Business group prevalence and impact across countries and over time
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine two prominent perspectives on business group functioning, institutional void (IV) and entrenchment/exploitation (EE), that make different predictions about the effect of business group (BG) on the economy. The authors examine the effects of BG prevalence in an economy and its effect on macroeconomic outcomes including foreign direct inward and outward investment, innovation and development of the financial sector. Design/methodology/approach The authors build a unique database by extracting estimates of BG prevalence for multiple countries between 1978 and 2012 from the existing literature and use this to test conflicting predictions derived from the IV and EE perspectives, respectively. Findings The authors find no consistent evidence that BG prevalence diminishes over time with economic development as IVs diminish, which is predicted by the IV perspective. Instead, the long-term persistence of BGs in many countries appears to be more consistent with the EE perspective. However, this study also finds no support for the perspective that high levels of BG prevalence are negatively associated with country-level indicators and determinants of economic development and competitiveness, as suggested by that perspective. Originality/value The authors conclude that there is no robust support for either the IV or the EE perspective and highlight the need for more contextualized theorizing about the evolution of BGs.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".