Relationship between key audit matters reporting and audit fees: moderating role of audit firm quality and client complexity

The International Auditing and Assurance Standards Board introduced key audit matters (KAMs) reporting in 2015 and mandated it for listed firms in many countries from December 15, 2016 (ISA 701). The purpose of ISA 701 is to enhance transparency in audit reports by highlighting the most significant matters in the audit of financial statements. This initiative is more likely to address information asymmetry concerns as part of agency problems between management and shareholders and increase auditors’ accountability (audit quality). Agency problems between management and stakeholders increase the likelihood of financial statement misstatements. Therefore, stakeholders pay greater attention to the quality of financial reporting. Auditors play a central role in ensuring the reliability, transparency and quality of financial reporting. A detailed audit report, including KAMs, is likely to enhance the value of audit reports. Not surprisingly, several studies have focused on KAM reporting since the introduction of KAM reporting (e.g. Camacho-Miñano, Muñoz-Izquierdo, Pincus, & Wellmeyer, 2024; Alharasis, 2024; Rousseau & Zehms, 2024; Hoang, Moroney, Phang, & Xiao, 2023; Reid, Carcello, Li, Neal, & Francis, 2019). For example, Camacho-Miñano et al. (2024) find a positive association between the number of key audit matters (NKAMs) and firms’ financial distress levels. Therefore, stakeholders’ attention to KAM reporting outcomes has increased because of the potential outcomes of KAMs.

This study extends auditing literature by examining the relationship between the NKAMs and audit fees (AFEE), and the moderating role of audit firm quality (proxied by Big4 auditors) and client complexity (proxied by client size and revenue) in this relationship. Stakeholders place greater attention on the determinants of AFEE because it is a major cost to firms and is intertwined with numerous firm-related and auditor-related factors. This study was motivated by the following factors.

First, listed firms are required to present audited financial statements. Similarly, KAM reporting is a mandatory audit reporting requirement for listed firms; therefore, auditors and firms must follow it carefully. KAMs relate to significant risks, transactions, events and auditor judgments (EU, 2014; IAASB, 2015; Pinto & Morais, 2019). KAMs are more likely to increase AFEE because identifying, addressing and reporting KAMs requires additional audit effort, time and specialized skills. Moreover, auditors’ accountability has increased in KAM reporting; therefore, KAMs lead to more audit procedures and reviews. Thus, KAM reporting is more likely to consume additional time, especially for senior staff, to review KAM disclosure, particularly how they addressed the identified KAMs. Some prior studies have documented that auditors’ efforts and workload have increased since the introduction of KAM reporting (e.g. Alharasis, 2025; Bepari, Nahar, & Mollik, 2024; Zeng, Zhang, Zhang, & Zhang, 2021; Rautiainen, Saastamoinen, & Pajunen, 2021).

Second, management is accountable for the firm’s costs, whereas statutory auditing is an independent professional service, and firms pay significant AFEE. It is one of the major expenses for listed firms. Several factors determine AFEE, including complexity, material misstatement risks, audit efforts, workload, audit firm size/reputation and the adoption of new standards and regulations. Notably, KAM reporting is the latest significant change in auditing, and it is mandatory for listed firms. This study aims to assess the effect of new audit reporting on costs. This is because stakeholders, especially management and shareholders, are more concerned about AFEE.

Third, prior studies have documented a significant positive and non-significant relationship between KAMs and AFEE. For example, Reid et al. (2019) document no significant increase in AFEE during the post-adoption of KAM reporting in the UK. However, Espahbodi, Lin, Liu, Mock, and Song (2023) document a significant positive association between KAMs and AFEE in Asian countries. These results indicate that the relationship between KAMs and AFEE may differ across geographical contexts and economies. To the best of the author’s knowledge, no prior studies have examined the relationship between NKAMs and AFEE in Germany. Germany is the largest economy in Europe and ranks among the top ten largest economies in the world. Prior studies document higher quality of enforcement and accounting standards, the strength of auditing and reporting standards, strong corporate governance and monitoring in Germany (La Porta, de Silanes, Shleifer, & Vishny, 2012; Krishansing Boolaky, 2011; Rahman, Yammeesri, & Perera, 2010; Werder, Talaulicar, & Kolat, 2005). For example, La Porta et al. (2012) document that the quality of enforcement and accounting standards is highest in Scandinavian and German civil law systems. Moreover, German listed firms operate under a two-tier board monitoring system, in which the separation between the management board and the supervisory board strengthens monitoring and has implications for financial reporting quality and audit quality/outcomes (e.g. Weber, 2020; Gros, Koch, & Wallek, 2017). Therefore, prior findings on the relationship between KAMs and AFEE are less likely to be generalizable to the German context, which represents a unique institutional setting.

Finally, prior studies have documented that audit firms’ size and client complexity are key determinants of AFEE (e.g. Gonthier‐Besacier & Schatt, 2007; Thinggaard et al., 2008; Francis, 1984; Palmrose, 1986; Liu & Subramaniam, 2013; DeFond & Zhang, 2014; Choi, Kim, Liu, & Simunic, 2008; Gunn, Kawada, & Michas, 2019; Hay, Knechel, & Wong, 2006). For example, Francis (1984) documents that large accounting firms charge higher AFEE, and a meta-analysis by Hay et al. (2006) ^[1] documents a positive association between client sales and AFEE. However, no study has examined the moderating role of audit firm size and client complexity in the relationship between NKAMs and AFEE.

This study makes several important contributions to audit literature. First, it provides the first empirical evidence from the German context on the relationship between NKAMs and AFEE, offering new insights into the cost implications of ISA 701 implementation in a major European economy. Second, it extends the literature by examining the moderating roles of audit firm quality and client complexity, which have not been jointly tested in this context before. Third, this study addresses endogeneity concerns by applying advanced techniques such as entropy balancing (EB) and propensity score matching (PSM), enhancing the reliability and robustness of its findings. Finally, the results have practical implications for auditors, firms, regulators and investors by providing empirical evidence of KAMs’ influence on audit costs, especially for large or complex clients. These are useful for better resource planning, regulatory assessment and auditing pricing decisions.

This study draws on the audit quality literature and important theories, including legitimacy, agency, information asymmetry and disclosure. The audit quality literature suggests that audit quality depends on auditor competence and independence (DeAngelo, 1981). Auditors are more likely to identify and disclose KAMs when they are competent and independent. Furthermore, audit quality literature shows a positive link between AFEE and audit quality (e.g. DeFond & Zhang, 2014; Fung, Gul, & Krishnan, 2012; Dao, Raghunandan, & Rama, 2012; Asthana & Boone, 2012; Hoitash, Markelevich, & Barragato, 2007). By integrating the audit quality literature, this study examines the relationship between NKAMs and AFEE, as well as the moderating role of Big4 auditors in this relationship.

Legitimacy Theory suggests that professional actors strive to ensure that their actions are perceived as appropriate and credible, aligning with societal expectations to maintain their social license to operate (Suchman, 1995). The mandatory introduction of KAMs under ISA 701 represents not only a technical reporting change but also a regulatory response to legitimacy concerns arising from audit quality, including the low communicative value of significant audit matters. By mandating the enhanced disclosure of significant audit matters, regulators aimed to improve transparency, restore public confidence and reinforce auditor accountability. Consequently, AFEE related to KAM disclosures reflects not only additional audit effort but also auditors’ investments in legitimacy management, including the use of senior expertise, heightened professional judgment and careful disclosure to mitigate reputation and litigation risks. Legitimacy Theory suggests a potential relationship between NKAMs and AFEE and the moderating role of Big4 auditors and client complexity as investments in legitimacy management. Notably, Big4 auditors are likely to charge higher AFEE not only for quality but also to mitigate litigation and reputation risks. Similarly, the high visibility and scrutiny of complex clients (large) are more likely to pose higher legitimacy, suggesting a potential moderating role in the relationship between NKAMs and AFEE.

More KAMs and higher AFEE are more likely to be associated with agency problems because agency problems are more likely to increase audit risk and efforts. Furthermore, KAM disclosure is more likely to enhance the communicative value of audit reports; therefore, it is more likely to reduce information asymmetry. For example, Seebeck and Kaya (2023) document that KAM reporting increases communicative value and find a significant positive relationship between more specific KAMs information and capital market reactions. Similarly, Sirois, Bédard, and Bera (2018) document that KAMs have an attention-directing impact, as participants access KAM-related disclosures more quickly and devote greater attention to them when they are included in the auditor’s report. Furthermore, Zhai, Lu, Shan, Liu, and Zhao (2021) document that KAM disclosures offer more firm-specific information and reduce share price synchronicity in China. KAMs are disclosed for both internal and external users; therefore, KAM disclosure is connected to the broader view of disclosure theory.

KAM reporting requires auditors to identify, assess and disclose matters of most significance, including significant risks, complex transactions, estimates and judgments in clients’ current-year financial statements. This inherently increases the audit effort and time. From a traditional cost-based perspective, auditors charge higher AFEE when engagements require more resources. From a legitimacy perspective, AFEE associated with KAMs can also be interpreted as compensation for the reputational and liability risks associated with signaling transparency. Auditors invest additional effort to ensure high-quality KAM disclosures, not only to satisfy regulatory requirements but also to maintain their social license to operate. Not surprisingly, some studies have examined the relationship between KAMs and audit costs (e.g. Espahbodi et al., 2023; Zeng et al., 2021; Elmarzouky, Hussainey, & Abdelfattah, 2022; Kitiwong, Ekasingh, & Sarapaivanich, 2024). Accordingly, the first hypothesis is as follows:

Big4 auditors are generally perceived as highly competent, independent and risk-averse, with greater exposure to litigation and reputation risks. They are more likely to deploy senior teams, specialists and advanced audit technologies to ensure compliance and quality in KAM reporting. According to Legitimacy Theory, this reflects an intentional strategy to maintain professional credibility. Consequently, the positive relationship between NKAMs and AFEE is expected to be stronger for Big4 clients, reflecting both the higher quality of service and the premium charged for legitimacy management. Moreover, prior studies have examined the relationship between audit firm size and AFEE (e.g. Choi, Kim, Kim, & Zang, 2010; Gonthier-Besacier & Schatt, 2007; Thinggaard et al., 2008; Francis, 1984; Palmrose, 1986; Liu & Subramaniam, 2013; DeFond & Zhang, 2014). For example, Liu and Subramaniam (2013) find a positive association between audit firm size and AFEE. Furthermore, Big4 auditors are more likely to disclose more KAMs because they are more competent and independent, and they are likely to avoid litigation and reputation risks. Therefore, they are more likely to extend audit work in the KAM reporting context. Not surprisingly, Wuttichindanon and Issarawornrawanich (2020) find a positive association between Big4 auditors and KAM disclosure. A contrarian possibility exists: experienced Big4 auditors may achieve greater efficiency in handling KAMs because of their expertise and standardized procedures; however, allocating such experienced auditors inherently increases costs. However, prior evidence suggests that reputational and compliance motives (reflecting higher investments in audit quality and legitimacy management) generally dominate, resulting in a net positive moderation effect. Thus, the following hypothesis is proposed.

Client complexity (proxied by firm size and revenue) poses additional challenges for auditors to detect fraud. Larger and higher-revenue clients are more visible to stakeholders, increasing their potential reputational exposure. They also tend to have more complex transactions, estimates and provisions that require greater audit effort. Auditors are more likely to charge a higher AFEE for complex clients because they spend more resources, particularly time on auditing. Some prior studies have examined the association between client complexity and AFEE (e.g. Gonthier-Besacier & Schatt, 2007; Simunic, 1980; Joshi & Al-Bastaki, 2000; Karim & Moizer, 1996; Choi et al., 2008; Gunn et al., 2019; Hay et al., 2006). For example, Gonthier-Besacier and Schatt (2007) find a positive association between firm size and the AFEE. Similarly, a prior meta-analysis reports that 22 out of 24 studies document a significant positive relationship between client sales and AFEE. From a Legitimacy Theory perspective, auditors expend additional resources on complex clients not only to reduce material misstatement risks but also to protect their social license by ensuring transparent and credible KAM disclosures. Thus, the following hypotheses are proposed.

Based on the availability of all variables, this study examines 1,163 firm-year observations from listed non-financial firms on the XETRA primary exchange over the period 2018–2023. The study period began in 2018 because 2017 marked the initial year of KAM reporting, an early adoption phase characterized by transitional reporting practices, learning effects and heterogeneity in KAM implementation. The informational content and economic consequences of KAM reporting are more likely to stabilize after the initial adoption period. Data were collected from the Audit Analytics database, and firm age was collected manually using this database.

The following four statistical models are used to test the hypotheses.

The first statistical model assesses the relationship between the NKAMs and AFEE:

The second statistical model assesses the moderating role of Big4 auditors in the relationship between NKAMs and AFEE.

The third and fourth statistical models assess the moderating role of client complexity in the relationship between NKAMs and AFEE.

All variables were winsorized at the 1% level, except for the binary variables used in this study. The dependent variable is the AFEE. NKAMs, BIG4*NKAMs, CSIZE*NKAMs and REV*NKAMs are the predicting variables in Models 1, 2, 3 and 4, respectively. The above statistical models control for ten important variables based on prior studies (e.g. Espahbodi et al., 2023; Reid et al., 2019; Zeng et al., 2021; Elmarzouky et al., 2022). The data cover six years; therefore, year-fixed effect controls for the potential effects. Furthermore, the models control for the potential effect of COVID-19 (Al-Qadasi, Baatwah, & Omer, 2023; Murphy, McLaughlin, & Elamer, 2025; Alharasis, Alkhwaldi, & Hussainey, 2024). The definitions of variables are AFEE (natural log of audit fees), NKAMs (number of KAMs reported), BIG4*NKAMs (interaction between Big4 auditors and NKAMs), CSIZE*NKAMs (interaction between client size and the number of KAMs), REV*NKAMs (interaction between client revenue and NKAMs), BIG4 (if the auditor is one of the Big4 auditors, 1; otherwise, 0), GCOs (if firms received going concern opinion 1, otherwise 0), ARL (audit report lag is the number of days between the financial year end and the auditor’s signing date), NAFEE (natural log of non-audit fees), ATURN (asset turnover is revenue divided by total assets), CSIZE (client size is the natural log of total assets), CAGE (client’s age in years from the initial public offering), MCAP (natural log of market capitalization), REV (natural log of revenue) and COVID [1 for firm-year observations period seriously affected by COVID-19 (2020 and 2021), otherwise 0].

The EB method was employed to create balanced covariates between the treatment and control groups, ensuring that the distributions of observed variables were similar across groups before estimating the treatment effects. This reduces bias due to confounding factors. Additionally, I used PSM to assess selection bias by matching treated and untreated observations with similar probabilities (propensity scores) of receiving treatment based on observed characteristics. This mimics the random assignment of observational data.

Table 1 presents the descriptive statistics, showing that the average AFEE and NKAMs are 13.41 and 2.60, respectively. Approximately 71% of the sample firm-year observations were audited by Big4 auditors, while 4% received GCOs. The average ARL is approximately 80 days, indicating timely audit reporting. The mean asset turnover ratio is 1.56, highlighting that the sample firms generated revenue 1.56 times their total assets. Notably, 35% of the firm-year observations pertain to the COVID-19–affected period.

The Pearson correlation matrix and variance inflation factor (VIF) are presented in Table 2. The correlation matrix shows that NKAMs exhibit a significant positive correlation with AFEE. In addition, BIG4, NAFFE, CSIZE, CAGE, MCAP and REV were positively and significantly correlated with AFEE. In contrast, GCOs and ARL were significantly and negatively correlated with AFEE. The mean VIF for all variables is 2.59, which is well below the threshold of 10 (VIF <10), suggesting that no multicollinearity issues exist among the variables used in this study based on existing literature (Kalnins & Praitis Hill, 2025).

Table 3 reports regression results. Results of Model 1 show a significant positive relationship between NKAMs and AFEE (p < 0.01), supporting H1, consistent with the prediction that KAMs increase AFEE. KAM reporting is an additional and mandatory requirement for the listed firms. Furthermore, auditors are solely responsible for KAM disclosure, where KAMs relate to significant risks, transactions, events and the auditor’s judgment in the client’s financial statements. At this juncture, the positive association between NKAMs and AFEE supports the notion that the KAM-related fee increase is driven more by additional effort or a liability risk premium, or collectively, thereby adding a new layer of understanding to audit pricing. Understandably, if auditors identify more KAMs, they are more likely to spend more time and resources addressing these KAMs. Furthermore, if auditors identify some KAMs related to technical aspects (e.g. fair value measurement, revaluation and specialist areas), then they are more likely to spend on hiring experts. These KAMs are more likely to increase auditing costs.

The results are consistent with the broader audit quality literature, legitimacy theory, agency theory, information asymmetry theory and disclosure theory. Prior findings support these results (e.g. Espahbodi et al., 2023; Zeng et al., 2021; Elmarzouky et al., 2022; Kitiwong et al., 2024) ^[2]. Moreover, the literature supports the significant positive association between KAMs and AFEE in the context of Germany because they have a two-tier monitoring system, higher quality of enforcement and accounting standards, the strength of auditing and reporting standards, strong corporate governance and monitoring (La Porta et al., 2012; Krishansing Boolaky, 2011; Rahman et al., 2010; Werder et al., 2005; Weber, 2020; Gros et al., 2017).

However, the results deviate from some prior studies, which have documented no significant association between KAMs and AFEE (Reid et al., 2019; Lee, Khalaf, Farag, & Gomaa, 2024). In particular, Reid et al. (2019) document no significant changes in audit fees when comparing pre- and post-adoption of KAM reporting (binary variable) in the UK. This study represents the early period of KAM reporting, focusing on two years pre- and post-adoption of KAMs. Similarly, Lee et al. (2024) document no significant association between CAMs and AFEE in the United States. This study also represents the early adoption of CAMs reporting in the United States from 2018 to 2020. Moreover, these two studies were conducted in common law countries.

Model 2 indicates that the relationship between NKAMs and AFEE is significantly stronger for Big 4 clients (p < 0.01), supporting H2 and consistent with the broader audit literature and agency theory. Big4 auditors are generally perceived as more competent and independent, and they are also more concerned about litigation and reputation risks. Given the mandatory nature of KAM reporting, Big4 auditors are more likely to deploy experienced audit teams, senior-level oversight, advanced audit technologies and greater resources to ensure high audit quality. The stronger moderating effect suggests that Big4 auditors are more likely to identify, assess and report more KAMs, thereby increasing audit effort and associated fees. In addition, the Big4 auditors may charge a risk premium when more KAMs are identified, reflecting heightened exposure to litigation and reputational risks. Moreover, this finding is consistent with prior empirical evidence showing a significant positive association between Big4 auditors and audit fees (e.g. Francis, 1984; Palmrose, 1986; Gonthier-Besacier & Schatt, 2007; Thinggaard et al., 2008; Liu & Subramaniam, 2013; DeFond & Zhang, 2014).

The results of Models 3 and 4 indicate that the association between NKAMs and AFEE is significantly stronger for complex clients, measured by both firm size and revenue (p < 0.01), thereby supporting H3 and H4. Auditors typically allocate more audit effort, in terms of time and resources, to larger and higher-revenue clients because such engagements involve higher operational complexity and audit risk and are subject to greater public scrutiny and visibility. Consequently, the positive NKAMs–AFEE relationship is more pronounced for complex firms, as auditors are more likely to identify and disclose more KAMs and charge higher audit fees to compensate for the increased effort and risk exposure. These findings are consistent with the broader audit and corporate governance literature and are well explained by audit, legitimacy, agency, information asymmetry and disclosure theories. Moreover, the results align with prior empirical evidence documenting a positive relationship between client complexity (proxied by firm size and revenue) and audit fees (e.g. Choi et al., 2008; Gunn et al., 2019; Hay et al., 2006) ^[3].

EB, achieving covariate balance through reweighting, enhances the validity of causal inferences and reduces bias, ensuring more reliable and interpretable results (Hainmueller & Xu, 2013). This study defines NKAMs as a binary variable for EB, equal to 1 if a firm has more than two KAMs and 0 otherwise. Then, I balanced the groups’ observed covariates using EB to ensure comparability and reduce selection bias before estimating the treatment effects. In post-matching, the means in the weighted control group (firms with fewer KAMs) align with those in the treatment group (firms with more KAMs), confirming the effectiveness of the matching process, as per the guidelines of Hainmueller and Xu (2013). Table 4 shows a significant relationship between NKAMs and AFEE. This is consistent with the baseline regression results after balancing the variables for the two groups, which confirms no causal inference.

To ensure that the difference in AFEE between firms with more and fewer KAMs is not caused by cross-sectional heterogeneity, I construct treatment and control groups similar to EB. I used the PSM method to identify a control firm for each treated firm (Lennox, Francis, & Wang, 2012). PSM regression results show a significant positive association between NKAMs and AFEE (Table 4). This is consistent with the baseline results and ensures that the differences between the treatment and control groups (more KAMs vs. fewer KAMs) are not simply due to observable confounders.

This study examines the relationship between NKAMs and AFEE and the moderating roles of Big4 auditors and client size and revenue as proxies for client complexity in Germany. The results show a significant positive association between NKAMs and AFEE, suggesting that new audit reporting (KAMs) increases audit effort, time and resources. This relationship is stronger for Big4 clients, reflecting Big4’s higher audit quality and greater sensitivity to litigation and reputational risks. Furthermore, the positive association is more pronounced for larger and higher-revenue firms, consistent with greater operational complexity, business segment diversity and public scrutiny. The robustness of the findings is confirmed using EB and PSM techniques. Notably, some prior studies document no significant association between KAMs and AFEE, even in the same geographical context (i.e. Europe). This study concludes that KAM reporting increases audit fees in Germany, validates the efficiency of the two-tier monitoring system, which potentially demands higher audit quality/KAMs, and supports the notion that quality of enforcement and accounting standards, the strength of auditing and reporting standards, strong corporate governance and monitoring in Germany enhance audit quality. Finally, it concludes that this relationship is stronger for Big4 and complex clients in Germany than in other countries.

The findings offer implications for stakeholders, including auditors, firms, regulators and standard-setters, and investors, by highlighting the relationship between new audit reporting and audit fees, and the moderating role of Big4 auditors and client complexity in this relationship. For auditors, the positive association between NKAMs and AFEE, particularly for Big4 engagements, offers empirical justification for tiered audit pricing models that explicitly account for KAM intensity, reflecting both additional audit effort and risk premiums. For corporate management, the stronger NKAMs–AFEE relationship for large and high-revenue firms highlights the tangible audit cost of client complexity, suggesting that enhancing internal controls and simplifying operations may mitigate KAMs-related AFEE. For regulators and standard-setters, the findings raise concerns regarding the additional cost implications of ISA 701, highlighting the need to evaluate whether the standard incentivizes procedural expansion over concise, decision-useful communication and to carefully weigh the benefits and costs of the new audit reporting. For investors, a higher AFEE accompanied by extensive KAM disclosure may signal increased audit scrutiny and legitimacy assurance, especially for complex firms, rather than operational inefficiency.

This study had some limitations. First, the results of this study should be generalized to other countries and periods with caution. Second, the analysis is based on firm-year observations of listed non-financial firms between 2018 and 2023. Third, although the models control for some important variables, other factors may also influence audit fees. Furthermore, this study uses only the number of KAMs as a measure of KAM intensity. Future research should examine different types or content of KAMs and extend the analysis to a cross-country setting. Qualitative approaches may also provide deeper insights into the mechanisms through which KAMs affect audit efforts and pricing decisions. Finally, despite robustness checks, potential endogeneity issues cannot be fully ruled out and remain avenues for future research.