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Impact creation approaches of community-based enterprises: A configurational analysis of enabling conditions

2024· article· en· W4401744168 on OpenAlexfundno aff
Jana Coenen, Florian Noseleit, Christian Rupietta

Bibliographic record

VenueJournal of Business Venturing · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
FundersQueen's UniversityNederlandse Organisatie voor Wetenschappelijk OnderzoekBundesministerium für Bildung, Wissenschaft, Forschung und TechnologieRadboud UniversiteitQueen's University Belfast
KeywordsBusinessIndustrial organizationProcess managementComputer science

Abstract

fetched live from OpenAlex

This study investigates which local conditions enable community-based enterprises (CBEs) to create impact. Advancing our limited understanding of the various contexts that enable CBEs to tackle societal issues locally, we investigate supportive conditions across 77 CBEs driving the energy transition in their geographic community. Through qualitative comparative analysis, we identify four condition configurations for CBE impact creation. Across these configurations, we reveal transferable mechanisms helping CBEs to engage community members ( Opportunity- and Community-anchoring ) and handle the absence of a supportive condition (Circumventing and Compensating). Our study suggests how CBEs can combine these mechanisms to create impact in varied local contexts. • Our study informs how community-based enterprises (CBEs) can generate local impact based on available local conditions. • No single condition is necessary or (by itself) sufficient for impact creation. • CBEs can build upon various condition combinations that can help deal with a barrier (lacking condition). • CBEs enhance resourcefulness by passing barriers with Circumventing and overcoming them with Compensating strategies. • CBEs engage communities by highlighting benefits ( Opportunity-anchoring ) or emphasizing relationships ( Community-anchoring ).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.057
GPT teacher head0.296
Teacher spread0.239 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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