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Record W3120341663 · doi:10.1007/s11266-020-00300-y

Institutional Constraints, Market Competition, and Revenue Strategies: Evidence from Canadian Social Enterprises

2021· article· en· W3120341663 on OpenAlexaboutno aff
ChiaKo Hung, Lili Wang

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCompetition (biology)RevenueBusinessIndustrial organizationMarket competitionMarket economyMicroeconomicsEconomicsFinance

Abstract

fetched live from OpenAlex

Abstract This study examines the impact of external environment on social enterprises’ revenues strategies. Using a national survey of 1250 social enterprises in Canada, this study tests whether institutional constraints and market competition affect commercialization and revenue diversification of social enterprises. The results suggest that social enterprises’ revenue strategies are associated with both institutional constraints and market competition. With institutional constraints, for-profit and cooperative social enterprises rely more on commercial revenues and have a less diverse revenue structure than their nonprofit counterparts. In addition, social enterprises with parent organizations tend to be more commercialized. With market competition, specialist social enterprises providing social services have a more concentrated revenue structure than generalist social enterprises, while specialist social enterprises providing culture services rely less on commercial revenues and have a more diverse revenue structure than generalist social enterprises. The findings offer implications for social enterprises to rethink financial flexibility and autonomy.

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)0.000
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 score0.096
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.000

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 head0.239
Teacher spread0.220 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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