Journal of Applied Accounting
Research

Received 22 December 2024
Revised 10 June 2025
Accepted 19 January 2026

Journal of Applied Accounting Research © Emerald Publishing Limited e-ISSN: 1758-8855 p-ISSN: 0967-5426
DOI 10.1108/JAAR-12-2024-0492

Increasing audit effort through the
public disclosure of audit hours:
insights from ESG ratings in
South Korea
Hyoung Joo Lim
Department of Accounting and Tax, Kyonggi University, Suwon, South Korea, and
Dafydd Mali
University of East Anglia, Norwich, UK
Abstract
Purpose – Based on the assertion that audit hours/effort enhances audit quality, we posit that as Environmental, Social and Governance (ESG) ratings increase, shareholders have an incentive to secure increasing levels of audit hours as an ESG rating assurance strategy (audit demand theory).
Design/methodology/approach – Using a sample of South Korean listed client-firms over the 2011–2019 period, the study captures the association between ESG, (1) audit hours, (2) audit fees and (3) fees per hour. Findings – Empirical results show that clients with higher ESG ratings secure increasing levels of audit effort/ hours. Moreover, based on ESG status, no fee premium is imparted by audit firms. Results are robust to various forms of additional analysis, including Big4/NonBig4 division, endogeneity tests, amongst others.
Originality/value – The study contributes to policymaking by reporting that in a rare instance of audit hour information availability on annual reports, a basis exists for clients to secure increasing levels of audit hours, as an ESG rating assurance strategy. The study therefore extends the ESG assurance and audit literatures.
Keywords ESG, Forecasting, Audit hours/effort, Audit demand theory, ESG assurance
Paper type Research article
1.		Introduction
The Enron scandal revealed that high audit fees (premium) were secured by Andersen as compensation; however, audit testing was notably insufficient (Arnold and De Lange, 2004; Cullinan, 2004; Duska, 2005). This “high fee-low effort” dynamic remained obscured from market participants due to the absence of a policy mandating that audit hour information be publicly disclosed. We posit that had audit hour information been accessible ex ante, the Enron-Andersen relationship could have been detected, prior to the infamous collapse of client and audit firm. Remarkably, two decades on, no policy exists to mandate that clients disclose audit hour information on financial reports. In this study, we are motivated to investigate whether South Korea’s unique policy to disclose numerical audit hour information on annual reports is a mechanism that influences a client’s incentives to secure an increasing level of audit hours/effort, based on Environmental, Social and Governance (ESG) ratings. By providing evidence that stakeholders utilise audit hour information for decision-making purposes, in rare instances of public availability, the study can offer unique insights into the audit/accounting profession and contribute to policymaking debates.
We have several motivations to conduct this study. First, the association between audit effort and ESG ratings is not well-established in the extant literature. Empirical evidence suggests that the association between audit fees (effort) and ESG is negative (Asante-Appiah, 2020; Burke et al., 2019). ESG discrepancies and complexities are also shown to increase audit fees/effort (Gou and Li, 2025; Lee et al., 2022). These studies offer an audit supply theory interpretation, inferring that based on ESG levels, audit firms secure increasing levels of fees as compensation, due to audit risk perceptions. On the other hand, based on an audit demand theory interpretation, Sharma et al. (2018) report that clients with high ESG scores and Tobin’s Q levels, secure higher levels of audit fees as a legitimacy strategy. DeFond and Zhang (2014, JAAR	p. 240) note that “audit fees capture the joint outcome of both supply and demand factors”. This criticism contextualises how “raw” audit fees can have both positive and negative associations with ESG ratings in different studies. We therefore envision that the literature can be extended with an investigation that speaks to whether the main driver of audit effort, based on ESG scores, is the supply incentives of audit firms or the demand incentives of clients.
We borrow from Simunic (1980) seminal framework to distinguish between audit demand/ supply incentives. Simunic (1980) asserts that in an instance where audit firms secure a fee premium (increasing rate per hour), it is indicative of audit supply (theory) incentives. In an instance where audit effort/hours increase, and no fee premium is imparted, this situation is indicative of a client’s demand (theory [1]) incentives. Because audit hour information is not widely available internationally, audit demand/supply distinctions are not provided in previous ESG – audit hour studies. South Korea is a unique setting, because audit hours are disclosed on mandatory basis on annual reports. In instances where no free premium is secured by auditors, South Korea clients are shown to have an incentive to secure audit hours to legitimise key financial/risk indicators, including operational performance, credit rating and to influence borrowing costs (Lim and Mali, 2021, 2024; Mali and Lim, 2021, 2023). Audit hours are also shown to reduce stock price volatility (Lim and Mali, 2024). In this study (see hypothesis section), we envision that, based on ESG ratings, shareholders equally have an incentive to secure audit hours in contracts (without incurring a fee) to protect their own self-interest (agency/inspired confidence theory). We also envision that management and audit firms will accept the ESG assurance incentives of shareholders (credibility theory). Empirical evidence consistent with the above contributes to knowledge by indicating that stakeholders take action to seek/provide assurance about ESG scores, in a similar fashion to key financial/risk indicators. This interpretation differs from the audit supply theory interpretation, which is commonly discussed in the extant literature (Asante-Appiah, 2020; Burke et al., 2019; Gou and Li, 2025; Lee et al., 2022).
Second, Willis (2003) surmises that ESG assurance strategies are underdeveloped. Presently, clients can acquire ESG ratings for payment from external agencies and/or audit firms. ESG ratings are also freely provided by non-profit firms, charities and some governments. Thus, in 2025, due to the different incentives/approaches of clients/ESG providers, it can be argued that an ESG assurance framework remains a work-in-progress. Only 6.2% of S&P500 US clients offer ESG audit assurance (Schrank, 2022). The European Commission (2021) reports that ESG audit assurance offers market participants with valuable information. However, a framework is not established. Schrank (2022) reports that in countries such as the United Kingdom, auditors provide ESG ratings, but they are also the ESG assurance provider. Due to greenwashing, this arrangement can be considered an agency problem (see Hrasky, 2012). In South Korea, ESG ratings are provided by the Korea Corporate Governance Service (KCGS), a South Korean non-profit. ESG audit assurance is provided by audit firms. Thus, a second unique contribution of this study is to show how the distinct separation of 1) ESG “rating provider” (KCGS), and 2) “assurance facilitator” (audit firms), influences the incentives of clients to secure audit effort/hours, as an ESG rating assurance strategy.
Third, there is extensive literature documenting that auditor time constraints adversely affect audit quality (Ettredge et al., 2014; Gu^enin-Paracini et al., 2014; Lambert et al., 2017). However, the accounting framework omits audit hour data, leaving stakeholders without a means to assess audit quality based on imparted audit effort. The study therefore contributes to audit policy debates by underscoring how disclosing audit hours can influence the demand for assurances about ESG ratings. The study may find that shareholders have an incentive to ensure that a client’s perceived ESG scores are a genuine reflection of business activities. The study therefore also contributes to knowledge by speaking to; the suspicion that shareholders possess in terms of managers’ incentive for greenwashing; how this can be addressed in a South Korean context and how policy oversights can be a limiting factor on an international basis. The study therefore speaks to how societal stakeholder’s expectations may not be met within the mainstream accounting framework, due to regulatory oversights.
Fourth, the quality of Big4 audits is recognised to be higher in comparison to NonBig4 audits (Becker et al., 1998; Behn et al., 2008; Che et al., 2020; DeAngelo, 1981; DeFond et al., 2017; Fung et al., 2016; Khurana and Raman, 2004; Lisic et al., 2015). We posit that clients may be incentivised to obtain higher levels of audit hours from Big4 audit firms as an ESG rating assurance strategy, relative to NonBig4 audit firms, due to the perceived higher quality provided by Big4 auditors. By revealing that clients secure higher levels of audit hours from Big4 audit firms in the ESG context, the study infers that shareholders take a nuanced approach in securing audit hours/effort from those auditors with higher perceived audit quality, to reduce potential agency problems associated with ESG scores. The study, therefore, extends the audit literature.
Using a sample of South Korean client-firms (8,384), over the 2011–2019 sample period, OLS regression results reveal that the association between ESG ratings and audit hours (effort) is positive. Additional analysis shows that (1) the association between audit fees and ESG ratings is incrementally less positive compared to audit hours. (2) Audit fees per hour have no relationship with ESG ratings. (3) After controlling for the fee premium (compensation), audit firms are expected to secure, based on audit risk, the association between audit hours and ESG ratings remains positive. Furthermore, we find that incrementally higher levels of audit hours are secured from Big4 audit firms, relative to NonBig4 audit firms. This result supports the assertion that nuanced shareholders deliberately seek higher levels of audit effort from Big4 auditor, with perceived superior audit quality, to validate ESG ratings. The above findings offer strong evidence in support (against) of audit demand (supply) theory. Specifically, empirical evidence suggests that the public disclosure of audit hours in South Korea motivates clients to secure greater audit effort/hours as an ESG rating assurance strategy.
To add robustness to our findings, a battery of additional tests has been conducted, including two-stage least squares regression to address potential endogeneity concerns, fixed- effect models to account for year-specific effects, clustering of standard errors and disaggregation of the ESG rating into its Environmental, Social, and Governance components, among other tests. Results remain consistent in all robustness tests. The above empirical findings provide numerous contributions. To avoid unnecessary repetition, contributions are discussed in more detail in Section 6.
The article proceeds as follows. In Section 2, key theories are introduced. Moreover, the hypothesis is developed. Section 3 includes details about sample selection and model specification. The results of the main analysis are included in Section 4. Additional analysis is conducted in Section 5. In section 6, we explain how findings contribute to the accounting/ audit profession, policymaking and extend the literature. Moreover, limitations and avenues for future research are discussed.
2.		Literature review and hypothesis development
2.1		Literature review
In Simunic (1980) seminal study, the theoretical distinction between audit demand and supply theory is introduced. More specifically, higher fees per hour (premium) infers that partners who are specialists are required for high-risk audits, which minimises audit supply (theory). In an instance, a balanced team conducts an audit without incurring a premium, it infers a low- risk audit, and that effort/hours increase with client demand (theory). In the 21st century, audit demand theory includes four subcategories, including policeman theory [2], credibility theory [3], confidence theory [4] and agency theory [5] (Akinadewo et al., 2024; DeZoort et al., 2012; Eferakeya and Edgars, 2021; Hayes et al., 2005; Igbekoyi et al., 2024). These distinctions provide a comprehensive conceptualisation of how ESG is likely to affect audit hour demand. As discussed in more detail in the hypothesis section, using audit demand theory as an overarching framework, we envision that, based on high ESG scores, shareholders have an incentive to seek assurances that ESG scores reflect business activities, to reduce information asymmetry associated with greenwashing (inspired confidence and agency theory).
JAAR	Furthermore, based on credibility theory, there is an expectation that in South Korea, due to the public availability of audit hour information, audit firms and management are likely to accept the audit demand incentives of shareholders, due to the perceived importance of ESG scores. Amel-Zadeh and Serafeim (2018) conduct a survey analysis to interpret how investors use ESG information. They find that ESG ratings provide investors with valuable information about sustainability/ethics, business strategy and market conditions. However, they argue that the most important consideration associated with ESG ratings is investment performance. ESG ratings are shown to be positively associated with financial performance (Atan et al., 2018; Fafaliou et al., 2022; Huang, 2021 Orlitzky et al., 2003), growth, firm value, and market returns (Al-Najjar and Animator, 2012; Bassen and Kov^acs, 2020; Gollier and Pouget, 2009). Firms with higher ESG ratings are also shown to have, higher credit ratings (Kiesel and L€ucke, 2019) and lower levels of stock price volatility (Sassen et al., 2016; Shakil, 2020), borrowing costs (El Ghoul et al., 2011; Ng and Rezaee, 2015) and business risk (Zhu et al., 2023). Survey analysis also suggests that ESG ratings are associated with a firm’s ability to implement a successful corporate strategy (Adams, 2017) and accountability Rietz (2018). Based on the above, management has a strong incentive to report the highest ESG rating possible.
Greenwashing is recognised as the practice of management to provide deceptive ESG disclosures to mislead stakeholders (Michelon et al., 2015). It is widely reported that management enacts Greenwashing to positively affect their corporate image (Belal et al., 2013; Capelle-Blancard and Petit, 2019; Marquis et al., 2016; Olatubosun and Nyazenga, 2019; Yu et al., 2020). Given the potential agency problems that can exist as a result of management’s incentive to greenwash ESG ratings, and the incentives of shareholders to use ESG information for investment decision-making purposes, it is surprising that very few studies examine the relationship between audit effort and ESG ratings.
The association between audit effort and ESG ratings can be interpreted from two perspectives. First, Duong et al. (2022) report that clients with lower business ethics pay higher fees, because auditors perceive such clients as riskier and thus charge greater risk premiums. Burke et al. (2019) show that negative ESG media coverage is associated with higher audit fees, inferring that audit firms impart higher levels of audit effort based on “bad news”. Asante- Appiah (2020), Hua and Alam (2021) and Moradi and Salehi-Vaziri (2022) explain that, based on audit risk perceptions, a negative association between ESG ratings and audit fees is indicative of audit firms securing higher (lower) levels of compensation from clients with low (high) ESG ratings. Discrepancies in ESG scores are shown to increase audit fees (Gou and Li, 2025). ESG is considered to enhance audit complexity, hence increasing the effort exerted by audit firms (Lee et al., 2022). Likewise, Hartlieb and Eierle (2022) report that climate-related risks are associated with higher audit fees. The above studies interpret the negative ESG-audit fee relationship through an audit supply theory lens. More specifically, they infer that audit firms control contract negotiations based on ESG status. This theoretical perspective is introduced in countless studies, which positively associate “raw” audit fee data with audit risk determinants (Abbott et al., 2006; Bedard and Johnstone, 2004; DeAngelo, 1981; Gul et al., 2003; Kinney et al., 2004; Lai, 2019; Lyon and Maher, 2005; Oradi, 2021; Simunic and Stein, 1996; Xue and O’Sullivan, 2023; Yang et al., 2018).
On the other hand, Sharma et al. (2018) explain that “investors value a firm (client) more highly when environmental initiatives occur in conjunction with higher audit fees” (2018:207). This study infers that clients may demand audit fees/effort as an assurance strategy to make inferences about a client’s market value. These differing interpretations identify a limitation in the audit literature. More specifically, as explained by DeFond and Zhang (2014), dependent on context and empirical results, audit fees can be interpreted as audit effort or audit risk. For example, based on a demand theory interpretation, audit fees (effort) are shown to be associated with and increased incentive for enhanced governance (Hay, 2013; O’Sullivan, 2000; Pronobis and Schaeuble (2022). Moreover, higher fees/effort is shown to be secured by clients to defend against credit rating downgrades (Gul and Goodwin, 2010)	. The literature can therefore be extended with studies that disentangle client demand incentives and audit firm supply incentives, in an ESG ratings context.
Historically, it has been argued that the inclusion of higher levels of audit hours in contracts can be indicative of the supply (theory) incentive of audit firms (Caramanis and Lennox, 2008; Eilifsen et al., 2001). However, more recently, it is widely accepted that it is clients who secure increasing levels of audit hours to enhance audit effort, based on an incentive to improve the usefulness of accounting information (Jung, 2016; Kim et al., 2011; Lennox and Pittman, 2011; Minnis, 2011). This view is contextualised by authors who consider audit hours to be a felicitous proxy for audit effort demand, in the sense that audit hours are a numerical representation of substantive and control tests conducted by audit firms (Reinstein and McMillan, 2004; Vinten et al., 2005).
In South Korea, where audit hour information is publicly disclosed, management is shown to secure increasing levels of audit hours in contracts as a financial reporting quality signalling strategy, based on the expectation that market participants monitor audit hours/effort. Mali and Lim (2021) provide evidence that in South Korea, securing increasing levels of audit effort/ hours provides clients with economically significantly lower borrowing costs (if not a fee premium is imparted). They surmise that capital providers monitor both audit hours and audit fees to make audit quality assertions when determining the weighted average cost of capital (WACC). Lim and Mali (2021) demonstrate that clients secure increasing levels of audit hours as a strategy to enhance their higher credit status. Mali and Lim (2020) show that management with higher levels of operational efficiency secures increasing levels of audit hours as a financial performance signalling strategy (if audit hours are provided at a discounted fee). Mali and Lim (2023) develop a residual model to determine if large domestic/foreign shareholders have different audit effort demand expectations. They show that South Korean audit firms meet the higher audit demand expectations of foreign shareholders, based on increased shareholder power. Taken together, we envision that knowledge can be extended with a study that provides evidence that ESG scores can be considered equal in terms of requiring audit hours, in a similar fashion to the aforementioned key business risk/performance indicators (credit ratings, WACC and operational performance).
2.2		South Korea dataset
Following the 1997 Asian Financial Crisis and instances of earnings management leading to concurrent large-scale financial collapses between 2000 and 2003, legislators introduced two policies to restore public confidence in the audit profession. First, based on the entrenchment hypothesis, the Mandatory Audit Firm Rotation Policy was introduced in 2006. However, the policy did not have the desired effect, thus, it was ceased in 2010 (Choi et al., 2017; Mali and Lim, 2018). The Auditor Engagement Policy was introduced in 2003. The policy mandates that 1) audit hour and 2) audit fee information must be disclosed on annual reports. The Auditor Engagement Policy has been considered a strategy to enhance public confidence in the audit profession, in the sense that audit fee/hour information provides transparency about auditor– client relationships and provides insights about audit contract negotiations. The policy remains in effect in 2025. Because of the nature of audit hour reporting in South Korea, we envision that audit effort information will almost certainly be scrutinised by market participants. As discussed in the introduction, KCGS, the country’s largest ESG provider is a non-profit. Thus, there is a separation between ESG “rating provider”, and the ESG “assurance facilitator”. As a result, the South Korean market is an ideal dataset to make inferences about how the public availability of audit hour information and independently derived ESG ratings can influence audit hour demand.
2.3		Hypothesis development
Based on the supposition that low ESG ratings provide evidence of weak governance and business/audit risk, clients are shown to pay higher audit fees (Asante-Appiah, 2020; Burke
JAAR	et al., 2019; Moradi and Salehi-Vaziri, 2022). This audit supply theory supposition infers that audit firms utilise ESG rating information to make an assertion about a client’s audit risk. Based on this interference, higher levels of audit effort have the potential to be imparted onto clients with lower ESG ratings, by audit firms, to mitigate potential litigation risk and reputational damage. However, based on Simunic (1980), and DeFond and Zhang (2014), in the above situation, a more negative association can be expected between audit fees and ESG ratings, relative to audit hours (higher fee premium). More specifically, if ESG ratings increase audit risk, higher levels of specialists and partners would be included on the audit, rather than a balanced team, increasing the fee per hour required. Capturing this association would imply that audit firms control the levels of audit effort required based on ESG ratings, not clients.
However, because audit hour information is publicly disclosed in South Korea, based on three audit demand theory subcategories, we conjecture that a different relationship will exist. First, an ESG rating can be considered an important business sustainability benchmark (Busch et al., 2016; Friede et al., 2015). ESG ratings are positively associated with financial performance (Al-Najjar and Animator, 2012; Atan et al., 2018; Bassen and Kov^acs, 2020; Fafaliou et al., 2022; Huang, 2021 Orlitzky et al., 2003; Gollier and Pouget, 2009). ESG ratings are also negatively associated with business risk (El Ghoul et al., 2011; Kiesel and L€ucke, 2019; Ng and Rezaee, 2015; Sassen et al., 2016; Shakil, 2020). Shareholders therefore have an incentive to seek assurances that potential investment benefits associated with ESG status will be realised. In South Korea, market participants are shown to associate audit hours with audit quality (Lim and Mali, 2021; Mali and Lim, 2021). Clients are also shown to secure audit hours to legitimise efficiency performance (Mali and Lim, 2020). We therefore hypothesise that because ESG can equally be perceived as an important business performance benchmark, clients (management and shareholders) will have an incentive to secure increasing levels of audit hours/effort, as an assurance strategy that ESG ratings reflect business activities, consistent with inspired confidence theory.
Second, because of the economic/reputational benefits associated with ESG ratings, shareholders may perceive that an agency problem exists, due to the incentives of management to strategically utilise resources to greenwash ESG ratings. There is an expectation that the audit process plays a critical role in promoting sustainable business activities associated with ESG (Ionascu and Ciumacencu, 2024). In South Korea, KCGS, a non-profit, is the country’s largest ESG rating provider. South Korean audit firms provide assurances about financial/ business information, which includes ESG. Shareholders are shown to secure increasing levels of audit effort to reduce agency problems (Caramanis and Lennox, 2008; Houqe et al., 2017; Lobo and Zhao, 2013; Niemi, 2005). Moreover, it is recognised that the perception of an agency (theory) problem, not its existence, will motivate stakeholders to take action to reduce information asymmetry (Dopuch et al., 1986; Jensesn and Meckling, 1976; Watts, 1977; Watts and Zimmerman, 1983). To reduce agency problems associated with the greenwashing incentives, we hypothesise that shareholders will be motivated to secure increasing levels of external monitoring by audit firms (audit hours/effort) to legitimise the ESG ratings provided by KCGS. We also envision that, consistent with credibility theory, the management of clients with high ESG ratings should have no objection to accepting such a request from shareholders.
Third, Mali and Lim (2023) show that as the power of foreign/domestic shareholders increases, South Korean audit firms meet the differing (higher/lower) audit effort demand expectations of shareholders. They also find that audit fees and hours increase at a qualitatively indifferent rate, inferring that a balanced team (junior and senior auditors) is deployed, signalling low audit risk. We posit that, based on higher ESG status, audit firms will equally meet the audit demand expectations of clients, consistent with a credibility theory assertion. Hence, in this low audit risk situation, audit hours and fees will increase at a qualitatively indifferent rate. This relationship will be captured empirically as a positive association between audit hours and ESG, and an equivalent (or less positive) association between ESG and audit fees. Based on the above, the following hypothesis is introduced:
H1. Based on increasing ESG ratings and the public disclosure of audit hour information, Journal of Applied clients demand increasing levels of audit hours/effort (if audit effort does not Accounting command a fee premium).	Research
3.		Methodology
3.1		Sample and data collection
Data is collected from 2011 because the Mandatory Audit Firm rotation Policy was ceased in 2010. Data is not provided after 2019, due to COVID-19’s effect on (reducing) audit hours. Over the 2011–2019 sample period, all financial and audit data have been downloaded from the TS-2000 database for all clients listed on the Korean stock exchange. ESG data is collected from the Korea Corporate Governance Service (KCGS). The data are then merged. Excluding financial clients, 8,384 client-year observations are available to conduct empirical analysis. 1,736 (10,120–8,384) observations are removed because clients did not report the required data listed in Table 1.
3.2		Measurement of variables
Independent variables are collected following Woodside (2016) assertion that including statistically insignificant variables into models reduces the predictive validity of empirical tests. Therefore, to develop the most robust model, as illustrated in Table 2, key determinants known to influence audit hours from previous studies are listed (column 3). Then, based on each determinant, various proxies are considered for inclusion. For example, for firm performance, return of asset (ROA), return on equity, earnings per share, amongst other accounting ratios, have been considered to proxy for financial performance. However, only the proxy with the strongest association is included in the model (ROA). An identical procedure was followed for each determinant. As shown in the result’s section, based on model VIF, adjusted R2 , F static and 2SLS endogeneity analysis, this procedure can be considered felicitous.
Audit Hoursi ;t ¼ β0 þ β1 ESGi ;t þ β2 Sizei ;t þ β3 Big4i ;t þ β4 Levi ;t þβ5 Betai ;t þ β6 Lossi ;t
þ β7 ROAi ;t þ β8 BigOwni ;t þ β9 Foreigni ;t þ β10 Directori;t þ ID þ YD
þ εi ;t
(1)
3.3		Empirical model
Equation (1) represents the main OLS regression model. The dependent variable, Audit_Hours (effort), is estimated as the natural logarithm of audit hours. The main variable of interest, ESG represents a client’s Environmental, Social and Governance ratings. ESG is provided by KCGS, the largest ESG rating provider in South Korea.
Table 1. Sample selection
Panel A: ESG and audit hour sample from 2011–2019	
Initial ESG Sample from 2011–2019	10,120	
Excluding firms with insufficient ESG obs	(989)	
Potential Sample	9,131	
Excluding firms with no audit and financial data available	(747)	
Final Sample (final data used from 2011 to 2019)	8,384	

Table 2. Variable definition
Dependent variable	Sign	Determinant	Definition	
Audit_Hours		Audit effort	Natural logarithm of audit hours	
Variable of interest
ESG	þ
Social	þ
Governance þ
Environment þ	ESG performance Social performance
Governance performance Environmental performance	Provided by Korea Corporate Governance Service Provided by Korea Corporate Governance Service Provided by Korea Corporate Governance Service
Provided by Korea Corporate Governance Service	
Control variables
Size	þ
Big4	þ
Lev	þ
Beta	þ
Loss	þ
ROA	–
BigOwn	–
Foreign	þ
Director	þ	Firm size
Audit quality
Business risk
Market risk
Financial risk
Firm performance
Ownership structure Ownership structure
Corporate governance	Natural logarithm of total assets
A dummy variable that takes 1 if an auditing firm is Big4
auditor, 0 otherwise
Debt ratio (5total liabilities / total assets)
Standard deviation of yearly stock return 3 Square root of trading days
A dummy variable that takes 1 if previous NI is negative, 0 otherwise
Absolute efficiency, return on assets (net income/total assets)
Largest domestic shareholder’s share holdings (%)
Largest foreign investors’ share holdings (%)
Ordinal value of the number of listed firm external directors divided by the total number of directors	
Fixed effect ID
YD			Industry fixed effect
Year fixed effect	

JAAR

The estimation procedure for control variables and expected signs is reported in Table 2. It is accepted that larger firms require higher levels of audit hours due to business complexity (O’Keefe et al., 1994). Thus, Size is expected to have a positive association with Audit_Hours. Big4 is expected to be positively associated with Audit_Hours, based on evidence that Big4 audit firms secure higher levels effort reduce reputational damage and litigation risk (Basu et al., 2001; DeAngelo, 1981; Fung et al., 2016). Lev (business risk), Beta (market risk) and Loss (financial risk) are known measures of firm risk that increase audit effort (Lim and Mali, 2018; Deis and Giroux, 1992; Lim and Mali, 2018; Mali and Lim, 2019, 2022; O’Keefe et al., 1994). Thus, a positive relation is expected between Audit_Hours and Lev/Beta/Loss. It is well established that firm performance is negatively associated with audit hours. Thus, a negative association is expected between ROA and Audit_Hours.
There is evidence that the largest South Korean shareholder demands lower levels of audit hours/effort, because it can be considered an unnecessary expense, due to their accumulation of local business knowledge (Mali and Lim, 2023). They also explain that foreign shareholders will secure increasing levels of audit hours to enhance external monitoring, which promotes effective governance, due to a lack of understanding of local business practices. Thus, a positive relationship is expected between Foreign and Audit_Hours, but a negative relation between BigOwn and Audit_Hours. Hay (2013) shows that external directors secure higher levels of audit effort/hours, as a strategy to reduce the potential for executive directors to act in their own self-interest. Therefore, a positive association is expected between Director and Audit_Hours, based on the assertion that higher levels of external audit monitoring promote corporate governance. Because the level of audit effort supplied/demanded by clients can change as a result of audit policies, we add a dummy variable to control for fixed-year effects.
A dummy variable is also added for each SIC code to control for industry fixed-effects. Journal of Applied To control for outliers, all variables are winzorised at the top/bottom 1%.	Accounting
Research
4.		Empirical results
4.1		Descriptive statistics
Table 3 provides the results of descriptive statistics. For all variables, excluding dummy variables, the mean and median levels are virtually at parity, inferring a normal sample distribution. ESG ratings are within a range of 0.14–0.86. The average ESG rating is 0.30. This result indicates that KCGS tends to provide conservative ESG ratings. Table 3 also presents the findings from Pearson correlation analysis. Consistent with our expectation, there is a statistically significant positive correlation between ESG scores and audit hours/effort, quantified at 0.58***. This finding underscores the hypothesis that higher ESG ratings are associated with increased audit hour/effort demand. All other control variables are statistically significant and show expected associations.
4.2		Multivariate tests
Table 4 provides the results of the main regression analysis, illustrated in Equation (1). The results demonstrate that after controlling for audit effort/hour determinants, ESG is positively associated with Audit_Hours (0.27***). All control variables show expected positive (Size 0.40***; Big4 0.28***; Lev 0.00***; Beta 0.08; Foreign 0.00**; Director 0.03*** and Loss 0.07***) and negative signs (ROA, -0.01*** and BigOwn 0.00***). The empirical results provide support for the assertions put forward in H1 More specifically, that based on an audit demand theory interpretation (1) shareholders have an incentive to secure audit hours/effort as an assurance strategy to enjoy the investment benefits associated with ESG status; (2) clients (management and shareholders) have an incentive to secure increasing levels of external monitoring by audit firms to reduce agency problems, and based on ESG status; (3) audit firms meet the demand expectations of clients. Adjusted R2 (0.72) and F values (300.47) are highly statistically significant, demonstrating model robustness.
5.		Additional analysis
5.1		Audit fee interpretation
In Table 4, we observe a positive relationship between audit effort/hours and ESG ratings. The result is contrary to the audit supply theory relationship introduced in previous studies (Asante-Appiah, 2020; Burke et al., 2019). To provide additional evidence in support of audit demand theory (against audit supply theory), additional analysis is conducted. In this analysis, the audit hour dependent variable is substituted with (1) audit fees and (2) fee per hour. Specifically, in Equation (2), the natural logarithm of audit hours is replaced by the natural logarithm of audit fees (Audit_Fees). Because both inputs are equivalent and provided on a logarithmic basis, a direct comparison can be drawn using the coefficients presented in Equations (1) and (2) (See Mali and Lim, 2021). In Equation (3), FeePerHour represents the natural logarithm of audit fees, divided by the natural logarithm of audit hours. The variable FeePerHour signifies the increased fee premium that audit firms would secure, based on a client’s perceived audit risk. Based on audit demand theory interpretations introduced in H1, we predict that Audit_Fees will have a qualitatively indifferent, or less positive association with ESG ratings, compared to Audit_Hours. Moreover, we predict that there will be no association between FeePerHour and ESG ratings. In both instances, these empirical associations would signal that the increased levels of audit hours included in contracts are based on the incentives of clients, not the incentives of audit firms.
Table 3. Descriptive statistics and Pearson correlations
Stats		Obs	Mean		Median		SD		Max	Min	
Panel A: Descriptive statistics										
Audit_Hours		8,384	7.13		7.00		0.86		11.14	1.38	
ESG		8,384	0.30		0.28		0.20		0.86	0.14	
Size		8,384	19.47		19.25		1.20		26.11	15.18	
Big4		8,384	0.60		1		0.49		1	0	
Lev		8,384	39.64		39.30		21.30		89.56	2.01	
Beta		8,384	0.85		0.83		0.49		1.60	^0.04	
Loss		8,384	0.25		0		0.43		1	0	
ROA		8,384	3.57		3.24		6.54		23.22	^17.77	
BigOwn		8,384	41.13		40.82		16.71		80.13	7.3	
Foreign		8,384	8.88		3.64		12.01		57.95	0.0	
Director		8,384	5.48		5		2.26		31	1	
	1	2	3	4	5	6	7	8	9	10	11	
Panel B: Pearson correlations
1. Audit_Hours	1										
2. ESG	0.58***	1									
3. Size	0.81***	0.67***	1								
4. Big4	0.47***	0.31***	0.42***	1							
5. Lev	0.17***	0.08***	0.16***	0.02*	1						
6. Beta	0.07***	^0.06***	0.03***	^0.02**	0.09***	1					
7. Loss	0.03***	^0.07***	^0.14***	^0.10***	0.28***	0.08***	1				
8. ROA	^0.05***	0.06***	0.15***	0.09***	^0.21***	^0.02	^0.59***	1			
9. BigOwn	^0.03***	0.12***	0.13***	0.13***	^0.08***	^0.22***	^0.18***	0.09***	1		
10. Foreign	0.41***	0.32***	0.46***	0.24***	^0.13***	^0.02*	^0.16***	0.26***	^0.08***	1	
11. Director	0.31***	0.25***	0.28***	0.09***	^0.01	0.01	0.00	^0.01	^0.07***	0.17	1	
Note(s): (1) * See Table 1 for variable definitions. (2) *, **, *** indicate significance level at 10%, 5%, 1% respectively				

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Table 4. ESG and audit effort/hours			Journal of Applied Accounting
Research	
	Pred. sign	Parameter estimate	t-statistic		
Intercept	þ/^	^1.26***	^12.37		
ESG	þ/^	0.27***	7.96		
Size	þ	0.40***	70.37		
Big4	þ	0.28***	23.66		
Lev	þ	0.00***	5.08		
Beta	þ	0.08***	6.85		
Loss	þ	0.07***	4.57		
ROA	–	^0.01***	^5.36		
BigOwn	–	^0.00***	^7.61		
Foreign	þ	0.00**	2.38		
Director	þ	0.03***	11.75		
ID		Included			
YD		Included			
Mean VIF		1.32			
F value		300.47***			
Adj. R2		0.7196			
Obs		8,384			
Note(s): *, **, *** indicate significance level at 10%, 5%, 1%, respectively. Figures in parenthesis indicate t-value
See Table 1 for variable definitions
H1. Based on increasing ESG ratings and the public disclosure of audit hour information, clients demand increasing levels of audit hours/effort (if audit effort does not command a fee premium)
Model: Audit Hoursi ;t ¼ β0 þ β1 ESGi ;t þ β2 Sizei ;t þ β3 Big4i ;t þ β4 Levi ;t þβ5 Betai ;t þ β6 Lossi ;t þβ7 ROAi ;t þ β8 BigOwni ;t þ β9 Foreigni ;t þ β10 Directori ;t þ ID þ YD þ εi ;t

Audit Feesi ;t ¼ β0 þ β1 ESGi ;t þ β2 Size i;t þ β3 Big4i ;t þ β4 Levi ;t þβ5 Betai ;t þ β6 Lossi ;t
þ β7 ROAi ;t þ β8 BigOwni ;t þ β9 Foreigni ;t þ β 10 Directori;t þ ID þ YD þ εi ;t
(2)
FeePerHouri;t ¼ β0 þ β1 ESGi ;t þ β2 Sizei ;t þ β3 Big4i ;t þ β4 Levi ;t þβ5 Betai ;t þ β6 Lossi ;t
þ β7 ROAi ;t þ β8 BigOwni ;t þ β9 Foreign i;t þ β10 Directori;t þ ID þ YD
þ εi ;t
(3)
Table 5 illustrates the results of Equations (2) and (3). A statistically significant positive association exists between ESG ratings and audit fees (0.23***). As expected, the association is incrementally less positive compared to the association between audit hours and ESG (0.27***). Furthermore, as expected, the association between ESG and fee per hour is not significant (^0.99). The results infer that no fee premium (per hour) is levied by audit firms, based on the increasing levels of audit hours secured by clients as an ESG rating assurance strategy. The inconsequential difference between audit fees and audit hours also supports the assertion that audit firms utilise a balanced audit team to provide assurances about the ESG ratings provided by KCGS. Consistent with H1, the above findings infer that audit firms are compensated based on the increased audit hours/effort demanded by clients, based on ESG ratings.

	Pred. sign	Parameter estimate	t-statistic	
Panel A: Audit fee analysis				
Intercept	þ/^	3.15***	34.06	
ESG	þ/^	0.23***	7.20	
Size	þ	0.39***	77.17	
Big4	þ	0.16***	15.60	
Lev	þ	0.00***	10.01	
Beta	þ	0.09***	8.03	
Loss	þ	0.08***	6.28	
ROA	–	^0.01***	^5.32	
BigOwn	–	^0.00***	^12.21	
Foreign	þ	0.00***	8.48	
Director	þ	0.02	11.36	
ID		Included		
YD		Included		
Mean VIF		1.32		
F value		342.95***		
Adj. R2		0.7457		
Obs		8,384		
Panel B: Fee per hour analysis				
Intercept	þ/^	2.20***	65.94	
ESG	þ/^	^0.06	^0.99	
Size	þ	^0.03***	^13.59	
Big4	þ	^0.05***	^11.96	
Lev	þ	^0.00	^0.01	
Beta	þ	^0.01**	^2.55	
Loss	þ	0.00	0.24	
ROA	–	0.00*	2.44	
BigOwn	–	0.00	1.29	
Foreign	þ	0.00*	1.67	
Director	þ	0.01***	3.54	
ID		Included		
YD		Included		
Mean VIF		1.50		
F value		120.20***		
Adj. R2		0.1256		
Obs		8,384		
Note(s): *, **, *** indicate significance level at 10%, 5%, 1%, respectively. Figures in parenthesis indicate t-value
See Table 1 for variable definitions
Model: FeePerHouri;t ¼ β0 þ β1 ESGi ;t þ β2 Sizei ;t þ β3 Big4i ;t þ β4 Levi ;t þβ5 Betai ;t þ β6 Lossi ;t þβ7 ROAi ;t þβ8 BigOwni ;t þβ9 Foreigni ;t þ β10 Directori ;t þ ID þ YD þ εi ;t
M o d e l : Audit Feesi ;t ¼ β0 þ β1 ESGi ;t þ β2 Sizei ;t þ β3 Big4i ;t þ β4 Levi ;t þβ5 Betai ;t þβ6 Lossi ;t þ β7 ROAi ;t þβ8 BigOwni ;t þβ9 Foreigni ;t þ β10 Directori;t þ ID þ YD þ εi ;t

5.2		Controlling for fee premium
Next, we revisit the primary analysis, where Audit_Hours served as the dependent variable. In this iteration, an additional independent variable is incorporated to control for the compensation (fee premium) an audit firm would require, based on a client’s perceived audit risk. For the execution of this analysis, we borrow from the methodology developed by Mali and Lim (2023), who propose a two-stage model. In the initial stage, illustrated in Equation (4), the natural logarithm of audit fees is designated as the dependent variable. Control variables are recognised audit risk determinants that influence an audit firm’s utility to
secure higher audit fees (Mali and Lim, 2023) [6]. The residual from the first stage regression can therefore be considered as representing the premium an audit firm would require, based on client risk.
A Audit Feei ;t ¼ β0 þ β1 Sizei ;t þ β2 Levi ;t þ β3 Lossi ;t þ β4 CurrentR i;t þ β5 ROAi ;t
þ β6 BIG4i ;t þ β7 TRMi;t þ β8 AEMi;t þ β9 Interest Coveragei ;t þ β 10 Betai ;t
þ ID þ YD þ εi ;t
(4)
In Equation (5), the residual from Equation (4), is included as an additional control variable (β11). Empirical results in Table 6 reveal that after controlling for the “audit fee premium effect”, the association between ESG and audit hours remains positive (0.14***). This finding provides further evidence in support of H1
Audit Hoursi ;t ¼ β0 þ β1 ESGi ;t þ β2 Sizei ;t þ β3 Big4i ;t þ β4 Levi ;t þβ5 Betai ;t þ β6 Lossi ;t
þ β7 ROAi ;t þ β8 BigOwni ;t þ β9 Foreigni ;t þ β10 Directori;t
þ β11 Abnormal Audit Feei ;t ID þ YD þ εi ;t	(5)
5.3		Big4 and NonBig4 comparative analysis
The census in the literature is that the audit quality of Big4 audit firms is higher, relative to NonBig4 counterparts. This superior quality is attributed to the extensive resources, specialised expertise and stringent audit methodologies employed by Big4 firms. Consequently, we surmise that the clients of Big4 firms are likely to be inclined to leverage
Table 6. Analysis after controlling for fee premium
	Pred. Sign	Parameter estimate	t-statistic	
Intercept	þ/^	^1.38***	^17.65	
ESG	þ/^	0.14***	4.78	
Size	þ	0.42***	91.63	
Big4	þ	0.39***	29.79	
Lev	þ	0.00**	2.59	
Beta	þ	0.04***	3.55	
Loss	þ	0.09***	7.78	
ROA	–	^0.01***	^6.45	
BigOwn	–	^0.00	^1.40	
Foreign	þ	0.00**	2.35	
Director	þ	0.02***	6.14	
Fee_Premium	þ	0.64***	64.58	
ID
YD
Mean VIF
F value
Adj. R2
Obs
Note(s) Model: Audit Hoursi ;t	¼ β0 þ β1 ESGi ;t þ β2 Sizei ;t	Included
Included
1.49
3069.40***
0.8147
8,384
þ β3 Big4i ;t þ β4 Levi ;t þβ5 Betai ;t þβ6 Lossi ;t	
þβ7 ROAi ;t þβ8 BigOwni ;t þ β9 Foreigni ;t þ β10 Directori ;t þ	β11 Fee Premiumi ;t þ ID þ YD þ εi ;t		

JAAR

this perceived quality to secure enhanced audit assurance, particularly as it relates to ESG ratings. In Table 7 Panel A, descriptive statistics are provided for Big4 and NonBig4 clients. Big4 clients are shown to secure higher levels of audit fees (43.31***) and hours (48.72***). Big4 clients also have higher ESG ratings (29.16***).
Table 7. Comparative analyses: Big4 vs Non-Big4
	(1) Big4	(2) Non-Big4	(3) Diff (1)–(2) Mean (med)	Mean (med)	t (z)	
Panel A: Univariate analyses
Obs
Audit_ Hours	8,384
Audit_ Fees	8,384
ESG	8,384	5,064	3320
7.46(7.33)	6.64(6.63)	48.72***(47.90***)
11.76(11.61)	11.05(11.32)	43.31***(41.88***)
0.35(0.43)	0.23(0.29)	29.16***(28.73***)	
DV: Audit	Pred.
hours	sign	Big4 vs Non-Big4	Big4	Non-Big4	

Panel B: Multivariate analyses using audit hours as dependent variable
Intercept	þ/^
ESG	þ
Big4	þ/^
ESG 3	Big4	þ/^
Controls/ID/YD
Mean VIF
F value
Adj. R2
Obs	^1.15***(^10.94)	^1.40***(^11.23)	0.31(1.50)
0.13***(2.73)	0.29***(6.33)	0.28***(5.24)
0.21***(11.28)
0.27***(4.55)
Included	Included	Included
1.46	1.34	1.39
297.29***	202.43***	36.48*** 0.7203	0.7247	0.3977
8,384	5,064	3320	
Pred. DV: Audit fees	sign	Big4 vs Non-Big4	Big4	Non-Big4	

Panel C: Multivariate analyses using Audit fees as dependent variable
Intercept	þ/^
ESG	þ
Big4	þ/^
ESG 3	Big4	þ/^
Controls/ID/YD
Mean VIF
F value
Adj. R2
Obs	3.35***(35.38)	2.90***(25.43)	5.06***(28.36)
0.11(2.58)	0.27***(6.51)	0.26***(4.49)
0.05***(2.78)
0.26***(9.01)
Included	Included	Included
1.46	1.34	1.39
1997.40***	256.46***	41.60***
0.7243	0.7694	0.4300
8,384	5,064	3320	
Note(s): Audit_Size is excluded because of collinearity, ESG 3 Big4 is an interaction variable that is ESG
3 Big4
*, **, *** indicate significance level at 10%, 5%, 1%, respectively. Figures in parenthesis indicate t-value
See Table 1 for variable definitions
Model: Big4 vs Non-Big4
Audit Hours=Feesi ;t ¼ β0 þ β 1 ESGi ;t þ β2 Sizei ;t þ β3 Big4i ;t þ β4 Levi ;t þβ5 Betai ;t þβ6 Lossi ;t þ β7 ROAi ;t þβ 8 BigOwni ;t þβ9 Foreigni ;t þ β10 Directori;t þID þ YD þ εi ;t
Model: Big4, Non-Big4 Audit Hours=Fees i;t ¼ β0 þ β1 ESGi ;t þ β2 Big4i ;t þβ3 ESG * Big4i ;t þβ4 Sizei ;t þβ5 Levi ;t þβ6 Betai ;t þ β7 Lossi ;t þ β8 ROAi ;t þ β9 BigOwni ;t þ β10 Foreigni ;t þ β11 Directori;t þID þ YD þ εi ;t
Audit Hours=Feesi ;t ¼ β0 þ β1 ESGi ;t þ β2 Sizei ;t þ β3 Big4i ;t þ β4 Levi ;t þβ5 Betai ;t þ β6 Lossi ;t þ β7 ROAi ;t þ β8 BigOwni ;t þ β9 Foreigni ;t þ β10 Directori ;t þ ID
þ YD þ εi ;t
(6)
In Panels B and C, empirical tests are conducted after dividing the sample into Big4 and NonBig4 (client) samples. For the Big4 sample, empirical results derived from Equation (6) reveal a consistently more positive relationship between ESG ratings and audit hours (0.29***), relative to audit fees (0.27***). For the NonBig4 sample, a more positive association is also captured between ESG and audit hours (0.27***), relative to fees (0.26***).
Audit Fees=hoursi ;t ¼ β0 þ β 1 ESGi ;t þ β2 Big4i ;t þ β3 ESG* B ig4i ;t þ β4 Firm Sizei ;t
þ β5 Auditor Sizei ;t þ β6 Levi ;t þβ7 Market Riski;t þ β8 Lossi ;t þ β9 ROAi ;t þ β10 BigOwni ;t þ β11 Foreign i;t þ β12 Directori;t þ ID
þ YD þ εi ;t
(7)
In Equation (7), the ESG 3 Big4 interaction term is introduced to reflect whether there is an incremental increase in the audit effort (fees/hours) secured by Big4 clients (compared to NonBig4 clients) as ESG levels increase. The ESG 3 Big4 interaction term (column 1, Panel B and C) provides evidence that Big4 clients with higher ESG ratings secure incrementally higher levels of audit hours (0.27***) and fees (0.26***), relative to NonBig4 clients. These results suggest that based on the perceived audit quality of Big4 auditors, as ESG ratings increase, Big4 clients exhibit a stronger propensity to demand higher levels of audit effort, relative to NonBig4 clients.
5.4		Environment, social and governance analysis
Next, an analysis is conducted for ESG ratings, after ESG is divided into individual Environmental, Social and Governance rating components. We report untabulated results for brevity. For all ESG ratings, audit hours are shown to increase at an incrementally higher rate, relative to audit fees (Environmental, audit hours: 0.22***, audit fees: 0.21***; Social, audit hours: 0.35***: audit fees, 0.32***; Governance, audit hours, 0.21***: audit fees 0.19***). These findings add robustness, by demonstrating that regardless of ESG performance/rating characteristic, results remain consistent.
Audit Hours=Feesi ;t ¼ β0 þ β1 Environment=Social=Governancei ;t þ β2 Sizei ;t þ β3 Big4i ;t
þ β4 Levi ;t þβ5 Betai ;t þ β6 Lossi ;t þ β7 ROAi ;t þ β8 BigOwni ;t
þ β9 Foreigni ;t þ β10 Directori ;t þ ID þ YD þ εi ;t
(8)
5.5		Test for endogeneity
In Table 8, we conduct a Two-Stage Least Squares (2SLS) endogeneity test, as delineated in Equations (8) and (9). In 1st stage, we estimate the predicted values for ESG, Environmental,

	DV: ESG	DV: social	DV: environment	DV: governance	
Panel A: First-stage regressions, DV 5 ESG, social, environment, governance		
Intercept	^0.37***(^17.54)	^0.39***(^17.97)	^0.29***(^14.12)	^0.10***(^6.15)	
ESG_t-1	0.67***(83.77)				
SOCIAL_t-1		0.71***(89.17)			
ENVIRONMENT_t-1			0.76***(111.15***)		
GOVERNANCE_t-1				0.55***(51.01)	
Controls	Included	Included	Included	Included	
ID and YD	Included	Included	Included	Included	
F value	1708.28***	2001.64***	2275.28***	506.85***	
Adj. R2	0.7365	0.7661	0.7883	0.4530	
Obs	6,720	6,720	6,720	6,720	
Table 8. 2SLS Analyses for endogeneity

Panel B: Second-stage regressions, DV 5 audit hours
Intercept	^0.22***(^3.03)	^0.14	^0.24***(^3.53)	^0.31***(^5.28)	
ESG	0.08**(2.34)				
SOCIAL		0.13***(3.93)			
ENVIRONMENT			0.06**(2.23)		
GOVERNANCE				0.10*(1.85)	
Firm Size	0.11***(21.51)	0.11***(21.13)	0.11***(22.58)	0.12***(23.83)	
Auditor size	0.12***(14.37)	0.12***(14.25)	0.12***(14.50)	0.12***(14.28)	
Indebtedness	0.00***(3.03)	0.00***(3.18)	0.00***(2.77)	0.00***(3.11)	
Market risk (Beta)	0.03***(4.20)	0.03***(4.27)	0.03***(4.06)	0.03***(4.04)	
Loss	0.03***(2.93)	0.03***(2.89)	0.03***(2.93)	0.03***(3.09)	
ROA	^0.00***(^2.72)	^0.00***(^2.71)	^0.00***(^2.69)	^0.00***(^2.89)	
Bigown	^0.00***(^3.59)	^0.00***(^3.74)	^0.00***(3.55)	^0.00***(^3.26)	
foreign	0.00*(1.83)	0.00*(1.86)	0.00*(1.84)	0.00(1.62)	
Directors	0.01***(5.41)	0.01***(5.19)	0.01***(5.50)	0.01***(5.67)	
ID and YD	Included	Included	Included	Included	
Wald χ2	48998.06***	49096.58***	48888.43***)	48983.74***	
Adj. R2	0.8794	0.8996	0.8791	0.8794	
Obs	6,720	6,720	6,720	6,720	
Note(s): First stage Model: ESG=Environment=Social=Governancei ;t ¼ β0 þ β1 Sizei ;t þ β2 Big4i ;t þβ3 Levi ;t þβ4 Betai ;t þ β5 Lossi ;t þ β6 ROAi ;t þ β7 BigOwni ;t þ β8 Foreigni ;t þ β9 Directori ;t þβ10 ESG= Environment = Social =Governancei ;t-1 ID þ YD þ εi ;t
Second stage Model: Audit Hoursi ;t ¼ β0 þ β1 ESG=Environment=Social=Governancei ;t þ β2 Sizei ;t þ β3 Big4 i;t þ β4 Levi ;t þβ5 Betai ;t þ β6 Lossi ;t þ β7 ROAi ;t þ β8 BigOwni ;t þ β9 Foreigni ;t þ β10 Director i;t þ IDþ YD þ εi ;t

JAAR

Social and Governance metrics for period t. To serve as instrumental variables, we utilise the lagged values of these metrics from the previous period. This approach is grounded on the premise that a previous period’s ESG metrics exert an influence on the subsequent period, thereby fulfilling the criteria for an instrumental variable in 2SLS analysis. An ideal instrumental variable must exhibit a direct influence on the variable of interest, while maintaining no direct association with the dependent variable, in this case, audit hours and fees. ESG ratings are released on a quarterly basis in South Korea. ESG ratings from period t-1 (4 periods before) are believed to be a particularly potent as instrumental variable. A temporal delay suggests that although ESG ratings from the preceding period should significantly influence the current ESG, there is no reason to suggest that ESG ratings from 4 reporting periods prior would influence “current” audit effort, thereby satisfying the exogeneity criterion. This delineation underscores the efficacy of lagged ESG metrics as instrumental variables, enabling a more nuanced analysis of their impact on ESG on audit effort, while controlling for endogeneity.
In the preliminary regression (Equation 8), we explore the persistence of ESG/ Environmental/Social/Governance from period t-1, against the determinants highlighted in the primary analysis, in period t, which include Size, Big4, Lev, Beta, Loss, ROA, BigOwn, Foreign and Director. As shown in Table 8, ESG/Environmental/Social/Governance ratings are associated with equivalents from 12 months prior (ESG: Coefficient 0.67, t-value 83.77; Environment: Coefficient 0.71, t-value 89.1; Social: Coefficient 0.76, t-value 111.15; Governance: Coefficient 0.55, t-value 51.01).
1st stage : ESG=Environment=Social=Governancei ;t
¼ β0 þ β1 ESG=Environment=Social=Governancei ;t-1 þ β2 Sizei ;t þ β3 Big4i ;t
þ β4 Levi ;t þβ5 Betai ;t þ β6 Lossi ;t þ β7 ROAi ;t þ β8 BigOwni ;t þ β9 Foreigni ;t
þ β10 Directori ;t þ ID þ YD þ εi ;t	(9)
2nd stage : Audit Hoursi ;t
¼ β0 þ β1 ESG=Environment=Social=Governancei ;t þ β2 Sizei ;t þ β3 Big4i ;t
þ β4 Levi ;t þβ5 Betai ;t þ β6 Lossi ;t þ β7 ROAi ;t þ β8 BigOwni ;t þ β9 Foreigni ;t
þ β10 Directori;t þ ID þ YD þ εi ;t
(10)
In the second stage regression, the predicted ESG/Environment/Social/Governance estimates taken from Equation (9) are included in Equation (10). The analysis reveals that the association between ESG metrics and audit hours persists, remaining substantively unchanged (Predicted ESG 0.08***, Social 0.13***, Environment 0.06**, Governance 0.10*). Across all models, the minimal Wald Chi-square and Adjusted R-square values (48888.43/0.8791) robustly affirm the model’s predictive accuracy. Following the above two-stage least squares analysis, the Durbin and Wu-Hausman test was applied. The null hypothesis infers that the ESG/ Environmental/Social/Governance variables are exogenous. The Wooldridge’s test does not reject the null hypothesis that ESG/Environment/Social/Governance are exogenous, at a conventional significance level (ESG rating 1.04, p-value 0.31; Social rating 1.53, p-value 0.21; Environment rating 1.20, p-value 0.41; Governance rating 0.40, p-value 0.52). The results indicate that the model employed to report a positive association between audit hours and ESG ratings is devoid of endogeneity bias.
5.6		Additional robustness tests
Next, we control for standard errors by year and industry to provide further evidence of model robustness. Using this approach, empirical findings confirm that the relationship between audit hours and ESG, Environmental, Social, and Governance ratings remains qualitatively consistent with our primary analysis. Specifically, the association between audit hours and Environmental (0.12**), Social (0.34***), Governance (0.11**) and overall ESG (0.23***) ratings persists, reinforcing initial findings. Additionally, we incorporate firm fixed effects into Equation (1) to control for unobserved heterogeneity that might influence audit hours. We report untabulated results that audit hours have a positive relationship with ESG ratings (Environmental: 0.08**, Social: 0.29***, Governance: 0.12**, ESG: 0.18**). This finding further corroborates the stability of our model.
6.		Discussion and conclusion
The study makes numerous contributions. First, in countries where audit hour information is not publicly disclosed, the relationship between audit fees/effort and ESG ratings is negative JAAR	(Asante-Appiah, 2020; Burke et al., 2019). This relationship is interpreted as a situation where audit firms secure higher compensation from clients with lower ESG ratings, to mitigate litigation risk and reputational damage (audit supply theory). Contrary to this finding, we discover that in a unique situation where audit hour information is publicly disclosed on annual reports (South Korea), ESG ratings have a positive effect on audit effort/hours (fees). The results can be interpreted as follows. Shareholders use ESG ratings for investment decisionmaking purposes (Amel-Zadeh and Serafeim, 2018). Because of the reputational and financial performance benefits associated with ESG (see literature review), investors have strong incentives to seek assurances that ESG ratings reflect business activities. On the other hand, management can be perceived as having an incentive greenwash ESG ratings (Yu et al., 2020), causing an agency problem. Our results infer that, based on the expectation that audit effort enhances audit quality, the shareholders of clients with high ESG ratings secure increasing levels of audit hours as an ESG rating assurance strategy (confidence theory and agency theory). The results also suggest that audit firms and management meet the demand expectations of shareholders (credibility theory). To the best of our knowledge, this is the very first study to explain how ESG ratings enhance audit hour demand. The study, therefore, contributes to knowledge by demonstrating how South Korea’s unique audit hour reporting policy influences audit contract negotiations, in an ESG rating context.
Second, DeFond and Zhang (2014) infer that audit fees are limited as an audit effort proxy because they capture both client demand and audit firm supply incentives. South Korean studies extend the literature by offering both audit hour, and audit fee interpretations. If no fee premium is secured by audit firms, South Korean clients are shown to secure increasing levels of audit hours; as an operational efficiency legitimacy strategy (Mali and Lim, 2021); a strategy to reduce borrowing costs (Mali and Lim, 2020) and a strategy to improve credit ratings (Lim and Mali, 2021). Likewise, we find that ESG ratings influence audit hour demand, as an ESG rating assurance strategy (in a situation where not fee premium is imparted). The study, therefore, extends the ESG literature by speaking to the importance of ESG ratings from the perspective of market participants. More specifically, in terms of audit effort/hour demand, ESG ratings are shown to be considered equivalent in significance to WACC, credit risk and firm performance.
Third, a policy suggestion is introduced. It is widely reported that time pressure to conduct audits reduces earnings quality and increases auditor stress (Caramanis and Lennox, 2008; Ettredge et al., 2014; Gu^enin-Paracini et al., 2014; Lambert et al., 2017). Furthermore, it is acknowledged that Enron paid high audit fees to Andersen; however, few audit tests were conducted (Arnold and De Lange, 2004; Cullinan, 2004; Duska, 2005). We provide evidence that audit hour information presents clients with audit quality/effort insights. Thus, if a similar policy to the Auditor Engagement Policy were to be introduced internationally, we posit that the audit profession can be enhanced. To offer a normative perspective, we believe that a comparative assessment of the audit hour/fees/tests included in client A’s contract, relative to client B, would provide useful insights about the auditor–client relationships, as well as offering audit risk insights (high fees / low hours). To enhance audit quality and transparency, we recommend that international legislators consider introducing a policy equivalent to the Auditor Engagement Policy, currently employed in South Korea.
Fourth, market participants value ESG audit assurance (European Commission, 2021; Schrank, 2022). However, an ESG assurance framework is not established (Zahid et al., 2022). We extend the ESG audit assurance literature by providing evidence that, in the absence of a structured ESG audit assurance framework, South Korean stakeholders use the public disclosure of audit hours information as a substitute mechanism to meet ESG assurance expectations. This finding infers that, in a similar fashion to South Korea, international stakeholders can have an incentive to secure audit hours as an ESG assurance strategy. However, because audit hour information is rarely disclosed internationally, it is not possible to make such a distinction in the majority of countries. The study, therefore, contributes to practice/society by illustrating that if the South Korean ESG audit hour framework would be
applied internationally, shareholders and societal stakeholders could have higher confidence in ESG scores. The study therefore provides insights into how the demands of stakeholders to promote good business practice, which benefits society, are not embedded within the mainstream accounting framework, using South Korea as a comparative benchmark.
Fifth, a characteristic of the South Korean sample is that the ESG “rating provider” (KCGS) and (2) “assurance facilitator” (audit firms) are different organisations. We provide evidence that, based on increasing ESG status, clients secure incrementally higher levels of audit hours/ effort from Big4 clients. It is widely recognised that Big4 audit firms have higher audit quality and are more independent, compared to NonBig4 auditors (Becker et al., 1998; Behn et al., 2008; Che et al., 2020; DeAngelo, 1981; DeFond et al., 2017; Fung et al., 2016; Khurana and Raman, 2004; Leng and Zhang, 2024; Lisic et al., 2015). This finding implies that South Korean clients are nuanced when it comes to selecting higher levels of quality (Big4) as an ESG ratings assurance strategy. We interpret that clients perceive that the increasing effort imparted by Big4 audit firms has a higher potential to uncover greenwashing, relative to NonBig4 auditors. This finding, therefore, extends the literature and offers further insights into the accounting profession.
Next, avenues for future research and limitations are discussed. Audit hour information is included on South Korean annual reports as a strategy to restore public confidence in the audit profession. Therefore, the positive ESG rating, audit effort/hour demand association may be unique to South Korea, due to high-profile audit failures. International comparative analysis in countries with higher confidence in the audit profession (and audit hour availability) may disentangle whether the positive ESG rating, audit effort/hour demand association, is an international phenomenon or unique to South Korea. Furthermore, the study does not collect evidence to determine whether increasing levels of audit hour are included in contracts based on the expectations of shareholders or the incentives of management. Shareholders have an incentive to secure increasing levels of audit effort as an ESG rating assurance strategy. However, management is also likely to have an incentive to secure increasing levels of audit hours in audit contracts (if no fee premium is imparted) as a financial/audit reporting signalling strategy. Future studies may collect questionnaire data from audit managers to investigate whether it is shareholders or management that are the drivers of increased audit hours, based on ESG ratings. Furthermore, based on evidence that director characteristics can influence corporate governance (Attah-Boakye et al., 2020, 2024), future studies may investigate the effect of director characteristics on audit effort demand, using ESG as a theoretical lens.
Notes
1.		As referred to in the literature review, audit demand theory consists of 4 sub-categories: the Policeman theory, (lending) credibility theory, theory of inspired confidence and agency theory (Hayes et al., 2005)
2.		The auditor’s role as a watchdog, focusing on the detection and prevention of fraud, similar to the role of a police officer.
3.		Auditors provide credibility to financial statements, enhancing stakeholders’ faith in management’s stewardship. By verifying the accuracy of financial statements, auditors give investors and other stakeholders confidence in the information presented.
4.		Stakeholders demand accountability from management in exchange for their contributions to the company. Auditors play a role in this by providing assurances that management is acting in accordance with the interests of the company’s stakeholders.
5.		The role of auditors is to monitor management’s actions and ensure they are acting in the best interests of the owners.
6.		Size, Lev, Loss, ROA, Big4, ID and YD were previously defined in Table 1.
TRM: Aggregated real earnings management measure based on Cohen and Zarowin (2010),
AEM: (Performance adjusted discretionary accruals based on Kothari et al., 2005)
Interest Coverage: (Operating income divided by interest expense)

JAAR

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Further reading
Azizkhani, M., Sami, H., Amirkhani, K. and Monroe, G.S. (2022), “Competition effects on audit quality and pricing in a non-big 4 market”, The International Journal of Accounting, Vol. 57 No. 4, 2250015, doi: 10.1142/s1094406022500159.
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In, S.Y. and Schumacher, K. (2021), “Carbonwashing: a new type of carbon data-related ESG greenwashing”, SSRN.
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Corresponding author
Dafydd Mali can be contacted at: d.mali@uea.ac.uk
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