How do Leading Retail MNCs Leverage CSR Globally? Insights from Brazil
Bibliographic record
Abstract
This study examines how multinational corporations (MNCs) from the retail sector deal with four challenges they face when adopting Corporate Social Responsibility (CSR) policies: the challenge of developing well-performing CSR projects and programs, building competitive advantages based on CSR, responding to local stakeholder issues in the host countries and learning from different CSR experiences on a worldwide basis. Based on in-depth case studies of two globally leading retail MNCs (with strong operations in Latin America), the concept of Transverse CSR Management emerged. Transverse CSR Management is defined as a distinctive form of organizational configuration that crosses different functional areas, country operations, and the boundaries of the firm. In particular, this article makes three main contributions: (1) we identify four central challenges faced by MNC managers when developing their CSR strategies; (2) we propose the concept of Transverse CSR Management to face these central challenges (together, at the same time) and identify its key elements (top management, external stakeholders, functional areas, and country subsidiaries); and (3) we propose four mechanisms (hierarchical, relational, cultural, and collaborative) through which the concept of Transverse CSR Management can be implemented by practicing managers. This study provides valuable insights for MNC managers in headquarters and subsidiaries on the issues they need to address in order to successfully deal with the four CSR-related challenges.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".