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Record W2078850365 · doi:10.1177/0007650314564783

Corporate Social Responsibility and Job Choice Intentions

2014· article· en· W2078850365 on OpenAlexaff
Cedric E. Dawkins, Dima Jamali, Charlotte M. Karam, Lianlian Lin, Jixin Zhao

Bibliographic record

VenueBusiness & Society · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCorporate social responsibilityTheory of planned behaviorSocial psychologyPsychologyNorm (philosophy)PerceptionVariance (accounting)Control (management)Job performanceJob attitudeJob satisfactionPublic relationsBusinessPolitical scienceManagementEconomics

Abstract

fetched live from OpenAlex

A theory of planned behavior (TPB) framework was employed to investigate the impact of corporate social responsibility (CSR) perceptions on the job choice intentions of American, Chinese, and Lebanese college students. Attitudes toward CSR, subjective norm, and perceived behavioral control explained moderate levels of the variance in job choice intention in all three countries. Attitudes toward CSR, which entailed individual evaluations of CSR, were positively related to job choice intentions among Lebanese and American respondents, but not Chinese respondents. Subjective norm, the importance accorded the views of significant others, was most strongly related to job choice intentions among Chinese respondents. Perceived behavioral control, the perceived degree of control over one’s actions and outcomes, had the strongest relationship to job choice intentions among American respondents. The authors concluded that respondents in the three countries did not differ in the extent to which they intend to work for socially responsible firms but tended to derive their intentions in different ways. Implications for tailoring CSR and recruitment efforts across countries are derived based on the findings.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.046
GPT teacher head0.268
Teacher spread0.222 · 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.

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

Citations53
Published2014
Admission routes1
Has abstractyes

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