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Record W4210792392 · doi:10.5267/j.uscm.2022.1.004

Business performance model of herbal community enterprise in Thailand

2022· article· en· W4210792392 on OpenAlexvenueno aff
Chayanan Kerdpitak

Bibliographic record

VenueUncertain Supply Chain Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsnot available
Fundersnot available
KeywordsStructural equation modelingCompetitive advantageBusinessMarketingConceptual modelData collectionKnowledge managementComputer science

Abstract

fetched live from OpenAlex

The research study was carried out to investigate the actual business performance model in herbal medicine business in Thailand. The conceptual framework was developed from resource-based theory of herbal medicine community enterprise, and other contemporaneous research in herbal medicine business performance. Accordingly, the study considered the importance of the factors of marketing channel, competitive advantage, logistics integration and innovative management. In this direction, the study employed a quantitative research approach. Questionnaire was used for data collection. Data were collected from 340 entrepreneurs of herbal community enterprise. Finally, data were analyzed using structural equation modeling (SEM) to examine the actual herbal business performance of the organizations studied through all operational links in the marketing channel, competitive advantage, logistics integration and innovative management. Results of the study found that marketing channels had a positive effect on herbal business performance. Competitive advantage also had a positive effect on herbal business performance. Furthermore, logistics performance had a positive effect on herbal business performance. Finally, similar results were found in case of innovative management which shows the positive effect on herbal business performance.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.027
GPT teacher head0.234
Teacher spread0.207 · 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 designSimulation or modeling
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

Citations16
Published2022
Admission routes1
Has abstractyes

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