Toward a typology of business groups: A qualitative content analysis
Bibliographic record
Abstract
By facilitating wider communication networks and improving the performance of their affiliated businesses in complex environments, businesses can increase their competitiveness. Understanding the characteristics and diversity of business groups is necessary for developing and implementing them. In this study, we examined the question of how business groups can be classified. What criteria can be used to separate them? We conducted a qualitative analysis of the content of 48 scientific journals published between 1999 and 2020 and selected 215 articles based on purposive sampling during two stages of screening. As a result of the content analysis, three main themes were identified: “origins of group control and ownership”, “groups' institutional origins”, and “intergroup relations”. Also, at the first subtheme level, six categories were identified: group control level, group ownership type, diversity of group relations, dependence and cooperation level, relationship structure, and institutional contexts. There are 12 subcategories included in the second-level subthemes. “origin of corporate governance”, “type of group ownership”, “type of institutional contexts”, “intra-group diversification”, “extra-group diversification”, “internal cooperation”, “formalization ratio”, “length of relations”, “external cooperation”, “geographical area”, “depth of cooperation”, “group maturity level”. Lastly, axis factors related to the diversity of business groups were used to develop a set of typologies.
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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 | 0.052 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".