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Record W2997205324 · doi:10.6000/1929-7092.2019.08.126

Entrepreneurial Marketing and Performance of Small and Medium Enterprises in South Africa

2019· article· en· W2997205324 on OpenAlexvenueno aff
Olawale Fatoki

Bibliographic record

VenueJournal of Reviews on Global Economics · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsSnowball samplingCronbach's alphaData collectionDescriptive statisticsMarketingReliability (semiconductor)Survey methodologyRegression analysisBusinessMeasure (data warehouse)Survey researchStatisticsBusiness administrationComputer scienceData miningMathematics

Abstract

fetched live from OpenAlex

The study investigated the effect of entrepreneurial marketing (EM) on the performance of SMEs. Performance was measured using both organisational and personal criteria. The study utilised the quantitative research approach. The cross-sectional survey method was used for data collection. Data was collected from small business owners through the survey method. The self-administered questionnaire method was used to collect data from the participants. Convenience and snowball methods were used for sampling. Descriptive statistics and multiple regression were used for data analysis. The Cronbach’s alpha was used as the measure of reliability. The results of this study showed significant positive relationships between some dimensions of EM and organisational and personal performance. Theoretically, the study linked EM to the personal performance of the owners of SMEs. Empirically, the study adds to the literature on the relationship between EM and the financial performance of SMEs. Practically, the study suggested recommendations that can improve EM by SMEs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.104
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.212
Teacher spread0.195 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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