Entrepreneurial orientation in the forestry industry: a population ecology perspective
Bibliographic record
Abstract
Purpose This study primarily seeks to focus on how entrepreneurial orientation (EO) may have influenced the evolution of SMEs in a constrained environment, namely the forestry industry. It also aims to find out how EO still acts on strategic intentions, management behaviour of the business leader and the SME's performance. Design/methodology/approach The approach takes the form of an empirical study based on a sample of 717 forestry SME owner‐managers, with cluster analysis of the data, taking a population ecology perspective. Findings The study reveals the existence of two types of forestry SMEs. The first, which could be referred to as an entrepreneurially‐oriented enterprise, generates a large portion of its revenues from out‐of‐forest activities. The second type is a small‐business‐oriented enterprise. In the context of the forestry sector, many opportunities to start a business were created following the decision of large corporations to subcontract their wood supply. The study shows how entrepreneurial orientation may influence the SMEs population distribution within different categories. Research limitations/implications Forestry SMEs should no longer be considered as mere subcontractors on the payroll of large firms, as the presence of genuine forestry entrepreneurs has been confirmed. These exhibit a strong entrepreneurial orientation and overcome the scarcity of opportunities in the industrial sector to sustain their growth willingness. With a shortage of entrepreneurs expected in the coming years, these “true” entrepreneurs may be called on to perform a more important role within the forest value chain. Practical implications Even when environment is not munificent, entrepreneurially‐oriented businesses find strategies to pursue growth opportunities. In the forest sector, diversification within the sector by offering turnkey projects to large contractors seems to be the first step to fuel further diversification outside the forest. A transition towards increasing the scope of forestry businesses as well as supporting diversification could be important avenues to pursue. Originality/value This may be the first time that empirical investigation of the entrepreneurial orientation has been done in a constrained environment and from a population ecology perspective. The study confirms the role of this concept in the development of entrepreneurship.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".