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Job Type and Reactions to Men’s and Women’s Performance and Altruistic OCB: Does Culture Matter?

2025· article· en· W4416002039 on OpenAlexaff
Kubilay Gok, Mahfooz A. Ansari, Salvador Barragan, Rehana Aafaqi, Maria Cristina Ferreira, David Iremadze, Do‐Yeong Kim, Rocío Martínez Jiménez, Sebastián Steizel, Ricky Yoon, Anthony Sadler

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsThompson Rivers UniversityUniversity of Lethbridge
Fundersnot available
KeywordsCollectivismAltruism (biology)Job performanceIndividualismAffect (linguistics)Hofstede's cultural dimensions theory

Abstract

fetched live from OpenAlex

We examined the effects of job type and target gender on observers' reactions to men’s and women’s job performance and altruistic citizenship behavior in the cultural context. A total of 2,642 undergraduate business students from 11 countries responded to experimental vignettes. In a 2 × 2 × 2 between-participants factorial design, the analysis revealed that controlling for respondents’ gender and job type significantly differed in the reactions to altruistic citizenship behavior: participants considered this behavior more optional for the masculine job type than the feminine job type. In addition, country culture made a significant difference in both job performance and altruistic behaviors. Job performance and altruistic behavior were required significantly more in vertical collectivist cultures than horizontal individualist cultures. The target's gender did not affect either behavior. Directions for future research and implications of the findings for those in leadership roles are suggested. Key Words: Gender roles, cross-cultural study, OCB expectations, gender typed jobs, experiment

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)0.005
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
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.325
Teacher spread0.300 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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