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Record W4323313012 · doi:10.3390/jrfm16030176

Community Leadership at a Hindu Non-Profit Organization Leads to Outperforming in Indian Microfinance Market

2023· article· en· W4323313012 on OpenAlexvenueno aff
Arvind Ashta, Nadiya Parekh

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Spirituality and Leadership
Canadian institutionsnot available
FundersConseil régional de Bourgogne-Franche-Comté
KeywordsHinduismMicrofinanceSocial capitalLeadership stylePublic relationsProfit (economics)ConceptualizationBusinessMarketingSociologyManagementEconomicsPolitical scienceEconomic growthSocial science

Abstract

fetched live from OpenAlex

There are isolated streams of research in spiritual capital, spiritual leadership, and community leadership. We put together these three notions and indicate that taken together, a spiritual leader with a community leadership style can use his spiritual capital to boost both the social and financial performance of the organization and reduce risk. We document a case where a Hindu non-profit organization is more resilient compared to the other top Indian firms which are for-profit organisations. This challenges the popular belief that creating sustainable organizations with social impact requires a purely business logic. This case study reports the results of interviews with the top management of the organization explaining how religion is related to management inputs, the social business model, and financial performance outcomes. We add to the meager literature on Hinduism in social business leadership. We generate five propositions that expand the extant theoretical conceptualization of community leadership with a case example from a non-profit Hindu spiritual leadership domain. They serve as lessons that managers can reflect on while working with their community and building trust.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.143
Threshold uncertainty score0.653

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.278
Teacher spread0.237 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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