IMPLICATIONS OF THE SME OWNER-MANAGER'S BUSINESS VENTURING MODE: COMPARING FOUNDERS, ACQUIRERS AND SUCCESSORS
Bibliographic record
Abstract
The entrepreneurship literature teaches us that the aspirations and competence of SME owner-managers as well as their strategic management behaviour can influence both the development and performance of their firm. However the research issues that surround the owner-manager's business venturing mode, that is, whether he or she has created a new firm, acquired an already existing firm, or acceded to a family firm's leadership and ownership by succession and/or inheritance, have rarely been addressed in an integrated manner. Now, founders, acquirers and successors may have fundamentally different strategic profiles, that is, in terms of the strategic capabilities they aim to develop and the type of performance they seek for their firm. In aiming to identify individual correlates and organizational effects of the entrepreneur's business venturing mode, an empirical study of 357 Canadian and French SMEs was thus undertaken. The results reveal significant differences between the three groups of owner-managers, that is, between the 196 founders, 96 acquirers and 65 successors with regard to their competence and motivations and with regard to the strategic capabilities and business performance of their firm. The results also reveal the owner-manager's business venturing mode to be a significant predictor of the firm's market and HR capabilities as well as its growth, productivity and profitability.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".