Beacons not burdens: Business groups and corporate social performance around the world
Bibliographic record
Abstract
Abstract Research Summary Prior studies on business groups (BGs) have predominantly focused on the impact of group affiliation on financial performance. In contrast, we argue that BG affiliates will outperform standalone firms in terms of corporate social performance (CSP) and that this effect will be positively moderated by the strength of formal and informal institutions. Moreover, we examine also differences among BGs and hypothesize that diversification and hierarchy of the group will negatively affect the CSP of affiliates. Employing a panel of 4368 firms from 43 countries between 2003 and 2016 and a propensity score matching approach in our regressions, we find robust support for these predictions. Our findings advance two distinct strands of literature on BGs and, respectively, corporate social responsibility. Managerial Summary BG are a common organizational structure in many countries. Despite this, we still do not know much about them beyond their financial performance. In this study, we focus on examining the impact of BG affiliation on non‐financial performance (i.e., CSP) in the light of growing societal grand challenges. Using an international dataset of several thousands of firms, we find out that BG affiliates exhibit superior CSP results compared to non‐affiliated firms. These positive effects of affiliation are increased in environments with strong formal and informal institutions but reduced within groups that are more diversified and hierarchical. Our findings showcase the importance of BGs in tackling some of today's grand challenges and provide support for more nuanced approaches to study BGs across countries.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".