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Record W2438771039 · doi:10.12735/jbm.v4i3p34

Relationship with Supervisor and Co-Workers, Psychological Condition and Employee Engagement in the Workplace

2015· article· en· W2438771039 on OpenAlexvenueno aff
Dorothea Wahyu Ariani

Bibliographic record

VenueJournal of Business & Management · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsnot available
Fundersnot available
KeywordsSupervisorEmployee engagementPsychologyEmployee resource groupsSocial psychologyApplied psychologyEmployee researchPublic relationsManagementOrganizational commitmentPolitical scienceEconomics

Abstract

fetched live from OpenAlex

This study aims to examine the relationship model of supervisor relations, co-worker relations, psychological conditions, and employee engagement. In particular, this study aims to test models of influence psychological conditions on employee engagement in the workplace. In addition, this study also aims to examine the influence of psychological condition variable as a mediator variable on the relationship between good relations with co-workers and supervisors and employee engagement. This research was conducted at the private companies in Yogyakarta, with a sample of 191 employees. Testing four models of the relationship is done by using structural equation modeling with AMOS program. Results of this study show that most models fit to the data. There is mediating model of psychological conditions on the relationship between supervisor relations, co-worker relations and employee engagement. This study confirmed previous research showing that psychological conditions as mediated variable between antecedents and employee engagement. A thorough discussion on the relationship among the variables as well as on self rating is presented in this paper.

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)0.005
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.087
GPT teacher head0.357
Teacher spread0.270 · 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

Citations40
Published2015
Admission routes1
Has abstractyes

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