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Record W4200214657 · doi:10.5539/ibr.v15n1p52

Empowerment of Micro, Small Medium Enterprises (MSMES) in Coconut Oil Development

2021· article· en· W4200214657 on OpenAlexvenueno aff
Kadek Wulandari Laksmi P, Ni Wayan Lasmi, Desak Made Sukarnasih, Wayan Sri Maitri

Bibliographic record

VenueInternational Business Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsCoconut oilBusinessProduction (economics)EmpowermentProduct (mathematics)Small and medium-sized enterprisesMarketingEntrepreneurshipEconomicsEconomic growthFinanceFood science

Abstract

fetched live from OpenAlex

Entrepreneurship development at the household level is the forerunner to the growth of an MSME. In the village of Antiga, Manggis District, Karangasem Regency, there is a home-based business engaged in the production of coconut oil (kelentik oil). Currently the level of marketing is only limited to traditional markets while the target is to be able to market products in modern markets. Therefore, this study has the objective to know the level of efficiency of production costs incurred by the producers of coconut oil (kelentik oil) in Antiga village, Manggis district, Karangasem regency; to know how to empower Micro, Small and Medium Enterprises producing coconut oil (kelentik oil) and to analyze the factors that influence the intention of buyers or consumers to buy coconut oil (kelentik oil) in Bali. The research method is a qualitative method. The result is that the production process is still traditional so that production cost efficiency is achieved. Furthermore, the empowerment of MSMEs is not yet optimal as business partners in marketing their products. Several factors influence consumers' intention to buy coconut oil (kelentik oil). So the input that can be given is to improve relationships or networks to be able to expand the market and improve product packaging.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.383
Teacher spread0.308 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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