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Record W2319013921 · doi:10.1016/j.burn.2016.03.002

The psychological costs of owning and managing an SME: Linking job stressors, occupational loneliness, entrepreneurial orientation, and burnout

2016· article· en· W2319013921 on OpenAlexafffund
Claude Fernet, Olivier Torrès, Stéphanie Austin, Josée St‐Pierre

Bibliographic record

VenueBurnout Research · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersUniversité du Québec à Trois-Rivières
KeywordsLonelinessEntrepreneurial orientationPsychologyModerated mediationBurnoutStressorPerspective (graphical)Occupational stressFeelingMediationSocial psychologyApplied psychologyBusinessEntrepreneurshipClinical psychologyFinanceSociology

Abstract

fetched live from OpenAlex

The aim of this study was to gain a deeper understanding of occupational stress in small-to-medium enterprise (SMEs) owner-managers by delving further into individual and contextual factors that make them vulnerable to burnout. From a relational perspective, the authors propose that job stressors related to SME management can predict burnout through the feeling of occupational loneliness, and that this indirect relationship is moderated by the entrepreneurial orientation of the owner-manager. The proposed moderated mediation model was supported by multiwave data collected from 377 owner-managers in France as well as its invariance across business size. The results showed that the conditional indirect effect of loneliness was stronger and significant when entrepreneurial orientation is low, but weaker and not significant when entrepreneurial orientation is high. This finding provides a starting point for further investigations of burnout in SME owner-managers, and more specifically, the complex pathways by which job stressors are related to burnout. © 2016 The Authors. Published by Elsevier GmbH.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.117
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.063
GPT teacher head0.368
Teacher spread0.305 · 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

Citations125
Published2016
Admission routes2
Has abstractyes

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