MétaCan
Menu
Back to cohort
Record W4286203088 · doi:10.3390/jrfm15070316

The Effects of ESG Activity Recognition of Corporate Employees on Job Performance: The Case of South Korea

2022· article· en· W4286203088 on OpenAlexvenueno aff
Minsuck Jin, Boyoung Kim

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
Fundersnot available
KeywordsMediationBusinessOrganizational cultureCorporate governanceStructural equation modelingJob performanceAffect (linguistics)Knowledge managementMarketingPublic relationsJob satisfactionPsychologyComputer scienceSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Corporate environment, society, and governance (ESG) management activities have recently been consolidated in the business ecosystem, and many firms are considering their employees’ recognition and job changes according to organizational ESG strategy. This study aims to verify the effects of ESG activity recognition of corporate employees on job performance by mediating change support behavior, innovative organization culture, and job crafting. This study designs a structural equation model with a hypotheses based on previous studies. A questionnaire survey was carried out targeting large Korean manufacturing companies, and an analysis of 329 response copies was performed. As a result, ESG activity recognition did not directly affect job crafting, but it affected job crafting with the mediation of innovative organizational culture and change support behavior. ESG activity recognition also positively affected job crafting and job performance by mediating change support behavior and an innovative organization culture. Hence, the research shows that an innovative culture and change support behavior within an organization should be considered to improve ESG management performance.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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

Citations38
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of risk and financial managementSame topicEnvironmental Sustainability in BusinessFrench-language works237,207