Duality of diversification: the effect of internal and external configuration of diversification on firm performance
Bibliographic record
Abstract
This thesis is the first to connect the diversification literature in strategic management with the literature on multi-market contact. I argue that there exists a duality of diversification, and that both sides of the duality have an impact on firm performance. The internal configuration, which potentially generates the synergistic use of resources, minimizes transactional costs among business divisions, and cross-fertilizes social capital of the corporate umbrella, is represented by the multi-divisional corporate structure of a diversified firm. The external configuration, which influences firm performance in that a focal firm can benefit from a coexistence with its rivals in a coordinated way, includes the first- and second-order multi-market contacts among firms, and the macro-level pattern of the inter-firm network. The second-order multi-market contact is further divided into Simmelian ties and quasi-Simmelian ties. Simmelian ties are referred to as triadic ties. Quasi-Simmelian ties are a focal firm's connections with the rivals of the focal firm's directly connected rivals. My arguments are tested in the Canadian general insurance industry through the use of spatial econometrics modeling. The analyses do not provide support for the beneficial effects of the internal configuration and the first-order multi-market contact. However, my results find evidence for the performance advantage of establishing Simmelian ties and quasi-Simmelian ties with rivals. Finally, my results reveal that the inter-firm network in the Canadian general insurance industry is negatively connected. A given firm's performance in such a network is harmed by the existence of all other firms, and weakened by those firms with which the given firm is directly connected. In addition, the given firm benefits from those far away rivals, whose existence undercuts the competitive blunt of those rival firms with which the given firm is directly confronted.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".