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Record W4402518200 · doi:10.1111/joms.13138

Outcome‐Based Typology of Social Enterprises: Interlacing Individual Transformation, Capital Provision, and Societal Influence

2024· article· en· W4402518200 on OpenAlexaboutno aff
Georgios Polychronopoulos, Martin Lukeš, Giuliano Sansone, Anirudh Agrawal, Florian Diener, Veronika Šlapáková Losová

Bibliographic record

VenueJournal of Management Studies · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsTypologyInterlacingSocial capitalOutcome (game theory)BusinessTransformation (genetics)Capital (architecture)EconomicsSociologyMicroeconomicsSocial scienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Social entrepreneurship has emerged as a global phenomenon aimed at tackling societal grand challenges through market‐based activities. A holistic understanding of social enterprise outcomes is crucial for reflecting their effectiveness in meeting social objectives and informing internal organizational processes. This study explores the outcomes of social enterprises through a comparative qualitative analysis of 49 social ventures in Austria, Canada, Czechia, Denmark, Germany, Greece, India, Italy, the Netherlands, and the United States, spanning diverse sectors. Three key outcome dimensions are identified: individual transformation, capital provision, and societal influence. Our analysis results in a typology of seven distinct types of social enterprises, each integrating these dimensions to varying degrees. Utilizing this typology, we reveal how social enterprises navigate barriers to solving complex social and environmental problems, illustrating the dynamic interplay between outcome dimensions and the importance of multi‐objective organizing – beyond hybrid organizing – in addressing complex societal issues.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.484

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 communication0.0000.001
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 head0.029
GPT teacher head0.306
Teacher spread0.277 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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