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Record W2967413731 · doi:10.1111/apps.12217

Supportive Organizations, Work–Family Enrichment, and Job Burnout in Low and High Humane Orientation Cultures

2019· article· en· W2967413731 on OpenAlexafffund
Ariane Ollier‐Malaterre, Jarrod Haar, Albert Sunyer Torrents, Marcello Russo

Bibliographic record

VenueApplied Psychology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsBurnoutPsychologyWork–family conflictSocial psychologyGlobeSample (material)MediationKindnessAltruism (biology)Work (physics)Clinical psychologySociologyPolitical science

Abstract

fetched live from OpenAlex

The present study draws on the work–family and cross‐national management literature to examine the relationships between Family‐Supportive Organizational Perceptions (FSOP), work–family enrichment, and job burnout across five countries with different cultural backgrounds: Malaysia, New Zealand, France, Italy, and Spain. Using a combined sample of 980 employees, we find support for a partial mediation model in which FSOP is positively associated with work–family enrichment, which in turn is negatively related to job burnout. Given our focus on support, we test the moderating role of the cultural value humane orientation, that is, the extent to which a society values altruism, kindness, and compassion. The five countries in our sample offer variation in their country‐level scores as determined by the GLOBE study (House et al., 2004). We found that individuals from cultures that scored higher in “as is” humane orientation (i.e., scores for actual practices) experienced lower job burnout when FSOP increased. This pattern was reversed when considering “should be” humane orientation (i.e., scores for ideal values). The implications for the work–family and the cross‐national management literature, and for practice, are discussed.

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 score0.087
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.313
Teacher spread0.303 · 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

Citations24
Published2019
Admission routes2
Has abstractyes

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