The Multiple Dimensions of Institutional Complexity in International Business Research
Bibliographic record
Abstract
Going back into previously exited markets is a significant management risk. But, how are re-entry risks managed? By adding strategic reference point (SRP) rationales to the risk management literature, this chapter examines re-entry after initial entry and divestment on a sample of 654 multinational enterprise (MNE) re-entrants. The authors move away from narrow risk management lenses according to which risks happen in isolation and theorize that MNEs simultaneously manage international risk by exploiting the trade-offs among external and internal sources of risk. The authors explain that, for re-entrants, exit may become the SRP for evaluating future strategic choices. The results suggest that re-entrants tend to manage re-entry risk by choosing partner-based modes that enable them to maintain strategic flexibility at re-entry. Surprisingly perhaps, market-specific experience acquired during the initial market foray does not provide strategic flexibility, in that highly experienced firms still experience risk trade-offs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".