Modeling Optimal FSA Recombination Decisions Using Real Options Theory
Bibliographic record
Abstract
MNEs can create a competitive advantage by ‘melding’ or adapting location-bound and non-location-bound FSAs across international borders. However, this process of melding also entails significant transaction costs which can be abated via internal and external modes of governance, prompting the question: which mode of governance should MNEs choose when engaging in cross-border FSA recombination? Recently, scholars have argued that the ‘optimal’ mode of governance is contingent upon the governance capabilities an MNE possesses: MNEs endowed with high external (Ote) governance capabilities should choose external modes of governance, while MNEs endowed with high internal (Oti) governance capabilities should choose internal or hierarchical modes of governance. However, this perspective has been strongly criticized on the basis of being tautological. In this paper, we seek to demonstrate that the optimal mode of governance is actually contingent upon the type and degree of uncertainty (i.e., endogenous vs. exogenous) in the external environment, rather than the governance capabilities the MNE is endowed with. We adopt a Real Options Theory (ROT) perspective to model the value of internal versus quasi-internal modes of governance under different forms of structural uncertainty, and use option pricing simulations to explore how the optimal choice of governance mode changes in response to changes in the degree of structural uncertainty.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".