Calm in the Storm: Job Security and Post-Merger Performance in Family versus Nonfamily Firms
Bibliographic record
Abstract
Building on social identity theory, we theorize and find that in a merger, paired family firms are better able to retain employees and improve post-merger performance compared to other merger pairs. We contribute to social identity theory by theorizing better post-merger performance as mediated by job security for family firm combinations. We also contribute to the job security and merger and acquisition literature by examining how job security and post-merger performance vary based on the paired social identity of owners. In addition to identity similarity, the type of identity also matters in mergers. We argue that family owner social identity similarity fosters greater integration between merging parties while allowing family owner pairs to retain some autonomy through their employees, thereby maximizing post-merger performance. Our data on private Swedish firms, complemented by 11 qualitative interviews across five countries and three continents, confirm that family mergers outperform other merger combinations via job security. In a supplementary critical experiment examining industry dissimilarity, we compare the socioemotional wealth perspective—which emphasizes loss aversion and predicts family firms’ unrelated diversification avoidance—to social identity theory. Consistent with social identity theory, our results show that both job security and post-merger performance improve with unrelated family firm mergers.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".