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Explaining Nascent Entrepreneurs’ Goal Commitment: An Exploratory Study

2009· article· en· W2109489311 on OpenAlexafffundabout
Dirk De Clercq, Teresa V. Menzies, Monica Diochon, Yvon Gasse

Bibliographic record

VenueJournal of Small Business & Entrepreneurship · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversité LavalSt. Francis Xavier UniversityBrock University
FundersIndustry Canada
KeywordsEntrepreneurshipNormativePerceptionExpectancy theoryTheory of planned behaviorSample (material)Exploratory researchValue (mathematics)Goal settingMarketingPsychologyBusinessSocial psychologyEconomicsSociologyManagementPolitical scienceFinance

Abstract

fetched live from OpenAlex

Abstract Expectancy theory and goal setting theory serve as conceptual frameworks to examine factors associated with nascent entrepreneurs’ goal commitment, or the extent to which nascent entrepreneurs exhibit positive attitudes toward devoting substantial energy to their start-up activities. Nascent entrepreneurs’ goal commitment may be influenced by personal and environmental factors that reflect the feasibility and desirability of attaining the goal of establishing a business. Tests of the study's hypotheses use a random sample of 81 Canadian nascent entrepreneurs. In terms of the feasibility of goal attainment, nascent entrepreneurs’ selfefficacy and their perception of the availability of external private financial support relate positively to goal commitment; the perception of the availability of public financial support relates negatively to goal commitment. In terms of the desirability of goal attainment, the value that nascent entrepreneurs attribute to entrepreneurship as a career choice and the perception of normative support for entrepreneurship both relate positively to goal commitment. Implications and limitations of the findings and directions for further research are discussed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.256
Teacher spread0.214 · 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

Citations49
Published2009
Admission routes3
Has abstractyes

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