When Does Prior Experience Pay? Institutional Experience and the Multinational Corporation
Bibliographic record
Abstract
This study reexamines organizational learning theories to reconcile the conditions under which prior internationalization experience leads to performance gains for multinational corporations (MNCs) with varying host-country institutional experiences in different regulatory environments. Using field studies on telecommunications regulation, executive interviews conducted in Brazil, Spain, Portugal, Canada, and the U.S., and foreign direct investment data for 96 subunit operations investing in the Brazilian telecommunications industry from 1997 to 2004, I develop an experiential-learning theoretical framework to explain the mechanisms driving MNCs’ performance in subsequent host-country institutional environments given the prior experience they acquired in 80 heterogeneous regulatory environments. I predict and find that MNCs with highly similar institutional experience compared with the target country’s institutional environment will succeed. Empirical evidence suggests that similarity, breadth, and depth of prior regulatory experience significantly prolong survival. In contrast, firms with institutional experience unrelated to the target country’s regulatory environment experience learning penalties and are six times more likely to fail. These findings suggest that variations in learning contexts affect organizations’ learning curves.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".