Effect of Corporate Social Responsibility Disclosure on Financial Performance of Manufacturing Firms Quoted on Nairobi Securities Exchange
Bibliographic record
Abstract
The purpose of this study was to determine the effects of corporate social responsibility disclosure on the organization’s financial performance. Specifically, the study examined the effect of environmental disclosure, community disclosure, employee disclosure and financial performance of quoted manufacturing companies in Kenya. The study was anchored on stakeholder’s theory, legitimacy theory and stewardship theory. The study employed census research design. The target population was all the manufacturing firms that are listed in the NSE from 2007 to 2017. The researcher identified manufacturing firms because they impact heavily on environment through waste and pollution they discharge and in addition they are capital and labour intensive organizations. This study used secondary data and content analysis of data from the published financial records and analysis of other reports of the companies. Stata version 12 was used to analyze the data using both descriptive and inferential methods. The findings were presented in form of tables and figures. It was expected that the study findings help business owners and managers to make more informed decisions on whether or not to adopt corporate social responsibility disclosure. The study also expected to help investors to understand the relationship between corporate social responsibility and financial performance which will help them design and allocate their portfolio in a manner that maximizes returns by investing in firms and organizations that make decisions based on ethical concerns. Further, the study also expected to enrich the discussion on corporate social responsibility and contribute to the existing literature and theories. The study found positive and non-significant effect of environmental disclosure, community disclosure and financial performance of quoted manufacturing companies in Kenya. Moreover, employee disclosure had inverse and non-significant effect on financial performance of quoted manufacturing companies in Kenya.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".