The purpose of this research is to examine how environmental committees, institutional shareholdings, and board independence affect managerial carbon disclosure decisions, particularly those of firms belonging to highly polluting industries. We focus on Italian firms that operate in a code law environment but that have the option either to adopt the unitary corporate structure prevalent in common law countries or to retain the dual corporate structure used in code law countries. We use weighted and unweighted carbon disclosure indexes based on the Kyoto Protocol requirements. The findings show that all factors greatly affect voluntary carbon disclosure and that their impact is especially strong for firms in highly polluting industries. This study has important implications for managers and regulators.

Purpose – The purpose of this paper is to better understand how mandatory risk categories are disclosed and to provide a better understanding of the reasons why risk disclosure looks less useful than it ought to be. Design/methodology/approach – We analyze how Italian banks provide risk information, by focusing on its characteristics to find out any differences between the notes to the financial statements and the public report, both prepared in compliance with the instructions of the Bank of Italy. We assess the risk-related reporting practices of 66 Italian banks, based on a content analysis of the two mandatory reports, and verify whether bank-specific factors explain any differences. Findings – Italian banks formally comply with the Bank of Italy’s instructions, but there is discretion to choose the characteristics of the information provided. Despite different risk categories to disclose in each report, disclosure is quite uniform, although banks tend to provide denser information in the notes to the financial statements and the difference in the economic signs between the two reports decreases as the level of risk increases. Practical implications – The significance of this study goes beyond the debate taking place in the academic arena, as it can be largely relevant for preparers, those responsible for setting international and national accounting standards, the Basel Committee on Banking Supervision and the domestic supervisory authorities, particularly concerning the possible introduction of requirements that are more explicit than the existing ones. Originality/value – The Italian setting is very relevant because unlike other countries, Italy adopts “interventionist enforcements”, which are regarded as a critical tool for achieving the minimum disclosure requirements. Moreover, the two sets of disclosure required by the Bank of Italy have never been investigated in a single data set.

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Purpose The purpose of this paper is to develop a model for assessing the audit evidence of the going-concern (GC) assumptions underlying the preparation of financial statements. Design/methodology/approach This research analyses 678 audit opinions of Italian listed firms from 2007 to 2016 and uses a multiple linear discriminant analysis to create a GC score, which includes variables suggested by the international standards on auditing (ISA) 570 and by literature on GC. Findings The model provides three cut-off scores which can orient auditors towards issuing the most appropriate GC audit opinions (unmodified opinion, unmodified opinion, which includes emphases of matter, qualified opinion or disclaimer of opinion). Research limitations/implications The development of the model is mainly based on public data and does not assess confidential information that is not disclosed in audit opinions. Practical implications This model can enable auditors to identify the most appropriate GC opinion and align auditor’s opinions in similar circumstances, thereby reducing their reliance on discretion and increasing the reliability of their judgement with a higher degree of accuracy. Moreover, this research lists additional events or conditions that may individually or collectively cast significant doubt on GC assumptions. Originality/value This study goes beyond the traditional decision-making process, apparently binary in nature, between “continuity” and “failure” or between “unmodified” and “modified” opinions. It is conceived to detect the different degrees of uncertainty that affect GC evaluations to orient auditors’ professional judgements.

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PurposeThis study aims to ascertain the intentions of risk managers to use artificial intelligence in performing their tasks by examining the factors affecting their motivation.Design/methodology/approachThe study employs an integrated theoretical framework that merges the third version of the technology acceptance model 3 (TAM3) and the unified theory of acceptance and use of technology (UTAUT) based on the application of the structural equation model with partial least squares structural equation modeling (PLS-SEM) estimation on data gathered through a Likert-based questionnaire disseminated among Italian risk managers. The survey reached 782 people working as risk professionals, but only 208 provided full responses. The final response rate was 26.59%.FindingsThe findings show that social influence, perception of external control and risk perception are the main predictors of risk professionals' intention to use artificial intelligence. Moreover, performance expectancy (PE) and effort expectancy (EE) of risk professionals in relation to technology implementation and use also appear to be reasonably reliable predictors.Research limitations/implicationsThus, the study offers a precious contribution to the debate on the impact of automation and disruptive technologies in the risk management domain. It complements extant studies by tapping into cultural issues surrounding risk management and focuses on the mostly overlooked dimension of individuals.Originality/valueYet, thanks to its quite novel theoretical approach; it also extends the field of studies on artificial intelligence acceptance by offering fresh insights into the perceptions of risk professionals and valuable practical and policymaking implications.

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