Beyond Good Intentions: Designing CSR Initiatives for Greater Social Impact
Bibliographic record
Abstract
Are corporate social responsibility (CSR) initiatives providing the societal good that they promise? After decades of CSR studies, we do not have an answer. In this review, we analyze progression of the CSR literature toward assessing the performance of CSR initiatives, identify factors that have limited the literature’s progress, and suggest a new approach to the study of CSR that can overcome these limits. We begin with comprehensive bibliometric mapping illustrating that although social impact has infrequently been its explicit focus, the CSR literature has measured outcomes other than firm performance, especially in the current decade. Thereafter, we conduct a more fine-grained analysis of recent CSR studies. Adapting a logic model framework, we show that even the most highly cited studies have stopped short of assessing social impact, often measuring CSR activities rather than impacts and focusing on benefits to specific stakeholders rather than to wider society. In combination, our analyses suggest that assessment of the performance of CSR initiatives has been driven by the availability of large, public secondary data sources. However, creating more such databases and turning to “big data” analyses are inadequate solutions. Drawing from the impact evaluation literature of development economics, we argue that the CSR field should reconceive itself as a science of design in which researchers formulate CSR initiatives that seek to achieve specific social and environmental objectives. In accordance with this pursuit, CSR researchers should move toward “small data” research designs, which will enable studies to better determine causation rather than just identify correlation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.061 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".