Professionalizing entrepreneurial firms: Managing the challenges and outcomes of founder‐CEO succession
Bibliographic record
Abstract
Abstract Research summary The transition from a founder‐led start‐up to a professionally managed firm entails significant change in the firm's organizational design. This transition can constitute a critical juncture for the entrepreneurial firm, and there is a risk of losing key talent. We posit that limiting the disruptive effect of changing organizational structures requires organizational members to not only adopt new roles but also embrace new behavioral norms regarding how the firm operates. We use an inductive multicase study paired with exogenous data on company morale to explore outcome variation in such transitional processes and elicit managerial strategies that can guide successful founder‐CEO succession and the accompanying organizational change of the entrepreneurial firm. Managerial summary Adapting the organizational structures of an entrepreneurial firm to match the needs of its expanding operations represents a critical moment in a firm's life. During this phase, the founder‐CEO is often replaced by a professional CEO. This event coupled with reorganization can be highly unsettling for the venture's workforce and can lead to turnover with negative performance implications. To minimize disruption, we studied change strategies employed by incoming professional CEOs. We find that most effective CEOs jointly use three change levers—change readiness activation, shared pathway creation, and founder legacy fairness—to help team members adapt to the new situation and align their behaviors with how mature firms operate.
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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 | 0.011 | 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 | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".