Determinants/Motivations of Corporate Social Responsibility Disclosure in Developing Economies: A Survey of the Extant Literature
Bibliographic record
Abstract
The main purpose of this study is to systematically analyse and synthesise the empirical literature on the drivers and motivations of CSR disclosure in developing countries. Previous studies on CSR disclosure have primarily investigated the accuracy of disclosure claims, impact on various actors, and the factors deriving CSR disclosure. While literature on CSR disclosure dates back to 1983, the number of studies have increased substantially in recent years, with 86% of studies being published in the last decade and a half. The results revealed that both internal and external factors influence the disclosure of CSR information. Internal factors influencing CSR disclosure include company characteristics such as size, industry, financial performance, corporate governance elements such as board size and board independence, and types of ownership. In addition, corporate polices and concerns also influence the disclosure of CSR-related information. External category factors influencing CSR disclosure include, regulatory pressures, government pressure, media concerns, social-cultural factors, and industry-level factors such as the level of industry competition, customers’ concerns, and multiple listing of a firm. Furthermore, global value chains, international buyers, international NGOs, and international regulatory bodies pressure companies in developing countries to disclose social and environmental information. In terms of motivations, companies disclose CSR information to improve their corporate reputation, improve their financial performance, access investment opportunities, and manage key stakeholders. The dominant theoretical frameworks used to explain the determinants of CSR disclosure include legitimacy theory and stakeholder theory.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".