Organisational Characteristics, Corporate Governance and Corporate Risk Disclosure: An Overview - AdamuMusa Uba (Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438, 2021)
Certain attributes of corporate governance behaviour have been identified in academic research as major factors correlating with corporate risk disclosure amongst listed companies. This is in spite of the fact, however, that much of the empirical research in the area reveals mixed results. This study analyses corporate risk disclosure practice involving listed companies and investigates whether such diverse results are attributable to regulation, jurisdiction, operating industry, business environment, or the methodologies employed. We use risk disclosure, corporate governance and organisational characteristics keywords to search the relevant studies on which 46 empirical research papers were sampled, and employ a meta-analysis procedure to evaluate the findings of the previous empirical research. Our analyses reveal that firm size is the major organisational-specific characteristic affected by moderators, and board size and institutional investors are the major corporate governance variables that affect moderators. On the analysis of the nature of disclosure, financial risk information is higher for companies operating in the banking sector, while operational risk disclosure is higher for non-financial companies. Additionally, the study finds that the data generating procedure, time interval, diversity of sample and size, and the statistical technique employed are among the major factors that influence discrepancies among the prior studies. Such variables complicate stakeholders’ effort to comprehend the main factors that influence companies to unveil their risks profile. We propose that the current data collection process is labour intensive and time consuming, and promote the selection of smaller sample sizes compared to most of the existing research. It may be the case that constraints can be overcome through research that employs an automated procedure for analysis of textual data.