A Discourse on Foresight and the Valuation of Explicit and Tacit Synergies in Strategic Collaborations
Bibliographic record
Abstract
One of the most important questions in business partners’ collaboration is whether their strategies create a collaborative synergy and thus add market value. This paper aims to develop a conceptual framework that will be useful for scholars and practitioners in developing foresight for explicit synergies and valuing tacit synergy in strategic collaborative ventures. The paper comprises a novel theoretical and empirical contribution to the foresight that is required for an explicit competence-based synergy in collaborative ventures from a resource-based view. It employs the ARCTIC framework and values a tacit competence-based synergy using simple and compound real options. Moreover, the paper makes several theoretical and empirical contributions to the study of strategic management, international business, and corporate finance disciplines. Finally, the 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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.010 | 0.025 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".