THE ACCOUNTING REVIEW
Vol. 93, No. 3
May 2018
pp. 59–82
American Accounting Association
DOI: 10.2308/accr-51889
The Accounting Review 2018.93:59-82.
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Supplier Internal Control Quality and the Duration of
Customer-Supplier Relationships
Andrew M. Bauer
University of Waterloo
Darren Henderson
Wilfrid Laurier University
Daniel P. Lynch
University of Wisconsin–Madison
ABSTRACT: Internal controls influence information quality, thus affecting the ability of supply chain partners, who
rely on collaborative systems of information sharing, to reliably contract. Using SOX-related internal control
assessments as a proxy for internal control quality and U.S. GAAP-mandated major customer disclosures, we find
that supplier internal control quality influences supply chain relationship duration. Specifically, our evidence
demonstrates that: (1) poor internal control quality increases the likelihood of subsequent customer-supplier
relationship termination; (2) timely control weakness remediation attenuates termination likelihood; and (3)
weaknesses affecting customer contracting drive the effect of internal control quality on relationship termination. Our
results control for supplier operational quality and performance, and are robust to propensity score matching
techniques, controls for reverse causality, and alternative proxies for relationship termination and internal control
quality. Overall, our findings are consistent with customers viewing strong supplier controls as important, albeit
overlooked, contracting elements with significant implications for supply chain relationships.
Keywords: internal control; supply chain; contracting; information quality.
I. INTRODUCTION
I
n this study, we examine the effect of supplier internal control quality on customer-supplier relationships and ask two
research questions: (1) Does poor supplier internal control quality increase the likelihood of customer-supplier relationship
termination? (2) Do subsequent improvements in internal control quality reduce the likelihood of termination? The nature
and economic consequences of interdependent supply chain relationships is the subject of longstanding scholarly interest (e.g.,
Galbraith 1952; Porter 1974), and prior literature documents that a company’s interactions with its supply chain partners affect
its operational and financial performance (Baiman and Rajan 2002b; Hertzel, Li, Officer, and Rodgers 2008; Fee and Thomas
2004; Johnstone, Li, and Luo 2014). We aim to incorporate an understanding of internal control quality on supply chain
relationships. Kinney (2000) argues that the internal control quality of a firm affects the value chain of customers and suppliers
because it affects each member’s future welfare from the relationship. Theoretical models (Baiman and Rajan 2002a)
demonstrate that supply chains depend on the exchange of reliable information. Moreover, Baiman and Rajan (2002a) call for
studies to investigate mechanisms that mitigate opportunism and ensure the reliable exchange of information.
We conjecture that internal control is one such mechanism and, thus, is important within the supply chain for several
reasons: (1) information asymmetry is a problem for customer-supplier relationships (Costello 2013), and the information
We gratefully acknowledge helpful comments from Elaine G. Mauldin (editor), two anonymous reviewers, Amanda Convery, Sean Dennis, Yu Hou,
Andy Imdieke, Karla Johnstone, Natalia Kochetova-Kozloski, Stacie Laplante, Logan Steele, Brian Mayhew, Miguel Minutti-Meza, Janet Morrill, Jeff
Pittman, Gord Richardson, Bridget Stomberg, Terry Warfield, Dave Weber, and workshop participants at the University of Wisconsin–Madison, the 2015
AAA Annual Meeting, and the 2016 CAAA Annual Conference.
Supplemental material can be accessed by clicking the link in Appendix B.
Editor’s note: Accepted by Elaine G. Mauldin, under the Senior Editorship of Mark L. DeFond.
Submitted: July 2016
Accepted: August 2017
Published Online: September 2017
59
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60
Bauer, Henderson, and Lynch
advantage that suppliers have over their customers leads to a demand from customers for truthful information sharing (Cen,
Dasgupta, Elkamhi, and Pungaliya 2016); (2) extending from a debt contracting perspective (e.g., Leftwich 1983), major
customers require accounting information from suppliers that signals their ability to meet required deliveries with products of
the appropriate specifications. Indeed, based on conversations with the controller of a large consumer products firm, customers
will monitor the internal control systems of key suppliers; and (3) prior literature documents that firms with internal control
weaknesses (ICWs) have lower investment efficiency due to reduced information quality (Cheng, Dhaliwal, and Zhang 2013),
and this lack of efficiency will likely affect the ability of supply chain participants to contract. We conjecture that poor internal
control quality reduces information quality, which customers view as a threat to the trust, cooperation, and investment
efficiency within a supply chain relationship. This reduction in information quality decreases suppliers’ ability to reliably
contract and increases the probability that customer-supplier relationships will be terminated.1
Alternatively, supplier internal control quality might not significantly influence the probability that customer-supplier
relationships will be terminated, for several reasons. Customers could substitute other governance mechanisms, such that
internal control quality would have little influence on relationship duration. Further, if customers influence internal controls
through direct monitoring of suppliers, then such monitoring could prevent supplier internal control quality from reaching a
critically poor level or reduce the value of information contained in public internal control disclosures. In fact, relationship
termination may occur prior to the disclosure of such reports. Thus, whether supplier internal control quality provides
incremental information that affects relationship duration is an open empirical question.
To examine our research questions, we use data on major customers from the Compustat Segment file to build a
comprehensive set of two-member supply chain dyads of customers and suppliers. We use Audit Analytics to identify the
disclosure of ICWs under the Sarbanes-Oxley Act, Sections 404 (SOX 404) and 302 (SOX 302), which are material negative
events (Johnstone, Li, and Rupley 2011), and an observable proxy for poor internal control quality.2 As we test the association
between disclosed ICWs and the probability of customer-supplier relationship terminations, our sample is exclusively postSOX and runs from 2004–2014.
We base our empirical strategy on the use of hazard models where the dependent variable is an indicator for the customersupplier relationship ending in the subsequent period. Consistent with prior literature, we define relationship termination as a
customer-supplier relationship that falls below the 10 percent of sales threshold prescribed by Statement of Financial
Accounting Standards (SFAS) 131 and, therefore, is no longer reported (Fee, Hadlock, and Thomas 2006; Raman and Shahrur
2008). Across our empirical specifications, which control explicitly for supplier operational quality and performance, we find
that supplier ICWs are associated with a significant increase in the probability of a relationship ending. The marginal effect
from the logistic regression suggests that the probability of relationship termination increases from 9.5 percent without an ICW
to 13.7 percent with an ICW. Overall, these findings demonstrate the powerful economic consequences of poor internal control
quality on the ability to contract with key customers.
We then examine whether remediation of disclosed supplier ICWs affects the probability of relationship termination. Our
results suggest that the remediation of supplier ICWs significantly reduces the likelihood of relationship termination, which
implies that customers are more willing to maintain relationships with suppliers who promptly address material control issues.
This result also corroborates our earlier finding of the association between ICWs and relationship termination by demonstrating
that increases in internal control quality also affect the probability of relationship termination.
We conduct several robustness tests that strengthen confidence in our primary results. First, we confirm that the
associations among ICWs, their remediation, and relationship termination are robust to propensity score matching, mitigating
concerns of bias due to functional form misspecification. Second, we find that only ICWs disclosed at time t are positively
associated with relationship termination at time tþ1, which helps rule out the potential for reverse causality. Third, we use a
binary construct of sales decreases to major customers in year tþ1 as an alternative customer-supplier relationship measure and
find consistent results. Fourth, we use restatements as an alternative measure that captures supplier internal control quality, and
find that our results are robust to using this measure.
Finally, in additional analyses, we document that ICWs related to inventory tracking, which are likely to directly affect
customer relationships, have the strongest correlation with relationship termination. Further, using a multivariate model with
ICW types, we find that ICWs linked to customer-related accounts (inventory tracking, revenue, and receivables) are the
1
2
The literature on contract theory for inter-firm trade and financing is well established; we refer readers to a review by Christensen, Nikolaev, and
Wittenberg-Moerman (2016) for a detailed summary, particularly for incomplete contracting. In contrast, the implications of this theory for supply
chain relationships are less established (Holmström and Roberts 1998) and, thus, help to motivate our research setting.
These disclosures relate to the effectiveness of internal control over financial reporting (ICFR). However, as is argued and supported by evidence in the
literature (e.g., Cheng et al. 2013; Feng, Li, McVay, and Skaife 2015; Gallemore and Labro 2015; Bauer 2016), effective ICFR is associated with, and
has potential spillover effects for, improving the quality of information used to make operational decisions, particularly as some controls serve both
financial and operational roles.
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Supplier Internal Control Quality and the Duration of Customer-Supplier Relationships
61
primary drivers of relationship termination. Overall, we believe these additional analyses increase the internal validity of our
study.
This study contributes to the literature in at least three ways. First, prior supply chain literature tends to focus on customers
exercising buyer power to pressure profitable suppliers into advantageous pricing terms (Schumacher 1991; Snyder 1996), with
limited empirical evidence on determinants of relationship termination (Fee et al. 2006; Raman and Shahrur 2008; Cen, Chen,
Huo, and Richardson 2015; Hollmann, Jarvis, and Bitner 2015). We help fill this gap in the literature, and respond to calls for
more supply chain research on the reliable exchange of information (Baiman and Rajan 2002a) and the identification of specific
‘‘negative critical incidents’’ affecting relationship termination (Van Doorn and Verhoef 2008). Second, our evidence suggests
that internal control quality plays an important information role, incremental to firm operational quality and performance, in the
contracting between customers and suppliers. These results have implications for studies of contract efficiency, complementing
research that focuses on aspects of contracts between supply chain buyers and sellers, such as financial covenants (Costello
2013) and the effects of customer concentration on inventory management and operational efficiency (Gavirneni, Kapuscinski,
and Tayur 1999; Lee, So, and Tang 2000). Third, we also demonstrate that internal control quality is important to a key
stakeholder group (i.e., trade partners) beyond the firm or its investors by illustrating the consequences of poor internal control
quality on customer-supplier relationships (Kinney 2000). Considering the significant investments made in a typical supply
chain (Raman and Shahrur 2008), the economic implications of termination are substantial.
Our findings also provide insights for managers and practitioners. Given the significant economic consequences of poor
internal controls that we document, managers should be aware that internal controls affect their ability to contract with key
customers. Additionally, if weaknesses in internal control are remediated in a timely fashion, then the improvements in internal
control quality can reduce the probability that customers will terminate the relationship. Relatedly, practitioners can
communicate the effects of poor internal controls to their clients, and the importance of timely remediation to salvage key
customer relationships.
Our paper proceeds as follows. In Section II, we provide motivation and background for our hypotheses. In Section III, we
describe our research methodology. In Section IV, we discuss our empirical results. In Section V, we discuss additional
analyses, and in Section VI, we conclude.
II. MOTIVATION AND HYPOTHESES DEVELOPMENT
Contracting and Internal Control Quality
Research suggests that over the past several decades, supply chains have become more concentrated (e.g., Choi and Krause
2006; Patatoukas 2012). This trend is due to the belief that collaborative partnerships provide greater competitive advantage to
the parties than traditional adversarial models of competition (O’Neal 1989). Under this modern view of interdependent supply
chain relationships, an integrated system of information sharing allows both the supplier and customer to reap net benefits from
the relationship (Kalwani and Narayandas 1995; Kumar 1996; Kinney and Wempe 2002; Vickery, Jayaram, Droge, and
Calantone 2003; Arend and Wisner 2005). Indeed, recent research finds that suppliers with high customer sales concentration
achieve higher accounting rates of return, consistent with this modern view (Patatoukas 2012; Matsumura and Schloetzer
2018).
However, the benefits of collaboration are contingent upon the ability of supply chain members to reliably contract with
one another. Baiman and Rajan (2002a) argue that the supply chain relationship is predicated on the exchange of information
relevant to trade. Customers are concerned about asymmetric information regarding product quality held by suppliers, and
suppliers are concerned about asymmetric information regarding product demand held by customers (Costello 2013).
Furthermore, when each party’s actions are not perfectly observable, the risk of opportunistic behavior increases (Holmström
1979), such as the use of discretion in accounting information to induce investments in relationship-specific assets (Raman and
Shahrur 2008).
For several reasons, SOX-related internal control disclosures provide a unique opportunity to test whether the internal
control quality of suppliers affects the duration of customer-supplier relationships. First, SOX disclosures are an observable
proxy for the quality of a firm’s internal controls that the prior literature shows are associated with the quality of firm-specific
information. For example, Feng et al. (2015) find that effective internal controls provide high-quality information that leads to
increases in inventory turnover and reductions in impairment. Next, a firm’s investment in internal control can provide a means
by which to reduce information asymmetry between a firm and its stakeholders (Gallemore and Labro 2015), such as supply
chain participants. As Kinney (2000) alludes to, customers and suppliers may view transaction conditions more favorably if
they are assured of the quality of information that is shared among supply chain members. Furthermore, Baiman and Rajan
(2002a) argue that the risk of a supplier misappropriating information is not completely contractible, which increases the
importance of other mechanisms that assure the quality of information, such as internal controls.
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Second, internal controls help achieve various entity objectives, including operational, financial, and strategic decisionmaking (Committee of Sponsoring Organizations of the Treadway Commission [COSO] 2013). While SOX disclosures reflect
internal controls over financial reporting, prior research demonstrates spillover to nonfinancial outcomes (e.g., Cheng et al.
2013; Feng et al. 2015; Gallemore and Labro 2015; Bauer 2016). Prior supply chain research examines financial performance
outcomes (e.g., Galbraith and Stiles 1983; Lanier, Wempe, and Zacharia 2010; Patatoukas 2012; Su, Zhao, and Zhou (2014);
Kim and Henderson 2015). We conjecture that internal control quality also affects a supplier’s ability to reliably contract with
key customers. Third, SOX reporting standards are formal and uniform, which enhances the comparability of empirical
measures based on SOX disclosures.
Internal Control Quality and Customer-Supplier Relationships
Relationships between customers and suppliers are important because of direct economic ties and mutual dependence. In
the supply chain context, major customers require information from suppliers about their ability to meet required deliveries
with products and services of the appropriate quantity and quality. Information asymmetry is a key factor in customer-supplier
contracting due to adverse selection (Akerlof 1970; Jensen and Meckling 1976; Costello 2013). This information asymmetry,
where the supplier has an information advantage over its customers, leads to a demand from customers for truthful information
sharing (Cen et al. 2016), particularly in relationships where repeated transactions are expected (Gulati 1995; Holmström and
Roberts 1998). Consequently, major customers will demand a commitment to strong internal controls that engender quality
information from suppliers.
Strong internal controls ensure that the information is relevant and free from error, and this reliable information generated
by well-controlled suppliers improves contracting efficiency (Christensen et al. 2016) and reduces costs associated with direct
monitoring.3 Additionally, customers could rely on strong supplier internal controls to ensure that goods and services provided
are of the appropriate quality and quantity. In the absence of strong internal controls, reliable information, a key contracting
demand between supplier and customer, will be lower.
Although customers can monitor key suppliers, suppliers do possess private information about internal control quality due to
the inability to achieve perfect monitoring (Holmström 1979). Furthermore, the existence of imperfect monitoring can lead to
opportunistic behavior within the supply chain (Gulati 1995; Raman and Shahrur 2008; Costello 2013). Incomplete contract
theory argues that accounting information provides an important signal about the threat of opportunism (e.g., hold-up concerns)
and, thus, acts to improve contract efficiency (Christensen et al. 2016). The strength of a firm’s system of internal controls
improves the reliability of accounting information, which can improve contract efficiency. Strong internal controls also improve
investment efficiency (Cheng et al. 2013), which can improve both the ability to contract and the ability to subsequently meet
contract demands. Relatedly, as the risk of opportunistic behavior and investment inefficiency increases, customers are more likely
to terminate the relationship because of these contracting risks. Consistent with this argument, prior studies document shorter
supply chain relationships when suppliers or customers engage in opportunistic income-smoothing behavior (Raman and Shahrur
2008) or when suppliers withhold negative information about lawsuits (Cen et al. 2015). Thus, we conjecture that ICWs will be
positively associated with relationship termination, and propose the following hypothesis, stated in alternative form:
H1: Poor supplier internal control quality increases the likelihood of customer-supplier relationship termination.
However, it is possible that internal control quality may not be a primary consideration for major customers, which implies
that the duration of supply chain relationships will not vary with supplier internal control quality. Furthermore, if customers
substitute other external monitors for supplier internal control quality, then any signals about internal control quality could be
relatively less valuable. Also, anecdotal evidence suggests that some major customers directly monitor key suppliers’ internal
control systems, which could provide advance information about suppliers’ inherent internal control quality. Such monitoring
could prevent supplier internal control quality from reaching a critically poor level or result in relationship termination prior to
the disclosure of an ICW.
Improvement in Internal Control Quality and Supply Chain Duration
Next, we consider how increases in internal control quality influence the duration of supply chain relationships. Prior
supply chain literature argues that positive and negative signals accumulate to influence the decision to continue a relationship
3
Prior research argues that transaction cost economics, as introduced by Williamson (1979, 1985), is a primary theory in explaining supply chain
integration (e.g., Gulati 1995; Zhao, Huo, Flynn, and Yeung 2008; Lanier et al. 2010). Specifically, this research posits that four types of transaction
costs—search, contracting, monitoring, and enforcement—are reduced in supply chains. Our focus on internal control quality is consistent with
reductions in contracting and monitoring costs.
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Supplier Internal Control Quality and the Duration of Customer-Supplier Relationships
63
(Hollmann et al. 2015). If effective internal controls provide reliable information, then remediating conditions that contribute to
poor internal control quality should improve the reliability of information. Thus, suppliers that address internal control issues
could salvage relationships by providing a positive signal to their customers regarding the future quality of information and
ability to rely on supplier controls. Customers will likely be aware of these investments due to private information sharing and
because the Securities and Exchange Commission (SEC) requires that firms disclose planned remediation efforts for disclosed
ICWs in their 10-K filings.
Prior research documents significant benefits from the remediation of underlying internal control issues; specifically,
Ashbaugh-Skaife, Collins, Kinney, and LaFond (2008) find that firms undertaking remediation have subsequently lower
discretionary accruals. Additionally, remediation of ICWs is associated with nonfinancial outcomes, including improved
inventory management (Feng et al. 2015), investment efficiency (Cheng et al. 2013), and tax avoidance (Bauer 2016; Lynch
2016). Consistent with prior research that suggests remediation efforts improve information quality, we propose the following
hypothesis, stated in alternative form:
H2: Remediation of poor supplier internal control quality reduces the likelihood of customer-supplier relationship
termination.
However, remediation may not reduce the likelihood of relationship termination for several reasons. First, the severity of
the underlying issues could lead a customer to terminate the relationship regardless of remediation efforts. Second, when a
customer learns the details of the planned remediation effort, they may not believe the remediation plan is credible. Third, even
if timely remediation significantly improves information quality, it may not fully offset the negative implications of the initial
lack of internal control quality.
III. METHODOLOGY
Sample
To test our hypotheses, we use all U.S. public firms with the necessary data to identify both ICWs and major customersupplier relationships. Since ICW disclosures under SOX are available starting in 2004, our sample stretches from 2004 to 2014
and includes both accelerated and non-accelerated filers. Our data include 2015, but our sample ends in 2014, as we measure
subsequent relationship termination at time tþ1. In our setting, we observe the disclosure of an ICW. As discussed in Section II,
we believe disclosed ICWs are the best publicly available proxy for our theoretical construct of internal control quality.
We identify specific customers within supply chains by matching customer names to firm names in Compustat. SFAS 131,
para. 39 requires that firms identify when a single customer represents more than 10 percent of sales, and SEC regulations
require firms to disclose the identities of such customers. We match disclosed customer names to Compustat identifiers by first
parsing the disclosed customer names. We then investigate the remaining unmatched customer names by manually searching
for a customer name match among all U.S. firms within Compustat. We match suppliers to ICW data from Audit Analytics and
obtain control variable data from Compustat.
Table 1 outlines the steps in determining our final sample. As we are testing relationships, we seek to identify each
supplier’s relationships with major customers (12,825 observations). We then exclude observations where either the supplier or
the major customer ceases to exist in the subsequent year (i.e., due to bankruptcy, acquisition, etc.), as such relationship
cessations are not due to choice. Next, we exclude observations with missing data for our primary models, which reduces the
sample to 8,291 observations. Finally, we choose each supplier’s most significant major customer for each firm-year
determined by supplier sales percentage, to include each supplier only once in a given year. This choice mitigates a lack of
independence among observations included in our empirical regressions (i.e., multiple firm-year observations from the same
supplier).4 Our final sample includes 5,166 observations with 1,740 customer-supplier dyads, 1,233 unique suppliers, and 562
unique customers.
The Effect of Internal Control Quality on Customer-Supplier Relationships (H1)
To examine the association between internal control quality and the duration of customer-supplier relationships, we
employ a hazard design that models the probability of a relationship ending. Ideally, we would observe the relationship ceasing;
4
In untabulated analyses, we find that our results are robust to (1) including each of our suppliers’ major customers in the sample, or (2) re-specifying our
dependent variable as equal to 1 if a relationship with any major customer ceases in year tþ1 (and using customer sales weighted averages for the
customer-level control variables). Specifically, in each analysis, we find results consistent with subsequent Tables 5 and 6, supporting our primary tests
of H1 and H2.
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TABLE 1
Sample Selection
# of
Observations
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Observations available in merged Compustat-Audit Analytics database for 2004–2014
Merge with and retain supplier firm-year observations disclosing identifiable major customers
43,898
(31,073)
Supplier firm-year observations with identifiable major customers
Exclude observations where supplier or customer firm ceases to exist in the next fiscal year
Exclude observations with missing variable data
12,825
(2,391)
(2,143)
Supplier firm-year observations including all major customers
Keep customers to which the largest amount of sales are made per supplier-year
8,291
(3,125)
Final Sample
5,166
however, we can observe only when a customer relationship falls below the 10 percent sales threshold prescribed by SFAS 131.
We take a relationship falling below this threshold as ceasing, consistent with prior accounting literature (Raman and Shahrur
2008) and the broader supply chain literature that considers both full and partial reductions in relationship transactions in its
concept of ‘‘customer defection’’ (Hollmann et al. 2015). Using logistic regression (Logit), as well as the Cox proportional
hazard model (Cox) and the accelerated failure time model assuming a Weibull distribution (Weibull), we estimate the effect of
the disclosure of an ICW, our proxy for poor internal control quality, on the probability of a relationship ending for individual
customer-supplier relationships. For expositional purposes, we present the Logit model as follows:
Rel Stopi;tþ1 ¼ b0 þ b1 ICWi;t þ kðRelationship ControlsÞi;t þ lðSupplier General ControlsÞi;t
þ mðSupplier Operations ControlsÞi;t þ uðSupplier Performance ControlsÞi;tþ1
þ qðCustomer General ControlsÞi;t þ sðIndustryÞi þ tðYearÞt þ ei;t
ð1Þ
The dependent variable (Rel_Stop) is an indicator variable equal to 1 if the customer-supplier relationship is terminated in the
following fiscal year (i.e., year tþ1), and 0 otherwise. For purposes of the Cox and Weibull models, we have censored data,
where Rel_Stop equal to 1 represents an observation with failure (i.e., terminated; uncensored), and Rel_Stop equal to 0, for all
firm-years, represents an observation without failure (i.e., unterminated; right-censored). We measure time to failure, or time at
risk, beginning in the year prior to the first year of our sample period (i.e., 2003, before SOX).5 We use all three approaches to
triangulate evidence and ensure that our results are not sensitive to varying assumptions about the baseline hazard rate.
Our primary variable of interest is ICW, which is equal to 1 if the supplier discloses a Section 404 or Section 302 material
weakness in internal controls during the fiscal year. A positive and significant coefficient on ICW is consistent with weak
internal controls contributing to the termination of customer-supplier relationships in the subsequent year (H1).
In Model (1), we include vectors of variables to control for other factors that could affect relationship duration.
Specifically, we include vectors for: (1) relationship variables, (2) supplier and customer general variables, (3) supplier
operational variables, and (4) supplier performance variables. The relationship variables control for effects of customer
concentration (CustConc) and supplier concentration (SuppConc). We also control for supplier bargaining power (MktShare)
and for the length of the customer-supplier relationship (Tenure; Logit model only).
The supplier and customer general variables control for firm characteristics that influence customer-supplier relationships
in prior research (e.g., Fee et al. 2006; Raman and Shahrur 2008). Specifically, we control for supplier (customer) firm size by
including Size (CSize), firm age by including Age (CAge), research and development expenditures by including RD (CRD), and
negative free cash flow by including NegFCF (CNegFCF).
Next, supplier operational variables control for underlying supplier problems that could affect relationship duration and be
correlated with ICWs, leading to erroneous inferences about the association between ICWs and relationship duration.
5
Arguably, the introduction of SOX regulation is a shock to the process by which the system of internal control is assessed, maintained, and assured for
all public firms. This shock supports the measurement of time to failure in the Cox and Weibull models from the advent of SOX. The logistic regression
is unaffected by this choice. Furthermore, in untabulated analysis, we confirm that our results are robust to measuring time to failure from the year each
customer-supplier relationship is first identified in the Compustat SFAS 131 dataset.
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Supplier Internal Control Quality and the Duration of Customer-Supplier Relationships
65
Specifically, we include the following operational controls: inventory turnover (Inv_TO); inventory holding period (IHld); fixed
asset turnover (PPE_TO); days of accounts receivable, accounts payable, and inventory (Days_AR, Days_AP, and Days_Inv);
capital expenditure intensity (Capex); and profit and gross margins (PM and GM). These variables control for the operational
performance of the supplier (Patatoukas 2012; Feng et al. 2015; Matsumura and Schloetzer 2018).
Additionally, we control for supplier performance in year tþ1. Feng et al. (2015) and Su et al. (2014) demonstrate that
ICWs reduce future performance and sales growth, respectively. These performance reductions may influence customer
relationship duration independent of ICWs. To control for these effects, we include the following performance variables
measured in year tþ1: return on assets (ROA), change in return on assets (DROA), and sales growth (Sales_Growth).6
Finally, we include industry fixed effects in all duration models, and year fixed effects in the Logit model. We employ
robust standard errors clustered by supplier throughout our analyses.7 In Appendix A, we define all variables used in the
duration models.
The Effect of Remediation on Customer-Supplier Relationships (H2)
To examine the association between improvements in internal control quality from remediation and relationship duration,
we modify Model (1) to substitute the variables Weak and Fixed for ICW, consistent with the remediation model of AshbaughSkaife et al. (2008). Weak is equal to 1 if a supplier discloses an ICW in the current or prior year, and 0 otherwise. Fixed is
equal to 1 if the supplier discloses an ICW in the prior year (i.e., year t1), but does not disclose an ICW in the current year
(i.e., year t), and 0 otherwise. Fixed represents an interaction term between Weak and a dummy variable equal to 1 if a firm had
an ICW previously, but no weakness presently. Consequently, for suppliers that remediate their ICWs, Fixed equals 1. This
ensures that potential relationship termination is measured after the year that remediation occurs (i.e., in year tþ1). We estimate
the following regression model to test H2:
Rel Stopi;tþ1 ¼ b0 þ b1 Weaki;t þ b2 Fixedi;t þ kðRelationship ControlsÞi;t þ lðSupplier General ControlsÞi;t
þ mðSupplier Operations ControlsÞi;t þ uðSupplier Performance ControlsÞi;tþ1
þ qðCustomer General ControlsÞi;t þ sðIndustryÞi þ tðYearÞt þ ei;t
ð2Þ
Our primary variable of interest is Fixed; a negative and significant coefficient on Fixed is consistent with the remediation
of poor internal control quality reducing the probability of customer-supplier relationship termination (H2). Furthermore, we
test whether the combined estimate of Weak and Fixed is significantly greater than zero to determine whether remediation fully
attenuates the association between ICWs and relationship termination. We continue to use the control variables from Model (1).
IV. RESULTS
Descriptive Statistics and Correlations
We provide sample composition details by fiscal year and industry in Table 2. Consistent with prior research, disclosed
ICWs are more frequent in the early half of our sample period. In Table 3, we provide descriptive statistics for the variables
used in the Logit and hazard model analyses that predict the probability of relationship termination. We observe that 13.4
percent of customer-supplier relationships end in the following year (Rel_Stop).8 With respect to our variables of interest,
suppliers report ICWs in 10.4 percent of firm-years (i.e., ICW ¼ 1 in 536 of the 5,166 sample firm-years) and, in those firmyears, report an average of 3.9 weaknesses (with a range of one to 12 reported weaknesses; untabulated). Of control
weaknesses, 194 are remediated in the following year (Fixed). For control variables, we note that, on average, the major
customer represents 19.7 percent of total supplier sales (CustConc), which represents an average of 2.9 percent of customer cost
of goods sold (SuppConc). Customer-supplier relationships last approximately six years, on average, and stretch from one to 37
years (Tenure). Finally, customers are much larger (CSize versus Size) and older (CAge versus Age) than suppliers (untabulated
t-test p-values , 0.01), consistent with prior literature (Patatoukas 2012).
6
7
8
In untabulated analysis, we include supplier stock returns in years t and tþ1 to control for other aspects of performance not captured by ROA and sales
growth. Although data availability limits our sample observations to 4,624 for this analysis, we find consistent results to those presented in Tables 5 and
6.
We find consistent results for our subsequent analyses if we cluster standard errors by both supplier and fiscal year.
Some relationships reported as significant have customer sales that are less than 10 percent of total sales. Our core results, reported in Tables 5 and 6,
are robust to either excluding these ‘‘low sales’’ observations from the analysis or coding them as additional terminations.
The Accounting Review
Volume 93, Number 3, 2018
Bauer, Henderson, and Lynch
66
TABLE 2
Sample Composition
Panel A: Time Distribution of Observations and ICWs
Observations
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Fiscal Year
Frequency
ICWs
%
Frequency
%
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
281
434
464
506
524
524
522
494
510
506
401
5.4
8.4
9.0
9.8
10.1
10.1
10.1
9.6
9.9
9.8
7.8
52
74
85
79
60
29
19
32
35
30
41
9.7
13.8
15.9
14.7
11.2
5.4
3.5
6.0
6.5
5.6
7.7
Total
5,166
100.0
536
100.0
Panel B: Industry Distribution of Observations
Observations
Industry (One-Digit SIC)
ICWs
Frequency
%
Frequency
%
0–1 (Agriculture, mining, oil, and construction)
2 (Food, tobacco, textiles, paper, and chemicals)
3 (Manufacturing, machinery, and electronics)
4 (Transportation and communications)
5 (Wholesale and retail)
6 (Financial institutions, insurance, and real estate)
7 (Services)
8–9 (Health, legal and educational services, and other)
648
1,116
2,109
264
193
211
547
78
12.6
21.6
40.8
5.1
3.7
4.1
10.6
1.5
57
93
244
21
21
12
74
14
10.6
17.4
45.5
3.9
3.9
2.3
13.8
2.6
Total
5,166
100.0
536
100.0
Table 4 displays pairwise correlations for regression variables from Models (1) and (2). For brevity, we tabulate Pearson
correlations only.9 Relationship termination in the subsequent period is significantly positively correlated with disclosed ICWs
(ICW and Weak) and significantly negatively correlated with remediated ICWs (Fixed) (p-values , 0.05), providing
preliminary evidence consistent with H1 and H2.
Multivariate Results—H1 (Effect of Internal Control Quality on Supply Chain Relationships)
Table 5 reports the results of estimating Model (1) using the Logit (Column (1)), Cox (Column (2)), and Weibull (Column
(3)) models. In all models, consistent with the predictions of H1, we find a positive and statistically significant association
between supplier ICWs and the probability of customer-supplier relationships ending in the following year (p-values , 0.01).
The hazard ratios imply that poor internal control quality increases the risk of relationship termination from one year to the
next, by a factor of 1.52 (Logit), 1.49 (Cox), and 1.59 (Weibull) (untabulated).10 Furthermore, the marginal effect from the
9
10
In our primary regression models, we find that multicollinearity is of limited concern because individual variance inflation factors (VIFs) never exceed
10, with average VIFs between 2.5 and 3.
Using a likelihood ratio test, we also find that ICWs significantly increase the explanatory power of each model (p-values , 0.01).
The Accounting Review
Volume 93, Number 3, 2018
Supplier Internal Control Quality and the Duration of Customer-Supplier Relationships
67
TABLE 3
Descriptive Statistics for Relationship Duration Models
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n
Mean
Dependent Variable
Rel_Stop
5,166
0.134
Key Independent Variables
ICW
5,166
0.104
Weak
5,166
0.141
Fixed
5,166
0.038
ICW_Customer
5,166
0.049
ICW_Other
5,166
0.055
Relationship Variables
CustConc
5,166
0.197
SuppConc
5,166
0.029
MktShare
5,166
0.089
Tenure
5,166
5.85
Supplier Variables—General
Size (raw)
5,166
3,897.0
Age
5,166
19.1
RD
5,166
0.055
NegFCF
5,166
0.299
Supplier Variables—Operational
Inv_TO
5,166
10.4
IHld
5,166
0.103
PPE_TO
5,166
11.5
Days_AR
5,166
57.7
Days_AP
5,166
72.4
Days_Inv
5,166
69.8
Capex
5,166
0.064
PM
5,166
0.017
GM
5,166
0.430
Supplier Variables—Performance
ROA
5,166
0.005
DROA
5,166
0.007
Sales_Growth
5,166
0.101
Customer Variables—General
CSize (raw)
5,166
101,595.9
CAge
5,166
36.3
CRD
5,166
0.020
CNegFCF
5,166
0.143
SD
Min.
25%
Median
75%
Max.
0.340
0
0
0
0
1
0.305
0.348
0.190
0.216
0.227
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
0.111
0.001
0.002
2
0.160
0.005
0.014
4
0.232
0.018
0.069
8
0.999
1
1
37
889.3
14
0.011
0
2,905.3
26
0.079
1
212,949.0
64
3.412
1
0.140
0.096
0.182
5.33
12,241.2
16.1
0.117
0.458
,0.001
,0.001
,0.001
1
1.5
0
0
0
280.1
7
0
0
23.9
0.114
19.3
30.2
99.7
74.2
0.095
0.365
0.236
0
0
0
0
0
0
0
2.382
0.072
2.1
0.011
2.4
38.2
30.4
14.2
0.017
0.014
0.245
4.4
0.077
5.7
53.9
47.9
53.1
0.032
0.044
0.404
8.6
0.157
10.9
70.4
71.4
98.3
0.064
0.107
0.594
180.8
2.132
126.9
198.1
735.6
417.8
0.576
1
1
0.151
0.134
0.285
0.700
0.508
0.582
0.018
0.036
0.036
0.038
0.002
0.067
0.078
0.026
0.193
0.298
0.534
1.312
146,001.3
55
0.025
0
2,415,689.0
65
0.887
1
186,872.4
18.5
0.043
0.350
1.6
0
0
0
11,864.0
20
0
0
39,808.0
37
0
0
Supplier variables—operational and performance—are winsorized at 1 percent and 99 percent.
See Appendix A for variable definitions.
Logit model (untabulated) suggests that the probability of relationship termination increases from 9.5 percent without an ICW
to 13.7 percent with an ICW.11
We also find that customer concentration (CustConc) is negatively associated with relationship termination, which implies
that relationships that are more important are less likely to be terminated. Furthermore, size (Size and CSize), supplier market
power (MktShare), and relationship length (Tenure) are negatively associated with the likelihood of termination. Supplier age
(Age) is also negatively related to relationship termination, suggesting that supplier maturity affects the stability of customer
relationships. Supplier concentration (SuppConc) and research and development (RD and CRD) are positively associated with
relationship termination. For the supplier operational controls, inventory turnover (Inv_TO), inventory holding (IHld), and days
11
We estimate the marginal effect of ICW using the margins command in Stata, holding all other control variables at their respective means. See: http://
www.stata.com/meeting/italy10/drukker_sug.pdf/ for details.
The Accounting Review
Volume 93, Number 3, 2018
Bauer, Henderson, and Lynch
68
TABLE 4
Variable Correlations
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Panel A: Correlation Variables Rel_Stop to IHld
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
(25)
(26)
(27)
(28)
Rel_Stop
ICW
Weak
Fixed
CustConc
SuppConc
MktShare
Tenure
Size
Age
RD
NegFCF
Inv_TO
IHld
PPE_TO
Days_AR
Days_AP
Days_Inv
Capex
PM
GM
ROA
DROA
Sales_Growth
CSize
CAge
CRD
CNegFCF
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
1
0.05*
0.03*
0.03*
0.15*
0.05*
0.06*
0.13*
0.05*
0.07*
0.02
0.08*
0.06*
0.01
0.01
0.05*
0.05*
0.03*
0.09*
0.03*
0.01
0.05*
0.01
0.02
0.07*
0.02
0.03*
0.05*
1
0.84*
0.07*
0.06*
0.02
0.06*
0.04*
0.10*
0.06*
0.03*
0.11*
0.03*
0.01
0.02
0.05*
0.01
0.002
0.02
0.10*
0.07*
0.15*
0.002
0.05*
0.003
0.01
0.04*
0.01
1
0.49*
0.06*
0.02
0.07*
0.04*
0.12*
0.06*
0.04*
0.12*
0.04*
0.002
0.03*
0.07*
0.01
0.01
0.02
0.11*
0.06*
0.16*
0.001
0.05*
0.01
0.02
0.03*
0.01
1
0.01
0.001
0.04*
0.01
0.07*
0.02
0.03*
0.03*
0.02
0.02
0.02
0.04*
0.01
0.01
0.003
0.04*
0.01
0.05*
0.001
0.01
0.01
0.01
0.01
0.004
1
0.01
0.10*
0.07*
0.19*
0.13*
0.16*
0.12*
0.01
0.03*
0.08*
0.01
0.11*
0.06*
0.04*
0.13*
0.05*
0.13*
0.03*
0.05*
0.03*
0.04*
0.05*
0.04*
1
0.14*
0.06*
0.19*
0.06*
0.05*
0.03*
0.02
0.07*
0.04*
0.07*
0.01
0.04*
0.04*
0.03*
0.05*
0.06*
0.02
0.02
0.28*
0.16*
0.06*
0.09*
1
0.15*
0.36*
0.36*
0.15*
0.13*
0.01
0.15*
0.02
0.16*
0.11*
0.03*
0.08*
0.08*
0.19*
0.13*
0.02
0.07*
0.14*
0.09*
0.10*
0.07*
1
0.08*
0.31*
0.07*
0.14*
0.04*
0.15*
0.03*
0.05*
0.12*
0.05*
0.13*
0.09*
0.15*
0.11*
0.01
0.07*
0.21*
0.20*
0.01
0.07*
1
0.27*
0.25*
0.17*
0.06*
0.05*
0.17*
0.10*
0.01
0.07*
0.07*
0.24*
0.01
0.22*
0.02
0.05*
0.17*
0.07*
0.16*
0.01
1
0.12*
0.16*
0.07*
0.16*
0.06*
0.07*
0.08*
0.08*
0.12*
0.11*
0.13*
0.15*
0.01
0.13*
0.15*
0.16*
0.09*
0.08*
1
0.14*
0.09*
0.07*
0.06*
0.02
0.19*
0.16*
0.15*
0.40*
0.25*
0.34*
0.11*
0.03*
0.10*
0.07*
0.23*
0.06*
1
0.09*
0.04*
0.10*
0.11*
0.16*
0.02
0.39*
0.33*
0.09*
0.37*
0.04*
0.06*
0.06*
0.04*
0.07*
0.13*
1
0.17*
0.03
0.02
0.04*
0.24*
0.18*
0.03
0.14*
0.03*
0.02
0.03*
0.01
0.01
0.07*
0.12*
1
0.12*
0.13*
0.17*
0.43*
0.18*
0.05*
0.37*
0.09*
0.01
0.02
0.02
0.08*
0.03*
0.08*
Panel B: Correlation Variables PPE_TO to CNegFCF
(15)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
(25)
(26)
(27)
(28)
PPE_TO
Days_AR
Days_AP
Days_Inv
Capex
PM
GM
ROA
DROA
Sales_Growth
CSize
CAge
CRD
CNegFCF
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
1
0.01
1
0.03* 0.28* 1
0.05* 0.05* 0.10* 1
0.25* 0.06* 0.25* 0.20* 1
0.02 0.07* 0.16* 0.08* 0.06* 1
0.03
0.13* 0.41* 0.23* 0.09* 0.17* 1
0.004 0.06* 0.18* 0.003 0.02
0.51* 0.07* 1
0.01
0.02 0.04* 0.03 0.10* 0.31* 0.08* 0.45* 1
0.03
0.20* 0.14* 0.10* 0.11* 0.10* 0.09* 0.17* 0.24*
0.01
0.01 0.08* 0.09* 0.05* 0.05* 0.11* 0.04* 0.002
0.05* 0.01 0.09* 0.02 0.03* 0.04* 0.15* 0.07* 0.02
0.01
0.11* 0.06* 0.12* 0.11* 0.20* 0.004 0.14* 0.002
0.10* 0.03* 0.02 0.11* 0.18* 0.01 0.05* 0.03* 0.02
(24)
(25)
(26)
(27) (28)
1
0.07* 1
0.03* 0.40* 1
0.003 0.09* 0.04* 1
0.001 0.16* 0.07* 0.01
1
* Coefficients with a two-tailed p-value of , 0.05.
Pearson pairwise correlations.
See Appendix A for variable definitions.
The Accounting Review
Volume 93, Number 3, 2018
Supplier Internal Control Quality and the Duration of Customer-Supplier Relationships
69
TABLE 5
Relationship Duration Models
Logistic Regression and Cox and Weibull Hazard Models
Variables
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ICW
(1)
Rel_Stop
Logit
(2)
Rel_Stop
Cox
(3)
Rel_Stop
Weibull
0.42***
(3.13)
0.40***
(3.85)
0.46***
(4.44)
5.67***
(7.05)
1.07***
(3.04)
0.66*
(1.74)
0.07***
(5.12)
4.76***
(7.19)
0.80***
(3.51)
0.52
(1.52)
4.78***
(7.09)
0.72***
(3.27)
0.53
(1.55)
0.36**
(1.99)
Age
0.01
(1.63)
RD
1.25***
(3.08)
NegFCF
0.09
(0.75)
Supplier Variables—Operational
Inv_TO
0.003*
(1.70)
IHld
0.79
(1.51)
PPE_TO
0.001
(0.56)
Days_AR
0.001
(0.51)
Days_AP
0.001**
(2.04)
Days_Inv
0.0004
(0.42)
Capex
0.92
(1.49)
PM
0.13
(0.62)
GM
0.51*
(1.68)
Supplier Variables—Performance
ROA
0.25
(0.45)
DROA
0.19
(0.32)
Sales_Growth
0.17
(0.85)
Customer Variables—General
CSize
0.26
(1.55)
CAge
0.004
(1.57)
0.31**
(2.14)
0.01***
(2.87)
1.03***
(3.39)
0.07
(0.70)
0.23
(1.60)
0.01***
(3.28)
0.98***
(3.27)
0.06
(0.57)
0.002*
(1.71)
0.68*
(1.81)
0.001
(0.27)
0.0003
(0.20)
0.001**
(2.26)
0.0003
(0.38)
0.68
(1.48)
0.05
(0.33)
0.35
(1.47)
0.002*
(1.95)
0.79**
(2.04)
0.001
(0.40)
0.0001
(0.10)
0.001**
(2.03)
0.0002
(0.30)
0.80*
(1.72)
0.01
(0.08)
0.31
(1.32)
0.13
(0.31)
0.07
(0.15)
0.16
(1.02)
0.16
(0.37)
0.08
(0.17)
0.19
(1.28)
0.26*
(1.89)
0.002
(0.82)
0.31**
(2.16)
0.002
(0.82)
Relationship Variables
CustConc
SuppConc
MktShare
Tenure
Supplier Variables—General
Size
(continued on next page)
The Accounting Review
Volume 93, Number 3, 2018
Bauer, Henderson, and Lynch
70
TABLE 5 (continued)
Variables
CRD
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CNegFCF
(1)
Rel_Stop
Logit
(2)
Rel_Stop
Cox
1.44*
(1.66)
0.03
(0.25)
1.23*
(1.89)
0.03
(0.35)
(3)
Rel_Stop
Weibull
1.36**
(2.06)
0.07
(0.69)
Std. errors clustered by supplier
# of clusters
Fiscal year dummies
Industry dummies
Yes
1,233
Yes
Yes
Yes
1,233
No
Yes
Yes
1,233
No
Yes
Wald Chi-square
Observations
300.3
5,166
312.3
5,166
322.7
5,166
***, **, * Denote significant two-tailed p-values at p , 0.01, p , 0.05, and p , 0.10, respectively.
Z-statistics are in parentheses.
See Appendix A for variable definitions.
of accounts payable (Days_AP) have positive associations with relationship termination.12 Overall, the results in Table 5 are
consistent with significant economic consequences of poor internal control quality, incremental to relationship factors, firm
characteristics, and the quality of supplier operations and performance. Specifically, weak supplier internal controls are
positively associated with the subsequent termination of major customer relationships.13
Multivariate Results—H2 (Effect of Remediation on Supply Chain Relationships)
We next examine whether remediation of poor internal control quality influences the duration of supply chain
relationships. We tabulate the results for Model (2) in Table 6. Consistent with the results in Table 5, we find a significant
positive association between control weaknesses and relationship termination (Weak p-values , 0.01). In support of H2, we
find that remediation of disclosed supplier ICWs (Fixed) is associated with a significant reduction in the likelihood that supply
chain relationships are terminated (p-values , 0.01).14 These results are consistent with our conjecture that improvements in
internal control quality mitigate the threat of relationship termination. Using Chi-square tests, we reject the hypothesis that the
sum of the coefficients of Weak and Fixed is greater than zero across all three specifications (p-values , 0.05). Overall, these
results suggest that the remediation of ICWs can fully mitigate the effect of poor internal control quality on the probability of
relationship termination.
Furthermore, this analysis helps dispel concerns that unobservable conditions existing concurrently in suppliers with poor
internal control quality confound our inferences, although we acknowledge that no set of tests completely mitigates these
threats. In particular, our variable of interest in the remediation analysis represents an increase in the internal control quality in
firms with poor internal controls. If unobservable issues exist that lead to relationship termination, such as customer service
problems or inefficient logistics, and these issues are not mitigated by a strong set of controls, then remediation of weak
controls should have no effect on the likelihood of termination. Consequently, our remediation results corroborate our findings
for H1.15
12
13
14
15
In untabulated robustness tests, we interact ROA at time t (acting as a summary operational performance measure) with ICW. We measure ROA both
continuously and as an indicator for weak performance based on lowest quartile. Neither measure interacts significantly with ICW to influence
relationship duration. We also perform a principal components analysis on the nine supplier operational control variables to isolate four factors. We fail
to find any statistically significant coefficients on interaction terms of each factor with ICW. In all of these tests, we continue to find a significant
positive association between ICWs and relationship termination.
In untabulated robustness tests, we control for supplier governance mechanisms: the G-index or E-index (Gompers, Ishii, and Metrick 2003; Bebchuk,
Cohen, and Ferrell 2009) and/or total institutional ownership. Results are robust to controlling for these governance mechanisms.
We find that Weak and Fixed significantly increase the explanatory power of each model (p-values , 0.01 using a likelihood ratio test).
We confirm that our results for H1 and H2 are robust to limiting our sample to accelerated filers under SOX regulations, and to controlling for
accelerated filers using an indicator variable (accelerated filers represent 2,965 of 5,166 observations).
The Accounting Review
Volume 93, Number 3, 2018
Supplier Internal Control Quality and the Duration of Customer-Supplier Relationships
71
TABLE 6
Relationship Duration Models
Remediation Tests
Logistic Regression and Cox and Weibull Hazard Models
(1)
Rel_Stop
Logit
(2)
Rel_Stop
Cox
(3)
Rel_Stop
Weibull
0.39***
(2.86)
1.03***
(3.47)
0.37***
(3.57)
0.88***
(3.53)
0.44***
(4.17)
0.90***
(3.62)
5.69***
(7.07)
1.12***
(3.12)
0.66*
(1.74)
0.07***
(5.12)
4.76***
(7.22)
0.82***
(3.57)
0.52
(1.52)
4.78***
(7.11)
0.74***
(3.34)
0.53
(1.55)
0.38**
(2.10)
Age
0.01
(1.63)
RD
1.29***
(3.02)
NegFCF
0.09
(0.75)
Supplier Variables—Operational
Inv_TO
0.003*
(1.74)
IHld
0.75
(1.43)
PPE_TO
0.002
(0.61)
Days_AR
0.001
(0.63)
Days_AP
0.001**
(2.05)
Days_Inv
0.0004
(0.49)
Capex
0.93
(1.51)
PM
0.14
(0.65)
GM
0.54*
(1.75)
Supplier Variables—Performance
ROA
0.28
(0.49)
DROA
0.17
(0.29)
Sales_Growth
0.16
(0.80)
0.33**
(2.26)
0.01***
(2.90)
1.05***
(3.29)
0.07
(0.70)
0.25*
(1.72)
0.01***
(3.31)
1.02***
(3.27)
0.05
(0.54)
0.002*
(1.73)
0.65*
(1.72)
0.001
(0.31)
0.0005
(0.35)
0.001**
(2.26)
0.0003
(0.40)
0.68
(1.49)
0.06
(0.37)
0.37
(1.54)
0.003**
(1.97)
0.76**
(1.96)
0.001
(0.44)
0.0001
(0.03)
0.001**
(2.03)
0.0002
(0.33)
0.81*
(1.75)
0.02
(0.13)
0.34
(1.42)
0.16
(0.37)
0.05
(0.12)
0.15
(0.99)
0.19
(0.43)
0.06
(0.14)
0.19
(1.25)
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Variables
Weak
Fixed
Relationship Variables
CustConc
SuppConc
MktShare
Tenure
Supplier Variables—General
Size
(continued on next page)
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Bauer, Henderson, and Lynch
72
TABLE 6 (continued)
Variables
Customer Variables—General
CSize
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CAge
CRD
CNegFCF
Test Weak þ Fixed . 0
(1)
Rel_Stop
Logit
(2)
Rel_Stop
Cox
(3)
Rel_Stop
Weibull
0.25
(1.51)
0.004
(1.53)
1.35
(1.56)
0.03
(0.25)
5.25**
(0.01)
0.25*
(1.83)
0.002
(0.77)
1.16*
(1.77)
0.03
(0.32)
4.59**
(0.02)
0.30**
(2.11)
0.002
(0.79)
1.29*
(1.94)
0.06
(0.65)
3.82**
(0.03)
Std. errors clustered by supplier
# of clusters
Fiscal year dummies
Industry dummies
Yes
1,233
Yes
Yes
Yes
1,233
No
Yes
Yes
1,233
No
Yes
Wald Chi-square
Observations
303.2
5,166
320.0
5,166
330.5
5,166
***, **, * Denote significant two-tailed p-values at p , 0.01, p , 0.05, and p , 0.10, respectively.
Z-statistics are in parentheses. Chi-square test of Weak þ Fixed . 0 (probability in parentheses).
See Appendix A for variable definitions.
Robustness Tests
Propensity Score Matching
We conduct additional robustness tests to address the concern that our results may be spurious due to misspecification.
First, we use a propensity score matched (PSM) design to support that our primary findings do not suffer misspecification of
functional form (i.e., our ICW treatment firms are dissimilar to our non-ICW control firms) (Shipman, Swanquist, and Whited
2017).
In Panel A of Table 7, we report a match of 528 (of 536) ICW to non-ICW observations using supplier relationship,
general, operational, and performance variables. The model has significant discriminatory power, with an area under the ROC
curve of 0.73. In Panel B, mean tests reveal no significant differences, suggesting that we have obtained sufficient covariate
balance, while median tests show four covariates (Days_AP, PM, ROA, and DROA) with statistically significant differences.
The second-stage model includes all four of these variables to control for any remaining covariate imbalance.16
Multivariate results with this matched sample, including all available observation-years for treatment and control firms
(3,181 firm-years), are displayed in Table 7, Panel C and are consistent with H1 and H2. We find significant positive
coefficients on ICW and Weak, and significant negative coefficients on Fixed (p-values , 0.01) across all specifications.
Additionally, we reject the null that the sums of coefficient estimates for Weak and Fixed are greater than zero (p-values ,
0.05). Overall, these results reduce concerns that functional form misspecification leads to erroneous conclusions about the
association between internal controls and relationship duration.
Reverse Causality
It is possible that customers possess more timely information about supplier internal control quality and terminate
relationships or decrease purchases prior to the disclosure of an ICW. This change in the relationship could alert auditors to
potential internal control issues and lead to the discovery of the ICW. In Models (1) and (2), we attempt to address the potential
for reverse causality by defining ICWs at time t and then looking at relationship terminations at time tþ1, as well as conducting
16
We use PSM without replacement, employing a 3 percent caliper to match ICW observations to non-ICW observations. For greater discrimination in
matching, we measure firm size as a continuous variable. We find consistent results when matching with replacement or when using alternative calipers
of 1, 5, and 10 percent.
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Supplier Internal Control Quality and the Duration of Customer-Supplier Relationships
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TABLE 7
Relationship Duration Models
Propensity Score Matched Sample
Logistic Regression and Cox and Weibull Hazard Models
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Panel A: First-Stage Logit Model
Variables
CustConc
MktShare
Size
Age
Sales_Drop
Inv_TO
IHld
PPE_TO
Days_AR
Days_AP
Days_Inv
Capex
PM
GM
ROA
DROA
Sales_Growth
Std. errors clustered by firm
Fiscal year dummies
Industry dummies
ROC
Observations
(1)
ICW
Logit
0.46
(1.42)
0.58
(1.56)
0.12***
(3.26)
0.001
(0.39)
0.02
(0.21)
0.003
(1.63)
0.27
(0.44)
0.003
(1.08)
0.005***
(3.11)
0.001
(1.24)
0.001
(0.70)
0.57
(0.80)
0.70***
(3.41)
1.13***
(3.84)
3.35***
(6.60)
2.61***
(5.34)
0.70***
(3.63)
Yes
Yes
Yes
0.73
5,158
(continued on next page)
remediation tests to further our causal inferences. However, to more directly address this issue, we rerun Model (1) including
contemporaneous ICWs (i.e., at time tþ1). If customers with private information about internal control issues terminating
relationships lead to ICW disclosures, then we would expect a positive contemporaneous association between ICWs and
relationship termination. In untabulated tests, we continue to observe a significantly positive coefficient on ICWt, and fail to
find a significant positive contemporaneous association between ICWtþ1 and relationship terminations at time tþ1. This result
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Bauer, Henderson, and Lynch
74
TABLE 7 (continued)
Panel B: Covariate Balance
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ICW ¼ 1 (Treatment)
n ¼ 528
CustConc
MktShare
Size
Age
Sales_Drop
Inv_TO
IHld
PPE_TO
Days_AR
Days_AP
Days_Inv
Capex
PM
GM
ROA
DROA
Sales_Growth
ICW ¼ 0 (Control)
n ¼ 528
Mean
Median
Mean
Median
0.22
0.06
6.09
16.46
0.26
12.87
0.11
12.67
62.21
76.08
69.40
0.06
0.11
0.39
0.06
0.01
0.06
0.18
0.01
6.00
12
0
4.92
0.08
6.40
58.55
51.92**
53.77
0.03
0.001***
0.34
0.01**
0.003***
0.03
0.22
0.05
6.12
15.69
0.27
13.70
0.10
13.35
63.36
72.53
66.63
0.06
0.10
0.39
0.05
0.02
0.06
0.17
0.01
6.01
11
0
4.36
0.07
6.11
59.21
47.55
45.37
0.03
0.03
0.35
0.02
0.01
0.05
***, **, * Denote significant two-tailed p-values at p , 0.01, p , 0.05, and p , 0.10, respectively.
t-test of means or non-parametric equality of medians.
See Appendix A for variable definitions.
Panel C: Second-Stage Regression Models
H1: IC Quality
Variables
ICW
(1)
Rel_Stop
Logit
0.66***
(4.60)
(2)
Rel_Stop
Cox
0.60***
(5.36)
H2: Remediation
(3)
Rel_Stop
Weibull
Fixed
Test Weak þ Fixed . 0
Wald Chi-square
Observations
(5)
Rel_Stop
Cox
(6)
Rel_Stop
Weibull
0.62***
(4.20)
1.32***
(3.88)
0.56***
(4.94)
1.15***
(3.80)
0.63***
(5.48)
1.19***
(3.89)
4.60**
(0.02)
3.89**
(0.02)
3.44**
(0.03)
0.67***
(5.92)
Weak
Std. errors clustered by supplier
# of clusters
Fiscal year dummies
Industry dummies
(4)
Rel_Stop
Logit
Yes
614
Yes
Yes
Yes
614
No
Yes
Yes
614
No
Yes
Yes
614
Yes
Yes
Yes
614
No
Yes
Yes
614
No
Yes
228.1
3,181
260.4
3,181
276.2
3,181
232.4
3,181
269.9
3,181
287.2
3,181
***, **, * Denote significant two-tailed p-values at p , 0.01, p , 0.05, and p , 0.10, respectively.
Controls included are: CustConc, SuppConc, MktShare, Tenure (Logit only), Size, Age, RD, NegFCF, Inv_TO, IHld, PPE_TO, Days_AR, Days_AP,
Days_Inv, Capex, PM, GM, ROA, DROA, Sales_Growth, CSize, Cage, CRD, and CNegFCF. Z-statistics are in parentheses. Chi-square test of Weak þ
Fixed . 0 (probability in parentheses).
See Appendix A for variable definitions.
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Supplier Internal Control Quality and the Duration of Customer-Supplier Relationships
75
helps to rule out reverse causality and establishes temporal precedence of the ICW, thus increasing the internal validity of our
study (Cook and Campbell 1979).17
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Alternative Customer-Supplier Relationship Variable—Sales Drops
In our primary testing, we presume that a customer-supplier relationship ceases when a supplier no longer discloses that
customer under SFAS 131. However, other reasons could affect the decision to not disclose a customer as a major customer. To
alleviate concerns that such unknown reasons are correlated with weak internal controls, we employ an alternative customersupplier relationship variable based on sales decreases. Specifically, we specify Sales_Droptþ1 as a year-ahead construct, equal
to 1 if the sales to a supplier’s largest major customer decrease from year t to year tþ1 or if the customer relationship is
terminated, and 0 otherwise. In untabulated analysis, we observe that supplier ICWs are associated with a higher probability of
decreasing sales to major customers in the next period (ICW Logit p-value , 0.1; Cox and Weibull p-values , 0.01). For
remediation testing, we find that the remediation of ICWs is associated with a lower likelihood of sales decreases to major
customers in the next period (Fixed Logit p-value , 0.1; Cox and Weibull p-values , 0.05). These results corroborate our
primary analysis, and provide additional evidence of the economic consequences of internal control quality to supply chain
relationships.
Alternative Internal Control Quality Proxy—Restatements
While ICWs represent a strong proxy for internal control quality, ICW disclosures could be affected by management’s
desire to avoid reporting such weaknesses (Rice and Weber 2012). To alleviate these concerns, we use supplier restatement
announcements as an additional proxy for poor internal control quality. Restatement announcements imply that the underlying
accounting information used by supply chain partners is unreliable. We rely on Hennes, Leone, and Miller (2008) and measure
Restatement equal to 1 if a supplier announces an irregularity restatement during the 365-day period ending 60 days after the
fiscal year-end (and 0 otherwise), as irregularities are associated with significant negative firm outcomes (e.g., Hennes et al.
2008). In untabulated analysis, we find that restatements are positively associated with customer-supplier relationship
termination (p-values , 0.05), which corroborates our primary finding of poor internal control quality being associated with
relationship termination.
V. ADDITIONAL ANALYSES
Types of Internal Control Weaknesses
Certain types of supplier control weaknesses could be more indicative of problems with information reliability related to
major customers’ contracting needs and, thus, could be more likely to lead to relationship termination. We categorize
underlying individual ICWs disclosed within each firm-year into 14 categories, consistent with prior literature on internal
controls (Ge and McVay 2005).18 We also subdivide inventory ICWs into tracking (80) and valuation (153) issues, consistent
with Feng et al. (2015), based on manual coding of the disclosed weakness. We posit that tracking issues are more likely to
affect the ability to contract with customers, as they often relate to challenges in identifying quantity of goods or invoicing
errors. In contrast, inventory valuation ICWs primarily relate to obsolescence reserves or applying the lower of cost or market
valuation. These latter types of inventory ICWs are less likely to affect customer contracting ability, as they focus on the
financial reporting of inventory.
In Panels A and B of Table 8, we report the mean probability of relationship termination (Rel_Stop) for observations with
various ICW types (i.e., ICW_Type ¼ 1 versus ICW_Type ¼ 0). We test the difference in means using a proportion ratio test. We
note that these basic tests do not consider the overlap of ICW categories and, thus, we cannot determine how much of the
observed difference in relationship termination is attributable to each ICW type (Bauer 2016).
17
18
To address the concern that customers are decreasing sales, but not necessarily terminating relationships, we examine the correlation between an
indicator variable for sales decreases at time t (Sales_Dropt) and ICWs at times t and tþ1. We fail to find any statistically significant correlations
between contemporaneous sales decreases and ICWs (negative correlation, with p-value . 0.50). Multivariate results are also robust to including the
Sales_Dropt variable in all of our specifications to control for the possibility that a decrease in sales to a customer leads to the discovery of an ICW.
Finally, we reestimate our models using changes rather than levels in our relationship and operational control variables, and our results remain
unchanged (untabulated).
The high frequency of other liabilities is tied specifically to ICWs related to lease accounting. As documented in Ashbaugh-Skaife, Collins, and Kinney
(2007, 167), the SEC released new generally accepted accounting principles (GAAP) guidance on leases in February 2005, which many ‘‘managers
were unaware of’’ previously, particularly with respect to disclosure protocol under SOX 302. In our sample, no observation has an ICW related to
other liabilities (i.e., leases) without some other type of ICW disclosed.
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Bauer, Henderson, and Lynch
76
TABLE 8
Relationship Duration Models
Analysis of ICW Types
Panel A: Univariate Tests of ICW Types
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ICW Type
(1)
Revenues
(2)
Receivables
(3)
Payables
(4)
Inventory
Tracking
(5)
Inventory
Valuation
(6)
Tax
(7)
Expense/
Restructure
Mean
Rel_Stop
(ICW_Type ¼ 0)
0.13
0.13
0.13
0.13
0.13
0.13
0.13
Mean
Rel_Stop
(ICW_Type ¼ 1)
Difference
0.22
0.20
0.18
0.31
0.12
0.13
0.16
0.09
0.07
0.05
0.18
0.01
0.004
0.03
1.84*
(0.07)
4.73***
(,0.001)
0.59
(0.55)
0.15
(0.88)
1.02
(0.31)
PR Test
Rel_Stop
(ICW_Type ¼ 0)
¼ Rel_Stop
(ICW_Type ¼ 1)
Observations
(ICW_Type ¼ 0)
(ICW_Type ¼ 1)
3.47***
(,0.001)
4,984
182
2.02**
(0.04)
5,049
117
5,001
165
5,086
80
5,013
153
4,989
177
5,038
128
Panel B: Univariate Tests of ICW Types (continued)
ICW Type
(8)
Compensation
(9)
Derivatives/
Securities
(10)
PPE/
Intangibles
(11)
Inter-company
(12)
Other
Assets
(13)
Other
Liabilities
(14)
Other
Mean
Rel_Stop
(ICW_Type ¼ 0)
0.13
0.13
0.13
0.13
0.13
0.13
0.13
Mean
Rel_Stop
(ICW_Type ¼ 1)
Difference
0.22
0.23
0.16
0.23
0.14
0.18
0.24
0.09
0.10
0.03
0.10
0.01
0.05
0.11
0.75
(0.45)
4.01**
(,0.001)
0.25
(0.81)
PR Test
Rel_Stop
(ICW_Type ¼ 0)
¼ Rel_Stop
(ICW_Type ¼ 1)
Observations
(ICW_Type ¼ 0)
(ICW_Type ¼ 1)
2.34**
(0.02)
5,079
87
2.48**
(0.01)
5,087
79
5,031
135
4,953
213
5,082
84
2.85***
(0.004)
4,786
380
3.96***
(0.001)
5,007
159
***, **, * Denote significant two-tailed p-values at p , 0.01, p , 0.05, and p , 0.10, respectively.
Rel_Stop represents the proportion of relationships that ended in the subsequent year. ICW_Type equals 1 if the ICW type was observed in the firm year,
and 0 otherwise. Z-statistics are shown for the proportion ratio test with related p-values in parentheses.
(continued on next page)
In Table 8, Panels A and B, we find that 12 of the 14 ICW types are positively related to relationship termination, with
eight (Revenue, Receivables, Inventory Tracking, Compensation, Derivatives/Securities, Intercompany, Other Liabilities, and
Other) having p-values , 0.05. For inventory ICWs, Inventory Tracking has a p-value , 0.001, while Inventory Valuation has
a p-value ¼ 0.55. Of all ICW types, Inventory Tracking has the strongest correlation with relationship termination. Thus, while
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Supplier Internal Control Quality and the Duration of Customer-Supplier Relationships
77
TABLE 8 (continued)
Panel C: Regression Models of Customer-Related ICWs versus Other ICWs
Variables
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ICW_Customer
ICW_Other
Test ICW_Customer . ICW_Other
(1)
Rel_Stop
Logit
(2)
Rel_Stop
Cox
(3)
Rel_Stop
Weibull
0.68***
(3.92)
0.16
(0.83)
0.57***
(4.46)
0.21
(1.40)
0.68***
(5.16)
0.24
(1.60)
4.71**
(0.02)
4.05**
(0.02)
5.65***
(0.01)
Std. errors clustered by supplier
# of clusters
Fiscal year dummies
Industry dummies
Yes
1,233
Yes
Yes
Yes
1,233
No
Yes
Yes
1,233
No
Yes
Wald Chi-square
Observations
324.6
5,166
319.4
5,166
334.3
5,166
***, **, * Denote significant two-tailed p-values at p , 0.01, p , 0.05, and p , 0.10, respectively.
ICW_Customer is equal to 1 if the ICW disclosed has tagged issues related to revenues, receivables, or inventory tracking, and 0 otherwise. All other ICWs
are coded as ICW_Other equal to 1. Controls included are: CustConc, SuppConc, MktShare, Tenure (Logit only), Size, Age, RD, NegFCF, Inv_TO, IHld,
PPE_TO, Days_AR, Days_AP, Days_Inv, Capex, PM, GM, ROA, DROA, Sales_Growth, CSize, Cage, CRD, and CNegFCF. Z-statistics are in
parentheses. Chi-square test of ICW_Customer . ICW_Other (probability in parentheses).
See Appendix A for variable definitions.
most types of ICWs are correlated with subsequent relationship termination, our evidence implies that inventory tracking
internal controls, which more directly affect contractual terms, have the greatest effect on relationship duration.
To expand this analysis to a multivariate setting, we bifurcate all ICWs into those that likely affect the ability to contract
with key customers versus those that likely do not. Specifically, we focus on the presence of ICWs related to accounts and
issues directly related to customer activities: Inventory Tracking, Revenue, and Receivables. We code all other ICWs as noncustomer-related. In Panel C of Table 8, we present multivariate results of our primary model with these customer-related and
non-customer-related ICW categories. In all models, we find that customer-related ICWs have a significant positive association
with customer relationship termination, while other ICWs show no significant association. Further, using F-tests, we find that
the coefficients for ICW_Customer are significantly greater than those for ICW_Other (Logit and Cox p-values , 0.05; Weibull
p-value , 0.01).19 Overall, we believe these tests increase the internal validity of our study.20
Customer Dependency
We examine whether a customer’s dependency on a supplier is associated with the customer’s response to poor internal
control quality. Customers that are more dependent on their suppliers are more strongly affected by supplier problems (e.g.,
Krause, Handfield, and Tyler 2007). Consequently, dependent customers could treat suppliers with weak internal controls more
harshly, resulting in a greater likelihood of relationship termination. In contrast, higher customer dependency also means that a
customer will have stronger ties to the key supplier. This dependency may require investment and collaboration that could
increase the cost of replacing that supplier. As a result, highly dependent customers could be less likely to terminate a
relationship with a supplier that has poor internal control quality.
19
20
We conduct similar untabulated analysis of remediation of control weaknesses by type. We find that remediation of customer-related weaknesses is
associated with a significant reduction in the probability of relationship termination (p-values , 0.01), while other remediations show a much weaker
association (Weibull p-value ¼ 0.09; Logit and Cox p-values . 0.1). The reduction from remediating customer-related weaknesses versus other
weaknesses is also significantly larger (Logit and Weibull p-values , 0.05; Cox p-value , 0.1).
We would also expect the negative consequences for supply chain duration to increase as the lack of internal control quality becomes more severe.
Doyle, Ge, and McVay (2007) consider ICW observations more severe when the number of individual weaknesses is greater than or equal to three. In
untabulated analysis, we measure severity based on a count of ICW types from Panels A and B of Table 8 (ICW_Count). Further supporting internal
validity, we find a significantly positive association between ICW_Count and the likelihood of relationship termination; we estimate that each
subsequent individual weakness increases the probability of relationship termination by a factor of roughly 1.07.
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Bauer, Henderson, and Lynch
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FIGURE 1
Relationship Duration Models
Customer Dependency
This graph depicts the coefficient values of ICW for each quartile of customer dependency (SuppConc). The median value of SuppConc is 0.0005 for
Quartile 1; 0.002 for Quartile 2; 0.009 for Quartile 3; and 0.046 for Quartile 4. The values are computed in untabulated quartile regressions of Model (1).
Using quartiles of supplier concentration (SuppConc) to proxy for varying degrees of customer dependency, Figure 1
illustrates a nonlinear pattern of ICWs and relationship continuance by degrees of customer dependency. Each point in the
figure represents the coefficient value of ICW from the respective multivariate regression by quartile, where higher values of
SuppConc represent higher customer dependence. We find some evidence that the association between supplier ICWs and
relationship termination is increasing in customer dependency from Quartile 1 to Quartile 3, and decreasing from Quartile 3 to
Quartile 4. This evidence is consistent with the notion that customers are more likely to sever supplier ties when they are
affected to a greater degree by suppliers’ control problems; however, once a customer is highly dependent, the costs of
terminating an integrated relationship often outweigh the benefits.
Contracting Demand for Supplier Investment in Internal Control
The discussion leading to H1 implies that a major customer’s demand for truthful reporting could result in higher levels of
supplier investment in internal control, ceteris paribus. Major customers, through explicit or implicit contracts, will demand
that suppliers invest in internal controls to decrease the risk of unreliable information. As a result, suppliers with major
customers should invest more in internal controls, which would decrease the risk of poor internal control quality. We provide a
brief analysis to confirm this underlying implication, but leave a detailed examination of this phenomenon to future research.
In results tabulated in the Online Appendix (see Appendix B for the link to the downloadable Word document), we find
evidence consistent with the contracting demand for investment in internal controls leading to lower observed rates of ICWs.
Using a univariate t-test that compares firms with major customers to a matched sample of firms without major customers, we
find a significantly lower ICW rate for firms with major customers than firms without (10.9 percent versus 13.0 percent; p-value
, 0.01). We also find corroborating evidence in a multivariate logistic regression model that controls for determinants of ICWs
(Ashbaugh-Skaife et al. 2007; Doyle et al. 2007). Specifically, we find a significant negative coefficient on the major customer
indicator using both pooled regression and PSM models (p-values , 0.05). Overall, these results suggest that the presence of
major customers is associated with greater investment in internal controls.
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VI. CONCLUSION
Over time, supply chains have become an integrative and collaborative system of information sharing where both the
customer and supplier can reap net benefits from the relationship. However, to reap the potential benefits of these relationships,
we hypothesize that suppliers must have adequate internal controls to provide and share reliable information, which are
necessary conditions for effective contracting with key customers. We expect such interdependent processes to be a factor in
the decision to continue or terminate an ongoing supply chain relationship.
To examine the association between relationship duration and poor internal control quality, we use major customer
disclosures to build a large sample of customer-supplier relationships from 2004–2014, and use SOX-related ICW disclosures
as a proxy for weak supplier internal controls. We demonstrate that poor supplier internal control quality impairs key customersupplier relationships, as the presence of ICWs is positively associated with the probability of a relationship ending in the
subsequent period. However, we also find that remediation of ICWs by suppliers is associated with a lower probability of
relationship termination. This finding suggests that managers may be able to preserve such relationships by investing in internal
controls following the disclosure of an ICW.
We contribute to the supply chain literature by providing evidence consistent with weak internal controls eroding the
reliability of information and increasing the risk of relationship termination. Our proxy for internal control quality represents a
specific disclosure that is associated with relationship termination, which expands prior research on ‘‘negative critical incidents’’
that can move customers closer to severing relationships (Van Doorn and Verhoef 2008). Furthermore, we contribute to the
literature on the economic consequences of poor internal control and its remediation. Our focus on a unique stakeholder group,
major customers, provides evidence of an additional benefit of strong internal controls. Managers should be aware that internal
controls affect the ability to contract with key customers.
Our study is subject to several limitations. First, we use major customer disclosures that are required under SFAS 131. This
guidance provides a threshold of 10 percent for identifying major customers; as a result, we do not observe significant
customers that may be close to that threshold and we must assume that relationships have ceased when that threshold is not met.
Second, we observe the disclosure of ICWs rather than a continuous measure of underlying internal control quality. Finally,
although we extensively control for supplier operational quality and performance, we cannot completely rule out that
relationship termination is a mechanism through which ICWs affect supplier operations and performance.
Despite these limitations, we believe that our study provides valuable insights into how internal control quality affects the
duration of supply chain relationships. We look forward to future research in this area, including studies that explore how
customers shape supplier internal control quality by investing in relationships they hope to maintain or by taking advantage of
certain weaknesses. Future research could also explore the influence of customer internal control quality, and its interaction
with supplier internal control quality, on relationship characteristics.
REFERENCES
Akerlof, G. 1970. The market for ‘‘lemons’’: Qualitative uncertainty and the market mechanism. Quarterly Journal of Economics 84 (3):
488–500. https://doi:10.2307/1879431
Arend, R., and J. Wisner. 2005. Small business and supply chain management: Is there a fit? Journal of Business Venturing 20 (3): 403–
436. https://doi:10.1016/j.jbusvent.2003.11.003
Ashbaugh-Skaife, H., D. Collins, and W. Kinney, Jr. 2007. The discovery and reporting of internal control deficiencies prior to SOXmandated audits. Journal of Accounting and Economics 44 (1/2): 166–192. https://doi:10.1016/j.jacceco.2006.10.001
Ashbaugh-Skaife, H., D. Collins, W. Kinney, Jr., and R. LaFond. 2008. The effect of SOX internal control deficiencies and their
remediation on accrual quality. The Accounting Review 83 (1): 217–250. https://doi:10.2308/accr.2008.83.1.217
Baiman, S., and M. V. Rajan. 2002a. The role of information and opportunism in the choice of buyer-supplier relationships. Journal of
Accounting Research 40 (2): 247–278. https://doi:10.1111/1475-679X.00046
Baiman, S., and M. V. Rajan. 2002b. Incentive issues in inter-firm relationships. Accounting, Organizations and Society 27 (3): 213–238.
https://doi:10.1016/S0361-3682(00)00017-9
Bauer, A. M. 2016. Tax avoidance and the implications of weak internal controls. Contemporary Accounting Research 33 (2): 449–486.
https://doi:10.1111/1911-3846.12151
Bebchuk, L., A. Cohen, and A. Ferrell. 2009. What matters in corporate governance? Review of Financial Studies 22 (2): 783–827. https://
doi:10.1093/rfs/hhn099
Cen, L., F. Chen, Y. Huo, and G. Richardson. 2015. Customer-Supplier Relationships and Strategic Disclosures of Litigation Loss
Contingencies. Working paper, University of Toronto and Queen’s University.
Cen, L., S. Dasgupta, R. Elkamhi, and R. S. Pungaliya. 2016. Reputation and loan contract terms: The role of principal customers. Review
of Finance 20 (2): 501–533. https://doi:10.1093/rof/rfv014
The Accounting Review
Volume 93, Number 3, 2018
The Accounting Review 2018.93:59-82.
Downloaded from aaajournals.org by Kings College London-FWIC Journals on 08/27/19. For personal use only.
80
Bauer, Henderson, and Lynch
Cheng, M., D. Dhaliwal, and Y. Zhang. 2013. Does investment efficiency improve after the disclosure of material weaknesses in internal
control over financial reporting? Journal of Accounting and Economics 56 (1): 1–18. https://doi:10.1016/j.jacceco.2013.03.001
Choi, T., and D. Krause. 2006. The supply base and its complexity: Implications for transactions costs, risks, responsiveness, and
innovation. Journal of Operations Management 24 (5): 637–652. https://doi:10.1016/j.jom.2005.07.002
Christensen, H. B., V. V. Nikolaev, and R. Wittenberg-Moerman. 2016. Accounting information in financial contracting: The incomplete
contract theory perspective. Journal of Accounting Research 54 (2): 397–435. https://doi:10.1111/1475-679X.12108
Committee of Sponsoring Organizations of the Treadway Commission (COSO). 2013. Internal Control—Integrated Framework.
Available at: https://www.coso.org/Pages/ic.aspx
Cook, T. D., and D. T. Campbell. 1979. Quasi-Experimentation: Design and Analysis Issues for Field Settings. Boston, MA: Houghton
Mifflin.
Costello, A. M. 2013. Mitigating incentive conflicts in inter-firm relationships: Evidence from long-term supply contracts. Journal of
Accounting and Economics 56 (1): 19–39. https://doi:10.1016/j.jacceco.2013.02.001
Doyle, J., W. Ge, and S. McVay. 2007. Determinants of weaknesses in internal control over financial reporting. Journal of Accounting
and Economics 44 (1/2): 193–223. https://doi:10.1016/j.jacceco.2006.10.003
Fee, C. E., and S. Thomas. 2004. Sources of gains in horizontal mergers: Evidence from customer, supplier, and rival firms. Journal of
Financial Economics 74 (3): 423–460. https://doi:10.1016/j.jfineco.2003.10.002
Fee, E., C. Hadlock, and S. Thomas. 2006. Corporate equity ownership and the governance of product market relationship. Journal of
Finance 61 (3): 1217–1251. https://doi:10.1111/j.1540-6261.2006.00871.x
Feng, M., C. Li, S. McVay, and H. Skaife. 2015. Does ineffective internal control over financial reporting affect a firm’s operations?
Evidence from firms’ inventory management. The Accounting Review 90 (2): 529–557. https://doi:10.2308/accr-50909
Galbraith, C., and C. Stiles. 1983. Firm profitability and relative firm power. Strategic Management Journal 4 (3): 237–249. https://
doi:10.1002/smj.4250040305
Galbraith, J. 1952. American Capitalism: The Concept of Countervailing Power. Boston, MA: Houghton Mifflin.
Gallemore, J., and E. Labro. 2015. The importance of the internal information environment for tax avoidance. Journal of Accounting and
Economics 60 (1): 149–167. https://doi:10.1016/j.jacceco.2014.09.005
Gavirneni, S., R. Kapuscinski, and S. Tayur. 1999. Value of information in capacitated supply chains. Management Science 45 (1): 16–
24. https://doi:10.1287/mnsc.45.1.16
Ge, W., and S. McVay. 2005. The disclosure of material weaknesses in internal control after the Sarbanes-Oxley Act. Accounting
Horizons 19 (3): 137–158. https://doi:10.2308/acch.2005.19.3.137
Gompers, P., J. Ishii, and A. Metrick. 2003. Corporate governance and equity prices. Quarterly Journal of Economics 118 (1): 107–156.
https://doi:10.1162/00335530360535162
Gulati, R. 1995. Does familiarity breed trust? The implications of repeated ties for contractual choice in alliances. Academy of
Management Journal 38 (1): 85–112. https://doi:10.2307/256729
Hennes, K., A. Leone, and B. Miller. 2008. The importance of distinguishing errors from irregularities in restatement research: The case
of restatements and CEO/CFO turnover. The Accounting Review 83 (6): 1487–1519. https://doi:10.2308/accr.2008.83.6.1487
Hertzel, M. G., Z. Li, M. S. Officer, and K. J. Rodgers. 2008. Inter-firm linkages and the wealth effects of financial distress along the
supply chain. Journal of Financial Economics 87 (2): 374–387. https://doi:10.1016/j.jfineco.2007.01.005
Hollmann, T., C. B. Jarvis, and M. J. Bitner. 2015. Reaching the breaking point: A dynamic process theory of business-to-business
customer defection. Journal of the Academy of Marketing Science 43 (2): 257–278. https://doi:10.1007/s11747-014-0385-6
Holmström, B. 1979. Moral hazard and observability. Bell Journal of Economics 10 (1): 74–91. https://doi:10.2307/3003320
Holmström, B., and J. Roberts. 1998. The boundaries of the firm revisited. Journal of Economic Perspectives 12 (4): 73–94. https://
doi:10.1257/jep.12.4.73
Jensen, M., and W. Meckling. 1976. Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial
Economics 3 (4): 305–360. https://doi:10.1016/0304-405X(76)90026-X
Johnstone, K. M., C. Li, and K. Rupley. 2011. Changes in corporate governance associated with the revelation of internal control material
weaknesses and their subsequent remediation. Contemporary Accounting Research 28 (1): 331–383. https://doi:10.1111/j.19113846.2010.01037.x
Johnstone, K. M., C. Li, and S. Luo. 2014. Client-auditor supply chain relationships, audit quality, and audit pricing. Auditing: A Journal
of Practice & Theory 33 (4): 119–166. https://doi:10.2308/ajpt-50783
Kalwani, M., and N. Narayandas. 1995. Long-term manufacturer-supplier relationships: Do they pay off for supplier firms? Journal of
Marketing 59 (1): 1–16. https://doi:10.2307/1252010
Kim, Y. H., and D. Henderson. 2015. Financial benefits and risks of dependency in triadic supply chain relationships. Journal of
Operations Management 36 (1): 115–129. https://doi:10.1016/j.jom.2015.04.001
Kinney, M., and W. Wempe. 2002. Further evidence on the extent and origins of JIT’s profitability effects. The Accounting Review 77 (1):
203–225. https://doi:10.2308/accr.2002.77.1.203
Kinney, W. 2000. Research opportunities in internal control quality and quality assurance. Auditing: A Journal of Practice & Theory 19
(Supplement): 83–90. https://doi:10.2308/aud.2000.19.supplement.83
The Accounting Review
Volume 93, Number 3, 2018
The Accounting Review 2018.93:59-82.
Downloaded from aaajournals.org by Kings College London-FWIC Journals on 08/27/19. For personal use only.
Supplier Internal Control Quality and the Duration of Customer-Supplier Relationships
81
Krause, D., R. Handfield, and B. Tyler. 2007. The relationships between supplier development, commitment, social capital accumulation
and performance improvement. Journal of Operations Management 25 (2): 528–545. https://doi:10.1016/j.jom.2006.05.007
Kumar, N. 1996. The power of trust in manufacturer-retailer relationships. Harvard Business Review 74 (November): 92–106.
Lanier, D., Jr., W. Wempe, and Z. Zacharia. 2010. Concentrated supply chain membership and financial performance: Chain- and firmlevel perspectives. Journal of Operations Management 28 (1): 1–16. https://doi:10.1016/j.jom.2009.06.002
Lee, H., K. So, and C. Tang. 2000. The value of information sharing in a two-level supply chain. Management Science 46 (5): 626–643.
https://doi:10.1287/mnsc.46.5.626.12047
Leftwich, R. 1983. Accounting information in private markets: Evidence from private lending agreements. The Accounting Review 58 (1):
23–42.
Lynch, D. 2016. Can Strong Tax-Related Internal Controls Improve Tax Planning Effectiveness? The Effects of Remediating Material
Weaknesses in Internal Control on Tax Avoidance. Working paper, University of Wisconsin–Madison.
Matsumura, E. M., and J. Schloetzer. 2018. The structural and executional components of customer concentration: Implications for
supplier performance. Journal of Management Accounting Research 30 (1). https://doi.org/10.2308/jmar-51605
O’Neal, C. 1989. JIT procurement and relationship marketing. Industrial Marketing Management 18 (1): 55–63. https://doi:10.1016/
0019-8501(89)90021-7
Patatoukas, P. 2012. Customer-base concentration: Implications for firm performance and capital markets. The Accounting Review 87 (2):
363–392. https://doi:10.2308/accr-10198
Porter, M. 1974. Consumer behavior, retailer power and market performance in consumer goods industries. Review of Economics and
Statistics 56 (4): 419–436. https://doi:10.2307/1924458
Raman, K., and H. Shahrur. 2008. Relationship-specific investments and earnings management: Evidence on corporate suppliers and
customers. The Accounting Review 83 (4): 1041–1081. https://doi:10.2308/accr.2008.83.4.1041
Rice, S., and D. Weber. 2012. How effective is internal control reporting under SOX 404? Determinants of the (non-)disclosure of
existing material weaknesses. Journal of Accounting Research 50 (3): 811–843. https://doi:10.1111/j.1475-679X.2011.00434.x
Schumacher, U. 1991. Buyer structure and seller performance in U.S. manufacturing industries. Review of Economics and Statistics 73
(2): 277–284. https://doi:10.2307/2109518
Shipman, J., Q. Swanquist, and R. Whited. 2017. Propensity score matching in accounting research. The Accounting Review 92 (1): 213–
244. https://doi:10.2308/accr-51449
Snyder, C. M. 1996. A dynamic theory of countervailing power. RAND Journal of Economics 27 (4): 747–769. https://doi:10.2307/
2555880
Su, L., X. Zhao, and G. Zhou. 2014. Do customers respond to the disclosure of internal control weakness? Journal of Business Research
67 (7): 1508–1518. https://doi:10.1016/j.jbusres.2013.06.009
Van Doorn, J., and P. C. Verhoef. 2008. Critical incidents and the impact of satisfaction on customer share. Journal of Marketing 72 (3):
124–142. https://doi.org/10.1509/jmkg.72.4.123
Vickery, S., J. Jayaram, C. Droge, and R. Calantone. 2003. The effects of an integrative supply chain strategy on customer service and
financial performance: An analysis of direct versus indirect relationships. Journal of Operations Management 21 (5): 523–539.
https://doi:10.1016/j.jom.2003.02.002
Williamson, O. E. 1979. Transaction cost economics: The governance of contractual relations. Journal of Law and Economics 22 (2):
233–261. https://doi:10.1086/466942
Williamson, O. E. 1985. The Economic Institutions of Capitalism: Firms, Markets, Relational Contracting. New York, NY: The Free
Press.
Zhao, X., B. Huo, B. B. Flynn, and J. H. Y. Yeung. 2008. The impact of power and relationship commitment on the integration between
manufacturers and customers in a supply chain. Journal of Operations Management 26 (3): 368–388. https://doi:10.1016/j.jom.
2007.08.002
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APPENDIX A
Variable Definitions
Variable
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Dependent Variables
Rel_Stop
Key Variables
ICW
Weak
Fixed
ICW_Customer
ICW_Other
Relationship Variables
CustConc
SuppConc
MktShare
Tenure
Definition
Indicator variable equal to 1 if the supplier firm’s relationship with a customer ceases in the subsequent year
tþ1, and 0 otherwise.
Indicator variable equal to 1 if the firm discloses a Section 404 internal control weakness (ICW) or Section
302 material weakness (MW) in the current year t, and 0 otherwise.
Indicator variable equal to 1 if the firm discloses a Section 404 ICW or Section 302 MW in the current year t
or prior year t1, and 0 otherwise.
Indicator variable equal to 1 if the firm discloses either a Section 404 ICW or Section 302 MW in the prior
year t1, but neither one in the current year t, and 0 otherwise.
Indicator variable equal to 1 if the firm discloses a Section 404 ICW or Section 302 MW related to revenue,
accounts receivable, or inventory tracking in the current year t, and 0 otherwise.
Indicator variable equal to 1 if ICW equals 1 and ICW_Customer equals 0 in the current year t, and 0
otherwise.
Customer concentration; sales to a specific major customer (SALECS) divided by total supplier sales (REVT).
Supplier concentration; purchases from supplier (i.e., SALECS) divided by total customer cost of goods sold
(COGS).
Total sales (REVT) in year t divided by total industry sales, defined as the sum of total sales by all firms in
year t within that firm’s four-digit SIC code.
The length of the customer-supplier relationship at the beginning of the year.
Supplier and Customer Variables—General
Size (CSize)
Rank of total assets (AT) of supplier (customer) firm.
Age (CAge)
The number of years the supplier (customer) is listed in Compustat.
RD (CRD)
Supplier (customer) research and development expense (XRD) scaled by total assets (AT).
NegFCF (CNegFCF) Indicator variable equal to 1 if supplier (customer) free cash flow (OANCF CAPX) is negative, and 0
otherwise.
Supplier Variables—Operational
Inv_TO
Inventory turnover; cost of goods sold (COGS) divided by two-year average FIFO inventory (INVT) (Feng et
al. 2015).
IHld
Inventory holding period; inventory (INVT) divided by opening total assets (AT).
PPE_TO
Property, plant, and equipment turnover; revenue (REVT) divided by net property, plant, and equipment
(PPENT).
Days_AR
Days accounts receivable; accounts receivable (RECT) divided by revenue (REVT) multiplied by 365.
Days_AP
Days accounts payable; accounts payable (AP) divided by cost of goods sold (COGS) multiplied by 365.
Days_Inv
Days inventory; inventory (INVT) divided by cost of goods sold (COGS) multiplied by 365.
Capex
Capital expenditure intensity; capital expenditures (CAPX) divided by opening total assets (AT).
PM
Profit margin; income before extraordinary items (IB) divided by revenue (REVT).
GM
Gross margin; revenue (REVT) less cost of goods sold (COGS) divided by revenue (REVT).
Supplier Variables—Performance
ROA
Return on assets in year tþ1; income before extraordinary items (IB) in year tþ1 divided by average total
assets (AT) in year tþ1.
DROA
Change in return on assets for year tþ1; ROAtþ1 minus ROAt.
Sales_Growth
Sales growth in year tþ1; (revenue [REVT] in year tþ1 minus revenue in year t) divided by revenue in year t.
APPENDIX B
accr-51889_Online Appendix: http://dx.doi.org/10.2308/accr-51889.s01
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