Thriving Leadership in Family Business Succession: A Systematic Review of Educational Strategies
Bibliographic record
Abstract
This systematic review investigated educational factors that support thriving leadership development in family business succession within diverse cultural contexts. Leadership transitions in family-owned enterprises were often challenges from inadequate preparation, generational conflict, and the need for culturally responsive pedagogical approaches. Employing PRISMA 2020 guidelines, 45 peer-reviewed studies (2015–2025) from Scopus, ERIC, and JSTOR databases were systematically analyzed using predetermined inclusion and exclusion criteria to identify recurring educational strategies contributing to sustainable leadership development. Data were extracted using a structured template capturing research objectives, methodologies, findings, and educational implications. All studies underwent rigorous quality assessment using eight criteria adapted from the Secretariat of the Education Council (2009). Thematic synthesis employing Thomas and Harden’s three-stage approach yielded four core domains: (1) Learning-Centered Leadership Development (n = 10), emphasizing self-directed learning and reflective practices; (2) Intergenerational Knowledge Transfer (n = 11), involving mentorship programs, experiential learning, and professional networks; (3) Organizational Readiness and Structural Support (n = 12), focusing on succession planning, governance systems, and resource alignment; and (4) Socio-Cultural Context and External Influences (n = 12), highlighting family values, traditions, and cultural norms in leadership education. Findings indicated thriving leadership emerged from strategic alignment of psychological readiness, knowledge-sharing mechanisms, institutional structures, and culturally embedded practices. Western models stress formal educational systems and individual autonomy, while Asian, African, and Latin American contexts favor relational mentoring, communal learning approaches, and value-based education. This review contributes to educational leadership and family enterprise research by offering a comprehensive, culturally adaptive framework for developing next-generation leaders, providing practical implications for educators, consultants, and policymakers fostering intergenerational continuity through culturally informed educational strategies.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".