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Record W2995210167 · doi:10.5267/j.msl.2019.11.034

The role of cooperative mediation in increasing the number of entrepreneurs: Case study of the DKI credit cooperative

2019· article· en· W2995210167 on OpenAlexvenueno aff
Febrizal Rahmana, Agung Sudjatmoko, Aini Farmani

Bibliographic record

VenueManagement Science Letters · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
FundersBinus University
KeywordsMediationBusinessMarketingSociologySocial science

Abstract

fetched live from OpenAlex

Even though the investment sector generally governs economic growth in developed countries. Indonesia's economic growth so far has been dominated by the consumption sector. On the other hand, one of the significant contributors to the investment sector is the entrepreneurs' role in any country. Therefore, the challenge for Indonesia is to make the entrepreneurs reach substantial contributions. This study investigates on three variables; namely entrepreneurial orientation, cooperatives, and entrepreneurs. Entrepreneurial orientation is the exogeneous variable, cooperative is the mediator variable and, the entrepreneur is the endogenous variable. These three variables are formed into three sub-models and one complete model. Each sub-model is to find out whether the dimensions contribute to each variable, and the entire model is to find out a significant relationship among the variables. The method used is the structural equation model (SEM), with computer software LISREL. This research will produce a breakthrough in how cooperative can mediate from entrepreneurial orientation to entrepreneurs. Besides, the study will deliver how cooperative stakeholders to get some understanding of entrepreneurial orientation so that cooperatives as an institution can persuade members of organizations to become entrepreneurs to be ready to contribute to economic growth. Moreover, the results provide the science of sustainable management on how to apply creativity and innovation to the cooperative organization.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.266
Teacher spread0.257 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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