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The Role of Career Resilience in the Stress Adjustment of Recently Started Entrepreneurs

2024· article· en· W4400442845 on OpenAlexaff
Ismail Elalaoui, Étienne St-Jean

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsResilience (materials science)Stress (linguistics)PsychologyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Entrepreneurship is considered one of the most demanding careers, and one in which entrepreneurs must endure periods of considerable stress, threatening their well-being, commitment and future in this career. Drawing on transactional theories of stress, the career resilience literature and the Broaden and build theory, we propose that entrepreneurial career resilience can reduce the impact of entrepreneurs' perceived stress on their job satisfaction and intention to stay in entrepreneurship. We tested our model with a moderate mediation effect using a four-wave longitudinal analysis on a sample of newly-started entrepreneurs. The results showed that entrepreneurs' perceived stress exerted a significant direct negative influence on their job satisfaction, and a significant indirect negative influence on their intention to stay in entrepreneurship (by decreasing satisfaction). We also found that new entrepreneurs' career resilience positively moderated the impact of perceived stress on their job satisfaction, and that the latter fully mediated the effect of stress on intention to remain an entrepreneur. Furthermore, the results showed that the direct effect of stress on job satisfaction and the indirect effect of stress on intention to remain an entrepreneur were conditional on the level of career resilience, so that these effects were significantly weaker for entrepreneurs with high levels of resilience as opposed to those with low levels of resilience.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.221

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.267
Teacher spread0.247 · 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 designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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