Corporate Social Responsibility Theories in the Context of Global Transformational Events: A Scoping Review
Bibliographic record
Abstract
ABSTRACT This scoping review examines the applicability and evolution of corporate social responsibility (CSR) theories, which focus on voluntary corporate actions, during global transformational events. Additionally, the study explores the relevance of broader sustainability management (SUSM) theories, which provide comprehensive frameworks for integrating sustainability into corporate strategies, to assess their adaptability under such abnormal operating conditions. We identify and review 10 key theories pertinent to SUSM during normal and abnormal operating periods: Agency Theory, Cognitive Theory, Ecological Modernization, Institutional Theory, Leadership Theory, Legitimacy Theory, Neoclassical Theory, Shareholder Theory, Socio‐Ecological Systems Theory, and Stakeholder Theory. Our findings reveal that these theories are dynamic, evolving in response to global crises, thereby influencing and being influenced by corporate behaviors. This study contributes to the academic literature by highlighting the interplay between theoretical evolution and real‐world applications of CSR and SUSM. For managers, the study offers insights into building corporate resilience and adaptability, while policymakers are provided with guidance on fostering regulatory environments that support sustainable corporate practices during periods of disruption. These contributions underscore the importance of refining SUSM frameworks to guide corporate decision‐making in increasingly volatile environments.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".