Narratives of the Big-4 transparency reports: country effects or firm strategy?

Abstract

Purpose The purpose of this paper is to examine whether audit firms use transparency reports (TRs) as a tool to standardize their brand image or whether the semantic and content analysis in these reports indicates a higher importance of country effects. Design/methodology/approach The sample includes 28 TRs published in English by the Big-4 audit firms from five EU countries (the UK, Ireland, Luxemburg, Hungary and Malta), as well as in the USA and Australia. Findings Using content analysis, this research finds that there is variation in the language used in TRs both across audit firms and jurisdictions. Most TRs from different countries of the same firm tend to be clustered, suggesting that audit firms use transparency reporting as a strategy to differentiate themselves from their competitors. In fact, EY and KPMG seem to have more standardized internal procedures and standardized information. Regarding country effects, the results indicate that TRs in the UK are longer and show more detailed information. Originality/value Overall, this research is innovative in the sense that it applies a new methodological approach to an emerging topic such as audit transparency reporting. It identifies emerging topics of voluntary disclosure, such as financial data of the firm, gender and ethnic origin of employees, community involvement or sanctions, among other topics of interest which might be explored in detail by future research to understand the construction of the profession.

Editorial

“…Those who do not understand the intuition underlying models may draw unsupported conclusions from complex analytics reported by easy-to-use analytic software. The articles in this eBook explain the intuition underlying some complex machine learning algorithms (Boskou et al, 2019;Zorio-Grima and Carmona, 2019) and NLP techniques Stone (2019a, 2019b)).…”

“…Sources of textual data such as auditors’ reports, MD&A, and news articles from reputable journals will require less cleaning effort than customer social media comments (Applebaum et al , 2017). Loughran and McDonald (2016, p. 1192) elaborate on the challenges of transforming textual data for quantitative analysis and caution that “we must be careful that the imprecision of the method does not overwhelm any hoped-for gains in identifying meaning.” Zorio-Grima and Carmona (2019) demonstrate that both automated textual analytics and manual content analysis may be used in the same paper to address this challenge, i.e. they adopt a “mixed, partially automated content analysis” approach that is used in many textual analysis papers (Zhang et al , 2019, p. 151).…”

“…ignoring diagrams, charts and boilerplate language) and then applying additional preprocessing steps that depend upon the analytic methods and tools used. For example, in this issue, Boskou et al (2019) and Zorio-Grima and Carmona (2019) describe transforming text into a matrix that can be analyzed using statistical software and machine learning techniques. Transformations used in other studies range from straight-forward keyword or dictionary-based word counts in disclosure studies (Campbell et al, 2014) to vector-based measures in studies that compare document similarity (Brown and Tucker, 2011).…”

“…It also includes textual communications, reports, social media posts, images, spoken word data and videos. Accounting and audit research increasingly analyzes textual data in many different ways to investigate a wide range of research questions (Fisher, 2018; Fisher et al , 2016; Loughran and McDonald, 2016; Teoh, 2018; Vasarhelyi et al , 2015; Zhang et al , 2019), as illustrated by the following four studies in this eBook: Chang and Stone (2019a) examine the association between auditor selection and audit proposal readability, measuring readability using a simple linear combination of counts of syllables, words and sentences in the proposals; Chang and Stone (2019b) study the association between audit firm size and report readability using natural language processing (NLP) measures that are new to audit literature; Boskou et al (2019) use supervised machine learning to develop a proxy for internal audit quality based on textual disclosures extracted from management’s discussion and analysis (MD&A); and Zorio-Grima and Carmona (2019) use both manual content analysis and a variety of automated textual analysis approach to study transparency reports issued by Big 4 audit firms. …”

“…Zorio-Grima and Carmona (2019) use both manual content analysis and a variety of automated textual analysis approach to study transparency reports issued by Big 4 audit firms.…”