Family and Lone Founder Ownership and Strategic Behaviour: Social Context, Identity, and Institutional Logics
Bibliographic record
Abstract
There is controversy in the literature about the effects of ownership on strategy and performance. Some scholars have taken agency explanations as definitive, arguing that closely held firms outperform. Empirical studies, however, show conflicting findings for firms with concentrated ownership: lone founder firms outperform, family firms do not. Such conflicts may be due to the failure of agency theory to distinguish between the social contexts of these different types of owners. We argue that explanations of performance must take into account not simply ownership, but who are the owners or executives and how their social contexts may influence their strategic priorities. Family owners and CEOs, influenced by family stakeholders in the business, are argued to assume the role identities and logics of family nurturers and thus strategies of conservation. By contrast, lone founders, influenced by a wider set of market-oriented stakeholders, are argued to embrace the identities and logics of entrepreneurs and strategies of growth. Family founders and founder-executives are held to blend both orientations. These notions are supported in a study of Fortune 1000 companies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".