MétaCan
Menu
← Back to cohort
Record W2891756249

Exploring Job Satisfaction of IT Workers in Taiwan, Japan, and China: The Role of Employee Demographics, Job Demographics, and Uncertainty Avoidance

2020· article· en· W2891756249 on OpenAlexaff
Benjamin Yeo, Alexander Serenko, Prashant Palvia, Osam Sato, Hiroshi Sasaki, Jie Yu, Yue Guo

Bibliographic record

VenueResearch Portal (King's College London) · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsJob satisfactionChinaJob attitudeDemographicsContext (archaeology)Job performancePersonnel psychologyJob designPsychologyBusinessDemographic economicsApplied psychologySocial psychologyDemographyGeographyEconomicsSociology
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study is to explore the drivers of job satisfaction among IT workers in the East Asian context, particularly in Taiwan, Japan, and China. Using data collected from IT workers, decision tree analysis was employed to identify the predictors of job satisfaction. Results indicate that the level of education has no effect on job satisfaction. In Taiwan and Japan, higher uncertainty avoidance results in higher job satisfaction, and more experienced IT workers appear to be less satisfied. As Taiwanese and Japanese IT workers get older, they are likely to hold senior positions, spend more time on the job, and become increasingly dissatisfied with their jobs. The effect of uncertainty avoidance is less clear in China. The job role and industry matter only in China. Thus, management efforts to enhance job satisfaction among IT workers in China may be tailored towards specific industries and job roles.

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 score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.049
GPT teacher head0.278
Teacher spread0.229 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueResearch Portal (King's College London)→Same topicJob Satisfaction and Organizational Behavior→French-language works237,207→