It Takes a Village: Leveraging Organization Theory for Unpacking Impact in Business Families
Bibliographic record
Abstract
This symposium explores how business families can deploy their wealth and influence to foster positive social and environmental impact. While research in organization studies has focused on low-power actors striving for grassroots impact, less attention has been paid to elites who, as resource owners and long-term oriented wealth-holders, have the potential to catalyze systemic change. Business families, defined as extended kin networks managing shared assets and exerting significant influence over economic and societal outcomes, are uniquely positioned to integrate social impact into their investment strategies. Yet they face challenges in harmonizing family values and goals, overcoming entrenched interests, and addressing external scrutiny derived from their concentration of wealth and power. This symposium seeks to deepen understanding of how business families address these challenges and organize to create positive social and environmental impact. To do so, we bring together four papers that draw on core organization and management theories to investigate business families and impact from both an internal perspective, focusing on how business families transform their organizing practices and long-term objectives to embrace sustainability, and an external perspective, focusing on how business families ensure that they realize and scale positive social and environmental impact through their deployment of capital. By integrating insights from family business and organization theory, the symposium will offer fresh perspectives on how elite actors like business families can become key drivers of socially responsible and sustainable organizational transformations. Islands of Bureaucracy in a Patrimonial Sea: Investigating Political Organizing in Business Families Author: Bridget Kustin; Author: Luca Manelli; Politecnico di Milano Author: Marya Besharov; University of Oxford Author: Mary Johnstone-Louis; University of Oxford Holding it Together: A Systems Psychodynamics Perspective on Contested Corporate Sustainability Author: Sophia Jungk; EBS University of Business and Law Author: Marya Besharov; University of Oxford Author: Falko Paetzold; Author: Matthias Waldkirch; Reflecting and Refracting Purpose in Impact Investment Author: Timo Busch; University of Hamburg Author: Sarah Carroux; University of Hamburg Author: Ibrat Djabbarov; Imperial College London Author: Falko Paetzold; Investing for Systems Change: An Empirical Study of Private Wealth Investors Author: Alban (Ray-Pern) Yau; Massachusetts Institute of Technology Author: Jason Jesurum Jay; Massachusetts Institute of Technology
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".