Does explanatory language convey the auditor’s perceived audit risk? A study using a novel big data analysis metric

Abstract

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

ICT acceptance, language use and information culture as tools for enhancing information literacy within smallholder maize farmers in Uganda

“…This could be attributed to use of simple, specific, actual and logical language when communicating agricultural information which makes it easier for farmers to make meaning out of these messages, thus, promoting information utilization. This is in agreement with previous researchers (Uccelli, 2023; Choi et al. , 2023) who reported that proper use of language in a social activity presents socially accepted and authentic processes for participation in real-life activities.…”

“…This could be attributed to use of simple, specific, actual and logical language when communicating agricultural information which makes it easier for farmers to make meaning out of these messages, thus, promoting information utilization. This is in agreement with previous researchers (Uccelli, 2023;Choi et al, 2023) who reported that proper use of language in a social activity presents socially accepted and authentic processes for participation in real-life activities. Language aspects like grammatical gender marking, politeness in the use of pronouns and use of first-person singular pronouns influence an individual's perceptions, actions and outcomes about an intended communication studies (Berman et al, 2022;Hatani, 2023).…”

“…However, despite its importance, there is little evidence supporting the role of language in information literacy in small and informal practices. The majority of earlier academic work on language use centred on formal academic settings and social media (Bin-Hady et al, 2023;Choi et al, 2023;Nicolas et al, 2023;Hart et al, 2021;Harvey et al, 2022;Lytra et al, 2022), yet the outcomes of such studies are limited in their relevance to informal settings (Tankosi c et al, 2021), like the case of SHFs. Therefore, understanding how language use affects information literacy levels of SHFs is a priority, and will close that knowledge gap identified.…”