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Record W3004329167 · doi:10.1002/bse.2476

Exploring the effect of environmental orientation on financial decisions of businesses at the bottom of the pyramid: Evidence from the microlending context

2020· article· en· W3004329167 on OpenAlexafffund
Anton Shevchenko, Xiaodan Pan, Goran Calic

Bibliographic record

VenueBusiness Strategy and the Environment · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsMcMaster UniversityConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMicrofinanceBottom of the pyramidBusinessContext (archaeology)Pyramid (geometry)Emerging marketsMarketingCapital (architecture)Orientation (vector space)Industrial organizationEconomicsFinanceEconomic growth

Abstract

fetched live from OpenAlex

Abstract Existing research has accumulated substantial evidence on the effect that an environmental orientation has on businesses' economic performance. Yet this research does not cover small businesses from bottom‐of‐the‐pyramid (BOP) markets. In fact, despite increasing interest in research on BOP markets, the effect of environmental orientation on the financial decision‐making of small businesses from BOP markets has gone largely unexplored. Using a large multicountry data set from a microlending platform, we investigate how the environmental orientation of BOP businesses impacts their financial decisions related to microlending, which ultimately shapes their economic performance. The results indicate that an environmental orientation necessitates BOP businesses to request a higher level of financial capital and ask for longer time to pay it back. Surprisingly, environmental orientation increases the odds of BOP businesses paying back the borrowed capital. These results show that environmental orientation gives rise to both challenges and opportunities for sustainable development in BOP markets.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.424

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 studies0.0010.001
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 head0.043
GPT teacher head0.205
Teacher spread0.162 · 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

Citations14
Published2020
Admission routes2
Has abstractyes

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