Leadership succession planning “affects commercial success”
Bibliographic record
Abstract
Purpose To help organizations to identify and develop future leaders, to highlight good and bad practices in development, to specify areas for improvement and to build on previous research in this area. Design/methodology/approach The article is based on a survey of 115 respondents – 46 percent of whom report an annual turnover in excess of $1 billion – from 19 industries. About 90 percent of respondents are based in the US, with the remainder being located in the UK, France, The Netherlands, Switzerland, Canada, Russia and central and eastern Europe. Findings Careful planning for leadership succession at major organizations has a significant impact on their commercial success. Conversely, there is a corresponding significant negative correlation between an organization's need to hire outside leaders and its confidence to meet future growth needs. Practical implications Chief executives and other senior managers need to commit time and energy to on‐the‐spot development of high‐potential individuals. Originality/value The article is of value to organizations seeking to identify and nurture talented employees for future leadership positions.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".