In the Eye of the Beholder: Global Analysts’ Coverage of Family Firms in an Emerging Market
Bibliographic record
Abstract
How do analysts make decisions about which firms to cover? Previous research has not considered how such decisions can be influenced by cultural understandings about appropriate forms of corporate governance. Drawing upon the institutional logics perspective, we propose that analyst firms’ home-country institutional logics of corporate governance can shape analyst perception of coverage risks for family firms. Specifically, we argue that given the negative view towards family governance in shareholder-based logic, family firms are less likely to be covered by analyst firms from shareholder-based countries than by those from stakeholder-based countries. Furthermore, the coverage divergence between shareholder- and stakeholder-based analyst firms will be greater for family firms featuring higher risks of value assessment and expropriation. We test our framework in the context of global analysts’ coverage of publicly listed firms in Taiwan between 1996 and 2005 and find empirical support. Our study contributes to the institutional logics perspective by establishing the implications of corporate governance logics for analyst coverage and providing a boundary condition for agency theory. We also uncover a less-noted source of institutional variation among the analyst community.
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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 | 0.003 | 0.023 |
| 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 | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".