The impact of CEO succession choices on digital transformation in family firms
Bibliographic record
Abstract
Purpose This study investigates how CEO succession choices – specifically, the appointment of a family-successor CEO versus an external non-family CEO – affect digital transformation in family firms. Drawing on socio-emotional wealth theory and agency theory, it further examines how top management team (TMT) diversity and slack resources influence this relationship. Design/methodology/approach The analysis is based on a panel of family firms listed on the Shanghai and Shenzhen A-shares markets from 2014 to 2020. Using multiple regression models with controls for endogeneity and robustness checks, the study evaluates the impact of CEO succession choices on digital transformation. Findings Results show that family-successor CEO significantly promote digital transformation in family firms, with the effect being more pronounced in firms with stronger familial cultural characteristics. Strategic risk-taking is identified as a mediating mechanism in this process. Moreover, TMT heterogeneity and slack resources, not only enhance the link between family-successor CEOs and strategic risk-taking but also reinforce the indirect effect of strategic risk-taking in facilitating digital transformation. Originality/value This study contributes to the literature on digital transformation in the context of Chinese family firms and extends research on intergenerational succession and strategic change. It highlights the unique role of family governance in mitigating agency problems and shows how traditional family culture adapts to modern economic demands. The findings provide an Eastern perspective on corporate governance, enriching cross-cultural research in this field.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".