Valuing Collaborative Synergies with Real Options Application: From Dynamic Political Capabilities Perspective
Bibliographic record
Abstract
This paper aims to justify propositions that the dynamic political capabilities of collaborative partners to manage their institutional contexts are important drivers of collaborative synergies which can be valued by real options. To date, the institutional context of collaborative corporate strategies (strategic alliances, mergers, and acquisitions), particularly the analysis of the influence of government agencies on the synergies or unrealized synergies of collaborative ventures, remains unexplored. Moreover, the interdependence between the institutional dimensions of the collaborative strategies, the dynamic political capabilities of the collaborating partners, and collaborative synergies are needed to be integrated into new conceptual models and a new framework. This paper contributes to this request by providing a cohesive framework of micro-foundations with dynamic political capabilities and demonstrating an application of simple and compound sequentially combined real options for collaborative synergies’ valuation in the findings and discussion section. This paper makes several theoretical and empirical contributions to international business, strategic management, and corporate finance. The practical implication of the research is evidence that food retailers who want to grow with the latest consumer trends will need dynamic political capabilities to deal with the impact of an institutional context. Finally, this paper discusses research limitations and future work.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".