How Does Media Coverage of Corporate Social Irresponsibility Influence Cross-Border Acquisition Completion? Evidence from Chinese MNEs
Bibliographic record
Abstract
Abstract Previous studies have found that media coverage of a firm's corporate social irresponsibility (CSiR) often delays or blocks the completion of a cross-border acquisition when the acquiror is a multinational enterprise (MNE) from an emerging market. Drawing from the attention-based view, we argue that the effects of Chinese MNEs’ CSiR on deal completion vary depending on several contextual factors, as these factors garner more attention by making the deals more salient to stakeholders. Using a sample of cross-border acquisitions by Chinese MNEs from 2013 to 2020, we find that CSiR media coverage per se does not decrease the likelihood of a deal's completion. However, consistent with attention-based arguments, we find that CSiR media coverage negatively affects the deal's completion when the acquirors are state-owned enterprises and when the target country has high institutional quality. Our findings enhance our understanding of the effects of CSiR on cross-border acquisitions by highlighting the moderating roles of contextual factors related to stakeholder attention. Thus, it is important for MNEs to recognize the boundary conditions that may influence the potential sanctions from local stakeholders. Based on these findings, this study contributes to the literature on CSiR, cross-border acquisitions, and stakeholder attention.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".