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Record W2620706065

The Effect of Work-Home Conflict on IT Employees in Japan: The Moderating Role of Conscientiousness

2017· article· en· W2620706065 on OpenAlexaff
Alexander Serenko, Osam Sato, Prashant Palvia, Aykut Hamit Turan, Hiroshi Sasaki

Bibliographic record

VenueJournal of the Association for Information Systems · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsLakehead University
Fundersnot available
KeywordsConscientiousnessPsychologySocial psychologyWork (physics)PersonalityBig Five personality traitsEngineeringExtraversion and introversion
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study is to propose and test a model explicating the effect of work-home conflict on job satisfaction and professional self-efficacy of IT employees in Japan. Conscientiousness was included as a moderator of the relationships above. The model was subjected to structural equation modelling analysis by using the data collected from 312 Japanese IT employees. The results indicate that work-home conflict has a negative impact on both job satisfaction and professional self-efficacy. The negative effect of work-home conflict on job satisfaction is stronger for those who exhibit a higher degree of conscientiousness. In contrast to expectations, conscientiousness does not moderate the negative relationship between work-home conflict and professional self-efficacy of IT employees. Managers should be aware of the negative consequences of work-home conflict because it reduces their employees’ degree of job satisfaction and professional self-efficacy. They should also pay more attention to highly conscientious employees.

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)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.121
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.018
GPT teacher head0.297
Teacher spread0.279 · 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 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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