Professionalizing entrepreneurial firms: Managing the challenges and outcomes of founder‐CEO succession
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it