How Multinational’ Social Performance Influences Performance of Subsidiary in China: The Role of Distance
Bibliographic record
Abstract
This paper addresses how corporate social responsibility of multinationals in China influences their Chinese subsidiaries’ performance. Using panel data regression, (1) we investigate the effects of corporate social moderating effects of institutional and geographic distances between their home countries and China. Our findings show, first, that subsidiaries’ corporate social performance is associated positively with their profit in China. Second, we find that the greater the cultural, economic, and geographic distances between the home country and China, the less likely it is that a subsidiary will benefit from corporate social responsibility. We enrich the theoretical understanding of the institutional conditions under which corporate social responsibility leads to specific outcomes by adding new institutional elements—the differences of culture and the economies between multinationals’ host and home countries. Our findings suggest that although corporate social responsibility is still new to China, being socially responsibility provides financial benefits to multinationals. Their global and local corporate social responsibility strategies have to adjust based on local cultures and economic levels, especially with respect to fundamental beliefs about spirituality and values. The paper fulfilled the research gap between multinationals’ social practices and their performance in host countries at the subsidiary level by providing empirical evidence concerning the responses to corporate social responsibility initiatives in emerging markets and clarifying the conditions under which foreign affiliates’ corporate social responsibility engagement in the host country can gain better returns in local markets.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".