Repeat market entries in the internationalization process: The impact of investment motives and corporate capabilities
Bibliographic record
Abstract
Research Summary: This study examines strategic and resource contingencies that shape MNEs’ country location choices. Our analysis of overseas production investments by Japanese firms (1971–2006) finds that differences in investment motives (i.e., horizontal and vertical investment) and corporate capabilities (i.e., marketing and production capabilities) moderate how prior entries into a host country affect subsequent entry decisions. In general, the positive impact of prior entries on investment in a country is weaker for horizontal investments and stronger for vertical investments. A more nuanced relationship emerges when entry decisions are analyzed in conjunction with heterogeneous corporate marketing and production capabilities. The study illustrates the novel insights to be gained from analyzing the joint impact of path dependence, managerial intentions, and corporate capabilities on country location decisions. Managerial Summary: Multinational firms often make multiple investments over time in a concentrated set of countries, accumulating superior knowledge and capabilities in these environments. Researchers have nonetheless uncovered factors that can lead firms to deviate from strategic trajectories defined by their prior investments. In a statistical analysis of country entry decisions by Japanese manufacturing firms over a 35‐year period, we found that firms’ tendencies to reinvest in the same host countries were smaller for horizontal (i.e., market‐seeking) investments but greater for vertical (i.e., efficiency‐seeking) investments. We also found that organizational capabilities influence the geographic trajectory of international expansion: firms with stronger marketing and production capabilities were less likely to be influenced by the locations of prior entries and were more likely to invest in new countries.
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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 | 0.009 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".