The Two Faces of Corporate Social Irresponsibility Impacts in Cross-Border Acquisitions?
Bibliographic record
Abstract
When multinational enterprises (MNEs) pursue cross-border acquisitions, corporate social irresponsibility (CSiR) can significantly impact the time to complete acquisition. However, differing theoretical perspectives imply opposing outcomes. The stakeholder resistance view suggests that media coverage of CSiR provokes adverse reactions from local stakeholders, potentially delaying acquisition completion. In contrast, the preemptive strategy view posits that MNEs may proactively expedite the process to mitigate stakeholder opposition, thereby reducing the time required to complete the acquisition. We further argue that stakeholder attention serves as a moderating factor between these contrasting perspectives by functioning as either an enhancing or attenuating mechanism. Using a sample of Chinese MNE cross-border acquisitions from 2008 to 2020, we find that firms with higher CSiR media coverage complete acquisitions faster, largely supporting the preemptive strategy view. In addition, we find that when stakeholder attention to cross-border acquisitions is high, the effectiveness of these preemptive strategies diminishes. This study contributes to the literature on cross-border M&A and international stakeholder management by analyzing how CSiR media coverage, firms’ strategic actions, and stakeholder attention collectively impact acquisition completion time.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".