How Corporate Social Responsibility Helps MNEs to Improve their Reputation. The Moderating Effects of Geographical Diversification and Operating in Developing Regions
Bibliographic record
Abstract
Abstract Multinational enterprises (MNEs) can enhance their reputations through advanced social initiatives and management practices. These firms often locate facilities in developing countries to benefit from lax environmental and social regulations, and to reduce their operating costs. MNEs can, however, also contribute positively to the development of those countries through corporate social responsibility (CSR) activities. This paper argues that MNEs operating in developing regions can enhance their level of corporate reputation through the implementation of CSR initiatives that meet specific stakeholders' expectations of the firm's activities in these areas. In addition, we argue that MNEs with units based in different regions strengthen the impact of corporate social performance on corporate reputation. Based on a sample of 113 US MNEs from the chemical, energy, and industrial machinery industries over the period 2005–2010, our findings show that CSR has a positive effect on corporate reputation. In addition, MNEs' operations in developing regions intensify the positive relationship between corporate social performance and reputation, although geographical diversification does not necessarily enhance MNEs' reputation through corporate social performance. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".