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Record W2407064033 · doi:10.1111/joms.12201

Kicking Off Social Entrepreneurship: How A Sustainability Orientation Influences Crowdfunding Success

2016· article· en· W2407064033 on OpenAlexaff
Goran Calic, Elaine Mosakowski

Bibliographic record

VenueJournal of Management Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSustainabilityLegitimacyEntrepreneurshipEntrepreneurial orientationBusinessCreativitySocial entrepreneurshipSocial capitalMarketingSocial sustainabilityArgument (complex analysis)Public relationsFinanceSociologyPolitical science

Abstract

fetched live from OpenAlex

Abstract Research generally suggests that, relative to commercial entrepreneurs, social entrepreneurs stand at a disadvantage at acquiring resources through traditional financial institutions. Yet interest in social entrepreneurship appears to be at an all‐time high. The current paper advances the argument that an innovative institutional form – crowdfunding – has emerged to address the needs of social entrepreneurs and other entrepreneurs with limited access to traditional sources of capital. To examine this, we study whether and how a sustainability orientation affects entrepreneurs’ ability to acquire financial resources through crowdfunding and hypothesize that a venture's sustainability orientation will enhance its fundraising capability. We also suggest that project legitimacy and creativity mediate the relationship between a sustainability orientation and funding success. Our analysis produces two key findings: 1) a sustainability orientation positively affects funding success of crowdfunding projects, and 2) this relationship is partially mediated by project creativity and third party endorsements.

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)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.032
GPT teacher head0.294
Teacher spread0.262 · 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 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

Citations599
Published2016
Admission routes1
Has abstractyes

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