The workplace social performance of family firms: a configurational approach
Bibliographic record
Abstract
Purpose The purpose of this study is to explore the drivers for proactive workplace social performance in family firms through a configurational approach. Comparative research on family versus non-family firms and workplace social performance has produced mixed results. Consequently, several calls have been made to account for family business heterogeneity to understand better how family involvement in the business affects the workplace social performance. The authors respond to these calls by exploring the governance antecedents that can catalyze family firms’ workplace social performance. Design/methodology/approach Using qualitative comparative analysis, the authors analyze 131 family firms from the STEP survey data. Findings The authors find two governance configurations that lead to better family business workplace social performance. The first configuration is the combination of 100% family ownership, high family involvement in management and a mix of outside directors and family members on the board. The second configuration is the combination of less than 100% family ownership and low family involvement in management. Originality/value The study builds on and extends the nascent work suggesting the integration of agency and stewardship theories. The authors show that these two theoretical approaches are able to not only coexist, but that they can also be complementary in helping to understand the unique workplace social behaviors of family firms.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".