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Record W4256206110 · doi:10.17722/ijme.v11i1.995

Effect of Market Orientation, Organization Creativity and Management Knowledge of Innovation and Impact on Competitive Advantages

2018· article· en· W4256206110 on OpenAlexvenueno aff
Iwan Kurniawan Subagja, Widji Astuti, Junianto Tjahjo Darsono

Bibliographic record

VenueInternational Journal of Management Excellence · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsNonprobability samplingCreativityBusinessCompetitive advantageManufacturing sectorSample (material)Market orientationManufacturingIndustrial organizationMarketingStructural equation modelingEconomicsLabour economicsComputer science

Abstract

fetched live from OpenAlex

Manufacturing industry sector as one of an important sector in national economic development. Manufacturing industry sector is one of the supporters of the national economy because this sector contributes significantly to the economic growth of Indonesia. This study aims to analyze the influence of market orientation, organizational creativity and management knowledge on innovation and its impact on competitive advantage in manufacturing industry in Indonesia. Populations and samples conducted in manufacturing industries located in Bekasi with a total sample of 186 manufacturing industries. This research is explanatory with sampling purposive sampling technique and analysis used quantitative analysis by using AMOS 22. The result of research indicates that market orientation, organizational creativity, and management knowledge influence to competitive advantage through innovation.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.008
GPT teacher head0.326
Teacher spread0.318 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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