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Record W4387645384 · doi:10.1504/ijesb.2023.134174

The nature of the relationship between an entrepreneurial marketing orientation and small business growth: evidence from Malaysia

2023· article· en· W4387645384 on OpenAlexaff
Muhammad Iskandar Hamzah, James M. Crick, Dave Crick, Syukrina ni Mat Ali, N.A. Noor', Noor’ain Mohamad Yunus

Bibliographic record

VenueInternational Journal of Entrepreneurship and Small Business · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEntrepreneurshipEntrepreneurial orientationBusinessSmall businessMarketingOrientation (vector space)Business administrationFinance

Abstract

fetched live from OpenAlex

Guided by resource-based theory, this study examines the nature of the relationship between an entrepreneurial marketing orientation (the interplay between market-oriented and entrepreneurially-oriented behaviours) and small business growth. Survey responses were collected from 421 smaller-sized firms in Malaysia. After assessing the statistical data for all major robustness checks, hierarchical regression was used to evaluate the conceptual framework. The results suggested that on their own, market orientation and entrepreneurial orientation have linear (positive) relationships with small business growth. More importantly, an entrepreneurial marketing orientation exhibited a nonlinear (inverted U-shaped) association with small business growth. Thus, owner-managers are faced with the challenge of fostering an 'optimal-level' of an entrepreneurial marketing orientation to avoid potentially harmful performance consequences. Consequently, unique insights have emerged regarding the complexities of the marketing/entrepreneurship interface, with stronger evidence pertaining to the dangers of implementing 'too little' or 'too much' of an entrepreneurial marketing orientation.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.043
GPT teacher head0.270
Teacher spread0.228 · 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 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

Citations29
Published2023
Admission routes1
Has abstractyes

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