Applying the resource-based view to alliance formation in specialized supply chains
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore drivers of alliance formation in a specialized supply chain from a manager’s perspective, focussing on firm-specific resources, resources embedded in inter-firm relationships and capabilities under the control of the focal firm. Design/methodology/approach This paper focusses on the resource-based view to obtain insights from the analysis of a manager survey conducted in Canada’s beef sector, applying a logistic regression approach to study alliance formation. Findings In identifying significant roles for resource richness and diversification of resource usage, the analysis highlights the importance of resource characteristics underlying factor market imperfections as drivers of alliance formation in a single primary input supply chain. The results suggest that resource heterogeneity is important for alliance formation and organizational success in specialized supply chains. Research limitations/implications If previous alliance-related experience of managers, controlled for in the underlying cross-sectional survey, serves as an approximation for persistent unobservables impacting the alliance formation decision, we may face spurious state-dependence. Practical implications Managers interested in building compatible alliances in specialized single primary input supply chains may benefit from an improved understanding of the differential role of resource characteristics and resource heterogeneity for alliance formation, as these can function as a source of competitive advantage. Originality/value The analysis provides new insights from an individual manager’s perspective on alliance formation drivers in a specialized agri-food supply chain, thereby solidifying extant findings on alliance formation obtained in other sectors. The study contributes to the understanding of the role of resources in alliance formation with regard to prior relationship experience, resource heterogeneity and thus causal ambiguity, thereby also contributing to the debate of the role of relational capabilities vs firm-internal resources for sustained competitive advantage.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".