BUSINESS STRATEGY AND

INTERNAL CONTROL OVER FINANCIAL REPORTING*

KATHLEEN A. BENTLEY-GOODE, The University of New South Wales

NATHAN J. NEWTON, The University of Missouri-Columbia

ANNE M. THOMPSON, The University of Illinois at Urbana-Champaign

March 2015

* We thank Michael Drake, Karla Johnstone, Phil Lamoreaux, Elaine Mauldin, Thomas

Omer, Mark Peecher, and participants at the 2014 AAA Annual Meeting for helpful

comments. We are grateful to the University of Illinois at Urbana-Champaign, University of

Missouri-Columbia, and University of New South Wales for financial support.

BUSINESS STRATEGY AND

INTERNAL CONTROL OVER FINANCIAL REPORTING

ABSTRACT: This study examines whether a firm’s business strategy is an underlying

determinant of the quality of its internal control over financial reporting (ICFR).

Organizational theory suggests that firms following an innovative “prospector” strategy are

likely to have weaker internal controls than firms following an efficient “defender” strategy.

We find that business strategy is a significant predictor of material weaknesses, incremental

to known determinants of material weaknesses. We also find that relative to defenders,

prospectors are less likely to remediate or disclose material weaknesses on a timely basis.

Finally, we find that guidance in AS No. 5 instructing auditors to take a risk-based approach

to ICFR evaluation improved auditors’ ability to link business strategy risks to internal

control deficiencies by improving the timeliness with which material weaknesses are

reported. Our results indicate that prospectors are riskier audit clients and suggest that

business strategy is a useful summary indicator for evaluating firms’ internal control strength.

Keywords: business strategy; internal control; material weakness; Sarbanes-Oxley; Auditing

Standard No. 5

Data availability: Data are obtained from public sources as indicated in the text.

1

BUSINESS STRATEGY AND

INTERNAL CONTROL OVER FINANCIAL REPORTING

I. INTRODUCTION

Using organizational theory, we predict that the type of business strategy that a firm

follows serves as an underlying determinant of the quality of the firm’s internal control over

financial reporting (ICFR).1 The Sarbanes-Oxley Act of 2002 requires management to

annually review and report on the effectiveness of the company’s ICFR and requires auditors

to attest to the effectiveness of ICFR for large U.S. listed firms. Understanding why some

firms have ineffective controls, known as material weaknesses, is important to stakeholders

because material weaknesses are associated with lower quality accruals (Doyle, Ge, and

McVay 2007a; Ashbaugh-Skaife, Collins, Kinney, and LaFond 2008), adverse market

reactions (Gupta and Nayar 2007; Beneish, Billings, and Hodder 2008; Hammersley, Myers,

and Shakespeare 2008), and greater debt and equity costs (Ashbaugh-Skaife, Collins, Kinney,

and LaFond 2009; Dhaliwal, Hogan, Trezevant, and Wilkins 2011; Kim, Song, and Zhang

2011).2

Kinney (2000, 88) indicates that the largest obstacle academics face in the area of

internal control quality and quality assurance is “our own limited knowledge...of business

strategy and organization design, management processes, risk, and risk management.” Recent

research finds that firms following a particular business strategy experience higher

likelihoods of restatements and litigation despite higher audit fees (Bentley, Omer, Sharp

2013), which the authors suggest is due to auditors insufficiently identifying client business

risks. We propose a complimentary explanation for their results, namely, that firms following

certain business strategies maintain weaker ICFR and that auditors do not assess control risk

1 Strategy is a broad concept where a firm's strategy can be distinguished in at least two levels: business- and

corporate-level strategies. Business-level strategy involves determining how the company expects to compete

within a given industry whereas corporate-level strategy involves determining in which area of business the firm

should be involved and is the source of strategy variation between industries (Beard and Dess 1981; Hambrick

1983; Dent 1990; Bruggeman and Van der Stede 1993). Our study focuses on business-level strategy.

2 Refer to Schneider, Gramling, Hermanson, and Ye (2009) for a literature review.

2

appropriately for these clients. From an audit quality standpoint, incorporating business

strategy into the study of reported material weaknesses and identifying settings in which

auditors are less likely to assess control risk appropriately can potentially improve the quality

of auditors’ risk assessments and ultimately the quality of the audit report. In addition,

because business strategy is visible to outsiders, documenting the association between

business strategy and internal control environments provides useful insights to stakeholders,

auditors, and regulators for understanding which firms are more likely to have weaker

internal controls, particularly among firms exempt from internal control attestation

requirements.3

Organizational theory indicates that firms within industries follow different strategies

and that these strategies are observable based on firm-level characteristics. For example,

innovative “prospector” firms (e.g., first-to-market firms) are frequently evolving to focus on

new product-market opportunities, resulting in rapid growth, firm complexity, internal

control modifications, and profit volatility (Miles and Snow 1978, 2003; Hambrick 1983;

Simons 1987). Conversely, organizational theory suggests that firms following an efficiency-

oriented “defender” strategy (e.g., cost-leadership firms) focus on producing a stable set of

products and exhibit more stable growth patterns within existing product lines (Miles and

Snow 1978, 2003; Hambrick 1983). Defender firms are associated with firm-level

characteristics opposite to those of prospectors: gradual growth, less complexity and more

consistent profitability.4

3 The use of an externally visible signal of internal control quality would be particularly useful for investors,

analysts, and other stakeholders of informationally opaque issuers that are exempt from Sarbanes-Oxley 404(b)

such as small filers and recent IPOs. Stakeholders could consider business strategy in assessing the reliability of

management’s assertions in the financial statements, earnings forecasts, and other communications, particularly

among firms that are likely to have weaker internal controls.

4 The Miles and Snow typology includes a third viable strategy, which is a hybrid strategy consisting of

elements of both prospectors and defenders. We follow prior accounting research (e.g., Simons 1987, Ittner,

Larcker, and Rajan 1997; Bentley et al. 2013) in focusing our discussion on the two distinct strategies at the

endpoints of the strategy continuum (prospectors and defenders) as these firms are most easily distinguished

based on organizational attributes.

3

Organizational theory proposes that prospector firms are likely to have weaker

internal controls than defender firms, but there are several reasons that empirical data may

not support this prediction. First, prior studies in organizational management and managerial

accounting provide mixed evidence on the association between business strategy and internal

controls (e.g., Simons 1987; Dent 1990; Langfield-Smith 1997; Agbejule and Jokipii 2009).

Second, although prior studies link material weaknesses to some firm-level characteristics of

business strategy, such as growth or complexity attributes, (Ge and McVay 2005; Doyle et al.

2007b), the effect of business strategy may be subsumed by these other determinants of ICFR

deficiencies. Furthermore, because public companies are expected to maintain effective ICFR

under both the Sarbanes-Oxley Act and the Foreign Corrupt Practices Act, the theoretical

association between business strategy and internal control may not extend to ICFR due to

federal regulations.

Finally, Bentley et al. (2013) find that restatements and litigation are more likely

among prospector firms. Because financial restatements often imply weaknesses in ICFR

(Kinney and McDaniel 1989, Auditing Standard No. 5), their study suggests an association

between business strategy and ICFR. However, Bentley et al. (2013) attribute their findings

to client business risk (CBR) rather than to financial reporting risk. Business risk auditing

proposes that CBR and financial reporting risk are closely linked because auditors must gain

a sufficient understanding of CBR in order to assess inherent risk and control risk

appropriately (Bell, Mars, Solomon, and Thomas 1997; Bell, Peecher, and Solomon 2002;

Knechel 2007). Thus, an auditor’s failure to gain a sufficient understanding of CBR could

indicate a failure to assess control risk appropriately, leading to increased likelihoods of both

undetected internal control deficiencies and restatements. However, CBR encompasses risks

external to the organization as well as risks embedded in the client’s core business processes

that are not addressed by ICFR. As Bell et al. (1997, 64) note, “…for the auditor to judge

4

effectively whether accounting estimates and valuations reflect the proper levels of

uncontrolled business risk, he must look outside of the accounting system to the actual

sources of risks, and the processes in place to control them.” Thus, the increased risk of

restatements and litigation documented by Bentley et al. (2013) could be attributable to

uncontrolled business process risks rather than weaknesses in the accounting system and

ICFR controls.

We use the archival measure of business strategy developed by Bentley et al. (2013)

to investigate the relationship between business strategy and occurrences of internal control

weaknesses. First, we examine the role of business strategy on the probability of disclosing a

material weakness, incremental to known determinants of internal control weaknesses from

prior literature (e.g., Doyle et al. 2007b). Next, we examine whether business strategy is

associated with the number of material weaknesses reported as well as the remediation of

reported material weaknesses. We find that firms following a prospector strategy are

significantly more likely to report material weaknesses and are less likely to remediate

material weaknesses than firms following a defender strategy.

Next, we perform two tests that explore the association between business strategy and

reported material weaknesses from an audit perspective. First, we examine the timeliness of

firms’ material weaknesses reporting by adapting the framework in Rice and Weber (2012)

where material weaknesses reported in conjunction with a restatement are considered to be

untimely relative to material weaknesses reported in the absence of a restatement, which are

considered to be timely. Timely material weaknesses imply that management and/or the

auditor’s controls and substantive tests detected the material weakness prior to issuing the

audit report. In contrast, untimely material weaknesses imply that management and/or the

auditor failed to detect or report a material weakness in a prior period and that the material

weakness was not detected until the internal control system failed to prevent a material

5

misstatement in the financial statements.5 This test is also important for understanding the

role of business strategy in the audit process because prospector firms need to modify their

control systems more often than firms following other strategies, which may increase

auditors’ difficulty in identifying or assessing the severity of internal control deficiencies. We

find that firms following a prospector strategy are significantly more likely to report both

timely and untimely material weaknesses relative to defender firms, which implies both that

prospector firms have weaker controls than defender firms and that managers and auditors

have greater difficulty detecting and/or reporting deficiencies in ICFR among prospector

clients.

Finally, we examine whether the relationship between business strategy and reported

material weaknesses differs during the sample period due to changes in auditing standards

governing auditors’ attestation of ICFR. In contrast to Auditing Standard No. 2 (AS No. 2),

which was effective in the early years of the sample, Auditing Standard No. 5 (AS No. 5)

(effective for audits after November 15, 2007) directs auditors to adopt a “top down, risk

based” approach to evaluating ICFR. Because a firm’s business strategy is a key input to the

auditor’s risk assessment (AICPA 2006; IFAC 2009; Bentley et al. 2013), it is possible that

AS No. 5 strengthened the association between business strategy and reported material

weaknesses or improved the timeliness with which auditors detect material weaknesses.

However, if auditors exert insufficient effort to understand CBR for prospector clients as

implied by Bentley et al. (2013), AS No. 5 may have no effect on the association between

strategy and reported material weaknesses. While we find a strong association between

business strategy and material weaknesses in both the AS No. 2 and AS No. 5 periods, we

find that the association in the AS No 2 period is driven by untimely material weaknesses

announced in conjunction with a restatement whereas in the AS No. 5 period, business

5 Under current U.S. auditing standards, auditors do not include internal control deficiencies judged to be less

severe than a material weakness in their audit reports.

6

strategy is associated only with timely material weaknesses. Thus, the top-down risk based

approach to evaluating internal controls appears to have improved auditors’ detection of

material weaknesses among riskier prospector clients.

We make several contributions to the literature. First, by linking organizational theory

to the accounting literature, we provide a theoretical framework for understanding findings

from prior studies and illustrating why business strategy is an underlying determinant of

firms’ control systems. We also contribute to the newly developing research that suggests that

firms following a prospector strategy are riskier audit clients. From a conceptual and practice

standpoint, our paper provides a significant contribution beyond Bentley et al. (2013) by

identifying internal controls as an area of heightened risk for prospector firms and control risk

assessment as a specific area for audit quality improvement for prospector audit clients.

Third, our results showing that the relationship between business strategy and the timeliness

of material weakness reporting is different in the pre- versus post-AS No. 5 periods suggest

that the top-down, risk-based approach to auditing internal controls in the post-AS No. 5

period improved audit quality surrounding internal controls for certain riskier clients.

Finally, this study provides useful information to outside parties who rely on internal

control reports for decision making. Because a firm’s business strategy, such as a focus on

innovation versus cost leadership, is observable to outsiders, it is likely easier for

stakeholders to assess the probable strength of a firm’s internal controls based on business

strategy than by evaluating the individual determinants of material weaknesses documented

in the prior literature. This observation may be useful for regulators planning risk-based

inspections and is particularly salient for stakeholders in firms that are exempt from ICFR

attestation requirements (e.g., non-accelerated filers and recent IPOs) that are often

characterized by relatively weaker information environments and greater information

asymmetry.

7

The rest of our paper is organized as follows. Section 2 provides the literature review

and our hypothesis development. Section 3 details our research design. Section 4 describes

our descriptive data and empirical results. Section 5 presents our additional analyses. Section

6 concludes.

II. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT

Management Control Systems and Strategy

Prior research identifies a firm’s business strategy as an important component of the

management control system (e.g., Simons 1987; Dent 1990; Langsfield-Smith 1997; Henri

2006). A significant body of accounting and organizational management research investigates

the relationship between management control systems and strategy.6 Management control

systems encompass accounting-based controls systems for planning and monitoring, and

strategic and operational control elements (Langsfield-Smith 1997). Prior research examining

elements of accounting control systems focuses on the business strategy of the firm (Dent

1990; Langfield-Smith 1997). Strategic organizational theory indicates that “the

organization’s control system should be congruent with its strategy” (Agbejule and Jokipii

2009, 502), and early accounting studies concluded that a firm’s business strategy should be

one of the primary components in the design of an accounting control system (e.g., Dermer

1977; Otley 1980; Simons 1987).

Strategy theorists (e.g., Miles and Snow 1978, 2003; Miller and Friesen 1978; Porter

1980) hypothesize that control systems vary among firms depending on the strategy of the

firm. Miles and Snow (1978, 2003) hypothesize that the control structures of firms following

an entrepreneurial/innovation-oriented strategy (“prospectors”) are generally decentralized

and flexible to adapt quickly to changing market conditions. For example, Miles and Snow

suggest that controls within prospector firms are focused more on external scanning activities

6 Refer to Dent (1990) and Langsfield-Smith (1997) for comprehensive literature reviews.

8

to locate new market opportunities. The highly decentralized organizational form allows

prospectors to encourage “risk taking” and “creativity” in managerial decision-making, thus

enabling their strategic pursuit of new product/market opportunities (Joyce and Slocum 1990,

141). Firms with decentralized structure employ “little formal control over member

behaviors. . . [where] [c]ontrol systems monitor outputs and not behaviors” (Joyce and

Slocum 1990, 142). Thus, Simons (1987) predicts that prospectors will “de-emphasize

accounting controls in general, placing greater emphasis on fostering individual creativity and

innovation” (360).

In contrast, the control structures of firms following a cost/efficiency-oriented

strategy (“defenders”) are generally centralized and rigid in order to maximize efficiency

(Miles and Snow 1978, 2003; Porter 1980). Miles and Snow suggest that controls within

defender firms are focused more on activities related to cost control and monitoring than on

activities to identify new business opportunities (Simons 1987). In addition, defender firms

employ the “extensive use of rules and standard operating procedures [to] ensure that

individuals from different functional areas are exposed to similar work practices and

procedures . . . [resulting in] highly effective internalized set of controls” (Joyce and Slocum

1990, 139). For these reasons, Simons (1987) predicts that defenders will “place heavier

reliance on formal accounting procedures, especially those directed to cost control” (360).

While there are numerous strategy taxonomies, we focus on the Miles and Snow

(1978, 2003) typology throughout this paper because it “provides the richest portrayal of

organizational arrangement associated with particular strategies” (Dent 1990, 10-11) and also

because of its “comprehensiveness” (Zahra and Pearce 1990, 753).7 The Miles and Snow

(1978, 2003) typology is based on the rate of change that a firm alters its product-market mix.

Three viable strategies emerge within a particular industry (Miles and Snow 1978, 2003;

7 Refer to Bentley et al. (2013, 782-783) for an in-depth discussion of how the Miles and Snow (1978) typology

aligns with other commonly used strategy typologies (e.g., Miller and Friesen 1982; Porter 1980).

9

Hambrick 1983). Firms at one end of the strategy continuum frequently/rapidly change their

product mix (prospectors) while firms at the other end rarely/gradually change their product

mix (defenders).8, 9 Miles and Snow (1978, 2003) identify a third type of viable business

strategy within a particular industry, which is known as “analyzers.” Analyzers (occupying

the middle of the strategy continuum) are neither as solely innovation-oriented as prospectors

nor as efficiency-driven as defenders and compete on the basis of a hybrid strategy—i.e., they

focus on efficiency in some divisions and innovation in others. The empirical strategy-control

literature typically examines the extreme strategies of prospectors and defenders (Fisher

1995), which we follow as well in our hypothesis development.10

Although organizational theory proposes that control systems differ between firms

following different business strategies, limited empirical evidence supports these conjectures.

While some studies report results consistent with the link between organizational theory and

firm control systems (Miller and Friesen 1982; Govindarajan 1986; Bruggeman and Van der

Steede 1993), other studies indicate mixed support (Simons 1987; Agbejule and Jokipii

2009). Thus, Langfield-Smith (1997, 207) concludes that “our knowledge of the relationship

between [management control systems] and strategy is limited, providing considerable scope

for further research.” Similarly, in providing an overview of the strategy and organizational

8 Refer to Miles and Snow (1994) for strategy classification examples. For example, within the microprocessor

industry, Intel is classified as a prospector because this firm is a “leader in product innovation” while National

Semiconductor is classified as a defender because this firm “focus[es] narrowly on efficient chip production”

(Miles and Snow 1994, 14).

9 Business strategy is the process of aligning the firm to its market, where firms find “a way to respond to or

help shape current and future customer needs . . . . Over time, successful firms relate to the market and the

broader environment with a consistent approach that builds on their unique competencies and sets them apart

from their peers” (Miles and Snow 1994, 12). Each type of business strategy has relative advantages and

disadvantages and no one strategy is necessarily ideal. For prospectors, their primary advantage is market

innovation while their primary disadvantage is a tendency to overextend resources and hence risk lower

profitability. For defenders, their primary advantage is efficiency and stability while their primary disadvantage

is the risk of obsolescence due their inability to rapidly respond to market shifts. Refer to Miles and Snow

(1978, 2003) for further description. 10

Consistent with organizational theory predictions that prospectors and defenders are equally viable strategies

across industries, Bentley et al. (2013) show that both strategies exist within each industry and at various

investment opportunity set (IOS) levels “including industries where the IOS is the highest.” Refer to the

descriptive statistics for industry distribution details by strategy.

10

control literature concerning accounting control system design, Dent (1990, 21) concludes

that “[r]esearch at the interface between accounting and strategy is, as yet, undeveloped,”

especially in the area of control systems.

Further, while strategic management studies exploring the linkage between a firm’s

control system design and its strategy suggest that organizational structure is contingent upon

contextual factors such as firm size, environment, and technology (e.g., Gordon and Miller

1976; Merchant 1981, 1984, 1985; Govindarajan and Gupta 1985), Dent’s (1990, 10)

literature review concludes that “the relationships are weak and the conclusions

fragmentary.” Dent (1990, 10) calls specifically for more research “exploring relationships

between organizations’ strategies and their control systems, recognizing strategic posture as

an important variable in the contingency framework.” We extend the work in this area by

investigating whether firms’ business strategies are an underlying source of differences in

reported internal control weaknesses over financial reporting, as attested to by external

auditors.

Hypothesis Development

Section 404(a) of the Sarbanes-Oxley Act (SOX) requires that management annually

review and report on the effectiveness of their company’s internal controls and Section

404(b) requires that auditors attest to the effectiveness of internal controls over financial

reporting (effective November 15, 2004 for accelerated filers) (SEC 2002). Companies must

disclose “material weaknesses” in ICFR and are not required to disclose less severe internal

control weaknesses (SEC 2003, 2004).11

Due to the material weakness disclosure

requirements under SOX, several recent studies have examined firm-level determinants of

material weaknesses (e.g., Ge and McVay 2005; Doyle et al. 2007b).

11

Refer to Ge and McVay (2005) and Doyle et al. (2007b) for a thorough discussion of the differences between

significant deficiencies and material weaknesses in internal controls. We follow Doyle et al. (2007b) in focusing

on the mandated reporting on material weaknesses rather than the more voluntary significant deficiencies.

11

We first investigate whether business strategy is associated with the likelihood of

disclosing material weaknesses in ICFR. There are two primary reasons to expect a positive

association between business strategy and material weaknesses. First, organizational theory

predicts a theoretical link between business strategy and internal control as discussed

previously. Second, many of the firm-level attributes that are empirically linked to material

weaknesses in ICFR such as rapid growth, complexity, and profit variability are

characteristics of firms following an innovative prospector strategy. For example, rapid

growth and acquisitions or restructuring activities are associated with a higher likelihood of

internal control deficiencies (Ashbaugh-Skaife, Collins, and Kinney 2007; Doyle et al.

2007b). Likewise, firms with more stable operations are less likely to quickly outgrow their

control structure compared to rapidly growing firms (Doyle et al. 2007b). Prospector firms

are typically associated with more rapid and sporadic growth due to their continual pursuit of

new product-market opportunities in different domains as compared to their industry

counterparts. Conversely, defender firms are associated with stable growth patterns due to

narrowly-focused product-market areas where growth tends to occur within existing product

lines versus across product lines into new areas (Miles and Snow 1978, 2003).

Prior accounting research also finds that firm complexity is associated with material

weaknesses and/or internal control deficiencies (Ge and McVay 2005; Ashbaugh-Skaife et al.

2007; Doyle et al. 2007b). Miles and Snow (1978, 2003) predict that prospectors maintain

decentralized control to help facilitate their diverse and numerous operations which are

structured across product groups, resulting in greater complexity within their organizational

structure. Further, prospectors must be adept to quickly adapting to changing market

conditions to maintain their market-leadership position, and this results in greater complexity

(Miles and Snow 1978, 2003). Consistent with these predictions, Simons (1987) finds that

prospectors modify their internal control systems much more frequently than defenders, and

12

Hambrick (1983, 23) finds that prospectors have “more flexible, labor intensive capacity

configurations” compared to defenders, which have much more structured automated capital

configurations.

In relation to financial performance, prior accounting research finds that financially

weaker firms are more likely to encounter material weaknesses and/or disclose internal

control deficiencies (Ge and McVay 2005; Ashbaugh-Skaife et al. 2007; Doyle et al. 2007b;

Rice and Weber 2012). Prospectors risk lower profitability or reporting losses more

frequently compared to industry peers because they overextend their financial resources in

pursuit of riskier opportunities (Miles and Snow 1978, 2003). Conversely, defenders focus

heavily on cost reduction as their strategic advantage and thus are less likely to encounter

losses or overextend their resources. Prior research confirms that prospectors experience

lower profitability and financial distress more frequently than defenders (Hambrick 1983;

Ittner et al. 1997; Bentley et al. 2013). In summary, prior accounting research documents that

many characteristics of business strategy are associated with ICFR deficiencies, suggesting

empirical support for an association between strategy and material weaknesses in ICFR.

However, there are several reasons why the empirical data may not support this

prediction. First, prior studies in management and managerial accounting provide mixed

evidence on the association between business strategy and internal controls (e.g., Simons

1987; Dent 1990; Langfield-Smith 1997; Agbejule and Jokipii 2009). Second, although prior

studies link material weaknesses to some firm-level characteristics of business strategy, such

as growth or complexity attributes, (Ge and McVay 2005; Doyle et al. 2007b), the effect of

business strategy may be subsumed by these other determinants of ICFR deficiencies.

Furthermore, because public companies are expected to maintain effective ICFR under both

the Sarbanes-Oxley Act and the Foreign Corrupt Practices Act, the theoretical association

between business strategy and internal control may not extend to ICFR due to federal

13

regulations. Ultimately, whether business strategy is associated with reported internal control

weaknesses over financial reporting is an empirical question, so we state Hypothesis 1a in the

null form.

H1a: There is no association between business strategy and the likelihood of reported

material weaknesses in ICFR

Remediation of prior deficiencies can improve financial reporting quality and help

restore investor confidence (Ashbaugh-Skaife et al. 2008; Goh 2009). However, despite the

benefits of remediation, “management may not be willing to invest time and resources in

remediating these deficiencies because such efforts divert attention and resources from the

core businesses” (Goh 2009, 550). Prior research finds that firms are less likely to remediate

material weaknesses (or less likely to remediate material weaknesses in a timely manner)

when they report greater numbers of deficiencies and when they exhibit lower profitability

and more complex operations (Goh 2009; Johnstone, Li, and Rupley 2011), which are

characteristics of prospector firms (e.g., Miles and Snow 1978, 2003; Hambrick 1983).

Further, because prospector firms need to change their control systems more frequently

(Simons 1987), management may be less likely to remediate deficiencies in evolving or

outdated processes. For these reasons, we also examine the association between business

strategy and the likelihood of remediating reported deficiencies. We state Hypothesis 1b in

the null form:

H1b: There is no association between business strategy and the likelihood of

remediating reported material weaknesses in ICFR.

Next, Bentley et al. (2013) conclude that prospector firms have higher likelihoods of

restatements and litigation due to higher client business risk (CBR) rather than financial

reporting risk. They also find that auditors increase their effort for prospector firms but that

the incremental effort is insufficient to addresses this risk. We propose that weaker internal

controls and inappropriate control risk assessment among prospector clients provides a

14

complimentary explanation for these findings. Under current U.S. auditing standards, auditors

assess the client’s inherent and control risk as inputs to the nature, timing, and extent of

planned audit procedures. If the auditor assesses one of these inputs inappropriately, the

planned audit procedures may not reduce audit risk to an acceptably low level, leading to an

increased likelihood of restatement. Because a strong understanding of the client’s business

risk is a key input to the auditor’s control risk assessment (Bell et al. 1997; Bell et al. 2002),

Bentley et al.’s (2013) findings imply that auditors’ control risk assessments for prospector

clients may be inadequate due to an insufficient understanding of CBR. If this is the case,

then prospector clients should have higher likelihoods of undetected or unreported control

deficiencies in a given year which could later manifest as the restatements documented by

Bentley et al. (2013).

However, CBR also encompasses risks external to the financial reporting process such

that auditors could obtain a strong understanding of the client’s accounting information

system while simultaneously obtaining an inadequate understanding of CBR. As Bell et al.

(1997, 64) note: “Without looking outside of [the client’s accounting system,] it is difficult

for the auditor to learn whether it processes all business activities and measures them giving

due consideration to all relevant business risks.” Because it is unclear whether the failure to

assess CBR appropriately for prospector clients leads to inappropriate control risk

assessment, and therefore undetected or unreported control deficiencies, we pose Hypothesis

2 in the null form.

H2: There is no association between business strategy and the likelihood of

undetected or unreported control deficiencies.

Our final hypothesis examines the effect of Auditing Standard No. 5 (AS No. 5) in

2007 on the association between business strategy and ICFR deficiencies. AS No.5 adopts a

“top-down,” risk-based approach to internal control audits (PCAOB 2007; Doogar,

15

Sivadasan, and Solomon 2010). This principles-based approach to ICFR audits superseded

the more prescriptive “bottom-up” guidance offered under Auditing Standard No. 2 (AS No.

2) (PCAOB 2004, 2007). Under AS No. 5, auditors are encouraged to use risk-assessments to

guide ICFR audits and to scale ICFR audits to client size and complexity because “the size

and complexity of the company, its business processes, and business units, may affect the

way in which the company achieves many of its control objectives” (PCAOB 2007, AS 5.13).

Unlike the “one-size-fits-all” approach of AS No. 2, AS No. 5 allows for more variation in

audit approaches across clients, permits auditors to direct their attention towards higher risk

areas, and gives more room for auditors’ professional judgment (PCAOB 2007; Jiang and Wu

2009; Doogar et al. 2010).

As discussed in the previous section, business strategy is a contributing factor toward

a firm’s control structure and overall business risk. Hence, following AS No. 5, auditors may

alter control testing efforts depending on the client’s business strategy.

If auditors are responsive to AS No. 5, business strategy should influence their control risk

assessments because standards now permit a risk-based approach to evaluating ICFR. Thus,

under AS No. 5, auditors may be more likely to detect and/or report material weaknesses for

prospector audit clients, while control risk assessments for the less risky defender clients are

likely to remain unchanged or be scaled back. However, the results in Bentley et al. (2013)

suggest that auditors do not fully account for risky business strategy in their audits, so AS No.

5 may have no effect on the association between strategy and reported material weaknesses.

Ultimately, whether AS No. 5 improved auditors’ ability to use a risk-based approach to

identify material weaknesses is an empirical question, so we state our hypothesis in the null

form.

H3: The association between business strategy and reported material weaknesses is

no different in the pre- and post-AS No. 5 periods.

16

III. RESEARCH DESIGN

Sample Selection

To construct our sample, we identify 41,513 internal control opinions in Audit

Analytics over the period 2004-2011. We eliminate 10,329 observations missing data in

Compustat, CRSP, and Audit Analytics to calculate our control variables and 12,617

observations missing data to calculate the strategy variable. We exclude 1,069 Section 302

opinions where the firm reported a material weakness or significant deficiency but did not

report a material weakness under Section 404. Finally, we exclude 3,844 observations

associated with firms claiming an exemption from SOX Section 404 in the current year. Our

final sample consists of 13,654 observations. The sample selection is presented in Table 1.

[Insert Table 1 here]

Multivariate Model

Strategy Measure

We follow Bentley et al. (2013) in measuring a firm’s business strategy based on

Miles and Snow’s (1978, 2003) business strategy typology. The Bentley et al. (2013) archival

strategy measure extends Ittner et al.’s (1997) strategy measure and uses six ratios to capture

the different dimensions of business strategy. These six ratios are: (1) research and

development expense to sales (captures new product development), (2) selling, general and

administrative expenses to sales (captures marketing efforts), (3) annual percentage change in

sales (captures growth patterns), (4) employees to sales (captures production efficiency), (5)

net property, plant and equipment to total assets (captures capital structure), and (6) standard

deviation of total number of firm employees (captures organizational stability). Following

prior research (e.g., Ittner et al. 1997; Bentley et al. 2013), we compute these measures using

the rolling five-year average and rank these measures into quintiles for each firm-year

relative to other firms in the same industry.

17

Once all the six measures are ranked into quintiles, the quintile rank scores are

summed across each firm-year such that firms could receive a maximum score of 30 (where

the firm ranked in the top industry-quintile across all six measures) and a minimum score of 6

(where the firm ranked in the bottom industry-quintile across all six measures). Firms with

higher (lower) STRATEGY scores represent Prospector (Defender) firms. For instance, firms

with higher STRATEGY scores have more new product development, marketing and growth

activities, lower efficiency (i.e., a greater ratio of employees to sales and lower capital

intensity), and less organizational stability (i.e., greater fluctuations in total employees)

relative to industry competitors, which is characteristic of prospector firms.12

We interpret a

positive (negative) coefficient on STRATEGY to mean that firms with characteristics most

consistent with a prospector strategy are positively (negatively) associated with the dependent

variable of interest. However, for ease of exposition, we discuss STRATEGY in terms of

prospector firms relative to defender firms. The STRATEGY measure has been validated

using both archival (Bentley et al., 2013) and survey methods (Bentley, 2013).13

Regression Model

To test H1a, we estimate a logistic regression model predicting internal control

material weaknesses based on the models in Doyle et al. (2007b). The dependent variable in

this model equals one if the firm receives an adverse audit report concerning ICFR under

Section 404 of the Sarbanes-Oxley Act during year t and equals zero if the firm receives an

12

The capital intensity measure is reverse-coded “so that observations in the lowest (higher) quintile are given a

score of 5 (1)” (Bentley et al. 2013, 810). Bentley et al. (2013) provide more detail of how STRATEGY aligns

with firms following Prospector or Defender strategies in their Appendices 2 and 3.

13 For example, Bentley et al. (2013) perform factor analysis and find that all six STRATEGY components load

on a single factor. This finding suggests that the six ratios capture one underlying construct. Bentley et al.

(2013) also use canonical correlation analysis and redundancy index tests to find that STRATEGY is a different

construct than complexity and risk. Finally, Bentley (2013) concludes that firms following a Prospector or

Defender strategy are properly classified using survey responses from senior executives in management and

marketing positions to compare firms to the STRATEGY measure.

18

unqualified audit report on ICFR (MW).14

Firms that do not report on ICFR under Section

404 of the Sarbanes-Oxley act are excluded from the sample by construction. The

independent variable of interest is STRATEGY as discussed previously. We estimate Model 1

as follows:

(1)

We also test H1a using an alternative dependent variable (MW_COUNT) where the

dependent variable equals the number of material weaknesses reported during year t. We re-

estimate Model 1 as a negative binomial regression where the dependent variable equals the

number of material weaknesses reported during year t (MW_COUNT).15

The control variables represent firm-specific factors that prior literature indicates are

predictive of internal control deficiencies in order to determine whether business strategy is

predictive of material weaknesses incremental to these known determinants. Unless

otherwise specified, all variables are measured as of the year t year-end balance sheet date.

We control for firm size using the natural log of the market value of equity (LnMVE) and

control for firm age using the natural log of the number of years the firm has appeared in the

CRSP monthly return file (AGE). We include two controls for firm performance. First,

AGGR_LOSS is an indicator variable equal to one if earnings before extraordinary items

summed over the prior two years is less than zero. Second, we control for financial distress

using the probability of bankruptcy, following Shumway (2001) (BANKRUPTCY).

Doyle et al. (2007b) explore the role of complexity in material weakness disclosures

due to the increased costs involved in aggregating information and coordinating internal

controls across multiple divisions and geographic locations. As discussed previously, strategy

14

We perform supplemental tests by including significant deficiencies and material weaknesses reported under

Section 302 of the Sarbanes-Oxley Act over the period 2004-2011 and obtain consistent inferences

(untabulated).

15 We use negative binomial regression because the count dependent variable, MW_COUNT, is over-dispersed

(mean = 0.153, variance = 0.421) which can bias the standard errors downward in a Poisson regression (Long

and Freese 2006). Negative binomial regressions include an additional error term in a Poisson regression to

correct this bias in the standard errors.

19

and complexity reflect different constructs, although both are likely associated with internal

control deficiencies. For this reason, we include three controls for business complexity

following Doyle et al. (2007b): (1) the natural log of the number of special purpose entities

reported on Exhibit 21 to the firm’s 10-K (SPE), (2) the natural log of the number of business

and geographic segments as reported in Compustat Segment (SEGMENTS), and (3)

FOREIGN, an indicator variable equal to one if the firm reports foreign currency translations

during the year per Compustat.16

In addition, firm growth is an important determinant of both

business strategy and because firms can “outgrow” their internal controls through internal

growth and through acquisitions. The model includes three controls for growth following

Doyle et al. (2007b): (1) EXTREME_GROWTH, an indicator variable equal to one if change

in industry-adjusted sales growth is in the largest quintile; (2) the total dollar amount of

acquisitions in the current and prior year scaled by the firm’s market capitalization

(ACQ_VALUE); and (3) total restructuring charges in the current and prior year scaled by

market capitalization (RESTRUCTURING).

Because auditors’ abilities to detect internal control deficiencies are an important

dimension of audit quality, we include an indicator variable equal to one if the firm retains a

Big 4 auditor and equal to zero otherwise (BIGN). We include an indicator variable equal to

one if the firm reports a restatement in year t (RESTATEMENT) because restatements are

common indicators of material weaknesses (PCAOB 2004). We also control for management

changes because prior research suggests that corporate governance and management turnover

are associated with the disclosure of material weaknesses (Li, Sun, and Ettredge 2010;

Johnstone et al. 2011). In addition, prior research suggests that prospector firms experience

more frequent CEO turnover than other firms (Miles and Snow 1978, 2003; Thomas and

Ramaswamy 1994, 1996). We include an indicator variable equal to one if either the firm

16

We collect the number of SPEs reported by the firm following the procedure described in Feng, Gramlich,

and Gupta (2009).

20

experienced a change in CEO or CFO during the year (EXEC_TURN) or a change in the

board of directors during the year (BOD_TURN) as reported in the Audit Analytics Director

and Officer Changes module. We also control for the extent of outside monitoring using the

percentage of shares held by institutions as of year-end (INST_OWN). Finally, we include

indicator variables for industry using the Fama-French 12 industry classification and cluster

standard errors by firm and year to control for time-series and cross-sectional correlation

(Gow, Ormazabal, and Taylor 2010).

To test H1b proposing that prospector firms are less likely to remediate material

weaknesses, we examine only firms reporting a material weakness in year t and estimate a

logistic regression where the dependent variable equals one if the firm receives an

unqualified internal control report in year t+1 and equals zero otherwise (REMEDIATE).

Following prior research examining material weakness remediation, the independent

variables in this model are calculated as the average of the values in years t and t+1 (Goh

2009), when possible. Variables related to the original material weakness (RESTATEMENT

and MW_COUNT) and variables that are not meaningful when averaged (AGE, STRATEGY)

are included in the model at their values in year t. The value of BIGN for year t+1 is included

to capture the auditor in the year of remediation.

To test H2, we adapt Rice and Weber’s (2012) framework for identifying timely

versus untimely material weakness reporting. Because a restatement announcement implies a

material weakness in internal control (PCAOB 2007), Rice and Weber consider material

weaknesses announced in conjunction with, or in the next opinion following, a restatement to

be untimely whereas other material weaknesses are considered to be reported on a timely

basis. We estimate a multinomial logistic regression model where the dependent variable falls

into one of three categories each year: (1) a material weakness reported in conjunction with,

or in the next audit report following, a restatement of the financial statements or SOX 404

21

report (an untimely material weakness), (2) a material weakness reported in the absence of a

restatement announcement (a timely material weakness), or (3) no material weakness

reported during the year.17

The control variables follow Model 1. If auditors fail to assess

control risk appropriately due to an inadequate understanding of CBR, we should observe a

positive and significant coefficient for STRATEGY for untimely material weaknesses.

H3 proposes no difference in the relative associations between STRATEGY and MW in

the pre- and post-AS No. 5 periods. First, we estimate Model 1 separately in the pre- and

post-AS No. 5 periods (2004-2006 and 2007-2011, respectively) and test the equality of the

coefficients for STRATEGY across the two periods using seemingly unrelated estimation.

Second, we re-estimate the multinomial logistic regression examining the timeliness of

reported material weaknesses separately in the AS No. 2 and AS No. 5 periods. If AS No. 5

had no effect on auditor detection and reporting of material weaknesses, then we should

observe no difference in the associations between STRATEGY and both timely and untimely

material weaknesses.

IV. DESCRIPTIVE STATISTICS AND EMPIRICAL RESULTS

Table 2, Panel A presents descriptive statistics for the dependent and independent

variables in Model 1. Eight percent of audit opinions in the sample report a material

weakness in internal control over financial reporting. With respect to the business strategy

types, 7.4 percent of observations are classified as prospectors, 5.4 percent are classified as

17

Our approach differs from Rice and Weber (2012) whose unit of analysis is an individual restatement over the

period 2004-2008. Rice and Weber do not perform a firm-year level analysis and exclude subsequent

restatements announced by repeat restatement firms, firms that reported material weaknesses but no restatement,

and firms that never reported a material weakness. While these research design choices are appropriate for their

research question which examines the determinants of untimely reporting, we expand our analysis to include

non-restatement firms, non-material weakness firms, and repeat restatements in order to link the findings in

Table 4 to the timely versus untimely reporting framework. Furthermore, we conduct our analysis at the firm-

year level rather than the restatement level in order to include non-restatement observations. Our inferences are

consistent when we estimate a logistic regression comparing untimely to timely material weaknesses and when

restricting the sample only to firm years that are later restated.

22

defenders, and 87.2 percent are classified as analyzers.18

The mean market capitalization

(LnMVE) equals 6.9 or approximately $1 billion, consistent with the small-filer exclusion for

internal control attestation under Section 404 (Gao, Wu, and Zimmerman 2009). Table 2,

Panel B presents the industry composition of the sample. Business equipment is the most

commonly represented industry in the sample (24.4 percent). Manufacturing, Wholesale and

Retail, Healthcare, and Other Industries each comprise less than 15 percent of the sample and

all other industries each comprise less than 10 percent of the sample. Among prospector

firms, 30.3 percent operate in Business Equipment, 25.7 percent operate in Healthcare, and

13 percent operate in manufacturing with all other industries each representing less than 10

percent of the sample. Among defender firms, 22.9 percent operate in Other industries, 19

percent operate in Manufacturing, 16.3 percent in Chemicals, and 10.6 percent operate in

Business Equipment with all other industries each representing less than 10 percent of the

sample.19

[Insert Table 2 here]

Table 3 presents correlations among the variables. The correlations among most

variable pairs are small. STRATEGY is positively correlated with MW and MW_COUNT

(significant at the 5 percent level) indicating that firms following a prospector strategy (i.e.,

firms with higher STRATEGY scores) have more material weaknesses in their internal

controls. STRATEGY is negatively correlated with REMEDIATE indicating that firms

following a prospector strategy are less likely to remediate material weaknesses in their

internal controls. Regarding control variables, STRATEGY is positively correlated with

18

We follow Bentley et al. (2013) in defining “pure” prospector firms as those having the highest STRATEGY

scores (i.e., scores between 24 and 30), “pure” defender firms as those having the lowest STRATEGY scores

(i.e., scores between 6 to 12), and “hybrid” analyzer firms as occupying the middle of the STRATEGY

continuum (i.e., scores between 13 to 23). We find consistent results when we replace our discrete STRATEGY

measure with these indicator variables in untabulated robustness tests.

19 Bentley et al. (2013) find industry distributions between prospectors and defenders to be in relatively equal

proportions because they show the industry distributions of the STRATEGY measure prior to data cuts for

control variables. We show similar industry distributions when constructing the STRATEGY measure prior to

imposing our sample restrictions.

23

LNMVE, AGGR_LOSS, FOREIGN, EXTREME_GROWTH, EXEC_TURN, and BOD_TURN

and is negatively correlated with AGE, BANKRUPTCY, SPE, RESTRUCTURING, and

INST_OWN. These correlations are consistent with organizational theory and prior empirical

research. Although the correlation between BANKRUPTCY and LNMVE exceeds |0.50|,

multicollinearity diagnostics confirm the stability of the coefficient estimates and indicate

that these correlations are not problematic.20

[Insert Table 3 here]

Table 4 presents multivariate tests of H1. Columns 1 and 2 present the test of H1a

investigating whether firms following a prospector strategy are more likely to report a

material weakness and/or report a greater number of material weaknesses than firms

following a defender strategy. In Column 1, the coefficient for STRATEGY is positive and

significant (p<0.01) indicating that firms whose characteristics are most consistent with a

prospector strategy (hereafter prospectors) are more likely to report a material weakness. A

likelihood ratio test (untabulated) indicates that the addition of STRATEGY to the model adds

incremental explanatory power over a model based on prior research that omits STRATEGY

(χ2 =11.64, p<0.01). In Column 2, the coefficient for STRATEGY is positive and significant

(p<0.01) indicating that prospectors report a greater number of material weaknesses. Column

3 presents the test of H1b examining whether prospector firms are less likely to remediate

material weaknesses in year t+1. In Column 3, the coefficient for STRATEGY is negative and

marginally significant (p<0.10), indicating that prospectors are less likely than defenders to

remediate material weaknesses reported in year t during year t+1.

[Insert Table 4 here]

Table 5 presents the results of testing H2, which examines the timeliness of reported

material weaknesses. Column 1 presents the coefficient estimates comparing firms reporting

20

The largest variance inflation factor equals 3.74 and the condition index equals 19.42. Both tests indicate that

multicollinearity is not a concern in our models.

24

a timely material weakness to firms reporting no material weakness, and Column 2 presents

the coefficient estimates comparing firms reporting an untimely material weakness to firms

reporting no material weakness. If auditors fail to assess control risk appropriately for

prospector clients, the coefficient for STRATEGY in column 2 should be positive and

significant. The coefficient for STRATEGY is positive and significant (p<0.01) in Column 2

and also positive and marginally significant in Column 1 (p<0.10).21

The results in Table 5 indicate that firms with greater prospector characteristics are

more likely to report both timely and untimely material weaknesses relative to firms reporting

no material weaknesses. These results reveal that while firms and auditors are more likely to

report timely material weaknesses for prospector firms than other firms, they also have

greater difficulty identifying and reporting material weaknesses among prospector firms,

potentially due to inadequate control risk assessment as implied by a failure to gain a

sufficient understanding of the client’s CBR. This finding has three additional implications.

First, because prospectors are more likely to report untimely material weaknesses than other

firms, the absence of a material weakness in the auditor’s attestation report may be less

diagnostic of internal control quality to financial statement users of prospector firms than

other firms. Second, these results imply that the increased likelihood of material weaknesses

among prospector firms is not driven solely by prospector’s greater propensity for

restatements as documented in Bentley et al. (2013). Finally, these results suggest that

focusing on control risk assessment is a potential area for improved audit quality among

prospector audit clients.

[Insert Table 5 here]

Table 6 Panel A presents the test of H3 regarding the association between business

strategy and material weaknesses in the AS No. 2 and AS No. 5 periods. The coefficient for

21

When we estimate the same regression using a sample of restatement-year observations as in Rice and Weber

(2012), our results are similar.

25

STRATEGY is positive and significant (p<0.05) during the AS No. 2 period and positive and

significant (p<0.01) during the AS No. 5 period. The coefficients for STRATEGY during the

two periods are not statistically different from one another. Because the “top-down” risk-

based approach to internal control attestation did not change the strength of the association

between business strategy and material weaknesses, these results are consistent with the

conclusion in Bentley et al. (2013) that auditors devote insufficient effort to understanding

the risks faced by prospector firms.

Table 6 Panel B presents results examining how business strategy is associated with

the timeliness of material weakness reporting in the AS No. 2 and As No. 5 periods. The first

two columns display regressions for the AS No. 2 period. The coefficient for STRATEGY is

significant only in the “untimely” column (p<0.05). This finding indicates that auditors had

difficulty linking business strategy and the presence of material weaknesses during this

regulatory period. The third and fourth columns present the results for the AS No. 5 period.

In columns 3 and 4, STRATEGY is significant (p<0.10) only in the “timely” column. These

results, when combined with the results in Panel A, suggest that business strategy is a

determinant of material weaknesses in both regulatory periods. However, AS No. 5 improved

auditor detection of material weaknesses among riskier clients because material weaknesses

are more likely to be reported in a timely manner under AS No. 5 rather than upon revelation

of a restatement under AS No. 2. This result is consistent with the intent of AS No. 5, which

mandated a top-down, risk-based approach to internal control auditing.

[Insert Table 6 here]

Sensitivity Tests

In untabulated analysis, we examine whether firms following a prospector strategy are

more likely to report company-level, account-level, staffing, complexity, and general material

weaknesses (Doyle et al. 2007b; Ge and McVay 2005). The coefficient for STRATEGY is

26

positive and significant for each of these five types of material weaknesses. These results

indicate that business strategy consistently predicts a significantly higher likelihood of

internal control material weaknesses among prospectors than among defenders regardless of

the nature of the material weakness.

Next, Doyle et al. (2007b) argue that firms with stronger corporate governance should

have fewer internal control problems. Similar to Doyle et al. (2007b), many governance

variables limit our sample size due to missing data in commercial databases. Our main tests

include governance variables with minimal data restrictions: CEO/CFO turnover, board of

director turnover, and institutional ownership. When we include additional governance

variables from Corporate Library that significantly reduce our sample size including (1)

whether the CEO is chairman of the board, (2) the independence percentage of the board, and

(3) the number of board meetings, we continue to find consistent results in our hypothesis

tests.

While our descriptive statistics indicate no size difference between prospector and

defender firms (lnMVE), we consider whether firm size is correlated with strategy and hence

an alternative explanation for our results.22

In untabulated analysis, we split the sample based

on median firm size and continue to find consistent results in both small and large firm

subsamples. Therefore, we conclude that our results are not driven by effects related to firm

size.

In our tabulated regressions, we use a continuous measure to capture the strategy of each

firm. Lower values of this measure represent defenders, while higher values represent

prospectors. Following Bentley et al. (2013), we construct indicator variables for defenders

and prospectors and replace the continuous strategy measure with the indicator variables in

22

Finding insignificant size differences between prospectors and defenders is consistent with both

organizational theory expectations (Miles and Snow 1978, 2003) and prior empirical research findings (e.g.,

Smith, Guthrie, and Chen 1989; Bentley et al. 2013).

27

each of the tabulated regressions. Results using the indicator variables are consistent with the

tabulated results.

We consider a firm's business strategy to be an underlying determinant of internal

control weaknesses because of the relative stability of a firm’s strategy over time (Miles,

Snow, Meyer, and Coleman1978; Mintzberg 1978; Snow and Hambrick 1980; Hambrick

1983). To test this assumption, we examine the consistency of firm classification based on

strategy during our sample time period (2004-2011). Firms almost never switch from a

prospector to a defender (or vice-versa) in our sample period (only one firm makes this

switch), consistent with theoretical expectations. Further, we note that firms classified as

prospectors (defenders) at the start of our sample retain consistently high (low) STRATEGY

scores throughout the entire sample period. Finally, we find that less than 2 percent of firms

change their STRATEGY score by more than 3 values (out of a total scale of 24) from year-to-

year. Altogether, we confirm that a firm’s strategy is consistent over time throughout our

sample period. These results imply that business strategy serves as one of the underlying,

firm-specific characteristics that determine the firm’s control structure, which aligns with

theoretical expectations.

VI. CONCLUSION

This study links organizational theory to accounting to examine whether a firm’s

business strategy affects its internal controls over financial reporting (ICFR). Using archival

data, our results suggest that business strategy significantly impacts firms’ ICFR, incremental

to known determinants of material weaknesses. We find evidence that innovation-oriented

“prospector” strategy firms are significantly more likely to report material weaknesses than

efficiency-oriented “defender” strategy firms. Prospector firms also report a greater number

of material weaknesses and are less likely to remediate material weaknesses in the following

year. Second, we find that prospector firms are significantly more likely to report both timely

28

and untimely material weaknesses relative to defender firms. This result suggests that, in

addition to prospectors having more material weaknesses than defender firms, auditors have

greater difficulty in identifying and reporting material weaknesses for their prospector clients.

Third, we investigate whether auditors’ focus on top-down risk assessment in the post AS No.

5 period improved their internal control reporting. We find a strong association between

strategy and material weaknesses in both the pre- and post-AS No. 5 periods; however, AS

No. 5 appears to have improved auditors’ detection of material weaknesses among prospector

firms, as indicated by a shift in the reporting timeliness of MWs from untimely reporting

under AS No. 2 to timely reporting under AS No. 5. Altogether, our findings suggest that (1)

prospector firms have weaker controls than defender firms, (2) auditors have greater

difficulty detecting and/or reporting deficiencies in ICFR among prospector clients, and (3)

the implementation of AS No. 5 improved auditors’ ability to identify MWs for prospector

clients in a timely manner.

Our research is subject to two primary limitations. While we rely on Miles and

Snow’s (1978, 2003) strategy typology and prior empirical research to create our business

strategy measure, our measure is assessed with noise. Second, we cannot control for selection

bias associated with the firm’s decision to adopt a particular strategy. Organizational theory

posits that a firm’s business strategy is chosen early in a firm’s life cycle (initiated by

management’s commitment of resources towards pursing certain objectives) and remains

relatively stable over time. Firm strategy reflects a fundamental difference in the structure of

firms where firms following different strategies have to align their strategic objectives across

their firm-wide functions (i.e., entrepreneurial, engineering, and administrative functions) to

be successful (Miles and Snow 1978, 2003). Hence, a firm following a prospector strategy

and a firm following a defender strategy are inherently different types of firms. Therefore,

econometric corrections for self-selection such as propensity-score matching where firms are

29

matched on otherwise equal dimensions excluding strategy is inconsistent with theoretical

expectations. Similarly, because firms are more likely to "adjust rather than change their

strategies” (Snow and Hambrick 1980, 529, italics in text), the stickiness of the strategy

measure restricts the ability to define an appropriate instrument within a two-stage regression

model in order to correct for selection effects. However, because a firm’s business strategy is

generally constant over time, identifying a firm’s strategy may serve as a useful context for

understanding differences in the control structural arrangements and weaknesses among

firms.

This study contributes to the literature in several ways. First, by linking organizational

theory to the accounting literature, we provide a theoretical framework for understanding

determinants of material weaknesses reported in prior studies and illustrate that differences in

business strategy serve as an underlying determinant of firms’ control systems. Second, our

findings suggest that internal control evaluation and testing is a specific area for improvement

in audit quality among prospector clients and that Auditing Standard No. 5 appears to have

improved auditor detection of material weaknesses by focusing auditors on a top-down risk-

based approach to auditing internal controls. These findings have important implications for

stakeholders evaluating the likely strength of a firm’s internal controls, practitioners planning

and performing financial statement audits, and regulators conducting risk-based inspections.

30

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37

APPENDIX

Variable Definitions

Variable Definition

STRATEGY Discrete score with values ranging from 6 to 30 where high (middle) [low] values

indicate prospector (analyzer) [defender] firms, respectively; Refer to Bentley et al.

(2013) for score construction.

MW Indicator variable equal to 1 when the firm’s auditor reports a material weakness

under SOX Section 404.

MW_COUNT The number of material weaknesses reported under SOX Section 404 in year t.

REMEDIATE Indicator variable equal to 1 when the firm’s auditor reports no SOX Section 404

material weaknesses in year t+1 after having reported a material weakness in year t.

MW_TIMELINESS Indicates when a MW is reported: set to 0 if year t is never associated with a MW, set

to 1 if a MW is reported in connection with the filing of year t’s financial statements

(considered a timely report), and set to 2 if a MW is revealed through a restated SOX

section 404 disclosure or in the aftermath of a future restatement of year t’s financial

statements (considered a late report).

LnMVE The natural log of the firm’s market capitalization (shares outstanding times price per

share as of year-end).

AGE The number of years since the firm first appeared on CRSP.

AGGR_LOSS Indicator variable equal to 1 if the sum of income before extraordinary items in years

t-1 and t is less than zero.

BANKRUPTCY The probability of bankruptcy, following Shumway (2001).

SPE The natural log of the number of special purpose entities reported on Exhibit 21 of the

firm’s 10-k.

SEGMENTS The natural log of the sum of the firm’s business and geographic segments reported

on Compustat.

FOREIGN Indicator variable equal to 1 if the firm had foreign income.

ACQ_VALUE The value of acquisitions in the current and prior-year scaled by the firm’s market

capitalization.

EXTREME_GROWTH Indicator variable equal to one if change in industry-adjusted sales growth is in the

largest quintile.

RESTRUCTURING The total restructuring charges in the current and prior year scaled by the firm’s

market capitalization.

RESTATEMENT Indicator variable equal to 1 if the firm announces a restatement during year t.

BIGN Indicator variable equal to 1 if the company is audited by a Big 4 audit firm.

EXEC_TURN Indicator variable equal to 1 if the firm experiences turnover of the CEO or CFO

during year t as indicated by Audit Analytics.

BOD_TURN Indicator variable equal to 1 if the firm experiences turnover on the board of directors

during year t as indicated by Audit Analytics.

INST_OWN The percentage of the firm’s shares owned by institutional owners.

38

TABLE 1

Sample Selection

Internal control opinion data 2004-2011 41,513

Exclude firm years lacking COMPUSTAT data to construct control variables (6,553)

Exclude firm years lacking CRSP data to construct control variables (3,750)

Exclude firm years lacking Audit Analytics data to construct control variables (26)

Exclude firm years lacking data to construct STRATEGY measure (12,617)

Exclude Section 302 MWs/significant deficiencies without corresponding Section 404 MWs (1,069)

Exclude firm years claiming a SOX 404 exemption (3,844)

Final Sample 13,654

39

TABLE 2

Descriptive Statistics

Panel A: Comparative Descriptive Statistics

Full Sample (N=13,654) Prospectors (N=1,015) Defenders (N=738)

Variable Mean Med. Q1 Q3 Std. Dev. Mean Med. Mean Med.

STRATEGY 18.168 18.000 16.000 21.000 3.591 25.136 25.000 10.959 11.000

MW 0.081 0.000 0.000 0.000 0.273 0.106 0.000 0.046 0.000

MW_COUNT 0.153 0.000 0.000 0.000 0.649 0.233 0.000 0.095 0.000

REMEDIATE 0.681 1.000 0.000 1.000 0.466 0.600 1.000 0.769 1.000

LnMVE 6.915 6.753 5.599 8.036 1.776 6.572 6.339 6.505 6.390

AGE 2.841 2.773 2.398 3.332 0.687 2.481 2.398 2.875 2.833

AGGR_LOSS 0.260 0.000 0.000 1.000 0.439 0.500 1.000 0.234 0.000

BANKRUPTCY 4.501 5.000 2.000 7.000 2.871 4.916 5.000 5.102 5.000

SPE 1.008 0.693 0.000 1.792 1.203 0.726 0.000 1.129 1.099

SEGMENTS 1.521 1.609 1.099 1.946 0.574 1.474 1.609 1.490 1.609

FOREIGN 0.162 0.000 0.000 0.000 0.368 0.158 0.000 0.146 0.000

ACQ_VALUE 0.054 0.001 0.000 0.038 0.168 0.055 0.001 0.053 0.000

EXTREME_GROWTH 0.200 0.000 0.000 0.000 0.400 0.365 0.000 0.198 0.000

RESTRUCTURING 0.015 0.000 0.000 0.007 0.075 0.012 0.000 0.025 0.000

RESTATEMENT 0.073 0.000 0.000 0.000 0.261 0.064 0.000 0.070 0.000

BIGN 0.860 1.000 1.000 1.000 0.347 0.834 1.000 0.851 1.000

EXEC_TURN 0.237 0.000 0.000 0.000 0.425 0.249 0.000 0.215 0.000

BOD_TURN 0.470 0.000 0.000 1.000 0.499 0.488 0.000 0.432 0.000

INST_OWN 0.604 0.707 0.377 0.870 0.328 0.521 0.600 0.616 0.704

40

TABLE 2 (Continued)

Panel B: Industry Composition

Industry

Full Sample

(N=13,654)

Prospectors

(N=1,015)

Defenders

(N=738)

Consumer Non-durables 915 6.7% 41 4.04% 25 3.39%

Consumer Durables 464 3.4% 15 1.48% 25 3.39%

Manufacturing 1,808 13.2% 132 13.00% 140 18.97%

Energy 902 6.6% 70 6.90% 57 7.72%

Chemicals 466 3.4% 2 0.20% 120 16.26%

Business Equipment 3,325 24.4% 307 30.25% 78 10.57%

Telecom 553 4.1% 20 1.97% 33 4.47%

Wholesale and Retail 1,859 13.6% 90 8.87% 43 5.83%

Healthcare 1,439 10.5% 261 25.71% 48 6.50%

Other 1,923 14.1% 77 7.59% 169 22.90%

Note: The appendix provides definitions of variables in this table. The descriptive statistics in Panel A are

shown for the full set of observations and separately for prospectors and defenders. The sample sizes for the

variable REMEDIATE are 827, 70, and 26 for the full, prospector, and defender samples. All other sample sizes

are indicated in the table. Bolded means and medians are significantly different between prospectors and

defenders at the 5 percent level. The industry composition in Panel B is based on the 12 industries in Fama and

French (1988).

41

TABLE 3

Correlation Table

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18)

(1) STRATEGY

(2) MW 0.05

(3) MW_COUNT 0.05 0.79

(4) REMEDIATE -0.08 N/A -0.32

(5) LnMVE 0.03 -0.14 -0.12 0.03

(6) AGE -0.16 -0.08 -0.08 -0.01 0.27

(7) AGGR_LOSS 0.13 0.12 0.12 -0.04 -0.38 -0.16

(8) BANKRUPTCY -0.04 0.12 0.12 -0.03 -0.56 -0.14 0.45

(9) SPE -0.08 -0.07 -0.06 0.02 0.27 0.17 -0.06 -0.05

(10) SEGMENTS 0.00 0.00 0.01 -0.11 0.26 0.22 -0.07 -0.14 0.04

(11) FOREIGN 0.02 0.02 0.03 -0.01 0.09 0.01 -0.01 -0.04 -0.05 0.19

(12) ACQ_VALUE 0.01 0.01 0.00 0.03 -0.11 -0.01 0.07 0.10 0.10 0.01 -0.01

(13) EXTREME_GROWTH 0.11 0.02 0.01 -0.02 0.01 -0.15 -0.01 -0.10 -0.08 -0.01 0.01 0.08

(14) RESTRUCTURING -0.03 0.02 0.02 0.01 -0.16 0.00 0.22 0.18 0.02 0.04 0.02 0.13 -0.06

(15) RESTATEMENT 0.00 0.21 0.24 -0.09 -0.07 -0.03 0.05 0.07 -0.03 -0.02 0.00 0.00 -0.01 0.01

(16) BIGN -0.01 -0.06 -0.05 0.04 0.34 0.06 -0.09 -0.11 0.17 0.10 0.04 0.00 -0.05 0.02 -0.01

(17) EXEC_TURN 0.03 0.08 0.09 -0.01 -0.07 0.01 0.11 0.08 0.04 -0.01 -0.03 0.03 -0.04 0.06 0.06 -0.01

(18) BOD_TURN 0.02 0.01 0.02 0.00 0.03 0.09 0.07 0.01 0.12 0.01 -0.05 0.02 -0.03 0.05 0.03 0.03 0.27

(19) INST_OWN -0.06 -0.08 -0.08 0.08 0.18 0.15 -0.21 -0.20 0.16 -0.01 -0.05 -0.01 -0.04 -0.08 -0.01 0.15 0.02 0.11

Note: The appendix provides definitions of variables in this table. The table shows Pearson correlations among regression variables. Bolded correlations are significant at the

5 percent level. The variable REMEDIATE is constructed conditional on the existence of a material weakness (MW) in year t, thus the correlation between these two variables

is omitted.

42

TABLE 4

Business Strategy and the Probability of Reporting

and Remediating a Material Weakness

(1) (2) (3)

MW MW_COUNT REMEDIATE

STRATEGY 0.033*** 0.038*** -0.055*

(3.273) (3.300) (-1.828)

LnMVE -0.194*** -0.230*** 0.066

(-3.702) (-6.183) (0.843)

AGE -0.234*** -0.166** -0.281***

(-3.538) (-2.296) (-3.037)

AGGR_LOSS 0.248* 0.273*** -0.012

(1.880) (2.860) (-0.047)

BANKRUPTCY 0.056 0.073*** -0.009

(1.552) (4.044) (-0.251)

SPE -0.093*** -0.095** 0.011

(-3.547) (-2.169) (0.135)

SEGMENTS 0.284*** 0.374*** -0.362*

(2.603) (4.381) (-1.892)

FOREIGN 0.226 0.325*** -0.026

(1.521) (3.265) (-0.275)

ACQ_VALUE -0.152 0.154 0.101

(-0.791) (0.776) (0.479)

EXTREME_GROWTH 0.147* 0.151* -0.327**

(1.762) (1.701) (-1.981)

RESTRUCTURING -0.623*** -0.208 0.047

(-2.855) (-0.523) (0.058)

RESTATEMENT 1.643*** 1.482*** -0.063

(11.566) (17.840) (-0.510)

BIGN -0.137 -0.273** 0.204

(-0.886) (-2.275) (0.819)

EXEC_TURN 0.471*** 0.526*** -0.383

(3.581) (6.880) (-1.553)

BOD_TURN -0.032 0.031 0.169

(-0.323) (0.410) (0.694)

INST_OWN -0.305* -0.309** 0.185

(-1.774) (-2.273) (0.430)

MW_COUNT -0.452***

(-7.527)

Industry Indicators Yes Yes Yes

Year Indicators No Yes No

Cluster Firm / Year Firm Firm / Year

Observations 13,654 13,654 827

Pseudo R2 0.115 0.113

Wald p-value <0.000

ROC Curve 0.749 0.727

43

Note: The appendix provides definitions of variables in this table. The table shows coefficients (z-statistics)

from regressions examining the association of STRATEGY with reporting a MW (column 1), the number of

MWs reported (column 2), and the remediation of a reported MW (column 3). Columns (1) and (3) use logistic

regression, and Column (2) uses negative binomial regression. In Column (3) the following independent

variables are averaged over the remediation period (see Goh 2009): lnMVE, AGGR_LOSS, BANKRUPTCY,

SPE, SEGMENTS, FOREIGN, ACQ_VALUE, EXTREME_GROWTH, RESTRUCTURING, and INST_OWN.

BIGN, EXEC_TURN, and BOD_TURN represent the auditor or turnover occurring during the remediation

period, and STRATEGY, AGE, RESTATEMENT, and MW_COUNT are the values as of the end of year t when

the MW is reported. ***, **, and * indicate statistical significance at the 1, 5, and 10 percent levels, respectively

based on two-tailed tests except where indicated by a sign prediction.

44

TABLE 5

Business Strategy and the Timeliness of Reporting a Material Weakness

(1) (2)

Variables TIMELY LATE

STRATEGY 0.026* 0.048***

(1.841) (2.755)

LnMVE -0.227*** -0.220***

(-5.192) (-4.000)

AGE -0.101 -0.112

(-1.138) (-1.158)

AGGR_LOSS 0.363*** 0.274**

(3.157) (1.981)

BANKRUPTCY 0.063*** 0.045*

(2.984) (1.939)

SPE -0.074 -0.014

(-1.473) (-0.241)

SEGMENTS 0.390*** 0.197*

(4.019) (1.681)

FOREIGN 0.496*** -0.020

(4.472) (-0.149)

ACQ_VALUE 0.112 0.034

(0.487) (0.142)

EXTREME_GROWTH 0.172 0.209*

(1.588) (1.707)

RESTRUCTURING 0.139 -0.194

(0.360) (-0.277)

RESTATEMENT 1.128*** 1.499***

(9.669) (13.471)

BIGN -0.418*** 0.171

(-3.098) (0.986)

EXEC_TURN 0.557*** 0.358***

(5.974) (3.424)

BOD_TURN 0.030 -0.046

(0.345) (-0.478)

INST_OWN -0.385** 0.213

(-2.494) (1.043)

Industry & Year Indicators Yes

Cluster Firm

Observations 13,654

Pseudo R2 0.155

Note: The appendix provides definitions of variables in this table. The table shows coefficients (z-statistics)

from multinomial logistic regressions examining the association between business STRATEGY and the

timeliness of reported material weaknesses. The dependent variable is MW_TIMELINESS. The base group

includes all observations where a MW for year t is never reported. Column (1) compares the base group to

observations where a MW is reported for year t at the end of year t, and Column (2) compares the base group to

observations where a MW is revealed in association with the restatement of year t’s SOX 404 opinion or the

restatement of financial statements in a later year.

45

TABLE 6

The Effect of AS No. 5 on the Relation between

Business Strategy and Material Weaknesses

Panel A – Strategy and MWs in the Pre and Post-AS5 Periods

(1) (2)

Variables Pre AS 5 Post AS 5

STRATEGY 0.020*** 0.046**

(2.602) (2.350)

LnMVE -0.338*** -0.132***

(-17.116) (-2.971)

AGE -0.110** -0.208**

(-2.285) (-2.070)

AGGR_LOSS 0.446*** 0.207*

(4.228) (1.825)

BANKRUPTCY 0.009 0.091***

(0.254) (3.126)

SPE -0.042 -0.072

(-1.538) (-1.193)

SEGMENTS 0.472*** 0.180*

(3.271) (1.871)

FOREIGN 0.257 0.440*

(1.421) (1.688)

ACQ_VALUE 0.191 0.142

(0.898) (0.770)

EXTREME_GROWTH 0.143*** 0.237

(2.951) (1.472)

RESTRUCTURING 1.497 -0.055

(0.978) (-0.381)

RESTATEMENT 1.328*** 1.615***

(6.182) (9.249)

BIGN -0.035 -0.460***

(-0.337) (-2.774)

EXEC_TURN 0.404*** 0.604***

(3.567) (3.187)

BOD_TURN -0.058 0.005

(-0.844) (0.027)

INST_OWN -0.043 -0.469*

(-0.221) (-1.741)

Industry Indicators Yes Yes

Cluster Firm / Year Firm / Year

Observations 4,983 8,671

Pseudo R2 0.123 0.116

ROC Curve 0.744 0.768

Test of coefficients: STRATEGY in PRE vs. POST periods: Chi-square = 1.38, p = 0.2395

46

Panel B – The Timeliness of MW Reporting in the Pre and Post-AS5 Periods

Pre AS 5 Post AS 5

Variables TIMELY LATE TIMELY LATE

STRATEGY

0.015 0.045**

0.042* 0.051

(0.922) (2.362)

(1.826) (1.494)

LnMVE

-0.319*** -0.274***

-0.129** -0.159**

(-5.861) (-3.982)

(-2.166) (-2.072)

AGE

-0.050 -0.078

-0.167 -0.128

(-0.501) (-0.698)

(-1.198) (-0.811)

AGGR_LOSS

0.398*** 0.365**

0.320* 0.167

(2.659) (2.243)

(1.798) (0.747)

BANKRUPTCY

0.050* 0.013

0.062* 0.122***

(1.952) (0.457)

(1.773) (3.114)

SPE

-0.059 0.002

-0.060 -0.002

(-0.981) (0.032)

(-0.810) (-0.025)

SEGMENTS

0.541*** 0.267*

0.166 0.135

(4.332) (1.942)

(1.199) (0.708)

FOREIGN

0.413*** -0.081

0.582*** 0.065

(2.926) (-0.467)

(3.582) (0.299)

ACQ_VALUE

0.709 -0.422

0.018 0.031

(1.258) (-0.600)

(0.063) (0.134)

EXTREME_GROWTH

0.172 0.054

0.121 0.496**

(1.229) (0.350)

(0.729) (2.560)

RESTRUCTURING

1.839 -2.121

0.187 0.061

(1.157) (-0.983)

(0.413) (0.099)

RESTATEMENT

1.004*** 1.517***

1.394*** 1.450***

(7.013) (11.590)

(7.321) (6.694)

BIGN

-0.294* 0.251

-0.608*** 0.090

(-1.820) (1.209)

(-3.172) (0.335)

EXEC_TURN

0.595*** 0.340***

0.507*** 0.385**

(4.869) (2.716)

(3.617) (2.008)

BOD_TURN

0.013 -0.019

0.071 -0.058

(0.116) (-0.167)

(0.500) (-0.312)

INST_OWN

-0.167 0.429*

-0.641*** -0.048

(-0.886) (1.773)

(-2.843) (-0.143)

Industry & Year Indicators

Yes

Yes

Cluster

Firm

Firm

Observations 4,983 8,671

Pseudo R2 0.116 0.118

Note: The appendix provides definitions of variables in this table. Panel A shows coefficients (z-statistics) from

logistic regressions examining the association between reported material weaknesses and business STRATEGY

before and after implementation of Audit Standard No. 5. We perform the test of coefficient equivalence across

the two models using seemingly unrelated estimation. Panel B shows coefficients (z-statistics) from multinomial

logistic regressions examining the association between business STRATEGY and the timeliness of reported

material weaknesses before and after implementation of Audit Standard No. 5. ***, **, and * indicate statistical

significance at the 1, 5, and 10 percent levels, respectively based on two-tailed tests.