MNC foreign investment and industrial disasters: The moderating role of technological, safety management, and philanthropic capabilities
Bibliographic record
Abstract
Research Summary : We investigate how industrial disasters can discourage FDI and how MNCs' technological, safety management, and philanthropic capabilities can moderate these effects. Using two unique panel data sets of entry and expansion of U.S. wholly‐owned manufacturing subsidiaries overseas, we found that industrial disasters are associated with reduced foreign entry of wholly‐owned subsidiaries in the disaster industry, but not for all firms in the host country experiencing the disaster. We also found that MNCs' technological, safety management, and philanthropic capabilities can, in some cases, positively moderate the negative relationships between industrial disasters and the foreign entry and expansion of wholly‐owned subsidiaries. Additionally, three‐way interactions with government stability suggest that technological and safety management capabilities substitute government stability in managing industrial disasters, while philanthropic capability complements government stability. Managerial Summary : How can MNCs' technological, safety management, and philanthropic capabilities overcome the effects of industrial disasters such as chemical spills and explosions in host countries? Our results show that industrial disasters are associated with reduced foreign entry of wholly‐owned subsidiaries in the industry in which the industrial disaster occurs, but not for other firms operating in the country experiencing the disaster. However, an MNC's technological capability can, in general, lower the negative consequences of industrial disasters in both the entry and expansion of its wholly‐owned subsidiaries. Regarding the institutional quality of a host country, the results imply that MNCs should develop philanthropic capability when the government stability of the host country is strong, and develop technological and safety management capabilities when the government stability is weak.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".