Cross-border M&A and the marketing timing of economic crisis
Bibliographic record
Abstract
Purpose – Cross-border M & A is one of the most important ways of international capital flows, and scholars have paid a lot of attention to this area, but a general explaining model has still not been generated. The purpose of this paper, based on Lambrecht (2004) and Bolton et al. (2013), is to build a general explaining model in this area and use the new model to explain some real world issues. Design/methodology/approach – The model work in this paper is based on Lambrecht’s (2004) real option model, which is the classical modeling method in this area, and take the economic crisis method of Bolton et al. (2013) into consideration; the authors also use the relative market condition to illustrate the motivation and market timing of cross-border M & A in this paper to connected the bidders’ markets and targets’ markets together to build the general explaining model for cross-border M & A. Findings – By analyzing the new model the authors build in this paper, the authors get three conclusions. Conclusion 1: when the bidders’ technologies are more advanced than the targets’, the bidders prefer market-seeking cross-border M & A, and the relatively higher the bidders’ technologies are, the stronger the preference is; when the bidders’ technologies are less advanced than the targets’, the bidders prefer technology-seeking cross-border M & A, and there exists an optimal relative technology ratio at which the bidders have the strongest motivation to exercise the technology-seeking type cross-border M & A. Conclusion 2: host country’s high economic growth helps attracting market-seeking cross-border M & A, conversely host country’s low economic growth helps attracting technology-seeking cross-border M & A. Conclusion 3: the bidders prefer to exercise the technology-seeking cross-border M & A when the home markets are stable or when economic crises happen in targets markets; and the bidders prefer not to exercise the market-seeking cross-border M & A when economic crises happen in home markets; and the relationship between the motivation for bidders to exercise market-seeking cross-border M & A and the possibility of the happening of economic crisis in home countries presents an inverse N-shape curve. Originality/value – In this paper the authors first use the relative market condition to illustrate the motivation and market timing in the cross-border M & A research area and build a general model based on current literatures.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".