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Record W4311079416 · doi:10.1504/ijmfa.2023.127523

Impact of stakeholders as board members on sustainability and social outreach of microfinance institutions in developing markets

2022· article· en· W4311079416 on OpenAlexaff
Mohammad Delwar Hussain, Iftekhar Ahmed, Tareq Hossain

Bibliographic record

VenueInternational Journal of Managerial and Financial Accounting · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMicrofinanceOutreachBusinessSustainabilityStakeholderAccountingDeveloping countryResource dependence theoryCorporate social responsibilityContext (archaeology)Stewardship (theology)Public relationsEconomic growthEconomicsPolitical scienceManagement

Abstract

fetched live from OpenAlex

The purpose of this study is to explore how stakeholders on the board contribute to sustainability and outreach of microfinance institutions (MFI). Stakeholders as board members can influence the social outreach and sustainability of microfinance institutions (MFIs). By applying a multi-theoretical approach to a longitudinal dataset from a developing country perspective, this study analyses stakeholder involvement on the MFI board and its impact on double-bottom-line performance. The results suggest that independent directors on the board have significantly positive effects on achieving microfinance institutions' dual missions: sustainability and outreach. However, some stakeholders have produced mixed results. Employees, donors, and female board members play significant roles, although their impacts are moderated by the age and size of MFIs. CEO duality contributes to MFI sustainability but inversely affects outreach. The results support the stakeholder, stewardship, and resource dependence theories. This study recommends the appointment of an independent board member as a social director to widen the range of stakeholders' involvement in the boards of MFIs and contribute to achieving its objectives in a developing market context.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.039
GPT teacher head0.283
Teacher spread0.243 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Managerial and Financial AccountingSame topicMicrofinance and Financial InclusionFrench-language works237,207