How do entrepreneurial firms behave in the face of environmental turbulence and uncertainty? Evidence from the manufacturing sector
Bibliographic record
Abstract
Purpose In a context of greater environmental uncertainty, understanding the practices and strategies adopted by the SME owner-manager to deal with it is an important topic. Design/methodology/approach Based on a questionnaire survey of 583 SME owner-managers, a cluster analysis based on the degree of perceived uncertainty was conducted. Findings A statistical differences across a continuum with regard to entrepreneurial orientation, information gathering, management and absorption practices, innovation and internationalization was observed. These results show that the behaviors, and strategies deployed by SME owner managers are adapted to the degree of uncertainty these individuals perceive. Moreover, these results are not linked to their individual profiles nor to those of their companies. Practical implications The results show how SME owner-managers can increase their capacity to face uncertainty by collecting different types of information from different sources, by traveling abroad, by hiring personal with diverse profiles and by dealing with situations outside their norms. Public authorities in economic development interested to promote entrepreneurial decisions are invited to produce and diffuse valuable information to reduce uncertainty perceived by owner managers to support SMEs. Originality/value This research is original in that no study has holistically examined the link between uncertainty and the strategic and organizational practices of SMEs. It also responds to political and managerial concerns to effectively support SMEs under conditions of uncertainty – contexts that are increasingly important these days.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".