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Record W4384300055 · doi:10.1177/00076503231182665

Predisposed, Exposed, or Both? How Prosocial Motivation and CSR Education Are Related to Prospective Employees’ Desire for Social Impact in Work

2023· article· en· W4384300055 on OpenAlexaff
Ante Glavas, Tobias Hahn, David A. Jones, Chelsea R. Willness

Bibliographic record

VenueBusiness & Society · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsProsocial behaviorCorporate social responsibilityPsychologyLeverage (statistics)Social psychologySeekersTraitPublic relationsPolitical science

Abstract

fetched live from OpenAlex

Researchers have explored important questions about employees’ prosocial motivation to impact others through their work and about employees’ engagement in corporate social responsibility (CSR) initiatives. Studies show that job seekers are attracted to CSR-engaged employers, but little is known about whether and why prospective employees are attracted by job roles that allow them to have positive social impact. We used prosocial motivation theory to develop hypotheses about processes through which a greater desire for social impact in work is associated with being predisposed to it (due to trait-like prosocial motivation), being exposed to the possibility of it (through CSR-related educational choices), and both in partially mediated sequence. Analyses of data from 187 prospective employees provided support for most hypotheses. Our findings inform new directions for research on CSR and recruitment, the CSR education literature, and recruitment practices that leverage prospective employees’ desire for social impact through performing their regular work.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.287
Teacher spread0.257 · 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

Citations17
Published2023
Admission routes1
Has abstractyes

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