Vol. 33, No. 4 DOI: 10.2308/ajpt-50783 November 2014
pp. 119–166
Client-Auditor Supply Chain Relationships,
Audit Quality, and Audit Pricing
Karla M. Johnstone, Chan Li, and Shuqing Luo
SUMMARY:We investigate the association between auditors’ supply chain knowledge
and companies’ audit quality and audit pricing. Auditor supply chain knowledge is a specialized understanding of information and processes regarding accounting and auditing issues that relates to both a supplier and its major customer, regardless of industry commonalities, that is particularly useful for understanding complexities associated with the revenue cycle. We find that auditors’ supply chain knowledge at the city level is associated with higher audit quality and lower audit fees, compared to companies employing auditors with supply chain knowledge at the national level or employing auditors without supply chain knowledge. Such effects are stronger for supplier companies that derive a high proportion of revenue from their major customers, and when the revenue cycle for the supplier companies is more important. We obtain these results while controlling for the usual determinants of audit quality and fees, along with auditors’ industry specialization.
Keywords: audit pricing; audit quality; supply chain relationships; industry expertise.
INTRODUCTION
S
upply chain relationships between suppliers and their major customers are of strategicimportance in the modern economy, and prior research has investigated the effects of these
relationships on partnerswithinthe supply chain. However, little is known about howother
constituents in capital markets, such as auditors, might be affected by, or might respond to, supply
Karla M. Johnstone is a Professor at the University of Wisconsin–Madison, Chan Li is an Associate Professor at the University of Pittsburgh, and Shuqing Luo is an Assistant Professor at the National University of Singapore.
We thank participants at research workshops at National University of Singapore, The University of Auckland, The University of Melbourne, The University of Kansas, and University of Wisconsin–Madison for comments on drafts of this paper. We especially appreciate the comments of Jean Bedard, Steven Cahan, Mark DeFond, David Emanuel, David Hay, W. Robert Knechel, Brian Mayhew, Vic Naiker, Jay Thibodeau, and Terry Warfield. Karla M. Johnstone acknowledges support through her professorship with EY, as well as the Andersen Center for Financial Reporting at the University of Wisconsin–Madison. We thank the AAA’s Auditing Section for acknowledging this manuscript with the ‘‘Best Paper Award’’for the 2012 Midyear Meeting. Finally, we thank two anonymous reviewers and our editor for excellent suggestions and positive feedback throughout the review process.
Editor’s note: Accepted by W. Robert Knechel.
Submitted: April 2013 Accepted: April 2014 Published Online: April 2014
chain relationships. In this study, we investigate whether audit firms’ supply chain knowledge is associated with differential audit quality and audit pricing. We define auditor supply chain knowledge as specialized understanding of information and processes regarding accounting and auditing issues that relates to both a supplier and its major customer, regardless of industry commonalities, that is particularly useful for understanding complexities associated with the revenue cycle.
We examine audit quality and pricing implications of auditor supply chain knowledge for several reasons. First, supply chain relationships are quite common. About 35 percent of Big 4 clients covered in Audit Analytics report having major customer relationships, and there are wealth
transfer and interdependence implications among supply chain partners (e.g.,Hertzel, Li, Officer,
and Rodgers 2008). However, most prior studies focus exclusively on financial performance and
operational strategy implications for upstream suppliers or downstream major customers in the
same supply chain relationship (e.g., Baiman and Rajan 2002a; Baiman and Rajan 2002b;Kulp,
Lee, and Ofek 2004). Related issues that have received limited attention are (1) how a company’s
supply chain relationships may affect other constituents in capital markets, such as audit firms, when they provide audit services to both suppliers and their major customers, and (2) whether the knowledge derived from such integrated supply chain audit services is associated with higher audit
quality.1
Second, supply chain partners may use earnings management to influence the perception of their respective supplier or customer partners about their own financial performance, and such
earnings management activity adversely affects the duration of supply chain relationships (Raman
and Shahrur 2008). The supply chain knowledge that an auditor brings may help improve audit
quality and the assurance regarding the reliability of the financial statements of the respective supply chain partners. However, prior research has not addressed whether auditor supply chain knowledge is associated with audit quality as proxied by the extent of earnings management in financial statements of supply chain partners.
Third, while we predict a positive association between auditor supply chain knowledge and audit quality, it is unclear whether that association leads to fee premia or discounts reflecting production efficiencies. Auditors with supply chain knowledge may charge a fee premium reflecting a differentiation strategy in which that knowledge is valued by supply chain partners. On the other hand, there may be efficiencies for an audit firm in completing the audit engagement for both a supplier and its major customer(s), thereby yielding a fee discount reflecting such efficiencies.
We advance the literature on the development and transfer of knowledge at the individual and
audit-firm levels (e.g., Libby and Luft 1993; Bonner and Walker 1994; Solomon, Shields, and
Whittington 1999;Thibodeau 2003) by introducing the concept of knowledge redundancy in the
context of auditor supply chain knowledge.Sivakumar and Roy (2004)develop a model explaining
the distribution of private and common knowledge among supply chain partners. We adapt their model to explain the implications of supply chain knowledge redundancy with respect to common knowledge available to the supply chain partners and their auditors.
To examine the audit quality and pricing implications of auditor supply chain knowledge, we use a sample of 4,569 supplier company years, of which about 11 percent employ an auditor with supply chain knowledge at the city level and about 21 percent of which employ an auditor with supply chain knowledge at the national level, but not at the city level. We measure auditors’ supply
1
New research is beginning to examine how supply chain relationships affect other constituents.Luo and Nagarajan
(2014) andGuan, Wong, and Zhang (2014)examine how analysts benefit from the information shared within
members of a supply chain relationship when analysts issue forecasts for both a supplier and one or more of the supplier’s major customers. Both of these papers find that analysts gain an information advantage and issue more accurate earnings forecasts for the supplier when analysts follow both of the supply chain partners.
chain knowledge by calculating the number of supplier companies employing the same auditor (at the city level or national level, respectively) as their major customers, divided by the total number of supplier companies audited by that audit firm (at the city level or national level, respectively). We separately examine auditor supply chain knowledge at the city level and at the national level because we expect supply chain knowledge transfer to be more pronounced within a local audit office. We focus our sample on supplier companies rather than customer companies because by construction, supplier companies in our sample are much more dependent on major customers than vice versa.2
The results show that auditor supply chain knowledge at the city level (but not at the national level) is associated with higher audit quality for supplier companies, as indicated by lower discretionary accruals, a lower likelihood of having a restatement, and a lower likelihood of managing earnings to meet or beat analysts’ forecasts, compared to companies employing an auditor without supply chain knowledge. Further, we also find that such supply chain knowledge at the city level (but not at the national level) is associated with lower audit fees paid by supplier companies compared to those paid by companies employing an auditor without supply chain knowledge. In addition, we find that these results only hold in situations in which the customer is highly important to the supplier company, and when the revenue cycle of the supplier company in the audit engagement is more important, i.e., sales and accounts receivable accounts comprise a higher percentage of total assets for the supplier company. We obtain these results while controlling for the usual determinants of audit quality and fees, along with auditors’ industry specialization at the national and city levels.
We conduct several sensitivity analyses to control for potential selection bias, the effects of potential omitted variables on the association between auditors’ supply chain knowledge and audit quality and audit pricing, and the simultaneity of such effects. The main conclusions remain unchanged. These results contribute to research on audit quality differentiation and audit pricing.
Demand for quality-differentiated audits has long been documented (e.g.,Watts and Zimmerman
1986; Simunic and Stein 1987). However, prior literature generally finds that audit firms
differentiate themselves from competitors via either brand name or industry specialization. We report evidence on a new dimension of auditor knowledge acquisition and transfer and document evidence consistent with audit firms providing value-added, quality-differentiated services via supply chain specialization.
Our study also contributes to the supply chain literature that illustrates the importance of the information shared between supply chain partners. For example, the exchange of detailed customer demand and inventory information within the supply chain is associated with reduced total supply
chain costs (Chen 1998;Cachon and Fisher 2000) and improved efficiency in the use of resources
(Matsumura and Schloetzer 2014). We extend this literature by exploring how supplier companies
benefit both in terms of enhanced audit quality and more competitively priced auditing services when they purchase auditing services from the same audit firm as their major customer, but only when that auditor conducts the two audits out of the same city office.
The remainder of the study proceeds as follows. The second section provides background information and hypotheses. The third section describes the sample and method. The fourth section reports empirical results and discusses additional analyses. The fifth section concludes.
2
For example, in our sample, the mean (median) supplier sales dependence on major customer companies is 21.0 (16.0) percent (calculated as total sales made to a major customer, divided by total sales of the supplier in the year). The mean supplier dependence on the major customer is comparable to 19.23 percent reported inFee, Hadlock, and
Thomas (2006). In contrast, the mean (median) customer purchasing dependence on supplier companies is just 3.2
(2.1) percent (calculated as the purchases of a major customer from the supplier, divided by total purchases of the major customer in the year).
BACKGROUND AND HYPOTHESES The Importance of Supply Chain Relationships
A company’s strategic interactions with supply chain partners can have important performance
implications. Major customers influence suppliers’ revenue and earnings realization (e.g.,Baiman
and Rajan 2002a;Baiman and Rajan 2002b;Kulp et al. 2004;Hertzel et al. 2008). When a major
customer expects growth, that demand for products and services positively affects the supplier’s revenue. Similarly, when a major customer experiences financial distress, the customer may take actions (e.g., decrease purchases, delay or stop payment, or default on long-term contracts) that reduce the supplier’s revenue and, thus, have negative consequences for the supplier’s financial
performance (Hertzel et al. 2008).
Suppliers can also affect their customers’ expenses and resulting financial performance. For example, a major customer with a trusted relationship with a supplier will benefit from a dependable supply source and improved effectiveness of inventory management and operational efficiency,
each of which reduces costs and improves earnings (Gavirneni, Kapuscinski, and Tayur 1999;Lee,
So, and Tang 2000; Fee and Thomas 2004). Alternatively, when a supplier experiences a raw
material shortage or price increase, these adverse events negatively affect a major customer’s
production efficiency, inventory management, and product delivery (Gavirneni et al. 1999;Lee et
al. 2000;Fee and Thomas 2004).
Given the strategic importance of supply chain relationships to company performance,Raman
and Shahrur (2008)document the role of supply chain relationships in financial reporting decisions.
They argue that because the value of supply chain relationships to the suppliers/customers depends on each company’s future prospects, supply chain partners may use earnings management to inflate earnings in order to favorably influence the perception of suppliers/customers and their willingness to undertake continuing relationship-specific investment. Other studies also find that companies manage earnings to influence other terms of trade with suppliers/customers, such as input/output prices and terms of trade credit. For example, customers are willing to pay higher prices if they perceive that their suppliers have superior ability to honor explicit or implicit commitments, which
is likely to be the case for more profitable suppliers (e.g.,Bowen, DuCharme, and Shores 1995;
Burgstahler and Dichev 1997).
The Development and Transfer of Auditor Supply Chain Knowledge
From an auditing perspective, supply chain relationships are important to suppliers because they affect the suppliers’ revenue cycle. The revenue cycle is one of the most important transaction
cycles in any audit (Johnstone, Gramling, and Rittenberg 2013). Improper revenue recognition
practices are the most frequent cause of financial misstatements. In fact, 50–60 percent of the fraud
cases involve overstating revenues (Committee of Sponsoring Organizations of the Treadway
Commission [COSO] 1999,2010).
By providing integrated supply chain auditing services, we predict that both individual auditors and audit firms develop knowledge that is useful in understanding the supply chain partners and assessing their respective risks. Such an effect would be consistent with knowledge gains due to
industry specialization (e.g., Craswell, Francis, and Taylor 1995; Mayhew and Wilkins 2003;
Balsam, Krishnan, and Yang 2003; Reichelt and Wang 2010). However, the nature of this
knowledge is likely different from that captured in studies on industry specialization, since supplier and customer companies are generally in different industries. Our discussions with audit practitioners suggest that supply chain knowledge relates to a deep understanding of the risks inherent in the revenue cycle of supplier companies in their respective industries. In situations in which there is no audit team overlap between the two engagements, one partner with whom we
spoke explained that if a particularly complex issue arose, then consulting the other engagement team was helpful, particularly in auditing revenue cycle accounts. The partner explained that since customer incentive payments are often one of the more challenging issues, being able to more fully understand both sides of the agreement would be important, since revenue recognition is one of the highest restatement areas. That partner also described a situation in which he served on the engagement of a major customer whose supplier was also audited by his firm. In that situation, one client was on the wholesale side and the other on the retail side. He noted that the audit firm has significant internal knowledge leadership that is gathered from these clients that is shared at an aggregate basis and that is cleansed of specific client information.
Based on this discussion, we define auditor supply chain knowledge as specialized understanding of information and processes regarding accounting and auditing issues that relates to both a supplier and its major customer, regardless of industry commonalities, that is particularly useful for understanding the complexities associated with the revenue cycle. Supply chain knowledge exists at both the individual auditor level and at the organizational level through audit firm knowledge leadership, knowledge management systems, and personal communication networks. While it is logical to expect that there may be some overlap of auditor industry knowledge and auditor supply chain knowledge, supply chain knowledge differs in that it can develop across clients regardless of industry membership, and that it is likely more specialized, focusing especially on accounting and auditing issues concerning revenue cycle accounts. Further, it is logical to expect that auditor supply chain knowledge differs from nontransferrable client-specific knowledge because it is aggregated across clients in a way that ensures client confidentiality.
The Development of Supply Chain Knowledge at an Individual Level
A variety of factors play a role in the development of supply chain knowledge over the course of an auditor’s career. Underlying audit task knowledge develops early, including both explicit and
tacit knowledge, and is an important determinant of audit task performance (Libby and Luft 1993).
As the auditor specializes in certain industries, industry expertise grows (Bonner and Walker 1994;
Solomon et al. 1999;Thibodeau 2003). Over time, the auditor establishes personal and professional
contacts in the business community, which enables networking across individuals and organizations
through repeated interactions (Borgatti and Cross 2003). These interactions may yield
enhancements in the acquisition of supply chain knowledge (e.g., relating to accounts receivable, revenue, and associated contracting complexities). As the auditor gains further experience, the likelihood increases that the auditor will work on both engagements in a supply chain partnership, or will have close colleagues within the audit firm that do so. It also increases the likelihood of an individual auditor working on one client in the supply chain relationship and then transferring to also working on the other client in the relationship. Ultimately, over time and with experience, the auditor develops an understanding of commonalities and shared risks/opportunities among supply chain partners, which constitutes individual auditor-level supply chain knowledge.
The Transfer of Supply Chain Knowledge at an Organizational Level
In addition to developing supply chain knowledge at the individual level, audit firms face the organizational challenge of ensuring effective transfer of such knowledge among and between engagement teams. A significant body of the management literature is devoted to knowledge
transfer and organizational learning (e.g.,Borgatti and Cross 2003;van Wijk, Jansen, and Lyles
2008). An important construct in that literature is that ofknowledge redundancy,which is defined
as‘‘the degree of overlap in the knowledge base between two or more social actors’’(Rindfleisch
organizations manage interpersonal and interorganizational communication of knowledge,
processes, and ideas. Sivakumar and Roy (2004) extend the knowledge redundancy literature
into the supply chain domain, arguing that while in some contexts knowledge redundancy may be viewed as wasteful, in the supply chain domain it is essential for superior performance of the partners because they cannot operate effectively unless they have in-depth knowledge of their supply chain partner. Suppliers and major customers will find it in their mutual best interests to share knowledge, ideas, and processes with one another to achieve maximum performance. As an example from our sample, Apache Corporation is in the crude petroleum and natural gas industry (SIC 1311) and its supplier is OIL States International Inc., which is in the oil and gas field machinery and equipment industry (SIC 3533). The same audit firm conducts both engagements out of the Houston, TX office. When the auditor audits both Apache and OIL States, we expect that knowledge redundancy between the three parties will be enhanced, which should be beneficial from an auditing perspective.
Auditing standards demand client confidentiality.3 However, it is important to note that
specialized knowledge in a particular field is allowed to transfer among different client
engagements (McAllister and Cripe 2008).4 Audit firm knowledge leadership, knowledge
management systems, and informal knowledge sharing via personal networks enable the capture and dissemination of broad insights for companies up and down the supply chain. We expect supply chain-relevant knowledge will help auditors to make more informed judgments and more accurate risk assessments, especially with regard to the revenue cycle on the audits of the supplier companies, thus producing a higher-quality audit.
Sivakumar and Roy (2004)develop a model explaining the distribution of private and common
knowledge among supply chain partners. In Figure 1, we adapt their model to explain the implications of supply chain knowledge redundancy with respect to common knowledge available to the supply chain partners and their auditors. Each of the scenarios in Figure 1 depicts the knowledge possessed
by only the supplier (denoted Ksupplier), the knowledge possessed by only the customer (denoted
Kcustomer), the common knowledge shared between the supplier and auditor (denoted KC-S, A), the
common knowledge shared between the customer and auditor (denoted KC-C, A), and finally the
common knowledge of all three parties (denoted KC-S, C, A). Following the introduction of our
hypotheses, we discuss the various panels in Figure 1, including implications for differences in the application of supply chain-relevant knowledge at the city versus national levels, and in situations in which the supplier is more versus less reliant on the major customer or when the revenue cycle is more versus less important to the supplier.
3 TheAICPA’s (1992)confidentiality rules compel the individual auditor not to reveal private information about either
partner in the supply chain relationship to the other client. However, it is routine practice for engagement teams to discuss information about clients both audited by their firm. One partner that we contacted told us that individual auditors generally do not to talk with other teams about specific client matters that would not otherwise be publicly known. That said, he noted that situations do arise when it is appropriate to share knowledge that could benefit the individual office or the audit firm. Such situations tend to relate to particular accounting or auditing issues, not to confidential client information. He noted that such communications are usually informal and verbal, which is due to the need to delicately address a potential issue without compromising confidentiality.
4
McAllister and Cripe (2008, 53) provide a comprehensive discussion of the benefits and risks of knowledge transfer
across client engagements. Of most relevance, the authors state,‘‘paragraph 30 of Rule 301 allows auditors to provide ‘knowledge and expertise resulting in a special competence in a particular field’ to clients without violating the confidence of another client as long as details associated with a particular engagement are not disclosed. This indicates that auditors are allowed to apply technical knowledge and experience acquired during current audit engagements to future audit engagements.’’
Hypotheses Concerning Supply Chain Knowledge, Audit Quality, and Audit Pricing As discussed earlier, supply chain-relevant knowledge should help auditors to make more informed judgments and more accurate risk assessments, especially with regard to the revenue cycle on the audits of the supplier companies, thus producing a higher-quality audit. Our first set of hypotheses involves expectations comparing audit quality and audit pricing of supplier companies
audited by auditorswithsupply chain knowledge (at either the city or the national levels) to those
withoutsuch knowledge. Hereafter, we refer to an audit firm that provides audit services to both a
supplier and its major customer out of one office location as a ‘‘supply chain auditor at the city
level’’and we refer to an audit firm that provides audit services to both a supplier and its major
customer out of two separate audit office locations as a‘‘supply chain auditor at the national level.’’
H1a: Supplier companies audited by a supply chain auditor receive higher audit quality
compared to supplier companies audited by a non-supply chain auditor.
H1b: Supplier companies audited by supply chain auditors at the national level receive higher
audit quality compared to supplier companies audited by non-supply chain auditors. As an example from our sample relating to H1a, the same audit firm audits Micron Technology Inc. and its supplier, Netlogic Microsystems, Inc., both out of the San Jose, CA office. H1a predicts higher audit quality for Netlogic Microsystems compared to a supplier-customer pair audited by
FIGURE 1
Implications of Supply Chain Auditing Knowledge Redundancy
Note:Ksupplierdenotes supplier knowledge;Kcustomerdenotes customer knowledge;Kcdenotes common knowledge with subscript denoting supplier (S), customer (C), or auditor (A); dashed boarder denotes entire domain of auditor knowledge.
different audit firms. As an example from our sample relating to H1b, the same audit firm audits Barnes & Noble Inc. and its supplier, Source Interlink Inc., but out of different offices (Chicago, IL and New York, NY, respectively). H1b predicts higher audit quality for Source Interlink compared to a supplier-customer pair audited by different audit firms.
While H1a and H1b predict that supply chain knowledge can be leveraged by auditors at both the city and national levels, we expect that the effect should be more pronounced at the city level. Figure 1, Scenarios A and B depict knowledge sharing for city-level versus national-level auditor supply chain knowledge. When different auditor offices conduct the audits for a supplier and its major customer, these audits are typically conducted by different audit teams with different audit partners. At the national level, it is less likely that auditors from different offices have information communication concerning the related companies in their client portfolio. In such circumstances, the information advantages and the knowledge that auditors build by providing supply chain auditing services is likely to be less. In contrast, it is likely that informal communication concerning these parties within the same city office may help auditors assess the business risk of supplier companies. In situations in which an individual auditor is involved in the audit of both the supplier company and the customer company, that individual knowledge is naturally retained. For example, it would not be unusual to find overlap among members of an audit engagement team on both a supplier client and a major customer client, especially when the supplier and major customer are
audited by the same city office of the audit firm. The result is thatKC-S, C, Ain Scenario A is larger
thanKC-S, C, Ain Scenario B. Thus, our next hypothesis is:
H1c: Supplier companies audited by supply chain auditors at the city level receive higher
audit quality compared to supplier companies audited by supply chain auditors at the national level.
Continuing prior examples, H1c predicts higher audit quality for Netlogic Microsystems (same city-level auditor) compared to Source Interlink (same national-level auditor).
Regarding audit pricing, Raman and Shahrur (2008) document an earnings management
motivation in financial reporting for supply chain partners. They argue that because the value of supply chain relationships to the suppliers/customers depends on each company’s future prospects, supply chain partners may use earnings management to favorably influence the perception of their supply chain partner. This motivation creates both a demand for, and supply of, quality and fee
differentiated audit services (e.g.,Copley and Doucet 1993;Copley, Doucet, and Gaver 1994).
In addition to a risk-based argument, if supplier companies place value on auditors’ supply chain knowledge and resulting higher audit quality, auditors with supply chain knowledge may be able to charge a fee premium reflecting this differentiation strategy. For example, the audit committee of a supplier company may find it appealing during the bidding process to know that members of the audit engagement team have experience auditing the company’s major customer, especially when the supplier and major customer are audited by the same city office of the audit firm, which may then translate to the auditor being able to negotiate a relatively higher audit fee
than cases if members of the audit engagement team had no such relevant experience.5One partner
with whom we spoke stated his strategy in these cases is to highlight supplier experience during the proposal process when he thinks it will be beneficial to demonstrating additional knowledge, and when the potential client has not indicated concern about the auditor’s relationship with the major customer.
5 If the audit committee members were uncomfortable with the potential for the supplier’s private information leakage by the auditor with supply chain knowledge, even if it were indirect, to the major customer, then presumably the audit committee members simply would not hire the auditor with such knowledge in the first place.
A competing argument is that the auditor’s supply chain knowledge and resulting synergies could yield audit production efficiencies. Production efficiencies associated with auditor supply chain knowledge would enable the auditor to offer a fee discount reflecting such efficiencies; such synergies would of course be most pronounced when the supplier and the major customer are audited by the same city office of the audit firm. One partner with whom we spoke articulated the process by which this might occur, stating that when there is supply chain knowledge the audit firm, as a whole, would generally have greater experience, which could lead to efficiencies. He noted that if both clients were in the same office (even if the audit teams may not directly overlap) there would be experience in the office and/or an ability to consult on tough issues that otherwise may take additional time to resolve independently without such consultation. Prior research shows an
association between audit production efficiencies and audit pricing. For example, Gist (1994)
provides empirical evidence that audit firms with a more structured audit approach charge lower audit fees, although that study acknowledges that without data on audit hours (a direct measure of audit effort) a definitive link between audit firm structure and production efficiencies is unavailable.
Dopuch, Gupta, Simunic, and Stein (2003, 52) utilize empirical data on audit firm effort and pricing
and show that inefficiencies in audit production hours are associated with reductions in audit firm
realization rates, and they also note that‘‘efficient production could also lead to fee discounting.’’
More recently however,Knechel, Rouse, and Schelleman (2009)report that audit fees are not very
sensitive to audit efficiency, so the results with respect to audit production models are mixed. Based on these two competing arguments, it remains an empirical question as to whether providing audit services to supply chain partners is associated with higher or lower audit fees. As such, we formulate the following research question:
RQ: Do supplier companies audited by supply chain auditors at the national or city level pay
higher or lower audit fees compared to companies audited by non-supply chain auditors? Hypotheses Concerning Supply Chain Knowledge and Supplier Reliance and Supplier Revenue Cycle Importance
Supplier reliance on a major customer varies across supplier-customer pairs, and the importance of revenue cycle accounts also varies across individual suppliers. Comparing Scenarios C and D in Figure 1 helps to explain knowledge redundancy implications when the supplier and customer have asymmetric overlap of private versus common knowledge. Asymmetric overlap occurs when one party in the supply chain relationship has greater private knowledge than the other party. Scenario C represents a situation in which the supplier’s reliance on the customer is higher in terms of the percentage of revenue they earn through transactions with the customer, or for which the revenue cycle is relatively more important to the supplier, whereas Scenario D represents a situation in which the supplier’s reliance is lower or the revenue cycle is relatively less important. Comparing Scenarios C and D illustrates that when a supplier company is more dependent on the sales made to its major customer, or when the revenue cycle accounts of the supplier companies are the primary accounts affected by the supplier-customer relationship, the common knowledge available to an auditor with integrated supply chain knowledge is greater than when a supplier company is less dependent on its
major customer. Sivakumar and Roy (2004, 245) illustrate the extreme example of supply chain
dependence as one in which a supplier’s only source of revenue is manufacturing steering wheels for
Ford Motor Company. The authors note‘‘Clearly, such a supplier can be expected to be efficient and
to be almost as internalized and domesticated as a company department. . .knowledge redundancy
will be at a maximum between the supplier and Ford.’’
Finally, based on insights gained from comparing Scenarios C and D in Figure 1, we predict that the associations that we observe relevant to H1 and the research question will be stronger in two situations: (1) for supplier companies that derive a high proportion of their total revenue from
sales to their major customers, and (2) when the revenue cycle in the audit engagement for supplier companies is more important.
H2a: The association between audit quality and auditor supply chain knowledge at the
national or city level is stronger for supplier companies (1) that derive a greater proportion of their total revenue from sales to their major customers, and (2) when the revenue cycle in the audit engagement for the supplier companies is more important.
H2b: The association between audit fees and auditor supply chain knowledge at the national
level or city level is stronger for supplier companies (1) that derive a greater proportion of their total revenue from sales to their major customers, and (2) when the revenue cycle in the audit engagement for the supplier companies is more important.
SAMPLE AND METHOD Sample
To determine whether a supplier and its major customer employ the same audit firm in a given fiscal year, we require identification of each party in a company’s supply chain relationships and the
identity of the auditors of these companies. SFAS No. 14 (FASB 1976) requires companies to
report financial information for any industry segment that comprises more than 10 percent of the company’s consolidated yearly sales, assets, or profit. In addition, companies must disclose the name of any customer representing more than 10 percent of the total sales of the company. We obtain this information from the Compustat customer segment file, a subset of the Compustat segment database that records the identity of, and sales to, a company’s major customers based on
the company’s 10-K.6
Table 1 shows the sample selection process. We begin with all supplier-major customer relationships reported in the Compustat customer segment file during the period January 1, 2003 to December 31, 2010. We start in 2003 to avoid issues associated with the collapse of Arthur
Andersen in 2002. We use an algorithm similar to that inFee and Thomas (2004)that compares
the number and order of the letters for the customer names with the historical company names listed in the CRSP company name file and retain a list of 8,451 supplier company-years with a valid GVKEY for both the supplier and its major customer. When a supplier company discloses more than one major customer, we retain in our sample the customer to whom the supplier makes the greatest amount of sales. As noted previously, we focus our sample on supplier companies rather than customer companies because suppliers are much more dependent on the sales made to
major customers thanvice versa. We then delete 373 supplier-years in the financial sector (e.g.,
Francis, Reichelt, and Wang 2005;Reichelt and Wang 2010), 471 supplier-years with either the
supplier or the major customer not covered by Audit Analytics, and 504 supplier companies not headquartered in the U.S. We eliminate 1,136 (1,398) suppliers (major customers) with missing financial data from Compustat. We require data on major customers because we include the abnormal audit fee of the major customer as one of the control variables in the regression analyses. This yields a sample of 4,569 supplier company-year observations. In analyzing audit quality, we also examine the propensity of companies’ meeting or beating analysts’ earnings
forecasts within one penny of earnings per share (Reichelt and Wang 2010). The additional
6
Therefore, all customer companies included in our sample meet this requirement, i.e., customer companies that make less than 10 percent of the total sales to the supplier company will not be included in our sample.
requirement of analysts’ consensus forecast data from I/B/E/S reduces our sample to 2,391
supplier company-years for this test.7,8
Measuring Auditor Supply Chain Knowledge and Sample Industry Distribution
We measure auditors’ supply chain knowledge at both the city level and the national level. Note that auditors’ supply chain knowledge at the city level, by definition, is a subset of auditors’ supply chain knowledge at the national level. Therefore, we define these two variables as
TABLE 1 Sample Selection
Panel A: Sample for Audit Quality (UsingjDCAAjandjMISSTATEjas Proxies for Audit Quality) and Audit Fee in the Main Analyses
Procedures
Number of Observations Number of supplier company-years with valid GVKEY for both the supplier and
the major customer company from 2003–2010
8,451 Delete: Supplier companies in financial sector (SIC 6000–6999) (373) Delete: Supplier-years for which either the supplier or the customer is not
covered by Audit Analytics
(471)
Delete: Non-U.S. supplier company-years (504)
Delete: Supplier company-years with missing values for financial variables (1,136) Delete: Customer companies with missing values for financial variables to
generate the abnormal audit fee
(1,398) Number of observations for the main analyses, representing audit clients from
806 auditor city years
4,569
Panel B: Sample for Audit Quality (Using the Propensity of Meeting or Beating Analyst Consensus Forecast as the Proxy for Audit Quality)
Procedures
Number of Observations
Number of observations from Panel A (1,398þ4,569) 5,967
Delete: Supplier companies without analyst consensus forecast data from I/B/E/S (2,178) Delete: Customer companies with missing values for financial variables to
generate the abnormal audit fee
(1,398) Final sample in the analysis of meeting or beating analysts’ forecasts,
representing audit clients from 637 auditor city years
2,391
7
These two samples include a total of 347 (7.6 percent34,569¼347) and 234 (9.8 percent32,391¼234) supplier companies that are simultaneously reported as the major customer of other companies. In untabulated analyses, we exclude these observations. The results regarding the association between auditor supply chain knowledge and both audit quality and fees are not sensitive to excluding these observations.
8
For the sample in Panel A of Table 1, 19.2 percent of suppliers are in the same two-digit SIC code as their major customer; this represents 877 supplier company years (19.2 percent34,569¼877). For the sample in Panel B of Table 1, 19.9 percent of suppliers are in the same two-digit SIC code as their major customer; this represents 476 supplier company-years (19.9 percent32,391¼476). In untabulated analyses, we exclude these observations. The results regarding the association between auditor supply chain knowledge and both audit quality and fees are not sensitive to excluding these observations.
orthogonal to each other. In particular,CHAIN_CITYis a ratio measure of auditors’ supply chain knowledge at the city level, and it is defined as the number of supplier companies (i.e., companies that have reported having major customer relationships in Compustat with both the supplier and the major customer being covered in Audit Analytics) employing the same city-office auditor as their major customers, divided by the total number of supplier companies from the same auditor city in the year. For example, assume that Audit Firm X in City A has ten client companies that have major customer relationships. Further assume that Audit Firm X also audits two of these
supplier companies’ major customers out of the City A office. In this example, CHAIN_CITY
equals 20 percent. CHAIN_CITY equals 0 if Audit Firm X does not audit any of the supplier
companies’ major customers out of the City A office. CHAIN_NATION is a ratio measure of
auditors’ supply chain knowledge at the national level, and it is defined as the number of supplier companies employing the same auditor (but not from the same city) as their major customers, divided by the total number of supplier companies that the audit firm audits at the national level. For example, assume that Audit Firm X has 100 client companies that have major customer relationships nationwide. Further assume that Audit Firm X also audits two of these supplier companies’ major customers, but that the supplier and customer companies are audited out of
different city offices. In this example,CHAIN_NATIONequals 2 percent. Appendix A provides
further illustrations of the calculation of these variables using examples from the sample; our calculations reflect the convention in the industry specialization literature regarding the use of
relative market share to measure the underlying construct (e.g.,Craswell et al. 1995;Balsam et al.
2003).
Table 2 shows that about 11 percent of supplier companies (509/4,569) employ an auditor with city-level supply chain knowledge, 21 percent (979/4,569) employ an auditor with national-level
TABLE 2
Industry Distribution of Sample Companies
Fama-French’s 12 Industries
CHAIN_CITY.0 CHAIN_NATION.0
CHAIN_CITY¼0 and
CHAIN_NATION¼0 n Percent n Percent n Percent
Consumer nondurables 19 3.7% 120 12.3% 413 13.4%
Consumer durables 7 1.4% 48 4.9% 165 5.4%
Manufacturing 64 12.6% 150 15.3% 473 15.4%
Energy and coal extraction and products
31 6.1% 35 3.6% 244 7.9%
Chemicals and allied products 11 2.2% 29 3.0% 126 4.1%
Business equipment 200 39.3% 250 25.5% 878 28.5%
Telephone and television transmission
11 2.2% 28 2.9% 41 1.3%
Utilities 21 4.1% 14 1.4% 48 1.6%
Wholesale, retail, and some services
10 2.0% 39 4.0% 107 3.5%
Healthcare, medical equipment, and drugs
86 16.9% 137 14.0% 312 10.1%
Others 49 9.6% 129 13.2% 274 8.9%
supply chain knowledge, with the remainder employing an auditor with neither type of supply chain knowledge. Industry distribution is relatively similar across these groups, with manufacturing, business equipment, and the healthcare/medical equipment/drug sectors having the largest representation.
Research Design
We first consider the association between auditor supply chain knowledge at either the city or national level and three common proxies for audit quality (H1a and H1b). The first proxy is the
absolute value of discretionary accrualsjDACCj(e.g.,Frankel, Johnson, and Nelson 2002;Reichelt
and Wang 2010), which is calculated based on the cross-sectional modified Jones (1991) model
where expected accruals are estimated from the change in revenue, adjusted by the change in accounts receivable, the level of property, plant, and equipment, and the prior year’s operating
performance by industry at the two-digit SIC code level in yeart.jDACCjrepresents the amount of
unexpected accruals and is the amount of earnings that have been potentially distorted through managerial discretion in earnings management. See Appendix B for an illustration of the calculation ofjDACCj. See Appendix C for variable definitions.
The second proxy for audit quality is the existence of a restatement,RESTATE(e.g.,Palmrose
and Scholz 2004;Francis, Michas, and Yu 2012). This variable equals 1 if a company’s financial
reports for yeartare subsequently restated in future periods, and 0 otherwise. The third proxy for
audit quality relates to the existence of aggressive earnings management. FollowingReichelt and
Wang (2010), we use the propensity that a company meets or just beats analysts’ earnings forecasts
as a proxy for clients’ aggressive earnings management behavior. MEET equals 1 if earnings
exactly meet or beat the latest analyst earnings forecast by one cent per share in yeart, and equals 0
otherwise.9H1a and H1b predict a negative association between these three audit quality proxies
and CHAIN_CITY and CHAIN_NATION, respectively. Our research question explores the association between auditor supply chain knowledge and audit fees and, accordingly, we make
no directional prediction between the natural log of audit fees, logAUDITFEE, and either
CHAIN_CITYor CHAIN_NATION.
We include various control variables in the models. A particularly important control variable is auditor industry specialization. Prior studies find that industry specialist auditors are associated with
better earnings quality and that they charge higher audit fees (e.g.,Balsam et al. 2003;Francis et al.
2005;Reichelt and Wang 2010). Following prior research, we control for industry specialization at
the national level only (IND_NATION), the city level (IND_CITY), and at both the national and the
city levels (JOINT_IND).
Other control variables follow prior research in terms of measurement and directional predictions;
when divergent results in prior research exist we make nondirectional predictions (e.g.,Frankel et al.
2002;Ashbaugh, LaFond, and Mayhew 2003;Francis et al. 2005;Reichelt and Wang 2010). For the
audit quality models, we control for whether the auditor is a Big 4 auditor (BIG_4), total accruals
(TACC),10 company size (logAT), operating cash flows (CFO), cash flow volatility (STD_CFO),
financial condition (LEVERAGE, ROA,andLOSS), company growth as measured by market-to-book
value, sales growth, and research and development expenses (MB, SALE_GROWTH, and
RD_RATIO), whether the company is in a litigious industry (LITIGATION), the likelihood of
9
As we discussed earlier, auditors’ supply chain knowledge is important for the revenue cycle in the audit engagement. Thus, as a sensitivity test we also use the likelihood that companies meet or beat analyst consensus revenue forecasts as an alternative proxy for audit quality. Our results remain essentially the same.
10
Following prior studies, we include the absolute value ofTACCrather than unsignedTACCas a control variable when we use the absolute value of discretionary accruals as the measure for audit quality (e.g.,Reichelt and Wang 2010).
bankruptcy (ALTMAN), and auditor tenure (logAUDITOR_TENURE). We expectBIG_4, CFO,and
ROA to be positively associated with audit quality (i.e., negatively associated with jDACCj,
RESTATE, or MEET), and TACC, STD_CFO, LEVERAGE, LOSS, MB, SALE_GROWTH, RD_RATIO,and ALTMANto be negatively associated with audit quality (i.e., positively associated withjDACCj, RESTATE,orMEET). We make no directional predictions forlogATorLITIGATION orlogAUDITOR_TENURE due to variation in results in prior studies (e.g.,Ashbaugh et al. 2003;
Knechel and Vanstraelen 2007;Reichelt and Wang 2010).
In the audit fee models, we control for whether the auditor is a Big 4 auditor (BIG_4), whether
the company’s year-end is in the busy season (BUSY_SEASON), company size (logAT), long term
debt (LONG_DEBT), current liabilities (CURRENT_LIABILITY), inventory ratio (INVT_RATIO), a
modified auditor opinion (MODIFIED_OPN), financial risk (ROA and LOSS), volatility
(STD_CFO), growth (SALE_GROWTHand RD_RATIO), restructuring (RESTRUCTURE), special
items (SPECIAL_ITEM), foreign transactions (FOREIGN), complexity (NUM_SEG), and auditor
tenure (logAUDITOR_TENURE). We expectROAto be negatively associated with audit fees, and
the other variables to be positively associated with audit fees.
Finally, because characteristics of suppliers’ major customers may potentially affect supplier companies’ audit quality and audit fees, we control for such characteristics. To manage the number of independent variables, we include a variable representing the abnormal audit fee paid by major
customer companies (ABN_CU_logFEE).ABN_CU_logFEEis the residual value from the audit fee
model presented in Appendix D. We make no directional prediction for the association between ABN_CU_logFEEand audit quality, and expect a positive association between ABN_CU_logFEE and audit fees.
Below are the regression models that we use to examine the association between auditor supply
chain knowledge and audit quality. BecauseCHAIN_CITYandCHAIN_NATIONare defined at the
auditor-office-year and auditor-year level, respectively, we adjust the standard errors for the coefficient estimates in the regression analyses by clustering the observations at the supplier-company level.
jDACCtj;ðRESTATEtÞ;ðMEETtÞ ¼b0þb1CHAIN CITYtþb2CHAIN NATIONt
þb3IND NATIONtþb4IND CITYtþb5JOINT INDt
þb6BIG4tþb7jTACCtjðTACCtÞ þb8logATtþb9CFOt
þb10STD CFOtþb11LEVERAGEtþb12ROAt
þb13LOSStþb14MBtþb15SALE GROWTHt
þb16RD RATIOtþb17LITIGATIONtþb18ALTMANt
þb19logAUDITOR TENUREtþb20ABN CU logFEEt
þfixed industryþfixed yearþet
ð1Þ logðAUDITFEEtÞ ¼b0þb1CHAIN CITYtþb2CHAIN NATIONtþb3IND NATIONt
þb4IND CITYtþb5JOINT INDtþb6BIG4tþb7BUSY SEASONt
þb8logATtþb9LONG DEBTtþb10CURRENT LIABILITYt
þb11INVT RATIOtþb12MODIFIED OPNtþb13ROAtþb14LOSSt
þb15STD CFOtþb16SALE GROWTHtþb17RESTRUCTUREt
þb18SPECIAL ITEMtþb19FOREIGNtþb20NUM SEGt
þb21RD RATIOtþb22logAUDITOR TENUREtþb23ABN CU logFEEt
þfixed industryþfixed yearþet
RESULTS Descriptive Statistics
Table 3, Panel A reports descriptive statistics for for the full sample, as well as the three
subsamples of companies, i.e.,CHAIN_CITY. 0,CHAIN_NATION.0, andCHAIN_CITYand
CHAIN_NATION¼0. Using the full sample, the mean jDACCj is 0.097, 15.8 percent of the
companies restated their financial statements (RESTATE), and 12.5 percent of companies have met
or just beat analyst forecasts by one cent (MEET). The mean (median) oflogAUDITFEEis 13.130
(13.158), which is equivalent to an unlogged mean (median) value of $1.29 million ($573,000). Univariate comparisons at the city level show that, compared with companies for which CHAIN_CITY and CHAIN_NATION¼0, those companies for which CHAIN_CITY . 0 have
marginally lower discretionary accruals (jDACCj; p ¼0.08), a lower likelihood of restatement
(RESTATE; p¼0.045), a marginally lower likelihood of meeting or beating the latest analyst
earnings forecast by one cent (MEET; p¼0.08), and no significant difference in audit fees. In
addition, companies for whichCHAIN_NATION .0 also have a marginally lower likelihood of
restatement (RESTATE; p ¼ 0.08) compared with companies for which CHAIN_CITY and
CHAIN_NATION¼0. However, the differences in jDACCj, MEET,and logAUDITFEEbetween
these two groups are insignificant. When comparing companies for which CHAIN_CITY . 0 to
those companies for whichCHAIN_NATION.0 (untabulated), we find the former companies have
marginally lower discretionary accruals (p¼0.06), and lower audit fees (p¼0.02) compared to the
latter companies.
The Pearson/Spearman correlation matrix in Table 3, Panel B reveals thatjDACCjis negatively
associated with bothCHAIN_CITY(p,0.01) andIND_CITY(p¼0.02).RESTATEis negatively
associated with bothlogAUDITFEE(p,0.01) andCHAIN_CITY(p¼0.04).MEETis positively
associated with logAUDITFEE(p, 0.01). Due to the large number of control variables across
various models, we do not tabulate those correlations. While several correlations among independent variables exceed 0.35, the highest variance influence factor (VIF) is only 4.60 (2.31) in the model of audit quality using discretionary accruals as the measure and audit fee, respectively, suggesting multicollinearity is unlikely to be problematic.
Regarding correlations between auditor supply chain knowledge and industry specialization,
the results reveal thatCHAIN_CITYis positively associated with bothCHAIN_NATION(p,0.01)
and JOINT_IND (p , 0.01), and is negatively associated with IND_CITY (p , 0.01). CHAIN_NATION is positively associated with IND_NATION (p , 0.01), IND_CITY (p ,
0.01), andJOINT_IND(p,0.01). While statistically significant, the correlations between proxies
for auditor supply chain knowledge and industry specialization range from0.09 to 0.15, so these
constructs appear to be related but distinct. Hypothesis Testing
Table 4, Columns (1)–(3) report results relevant to testing H1a and H1b. The results show that CHAIN_CITYis negatively associated with the level of absolute discretionary accruals (p¼0.017),
the likelihood of a restatement (p¼0.045), and the likelihood of meeting or just beating analysts’
forecasts for supplier companies (p¼0.033). These results support H1a. To put these coefficients
into perspective, these results suggest a decrease of 5.2 percent (0.04530.112)/0.097) in the total
level of absolute discretionary accruals, a decrease in the probability of a restatement from 21.3 percent to 20.6 percent (representing a 3 percent decrease), and a decrease in the propensity to meet or beat analyst forecasts from 7.6 percent to 6.8 percent (representing a 10.5 percent decrease),
T ABLE 3 Descriptive Statistics and Corr elations Panel A: Descriptive Statistics V ariables Full Sample (Max. n ¼ 4,569) CHAIN_CITY . 0 (Max. n ¼ 509) CHAIN_N A TION . 0 (Max. n ¼ 979) CHAIN_CITY and CHAIN_N A TION ¼ 0 (Max. n ¼ 3,081) Mean Median Std. Mean Median Std. Mean Median Std. Mean Median Std. (1) (2) (3) (4 ) (5 ) (6 ) (7 ) (8) (9) (10) (11) (12) j DACC t j 0.097 0.038 0.176 0.090* 0.036 0.171 0.105 0.039 0.191 0.101 0.045 0.168 RESTATE t 0.158 0.000 0.365 0.138** 0.000** 0.383 0.153* 0.000* 0.386 0.178 0.000 0.344 MEET t a 0.125 0.000 0.331 0.112* 0.000 0.318 0.118 0.000 0.323 0.140 0.000 0.347 logAUDITFEE t 13.130 13.158 2.364 13.226 13.221 2.223 13.190 13.171 2.234 13.055 12.834 2.375 AUDITFEE t (i n million $) 1.287 0.573 2.054 1.657** * 0.809*** 2.438 1.612** * 0.783*** 2.336 0.942 0.374 1.635 IND_NATION t 0.242 0.000 0.428 0.263** * 0.000*** 0.441 0.311** 0.000** 0.463 0.193 0.000 0.395 IND_CITY t 0.105 0.000 0.306 0.082 0.000*** 0.275 0.123** * 0.000*** 0.328 0.101 0.000 0.302 JOINT_IND t 0.278 0.000 0.448 0.520** * 1.000*** 0.500 0.347** * 0.000*** 0.476 0.161 0.000 0.367 BIG_4 t 0.759 1.000 0.428 0.966** * 1.000*** 0.064 0.977** * 1.000*** 0.151 0.753 1.000 0.497 TACC t 0.023 0.009 0.261 0.022 0.006*** 0.233 0.004* 0.003 0.206 0.036 0.013 0.297 logAT t 5.521 5.488 2.082 6.360** * 6.328*** 1.872 6.034** * 5.928*** 1.791 4.946 4.871 2.141 AT t (i n 1,000 millions $) 1.655 0.241 4.316 2.601** * 0.560 5.162 2.077** 0.375 5.223 1.103 0.130 3.186 CFO t 0.004 0.070 0.378 0.016** 0.069*** 0.271 0.038* 0.079*** 0.255 0.020 0.065 0.461 STD_CFO t 0.336 0.381 8.178 0.101 0.311 6.175 0.776* 0.417*** 9.383 0.142 0.369 7.918 LEVERAGE t 0.231 0.156 0.279 0.187** * 0.122*** 0.221 0.201 0.133 0.246 0.263 0.183 0.309 ROA t 0.070 0.023 0.281 0.066** 0.020*** 0.236 0.034** * 0.034*** 0.216 0.094 0.019 0.324 LOSS t 0.414 0.000 0.493 0.435 0.000 0.496 0.373** * 0.000*** 0.484 0.432 0.000 0.495 MB t 1.962 1.841 5.487 2.022 2.099 5.520 2.408 2.052 4.774 1.666 1.645 5.869 SALE_GROWTH t 0.098 0.063 0.319 0.097 0.073*** 0.336 0.116 0.074 0.297 0.088 0.054 0.327 RD_RATIO t 0.083 0.021 0.104 0.136** * 0.140 0.116 0.090** * 0.036*** 0.103 0.062 0.009 0.093 LITIGATION t 0.077 0.000 0.267 0.085** 0.000 0.279 0.101 0.000 0.302 0.059 0.000 0.236 ALTMAN t 6.252 3.842 6.518 6.730* 4.383** 6.657 6.063** 3.795** 5.974 6.227 3.732 6.797 ABN_CU_logFEE t 0.396 0.412 0.602 0.413* 0.420*** 0.575 0.439 0.418 0.599 0.365 0.405 0.609 (continued on next page)
T ABLE 3 (continued) V ariables Full Sample (Max. n ¼ 4,569) CHAIN_CITY . 0 (Max. n ¼ 509) CHAIN_N A TION . 0 (Max. n ¼ 979) CHAIN_CITY and CHAIN_N A TION ¼ 0 (Max. n ¼ 3,081) Mean Median Std. Mean Median Std. Mean Median Std. Mean Median Std. (1) (2) (3) (4 ) (5 ) (6 ) (7 ) (8) (9) (10) (11) (12) BUSY_SEASON t 0.643 1.000 0.479 0.652 1.000 0.477 0.618 1.000 0.486 0.656 1.000 0.475 LONG_DEBT t 0.180 0.087 0.300 0.158*** 0.086*** 0.218 0.174 0.090 0.267 0.192 0.087 0.339 CURRENT_LIABILITY t 0.052 0.006 0.117 0.030*** 0.003** 0.066 0.031 0.003 0.070 0.072 0.011 0.145 INVT_RATIO t 0.121 0.084 0.125 0.088*** 0.058*** 0.099 0.111*** 0.081*** 0.110 0.137 0.099 0.138 MODIFIED_OPN t 0.063 0.000 0.243 0.031*** 0.000*** 0.174 0.017** 0.000** 0.130 0.101 0.000 0.302 RESTRUCTURE t 0.029 0.000 0.168 0.032 0.000* 0.177 0.036 0.000 0.187 0.023 0.000 0.151 SPECIAL_ITEM t 0.686 1.000 0.464 0.717*** 1.000 0.451 0.727 1.000 0.445 0.651 1.000 0.477 FOREIGN t 0.240 0.000 0.427 0.286*** 0.000*** 0.452 0.296 0.000 0.457 0.191 0.000 0.393 NUM_SEG t 2.908 2.000 2.549 3.000* 1.000*** 2.822 3.058 1.000 2.731 2.786 2.000 2.332 AUDITOR_TENURE t 8.912 6.000 8.355 8.674*** 7.000*** 7.128 9.429 7.000 8.577 8.657 6.000 8.544 logAUDITOR_TENURE t 1.926 1.946 0.917 1.973*** 2.079*** 0.840 1.996 2.079 0.893 1.867 1.946 0.951 CUSTOMER_IMPT t 0.211 0.160 0.251 0.237** 0.160*** 0.416 0.201*** 0.160 0.139 0.209 0.150 0.236 REV_CYCLE_IMPT t 0.031 0.020 0.047 0.025*** 0.015*** 0.044 0.028*** 0.019*** 0.028 0.034 0.022 0.057 *, **, *** Denote significant at the 10 percent, 5 percent, and 1 percent levels, respectively, based on two-tailed t-(z-) test statistics for the diffe rences in the means (median) values between CHAIN_CITY . 0 and CHAIN_NATION . 0 in comparison with CHAIN_CITY ¼ 0 and CHAIN_NATION ¼ 0, respectively. aThe numbers of observations for MEET t for the full sample, the CHAIN_CITY ¼ 1 and CHAIN_NATION . 0 and the CHAIN_CITY and CHAIN_NATION ¼ 0 group are 2,391, 411, 840, and 1,140, respectively. Variables are winsorized at 1 percent and 99 percent to reduce the effect of outliers. See Appendix C for variable definitions. (continued on next page)
T ABLE 3 (continued) Panel B: P earson/Spea rman Correlation Matrix (max. n ¼ 2,391) V ariables (1) (2) (3) (4 ) (5 ) (6 ) (7 ) (8) (9) (1) j DACC t j 0.03 0.02 0.06 0.03 0.09 0.06 0.03 0.06 (0.07 ) (0.22) ( , 0.01) (0.08) (0.13) ( , 0.01) (0.07 ) ( , 0.01) (2) RESTATE t 0.01 0.02 0.08 0.02 0.15 0.03 0.02 0.01 (0.78) (0.28) ( , 0.01) (0.04 ) (0.42) (0.07 ) (0.13) (0.16 ) (3) MEET t 0.01 0.02 0.14 0.02 0.02 0.03 0.03 0.01 (0.51) (0.28) ( , 0.01) ( 0.02) (0.42) (0.07 ) (0.13) (0.71) (4 ) logAUDITFEE t 0.01 0.07 0.15 0.07 0.07 0.05 0.04 0.20 (0.90) ( , 0.01) ( , 0.01) (0.08) ( , 0.01) ( , 0.01) (0.06 ) ( , 0.01) (5 ) CHAIN_CITY t 0.05 0.01 0.01 0.08 0.33 0.01 0.06 0.20 ( , 0.01) (0.04 ) (0.08) (0.10) ( , 0.01) (0.65 ) ( , 0.01) ( , 0.01) (6 ) CHAIN_NATION t 0.04 0.01 0.02 0.08 0.25 0.05 0.08 0.15 (0.12) (0.59) (0.38) ( , 0.01) ( , 0.01) ( , 0.01) ( , 0.01) ( , 0.01) (7 ) IND_NATION t 0.03 0.03 0.01 0.04 0.02 0.05 0.23 0.40 (0.16 ) (0.07 ) (0.46 ) (0.03) (0.30) ( , 0.01) ( , 0.01) ( , 0.01) (8) IND_CITY t 0.04 0.03 0.01 0.04 0.09 0.08 0.23 0.27 (0.02) (0.13) (0.57 ) (0.05 ) ( , 0.01) ( , 0.01) ( , 0.01) ( , 0.01) (9) JOINT_IND t 0.02 0.07 0.02 0.20 0.12 0.15 0.40 0.27 (0.51) (0.71) (0.39) ( , 0.01) ( , 0.01) ( , 0.01) ( , 0.01) ( , 0.01) Two-tailed p-values for the Pearson (Spearman) correlation are presented in parentheses. The sample for this table is that used to test MEET as the proxy for audit quality. See Appendix C for variable definitions.
TABLE 4
The Association between Audit Quality and Supply Chain Auditor Knowledge
Independent Variables Pred. Sign Dependent Variable: jDACCtj (1) RESTATEt (2) MEETt (3) jDACCtj (4) RESTATEt (5) MEETt (6) Constantt ? 0.048*** 2.242*** 4.052*** 0.033 0.168*** 4.453*** (3.14) (8.21) (10.55) (1.19) (2.66) (5.26) CHAIN_CITYt 0.045** 0.358** 1.058** 0.043** 0.108*** 0.870** (2.12) (1.79) (1.84) (1.77) (2.32) (1.72) CHAIN_NATIONt 0.017 0.943 0.082 (0.89) (1.34) (0.22) IND_NATIONt 0.004 0.241 0.123 0.002 0.045 0.074 (0.43) (1.52) (0.63) (0.21) (1.54) (0.28) IND_CITYt 0.029*** 0.024 0.075 0.026 0.006 0.108 (2.32) (0.13) (0.33) (1.46) (0.17) (0.35) JOINT_INDt 0.015* 0.136 0.086 0.008 0.034 0.182 (1.45) (0.86) (0.45) (0.60) (1.18) (0.74) BIG_4t 0.027** 0.487*** 0.230 0.010 0.011 0.220 (2.28) (2.93) (0.83) (0.41) (0.21) (0.28) jTACCtj þ 0.140*** 0.032 (4.95) (0.97) TACCt þ 0.144 0.198 0.008 0.195 (0.69) (0.42) (0.16) (0.32) logATt ? 0.002 0.029 0.309*** 0.003 0.008 0.344*** (0.78) (0.82) (6.62) (0.86) (1.21) (5.89) CFOt 0.015 0.100 1.307 0.045 0.003 0.858 (1.03) (0.64) (1.51) (1.27) (0.06) (0.76) STD_CFOt þ 0.000 0.011** 0.005 0.000 0.001 0.001 (1.12) (1.94) (0.65) (0.89) (1.04) (0.11) LEVERAGEt þ 0.001 0.059 0.476* 0.029 0.014 0.683* (0.08) (0.31) (1.35) (1.08) (0.32) (1.62) ROAt þ 0.019 0.342 1.078 0.040 0.112* 1.256 (0.75) (1.15) (0.98) (0.76) (1.39) (0.92) LOSSt þ 0.015 0.118 0.807*** 0.017 0.029 0.653** (1.62) (1.01) (3.05) (1.22) (1.20) (2.05) MBt þ 0.000 0.016** 0.024* 0.001 0.003** 0.008 (0.22) (1.85) (1.38) (0.47) (1.66) (0.38) SALE_GROWTHt þ 0.004 0.061 0.538* 0.031* 0.007 0.569 (0.34) (0.45) (1.85) (1.62) (0.24) (1.53) RD_RATIOt þ 0.019 0.484 1.498* 0.056 0.114 2.126** (0.43) (0.89) (1.65) (0.93) (1.10) (1.96) LITIGATIONt ? 0.224*** 0.004 0.266 0.254*** 0.001 0.403 (16.27) (0.02) (1.09) (14.93) (0.04) (1.47) ALTMANt þ 0.002*** 0.012 0.038*** 0.004*** 0.004*** 0.044*** (2.96) (1.38) (2.69) (3.08) (2.62) (2.64) logAUDITOR_TENUREt ? 0.001 0.033 0.043 0.000 0.011 0.107 (0.27) (0.62) (0.57) (0.01) (0.90) (1.14) ABN_CU_logFEEt ? 0.006 0.007 0.050 0.010 0.016 0.072 (1.23) (0.10) (0.43) (1.63) (0.95) (0.47)
when CHAIN_CITYincreases by one standard deviation over its sample mean, holding all other
variables at their sample mean values.11
In contrast to the results for the association between CHAIN_CITY and audit quality, the
coefficients on CHAIN_NATION in the models of audit quality using jDACCj, RESTATE, and
MEETare not significant. This suggests that when the supplier and major customer’s audits are
conducted by different auditor offices, the audit quality benefits of auditing both the supplier and the major customer are lost. These results are consistent with our expectation that the effects of supply chain auditing are most pronounced at the city level.
To directly compare audit quality for supplier companies with auditors who have city- versus national-level supply chain knowledge (H1c), we focus on the subsample of companies with supply
chain auditor knowledge (i.e., those in whichCHAIN_CITY . 0 or CHAIN_NATION . 0) and
exclude those in whichCHAIN_CITY¼0 andCHAIN_NATION¼0. Table 4, Columns (4)–(6)
present results of these tests. The results support H1c and show that CHAIN_CITYis negatively
associated with levels of absolute discretionary accruals (p¼0.039), the likelihood of a restatement
(p¼0.010), and the likelihood of meeting or just beating analysts’ forecasts for supplier companies
(p¼0.045). Control variables, when significant, are generally consistent with predicted signs.
Table 5 presents analyses that examine the research question regarding the association between
auditor supply chain knowledge and audit fees. The results in Column (1) show thatCHAIN_CITY
is negatively and marginally significantly associated withlogAUDITFEE(p¼0.08). Economically,
this implies a 1.9 percent decrease in audit fees for a one standard deviation increase of TABLE 4 (continued) Independent Variables Pred. Sign Dependent Variable: jDACCtj (1) RESTATEt (2) MEETt (3) jDACCtj (4) RESTATEt (5) MEETt (6) Include industry and year dummy
Yes Yes Yes Yes Yes Yes
Clustered by company
Yes Yes Yes Yes Yes Yes
Observations 4,569 4,569 2,391 1,488 1,488 1,251
Model p-value ,0.001 0.02 ,0.001 ,0.001 ,0.001 ,0.001
Adjusted (Pseudo) R2
0.21 0.05 0.12 0.27 0.07 0.123
*, **, *** Denote significant at 10 percent, 5 percent, and 1 percent, respectively, based on a one-tailed test for variables with a directional expectation and two-tailed for variables with no directional expectation.
All variables are winsorized at 1 percent and 99 percent to reduce the effect of outliers.
One-tailed (two-tailed) t- (z-) test statistics are reported in parentheses for signed (unsigned) predictions. We include industry and year fixed effects, and the standard errors are adjusted based on firm-level clustering. The sample used for the regression models in Columns (4)–(6) excludes the supplier companies in whichCHAIN_CITY ¼0 andCHAIN_NATION¼0.
See Appendix C for variable definitions.
11
The probability is calculated based onebX0/(1þebX0) when we increase each independent variable by one standard deviation over the sample mean, holding all other variables at their mean values. For example, the coefficient on
CHAIN_CITYis0.358, its mean is 0.037, and it has a standard deviation of 0.113. We calculate the changes in
ebX0/(1þebX0) whenCHAIN_CITYis at the 0.037 level versus at the 0.037þ0.113¼0.15 level, while holding all other variables at their sample mean value.
TABLE 5
The Association between Audit Pricing and Supply Chain Auditor Knowledge
Independent Variables
Predicted Sign
Dependent Variable:logAUDITFEEt
(1) (2) Constantt ? 9.489*** 9.538*** (184.37) (98.98) CHAIN_CITYt ? 0.171* 0.155* (1.75) (1.74) CHAIN_NATIONt ? 0.187 (1.64) IND_NATIONt þ 0.033 0.074** (0.96) (1.80) IND_CITYt þ 0.169*** 0.185*** (4.13) (3.45) JOINT_INDt þ 0.106*** 0.216*** (3.08) (5.40) BIG_4t þ 0.041 0.098 (1.08) (1.25) BUSY_SEASONt þ 0.117*** 0.145*** (4.96) (4.89) logATt þ 0.496*** 0.467*** (63.30) (48.35) LONG_DEBTt þ 0.030 0.059 (0.79) (1.03) CURRENT_LIABILITYt þ 0.016 0.502*** (0.15) (2.40) INVT_RATIOt þ 0.240*** 0.602*** (2.48) (4.23) MODIFIED_OPNt þ 0.114** 0.159* (2.10) (1.70) ROAt 0.381*** 0.459*** (6.56) (5.21) LOSSt þ 0.075*** 0.074** (2.57) (1.91) STD_CFOt þ 0.001 0.003** (0.49) (2.02) SALE_GROWTHt þ 0.011 0.075* (0.32) (1.66) RESTRUCTUREt þ 0.145** 0.243*** (2.26) (3.25) SPECIAL_ITEMt þ 0.144*** 0.138*** (5.68) (4.26) FOREIGNt þ 0.229*** 0.180*** (8.49) (5.72) NUM_SEGt þ 0.109*** 0.048*** (22.59) (6.91) RD_RATIOt þ 0.274** 0.209 (2.10) (1.30) logAUDITOR_TENUREt þ 0.096*** 0.083*** (7.76) (5.02)
CHAIN_CITYover the mean.12In contrast, the coefficient onCHAIN_NATIONis not statistically significant. We also examine the association between audit fees and auditor supply chain knowledge at the city level versus the national level by excluding suppliers audited by auditors
without supply chain knowledge. The results in column (2) show that CHAIN_CITY remains
marginally negative (p¼0.08). Taken together, the results in Tables 4 and 5 suggest that the
synergy gained by auditors with supply chain knowledge at the city level not only improves the effectiveness of auditing (in terms of higher audit quality), but also improves the efficiency of auditing (in terms of marginally lower audit fees). These results are interesting in the sense that they imply that auditors are willing to share the efficiency gain from their supply chain knowledge with their clients via a fee reduction. This result is in contrast with the fee premium on auditors’ industry
specialization, as shown by the positive and significant coefficients onIND_CITY(p,0.01) and
JOINT_IND (p , 0.01), which are consistent with Francis et al. (2005). Other variables, when
significant, are generally consistent with predicted signs.13
TABLE 5 (continued)
Independent Variables
Predicted Sign
Dependent Variable:logAUDITFEEt
(1) (2)
ABN_CU_logFEEt þ 0.063*** 0.052**
(3.44) (2.20)
Include industry and year dummy
Yes Yes
Clustered by company Yes Yes
Observations 4,569 1,488
Adjusted R2 0.75 0.76
*, **, *** Denote significant at 10 percent, 5 percent, and 1 percent, respectively, based on a one-tailed test for variables with a directional expectation and two-tailed for variables with no directional expectation.
All variables are winsorized at 1 percent and 99 percent to reduce the effect of outliers.
One-tailed (two-tailed) t- (z-) test statistics are reported in parentheses for signed (unsigned) predictions. We include industry and year fixed effects, and the standard errors are adjusted based on firm-level clustering. The sample used for the regression models in Column (2) excludes the supplier companies in whichCHAIN_CITY¼0
andCHAIN_NATION¼0.
See Appendix C for variable definitions.
12
As the dependent variable is the natural log of the audit fee, the fee discount is calculated as a one standard deviation change of the independent variable on the percentage change of the dependent variable, which is (e(0.171)30.112 (the standard deviation)
1)¼1.9 percent. 13
Regarding the lack of significance onlogAUDITOR_TENUREin Table 4, we note that insignificant results for auditor tenure in audit quality models do exist in the literature (e.g., J.Myers, L. Myers, and Omer 2003). We also explored whether there is an interaction between auditor tenure (logAUDITOR_TENURE) and the measures of auditor supply chain knowledge or industry specialization at the city level or the national level. Untabulated results show that the main effects on audit fees, presented in Table 5, for these variables remain essentially the same and there exists a positive association between the interaction of auditor tenure and auditors’ supply chain knowledge at the city level (p
,0.01), and the interaction of auditor tenure with industry specialization at the city level and at the joint industry specialization level (p,0.01 and p¼0.03, respectively). These results imply that the initial fee discount associated with supply chain knowledge is lower for long-tenured engagements, and that the initial pricing premium associated with industry specialization is higher for long-tenured engagements.
