Unpacking the relationship between entrepreneurial marketing activities and small firm performance
Bibliographic record
Abstract
Purpose A question remains unresolved in existing cross-disciplinary research at the marketing/entrepreneurship interface (MEI). This features circumstances when employing a combination of market-oriented and entrepreneurially-oriented activities, known as entrepreneurial marketing (EM) behavior, is likely to lead to positive performance outcomes. Earlier mixed findings provide the need to unpack the nuances of EM practices, in terms of their boundary conditions, regarding circumstances where this behavior does or does not lead to performance-enhancing outcomes. Consequently, the purpose of this study is to examine the complexities of the association between EM activities and small firm performance by assessing quadratic and moderating effects. Design/methodology/approach This study was underpinned by resource-based theory (RBT). Survey responses were collected from 214 smaller-sized companies in the United Kingdom. The statistical data passed all major checks for reliability, different forms of validity, common method variance and endogeneity bias. Findings EM activities had a quadratic connection with small firm performance, with this relationship being enhanced (in terms of a positive two-way interaction effect) by market dynamism (a counter-intuitive result regarding environmental conditions). Surprisingly, through a post-hoc test, coopetition (cooperation among competitors to leverage assets and overcome resource constraints) did not play any influential part in helping owner-managers to overcome the potential downsides of EM practices, like the time and cost implications of identifying and exploiting opportunities (i.e., a non-significant three-way interaction effect). Originality/value Unique insights outline how decision-makers in smaller-sized organizations can harness the potential benefits, and minimise the likely drawbacks, of employing EM activities. However, owner-managers should be cautious when implementing these organization-wide practices, since they are likely to enhance performance, but only up to a fixed point. Indeed, excessive forms of EM activities can weaken small firms’ performance. A counter-intuitive positive moderation effect regarding market dynamism challenges certain earlier findings. Specifically, in some dynamic market conditions, EM activities could be performance-enhancing, since certain environmental-level forces might assist owner-managers to amplify the merits of behavior at the MEI when implemented effectively.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".