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The Enactment of One’s Calling for Job Performance in Organizations: A Moderated Mediation Model

2013· article· en· W1995785778 on OpenAlexaff
Sung Soo Kim, Dong-Hoon Shin

Bibliographic record

VenueAcademy of Management Proceedings · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Spirituality and Leadership
Canadian institutionsMcGill University
Fundersnot available
KeywordsJob satisfactionModerationJob attitudeSocial psychologyIdeologyPsychologyJob performanceMediationJob designAffective events theoryContextual performanceModerated mediationCore self-evaluationsPerceived organizational supportPersonnel psychologyOrganizational commitmentAffect (linguistics)Empirical researchPublic relationsPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Calling research highlights that a sense of calling is a powerful source of job satisfaction and employee motivation. However, empirical evidence is scarce as to whether one’s calling is ultimately related to job performance. This study addresses this gap in knowledge by examining how and when one’s calling is related to perceived job performance. Our model involves job satisfaction as a mediating process and organizational support for professional ideology as a moderator. Results from a study of 185 journalists confirmed that people’s callings affect job satisfaction, which in turn, interact with organizational support for professional ideology, such that job satisfaction is positively related to perceived job performance when employees perceive that their professional ideology is valued in their organization. These findings contribute to the calling literature by demonstrating the link between calling and perceived job performance, and by illuminating the mitigating condition for the link in terms of organizational support for professional ideology.

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)0.011
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.004
Open science0.0030.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.052
GPT teacher head0.301
Teacher spread0.249 · 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 designSimulation or modeling
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
Published2013
Admission routes1
Has abstractyes

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Same venueAcademy of Management ProceedingsSame topicWorkplace Spirituality and LeadershipFrench-language works237,207