Real options theory and international investment strategy: past, present and future
Bibliographic record
Abstract
This study reviews the research on international investment strategy from a real options lens and discusses avenues for future research. Uncertainty has been a persistent feature in international business, and multinational enterprises (MNEs) have to deal with uncertainty (e.g., market, political, technological) in strategic decisions concerning foreign market entry mode, scale, and timing. The conventional wisdom in international business (IB) is to view uncertainty as an unfavorable condition that complicates the decision making process and exposes firms to downside risks and losses; as a result, much effort has been put into designing strategies to minimize potential negative outcomes in an uncertain environment. In contrast with the conventional wisdom, real options theory offers a fresh perspective to tackle uncertainty in international investment: Uncertainty in the host market does not necessarily pose a threat to MNEs’ profitability; it may also present valuable opportunities for MNEs to exploit. From this perspective, MNEs should design strategies such that they have the flexibility to benefit from upside potentials while containing downside losses in future. The unique contribution of real options theory is that it provides a general theoretical foundation on which IB scholars can conceptualize how MNEs make investment decisions in an uncertain environment as well as adjust their investment strategies in response to new information in the environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".