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Record W3021202466 · doi:10.1504/ijewe.2019.10029242

Employee perceptions of an expanded form of corporate social responsibility in predicting job attitudes and turnover intentions

2019· article· en· W3021202466 on OpenAlexaff
Paul Fairlie, Oxana Svergun

Bibliographic record

VenueInternational Journal of Environment Workplace and Employment · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsGeorge Brown College
Fundersnot available
KeywordsPerceptionStructural equation modelingNomological networkCorporate social responsibilityPsychologyTurnoverJob satisfactionEmployee engagementSocial psychologyTurnover intentionWork engagementWork (physics)BusinessPublic relationsManagementEconomicsPolitical science

Abstract

fetched live from OpenAlex

Multiple studies suggest that employee perceptions of CSR positively influence employee attitudes and behaviour as well as organisational performance. Despite existing research in this area, there remains an opportunity to expand our knowledge and achieve a better understanding of the interconnections among these variables. The present study involved measuring employee perceptions of an expanded form of CSR that is proactive, discretionary, and focused on positive impacts, as well as reactive, mandatory, and aimed at avoiding negative impacts. These perceptions were examined through structural equation modelling (SEM) in a larger, nomological net of employee variables, and were found to have positive effects on job satisfaction, organisational commitment, and work engagement, as well as negative effects on turnover intentions. As an aside, older employees, relative to younger employees, reported more positive CSR perceptions, higher levels of work engagement, and lower levels of turnover intentions.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.025
GPT teacher head0.278
Teacher spread0.253 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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