The Impact of Protectionism on the Completion and Duration of Cross-Border Acquisitions
Bibliographic record
Abstract
This study investigates the impact of protectionism in a host country on the completion likelihood of an announced cross-border acquisition and the time required to complete the acquisition. Adopting a legitimacy perspective, I identify and test boundary conditions at the firm and national levels to study the relationship between protectionism and cross-border acquisition completion and duration. I hypothesise that in host countries with a high level of protectionism, as reflected by the level of non-tariff barriers, cross-border acquisitions are less likely to be completed and the time taken to close the acquisition deal increases. I also propose that the relationships between protectionism and acquisition outcomes are moderated by critical target firm characteristics and the host country's economic condition. Specifically, these moderators include target firm size, target firm performance, the degree to which the target industry is sensitive to national security concerns, and the host country's GDP growth. I test these hypotheses using a sample of 675 cross-border acquisition attempts by firms in the manufacturing and services industries (excluding financial services) into the U.S. and Canada between 1995 and 2015. The results of the statistical analysis support the prediction that the higher the degree of protectionism, the lower likelihood of acquisition completion and the longer the duration is between acquisition announcement and completion. Findings also support the predicted moderating effects of the target firm size, performance and national security concern. However, the hypothesised moderating effect of the host country's GDP growth was not supported by the results. This finding suggests that host country protectionism impacts cross-border acquisition attempts, irrespective of the host country's economic development. These findings have significant implications for legitimacy-based explanations of cross-border acquisitions. In particular, the results of this study indicate that when protectionism is high, the host country is more likely to raise concerns around the legitimacy of foreign firms. In turn, these firms face adverse host country scrutiny which can result in a failed acquisition attempt, or an extended and therefore, costlier acquisition deal. The framework and findings of this study contribute to an institution-based view and, in particular, to a legitimacy-based perspective in the research on the internationalisation of firms.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".