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Record W2976604572

Mission Possible?: Funding and Accountability in Benefit Corporations

2018· article· en· W2976604572 on OpenAlexaboutno aff
Michael Cianos

Bibliographic record

VenueeScholarship (California Digital Library) · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
Fundersnot available
KeywordsShareholderCorporate social responsibilityAccountabilityCorporationTransparency (behavior)CertificationLegislationDominance (genetics)StakeholderOrder (exchange)CapitalismCorporate governanceBusinessPolitical sciencePublic relationsEconomicsManagementLawFinancePolitics
DOInot available

Abstract

fetched live from OpenAlex

There exists only a handful of empirical studies on B Corps, and even fewer on benefit corporations, yet understanding how social entrepreneurs wield these forms is an important area to continue investigating considering their rapid adoption. Though often used interchangeably, the two terms refer to different things: the benefit corporation is a legal entity (akin to a C Corp, LLC, etc.) designed to institutionalize a multi-stakeholder approach to business and more rigid transparency requirements, and B Corps are companies that have been certified by B Lab—the nonprofit behind the whole movement—as meeting certain social and environmental standards. Benefit corporations only explicitly exist in the U.S. and Italy, however there have been significant efforts to pass legislation in Australia, Argentina, Canada, Chile, and Colombia. B Corps, on the other hand, have spread all around the world, and B Lab now has an Asian headquarters in Taiwan, an East African headquarters in Kenya, and a European headquarters in Amsterdam. Both of these forms have arisen in response to the dominance of shareholder-value-driven capitalism, which was born in the U.S. but has since spread to many areas of the world by way of Washington Consensus-style economic reforms. Despite its prevalence, there is growing dissatisfaction with the model’s inability to address non-commercial objectives. Previous efforts to reconstitute corporate models in order to serve multiple functions (e.g. CSR, ethical certifications, triple bottom line approaches) have tended to run afoul of market pressures whereby competition in product, capital, and labor markets—coupled with the primacy of shareholder value—relegate the non-commercial functions to a secondary status. The benefit corporation and the B Corp were conceived as a direct response to these problems. This study asks whether and how they help provide committed social entrepreneurs the space they require to pursue their social goals (the wider problem of preventing strictly commercial companies masquerading as socially conscious businesses is beyond the scope of this project). This research set out to explore the experiences of early adopters of the benefit corporation in order to understand how the legal form might impact a company “on the ground.” However, since many of this study’s participants were also B Corps, the findings that resulted have a great deal to do with B Corps, as well. The study consists of 12 semi-structured interviews with founders, CEOs, general counsels, and executives of benefit corporations over the course of 3 months. Interviews focused primarily on how being a benefit corporation impacts funding and how, if at all, it impacts a company’s day-to-day operations. Two primary hypotheses resulted, which both can be better understood by applying the economic notions of signaling and screening: (1) being a benefit corporation acts as a screening mechanism to help founders find better alignment with “traditional” investors and (2) being a benefit corporation does not significantly change behavior or encourage internal and horizontal accountability, but being a B Corp does. The latter hypothesis is strengthened by explaining how being a B Corp acts as a signal during the hiring process, and how combining it with a screen may make the development of a strong internal accountability mechanism more likely. These findings have significant relevance for social entrepreneurs who are considering becoming either a benefit corporation or a B Corp (or both), as well as for legislators and investors.

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 categoriesScholarly communication, Insufficient 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 score0.272
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.251
Teacher spread0.195 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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