Managing an entrepreneurial marketing orientation in turbulent competitive business environments
Bibliographic record
Abstract
Mixed prior findings exist concerning the relationship between an entrepreneurial marketing orientation (EMO) and small-to-medium-sized enterprises’ (SMEs’) performance. This is a problem, since the performance-enhancing circumstances remain unclear concerning the utility of decision-makers employing EMO behaviours. This study unpacks the relationship between an EMO and SMEs’ performance under the moderating role of market dynamism. Survey responses were collected from 916 SMEs in Malaysia, and all major robustness checks were addressed. The findings showed that consistent with much of the existing research, an EMO drove SMEs’ performance. In contrast, this link was positively moderated by market dynamism (a counter-intuitive result). Consequently, this investigation offers unique insights regarding the circumstances where effectively managing an EMO is likely to assist SMEs to yield enhanced performance across dynamic markets. Furthermore, improved evidence is provided about the outside-the-firm perspective of resource-based theory, as a lens to conceptualise the nuances of EMO practices in turbulent environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".