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Record W3200202622 · doi:10.7202/1081421ar

Entrepreneurial Marketing Skills and Small-Scale Business Performance: The Case of Ghana

2021· article· en· W3200202622 on OpenAlexaffvenue
Andrews Ayiku, E. Stephen Grant

Bibliographic record

VenueJournal of Comparative International Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMarketingBusinessCompetition (biology)Marketing managementScale (ratio)Return on marketing investmentSkills managementMarketing strategyMarketing researchMarketing effectiveness

Abstract

fetched live from OpenAlex

The development of SMEs serves as a tool for creating employment opportunities in developing countries. SME growth can be attributed to how owners and managers use entrepreneurial marketing skills in their day-to-day operations. A quantitative study was used to gain an understanding of the application of marketing skills in SMEs and the development of SMEs in Ghana. Findings indicate that entrepreneurs acquire marketing skills during their years of operation, however, these skills are not in tune with modern business trends and market competition. Establishing marketing departments and equipping business owners with marketing skills enables their ability to deal with volatile consumer demands. Likewise, requisite marketing skills enable market understanding, new marketing approaches, and the design of demand driven products and services

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)0.002
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 score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.257
Teacher spread0.239 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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