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This paper contributes to risk management research with reference to disclosure of risk specific information within the oil and gas industry. This paper provides empirical evidence regarding voluntary and mandatory disclosure behaviour from both a quantitative and qualitative perspective.

A longitudinal empirical study examines probabilistic reserve quantum reporting of UK companies, over a time-period spanning voluntary and mandatory disclosure. The researchers analyse disclosure behaviour under voluntary and mandatory time spans using a logistical regression approach to measure determinants of risk reporting. Form of regulation is considered as the fundamental driver for disclosure whilst controlling for other relevant variables. Implications for developing international regulation are presented with suggestions for further research.

Mandatory reporting is not seen as a significant influence to disclosure. Degree of risk, quality of audit firms, level of stock exchange and organisational visibility each impact on disclosure. The findings indicate that a mandatory disclosure approach is ineffective, partially explained by mimetic and normative forces and a balancing of agency-related costs and benefits. There is an inverse relationship between level of risk and risk reporting.

Generalisation of the findings is limited due to the specific context of the extractive industry.

The paper seeks to inform the International Accounting Standards Board's (IASB) on-going consideration of risk reporting and also its extractive industries deliberations.

The paper provides original insight into the area of risk management with particular focus on risk specificity and quantitative metrics for risk profiling not previously tested. The paper introduces risk profiling as a variable in risk disclosure.

In this study the authors examine the nature and contents of key audit matters (KAMs), and the consequences of KAMs reporting on audit quality in the context of a developing country, Bangladesh. The authors’ proxies of audit qualities are discretionary accruals, small positive earnings surprise, audit report lag, earnings management via below the line items and audit fees.

The authors use content analysis of the KAMs for the period 2018–2021 to understand the nature and extent of KAMs reported by auditors in Bangladesh. The authors then use multivariate regression analysis to examine the effect of the number and content characteristics of KAMs on audit quality by using multivariate regression analysis.

Auditors in Bangladesh disclose a higher number of KAMs compared to other countries, disclose short descriptions of KAMs and industry generic KAMs. The authors document significant cross-sectional variations in the number and content characteristics of KAMs reported by auditors in Bangladesh. The authors’ pre-post analysis suggest that audit quality has improved after the adoption of KAMs. Cross-sectional analysis suggests that KAMs number and content characteristics are related to audit quality.

The authors’ findings imply that the KAMs reporting has the potential to play significant monitoring role in reducing the opportunistic behavior of managers. Hence, KAMs reporting can play a significant role in reducing the agency problem. For regulators, shareholders and corporate managers, the authors’ findings imply that if the audit quality is to be increased, the audit effort should be supported by an appropriate amount of audit fee.

The content characteristics of KAMs significantly influence managerial reporting behavior and affect the level of audit efforts.

Unlike developed countries (Gutierrez et al., 2018; Lennox et al. 2022), this study supports that KAMs reporting improves audit quality and control opportunistic behavior of managers in developing countries. The authors show that even though the KAMs disclosure quality is poor, it has the potential to improve financial reporting quality.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.