Corporate Social Responsibility Disclosure and the Value Relevance of Annual Reports for Listed Banks in Kenya
Bibliographic record
Abstract
This study investigated the relationship between corporate social responsibility disclosure and value relevance of annual reports for listed banks in Kenya. To do so, the study used content analysis and financial analysts’ perception to quantify corporate social responsibility disclosure, included by banks in their annual reports. The sample comprised of the annual reports of ten banks listed on the Nairobi Securities Exchange (NSE) over the entire period from year 2010 to year 2015. The study focused on banks due to additional regulation by the Central Bank of Kenya, (CBK). A survey research design was adopted. The study used both primary data and secondary data. Primary data was obtained through survey questionnaires administered on respondents who were financial analysts at a total of sixty one Kenya’s Capital Markets Authority (CMA) licensed firms (investment banks, stock brokers, fund managers and investment advisers) as at 30 April 2016. Secondary data was obtained from the corporate action register and handbook by the Nairobi Securities Exchange, the daily market statistics from the NSE data and annual reports released by the banks. Content analysis program ATLAS.ti 8, OneLook dictionary and Ms Excel 2007 were used for content analysis. Data analysis was carried out using SPSS version 20 and Stata 13. Descriptive statistics and inferential statistics were used for analysis. The results revealed that corporate social responsibility disclosure had a positive and significant relationship with value relevance of annual reports which was measured by the average market price per share, (MPS). This study therefore concluded that corporate social responsibility disclosure in annual reports of listed banks in Kenya affect the value relevance of the annual reports. The study recommends an expanded role of the auditor in reviewing the corporate social responsibility disclosure and other accounting narratives. Currently in accounting reporting, the auditor is not obligated to formally audit accounting narratives. Instead, an auditor reviews the accounting narratives to ascertain if the narratives are consistent with the financial statements. The study also recommends more guidelines and regulations in relation to non-financial disclosures to ensure that firms put clearer information in the hand of investors.
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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 | 0.003 | 0.018 |
| 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".