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Record W4283828399 · doi:10.5465/ambpp.2022.53

The Unintended Consequences of Supportive Institutional Infrastructure on Social Ventures

2022· article· en· W4283828399 on OpenAlexaff
Clodia Vurro, Peter A. Dacin, Derin Kent

Bibliographic record

VenueAcademy of Management Proceedings · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsQueen's University
Fundersnot available
KeywordsUnintended consequencesThrivingSocial entrepreneurshipPublic relationsBusinessNew VenturesContext (archaeology)Action (physics)Social WelfareEconomic systemSociologyEconomicsPolitical scienceEntrepreneurshipFinance

Abstract

fetched live from OpenAlex

Social ventures—newly founded organizations that blend social welfare and commercial logics to deliver innovation for societal change—while becoming an increasingly popular choice of organizational form for addressing complex social problems, often leave social entrepreneurs faced with tensions and trade-offs stemming from multiple and oftentimes contradictory social and business logics. Although tension is intrinsic to the hybridity of social ventures, it can also be unintendedly induced by the context in which social entrepreneurial activities occur. Extending current understanding of the complex role of institutions in both facilitating and hindering the effectiveness of social ventures, our research identifies conditions under which institutional infrastructures meant to coordinate action around a given social problem, unintendedly threaten the effectiveness of social ventures thriving to contribute to solving the problem. Our proposed theoretical framework suggests that, depending on the level of centralization and multiplicity of actors in the institutional infrastructure, social ventures face legitimating and organizing tensions that are likely to threaten their ability to address and manage social-business tensions and, thus, their ability to produce intended results. This enhanced understanding of institutional configurations and related unintended consequences allows us to provide a number of suggestions regarding institutional correction mechanisms targeted at mitigating the likelihood of causing unexpected drawbacks.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.508

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 studies0.0010.000
Scholarly communication0.0000.000
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 head0.049
GPT teacher head0.270
Teacher spread0.221 · 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 designTheoretical or conceptual
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

Citations0
Published2022
Admission routes1
Has abstractyes

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