Search results

The paper aims to estimate abnormal audit fees more precisely than the traditional audit fee model by applying an artificial intelligence (AI) method.

The AI technique employed in this paper is the deep neural network (DNN) model, which has been successfully applied to a wide variety of decision-making tasks. The authors examine the ability of the classic ordinary least squares (OLS) and the DNN models to describe the effects of abnormal audit fees on accounting quality based on recent research that demonstrates a systematic link between accruals-based earnings management and abnormal audit fees. Thus, the authors seek to imply that their new method provides a more precise estimate of abnormal audit fees.

The findings indicate that abnormal audit fees projected using the DNN model are substantially more accurate than those estimated using the classic OLS model in terms of their association with earnings management. Specifically, when abnormal audit fees predicted using the DNN rather than the OLS are incorporated in the accruals-based earnings management model, the adjusted R²s are larger. Additionally, the DNN-estimated coefficient of abnormal audit fees is more favorably associated to earnings management than the classic OLS-estimated coefficient. Additionally, the authors demonstrate that the DNN outperforms OLS in terms of explanatory power in a negative discretionary accruals subsample and a Big 4 auditor subsample. Finally, abnormal audit fees projected using the DNN method provide a better explanation for audit hours than those estimated using the OLS model.

This is the first approach that utilized a machine learning technology to estimate abnormal audit fees. Because more precise measurement yields more credible research results, the findings of this study imply a significant advancement in calculating unusually higher audit fees.

The purpose of this study is to examine the relationship between explanatory language, audit fees and audit hours to demonstrate that auditors use explanatory language in audit reports to explain perceived audit risk.

The authors construct the sentiment value, a novel audit risk proxy derived from audit reports, using big data analysis. The relationship between sentiment value and explanatory language is then investigated. The authors present the validity of their new metric by examining the relationship between sentiment value and accounting quality, taking audit fees and hours into account.

The authors first find that reporting explanatory language is positively related to audit fees. More importantly, the authors provide an evidence that explanatory language in audit reports is indicative of increased audit risk as it is negatively correlated with sentiment value. As a positive (negative) sentimental value means that the audit risk is low (high), the results indicate that auditors describe explanatory language in a negative manner to convey the inherent audit risk and receive higher audit fees from the risky clients. Furthermore, the relationship is strengthened when the explanatory language is more severe, such as reporting the multiple numbers of explanatory language or going-concern opinion. Finally, the sentiment value is correlated with accounting quality, as measured by the absolute value of discretionary accruals.

Contrary to previous research, the authors’ findings suggest that auditors disclose audit risks of client firms by including explanatory language in audit reports. In addition, the authors demonstrate that their new metric effectively identifies the audit risk outlined qualitatively in audit report. To the best of the authors’ knowledge, this is the first study that establishes a connection between sentiment analysis and audit-related textual data.

This chapter aims to build a systematization of the current theoretical and empirical academic contributions on smart working (SW) in the organization studies domain and to examine which are the main paths that researchers are concerning themselves with, with specific attention being paid to the new meaning that the work itself has acquired in the model proposed by SW. Particular consideration is devoted to an analysis of the characteristics of the present debate on this construct and the meaning of SW, identifying two different – and contrasting – approaches: one considers it as a totally new concept; the other is notable for its continuity with previous arrangements such as telework. Further, some relevant concepts, strictly related to that of SW in working environments are considered. In the last part of the chapter, some key points for further research are proposed to create stimuli for discussion in the community of organization studies and HRM scholars and among practitioners, given from the perspective of deepening the change in progress, the relevance for which there is general consensus.