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Abstract

This study aims to investigate the impact of shared auditors and same signing auditors on audit quality in M&A transactions. Existing research suggests that shared auditors in M&A transactions are considered to enhance information symmetry, thereby improving transaction quality. However, there is a lack of systematic research on how shared auditors influence audit quality in M&A, particularly regarding the interaction with same signing auditors. Using a sample of 1,083 M&A transactions from the Chinese market between 2004 and 2020, we analyze the impact of shared auditors and the same signing auditors on audit quality during the M&A process. The findings indicate that both shared auditors and same signing auditors contribute to improved audit quality in M&A. Further analysis reveals that this enhancement effect is more pronounced in M&A across industries and provinces. Moreover, the impact on audit quality is stronger when the bidder is larger, non-state-owned, or not audited by the Big 4 audit firms. This study provides empirical evidence on the roles of auditors at both the firm and individual levels in improving audit quality, highlighting the potential value of shared auditors. The conclusions offer recommendations for enhancing audit quality and make informed choices regarding auditors.

Jel Classification: M42.

Plain language summary

This study examines the impact of shared audit firms or signing auditors on audit quality in mergers and acquisitions (M&A). Auditors play a vital role in ensuring the reliability of financial information, which is essential for the success of M&A transactions. Analyzing data from over 1083 M&A deals in China between 2004 and 2020, the study finds that when companies use the same audit frms or signing auditors, audit quality improves. This effect is more pronounced in cross-industry or cross-regional transactions. The positive impact is especially significant for larger companies, non-state-owned enterprises, and those not audited by the Big 4 firms. These results suggest that shared auditors enhance access to reliable financial information, which reduces risks in M&A. The study offers practical guidance for businesses on how to strategically select auditors to improve both audit quality and M&A outcomes.

Keywords

shared auditors same signing auditors audit quality M&A

Introduction

Given the strategic imperative for expanding industry scale and capturing market share, Mergers and Acquisitions (M&A) have emerged as the preferred external growth strategy for listed companies. However, due to incomplete information disclosure, acquirers often face information opacity and low credibility in target’s financial reports. According to information asymmetry theory, uneven information distribution between transaction parties can hinder the transaction process. Informal systems and various information networks play a vital role in addressing this issue (Peng & Luo, 2000).

Recent studies highlight the role of social networks, particularly shared auditors, in M&A decision-making (Bedford et al., 2023; Y. Cai et al., 2016; Chahine et al., 2018; Chircop et al., 2018; Dhaliwal et al., 2016). Shared auditors is a term used when two firms select the same audit firm (T. Chen et al., 2012). When borrowers and lenders in lending relationships share the same auditor office, the audit quality is improved (Ton, 2023). The utilization of professional auditors and functioning within industries characterized by greater homogeneity is linked to enhanced audit quality (Sun et al., 2020). Moreover, common signing auditors contribute to greater financial statement comparability by applying consistent accounting standards and professional judgment (Jiu et al., 2020). They also increase information transparency, fostering better comparability across companies (Livne et al., 2024). In addition, the professional judgment of individual signing auditors also contributes positively to audit quality and consistency.

While prior research on shared auditors in M&A has focused on post-merger performance and M&A premiums, their impact on audit quality remains underexplored. This study aims to: (1) analyze the role of shared auditors and same signing auditors in enhancing audit quality, particularly for bidder in M&A; (2) examine the differential impact of shared auditors in cross-industry and cross-province transactions; and (3) investigate how the characteristics of the acquirer, such as firm size, ownership, or engagement with a “Big 4” audit firm, moderate the impact of shared auditors on audit quality.

For several reasons, first, social networks facilitate information sharing, reducing the high costs and low transparency of M&A (Baxamusa et al., 2024; T. Chen et al., 2012; Peng & Luo, 2000). Augmenting information exchange between the involved entities is deemed a critical approach to heighten the efficacy and advantages of M&A (L. Li et al., 2018). The moderate involvement of third-party intermediaries has a positive impact on M&A (Ahn et al., 2010; Bi & Wang, 2018; Cukurova, 2012; de Sousa Barros et al., 2020; Dhaliwal et al., 2016; Golubov et al., 2012). Firms with the same auditor exhibit similar financial reporting patterns, lowering transaction premiums and improving market returns (Bedford et al., 2023; Y. Cai et al., 2016; Dhaliwal et al., 2016; Robert Knechel et al., 2015).

Second, financial reports must be independently audited in compliance. The junior (engagement) auditor and the senior (review) auditor should both sign and seal the audit report, as Audit Standards No. 1501 and No. 1121 issued by the Department of Treasury. In China, disclosing signing auditors’ identities highlights their unique approaches, improving financial statement comparability (Jiu et al., 2020; L. Li et al., 2018). The personal methods employed by auditors play a noteworthy role in enhancing the comparability of financial statement consistency (J. Z. Chen et al., 2020; Jiu et al., 2020).

Third, industry-specific mergers benefit from faster information flow, lower costs, and specialized auditors (Dunn et al., 2000; Solomon et al., 1999). Cross-industry mergers face greater information challenges, where shared auditors mitigate asymmetry (Bathelt & Henn, 2021; Luypaert & Van Caneghem, 2014). While geographic proximity aids communication, shared auditors are crucial when distance hinders information flow (Ciobanu, 2016; Z. Jin et al., 2021). For state-owned enterprises (SOEs), mergers are often policy-driven, and shared auditors facilitate collaboration by mitigating information barriers (Gu et al., 2020). Larger firms tend to hire high-quality auditors (T. Chen et al., 2012) and set higher expectations for targets (Dhaliwal et al., 2016). While Big 4 auditors ensure independence and quality, non-Big 4 firms may better align with client needs (Jain & Agarwalla, 2022). Thus, challenges in information sharing arise from decentralized structures, cross-industry regulations, regional differences, and the scale of firms, with larger audit firms typically being more effective.

Overall, based on M&A data from China’s A-share market from 2004 to 2020, we analyze the impact of shared auditors and the same signing auditors on audit quality during the M&A process. We find empirical evidence that is largely consistent with most hypotheses, with the effect being more pronounced for same signing auditors. To further explore the role of shared auditors and same signing auditors in different scenarios, we conducted heterogeneity tests on five factors: (1) industry differences, (2) regional differences, (3) ownership, (4) relative size, and (5) audit firm size. We argue that the effect is more significant in cross-industry, cross-province, larger acquirer, non-SOEs and non-Big 4, where the role of the shared auditor and the same signing auditor in enhancing audit quality.

The study provides valuable additions to existing literature by focusing on the impact of same signing auditors and supplements the relevant literature on the impact of shared auditors (Ai et al., 2020; Bedford et al., 2023; Y. Cai et al., 2016; Chircop et al., 2018; Dhaliwal et al., 2016; Sun et al., 2020). Regardless of whether it involves shared auditors or same signing auditors, both can generate knowledge spillovers based on M&A. In addition, it expands the related research on the heterogeneous effects of shared auditors and same-signing auditors on audit quality, reflected in aspects such as industry, region, relative size, firm nature, and Big 4 accounting firms (Cao & Pham, 2021; Chahine et al., 2018; J. Z. Chen et al., 2020; Jiu et al., 2020; Tong et al., 2022).

The remaining sections include: Section 2 provides the existing literature concerning shared auditors and same signing auditors, and presents the hypotheses. Section 3 presents the data and outlines the empirical model employed. Section 4 presents the empirical analysis. Section 5 offers the conclusions.

Theoretical Framework and Hypotheses Development

Social networks refer to the relationships between various actors is affected by influencing the preferences and decisions (Granovetter, 1985). Information asymmetry theory highlights the uneven distribution of information, which can hinder M&A success, while social networks bridge these gaps to aid decision-making. Transaction cost economics further reveals the complexity of M&A, emphasizing the importance of efficient information flow (Williamson, 1975).

For audit firms, access to additional firm-level expertise. Shared auditors, interlocked directors, and advisors are key in transferring information (Burris, 2005; Y. Cai et al., 2016; C. Cai et al., 2019; Chiu et al., 2013; Cukurova, 2012; Dhaliwal et al., 2016). Auditing, as an external social network, promotes sustainability in economic systems. Shared auditors refer to two or more firms jointly hiring an audit firm for different purposes (T. Chen et al., 2012). Research shows that shared auditors contribute significantly across supply chains (C. Cai et al., 2019; T. Chen et al., 2012), M&A (Y. Cai et al., 2016; Dhaliwal et al., 2016), competitor relationships (Aobdia, 2015), banks and borrowers (Francis & Wang, 2021), investment and financing relationships (Cao & Pham, 2021; Sun et al., 2020) and Strategic alliances (Baxamusa et al., 2024). By facilitating private information exchange, shared auditors reduce transaction frictions and enhance M&A decision-making, particularly in high-risk or cross-border transactions (Y. Cai et al., 2016; C. Cai et al., 2019; T. Chen et al., 2012; Chircop et al., 2018; Francis et al., 2009). Additionally, they help lower acquisition premiums, improve market reactions, and increase asset returns (Bedford et al., 2023; Chircop et al., 2018).

For the signing auditor, in accordance with PCAOB Rule 3211, audit firms must reveal the audit partner’s name (Burke et al., 2020). The junior (engagement) auditor and the senior (review) auditor should both sign and seal the audit report, as Audit Standards No. 1501 and No. 1121 issued by the Department of Treasury. Individual auditors significantly influence audit quality and outcomes (Carey & Simnett, 2006; R. Chen et al., 2009; Chi & Chin, 2011; DeFond & Francis, 2005; Gul et al., 2013; Lennox et al., 2014). In China, signing auditors’ identities are publicly available, making their professional judgment and audit styles more visible (Jiu et al., 2020; L. Li et al., 2018). Notably, individual auditors significantly contribute to enhancing the comparability of client firms’ earnings and ameliorating financial statement comparability (J. Z. Chen et al., 2020; Jiu et al., 2020). Audit quality also improves when borrowers and lenders share the same audit office (Ton, 2023).

Overall, firm-level auditing prioritizes resource integration over individual professional judgment. Shared auditors promote information sharing through industry expertise and firm-wide coordination, ensuring consistent audit services and improving audit quality. In M&A, they strengthen mutual trust and financial reporting consistency. Same signing auditors further enhance information flow and risk control. This paper argues that auditor knowledge spillovers and information-sharing mechanisms improve audit quality, as reflected in higher audit fees and lower discretionary accruals.

Thus, the hypotheses can be formulated as follows:

H1: Audit quality is higher for shared auditors than for non-shared auditors.

H2: Audit quality is higher for same signing auditors than for non-same signing auditors.

In M&A decision-making, acquirers evaluate the target’s industry, resources, and market dynamics, aligning with the resource-based theory, which emphasizes resource heterogeneity. Successful mergers often rely on the integration of these diverse resources, making a comprehensive evaluation of both parties’ resource compatibility a critical factor.

When the acquirer and the target operate in related industries (e.g., upstream or downstream enterprises or direct competitors), shared market environments, business connections, and aligned credit policies reduce communication costs and mitigate information asymmetry (Betton et al., 2009). Auditors with industry expertise improve audit quality and financial reporting (Dunn et al., 2000; Solomon et al., 1999). And acquirers often select industry-expert auditors to better assess targets and enhance post-merger performance (Cui & Chi-Moon Leung, 2020; Dhaliwal et al., 2016). In regional economic integration, industry relevance strengthens synergies in resources, technology, and markets (Wilson & Pholo Bala, 2019). The characteristics of the industry significantly affect the policy implications in M&A decision-making (Shen et al., 2023).

However, cross-industry M&A presents greater integration challenges due to differences in industry scale and structure (Hu, 2024). Acquirers may lack understanding of the target’s industry, increasing information asymmetry (Luypaert & Van Caneghem, 2014). External mechanisms, such as improved geographical proximity, reduce information barriers between companies from different industries, thereby facilitating more efficient cross-industry M&A and increasing the frequency (Ahern & Harford, 2014; Z. Jin et al., 2021). Shared auditors play a crucial role in such transactions by helping acquirers grasp industry dynamics, business processes, information systems, and internal controls, thereby enhancing audit quality and mitigating information asymmetry challenges in cross-industry M&A.

Based on this, the following hypothesis is proposed:

H3: Shared auditors and same signing auditors play a more significant role in improving audit quality in unrelated-industry mergers.

Geographical proximity between acquirers and targets facilitates access to operational and developmental information, reducing information asymmetry (Ciobanu, 2016; Croci et al., 2023; Z. Jin et al., 2021; Kengelbach et al., 2010; C. Li et al., 2022). This advantage is evident in local supply chains, where firms often engage the same audit firm’s branch to oversee both parties within the same province or municipality. Proximity also enhances M&A returns by providing informational advantages (Uysal et al., 2008).

However, cross-industry or cross-regional M&A increases information acquisition challenges, leading to higher consulting costs and risks of overpayment (Bathelt & Henn, 2021; Bick et al., 2017; C. Li et al., 2022; Reddy & Fabian, 2020). Under these circumstances, rely more on external networks, with shared auditors playing a key intermediary role. Cross-regional M&A are more driven by local factors, such as product diversification and the degree of globalization (J. Wu et al., 2024). Through their professional audit activities, shared auditors can access accurate information about the potential target’s operational circumstances and future development. This information intermediary function is especially critical when the geographical distance between the acquirer and the target is greater.

Therefore, we hypothesize that in cross-province M&A, the role of shared auditors and same-signing auditors becomes more significant in improving the quality of audits. They help bridge the information gap that arises due to the geographical and industry distance between the acquirer and target, thereby contributing to a more informed and transparent M&A process. Based on the above analysis, this paper proposes the following hypothesis:

H4: Shared auditors and same signing auditors play a more significant role in improving audit quality in cross-province mergers.

The performance of SOEs in M&A is closely tied to the unique policy support, resource advantages, and market credibility, which make them less sensitive to external market constraints. Despite the transfer of operational authority, the government still uses SOEs to implement its policies (X. Jin et al., 2022). This allows SOEs to mitigate some negative effects of high corporate social responsibility (CSR) on M&A outcomes (Feng et al., 2024; Zou & Ma, 2024). However, SOEs are relatively weaker in resource reorganization capabilities and innovation drive, limiting their ability to fully capitalize on M&A opportunities (Wang et al., 2024). Local SOEs focus on policy goals, while central SOEs prioritize control over state-owned capital (Del Bo et al., 2017), with M&A decisions often driven by government objectives rather than maximizing corporate value (Gu et al., 2020).

In contrast, non-SOEs, lacking political background advantages, prioritize external legitimacy to improve their image and competitiveness. For example, they tend to adopt measures such as high-quality environmental information disclosure and green M&A (Feng et al., 2024; He et al., 2024). Non-SOEs also show greater flexibility and adaptability in M&A, particularly in technological innovation. This difference is evident in shared audit relationships, where government intervention in SOEs weakens auditors’ role in communication, while non-SOEs, with less government interference, are more likely to use shared auditors to reduce information asymmetry.

Based on this, this paper proposes a hypothesis:

H5: Shared auditors and same signing auditors play a more significant role in improving audit quality within non-SOE bidders.

In M&A, the relative size of the acquirer and the target has a significant impact on the decision-making, execution, and outcomes of the deal. Larger acquirers typically dominate the transactions and set higher expectations for targets (Qiao et al., 2023). In contrast, smaller and medium-sized acquirers, constrained by their resources and capabilities, focus more on cost control and risk management by selecting smaller or relatively simpler targets, thus reducing the complexity and potential costs of post-merger integration (J. Wu et al., 2024). And, larger clients are more likely to choose high-quality audit firms (T. Chen et al., 2012; Danos & Eichenseher, 1986; Healy & Lys, 1986; Johnson & Lys, 1990; Reynolds & Francis, 2000). When the acquirer is significantly larger than the target, it exercises greater control over the target, often prompting the target to engage the same auditor as the acquirer (Dhaliwal et al., 2016). This simplifies access to the target’s information and reduces audit and integration costs.

Larger acquirers tend to rely on external intermediaries, such as shared auditors, to obtain critical information about targets. They are better able to integrate the target’s resources, achieve higher synergies, and enhance post-merger innovation and strategic goals (Qiao et al., 2023; Wang et al., 2022). Shared auditors help mitigate information asymmetry, act as communication bridges, and enhance M&A efficiency, especially in complex transactions involving large firms.

Thus, larger acquirers demonstrate a stronger need for information collection and control, with shared auditors playing a crucial role in reducing uncertainties, optimizing resource integration, and achieving strategic and innovation goals. Based on this, we propose the following hypothesis:

H6: Shared auditors and same signing auditors play a more significant role in improving audit quality in larger bidders.

The Big 4 audit firms are renowned for their high standards and independence in auditing, leading to better audit quality and reduced earnings management (Jain & Agarwalla, 2022; Lopes, 2018). As key players in external governance, the Big 4 demonstrate greater independence and professional competence in environments characterized by information asymmetry (Zhang et al., 2024). Their expertise and resources are particularly important in complex industries. In M&A, the Big 4 enhance audit outcomes with their specialized M&A auditing expertise (Gal-Or et al., 2022). Also, the Big 4 primarily serve larger clients with more complex transaction structures and maintain a stable leadership position in high-end markets (Kitto, 2024).

In contrast, non-Big 4 firms face greater challenges due to limited resources and reputational constraints but are motivated to improve audit quality and meet client needs. Non-Big 4 firms place a stronger emphasis on building client relationships and improving service quality to strengthen their market position (Gal-Or et al., 2022). In competitive markets, they often attract clients by offering cost-effective services while optimizing cost structures through shared resources and expertise (Kitto, 2024).

In M&A, non-Big 4 firms use shared auditors as intermediaries to compensate for weaker brand effects and market share. These relationships promote information transparency and resource integration, supporting clients with focused informational needs. We expect the intermediary role of shared auditors or signing auditors is more evident in non-Big 4 firms. Based on this, we propose the following hypothesis:

H7: Shared auditors and same signing auditors play a more significant role in improving audit quality with non-Big 4 audit firm.

Sample Selection, Measures, and Research Design

Sample Selection

This study selects completed M&A in the Chinese A-share listed company listed in Shanghai Stock Exchange (SSE) and Shenzhen Stock Exchange (SZSE) from 2004 to 2020. M&A are characterized by the successful transfer of control or ownership rights between the bidder and the target. We exclude financial firms and failed deals. For multiple acquisitions completed by the same bidder in the same year, only the first acquisition is retained.

This procedure left us with 1,083 M&A transactions. To address potential outliers, all continuous variables are winsorized at 1% and 99%. Financial data, audit firm information and auditor information were sourced from the China Stock Market and Accounting Research (CSMAR) database, the missing data are manually searched and supplemented. The categorization of a company’s industry was determined by adhering to Guidelines for the Industry Classification of Listed Companies (2012).

Variable Definitions

Dependent Variables

Our main analysis investigates the impact of shared auditors on audit quality. Specifically, we use two audit quality proxies. The first proxy is AUDITFEE, the natural logarithm of total audit fees of the bidder, reflecting the effort and complexity involved in the audit process. As noted by Francis (2004), in most cases, high audit fees still serve as a signal of more resources being allocated by the audit firm, thereby improving audit quality (Doyle et al., 2007). Unified auditing, rather than reducing audit fees, actually tends to increase them (Y. N. Wu & Edwin Cheng, 2008). Higher audit fees are generally associated with better audit quality due to increased resources allocated to high-risk engagements.

The second proxy, |DA|, quantifies the absolute magnitude of discretionary accruals of the bidder calculated using the Modified Jones model, as described by Dechow et al. (1995).

Here, TACC_i,t represents the total accruals of firm i in year t, scaled by the lagged total assets (A_i,t−1). The absolute value (|DA|) serves as the second proxy for audit quality. Lower |DA| values indicate higher audit quality. The model is specified as:

\begin{matrix} TAC C_{i, t} / A_{i, t - 1} = β_{1} 1 / A_{i, t - 1} + β_{2} Δ RE V_{i, t} / A_{i, t - 1} \\ + β_{3} PP E_{i, t} / A_{i, t - 1} + ε_{i, t} \end{matrix}

(1)

\begin{matrix} D A_{i, t} = TAC C_{i, t} / A_{i, t - 1} - β_{1} 1 / A_{i, t - 1} \\ - β_{2} (Δ RE V_{i, t} - Δ RE C_{i, t}) / A_{i, t - 1} - β_{3} PP E_{i, t} / A_{i, t - 1} \end{matrix}

(2)

Where,

TACC_i,t = Accounting earnings- Cash Flow from Operating activities

DA_i,t = Discretionary accruals

A_i,t _-1 = Lagged total assets (total asset in year t-1)

ΔREV_i,t = Change in the revenues of company i from year t-1 to year t

ΔREC_i,t = Change in the net receivables of company i from year t-1 to year t

PPE_i,t = Property, plant, and equipment of company i at the end of year t

ε_i,t = Errors in company i in the year t

Independent Variables

Shared auditors (SHARE). Shared auditors refer to audit firms that provide auditing services simultaneously to both the acquirer and the target involved in an M&A. According to Y. Cai et al. (2016) and Dhaliwal et al. (2016), the variable is assigned a value of 1 if the acquirer and the target are audited by the same audit firm within the 2 years preceding the first announcement of the M&A; 0 otherwise.

Same signing auditors (SAMESIGN). The variable represents a stricter condition where the acquirer and target not only share the same audit firm but also have identical signing auditors listed in audit reports. This variable is coded as 1 if the individual signing auditors disclosed for both firms are the same within the 2 years preceding the first announcement of the M&A; 0 otherwise.

Moderator Variables

Industry differences (SI). The variable equals 1 when Shared auditor = 1 and the common auditors are from related industries; 0 otherwise (Faccio & Masulis, 2005; Megginson et al., 2004).

Region differences (SP). The variable equals 1 when Shared auditor = 1 and the common auditors are from the same province; 0 otherwise (Portes & Rey, 2005).

Firm nature (SOE). The variable equals 1 when the acquirer is a state-owned enterprise; 0 otherwise.

Relative size (RS). The ratio of the market value of the target’s equity is divided by the acquirer’s equity (Hayward & Hambrick, 1997; Lee & Kocher, 2011).

Audit Firm size (BIG4). The variable equals 1 when the audit firms are from “big 4”; 0 otherwise (Xie et al., 2013).

Control Variables

According to T. Chen et al. (2012) and Y. Cai et al. (2016), we control for two main factors that may affect the M&A decision, target characteristics and shareholding structure. The data are selected from the last fiscal year of M&A.

Controls represent the characteristics that are associated with the shared auditor and the same signing auditor. With respect to deal characteristics, we control for the acquirer’s size (SIZE) that the natural logarithm of the total assets of the real and potential targets 1 year prior to the date of the first announcement of the acquisition was used to measure the size of the company (Y. Cai et al., 2016; Dhaliwal et al., 2016). We also control for leverage (LEV) measured as total liabilities divided by total assets (Capron & Shen, 2007) and sales growth (GROWTH; Ashbaugh-Skaife et al., 2008; Gaur et al., 2013). We also control for whether the acquisition is financed with stock (PAY) because managers may have different incentives to make stock-based versus cash-based acquisitions (Erickson & Wang, 1999). We control for Big 4 audit firms(BIG) of the acquirer because they play an important role in facilitating M&A (Xie et al., 2013) and some prior research suggests that Big 4 auditors provide higher financial reporting quality (Khurana & Raman, 2004). We also control inventory (INVENTORY) scaled by lagged assets. We also control for shareholding ratios of the largest shareholders (LSR). We also keep track of the total shareholder equity to share ratio (NAVPS; T. Chen et al., 2012). In terms of profitability, this study measures profitability using return on equity (ROE), which is defined as the ratio of the target’s net income to the target’s equity (Capron & Shen, 2007). In addition, we use return on assets (ROA) to determine profitability (Bodnaruk et al., 2009). Asset quality is a function of M&A cost, which ultimately determines whether a company is the real target. To assess asset quality, we use the book-to-market ratio (BM) and the price-to-earnings ratio (PER; Y. Cai et al., 2016). The price-to-earnings ratio (PER) is taken as the ratio of share price to earnings per share of the target 1 year before the first announcement date. Also, we included industry dummies and year dummies to control for industrial effect and the effect of time-related contemporaneous correlation. The variables and measurements are listed in Appendix 1.

Research Design

We refer to previous research on the factors influencing audit fees and discretionary accruals. We design the model as follows.

\begin{matrix} AUDITFE E_{i, t} = α_{0} + α_{1} SHARE {(SAMESIGN)}_{i, t} + α_{2} SIZ E_{i, t} \\ + α_{3} GROWT H_{i, t} + α_{4} LE V_{i, t} \\ + α_{5} INVENTOR Y_{i, t} + α_{6} BI G_{i, t} \\ + α_{7} RO E_{i, t} + α_{8} RO A_{i, t} + α_{9} LS R_{i, t} \\ + α_{10} B M_{i, t} + α_{11} NAVP S_{i, t} + α_{12} PA Y_{i, t} \\ + \sum Year + \sum Industry + ε \end{matrix}

(3)

\begin{matrix} {| DA |}_{i, t} = β_{0} + β_{1} SHARE {(SAMESIGN)}_{i, t} + β_{2} SIZ E_{i, t} \\ + β_{3} GROWT H_{i, t} + β_{4} LE V_{i, t} + β_{5} INVENTOR Y_{i, t} \\ + β_{6} BI G_{i, t} + β_{7} RO E_{i, t} + β_{8} RO A_{i, t} + β_{9} LS R_{i, t} \\ + β_{10} B M_{i, t} + β_{11} NAVP S_{i, t} + β_{12} PA Y_{i, t} \\ + \sum Year + \sum Industry + ε \end{matrix}

(4)

Model (3) evaluates the correlation between audit fees and SHARE (SAMESIGN). We use the coefficient α₁ to verify the relationship between audit fees and shared auditor and same signing auditors. If α₁ is significantly positive, it indicates that the acquirer’s audit fees are higher and the audit quality is higher with shares auditors or same signing auditors. Otherwise, it indicates that the bidder’s audit fees are lower and the audit quality is lower.

Model (4) is used to measure the association between |DA| and SHARE (SAMESIGN), and we verify the relationship by observing the coefficient β₁. If β₁ is significantly negative, it indicates that the acquirer has less earning management behavior and higher audit quality with shares auditors or same signing auditors. Conversely, it indicates that the acquirer has more earning management behavior and has no effect on audit quality.

These variables inherently have binary attributes, and dummy variables can accurately reflect their characteristics. Additionally, due to data availability constraints, we are unable to obtain more detailed continuous or categorical data, making dummy variables a feasible alternative. Nevertheless, existing literature indicates that similar binary measurement methods effectively capture the core impact of these variables (T. Chen et al., 2012; Lopes, 2018).

Empirical Results

Descriptive Statistics

Full Sample Descriptive Statistics

In Table 1. The mean value of AUDITFEE (|DA|) is 13.894 (0.087). SHARE accounts for 19.2% of the total sample, while SAMESIGN accounts for 5.4%. The mean value of SOE is 25.7%, suggesting that the sample contains more non-SOEs. The mean value for Industry differences (SI) and Region differences (SP) are 0.200 and 0.366, respectively, indicating that the sample contains more cross-industry and cross-region M&A. As the market and industry become more saturated, companies must choose to seek opportunities in unrelated industries and exotic markets in order to achieve further growth. The mean value of relative size (RS) is −1.913, indicating that the acquirer is smaller. The average of BIG4 is 0.022, indicating that M&A employing big 4 auditors accounts for only 2.2% of the M&A sample and the sample size is small.

Table 1.

Full Sample Descriptive Statistics.

Variables	N	Mean	Std	Min	P25	P50	P75	Max
AUDITFEE	1,083	13.894	1.166	12.429	13.122	13.653	14.403	18.005
\|DA\|	1,083	0.087	0.136	0.001	0.015	0.047	0.099	0.944
SHARE	1,083	0.192	0.394	0	0	0	0	1
SAMESIGN	1,083	0.054	0.225	0	0	0	0	1
SI	1,083	0.200	0.400	0	0	0	0	1
SP	1,083	0.366	0.482	0	0	0	1	1
SOE	1,083	0.257	0.437	0	0	0	1	1
BIG4	1,083	0.022	0.147	0	0	0	0	1
RS	1,083	−1.913	3.785	−7.888	−4.533	−0.896	0.683	6.620
SIZE	1,083	22.536	1.988	19.908	21.169	22.258	23.545	29.005
GROWTH	1,083	0.270	1.375	−0.960	−0.159	0.085	0.375	10.644
LEV	1,083	0.500	0.244	0.071	0.327	0.527	0.684	1.018
INVENTORY	1,083	0.126	0.142	0	0.012	0.092	0.173	0.673
BIG	1,083	0.162	0.368	0	0	0	0	1
ROE	1,083	−0.136	0.579	−1.736	0.008	0.062	0.119	0.436
ROA	1,083	−0.036	0.174	−0.451	0	0.021	0.049	0.210
LSR	1,083	32.194	17.937	7.080	18.330	30.780	44.760	78.840
BM	1,083	0.605	0.307	0.144	0.338	0.651	0.872	1.135
NAVPS	1,083	4.217	3.538	0.046	1.934	3.542	5.636	17.834
PAY	1,083	0.250	0.433	0	0	0	1	1

Note. The table displays the full sample descriptive statistics. Detailed definition of variables is in Appendix 1.

Subsample Difference Statistics

We performed a T-test analysis to compare the means of two groups of audit firms, divided based on whether they are shared by two firms. Our findings are presented in Table 2. The findings reveal a significant positive correlation between shared auditors and audit fees. Specifically, the mean value of AUDITFEE is 14.014 for audit firms with shared auditors, compared to 13.866 for firms without shared auditors. Moreover, our results indicate that acquirers tend to exhibit lower mean discretionary accruals when they share auditors. Specifically, discretionary accruals’ mean value is 0.083 for acquirers with shared auditors, compared to 0.088 for those without shared auditors. This finding is consistent with theoretical expectations, providing preliminary support for hypotheses H1 and H2. In addition, the shared auditor group demonstrates notably higher values for same signing auditors (SAMESIGN), region (SP), and audit firm size (BIG4) compared to the non-shared auditor group.

Table 2.

Sub-Sample Difference Statistics.

Variable	Full sample		Shared auditor		Non-shared auditor		Difference
Variable	Obs	Mean	Obs	Mean	Obs	Mean	Diff.	T
AUDITFEE	1,083	13.894	208	14.014	875	13.866	−0.148*	−1.51
\|DA\|	1,083	0.087	208	0.083	875	0.088	0.005**	1.82
SAMESIGN	1,083	0.054	208	0.279	875	0.000	−0.279***	−8.95
SI	1,083	0.200	208	0.226	875	0.194	−0.032	−1.03
SP	1,083	0.366	208	0.644	875	0.299	−0.345***	−9.66
SOE	1,083	0.257	208	0.298	875	0.247	−0.051	−1.52
BIG4	1,083	0.022	208	0.115	875	0.000	−0.115***	−5.20
RS	1,083	−1.913	208	−1.910	875	−1.913	−0.003	−0.01
SIZE	1,083	22.536	208	22.343	875	22.582	0.2390*	1.71
GROWTH	1,083	0.270	208	0.209	875	0.285	0.076	0.71
LEV	1,083	0.500	208	0.491	875	0.503	0.012	0.64
INVENTORY	1,083	0.126	208	0.128	875	0.125	−0.003	−0.23
BIG	1,083	0.162	208	0.130	875	0.169	0.039	1.48
ROE	1,083	−0.136	208	−0.144	875	−0.134	0.010	0.22
ROA	1,083	−0.036	208	−0.037	875	−0.036	0.001	0.05
LSR	1,083	32.194	208	33.642	875	31.850	−1.792	−1.30
BM	1,083	0.605	208	0.621	875	0.601	−0.020	−0.83
NAVPS	1,083	4.217	208	4.155	875	4.231	0.076	0.28
PAY	1,083	0.250	208	0.245	875	0.251	0.006	0.19

Note. This table presents T-test result.

Significance levels are represented by *, **, and ***, indicating 10%, 5%, and 1%, respectively. Further explanation of variables is in the note of Appendix 1.

Correlation Analysis

The results of the Person and Spearman correlation analyses conducted between the dependent variable (AUDITFEE), independent variables (shared auditor and same signing auditors), moderating variables (firm nature, relative size, audit firm size, industry, and region), and control variables. In Table 3, The findings reveal that the coefficient between audit fees (AUDITFEE) and shared auditors (SHARE) is .0501 (p < .1), indicating a significant positive relationship. This suggests that companies with shared auditors tend to allocate higher audit fees to improve audit quality. Moreover, the correlation coefficient between discretionary accruals (|DA|) and shared auditors (SHARE) is −.0159 (p < .1), suggesting a significant negative relationship between these two variables. This finding implies that shared auditors can reduce acquirer’s discretionary accruals behavior, increase audit fees and improve financial statement audit quality. Hypothesis 1 is confirmed.

Table 3.

Correlation Matrix.

Var.	AUDITFEE	\|DA\|	SHARE	SAMESIGN	SI	SP	SOE	BIG4	RS	SIZE
AUDITFEE	1	−0.0094	0.0283*	0.0304*	0.0776**	−0.0057	0.0576*	0.2064***	0.3783***	0.1651***
\|DA\|	−0.0954***	1	−0.0096*	−0.0196	0.0216	−0.0152	0.1374***	−0.0185	0.1865***	−0.0458
SHARE	0.0501*	−0.0159*	1	0.4879***	0.0312	0.2820***	0.0462	0.3088***	0.0104	−0.0306
SAMESIGN	0.0305**	−0.0100	0.4879***	1	0.0756**	0.2196***	0.0480	0.1592***	0.0034	−0.0404
SI	0.0589*	0.0023	0.0312	0.0756**	1	0.0127	0.0544*	0.0343	0.0368	0.0388
SP	−0.0418	−0.0219	0.2820***	0.2196***	0.0127	1	0.0498	−0.0101	−0.0278	−0.1064***
SOE	0.0933***	0.0858***	0.0462	0.0480	0.0544*	0.0498	1	0.0121	0.2060***	−0.0681**
BIG4	0.3219***	−0.0224	0.3088***	0.1592***	0.0343	−0.0101	0.0121	1	0.0666**	0.1262***
RS	0.3588***	0.0502*	0.0004	−0.0125	0.0325	−0.0489	0.1926***	0.0538*	1	0.0014
SIZE	0.1340***	−0.0112	−0.0474	−0.0515*	0.0129	−0.1157***	−0.0676**	0.1413***	0.0319	1
GROWTH	−0.0318	0.0389	−0.0217	−0.0148	−0.0506*	−0.0350	−0.0342	0.0036	0.1797***	0.1293***
LEV	0.0093	0.0028	−0.0194	−0.0007	−0.0288	−0.0586*	0.0464	0.0550*	0.1305***	0.5674***
INVENTORY	−0.0158	0.0098	0.0071	0.0553*	0.0268	0.0122	0.0115	0.0204	0.1913***	0.1209***
BIG	0.5821***	−0.0581*	−0.0421	−0.0264	0.0497	−0.0104	0.1211***	0.3429***	0.1827***	0.0188
ROE	0.0133	0.0013	−0.0068	−0.0189	0.0129	−0.0965***	−0.0373	0.0740**	0.3653***	0.4244***
ROA	0.0245	−0.0058	−0.0016	−0.0147	0.0020	−0.0682**	−0.0096	0.0434	0.5031***	0.4675***
LSR	0.0997***	0.0394	0.0394	0.0429	0.0363	0.0084	0.0748**	0.1237***	0.3050***	0.2917***
BM	0.0833***	−0.0132	0.0253	−0.0224	−0.0008	−0.0346	0.1090***	0.0793***	0.2245***	0.6159***
NAVPS	0.0819***	−0.0217	−0.0085	−0.0060	0.0485	−0.0789***	−0.0898***	0.1094***	0.1435***	0.5882***
PAY	0.0812***	0.1142***	−0.0057	−0.0049	0.0091	−0.0535**	0.0070	0.0868***	−0.0321	0.0798***
	GROWTH	LEV	INVENTORY	BIG	ROE	ROA	LSR	BM	NAVPS	PAY
AUDITFEE	−0.0109	0.0094	−0.0311	0.4472***	−0.0143	−0.0131	0.0520*	0.0591*	0.0880***	0.0941***
\|DA\|	−0.0375	−0.0315	−0.0089	−0.0828***	−0.0386	−0.0345	0.0095	−0.0493	−0.0259	0.0550*
SHARE	−0.0542*	−0.0229	0.0353	−0.0421	0.0057	0.0058	0.0406	0.0252	−0.0212	−0.0057
SAMESIGN	−0.0498	0.0057	0.0363	−0.0264	−0.0099	−0.0144	0.0440	−0.0233	−0.0152	−0.0049
SI	−0.0369	−0.0300	0.0305	0.0497	0.0030	0.0080	0.0356	0.0025	0.0443	0.0091
SP	−0.0670**	−0.0503*	−0.0172	−0.0104	−0.0841***	−0.0504*	0.0072	−0.0363	−0.0857***	−0.0535*
SOE	−0.0678**	0.0402	0.0396	0.1211***	−0.0681**	−0.0572*	0.0788***	0.1083***	−0.0927***	0.0070
BIG4	−0.0424	0.0521*	0.0119	0.3429***	0.1260***	0.0488	0.0980***	0.0822***	0.0758**	0.0868***
RS	0.2555***	0.0385	0.3107***	0.2243***	0.0385	0.2637***	0.2845***	0.1368***	0.1400***	−0.0525*
SIZE	0.3304***	0.5373***	0.1936***	0.0244	0.3466***	0.3121***	0.3223***	0.6323***	0.6403***	0.0915***
GROWTH	1	0.2027***	0.4098***	−0.0721**	0.1896***	0.3382***	0.2927***	0.2710***	0.3507***	0.0435
LEV	0.1418***	1	0.2091***	0.0005	0.1953***	−0.1105***	0.1634***	0.4857***	0.1758***	−0.1075***
INVENTORY	0.3568***	0.2366***	1	−0.0117	0.1175***	0.2953***	0.3845***	0.2588***	0.2092***	−0.0208
BIG	−0.0403	−0.0032	−0.0037	1	0.0171	0.0215	0.0630**	0.0545*	0.0044	0.0591*
ROE	0.2566***	0.3549***	0.2315***	0.0015	1	0.7819***	0.2534***	0.0793***	0.3935***	0.1129***
ROA	0.3181***	0.2357***	0.2956***	0.0200	0.8098***	1	0.3936***	0.0996***	0.4955***	0.1598***
LSR	0.2178***	0.1818***	0.2900***	0.0754**	0.3742***	0.4955***	1	0.3418***	0.3348***	0.1213***
BM	0.1813***	0.4955***	0.2377***	0.0533*	0.4041***	0.4594***	0.3542***	1	0.4661***	−0.1063***
NAVPS	0.1492***	0.1984***	0.1153***	0.0055	0.4354***	0.4936***	0.2443***	0.4230***	1	0.1132***
PAY	0.0350	−0.1030***	−0.0189	0.0591*	0.0623**	0.0937***	0.1242***	−0.1065***	0.0925***	1

Note. The table displays the correlation matrix.

The *, **, and *** symbols indicate significance levels of 10%, 5%, and 1%, respectively. Further explanation of variables can be found in Appendix 1.

Similarly, the correlation coefficient between audit fees (AUDITFEE) and same signing auditors (SAMESIGN) is .0305 (p < .05), revealing a significant positive relationship. This indicates that the presence of same signing auditors can help to enhance audit quality. However, the correlation coefficient between discretionary accruals (|DA|) and same signing auditors (SAMESIGN) is −.0100, which is not significant. The audit quality, represented by audit fees, improves to some extent in the case of the same signing auditor, and Hypothesis 2 is confirmed.

Additionally, audit fees (AUDITFEE), industry (SI), firm nature (SOE), audit firm size (BIG4), and relative size (RS) demonstrate a significant positive relationship with audit fees. On the other hand, discretionary accruals (|DA|) exhibit a significant positive relationship with firm nature (SOE) and relative size (RS).

Regression Results

Table 4 illustrates the correlation between audit fees and shared auditors (same signing auditors) in M&A. The results indicate a significant positive coefficient of .2236 (p < .01) connecting shared auditors (SHARE) and audit fees (AUDITFEE). However, this also results in increased fees to account for the shared auditor’s increased workload and the overlap of audits. Shared auditors play a crucial role as information brokers. As such, the findings confirm Hypothesis 1. Furthermore, our regression analysis also uncovers a positive coefficient of .3161 (p < .01) between same signing auditors (SAMESIGN) and audit fees (AUDITFEE), indicating that same signing auditors have a favorable impact on audit fees, particularly at the team level, where information is more likely to be shared. The findings confirm Hypothesis 2.

Table 4.

Estimates of SHARE and SAMESIGN on AUDITFEE.

Var.	AUDITFEE
Var.	SHARE	SAMESIGN
Intercept	13.0041*** (23.70)	13.0807*** (23.83)
SHARE	0.2236*** (3.26)
SAMESIGN		0.3161*** (2.62)
SIZE	0.0361 (1.49)	0.0350 (1.45)
GROWTH	−0.0013 (−0.06)	−0.0019 (−0.09)
LEV	−0.0596 (−0.39)	−0.0734 (−0.47)
INVENTORY	0.0607 (0.28)	0.0395 (0.18)
BIG	1.6786*** (21.50)	1.6687*** (21.37)
ROE	−0.0735 (−0.88)	−0.0707 (−0.84)
ROA	−0.1682 (−0.55)	−0.1677 (−0.55)
LSR	0.0036** (2.01)	0.0035** (1.98)
BM	0.1194 (0.86)	0.1438 (1.03)
NAVPS	−0.0003 (−0.03)	−0.0006 (−0.06)
PAY	0.1730*** (2.61)	0.1767* (2.67)
Year	Included
Industry	Included
Adj. R²	.4578	.4597
N	1,083	1,083

Note. The table displays the result of SHARE (or SAMESIGN) and AUDITFEE.

The *, **, and *** indicate significance levels of 10%, 5%, and 1%, respectively. Further explanation of variables can be found in Appendix 1.

A regression analysis was performed to examine the impact of shared auditors and same signing auditors on another proxy variable for audit quality, |DA|, as presented in Table 5. The results indicate a significant negative coefficient of −.0574 (p < .01) between SHARE and |DA|, suggesting that the use of shared auditors has a significant adverse impact on discretionary accruals. The findings confirm Hypothesis 1. Moreover, the regression analysis also presents a negative coefficient of −.0320 (p < .01) between same signing auditors (SAMESIGN) and audit quality (|DA|), indicating that the experience and expertise of professional auditors at the team level of same signing auditors can be leveraged to reduce financial statement discretionary accruals and improve audit quality. Hypothesis 2 is verified.

Table 5.

Estimates of SHARE and SAMESIGN on |DA|.

Var.	\|DA\|
Var.	SHARE	SAMESIGN
Intercept	0.1158 (1.34)	0.0179 (0.22)
SHARE	−0.0574*** (−3.85)
SAMESIGN		−0.0320*** (−2.79)
SIZE	−0.0013 (−0.34)	0.0011 (0.29)
GROWTH	0.0044 (1.27)	0.0038 (1.19)
LEV	−0.0085 (−0.35)	−0.0040 (−0.18)
INVENTORY	−0.0482 (−1.39)	−0.0504 (−1.55)
BIG	−0.0209* (−1.67)	−0.0574*** (−3.85)
ROE	0.0133 (1.00)	0.0062 (0.50)
ROA	−0.0517 (−1.07)	−0.0431 (−0.95)
LSR	0.0003 (1.19)	0.0004 (1.39)
BM	0.0064 (0.29)	−0.0005 (0.00)
NAVPS	0.0007 (0.04)	−0.0003 (−0.18)
PAY	0.0354*** (3.39)	0.0390*** (4.00)
Year	Included
Industry	Included
Adj. R²	.1819	.1452
N	1,083	1,083

Note. The table displays the result of SHARE (or SAMESIGN) and |DA|.

The * and *** symbols indicate significance levels of 10% and 1%, respectively. Further explanation of variables can be found in Appendix 1.

Additional Analysis

Drawing on prior research, this study identifies five factors potentially affecting information sharing: firm nature, industry and regional differences, relative size, and audit firm size. We aim to examine how these factors may contribute to variations in the impact of our primary findings.

Firstly, the acquirer’s firm nature may influence the ease of information sharing due to differences in organizational structures and cultures. For example, decentralized firms may struggle more with information sharing compared to centralized ones. Secondly, industry differences, such as regulations and competitive dynamics, can affect information sharing. Thirdly, regional differences introduce challenges due to distinct cultural norms, legal frameworks, and market conditions. Fourthly, relative size between the acquirer and target impacts information sharing, with larger firms often requiring and facing more challenges in sharing information. Lastly, audit firm size influences the process, as larger audit firms typically possess more resources and expertise, enabling them to facilitate smoother information sharing compared to smaller firms.

By considering these factors, this analysis may help firms and regulators to better evaluate the potential benefits and challenges of auditor sharing arrangements and team audit work in M&A across different contexts.

Moderating Effect on Audit Fees

Moderating Effect of Industry Differences on the Relationship Between SHARE(SAMESIGN) and AUDITFEE

This study divided the sample into two subsamples based on whether the two firms were in the same industry and conducted separate regression analyses for each subsample. As shown in Table 6, when the acquirer and target are from unrelated industries, a significant positive moderating effect on the relationship between shared auditors and audit fees is observed (Coeff. = .2759, p < .01). Likewise, when the two firm operates in unrelated industries, a positive moderating effect is evident between same signing auditors and audit fees (Coeff. = .3073, p < .05). These results suggest that differing industry contexts increase the information acquisition challenge for the acquirer Thus, both the audit firm and auditor team have a stronger impact in cross-industry M&A. The findings are consistent with Hypothesis 3.

Table 6.

Further Tests Grouping by Industry Differences (AUDITFEE).

Var.	AUDITFEE
Var.	SI	NON-SI	SI	NON-SI
Intercept	13.3824*** (9.56)	13.3713*** (23.09)	13.4229*** (9.71)	13.4300*** (23.08)
SHARE	0.0795 (0.51)	0.2759*** (3.62)
SAMESIGN			0.3276 (1.38)	0.3073** (2.15)
Year	Included
Industry	Included
Adj. R²	.4475	.4882	.4526	.4829
N	217	866	217	866

Note. The table displays the heterogeneity analysis of SHARE (or SAMESIGN) and AUDITFEE grouping by industry differences.

Significance levels are represented by ** and ***, corresponding to significance at 5% and 1% levels, respectively. Further elaboration of variable definitions can be located in Appendix 1.

Moderating Effect of Regional Differences on the Relationship Between SHARE(SAMESIGN) and AUDITFEE

Firms in the same region often hire the same branch of an audit firm to reduce costs and improve information access, strengthening the “information sharing” effect. To examine regional differences, the sample was divided into same-province and cross-province groups. In Table 7, the coefficients of AUDITFEE and SHARE for the same-province group are not significant. However, for the cross-province group, the coefficients of AUDITFEE and SHARE are significantly positive (Coeff. = .2541, p < .01), indicating a stronger knowledge-sharing impact. Similarly, the coefficients of AUDITFEE and SAMESIGN are not significant in the same province group but are significantly positive in the different province groups (Coeff. = .6669, p < .05). The results provide support for Hypothesis 4.

Table 7.

Further Tests Grouping by Regional Differences (AUDITFEE).

Var.	AUDITFEE
Var.	SP	NON-SP	SP	NON-SP
Intercept	10.3708*** (12.02)	13.9803*** (18.79)	10.4195*** (11.94)	13.9795*** (18.85)
SHARE	0.1686 (1.45)	0.2541*** (2.95)
SAMESIGN			0.1227 (0.97)	0.6669** (2.39)
Year	Included
Industry	Included
Adj. R²	.4923	.4834	.4811	.4863
N	396	687	396	687

Note. The table displays the heterogeneity analysis of SHARE (or SAMESIGN) and AUDITFEE grouping by regional differences.

Significance levels are represented by ** and ***, corresponding to significance at 5% and 1% levels, respectively. Further elaboration of variable definitions can be located in Appendix 1.

Moderating Effect of Ownership on the Relationship Between SHARE(SAMESIGN) and AUDITFEE

The sample was divided into two subgroups to examine the effect of adding the factor of whether the acquirer is SOEs or non-SOEs, as shown in Table 8. Specifically, when the acquirer is SOEs, the coefficients of AUDITFEE and SHARE are not significant. However, when the acquirer is non-SOEs, AUDITFEE and SHARE are significantly positively related (Coeff. = .2756, p < .01). Regarding the association between AUDITFEE and SAMESIGN, the coefficients are insignificant for SOEs. However, in the case of non-SOEs, AUDITFEE and SAMESIGN show a significant positive relationship (Coeff. = .3629, p < .05). The results confirm Hypothesis 5. These results confirm Hypothesis 5, indicating that the acquirer’s nature moderates the relationship between shared auditors and audit quality in non-SOEs.

Table 8.

Further Tests Grouping by Firm Nature (AUDITFEE).

Var.	AUDITFEE
Var.	SOE	NON-SOE	SOE	NON-SOE
Intercept	12.5078*** (10.93)	13.4084*** (21.43)	12.5068*** (10.94)	13.4694*** (21.46)
SHARE	0.0674 (0.49)	0.2756*** (3.52)
SAMESIGN			0.1312 (0.60)	0.3629** (2.54)
Year	Included
Industry	Included
Adj. R²	.6024	.4285	.6026	.424
N	278	805	278	805

Note. The table displays the heterogeneity analysis of SHARE (or SAMESIGN) and AUDITFEE grouping by firm nature.

Significance levels are represented by ** and ***, corresponding to significance at 5% and 1% levels, respectively. Further elaboration of variable definitions can be located in Appendix 1.

Moderating Effect of Relative Size on the Relationship Between SHARE(SAMESIGN) and AUDITFEE

As the acquirer’s size increases, so does business complexity, motivating the audit firm to gather and cross-check more information, thus investing more in the audit process. This additional effort often focuses on the acquirer to prevent disguised financial reporting. On the flip side, the target’s shareholders may also be driven to disclose more to improve their financial clarity. To explore this, we segment the dataset into two groups based on the median relative size: larger acquirers and smaller acquirers. Table 9 shows a significant positive moderating effect of shared auditors on audit quality for larger acquirers (Coeff. = .2318, p < .05), implying a more pronounced role played by shared auditors. A similar effect is observed for same signing auditors (Coeff. = .3237, p < .05), highlighting the more pronounced role played by same signing auditors. Thus, there is a clear preference for larger acquirers, where the moderating effect is stronger. The findings confirm Hypothesis 6.

Table 9.

Further Tests Grouping by Relative Size (AUDITFEE).

Var.	AUDITFEE
Var.	Larger acquirer	Smaller acquirer	Larger acquirer	Smaller acquirer
Intercept	11.6312*** (14.85)	7.3201*** (6.56)	11.5626*** (14.89)	7.2960*** (6.51)
SHARE	0.2318** (2.08)	0.0055 (0.06)
SAMESIGN			0.3237** (2.01)	−0.1322 (−0.68)
Year	Included
Industry	Included
Adj. R²	.3196	.4487	.3251	.4444
N	542	541	542	541

Note. The table displays the heterogeneity analysis of SHARE (or SAMESIGN) and AUDITFEE grouping by relative size.

Significance levels are represented by ** and ***, corresponding to significance at 5% and 1% levels, respectively. Further elaboration of variable definitions can be located in Appendix 1.

Moderating Effect of Audit Firm Size on the Relationship Between SHARE(SAMESIGN) and AUDITFEE

This section delves into the examination of the moderating impact of audit firm size by categorizing shared auditors as either “Big 4” or “non-Big 4,” as outlined in Table 10. Results indicate a significant positive correlation between AUDITFEE and SHARE for non-Big 4 audit firms (Coeff. = .1887, p < .01), indicating a stronger moderating effect. Similarly, for the relationship between audit fees (AUDITFEE) and same signing auditors (SAMESIGN), a similar moderating effect is observed for non-Big 4 (Coeff. = .3131, p < .01), where the role of the signing auditor is more pronounced. The analysis supports Hypothesis 7.

Table 10.

Further Tests Grouping by Audit Firm Size (AUDITFEE).

Var.	AUDITFEE
Var.	BIG 4	NON-BIG 4	BIG 4	NON-BIG 4
Intercept	11.0293*** (4.37)	13.5051*** (28.01)	11.3893*** (4.78)	13.6242*** (28.4)
SHARE	−0.1244 (−0.34)	0.1887*** (3.17)
SAMESIGN			0.3488 (0.54)	0.3131*** (3.03)
Year	Included
Industry	Included
Adj. R²	.3060	.2162	.3019	.2155
N	24	1,059	24	1,059

Note. The table displays the heterogeneity analysis of SHARE (or SAMESIGN) and AUDITFEE grouping by audit firm size.

Significance levels are represented by ***, corresponding to significance at 1% levels. Further elaboration of variable definitions can be located in Appendix 1.

Moderating Effect on Absolute Value of Discretionary Accruals

Moderating Effect of Industry Differences on the Relationship Between SHARE(SAMESIGN) and |DA|

We also investigate the moderating effect of shared auditors on the absolute value of discretionary accruals (|DA|) by dividing the M&A into related and unrelated industry categories.

In Table 11, there is a significant positive moderating effect of shared auditors (SHARE) on discretionary accruals (|DA|) in unrelated industries (NON-SI, Coeff. = −.0709, p < .01). Likewise, for the relationship between discretionary accruals (|DA|) and same signing auditors (SAMESIGN), a significant positive moderating effect is observed in unrelated industries (Coeff. = −.0383, p < .01). This supports Hypothesis 3. Due to higher information asymmetry in unrelated industries, shared and signing auditors need more time and experience to improve audit work, reducing discretionary accruals, and enhancing audit quality.

Table 11.

Further Tests Grouping by Industry Differences (|DA|).

Var.	\|DA\|
Var.	SI	NON-SI	SI	NON-SI
Intercept	0.1682 (0.99)	0.1317 (1.33)	0.1192 (0.76)	0.0235 (0.26)
SHARE	−0.0230 (−0.86)	−0.0709*** (−4.01)
SAMESIGN			−0.0017 (−0.08)	−0.0383*** (−2.65)
Year	Included
Industry	Included
Adj. R²	.417	.1523	.4076	.1176
N	217	866	217	866

Note. The table displays the heterogeneity analysis of SHARE (or SAMESIGN) and |DA| grouping by industry differences.

Significance levels are represented by ***, corresponding to significance at 1% levels. Further elaboration of variable definitions can be located in Appendix 1.

Moderating Effect of Regional Differences on the Relationship Between SHARE(SAMESIGN) and |DA|

Accordingly, this study incorporates the distance factor and divides the sample by province. Table 12 shows that the coefficients of |DA| and SHARE are significantly negative for the cross provinces group (Coeff. = −.0599, p < .01), indicating that shared auditors enhance information sharing and reduce discretionary behavior in cross-province transactions. For SAMESIGN, the coefficients of discretionary accruals (|DA|) and SAMESIGN in the cross provinces group show a significant positive coefficient (Coeff. = −.0456, p < .05), suggesting a stronger moderating effect of same-signing auditors when the acquirer and target are in different provinces. These results support Hypothesis 4.

Table 12.

Further Tests Grouping by Regional Differences (|DA|).

Var.	\|DA\|
Var.	SP	NON-SP	SP	NON-SP
Intercept	0.1206 (0.81)	0.1227 (1.04)	0.0365 (0.28)	0.0182 (0.16)
SHARE	−0.0538** (−2.20)	−0.0599*** (−3.08)
SAMESIGN			−0.0180 (−1.02)	−0.0456*** (−2.68)
Year	Included
Industry	Included
Adj. R²	.291	.1255	.2542	.0861
N	396	687	396	687

Note. The table displays the heterogeneity analysis of SHARE (or SAMESIGN) and |DA| grouping by regional differences.

Significance levels are represented by ** and ***, corresponding to significance at 5% and 1% levels, respectively. Further elaboration of variable definitions can be located in Appendix 1.

Moderating Effect of Ownership on the Relationship Between SHARE(SAMESIGN) and |DA|

The sample was divided into SOE and NON-SOE subsets. In Table 13, for NON-SOEs, the coefficients of discretionary accruals (|DA|) and shared auditors (SHARE) are significantly negative (Coeff. = −.0778, p < .01). Similarly, the coefficients of discretionary accruals (|DA|) and same signing auditors (SAMESIGN) for NON-SOEs are significantly negative (Coeff. = −.0502, p < .01). The moderating effect of shared auditors and same signing auditors is more pronounced for NON-SOEs. The findings confirm Hypothesis 5.

Table 13.

Further Tests Grouping by Firm Nature (|DA|).

Var.	\|DA\|
Var.	SOE	NON-SOE	SOE	NON-SOE
Intercept	0.0281 (0.16)	0.1421 (1.33)	0.0336 (0.19)	−0.0094 (−0.10)
SHARE	0.0095 (0.31)	−0.0778*** (−4.32)
SAMESIGN			−0.0006 (−0.03)	−0.0502*** (−3.34)
Year	Included
Industry	Included
Adj. R²	.2332	.1533	.2343	.1117
N	278	805	278	805

Note. The table displays the heterogeneity analysis of SHARE (or SAMESIGN) and |DA| grouping by firm nature.

Significance levels are represented by ***, corresponding to significance at 1% levels. Further elaboration of variable definitions can be located in Appendix 1.

Moderating Effect of Relative Size on the Relationship Between SHARE(SAMESIGN) and |DA|

We divided the sample into two subsample sets based on the relative sizes. A larger acquirer is defined when the relative size is larger than the median, while a smaller acquirer is defined otherwise. In Table 14, the relationship between discretionary accruals (|DA|) and shared auditors (SHARE) shows a significant negative moderating effect for larger acquirers (Coeff. = −.0833, p < .01), while the coefficients are not significant for smaller acquirers. Similarly, a significant moderating effect is observed between discretionary accruals (|DA|) and same signing auditors (SAMESIGN) for larger acquirers (Coeff. = −.0695, p < .01). Both the audit firm and auditor team levels show a stronger moderating effect for larger acquirers, confirming Hypothesis 6.

Table 14.

Further Tests Grouping by Relative Size (|DA|).

Var.	\|DA\|
Var.	Larger acquirer	Smaller acquirer	Larger acquirer	Smaller acquirer
Intercept	0.3473** (2.06)	0.1144 (0.94)	−0.0106 (−0.08)	0.0741 (0.62)
SHARE	−0.0834*** (−3.40)	−0.0197 (−1.10)
SAMESIGN			−0.0695*** (−2.78)	−0.0105 (−0.86)
Year	Included
Industry	Included
Adj. R²	.172	.2892	.1196	.2885
N	542	541	542	541

Note. The table displays the heterogeneity analysis of SHARE (or SAMESIGN) and |DA| grouping by relative size.

Significance levels are represented by ** and ***, corresponding to significance at 5% and 1% levels, respectively. Further elaboration of variable definitions can be located in Appendix 1.

Moderating Effect of Audit Firm Size on the Relationship Between SHARE(SAMESIGN) and |DA|

As demonstrated in Table 15, when both parties employ non-big 4 audit firms, there is a significant negative correlation between discretionary accruals (|DA|) and shared auditors (SHARE; Coeff. = −.0653, p < .01). However, this correlation is not significant when both parties hire Big 4 firms. This indicates a stronger moderating effect of shared auditors on discretionary accruals with non-Big 4 firms. Similarly, a significant moderating effect is found for same signing auditors (SAMESIGN), when the signing auditor is non-Big 4 (Coeff. = −.0765, p < .05). The findings are consistent with Hypothesis 7.

Table 15.

Further Tests Grouping by Audit Firm Size (|DA|).

Var.	\|DA\|
Var.	BIG 4	NON-BIG 4	BIG 4	NON-BIG 4
Intercept	−0.1313 (−0.67)	0.1177 (1.22)	0.1170 (0.47)	0.0614 (0.40)
SHARE	−0.0030 (−0.07)	−0.0655*** (−4.06)
SAMESIGN			−0.0364 (−1.30)	−0.0765** (−2.23)
Year	Included
Industry	Included
Adj. R²	.2572	.1871	.2637	.1371
N	24	1,059	24	1,059

Note. The table displays the heterogeneity analysis of SHARE (or SAMESIGN) and |DA| grouping by audit firm size.

Significance levels are denoted by ** and ***, corresponding to significance at 5% and 1% levels, respectively. Elaborated definitions of variables are provided in the Appendix 1.

Robustness Tests

We further conducted robustness tests in three ways. We run the endogeneity test based on the propensity score matching method. Then, we used different key variables of shared auditors and same signing auditors to test the stability of the results. And also, this study carried out the regression to retest the model by using an alternative variable of audit quality.

Endogeneity Test

To overcome the endogenous problem, this study employed the propensity score matching method (PSM). First, taking SHARE (or SAMESIGN) as the independent variable and taking the largest shareholding’s ratio (LSR), dual position (Dual), company size (SIZE), sales growth rate (GROWTH), inventory to total assets (INVENTORY), asset to liability ratio (LEV), big 4 (BIG), return on equity (ROE), net asset value per share (NAVPS), book to market ratio (BM), the same year (YEAR), the same industry (INDUSTRY) as covariates. To calculate the propensity score, Model (4) was established. Subsequently, the nearest neighbor matching method was applied. The SHARE samples were matched 1:1. 207 samples of SHARE matched 184 samples, total of 390 samples. 57 samples of SAMESIGN matched 53 samples, total of 110 samples. Third, AUDITFEE and |DA| were utilized as the dependent variable, and SHARE was utilized as the independent variable. In Table 16, SHARE and SAMESIGN with AUDITFEE were significantly and positively correlated (p < .01), respectively. SHARE and SAMESIGN with |DA| were significantly negatively correlated (p < .01), respectively. The findings confirmed the persistence of the research conclusions.

\begin{matrix} SHARE or SAMESIGN = φ_{0} + φ_{1} LS R_{i, t} + φ_{2} SIZ E_{i, t} \\ + φ_{3} Dua l_{i, t} + φ_{4} GROWT H_{i, t} \\ + φ_{5} LE V_{i, t} + φ_{6} INVENTOR Y_{i, t} \\ + φ_{7} BI G_{i, t} + φ_{8} RO E_{i, t} \\ + φ_{9} B M_{i, t} + φ_{10} NAVP S_{i, t} \\ + \sum Year + \sum Ind + ε \end{matrix}

(5)

Table 16.

Robustness Tests Using PSM Method.

Var.	SHARE		SAMESIGN
Var.	AUDITFEE	\|DA\|	AUDITFEE	\|DA\|
Intercept	10.2036*** (12.49)	2.3659** (2.20)	−0.1281 (−0.37)	−0.1655 (0.91)
SHARE	0.3700*** (3.39)	−0.4147*** (−2.72)
SAMESIGN			0.1370*** (3.33)	−0.0456** (−2.03)
Year	Included		Included
Industry	Included		Included
Adj. R²	.3702	.1597	.5481	.0686
N	390	390	110	110

Note. The table displays the results of SHARE (SAMESIGN) and AUDITFEE (or |DA|) using PSM method.

**and ***Denote the significance at 5% and 1% level, respectively. Further elaboration of variable definitions can be located in Appendix 1.

Alternative Measures of Shared Auditors and Same Signing Auditor

This section uses shared auditors for the 3 years prior to the M&A to improve the reliability of the data. After replacing the proxy of shared auditors, as shown in Table 17, SHARE and SAMESIGN with AUDITFEE are significant at the 1% level. SHARE and SAMESIGN with |DA| are significant at the 5% and 10% level, respectively, with no change in the regression results. In summary, the findings are robust after replacing the key independent variable.

Table 17.

Robustness Tests Using Other Measures of SHARE and SAMESIGN.

Var.	SHARE			SAMESIGN
Var.	AUDITFEE	\|DA\|	AUDITFEE	\|DA\|
Intercept	12.6138*** (23.25)	0.0160 (0.20)	13.6446*** (26.65)	0.0229 (0.28)
SHARE	0.4232*** (6.97)	−0.0233** (−2.23)
SAMESIGN			1.8032*** (24.30)	−0.0865* (−1.83)
Year	Included
Industry	Included
Adj. R²	.4786	.1485	.5324	.1455
N	1,083	1,083	1,083	1,083

Note. The table displays the results of SHARE (or SAMESIGN) and AUDITFEE (|DA|) by replacing the independent variable.

*, **, and ***Denote the significance at 10%, 5%, and 1% level, respectively. Further elaboration of variable definitions can be located in Appendix 1.

Alternative Measures of Audit Quality

We employed two alternative measures to evaluate audit quality. The first measure is based on the calculation of the modified Jones model as described by Kothari et al. (2005). Specifically, we used the absolute value of discretionary accruals as a proxy, denoted as absDA, instead of using |DA| in this model. The modified Jones model of Kothari et al. (2005) is based on the modified Jones model of Dechow et al. (1995) by further considering the return on assets (ROA).

\begin{matrix} TAC C_{i, t} / A_{i, t - 1} = β_{0} 1 / A_{i, t - 1} + β_{1} (Δ RE V_{i, t} - Δ RE C_{i, t}) / A_{i, t - 1} \\ + β_{2} PP E_{i, t} / A_{i, t - 1} + β_{3} RO A_{i, t - 1} + ε_{i, t} \end{matrix}

(6)

Where,

TACC_i,t (Total accruals) = Accounting earnings-CFO

A_i,t- ₁ = Lagged total assets (total asset in year t-1)

ΔREV_i,t = Change in the revenues of company i from year t-1 to year t

ΔREC_i,t = Change in the net receivables of company i from year t-1 to year t

PPE_i,t = Property, plant, and equipment of company i at the end of year t

ROA_i,t- ₁ = Return On Assets of company i at the end of year t-1

ε_i,t = Errors in company i in the year t

As an alternative gauge of audit quality, we adopt a metric, the duration in days of the audit report, to signify audit delay or efficiency.

Table 18 presents regression results showing a significant positive relationship between shared auditors and audit quality. The coefficient for shared auditors (SHARE) with discretionary accruals (absDA) is −.0331 (p < .01), and for same signing auditors (SAMESIGN), it is −.0138 (p < .1), indicating that both reduce discretionary accruals, thereby improving audit quality. Similarly, the coefficient for SHARE with audit efficiency (lag) is −2.7199 (p < .1), and for SAMESIGN, it is −4.1652 (p < .05), suggesting both increase audit lag, further reinforcing audit quality. The findings remain robust after substituting the key dependent variables (absDA and lag).

Table 18.

Robustness Tests Using Other Measures of Audit Quality.

Var.	absDA		Lag
Var.	SHARE	SAMESIGN	SHARE	SAMESIGN
Intercept	−0.0089 (−0.15)	−0.0533 (−0.99)	3.6686*** (16.55)	3.5304*** (13.93)
SHARE	−0.0331*** (−3.24)		−0.0613* (−1.81)
SAMESIGN		−0.0138* (−1.78)		−0.0782** (−2.18)
Year	Included
Industry	Included
Adj. R²	.1848	.1827	.1082	.1006
N	1,083	1,083	1,083	1,083

Note. The table displays the results of SHARE (or SAMESIGN) and absDA (or lag) by replacing the independent variable.

*, **, and *** denote the significance at 10%, 5%, and 1% level, respectively. Further definition of variables is in the note of Appendix 1.

Conclusions

This research investigates the impact of shared auditors and same signing auditors on audit quality, based on M&A data from the Chinese A-share market spanning from 2004 to 2020. The result suggests that both shared auditors and same signing auditors enhance audit quality, with the effect being stronger at the individual auditor level. Shared auditors facilitate alignment between M&A parties and reduce uncertainties, a role more prominently reflected in same signing auditors. These findings remain robust across different model specifications and adjustments.

Heterogeneity tests reveal that this effect is more pronounced in cross-industry, cross-province, larger acquirer, non-SOEs, and non-Big 4. First, industry differences. Cross-industry M&A often face significant challenges due to differences in business practices and accounting techniques, making information acquisition more difficult. Second, regional differences. Greater geographical distances make information exchange between companies more challenging, making external networks particularly advantageous for information sharing. Third, ownership. M&A decisions by SOEs are often influenced by government policy objectives. Auditors can act as communication channels to reduce barriers. Fourth, relative size. Larger companies typically have higher expectations for targets and tend to gather information through external networks. Lastly, audit firm size. Non-Big 4 audit firms, due to their relatively weaker independence, are often more inclined to follow client directives and facilitate information sharing between the parties involved.

From a theoretical perspective, by examining the roles of shared auditors and same signing auditors in different business, industry, and regional contexts, it further enhances the existing literature on the role of auditors in complex corporate transactions (Ai et al., 2020; Bedford et al., 2023; Y. Cai et al., 2016; Chircop et al., 2018; Dhaliwal et al., 2016; Sun et al., 2020). Secondly, the study reveals how shared auditors and same signing auditors enhance audit quality in M&A through knowledge spillover effects, especially in M&A involving larger acquirers and non-SOEs. This finding provides an important supplement to the theoretical model of information sharing and decision-making in M&A (Cao & Pham, 2021; Chahine et al., 2018; J. Z. Chen et al., 2020; Jiu et al., 2020; Tong et al., 2022). The knowledge-sharing mechanism across businesses and industries helps deepen the understanding of the auditor network effect in M&A. Lastly, this study further emphasizes the unique importance of shared auditors and same signing auditors in specific contexts, highlighting the strategic role of shared auditors in M&A, especially in cases requiring cross-regional and cross-industry coordination (Jiu et al., 2020; Tong et al., 2022). It also emphasizes the interaction between the role of auditors and acquirer size (or audit firm size). These findings offer new insights into the relationship between auditor independence and audit quality in auditing theory (J. Z. Chen et al., 2020; Dhaliwal et al., 2016), while also contributing to the theory on the relationship between policy-driven M&A decisions and audit quality (Gu et al., 2020).

From a practical perspective, the findings guide companies in selecting audit services for mergers, suggesting that shared auditors with industry expertise and regional networks improve information-sharing efficiency and decision-making quality. This study also highlights the proactive role of non-Big 4 audit firms in information sharing and knowledge spillovers due to their flexibility in meeting client needs, offering practical insights for small and medium-sized audit firms to strengthen competitiveness. Additionally, the findings provide empirical evidence for policymakers on the role of auditors as information bridges, aiding the optimization of M&A regulations and improving audit quality in the industry.

The following suggestions are derived from this study. Firstly, it may be beneficial to consider using the same audit firm or signing auditor as the acquirer to enhance audit quality, during M&A. Secondly, to effectively utilize information resources, priority may be given to using shared auditors in cross-industry and cross-regional M&A to maximize the benefits of information sharing. Thirdly, shared auditors could be prioritized when the acquirer is larger in scale, non-SOEs, or audited by a non-Big 4 firm. Additionally, the improvement in audit quality or information quality for the acquirer may trigger a series of chain reactions, benefiting the target and leading to a significant enhancement in overall efficiency. Lastly, the government should promote market-oriented M&A and foster the development of market intermediaries to support the healthy growth of the M&A market.

This paper has some limitations. First, the study focuses on China’s A-share market, with a limited sample size due to the scarcity of listed company data. This limits the generalizability of the findings to unlisted companies and other countries. Future research could include unlisted companies and cross-national comparisons to improve external validity. Second, the study assumes a common interest between the acquirer and target, but conflicts of interest are unexplored. Future research could examine shared auditors’ behavior in such cases. Third, while the effects of shared and same signing auditors were explored, the role of social relationship networks between audit firms remains unexamined. Future research could investigate how these networks and their characteristics affect M&A and audit quality.

Footnotes

Appendix

Appendix 1.

Variable Definitions.

Variables	Definitions
Dependent variables
AUDITFEE	Natural logarithm of total audit fee of the audit report.
\|DA\|	Absolute values of discretionary accruals from modified Jones model (1995).
Independent variables
SHARE	Indicator variable takes the value of 1 if both sides of M&A are engaged in the same audit firm within the 2 years preceding the first announcement of the M&A; 0 otherwise.
SAMESIGN	Indicator variable takes the value of 1 if the acquirer and the target of M&A are engaged in the same audit firm and have the same signing auditors within the 2 years preceding the first announcement of the M&A; 0 otherwise.
Moderator variables
SI	Indicator variable equals 1 if shared auditor = 1 and the common auditors are from related industries; 0 otherwise.
SP	Indicator variable equals 1 if shared auditor = 1 and the common auditors are from same province; 0 otherwise.
BIG4	Indicator variable equals 1 if shared auditor = 1 and the common auditors are from the “Big 4”; 0 otherwise.
SOE	Indicator variable equals 1 if the acquirer is state-owned enterprises; 0 otherwise.
RS	Natural log of the ratio of the market value of target’s equity is divided by the acquirer’s equity.
Control variables
SIZE	Natural log of total assets.
LEV	Total liabilities are divided by total assets of the target.
GROWTH	Current year operating income minus prior year operating income is divided by prior year operating income.
INVENTORY	The ratio of net inventory to total assets.
ROE	The ratio of the net income to the target’s equity.
ROA	The ratio of the net income to assets of the target.
BM	The book value of the target’s equity is divided by the market value of the target’s equity.
BIG	Indicator variable takes the value of 1 if the audit firm of the acquirer is from the Big 4; 0 otherwise.
NAVPS	Net asset value per share. The ratio of the net asset value to the number of outstanding shares.
LSR	Shareholding ratio of largest shareholder.
PAY	Indicator variable equals 1 if the acquirer uses stock as a method of payment; 0 otherwise.
Dummy variables
Industry	Guidelines for Industry Classification of Listed Companies (2012).
Year	M&A took place from 2004 to 2020, with 16 years of dummy variables set.

Note. All accounting variables are derived from data of the preceding year and the variables related to M&A are as of the acquisition announcement year. The source of observations is all from CSMAR.

Acknowledgements

Special thanks to the reviewers for valuable comments.

ORCID iD

Lin Du

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Scientific Research Program for Higher Education Institutions of Hebei Provincial Department of Education (Grant No. QN2025869).

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval

This research did not involve human or animal participants, and thus, ethical approval was not required.

Data Availability Statement

All data are publicly available from the sources identified in the article.

References

Ahern

K. R.

Harford

(2014). The importance of industry links in merger waves. Journal of Finance, 69(2), 527–576. https://doi.org/10.1111/jofi.12122

Ahn

Jiraporn

Kim

Y. S.

(2010). Multiple directorships and acquirer returns. Journal of Banking & Finance, 34(9), 2011–2026. https://doi.org/10.1016/j.jbankfin.2010.01.009

Doucet

Myers

L. A.

Schuchard

(2020). Common auditors in mergers and acquisitions: The impact on post-acquisition financial reporting quality. SSRN Electronic Journal. Advance online publication. https://doi.org/10.2139/ssrn.3589811

Aobdia

(2015). Proprietary information spillovers and supplier choice: Evidence from auditors. Review of Accounting Studies, 20(4), 1504–1539. https://doi.org/10.1007/s11142-015-9327-x

Ashbaugh-Skaife

Collins

D. W.

Kinney

W. R.

LaFond

(2008). The effect of SOX internal control deficiencies and their remediation on accrual quality. Accounting Review, 83(1), 217–250. https://doi.org/10.1111/j.1475-679X.2008.00315.x

Bathelt

Henn

(2021). Knowledge exchanges, trust, and secretive geographies in merger and acquisition processes. Environment and Planning A: Economy and Space, 53(6), 1435–1453. https://doi.org/10.1177/0308518x211013360

Baxamusa

Jha

Raman

K. K.

(2024). Strategic alliances and shared auditors. Journal of Financial Stability, 72, 101271. https://doi.org/10.1016/j.jfs.2024.101271

Bedford

Bugeja

Czernkowski

Bond

(2023). Is the effect of shared auditors driven by shared audit partners? The case of M&As. British Accounting Review, 55(2), 101100. https://doi.org/10.1016/j.bar.2022.101100

Betton

Eckbo

B. E.

Thorburn

K. S.

(2009). Merger negotiations and the toehold puzzle. Journal of Financial Economics, 91(2), 158–178. https://doi.org/10.1016/j.jfineco.2008.02.004

10.

Bick

Crook

M. D.

Lynch

A. A.

Walkup

B. R.

(2017). Does distance matter in mergers and acquisitions? Journal of Financial Research, 40(1), 33–54. https://doi.org/10.1111/jfir.12115

11.

Wang

(2018). External sources of political connections: Financial advisors and Chinese acquisitions. International Journal of Finance & Economics, 23(4), 705–722. https://doi.org/10.1002/ijfe.1644

12.

Bodnaruk

Massa

Simonov

(2009). Investment banks as insiders and the market for corporate control. Review of Financial Studies, 22(12), 4989–5026. https://doi.org/10.1093/rfs/hhp043

13.

Burke

J. J.

Hoitash

(2020). The use and characteristics of foreign component auditors in U.S. multinational audits: Insights from form AP disclosures. Contemporary Accounting Research, 37(4), 2398–2437. https://doi.org/10.1111/1911-3846.12605

14.

Burris

(2005). Interlocking directorates and political cohesion among corporate elites. American Journal of Sociology, 111(1), 249–283. https://doi.org/10.1086/428817

15.

Cai

Zheng

Zhu

(2019). The effect of shared auditors in the supply chain on cost stickiness. China Journal of Accounting Research, 12(4), 337–355. https://doi.org/10.1016/j.cjar.2019.09.001

16.

Cai

Kim

Park

J. C.

White

H. D.

(2016). Common auditors in M&A transactions. Journal of Accounting and Economics, 61(1), 77–99. https://doi.org/10.1016/j.jacceco.2015.01.004

17.

Cao

V. N.

Pham

A. V.

(2021). Behavioral spillover between firms with shared auditors: The monitoring role of capital market investors. Journal of Corporate Finance, 68, 101914. https://doi.org/10.1016/j.jcorpfin.2021.101914

18.

Capron

Shen

(2007). Acquisitions of private vs. public firms: Private information, target selection, and acquirer returns. Strategic Management Journal, 28(9), 891–911. https://doi.org/10.1002/smj.612

19.

Carey

Simnett

(2006). Audit partner tenure and audit quality. Accounting Review, 81(3), 653–676. https://doi.org/10.2308/accr.2006.81.3.653

20.

Chahine

Hasan

Mazboudi

(2018). The role of auditors in merger and acquisition completion time. International Journal of Auditing, 22(3), 568–582. https://doi.org/10.1111/ijau.12142

21.

Chen

J. Z.

Chen

M. H.

Chin

C. L.

Lobo

G. J.

(2020). Do firms that have a common signing auditor exhibit higher earnings comparability? Accounting Review, 95(3), 115–143. https://doi.org/10.2308/accr-52522

22.

Chen

Dyball

M. C.

Wright

(2009). The link between board composition and corporate diversification in Australian corporations. Corporate Governance, 17(2), 208–223. https://doi.org/10.1111/j.1467-8683.2009.00734.x

23.

Chen

Martin

X. M.

Sun

A. X.

(2012). The effect of sharing a common auditor with customers on accounting restatements by supplier firms (Working Paper).

24.

Chi

H.-Y.

Chin

C.-L.

(2011). Firm versus partner measures of auditor industry expertise and effects on auditor quality. AUDITING: A Journal of Practice & Theory, 30(2), 201–229. https://doi.org/10.2308/ajpt-50004

25.

Chircop

Johan

Tarsalewska

(2018). Common auditors and cross-country M&A transactions. Journal of International Financial Markets Institutions and Money, 54, 43–58. https://doi.org/10.1016/j.intfin.2017.04.001

26.

Chiu

P.-C.

Teoh

S. H.

Tian

(2013). Board interlocks and earnings management contagion. Accounting Review, 88(3), 915–944. https://doi.org/10.2308/accr-50369

27.

Ciobanu

(2016). Does the geographic location influence takeovers? Economic Research-Ekonomska Istraživanja, 29(1), 782–798. https://doi.org/10.1080/1331677x.2016.1197553

28.

Croci

Mazur

Salganik-Shoshan

(2023). Coordinated monitoring and mergers and acquisitions. European Financial Management, 30(3), 1422–1459. https://doi.org/10.1111/eufm.12449

29.

Cui

Chi-Moon Leung

(2020). The long-run performance of acquiring firms in mergers and acquisitions: Does managerial ability matter? Journal of Contemporary Accounting & Economics, 16(1), 100185. https://doi.org/10.1016/j.jcae.2020.100185

30.

Cukurova

(2012). Interlocking directors and target selection in mergers and acquisitions. SSRN Electronic Journal. Advance online publication. https://doi.org/10.2139/ssrn.1966816

31.

Danos

Eichenseher

J. W.

(1986). Long-term trends toward seller concentration in the U.S. audit market. Accounting Review, 61(4), 633–650. http://www.jstor.org/stable/247361

32.

Dechow

P. M.

Sloan

R. G.

Hutton

A. P.

(1995). Detecting earnings management. Accounting Review, 70(2), 193–225. https://doi.org/10.2307/258852

33.

DeFond

M. L.

Francis

J. R.

(2005). Audit research after Sarbanes-Oxley. AUDITING: A Journal of Practice & Theory, 24(s-1), 5–30. https://doi.org/10.2308/aud.2005.24.s-1.5

34.

Del Bo

C. D.

Ferraris

Florio

(2017). Governments in the market for corporate control: Evidence from M&A deals involving state-owned enterprises. Journal of Comparative Economics, 45(1), 89–109. https://doi.org/10.1016/j.jce.2016.11.006

35.

de Sousa Barros

Cárdenas

Mendes-Da-Silva

. (2020). The effect of interlocking directorates on mergers and acquisitions in Brazil. Journal of Management & Governance, 25(3), 811–839. https://doi.org/10.1007/s10997-020-09529-7

36.

Dhaliwal

D. S.

Lamoreaux

P. T.

Litov

L. P.

Neyland

J. B.

(2016). Shared auditors in mergers and acquisitions. Journal of Accounting and Economics, 61(1), 49–76. https://doi.org/10.1016/j.jacceco.2015.01.005

37.

Doyle

McVay

(2007). Determinants of weaknesses in internal control over financial reporting. Journal of Accounting and Economics, 44(1–2), 193–223. https://doi.org/10.1016/j.jacceco.2006.10.003

38.

Dunn

S. V.

Lawson

Robertson

Underwood

Clark

Valentine

Walker

Wilson-Row

Crowder

Herewane

(2000). The development of competency standards for specialist critical care nurses. Journal of Advanced Nursing, 31(2), 339–346. https://doi.org/10.1046/j.1365-2648.2000.01292.x

39.

Erickson

Wang

S. W.

(1999). Earnings management by acquiring firms in stock for stock mergers. Journal of Accounting and Economics, 27(2), 149–176. https://doi.org/10.1016/s0165-4101(99)00008-7

40.

Faccio

Masulis

R. W.

(2005). The choice of payment method in European mergers and acquisitions. Journal of Finance, 60(3), 1345–1388. https://doi.org/10.1111/j.1540-6261.2005.00764.x

41.

Feng

Yang

Bhandari

(2024). The influence of CSR on cross-border mergers and acquisitions. Finance Research Letters, 70, 106273. https://doi.org/10.1016/j.frl.2024.106273

42.

Francis

J. R.

(2004). What do we know about audit quality? British Accounting Review, 36(4), 345–368. https://doi.org/10.1016/j.bar.2004.09.003

43.

Francis

J. R.

Richard

Vanstraelen

(2009). Assessing France’s joint audit requirement: Are two heads better than one?. AUDITING: A Journal of Practice & Theory, 28(2), 35–63. https://doi.org/10.2308/aud.2009.28.2.35

44.

Francis

J. R.

Wang

(2021). Common auditors and private bank loans. Contemporary Accounting Research, 38(1), 793–832. https://doi.org/10.1111/1911-3846.12617

45.

Gal-Or

Hoitash

(2022). Auditor expertise in mergers and acquisitions. AUDITING: A Journal of Practice & Theory, 41(4), 135–162. https://doi.org/10.2308/AJPT-2019-120

46.

Gaur

A. S.

Malhotra

Zhu

(2013). Acquisition announcements and stock market valuations of acquiring firms’ rivals: A test of the growth probability hypothesis in China. Strategic Management Journal, 34(2), 215–232. https://doi.org/10.1002/smj.2009

47.

Golubov

Petmezas

Travlos

N. G.

(2012). When it pays to pay your investment banker: New evidence on the role of financial advisors in M&As. Journal of Finance, 67(1), 271–311. https://doi.org/10.1111/j.1540-6261.2011.01712.x

48.

Granovetter

(1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91(3), 481–510. https://doi.org/10.1086/228311

49.

Gul

F. A.

Yang

(2013). Do individual auditors affect audit quality? Evidence from archival data. Accounting Review, 88(6), 1993–2023. https://doi.org/10.2139/ssrn.1888424

50.

Bao

(2020). The cross-border mergers and acquisitions of local state-owned enterprises: The role of home country government involvement. Sustainability, 12(7), 3020. https://doi.org/10.3390/su12073020

51.

Hayward

M. L. A.

Hambrick

D. C.

(1997). Explaining the premiums paid for large acquisitions: Evidence of CEO hubris. Administrative Science Quarterly, 42(1), 103–127. https://doi.org/10.2307/2393810

52.

Healy

Lys

(1986). Auditor changes following big eight mergers with non-big eight audit firms. Journal of Accounting and Public Policy, 5(4), 251–265. https://doi.org/10.1016/0278-4254(86)90022-0

53.

Wei

(2024). Research on the impact of green mergers and acquisitions of heavily polluting enterprises on the quality of environmental information disclosure: empirical evidence from listed companies in China. Environment Development and Sustainability. Advance online publication. https://doi.org/10.1007/s10668-024-04525-5

54.

(2024). Cross-Industry M & A and Target Output: Impact on Acquirors’ Performance. In Gartner

W. C.

(Ed.), New Perspectives and Paradigms in Applied Economics and Business. Springer, Cham, 191–204.

55.

(2024). Cross-industry M&A and target output: Impact on acquirors’ performance. In New perspectives and paradigms in applied economics and business.

56.

Jain

Agarwalla

S. K.

(2022). Big-4 auditors and audit quality: A novel firm life-cycle approach. Meditari Accountancy Research, 31(2), 1436–1452. https://doi.org/10.1108/medar-06-2021-1344

57.

Jin

Xin

Adhikari

(2022). Political governance in China’s state-owned enterprises. China Journal of Accounting Research, 15(2), 100236. https://doi.org/10.1016/j.cjar.2022.100236

58.

Jin

Yang

Zhang

(2021). Geographic proximity and cross-region merger and acquisitions: Evidence from the opening of high-speed rail in China. Pacific-Basin Finance Journal, 68, 101592. https://doi.org/10.1016/j.pacfin.2021.101592

59.

Jiu

Liu

(2020). How a shared auditor affects firm-pair comparability: Implications of both firm and individual audit styles. AUDITING: A Journal of Practice & Theory, 39(3), 133–160. https://doi.org/10.2308/ajpt-17-008

60.

Johnson

W. B.

Lys

(1990). The market for audit services. Journal of Accounting and Economics, 12(1–3), 281–308. https://doi.org/10.1016/0165-4101(90)90051-5

61.

Kengelbach

Schwetzler

Sperling

M. O.

(2010). How does geographical and legal proximity affect the performance of M&A transactions? Journal of General Management, 35(3), 81–97. https://doi.org/10.1177/030630701003500306

62.

Khurana

I. K.

Raman

K. K.

(2004). Litigation risk and the financial reporting credibility of big 4 versus non-big 4 audits: Evidence from Anglo-American countries. Accounting Review, 79(2), 473–495. https://doi.org/10.2308/accr.2004.79.2.473

63.

Kitto

A. R.

(2024). The effects of non-big 4 mergers on audit efficiency and audit market competition. Journal of Accounting and Economics, 77(1), 101618. https://doi.org/10.1016/j.jacceco.2023.101618

64.

Kothari

S. P.

Leone

A. J.

Wasley

C. E.

(2005). Performance matched discretionary accrual measures. Journal of Accounting and Economics, 39(1), 163–197. https://doi.org/10.1016/j.jacceco.2004.11.002

65.

Lee

H. W.

Kocher

(2011). Firm characteristics and seasoned equity issuance method: Private placement versus public offering. Journal of Applied Business Research, 17(3), 23–36. https://doi.org/10.19030/jabr.v17i3.2082

66.

Lennox

C. S.

Zhang

(2014). Does mandatory rotation of audit partners improve audit quality? Accounting Review, 89(5), 1775–1803. https://doi.org/10.2308/accr-50800

67.

Zhao

(2022). Geographic proximity and M&As: Evidence from China. Emerging Markets Review, 51, 100892. https://doi.org/10.1016/j.ememar.2022.100892

68.

Duan

Chan

K. C.

(2018). Linguistic distance and mergers and acquisitions: Evidence from China. Pacific-Basin Finance Journal, 49, 81–102. https://doi.org/10.1016/j.pacfin.2018.04.002

69.

Livne

Tsipouridou

Wood

(2024). Do audit committees and auditors coordinate effort? Evidence from risk areas, materiality, and meetings. Accounting Review, 99(3), 349–372. https://doi.org/10.2308/TAR-2020-0441

70.

Lopes

A. P.

(2018). Audit quality and earnings management: Evidence from Portugal. Athens Journal of Business & Economics, 4(2), 179–192. https://doi.org/10.30958/ajbe.4.2.4

71.

Luypaert

Van Caneghem

(2014). Can auditors mitigate information asymmetry in M&As? An empirical analysis of the method of payment in Belgian transactions. AUDITING: A Journal of Practice & Theory, 33(1), 57–91. https://doi.org/10.2308/ajpt-50590

72.

Megginson

W. L.

Morgan

Nail

(2004). The determinants of positive long-term performance in strategic mergers: Corporate focus and cash. Journal of Banking & Finance, 28(3), 523–552. https://doi.org/10.1016/s0378-4266(02)00412-0

73.

Peng

M. W.

Luo

(2000). Managerial ties and firm performance in a transition economy: The nature of a micro-macro link. Academy of Management Journal, 43(3), 486–501. https://doi.org/10.5465/1556406

74.

Portes

Rey

(2005). The determinants of cross-border equity flows. Journal of International Economics, 65(2), 269–296. https://doi.org/10.1016/j.jinteco.2004.05.002

75.

Qiao

Dong

(2023). The heterogeneous impacts of M&As on renewable energy firms’ innovation: Comparative analysis of China, the US and EU. International Review of Economics & Finance, 87, 306–323. https://doi.org/10.1016/j.iref.2023.04.015

76.

Reddy

R. K.

Fabian

(2020). Information asymmetry and host country institutions in cross-border acquisitions. Management International Review, 60(6), 909–938. https://doi.org/10.1007/s11575-020-00431-w

77.

Reynolds

J. K.

Francis

J. R.

(2000). Does size matter? The influence of large clients on office-level auditor reporting decisions. Journal of Accounting and Economics, 30(3), 375–400. https://doi.org/10.1016/s0165-4101(01)00010-6

78.

Robert Knechel

Vanstraelen

Zerni

. (2015). Does the identity of engagement partners matter? An analysis of audit partner reporting decisions. Contemporary Accounting Research, 32(4), 1443–1478. https://doi.org/10.1111/1911-3846.12113

79.

Shen

Yang

(2023). The influence mechanism of industrial policies on Chinese companies’ cross-border M&A decision-making. Scientific Reports, 13(1), 16162. https://doi.org/10.1038/s41598-023-43445-8

80.

Solomon

Shields

M. D.

Whittington

O. R.

(1999). What do industry-specialist auditors know? Journal of Accounting Research, 37(1), 191–208. https://doi.org/10.2307/2491403

81.

Sun

Wang

Kent

(2020). Does sharing the same network auditor in group affiliated firms affect audit quality? Journal of Accounting and Public Policy, 39(1), 106711. https://doi.org/10.1016/j.jaccpubpol.2019.106711

82.

Tong

Zhang

(2022). Do auditors’ early-life socioeconomic opportunities improve audit quality? Evidence from China. British Accounting Review, 54(2), 101040. https://doi.org/10.1016/j.bar.2021.101040

83.

Ton

(2023). Do shared auditors improve audit quality? Evidence from banking relationships. Accounting Review, 98(1), 423–451. https://doi.org/10.2308/TAR-2017-0179

84.

Uysal

V. B.

Kedia

Panchapagesan

(2008). Geography and acquirer returns. Journal of Financial Intermediation, 17(2), 256–275. https://doi.org/10.1016/j.jfi.2007.12.001

85.

Wang

(2024). Impact of mergers and acquisitions on enterprise innovation efficiency: The mediating role of factor endowment. Finance Research Letters, 66, 105652. https://doi.org/10.1016/j.frl.2024.105652

86.

Wang

Lai

(2022). Acquisition for innovations? M&A intensity and intra-firm innovation reallocations. Research in International Business and Finance, 62, 101721. https://doi.org/10.1016/j.ribaf.2022.101721

87.

Williamson

O. E.

(1975). Markets and hierarchies: Analysis and antitrust implications: A study in the economics of internal organization. Free Press

88.

Wilson

M. K.

Pholo Bala

(2019). Regional integration and cross-border mergers and acquisitions in Africa. Journal of Economic Integration, 34(1), 109–132. https://www.jstor.org/stable/26588466

89.

Yuan

Yang

(2024). Examining sectoral differences and locational determinants of acquisition target selection at the sub-national scale: Evidences from China. Applied Geography, 162, 103144. https://doi.org/10.1016/j.apgeog.2023.103144

90.

Y. N.

Edwin Cheng

T. C.

(2008). The impact of information sharing in a multiple-echelon supply chain. International Journal of Production Economics, 115(1), 1–11. https://doi.org/10.1016/j.ijpe.2008.02.016

91.

Xie

H. S.

Zhang

(2013). The value of big N target auditors in corporate takeovers. AUDITING: A Journal of Practice & Theory, 32(3), 141–169. https://doi.org/10.2308/ajpt-50405

92.

Zhang

Mao

(2024). M&A compensation commitment and stock price crash risk: Evidence from China. Asia-Pacific Journal of Accounting & Economics, 31(5), 757–787. https://doi.org/10.1080/16081625.2022.2157291

93.

Zou

(2024). Does carbon emission trading scheme improve corporate green mergers and acquisitions? Evidence from Chinese industrial enterprises. Environment Development and Sustainability. Advance online publication. https://doi.org/10.1007/s10668-024-05327-5

The Role of Shared and Same Signing Auditors in Enhancing Audit Quality for M&A Transactions

Abstract

Plain language summary

Keywords

Introduction

Theoretical Framework and Hypotheses Development

Sample Selection, Measures, and Research Design

Sample Selection

Variable Definitions

Dependent Variables

Independent Variables

Moderator Variables

Control Variables

Research Design

Empirical Results

Descriptive Statistics

Full Sample Descriptive Statistics

Subsample Difference Statistics

Correlation Analysis

Regression Results

Additional Analysis

Moderating Effect on Audit Fees

Moderating Effect of Industry Differences on the Relationship Between SHARE(SAMESIGN) and AUDITFEE

Moderating Effect of Regional Differences on the Relationship Between SHARE(SAMESIGN) and AUDITFEE

Moderating Effect of Ownership on the Relationship Between SHARE(SAMESIGN) and AUDITFEE

Moderating Effect of Relative Size on the Relationship Between SHARE(SAMESIGN) and AUDITFEE

Moderating Effect of Audit Firm Size on the Relationship Between SHARE(SAMESIGN) and AUDITFEE

Moderating Effect on Absolute Value of Discretionary Accruals

Moderating Effect of Industry Differences on the Relationship Between SHARE(SAMESIGN) and |DA|

Moderating Effect of Regional Differences on the Relationship Between SHARE(SAMESIGN) and |DA|

Moderating Effect of Ownership on the Relationship Between SHARE(SAMESIGN) and |DA|

Moderating Effect of Relative Size on the Relationship Between SHARE(SAMESIGN) and |DA|

Moderating Effect of Audit Firm Size on the Relationship Between SHARE(SAMESIGN) and |DA|

Robustness Tests

Endogeneity Test

Alternative Measures of Shared Auditors and Same Signing Auditor

Alternative Measures of Audit Quality

Conclusions

Footnotes

Appendix

Acknowledgements

ORCID iD

Funding

Declaration of Conflicting Interests

Ethical Approval

Data Availability Statement

References