Emotional Intelligence and Leadership Succession Planning: Strategies for Identifying and Developing Future Leaders
Bibliographic record
Abstract
The aims of this study are to investigate the relationship of leadership identification and development with emotional intelligence and leadership succession planning and to outline the effective strategies for identification of potential leaders and leadership development. Methodology - To achieve the aim, we used the scheme of interactive co-production research where researchers create the scientific partnership with business professionals and they are responsible for generation of theoretical and practical knowledge. To process the data, we applied mixed methodology to improve the data assessment and contributed to objectivity and accuracy of the research. The survey involved five companies operating in the information technology, car repair enterprise, sewing business, and food and beverages producer in the Western and Central regions of Ukraine. The survey was conducted between March-September 2023. A sample included 24 business professionals and leadership in different roles, ages, gender, work experience, and education background. The selection of respondents was based on their involvement in leadership identification and development procedures in the organization and depended on their level of professional competency in the industry. The result show that leadership identification and development is implemented through certain strategies which include the use of feedback, culture of high engagement, performance assessment, leadership training programs, identification of skill gaps and future needs, and introduction of continuous learning culture in an organization. The use of these strategies should be implemented in the individual, group, and organizational levels. The study contributed to elaboration of the model of leadership identification and development in an organization.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".