“Method in their madness”: understanding the behaviour of VSE owner‐managers
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand the sound practical reasons underlying the behaviour of very small enterprise (VSE) owner‐managers with respect to their perceived resistance to the dominant entrepreneurial and managerial models in areas such as management methods, marketing or internationalisation. Design/methodology/approach The current literature on VSE managers was reviewed in the light of Raymond Boudon's general theory of rationality. Starting from the premise that in science, the simplest explanation tends to be the best, the paper highlights the practical reasons why VSE owner‐managers behave the way they do. Findings While there may be cultural or personality‐based reasons why VSE owner‐managers often appear to reject the traditional entrepreneurial model, these are not the sole or even the main explanation. In most cases, the behaviour in question can be explained much more simply by practical, down‐to‐earth reasons. From the actor's point of view, his behaviour is always rational. Research limitations/implications This new model of the behaviour of VSE owner‐managers has not been empirically tested. Originality/value The paper presents a novel vision of the behaviour of VSE owner‐managers, based on the practical reasons underlying their actions, that goes beyond the existing typologies such as the “Traditional‐vs‐Opportunistic” entrepreneur.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".