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Record W2949480171 · doi:10.5430/ijfr.v10n5p430

The Sustainability of Muslim Women Entrepreneurs: A Case Study in Malaysia

2019· article· en· W2949480171 on OpenAlexvenueno aff
Endi Rekarti, Zakaria Bahari, Normaisarah M. Zahari, Caturida Meiwanto Doktoralina, Nor Asariah Ilias

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersUniversitas Mercu BuanaUniversiti Sains Malaysia
KeywordsWomen entrepreneursSustainabilityCapital (architecture)BusinessProduct (mathematics)Sustainable businessEconomic growthMarketingSocioeconomicsEntrepreneurshipEconomicsFinanceGeography

Abstract

fetched live from OpenAlex

The number of women who engage in small entrepreneurs (SMEs) in Malaysia, Kelantan has a high number of Muslim businesswomen whose efforts have been in place for over ten years and their businesses are able to increase family income on sustainable. This paper aims to identify the types of sustainability activities undertaken by Muslim small business women in Kelantan and to analyse the factors that influence the viability of Muslim women entrepreneurs doing business there. A case study was conducted to answer the question of how Muslim women entrepreneurs can be sustainable in business. Interview respondents were selected from a random sampling conducted on 15 Muslim women entrepreneurs in Kelantan. These findings indicate the dry food product business is more sustainable than wet goods because the sale of dry goods is always in demand and does not require much initial capital. This study shows that the factors that influence the survival of Muslim women small entrepreneurs in Kelantan are divided into three categories i.e., First financial capital, the human capital of an inherited business and the family experience gained in the field plays a role. Last, the patient's spiritual element, which is deliberate and honest.

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)0.002
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 score0.203
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.341
Teacher spread0.317 · 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

Citations21
Published2019
Admission routes1
Has abstractyes

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