Leadership Succession in Different Types of Organizations: What Business and Political Successions May Learn From Each Other
Bibliographic record
Abstract
We systematically review the recent impactful leadership succession literature in three types of organizations/contexts, namely publicly-traded, privately-owned (mostly family businesses), and political organizations. We compare and contrast these literatures, and argue that business and political leadership succession researchers and practitioners can learn from each other. The purpose of the review is fourfold. First, to take stock of the existing leadership succession research in these three related literatures – that examine the same essential phenomenon – but that have evolved separately. Previous reviews have focused mostly on CEO succession (not the broader phenomenon of leadership succession) mainly in publicly-traded firms; and to our knowledge no (recent) comprehensive literature reviews on the important topics of privately-owned and political organization leadership succession exist yet. Second, to develop an overarching integrative conceptual framework (ICF) that structures the overall leadership succession literature and shows the potential areas of integration and difference among the three literatures. Third, to develop three organizational frameworks – one for each organization type – that review what we know and what we should know about leadership succession in each type. Fourth, to critically compare the ICF, the three organizational frameworks, and the three literatures to better understand the similarities and differences among these literatures. By doing so and using a multidisciplinary approach we aim to contribute to the field in the following ways. Firstly, we seek to synthesize the field of leadership succession to identify important research questions that are ripe for study in the near future in the business and political science disciplines. Secondly, we strive to uncover what succession researchers and practitioners across these disciplines may learn from each other.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".