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Record W3008265794 · doi:10.5267/j.msl.2020.2.019

The role of innovativeness-based market orientation on marketing performance of small and medium-sized enterprises in a developing country

2020· article· en· W3008265794 on OpenAlexvenueno aff
Ari Riswanto, Rasto Rasto, Heny Hendrayati, Mohamad Saparudin, Ali Zaenal Abidin, Andi Primafira Bumandafa Eka

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsMarket orientationBusinessMediationMarketingAgency (philosophy)Government (linguistics)Small and medium-sized enterprisesVariable (mathematics)Industrial organization

Abstract

fetched live from OpenAlex

The aim of this study was to determine whether the innovativeness variable can mediate the influence between market orientation and marketing performance in the culinary industry in West Java Province, Indonesia. More specifically, this study analyzes the relationship among market orientation, innovation and marketing performance in the single mediation model by placing mediation variable of innovation. The samples involved a total of 209 culinary industries under the auspices of the agency of cooperative and SME services. By using a simple linear regression analysis with SPSS and AMOS v. 23 we understand that innovativeness had the ability to mediate the influence between market orientation and marketing performance. Likewise, market orientation has a positive influence on performance. The implication of this research is that entrepreneurs can improve their innovation capabilities to improve the SME performance. This study recommends that government policies should encourage innovation in SMEs and encourage SME managers to pay more attention and manage innovations to improve their operational 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 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)0.003
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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

Citations39
Published2020
Admission routes1
Has abstractyes

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