A Socioemotional Wealth Approach to CEO Career Horizons in Family Firms
Bibliographic record
Abstract
ABSTRACT This paper challenges the predominant view that as CEOs near retirement, they forgo risky long‐term strategic choices and instead focus on decisions that enhance their own short‐term self‐interests. Drawing on the socioemotional wealth (SEW) literature, we argue that unlike near‐retirement CEOs in widely held firms, near‐retirement CEOs in family firms are more concerned about transgenerational control and the legacy that they pass on to future generations. We further contend that the priority of SEW dimensions can change within family firms depending on the CEO's time to retirement. Consequently, near‐retirement CEOs in family firms differ from their counterparts in non‐family firms in that they are willing to continue to engage in international acquisitions as they approach retirement, despite the potential short‐term risks. We further hypothesize that this effect depends on whether the CEO is a family member, whether the CEO is succeeded by another family member, and whether the CEO is the founder. In analysing 3432 family and non‐family firm‐year observations from the S&P 500 for the period between 1997 and 2009, we find support for our hypotheses. Subsequent analyses indicate that near retirement, family CEOs acquire larger and culturally closer targets than their non‐family counterparts. Our paper confirms the need to more fully consider the characteristics of owners and managers in analyses of the CEO career horizon problem.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".