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The Impacts of Internal/External CSR on Job Applicants and the Moderating Role of Exchange Ideology

2025· article· en· W4416001909 on OpenAlexaff
Jianan Li, Jie Li, Jiangjin Li

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEmployer Branding and e-HRM
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsCorporate social responsibilityModerationIdeologyEconomic JusticeEmpathySocial exchange theoryProsocial behaviorOrganizational justice

Abstract

fetched live from OpenAlex

Although prior research has shown the positive effects of corporate social responsibility (CSR) on job applicants’ seeking intentions with an organization, it has not thoroughly examined whether these effects vary with different types of CSR practices or how individual applicants respond differently. This study aims to advance this stream of research by dividing CSR practices into two categories—internal and external CSR—and examining their distinct influences on applicants’ job-seeking intentions. Drawing on signaling theory and moral principle theory, we propose that internal CSR influences applicants’ job-seeking intentions by influencing their perceived justice within an organization, while the effect of external CSR operates through job applicants’ perceived empathy of an organization. Moreover, we introduce job applicants’ exchange ideology as a moderator that can further differentiate the effects of internal and external CSR. The findings from two experimental studies largely supported our hypotheses. In particular, we found that internal and external CSR practices influenced job applicants’ job-seeking intentions via perceived justice and empathy, respectively. Moreover, our results showed that an applicant’s exchange ideology strengthened the effect of external CSR on job-seeking intentions through empathy, but did not moderate the effect of internal CSR.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.413

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 science0.0010.001
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 head0.011
GPT teacher head0.247
Teacher spread0.237 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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