influence, Organizational climate had a significant effect on employee loyalty at PT Telkom Access Telkom North Surabaya with a magnitude of influence of 0.308 or 30.8% with a positive direction of influence. Employee Satisfaction has a significant effect on Employee Loyalty at PT Telkom Access Telkom North Surabaya with a magnitude of influence of 0.274 or 27.4% with a positive direction of influence Keywords: Organizational Climate, Employee Satisfaction, dan Employee Loyalty

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)0.000
metaresearch head score (Gemma)0.000
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 score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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 head0.033
GPT teacher head0.317
Teacher spread0.284 · 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
Published2023
Admission routes1
Has abstractyes

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