Profit-Seeking Corporate Social Responsibility in Developing Countries: The Risk of Conflating CSR and R&D
Bibliographic record
Abstract
Strategic corporate social responsibility (CSR) has drawn praise for representing the "sweet spot" between communities’ needs and firms’ resources, capabilities and efforts. But what if the concept is pushed to its limits? A firm can initiate CSR projects not just to help communities, but to directly realize profit from them. In this conceptual paper, we ask how CSR is understood and functions when the intent of CSR projects is to conduct a form of research and development (R&D). The intended innovations are not science-based, but socially oriented; they seek to determine how to profitably meet the needs of poor people in developing countries. We develop our argument from conversations with managers and teaching cases that explain how executives believe CSR helps firms (learn how) to profitably serve new potential customers – whether through developing new markets or new products and services with a social purpose. Using CSR as a form of "living R&D" allows firms to make mistakes and to avoid short-term shareholder pressures. But there are very real risks to what in essence is unregulated experimentation on poor people, and we highlight some of them. Our argument highlights the ways in which such innovation and profit-oriented CSR challenge thinking on both CSR and R&D, and we make practical recommendations for how to ensure that intended beneficiaries are not harmed, but can instead benefit.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| 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".