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Record W4285182891 · doi:10.5267/j.uscm.2022.4.004

The effect of supply chain corporate social responsibility (CSR) program on small business innovation through entrepreneurial orientation

2022· article· en· W4285182891 on OpenAlexvenueno aff
Bambang Hermanto, Achmad Romly, Iwan Sukoco, Margo Purnomo

Bibliographic record

VenueUncertain Supply Chain Management · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessEntrepreneurial orientationCorporate social responsibilityNonprobability samplingStructural equation modelingMarketingBusiness administrationOrientation (vector space)EntrepreneurshipPublic relationsSociology

Abstract

fetched live from OpenAlex

This research aimed to examine the effect of the model of Corporate Social Responsibility (CSR) Programs on SME Innovation (SI) by involving Entrepreneurial Orientation (EO) as the mediating variable. This quantitative research used a Structural Equation Model (SEM) analysis technique with SEM-PLS 3.2.9 software. Respondents in this research were small businesses as mitra binaan of the Oil and Gas SOE in Wast Java Province, consisting of 87 small businesses or enterprises engaged in fashion, food, beverages, toys, and educational props. Sampling was done using a purposive random sampling technique. The results showed that CSR programs had a significant direct effect on Entrepreneurial Orientation and SME Innovations. It was also found that Entrepreneurial Orientation was significantly and directly correlated with SME Innovations. CSR programs had a significant indirect effect on SME Innovations through Entrepreneurial Orientation. The study confirmed that CSR programs impacted SME Innovations by involving entrepreneurial orientation to strengthen the influence of CSR programs on small businesses.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.295
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes1
Has abstractyes

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