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Record W2990452979 · doi:10.1108/ijebr-03-2019-0185

Greater fit and a greater gap

2020· article· en· W2990452979 on OpenAlexaff
Steven A. Brieger, Dirk De Clercq, Jolanda Hessels, Christian Pfeifer

Bibliographic record

VenueInternational Journal of Entrepreneurial Behaviour & Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsBrock University
Fundersnot available
KeywordsExtant taxonOriginalityEntrepreneurshipLife satisfactionValue (mathematics)Profit (economics)BusinessSample (material)MarketingPsychologyEconomicsSocial psychologyMicroeconomicsCreativityFinance

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to understand how national institutional environments contribute to differences in life satisfaction between entrepreneurs and employees. Design/methodology/approach Leveraging person–environment fit and institutional theories and using a sample of more than 70,000 entrepreneurs and employees from 43 countries, the study investigates how the impact of entrepreneurial activity on life satisfaction differs in various environmental contexts. An entrepreneur’s life satisfaction arguably should increase when a high degree of compatibility or fit exists between his or her choice to be an entrepreneur and the informal and formal institutional environment. Findings The study finds that differences in life satisfaction between entrepreneurs and employees are larger in countries with high power distance, low uncertainty avoidance, extant entrepreneurship policies, low commercial profit taxes and low worker rights. Originality/value This study sheds new light on how entrepreneurial activity affects life satisfaction, contingent on the informal and formal institutions in a country that support entrepreneurship by its residents.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.153
GPT teacher head0.360
Teacher spread0.207 · 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.

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

Citations22
Published2020
Admission routes1
Has abstractyes

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