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Record W2797969745 · doi:10.1016/j.jbusres.2018.04.002

Family incivility, emotional exhaustion at work, and being a good soldier: The buffering roles of waypower and willpower

2018· article· en· W2797969745 on OpenAlexaff
Dirk De Clercq, Inam Ul Haq, Muhammad Umer Azeem, Usman Raja

Bibliographic record

VenueJournal of Business Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsBrock University
Fundersnot available
KeywordsEmotional exhaustionPsychologyIncivilitySocial psychologyOrganizational citizenship behaviorModerated mediationMediationWork–family conflictMechanism (biology)Work (physics)BurnoutOrganizational commitmentClinical psychologySociology

Abstract

fetched live from OpenAlex

This study unpacks the relationship between family incivility and organizational citizenship behavior (OCB), suggesting a mediating role of emotional exhaustion and moderating roles of waypower and willpower, two critical dimensions of hope. Three-wave data from employees and their peers in Pakistani organizations show that an important reason that family incivility diminishes OCB is that employees become emotionally overextended by their work. Employees' waypower and willpower buffer this harmful effect of family incivility on emotional exhaustion though, such that this effect is mitigated when the two personal resources are high. The study also reveals the presence of moderated mediation, such that the indirect effect of family incivility on OCB through emotional exhaustion is weaker for employees high in waypower and willpower. For organizations, this study accordingly identifies a key mechanism by which family adversity can undermine voluntary behaviors; this mechanism is less forceful among employees who are more hopeful though.

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)0.008
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.039
GPT teacher head0.299
Teacher spread0.260 · 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".

Quick stats

Citations72
Published2018
Admission routes1
Has abstractno

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