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Record W4403331448 · doi:10.1080/19420676.2024.2413074

Pathways to Scaling International Impact: Exploring Capabilities in B Corps

2024· article· en· W4403331448 on OpenAlex
Kelsey M. Taylor, Francesca Ciulli, Lydia Bals

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Social Entrepreneurship · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsUniversity of Manitoba
FundersDeutsche Forschungsgemeinschaft
KeywordsScalingSociologyPolitical scienceMathematics

Abstract

fetched live from OpenAlex

With the rising global awareness of sustainable development issues, hybrid ventures have increasingly brought their solutions across borders to scale their impact internationally. Yet, prior research has largely concentrated on international scaling undertaken via sales growth for economic goals. We instead offer an elaborated conceptualisation of international impact scaling, which includes internal growth, partnership, and dissemination downstream and upstream in the value chain. Stemming from prior research that views capabilities as critical ‘firm-specific advantages’ for international expansion, we investigate which capabilities hybrid ventures leverage when scaling impact internationally and to what extent they differ across scaling strategies. We adopt a qualitative method to analyse seven B Corps, a type of hybrid venture whose impact is certified. The findings reveal three clusters of foundational capabilities that B Corps leverage irrespective of the specific scaling strategy adopted, alongside a range of targeted capabilities that are specifically important to each international scaling strategy. The study contributes to international scaling research and the literature on hybrid ventures.

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.

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 score0.395
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.056
GPT teacher head0.276
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