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Negócios sociais e investimento de impacto: um estudo sobre as percepções dos atores do ecossistema

2017· dissertation· pt· W2771454929 on OpenAlexaff
Carlos Eduardo Alvares Gonçalves

Bibliographic record

Venuenot available
Typedissertation
Languagept
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsImpact
Fundersnot available
KeywordsExploratory researchValue (mathematics)Investment (military)PhenomenonPerceptionBusinessEntrepreneurshipSocial capitalBusiness administrationMarketingFinancePolitical scienceSociologySocial sciencePsychology

Abstract

fetched live from OpenAlex

Commonly, the creation of economic and financial value has been attributed to companies acting in the capitalist market relations while the creation of social value has been attributed to third sector organizations. This seeming duality is being questioned in the light of entrepreneurship, causing the emergence of the impact investing phenomenon as an alternative that allows capital to be allocated to initiatives and ventures, the so-called social businesses, which aim to create positive social impact together with financial returns. The rise of investors and entrepreneurs of social businesses has caused controversies and reflections about the impact evaluation, the rate of return for impact investments, and also the core concepts that support investment operations, such as social value and socio-environmental impact. In this scenario, this study aimed to analyze the perceptions of different groups of actors within the Brazilian social business and impact investing ecosystem related to these themes and their experiences in the sector -challenges, opportunities, and learnings. An exploratory research of qualitative approach has been designed, which research problem was a survey of opinions, perceptions and expectations of representatives of three groups of actors of this ecosystem: investors interested in social businesses; entrepreneurs and executives from social businesses; and representatives from intermediary organizations, such as accelerators and investment funds. Based on the construction of a theoretical framework and the analysis of secondary data on the studied phenomenon, a primary research has been conducted applying a script for semistructured interview in an intentional sample of eighteen people. The analysis of the answers showed that there is a great diversity of understandings about the concept of social value and that some interviewees tend to use this term as a synonym for social impact. Most of the interviewees highlighted elements related to access to education, health services, and citizenship rights. But none of them referred to the solid waste management as a generator of social value. Perhaps because these are relatively recent ventures, it was not possible to identify the extent to which impact investors are contributing to the growth of social value creation to the final beneficiaries. However, all social businesses that received impact investments stated that investors are contributing to the development of their businesses. Regarding the rate of return on investment, the respondents' opinions diverged, but most of them understood that, in the medium and long term, rates of return should follow average market rates. The preinvestment impact evaluation is still more approximate than based on social impact metrics, but all actors from the groups of investors and of social businesses which received investments stated that the generation of social value is monitored by investors after capital contribution. The main challenges are the access and volume of capital, the constraints of the Brazilian economic context, and the immaturity of the sector. However, all of them consider that the social business sector and impact investments have a promising future in Brazil, whether due to the need to solve the numerous social and environmental problems or due to the size of the market of potential beneficiaries of these initiatives. In this sense, this work hopes to contribute to the theoretical and practical advancement of the issues and dilemmas faced by the social entrepreneurship and the impact investment sector, as well as to foster the Brazilian Social Finance ecosystem.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0030.000
Scholarly communication0.0030.001
Open science0.0020.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.051
GPT teacher head0.318
Teacher spread0.268 · 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; both teacher heads agree on what is shown here.

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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