CEO Succession, Audit Pricing, and Firm Value: The Role ofSupply Chain Knowledge

Abstract

This study examines several issues in the context of CEOs’ supply chain knowledge.
Organizational complexity, particularly the diversification of organizational structure, isessential to the growing trend of Supply Chain Management. Consistent with this notion, we
find that CEOs with supply chain knowledge are more likely to be recruited by firms withhigher organizational complexity. We also show that among outsider CEOs audit fees are
greater when the CEO previously worked for a partner within the supply chain, and that
insider CEOs demand more audit effort or greater audit scope and coverage. Appointing a
new CEO who previously served for a partner within the supply chain or promoting a new
CEO from inside an organization adds greater value to firms than that outside a supply chain,suggesting that new CEOs with supply chain knowledge is valued at a premium.

These

results are robust to alternative specifications and analyses designed to mitigate the concernthat our findings are driven by factors leading to the CEO change.

Keywords: audit pricing; CEO succession; firm value; organizational complexity; supply
chain knowledge

CEO Succession, Audit Pricing, and Firm Value: The Role ofSupply Chain Knowledge

1. Introduction
Incoming Chief Executive Officers (CEOs) often commence significant changes after
succession.

Major changes to a firm’s mission and strategy may significantly affect both

operational decisions and financial policies (Bills, Lisic, and Sedel, 2017).

Even without thepresence of strategic changes, an incoming CEO may bring a distinctive management stylethat affects firms’ financial reporting decisions.

Extant research indicates that firms change

financial policies after CEO turnover (Pan, Wang, and Weisbach, 2016), and that executives
can exercise their influence over accounting quality (Ge, Matsumoto, and Zhang, 2011).
Risk sharing and the specialization of management are the major advantages of thecorporate form of organization, and yet at the expense of “separation of ownership andcontrol” (Fama and Jensen, 1983; Demsetz, 1983). In assessing the risk of material
misstatement, auditors evaluate the company’s control environment (Public Company
Accounting Oversight Board, 2010a, AS 1101, para. 6). The risk increases with amount ofevidence that the auditor should obtain (PCAOB, 2010b, AS 1105, para. 5), which may, inturn, affects audit pricing.
The CEO turnover process increases the risk of an audit litigation and therefore audit
fees. For instance, several studies explore the issues on CEOs subject to non-routine change
versus routine change as well as forced turnover versus voluntary turnover (e.g., Huang et al.,2014; Pourciau, 1993; Wells, 2002).

They generally find that non-routine/forced CEO

turnover negatively affects earnings or audit quality. Specifically, a non-routine CEO change1creates uncertainty regarding both the company’s future operating strategy and the new CEO’s
ability to effectively manage the organizational changes (Clayton, Hartzell, and Rosenberg,2005).
Another line of research argue that stakeholder perceptions of risk associated withCEO turnover may be lower when a new CEO is promoted from inside a firm relative toexternal replacement (Bebchuk and Stole, 1993; Bills et al., 2017; Laux, 2012). However,external CEOs come with considerable upside may be also accompanied by significant
downside – risk (Ranft et al., 2006). The positive relation between new CEOs and audit fees
is further mitigated when the new CEO is promoted from within the firm relative to those
hired from outside the firm, and that this mitigation effect is greater for heir apparent insiders
than for non-heir apparent insiders (Bills et al., 2017).
A growing body of research finds that CEO characteristics affect audit risk.

Forinstance, Johnson et al. (2012) and Judd, Olsen, and Stekelberg (2017) document that auditors
charge higher fees when a client’s CEO exhibits behavior and attitudes consistent withnarcissism.

Hribar et al. (2012) find that overconfident CEOs report more aggressively, andthat auditors charge higher fees to compensate for aggressive reporting. Harjoto, Laksmana,
and Lee (2015) provide evidence that firms with female and ethnic minority CEOs pay
significantly higher audit fees than those with male Caucasian counterparts. Kim, Li, and Li(2015) document a positive relation between CEO portfolio vega equity and audit fees,
suggesting that auditors consider higher earnings-management risk to be associated withequity compensation.

Moreover, Kalelkar and Khan (2016) find that firms with a financial

expert CEO pay lower audit fees. The findings of the above studies increase ourunderstanding that auditors consider the CEO’s characteristics to be a relevant factor in audit
2

pricing decisions.
We extend the relevant literature by investigating a relatively neglected aspect of CEO
characteristics around turnover, namely, whether a new CEO previously worked for acompany within/outside a supply chain influences audit pricing. Outsider CEOs arecommonly hired to formulate and implement strategic change or replace a poorly performing
CEO (Elosge et al., 2018). Audit committees responsible for setting and approving audit fees
may demand greater assurance surrounding uncertainty associated with new CEOs who areless likely to adapt their strategies in response to environmental changes, which would lead toan increase in audit fees. Moreover, auditors’ strategic risk assessment is associated with theoutcome of the audit process (Bruynseels and Willekens, 2012). Accordingly, a company’s
strategic interactions with the incoming CEO’s career experience can have important
implications of audit pricing.
Prior studies provide evidence on the negative relation between auditors’ supply chain
knowledge and audit pricing (Chen et al., 2014; Johnstone, Li, and Luo, 2014). They define
supply chain knowledge from the accounting and auditing perspectives as comprehension of
information and processes regarding accounting and auditing issues that relates to a supplierand its major customer, which is particularly beneficial for approaching the complexities
associated with the revenue cycle (Johnstone et al., 2014). Supply chain knowledge presents
at both the individual auditor and the entity level through audit firm expertise, knowledgeorganizing mechanisms, and personal communication systems. Accordingly, supply chain
knowledge helps auditors to make more informed opinions and more accurate risk
assessments, thus leading to higher audit quality (Chen et al., 2014; Johnstone et al., 2014).
While the auditing literature explores the effect of audit firms’ expertise and/or knowledge on
3

audit fees, relevant research neglects the potential impact on the audit pricing of CEO’s uniqueknowledge along the supply chain.
As CEOs gain further relationship- and transaction-specific experiences over time,they develop an understanding of commonalities and shared risks/opportunities among supply
chain partners, which forms exclusive CEO-level supply chain knowledge.

Supply chain

knowledge is defined as the conglomeration of all the information resources and knowledge
assets available for supply chain partners from a wide range of industries that would contributethe supply chain objectives’ achievement (Taher, Bandarian, and Moghadam, 2016).
Recognizing the importance of supply chain knowledge helps implement supply chain-relatedorganizational activities (Lee and Nam, 2016; Richey, Tokeman, and Wheeler, 2006).
Specifically, knowledge sharing with the supplier is a factor that leads to improved productand financial performance (Lakshman and Parente, 2008).
Additionally, extant research (e.g., Bozarth et al., 2009; Closs and Mollenkopf, 2004;
Russell and Hoag, 2004) indicates that organizational complexity, particularly thediversification of organizational structure, is essential to the growing trend of supply chain
management.1 However, to date, it has received minimal coverage among prior studies.
Berry et al. (2006) document that new CEOs in diversified firms are older, more educated, andare paid more when hired. Their results indicate that firm complexity and scope affect CEO
succession. Coles, Daniel, and Naveen (2008) and Klein (1998) argue that the CEO’s need

1

There appears to be little consensus on the definition of “supply chain management.” (Kauffman, 2002;
Burgess, Prakash, and Koroglu, 2006). Mentzer et al. (2001) propose a definition that is comprehensive andadequately reflecting the breadth of issues that are usually covered under this term. Specifically, the authors
defined supply chain management as “the systemic, strategic coordination of the traditional business functions
and the tactics across these business functions within a particular company and across businesses within the
supply chain, for the purposes of improving the long-term performance of the individual companies and the
supply chain as a whole.” Given that our issue of interest is “supply chain knowledge”, we have not strictly
adhered to all the nuances conveyed in this definition.

4

for advice will increase with the complexity of the organization. Furthermore, Zahavi andLavie (2013) provide evidence that companies that engage in related diversification alreadyhave some relevant experience and knowledge about the industry, which in turn helps thesecompanies learn and monitor their supply base.

The importance of supply chains withintoday's economy therefore raises the question of whether the CEO’s supply chain-relevant
knowledge is associated with organizational complexity.2
We perform the two-sample t-test on the diversification measures to test the hypothesisof mean differences between CEO-firm-year observation with supply chain knowledge andthose without supply chain knowledge. The results indicate that both mean number ofbusiness segment and Herfindahl-based measure for CEO-firm-year observations with supply
chain knowledge is significantly higher than those without supply chain knowledge. Ourfindings are consistent with the notion that CEOs with supply chain knowledge are more likely
to be recruited by firms with higher organizational complexity.3.4
Diversification may give rise to internal agency costs (Denis, Denis, and Sarin, 1997a,b).

Firms facing higher levels of agency costs rely more on their external auditors to relieve

such costs (Jensen and Payne, 2005). To the extent that firms with higher organizational
complexity are more likely to appoint a new CEO from within the supply chain, the successor

2

It is reasonable to expect that industry knowledge overlaps with supply chain knowledge. Industry knowledge
is defined as being able to maintain self-specialized knowledge, to follow the changes in industry and trends, andto build industry-based knowledge (Gulbahar and Kalelioglu, 2015) whereas supply chain knowledge is moregeneralized and defined as previously.
3
In contrast with relevant studies (i.e., Chen et al., 2014; Johnstone et al., 2014), we include competitors in the
supply chain partnership. That is, as the CEO gains further experience over time, he/she develops anunderstanding of commonalities and shared risks/opportunities among the firm itself and supply chain partners,
which include major suppliers, customers, and competitors.
4
Limiting the analysis to only observations of CEO turnover was common in earlier empirical studies in theeconomics and finance literature (Barron, Chulkov, and Waddell, 2011; Huson, Malatesta, and Parrino, 2004).
Following relevant studies (Bills et al., 2007; Huang et al., 2014), we do not compare pre- and post-CEO
succession observations because CEO turnover is endogenous and partially determined by the firm’s financial
performance (Murphy and Zimmerman, 1993).

5

has a higher propensity to demand more audit efforts in response to increased levels of agency
costs. Accordingly, we also examine whether the association between CEO succession
planning and audit pricing is moderated by supply chain management skills.
The results indicate that audit fees are higher for companies with a new CEO who
previously worked for a company within a supply chain or is promoted from inside anorganization than for those with a new CEO who previously served for a company outside thesupply chain. We also find that among outsider CEOs audit fees are greater when the CEOpreviously worked for a company within the supply chain, and that insider CEOs demand
more audit effort or greater audit scope and coverage, suggesting that both CEO succession
plans (identifying a new outsider CEO with supply chain management skills and identifying a
new insider CEO) demand more audit efforts to relieve agency costs.
Additional findings document that appointing a new CEO who previously served for acompany within the supply chain enhances firm value. We also find that among outsider
CEOs the value gains are more pronounced when the CEO previously worked for a company
within the supply chain, and that insider CEOs add greater value to firms than those without
supply chain knowledge. We believe that our results are consistent with the notion thatsuccessor CEOs with supply chain knowledge consider agency costs large enough to makeexternal audits valuable.
We conduct two additional analyses. First, due to uneven sample size between thetreatment (firm-years with CEO turnover) and control (firm-years without CEO change)
groups, we employ the propensity score matching approach to test the robustness of ourfindings. The propensity score matching approach yields 1,136 firm-year observations, 568
for firms with CEO change and the other 568 without CEO change. Our results are still hold6

to this robustness check. Secondly, to determine whether our results are driven byobservations where the firm has a new auditor, we repeat the analysis using a sub-sample
removing all observations where the auditor is within the first three years of tenure with theclient.

Our inferences hold for this alternative sample.
Our results reveals that new CEOs who previously worked for a company within asupply chain demand more audit efforts in response to high agency problems.

Increase inagency costs results in an increase need for intensive auditing, which leads to higher audit
quality. Accordingly, new CEOs with supply chain knowledge is valued at a premium.
Taken together, the combined evidence suggests that appointing a new CEO with supply
change knowledge to reduce agency costs can be an effective way to enhance firm value.
This paper contributes to audit-planning literature by providing evidence on an
unexplored implication. Specifically, we include the adaptation of the knowledge
distribution framework posited in Johnstone et al. (2014).

This study uses that foundation

and adapts it to the CEO turnover context. We extend the literature by exploring whether
new CEOs with supply chain-relevant knowledge affect the auditor’s fee-setting process.
Specifically, this study presents the first attempt in accounting literature to investigate whether
new CEOs’ supply chain knowledge plays an essential role in the audit risk and firm value
assessment of succession practices. This paper provides evidence on a new dimension ofCEOs’ knowledge acquisition and transfer via supply chain specialization.
The remainder of this paper is organized as follows.

Section 2 contains a review of

relevant research and development of the hypothesis. Section 3 reports the sample
distribution. Section 4 presents our research design. Section 5 reports empirical results.

7Section 6 presents the findings of additional analyses. Section 7 concludes and offers
directions for future research.

2. Literature review and research hypotheses
2.1. Tone at the top and transition of leadership
A CEO is an individual at the top of a firm whose personal reputation can have direct and long
lasting impact upon the organization (Ranft et al., 2006). A CEO has a strong influence on
the firm’s ‘‘tone at the top,’’ which is a fundamental way in which he/she proclaims
leadership. A firm’s “tone at the top” reflects the CEO’s personality and affects the auditor’s
assessments of the client’s inherent and control risks, due to its prevalent impact on theclient’s financial reporting and organizational practices (Judd, Olsen, and Stekelberg, 2017;
Patelli and Pedrini, 2015; Schmidt, 2014).
Transition of key leadership is a very important occasion for a firm because of thesubstantive and symbolic importance of the CEO position (Bills et al., 2017).

Firms

generally engage in multiple options and related processes to choose a successor CEO,including a comprehensive search of internal and external candidates and selection of an “heirapparent” (Zhang and Rajagopalan, 2004). Incoming CEOs often commence significant
changes after succession. Major changes to a firm’s mission and strategy may significantly
affect both operational decisions and accounting choices.

Even without the presence ofstrategic changes, a new CEO may bring a distinctive management style that affects firms’
financial reporting decisions and thereby stakeholders’ perceived risk of accounting errors or
improprieties (Bills et al., 2017).

8

2.2. CEO succession planning, supply chain knowledge, and organization complexity
CEO succession acts as a means by which firms can expedite adaptation to major changes intheir environment (Custodio, Ferreira, and Matos, 2013; Tushman, Newman, and Romanelli,1986). Prior studies have examined the factors that lead to CEO change. They generallyfind that firms with poor performance or wish to change strategy are more likely to hire newCEOs from outside the organizations (Cannella, Lubatkin, and Dapouch, 1991; Farrell andWhidbee, 2003; Friedman, 1991; Parrino, 1997).5 This is consistent with the notion that well
performing firms are more likely to choose insider CEOs because their abilities are suitable tocontinue current policies (Jalal and Prezas, 2012; Kesner and Sebora, 1994). Poor
performance may denote that firms exhibit a poor ability to cope with environmental change,
and that top management does not possess the required career specialization necessary to
manage a successful strategic adjustment (Friedman and Singh, 1989; White, Smith, andBarnett, 1997).
There is evidence that the appointment of an outsider CEO benefits stockholders morethan the appointment of an insider (Farrell and Whidbee, 2003; Huson, Parrino, and Starks,
2001). While insider CEOs bring firm-specific knowledge and skills from their priorexperience within the firm, the dominant stream of this research is within the organizational
demography tradition and argues that they lack the necessary skills to adapt their strategies in
response to environmental changes (Murphy and Zabojnik, 2007).

This avenue of researchalso suggests that an incoming CEO hired externally is more likely to signal ability or talent,
resulting in some degree of competitive advantage, as compared to inside hires (Boeker, 1997;

5

Their findings are consistent with our untabulated evidence that, relative to firms with new CEOs promoted
internally, those hired externally have poorer accounting-based performance. Specifically, a greater portion of
the latter has experienced a loss in at least two of the prior three years,

9Zhang and Rajagopalan, 2003, 2004).

On the contrary, some researchers are critical of thesestudies and argue that the “meaningfulness of this (outsider) distinction is somewhat unclear”
for its impact on the firm (Zajac and Westphal, 1996).

They further indicate that firms will

not choose outsider CEOs who are just marginally better than insider candidates, thus theformers are frequently handicapped in the CEO succession process (Agrawal, Knoeber, andTsoulouhas, 2006). Accordingly, prior studies examining the effects of outsider succession
versus insider succession have been mixed.
Although the extant literature provides valuable insights on several aspects of theinternal vs. external replacement, questions remain on possible differences among distinctgroups of incoming outsider CEOs. For instance, if firms choose a CEO successor, they havethe option of hiring from a company within or outside a supply chain.

To the extent that

CEO turnovers present an opportunity of generating a new fit between internal factors andvarying environmental requirements (Elosge et al., 2018), successors appointed from within
the supply chain may better “fit” in selected strategies.
A supply chain is a network of affiliated firms that work together, in competition withother such networks, to produce value for its end-user customers (Chen et al., 2014).

Lee(2004) finds that top-performing supply chains haves three distinct qualities. First, they areagile enough to react readily to sudden changes in supply or demand. Second, they adopt
over time when market environment changes. Third, they align the interests of all supply
chain partners in order to optimize the chains’ performance. These attributes (i.e., ability,
adaptability, and alignment) are feasible only when partners support knowledge flow in their
supply chain network.
Through repeated personal and professional contacts in the business community, CEOs10

establish networking across individuals and organizations, which may enhance the acquisitionof supply chain-relevant knowledge. New CEOs hired from within the industry possess
industry knowledge and skills which can be easily transferred to any firm within the industry(Castanias and Helfat, 1991). Although it is reasonable to expect that industry knowledge
overlaps with supply chain knowledge, the latter in this context is more generalized anddifficult to imitate and cannot be purchased in a market because it serves as an inter-firm
network of knowledge flow (Aman and Aitken, 2011).
Organizational complexity, particularly the diversification of organizational structure,is essential to the growing trend of supply chain management but, to date, has received
minimal coverage among prior research.

Rose and Shepard (1997) find that CEOs ofdiversified firms earn more than CEOs of focused firms, and that this wage premium is moreconsistent with an ability matching story than an entrenchment story. They also documentthat incumbents who diversify their firms are paid less than incoming CEOs at alreadydiversified firms. Likewise, Berry et al. (2006) document that new CEOs in diversified firms
are older, more educated, and are paid more when hired.

Their results indicate that firmcomplexity and scope affect CEO succession. Coles, Daniel, and Naveen (2008) and Klein(1998) argue that the CEO’s need for advice will increase with the complexity of theorganization. Moreover, Xu et al. (2016) indicate that organizational complexity is mainly
influenced by supply chain structure, scale, and internal relationship. This study is motivatedby the importance of supply chains within today's economy and is therefore intended toresolve the gap by testing the association between successor CEOs’ supply chain knowledge

11and organizational complexity.6

2.3. Successor CEOs’ supply chain knowledge, audit pricing, and firm value
Denis et al. (1997a, b) document that diversified firms tend to refocus following
external pressure or poor performance, and perceive this as evidence of agency costs indiversified firms. Goetz, Laeven, and Levine (2013) also provide evidence thatdiversification intensifies agency problems.

Their results reveal that the decrease in firm

valuations is associated with an increase in the benefits to corporate insiders and a reduction
in loan quality. Accordingly, diversification may give rise to internal agency costs.
Auditing serves as a bonding and monitoring mechanism to relieve agency costs
caused by information asymmetry among interested parties (Jensen and Meckling, 1976;
Watts and Zimmerman, 1983). Jensen and Payne (2005) document that municipalorganizations that rely more on their auditors to relieve higher levels of agency costs are
more likely to have better-developed audit-procurement practices.

They also find that well-developed audit-procurement practices are associated with the hiring of auditors who havehigher levels of industry experience, which in turn leads to higher audit quality. Griffin,
Lont, and Sun (2010) conclude that the agency problems of companies with high free cash
flow induce auditors of companies to raise audit fees to compensate for the additional risk
and effort. Accordingly, to the extent that firms with higher organizational complexity are
more likely to appoint a new CEO from within the supply chain, the successor has a higher
propensity to demand more audit efforts in response to increased levels of agency costs.

6

In contrast with relevant studies (i.e., Chen et al., 2014; Johnstone, Li, and Luo, 2014), we include competitors
in the supply chain partnership. That is, as the CEO gains further experience over time, he/she develops anunderstanding of commonalities and shared risks/opportunities among the firm itself and supply chain partners,
which include upstream suppliers, downstream customers, and competitors.

12

Auditing literature provides evidence that CEO characteristics affect audit fees. For
instance, Johnson et al. (2012) and Judd et al. (2017) document that auditors charge higher
fees when a client’s CEO exhibits behavior and attitudes consistent with narcissism. Hribar
et al. (2012) find that overconfident CEOs report more aggressively, and that auditors chargehigher fees to compensate for aggressive reporting. Harjoto, Laksmana, and Lee (2015)
provide evidence that firms with female and ethnic minority CEOs pay significantly higher
audit fees than those with male Caucasian counterparts. Kim, Li, and Li (2015) document a
positive relation between CEO portfolio vega equity and audit fees, suggesting that auditors
consider higher earnings-management risk to be associated with equity compensation.
Moreover, Kalelkar and Khan (2016) find that firms with a financial expert CEO pay lower
audit fees. We extend auditing literature by investigating a relatively neglected aspect ofCEO characteristics around turnover, namely, successor CEOs’ supply chain knowledge.
Prior auditing literature defines supply chain knowledge as comprehension of
information and processes regarding accounting and auditing issues that relates to a supplierand its major customer, which is particularly beneficial for approaching the complexities
associated with the revenue cycle (Johnstone et al., 2014). The upstream and downstream
relationship among supply chain partners can improve the auditor’s understanding of sales andpurchases transactions between the group-affiliated firms, which, in turn, lead to higher audit
quality.
Relevant studies indicate that supply chain knowledge presents at both the individual
auditor and the entity level through audit firm expertise, knowledge organizing mechanisms,
and personal communication systems.

For instance, Johnstone et al. (2014) investigate theeffects of upstream suppliers and downstream major customers along a supply chain engaging
13

the same auditor.

Their results reveal that the probability of engaging the same auditor with

supply chain knowledge increases with the operational relationship between the upstream anddownstream companies, which produces a higher-quality audit and results in lower audit fees.
This suggests that auditors share the efficiency gain from their supply chain knowledge with
their clients via a fee discount.
Chen et al. (2014) also examine whether an audit firm charges higher or reduced audit
fees to a client when it possesses exclusive knowledge derived from simultaneously auditing aclient and that client’s significant partners in the same supply chain.

They find that audit

firms’ supply chain knowledge has a negative impact on audit fees.

Specifically, their results

indicate that an auditor with more supply chain knowledge offers more audit fee discounts to
its clients in the same supply chain when it also provides auditing services to its client’s major
customer. However, Chen et al. (2014) further document that audit firms with only supplierrelated supply chain knowledge have no differential audit fee.
While the auditing literature explores the effect of audit firms’ expertise and/or
knowledge on audit fees, relevant research neglects the potential impact on the audit pricingof CEOs’ unique knowledge. Instances of CEO turnover provide a good setting to observe
the impact of incoming CEOs’ unique knowledge along the supply chain on auditor’s feesetting process.

Extant research documents that audit fees are greater for companies with

new CEOs (e.g., Bills et al., 2017). To the extent that new CEOs have gained supply chain
knowledge about the organization and the environment in which the firm operates, they are
more likely to consider several alternatives, have a more external focus, and are more open to
fresh ideas, change and experimentation than incumbent CEOs.

Agency costs may serve as

a motivating force for organizations to increase their audit efforts and improve their audit
14

quality.

It may also be useful in maximizing value (Jensen and Payne, 2005).7 Because

the benefits of higher audit quality are likely to be greater for those firms facing higher levelsof agency costs, it seems reasonable that new CEOs with supply chain knowledge demand
more audit efforts in response to high agency problems. Accordingly, new CEOs with
supply chain knowledge would be valued at a premium. This leads to the following
hypotheses (stated in alternative form):
H1a:

Audit fees are greater for companies with a new CEO who previously
worked for a partner within the supply chain than for those with a new CEO
who previously served for a company outside a supply chain.

H1b:

Audit fees are greater for companies with a new CEO promoted from inside
the firm than for those with a new CEO who previously served for acompany outside a supply chain.

H2a:

The value of the firm are greater for companies with a new CEO who
previously worked for a partner within the supply chain than for those with a
new CEO who previously served for a company outside a supply chain.

H2b:

The value of the firm are greater for companies with a new CEO promoted
from inside the firm than for those with a new CEO who previously served
for a company outside a supply chain.

3. Sample selection

7

Agency Theory claims that when the total agency costs are minimized, firm value is maximized. Prior studies(e.g., Classens et al., 2002, Lemmon and Lins, 2003, and Lins, 2003; Xiao and Zhao, 2009) examine the impactof agency costs on firm value. They find that in general, the divergence between the ultimate owner’s cash flow
rights and control rights has an adverse impact on firm value.

15

Panel A of Table 1 summarizes the sample selection process. To construct our sample, we
begin with the ExecuComp database to identify the chief executive officer (CEO) of all U.S.
listed companies from years from 2012 to 2016. We assume that CEO is the top ranking
position in the firm. We remove CEOs of subsidiaries and divisions from the sample.
When more than one person hold the position of CEO during a given year, ExecuComp
reports the names of the individuals who held the position and entitle them “co-chief executive
officer”. When a CEO is named, we exclude individuals holding the positions of president,
vice president, chief operating officer, chairman of the board or executive committee, anddirector from the sample unless one or more of those positions is also held by the CEO.
Merging the dataset with Audit Analytics database, Compustat database, andDatastream database results in available 35,871 firm-years representing 8,618 firms. We
exclude observations in financial industries and missing information in the combined dataset
because their characteristics are unique (Francis, Reichelt, and Wang, 2005; Reichelt andWang, 2010). We further exclude missing information in the combined dataset. Our final
sample consists of 5,367 firm-years representing 1,229 firms.
We use the Audit Analytics database and the Execucomp database to identify changes
in the position of CEO. The Audit Analytics database specifically provides categorizedreasons for the CEO change. We collect information on whether the new CEO was an
outsider/insider and the new CEO’s last position if promoted from within the firm from
ExecuComp.

For CEOs appointed from outside the firm, we hand-collect their priorpositions from firm disclosures and press releases. Of the 5,367 firm-year observations, there
are 568 cases with a CEO turnover (10.58 percent of total observations), including 432
observations with internal replacement and 136 observations with outside appointment.
16

To determine the identification of each party in a company’s supply chain
relationships, we hand-collect the names of a company’s major suppliers, customers, andcompetitor(s) from Bloomberg Professional Service for each sample year.8

We further

identify whether a new CEO previously worked for his/her incumbent company’s suppliers,
customers, or competitor(s) if he/she is appointed from outside the firm.

Of the 136

observations with outside appointment, there are 99 cases involved in a supply chain
relationship. Panel B of Table 1 details our sample distribution.
[Insert Table 1 here]

4. Research design
4.1. Empirical tests: The relation between CEO’s supply chain knowledge and organizational
complexity
Following prior studies (e.g., Berry et al., 2006; Naveen, 2006), We use diversification toproxy for organizational complexity. The first measure of diversification is equal to thenumber of business segments reported by a firm.

As a second measure of diversification, we

use one minus the firm’s segment sales based Herfindahl index, given by 1∑

. This Herfindahl-based measure, which is equal to zero for single

segment firms, places less weight on a firm’s smaller segments. The Herfindahl-based
measure is greater than zero for multi-segment firms.

We classify the firm-year observations

into two groups (CEOs’ with supply chain knowledge and those without supply chain

8

We include all of the major suppliers, customers, and competitors along a supply chain engaging a sample firmof interest. In contrast with relevant studies (i.e., Chen et al., 2014; Johnstone et al., 2014), we do not limit parties
in a sample firm’s supply chain relationships to suppliers and customers because CEOs at competitor firms may
also enhance the acquisition of supply chain-relevant knowledge through repeated personal and professional
contacts in the business community.

17

knowledge), and we compare the two measures of diversification measures between the twogroups.

4.2. Empirical tests: The relation between CEOs’ supply chain knowledge and audit fees
To test H1a and H1b, we extend the audit fee literature (e.g., Abbott, Parker, and Peters, 2006;
Abbott, et al., 2003; Bills et al., 2017; Huang et al., 2014) by including the proxy for CEO’s
supply chain knowledge as follows:
AUD_FEEit = λ0 + λ1CEO_PTNRit + λ2CEO_NOT_PTNRit+ λ3CEO_INTit +λ4SIZEit+λ5LOSSit + λ6ROAit + λ7INVRECit + λ8LEVit + λ9RETit + λ10GOCONit+λ11BIGit + λ12NEWAUDit + λ13MERGERit + λ14INSTOWNit+λ15INDSPECit + λ16NONAUD_FEEit + λjINDUSTRY_FE+λkYEAR_FE + εit(1)

The dependent variable is the natural logarithm of audit fees (AUD_FEE).

The key

independent variables involve CEO turnover. Three types of successor CEOs areinvestigated: those appointed from partners within the supply chain (CEO_PTNR), fromcompanies outside the supply chain (CEO_NOT_PTNR), and from inside an organization(CEO_INT). Each type is represented by a separate variable, which is coded 1 if that type ofturnover is applicable, and 0 otherwise.

Specifically, CEO_PTNR is indicator variable equal

to 1 if the CEO is within the first year of his/her tenure and he/she previously worked for oneof his/her major supplier, customer, or competitor firms; CEO_NOT_PTNR is an indicatorvariable equal to 1 if the CEO is within the first year of his/her tenure and he/she previously
worked for a company outside the supply chain; CEO_INT is an indicator variable equal to 1 if
the CEO is within the first year of his/her tenure and he/she was promoted from inside the18firm.

Firm‐year observations without CEO turnovers are set to 0 for each of these key

independent variables, and i and t represent firm and year indicators.
H1a (H1b) predicts that the coefficient on CEO_PTNR (CEO_INT) will be larger thanthe coefficient on CEO_NOT_PTNR, suggesting that more audit efforts would be demanded
when the new CEO previously worked for a company within the supply chain (was promoted
from inside the firm).9 To test H1a (H1b), we test the equality of the coefficients on
CEO_PTNR and CEO_NOT_PTNR (that on CEO_INT and CEO_NOT_PTNR).
The research on the determinants of audit fees is well developed, and explanatorymodels have adjusted R2s in the 70–90 percent range (Abbott, Parker, and Peter, 2003, Abbott,
et al., 2006; Bills et al., 2017; Craswell, Francis, and Taylor, 1995; Huang et al., 2014).
Following the above studies, we include in equation (1) a vector of control variables that arelikely to affect audit fees. We control for firm size by including the natural logarithm of total
assets (SIZE).

We use two measures to control for past and current financial performance,

respectively: an indicator variable equal to 1 if the client has experienced a loss in at least twoof the prior three years, and 0 otherwise (LOSS) and earnings before interest and taxes deflated
by total assets (ROA). We include the proportion of total assets in inventory and accounts
receivable (INVREC) to control for fraud, which is more likely when this proportion is greater(Cao, Myers, and Omer, 2012; Summers and Sweeney, 1998).

We control for debt-to-asset

ratio (LEV), a common proxy for business risk, related to the firm’s financial structure anddebt level.

We also control for stock returns in the current year (RET). We include anindicator variable (GOCON) coded 1 if the client receives a going-concern opinion during a

9Extant research (e.g., Bills et al., 2017) provides evidence that audit fees are higher for companies with newCEOs. Accordingly, the coefficients on the CEO_PTNR, CEO_NOT_PTNR, and CEO_INT are predicted to bepositive.

19

given year, and 0 otherwise. We use two indicator variables (BIG and NEWAUD) to control
for auditor type and shorter tenure. BIG is coded 1 if the firm is audited by a Big 4 auditor(Deloitte, Ernst & Young, KPMG, or PwC), and 0 otherwise; NEWAUD is set to 1 if theauditor is within the first three years of tenure with the client, and 0 otherwise. We control
for merger and acquisition activity by including an indicator variable (MERGER) that equals 1
if the firm has engaged in a merger or acquisition in year t, and 0 otherwise.

We control forthe percentage of shares owned by institutional investors (INSTOWN). We include audit
ﬁrm’s industry market share based on total sales audited within 2-digit SIC code (INDSPEC)to control for industry specialization. We also include the natural logarithm of non-audit fees
(NONAUD_FEE) to control for non-audit services.

Finally, we control for potential

influence of industry and year: industry fixed effects by two-digit SIC code (INDUSTRY_FE);and year fixed effects for the firm’s fiscal year (YEAR_FE).

4.3. Empirical tests: The relation between CEOs’ supply chain knowledge and firm value
To test H2a and H2b, we model firm value as a function of CEOs’ career experiences and otherfirm characteristics:
IND_Qit = π0 + π1CEO_PTNRit + π2 CEO_NOT_PTNRit + π3CEO_INTit + π4TAit+ π5LEVit + π6IAit + π7SEGit + π8CSit + π9CAPit + π10INDROAit+ πmYEAR_FE + εit(2)H2a (H2b) predicts that the coefficient on CEO_PTNR (CEO_INT) will be larger thanthe coefficient on CEO_NOT_PTNR, suggesting that the value of the firm increases to agreater extent for companies with a new CEO who previously worked for a partner within the
supply chain (promoted from inside the firm) than for those with a new CEO who previously
20

served for a company outside a supply chain. To test H2a (H2b), we test the equality of thecoefficients on CEO_PTNR and CEO_NOT_PTNR (that on CEO_INT and CEO_NOT_PTNR).
We devote the remainder of this section to defining the variables of interest in equation (2) anddescribing their measurement.

4.3.1. Dependent variable
We use Tobin’s q to proxy for firm value.

Tobin’s q, a forward-looking market-based

measure, captures potential future performance and value associated with international
diversification, which earnings-based accounting measures may not capture (Chari, Devaraj,and David, 2007).

Tobin’s q is linked theoretically to total economic ROI and reflects

investor expectations of future returns (Landsman and Shapiro, 1995).

Applying Tobin’s q

helps avoid some of the problems that beset earnings-based performance measures(Bharadwaj, Bharadwaj, and Konsynski, 1999), such as ignoring discrepancies in systematicrisk, temporary disequilibrium effects, tax laws, and accounting manipulation (Smirlack et al.,
1984; Wernerfelt and Montgomery, 1988).
Following prior studies (e.g., Huang et al., 2009; Villalonga and Amit, 2006), we
measure firm performance using industry-adjusted Tobin’s q (IND_Qit), which is the log
difference between q and the median q for each firm’s primary two-digit SIC classification.
Tobin’s q is the ratio of market value of assets to the book value of assets, where the market
value of assets is the book value of assets less the book value of equity plus the market valueof equity.10

10

We also use the natural logarithm of Tobin’s q as an alternative proxy for firm value. The univariate andmultivariate results do not vary substantially with the proxy used. Thus, we only report results using industryadjusted Tobin’s q.

21

4.3.2. Independent variables
CEO_PTNRit, CEO_NOT_PTNRit, and CEO_INTit, and YEAR_FE are as defined previously.
Equation (2) also controls for additional factors we expect to affect corporate valuation (seeHuang et al., 2009; Villalonga and Amit, 2006). We use the logarithm of total assets (TAit) tocontrol for firm size, and we use the ratio of total debt to total assets (LEVit) to control for the
impact of leverage on future performance. The investment-to-assets ratio (IAit), defined as thesum of the annual change in inventory and the annual change in gross property, plant, andequipment scaled by lagged total assets, is a proxy for investment growth.

The number ofbusiness segments (SEGit) controls for industry diversification. Cash stocks (CSit), defined as
net cash flows less cash dividends and capital expenditures scaled by lagged total assets, controls
for the free cash flow problem. The ratio of capital expenditures to sales (CAPit) controls fordifferences in growth options, and industry-adjusted ROA (IND_ROAit), defined as earnings
before interest and taxes divided by total assets less median industry ROA, controls forprofitability.

5. Empirical results
5.1.1. Descriptive statistics
Table 2 presents the descriptive statistics for the model variables (n = 5,367). We find thatapproximately 10 percent of the sample hires a new CEO with supply chain knowledge (i.e., 2
percent from supply chain partners and 8 percent from inside the firm).

Only 1 percent of thesample appoints a new CEO who previously worked for a company outside a supply chain.
The mean return on assets (ROA) is 9 percent, while 11.4 percent of observations report a22

loss.

We find 19 percent of firms have engaged in a merger or acquisition in the year ofobservations. Regarding governance, the mean institutional holdings is 17.2 percent. The
annual changes in property, plant, equipment, and inventories average nearly 4.4 percent oflagged total assets. The typical firm in our sample has multiple business segments. The
mean and median industry-adjusted returns on assets are approximately -0.1 percent and 0,
respectively. Distributions of other variables are consistent with findings in prior research.
[Insert Table 2 here]

Table 3 presents Pearson correlations among the variables. Panel A reports univariate
correlations among the variables in tests of the association between audit fees and CEO
succession planning. Regarding correlations between audit fees and new CEOs’ career
experiences, the results reveals that AUD_FEE is positively associated with CEO_INT (p ＜0.01). As we observe from Table 3, the correlation coefficient between AUD_FEE and eachof the control variables is statistically significant. The combined results suggest that audit
fees are significantly associated with almost all of the independent variables, including control
variables.
Panel B of Table 3 presents correlations among the variables in tests of the association
between firm value and CEO succession planning. The results reveal that IND_Q ispositively correlated with CEO_PTNR, suggesting that firms with new CEO who previously
worked for a partner within the supply chain have different valuation implications. We findno significant correlation between firm value and other proxies for CEO successions.
We find significant correlations, but to a lesser degree, between various pairs ofvariables. Specifically, our choice of variable considers multicollinearity. However,23multicollinearity is not significant within our specification because the variance inflation
factors (VIFs) on our independent variables are all less than 2.
[Insert Table 3 here]

5.1.2. Univariate results
We perform the two-sample t-test on the diversification measures to test the hypothesis ofmean differences between CEO-firm-year observation with supply chain knowledge and those
without supply chain knowledge. Untabulated results indicate that both mean number ofbusiness segment and Herfindahl-based measure for CEO-firm-year observations with supply
chain knowledge is significantly higher than those without supply chain knowledge at better
than p = 10%.

These results are consistent with the notion that CEOs with supply chain

knowledge are more likely to be recruited by firms with higher organizational complexity.

5.1.3. Multiple regression results
In Table 4, we examine whether the predicted positive relation between a new CEO and audit
fees is more pronounced when the new CEO previously served for a partner within a supply
chain or was promoted from inside the firm. As predicted, the coefficient values for
CEO_PTNR and CEO_INT are positive and significant at the 0.01 and 0.05 levels,
respectively. The coefficient on CEO_NOT_PTNR is insignificant (at the 0.10 level),suggesting that stakeholders do not perceive incremental risk with external appointment fromoutside the supply chain. We use F-tests to compare the coefficients on CEO_PTNR,
CEO_NOT_PTNR, and CEO_INT. We find that the coefficient on CEO_PTNR issignificantly larger (at less than the 0.01 level; λ1－λ2 = 0.265) than the coefficient on24

CEO_NOT_PTNR.

This result is consistent with H1a, indicating that among outsider CEOs

audit fees are greater when the CEO previously worked for a partner within the supply chain.
We also find that the coefficient on CEO_INT is significantly larger (at less than the 0.01
level; λ3－λ2 = 0.137) than the coefficient on CEO_ NOT_PTNR. This result is consistent
with H1b, suggesting that insider CEOs demand more audit effort or greater audit scope andcoverage. Our combined evidence suggests that hiring a CEO with supply chain knowledge
may not have a decreasing effect on audit fees.
Results for the control variables are consistent with prior audit fee studies (Abbott etal., 2003; Abbott, et al., 2006; Bills et al. 2017; Huang et al., 2014). Control variables thathave statistically significant coefficients (p < 0.01) include: SIZE, LOSS, ROA, INVREC, LEV,
BIG, NEWAUD, INSTOWN, INDSPEC, and NONAUD_FEE.
[Insert Table 4 here]

The regression results presented in Table 5 document that the coefficient values for
CEO_PTNR is positive and significant at the 0.10 level, suggesting that appointing a new CEO
who previously worked for a partner within the supply chain enhances firm value.

Thecoefficients on CEO_NOT_PTNR and CEO_INT are insignificant (at the 0.10 level).
Moreover, we use F-tests to compare the coefficients on CEO_PTNR, CEO_NOT_PTNR, and
CEO_INT. We find that the coefficient on CEO_ PTNR is significantly greater (at less thanthe 0.05 level; π1－π2 = 0.895) than the coefficient on CEO_NOT_PTNR.

This result is

consistent with H2a, indicating that among outsider CEOs the value gains are more pronounced
when the CEO previously worked for a partner within the supply chain. We also find that thecoefficient on CEO_INT is significantly larger (at less than the 0.10 level; π3－π2 = 0.630)
25

than the coefficient on CEO_ NOT_PTNR. This result is consistent with H2b, suggesting that
insider CEOs add greater value to firms than those who previously served for a company
outside a supply chain.11
The results reveals that new CEOs who previously work for a partner within a supply
chain demand more audit efforts in response to high agency problems. Increase in agency
costs results in an increase need for intensive auditing, which leads to higher audit quality.
Accordingly, new CEOs with supply chain knowledge is valued at a premium. We believethat the combined evidence is consistent with the notion that successor CEOs with supply
chain knowledge consider agency costs large enough to make external audits valuable.
[Insert Table 5 here]

6. Additional Analysis
6.1.Uneven sample size
6.1.1. Propensity score matching approach
Due to uneven sample size between the treatment (firm-years with CEO turnover) and control(firm-years without CEO change) groups, we employ the propensity score matching approachto test the robustness of our findings. In a regular matched-pair research design, eachobservation in the group of CEO turnovers is paired with an observation in the counterpart
group that is similar along each dimension Xi relevant to the decision to change the CEO.
Accordingly, the propensity score matching approach allows us to find a control group that issimilar to the treatment group except for CEO changes.

11

The number of firm-year observations (5,367) in Table 4 are smaller than those (4,536) in Table 5 due todiffering data requirements for the analyses. We re-estimate equations (1) using a reduced sample of 4,536 firmyear observations. The inferences remained unchanged.

26

We examine the nature of the CEO turnover and use the following logit model togenerate the propensity scores (see Farrell and Whidbee, 2003):
CEOit =δ0 +δ1AGEit +δ2EMPLOYit +δ3HOMOGENEITYit +δ4IND_RETit+δ5ADJ_ROAit +δ6 IND_FORECASTit + εit(3)where CEO is an indicator variable equal to 1 if the CEO is within the first year of his/her
tenure, and 0 otherwise; AGE is an indicator variable equal to 1 if CEO is older than 60, and 0
otherwise; EMPLOY is the log of the number of firm employees; HOMOGENEITY is
measured using the approach described by Parrino (1997); IND_RET is industry-adjusted
stock returns measured over the previous fiscal year; IND_ROAit is industry-adjusted ROA(net income/assets) measured over the previous fiscal year, defined as ROA less its medianindustry ROA (classified by its two‐digit SIC code); IND_FORECASTit is industry-adjusted
analyst forecast error (realized EPS for the previous year − forecasted EPS at the beginning of
the previous year) divided by stock price at the beginning of the previous year.
We collect categorized reasons for the CEO change based on firm disclosures from the
Audit Analytics Director and Officer Changes database. However, firms may not report a
precise reason for the turnover (e.g., Farrell and Whidbee, 2003; Goyal and Park, 2002;
Weisbach, 1988) and rarely cite poor firm performance as an explanation of a CEO change(DeFond and Park, 1999). As a result, there may be misclassification of forced and voluntary
turnover. To control for this potential error in our classification, we include a dummy
variable to indicate whether a firm’s CEO is older than 60 years of age in our model.12 We
expect CEO age to positively affect the likelihood of CEO turnover.

12

Because reported reasons for CEO turnovers are often not reliable, prior studies (Farrell and Whidbee, 2003;
Huang et al., 2014) generally assume that departure of CEOs around age 60 are more likely due to age-related
retirements than to forced turnovers.

27

Several studies find a positive relation between the likelihood of CEO turnover and firm
size (e.g., Farrell and Whidbee, 2003; Huson et al., 2001). Other studies document thatlarger firms are more likely to appoint an insider to replace an outgoing CEO (e.g., Fich, 2005;
Parrino, 1997). Potential explanations for these findings are that smaller firms have a higher
propensity to have fewer senior executives that are qualified for the CEO position, and that an
outside candidate is more likely to be effective in a smaller, less complex organization.
Following prior studies (e.g., Farrell and Whidbee, 2003), we use the natural log of thenumber of firm employees as a proxy for size.
Parrino (1997) finds evidence that CEO turnovers are more likely in homogeneous
industries due to the increased availability of strong outside candidates.

The appointment ofan executive with such experience probably reduces the likelihood that the new CEO will
make costly errors when the objective of the succession is to adapt organizational change.
Following Parrino, we construct a proxy for industry homogeneity and include this variable in
the analysis to control for the availability of an outside candidate on the CEO turnoverdecisions. First, we estimate an equally weighted return index for each industry using thefirms for which monthly returns are reported on the CRSP database between July 2010 andJune 2017. Second, the monthly return for each firm in each index is then regressed against
an equally weighted market return index and the industry return index. Finally, we determine
the average of the partial correlation coefficients for the industry return index in eachindividual-company regression, hereafter referred to as the mean partial correlation proxy.
We use monthly returns for up to 50 randomly selected firms from each industry and calculate
the industry return index. We place an upper bound on the number of firms in calculating theindustry index because the partial correlation coefficient estimated from the two-factor market
28

model is negatively related to the number of firms used to calculate the industry index. For
this same reason, we exclude those industries that do not have at least 35 firms with sufficient
return data.
Prior studies use both stock returns and reported earnings as measures of firm
performance in determining the likelihood of CEO turnover. Although prior studies (e.g.,
Farrell and Whidbee, 2003; Goyal and Park, 2002; Jensen and Murphy, 1990; Kaplan, 1994;
Murphy and Zimmerman, 1993) find that top executive turnover is significantly related tostock returns and earnings, it is unclear whether stock returns are more informative thanearnings in measuring CEO performance. Moreover, analyst forecast errors may capture
CEO performance as well as the impact of unanticipated events on firm performance (Farrell
and Whidbee, 2003). Accordingly, to ensure the robustness of our results, we use threedifferent measures of firm performance: industry-adjusted stock returns, industry-relative
earnings, and analysts’ earnings forecast errors.
Our first measure of firm performance is the industry-adjusted stock returns estimated as
the difference between the stock return for the firm in a fiscal year and the median return for
all the firms with the same two-digit SIC code in that fiscal year. The second measure isindustry-adjusted ROA (net income/assets) measured over the previous fiscal year, defined as
ROA less its median industry ROA (classified by its two‐digit SIC code). Our third measureof performance is industry-adjusted analyst forecast errors estimated as the difference between
realized EPS for the previous year and forecasted EPS for the previous year divided by stock
price at the beginning of the previous year, adjusted for the industry median. By using
industry-adjusted forecast errors in our analysis, we attempt to control for industry-wide
events that affect firm performance. We hypothesize that there will be a negative relation29

between firm performance and CEO turnover.
In the case where a binary treatment is present (i.e., treatment or control), we form
matched pairs by selecting an observation that received the treatment and selecting anotherobservation with the closest propensity score that did not receive the treatment. The
propensity score matching approach yields 1,136 firm-year observations, 568 for firms withCEO change and the other 568 without CEO change. Our results (unreported) are robust to
the use of an alternative sample that is propensity score matched on CEO turnover.

6.1.2. Bootstrapping approach
To account for this uneven sample size, whilst still using the entire data set, we also apply thebootstrapping approach to the estimations of the regression models in Tables 4 and 5 (seeEfron and Tibshirani, 1993). Specifically, we obtain the mean and variance based on 1,000
random parameter estimates to construct the confidence intervals. The results (unreported)
reveal that the inferences from that analyses are qualitatively similar to those presented inTables 4 and 5.

6.2. Auditor change
The auditor has less experience and familiarity with a new client.

This could aggravate theperceived uncertainty surrounding a new CEO who is appointed from a company within the
supply chain or promoted from inside the firm, leading to a greater audit fee increase.
Accordingly, to determine whether the results are driven by observations where the firm has a
new auditor, we perform an additional analysis where we drop from the sample all30

observations where NEWAUD is equal to 1 and re-estimate equation (1). Table 6 presents
the results of this analysis. Our inferences hold in this alternative sample.
[Insert Table 6 here]

7. Conclusion
Instances of CEO turnover provide a good setting to observe the impact of incoming CEOs’unique knowledge along the supply chain.

Organizational complexity, particularly thediversification of organizational structure, is essential to the growing trend of Supply Chain
Management.

However, to date, it has received minimal coverage among prior studies.

Our results document that CEOs with supply chain knowledge are more likely to be recruitedby firms with higher organizational complexity.
To the extent that firms with higher organizational complexity are more likely toappoint a new CEO from within the supply chain, our results also provide evidence that thesuccessor is likely to demand more audit efforts in response to increased levels of agency
costs. Specifically, we find that audit fees are higher for companies with a new CEO who
previously worked for a company within a supply chain or is promoted from inside anorganization. Moreover, the results support our theoretical argument that audit fees increases
to a lesser extent for companies with a new CEO who previously worked for a company
outside a supply chain than for those with either of the two CEO succession plans, suggestingthat both CEO succession plans (identifying a new outsider CEO with supply chain
management skills and identifying a new insider CEO) demand more audit efforts to relieve
agency costs.

31

Our results also provide evidence that appointing a new CEO who previously served
for a partner within the supply chain enhances firm value. In addition, we find that among
outsider CEOs the value gains are more pronounced when the CEO previously worked for apartner within the supply chain, and that insider CEOs add greater value to firms than those
without supply chain knowledge.
Taken together, the combined evidence suggests that hiring a CEO with supply chain
knowledge may not have a decreasing effect on audit fees. On the other hand, our findings
are consistent with the notion that new CEOs who previously work for a partner within asupply chain demand more audit efforts in response to high agency problems.

Increase inagency costs results in an increase need for intensive auditing, which leads to higher audit
quality. Accordingly, new CEOs with supply chain knowledge is valued at a premium.
Our results have potential implications for future research. First, although our study
has practical implications for companies by examining auditing pricing in the context of CEO
succession, we do not collect evidence of risk assessments and audit hours directly fromauditors. Future research could address this issue. Understanding the association between
incoming CEOs with supply chain knowledge and direct measures of audit effort would be
informative.

Second, future research will need to develop and examine precise measures ofagency costs before one can draw sharper inferences about the relation between successor
CEO’s supply chain knowledge and firm value. Third, we do not separately investigate thepotential impact on the audit pricing of CEOs’ unique knowledge regarding major suppliers,
customers, and competitors because of data limitations.

These limitations warrant interesting

avenues for future research. Finally, pre-existing differences, changes around CEO turnover,or both could drive the differences documented between CEO-firm-year observation with32

supply chain knowledge and those without supply chain knowledge. Future research could
investigate the two explanations by comparing the two groups of firms before CEO turnovers
and also determining the differences in the pre-and post-CEO turnover periods.

33

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43

Table 1
Sample Selection
Panel A: Sample selection process
Firm-Years
Available observations with Audit Analytics/Compustat/
35,871
Datastream/ExecuComp data over the period
2012-2016
Less: Financial sector (SIC 6000-6999)(15,331)
Less: Missing financial data
(15,173)(3,902)(3,487)

Final sample

1,229

5,367

Firms
8,618

Panel B: Sample distribution
Firms-Years Percentage
568
10.58%
4,799
89.42%
5,367
100.00%CEO switches
No CEO switches
Number of observations from Panel A
Internal replacement
External replacement
CEO switches

432
136
568

76.06%
23.94%
100.00%

Supply chain knowledge
No supply chain knowledge
CEO turnover with external replacement

99
37
136

72.79%
27.21%
100.00%44

Table 2
Descriptive StatisticsN

MeanMedianStd Dev

Q1

Q3

Test variables
AUD_FEE
CEO_PTNR
CEO_NOT_PTNR
CEO_INT
IND_Q

5,367
5,367
5,367
5,367
4,536

14.642
0.018
0.007
0.079
0.248

1.035
0.135
0.082
0.270
0.102

13.933
0.000
0.000
0.000
2.662

14.570
0.000
0.000
0.000
-0.48515.328
0.000
0.000
0.000
0.915

Control variables
SIZE
LOSS
ROA
INVREC
LEV
RET
GOCON
BIG
NEWAUD
MERGER
INSTOWNINDSPECNONAUD_FEE
TA
IA
SEG
CS
CAP
IND_ROA

5,367
5,367
5,367
5,367
5,367
5,367
5,367
5,367
5,367
5,367
5,367
5,367
5,367
4,536
4,536
4,536
4,536
4,536
4,536

7.882
0.114
0.090
0.241
0.251
0.130
0.003
0.901
0.037
0.190
0.172
0.288
11.84821.698
0.044
2.471
0.036
0.094
-0.001

1.680
0.317
0.125
0.167
0.213
0.680
0.058
0.298
0.189
0.393
0.115
0.206
3.543
21.596
0.027
2.000
0.041
0.035
0.000

6.665
0.000
0.054
0.103
0.089
-0.107
0.000
1.000
0.000
0.000
0.080
0.129
11.225
1.681
0.124
1.680
0.101
0.271
0.117

7.771
0.000
0.088
0.218
0.238
0.080
0.000
1.000
0.000
0.000
0.160
0.288
12.63020.484
0.003
1.000
0.001
0.020
-0.031

8.976
0.000
0.134
0.335
0.360
0.272
0.000
1.000
0.000
0.000
0.240
0.408
13.78822.778
0.069
3.000
0.080
0.067
0.037

The number of observations are unequal due to differing data requirements for the analyses.
Variable definitions:
AUD_FEE =CEO_PTNR =CEO_NOT_PTNR=

CEO_INT

=

IND_Q

=

SIZE
LOSS

=
=

the natural logarithm of audit fees;
an indicator variable equal to 1 if the CEO is within the first year of his/her tenure andhe/she previously worked for one of his/her major supplier, customer, or competitor
firms;
an indicator variable equal to 1 if the CEO is within the first year of his/her tenure andhe/she previously worked for a company outside the supply chain;
an indicator variable equal to 1 if the CEO is within the first year of his/her tenure andhe/she was promoted from inside the firm;
the log difference between q and the median q for each firm’s primary two-digit SIC
classification. Tobin’s q is the ratio of market value of assets to the book value of
assets, where the market value of assets is the book value of assets less the book valueof equity plus the market value of equity;
the natural logarithm of total assets;
an indicator variable equal to 1 if the client has experienced a loss in at least two of the

45

ROA
INVREC
LEV
RET
GOCON

=
=
=
=
=

BIG

=

NEWAUD=

MERGER=

INSTOWNINDSPECNONAUD_FEE
TA
IA

=
=
=
=
=

SEG
CS

=
=

CAP

=

IND_ROA =prior three years, and 0 otherwise;
earnings before interest and taxes deflated by total assets;
the proportion of total assets in inventory and accounts receivable;
total debt divided by total assets;stock returns in the current year;
an indicator variable coded 1 if the client received a going-concern opinion in the sample
year, and 0 otherwise;
an indicator variable that equals 1 if the firm is audited by a Big 4 auditor (Deloitte,
Ernst & Young, KPMG, or PwC), and 0 otherwise;
an indicator variable set to 1 if the auditor is within the first three years of tenure with theclient, and 0 otherwise;
an indicator variable that equals 1 if the firm has engaged in a merger or acquisition inyear t, and 0 otherwise;
the percentage of shares owned by institutional investors;
audit ﬁrm’s industry market share based on total sales audited within 2-digit SIC code;
the natural logarithm of non-audit fees;
the logarithm of total assets;
the sum of the annual change in inventory and the annual change in gross property,
plant, and equipment scaled by lagged total assets;
the number of business segment;
net cash flows less cash dividends and capital expenditures scaled by lagged total
assets;the ratio of capital expenditures to sales; andindustry-adjusted ROA (earnings before interest and taxes divided by total assets),measure as ROA less its median industry ROA (classified by its two‐digit SIC code).

46

Table 3
Pearson Correlation Matrix
Panel A: Variables Testing the Association between CEO Succession Planning and Audit Fee
Variable

AUD_FEE CEO_PTNR CEO_NOT_PTNR CEO_INT

SIZE

LOSS

ROA

INVREC

LEV

RET

GOCON

CEO_PTNR-0.013

CEO_NOT_PTNR-0.019-0.011

CEO_INT

0.064***-0.040***-0.024*SIZE

0.834***-0.050***-0.028**0.055***

LOSS-0.115***0.051***

0.028**

0.008

ROA

0.043***-0.030**-0.002-0.039*** 0.079*** -0.316***

INVREC-0.071***-0.0040.038***-0.004-0.212*** 0.005

LEV

0.276***-0.023*-0.0190.030**

0.319*** 0.072*** -0.068*** -0.158***

RET-0.073***0.038***-0.005-0.031** -0.074*** 0.041*** 0.048***

GOCON-0.023*-0.008-0.0050.031** -0.049*** 0.111*** -0.161*** 0.001

BIG

0.376***-0.0100.012

0.046*** 0.373*** -0.106*** 0.087*** -0.073*** 0.189*** -0.072*** -0.035**

NEWAUD-0.113***0.010

0.032**-0.014-0.084*** 0.066*** -0.043*** 0.036*** -0.024*

MERGER

0.051***-0.010-0.028**-0.022

0.011

INSTOWN-0.148***INDSPEC

0.291

***

-0.030NONAUD_FEE

0.527***-0.004

0.014

0.008
**

-0.017BIGNEWAUD MERGER INSTOWN INDSPEC-0.216***0.115***-0.044*** -0.013-0.162

***
***

0.014
-0.0900.065

0.039*** -0.029**-0.0090.001***

0.028** -0.012

***

0.034

**

-0.060***0.036*** 0.023

0.036*** 0.045*** -0.004
0.039

***

0.117

***

-0.037***-0.036***0.018-0.003

0.003

0.019

0.021

0.321

0.004

0.026*

0.467*** -0.114*** 0.106*** -0.070*** 0.151*** -0.032** -0.036*** 0.305***

All variables are defined in Table 2.

47

0.427

0.005
***

0.011

***, **, * denote significance at the 0.01, 0.05, and 0.10 level, respectively.

-0.017-0.104***

0.010
***

-0.045***-0.042***-0.085***0.056***-0.069***-0.0820.212***

Table 3 (continued)
Pearson Correlation Matrix
Panel B: Variables Testing the Association between CEO Succession Planning and Firm Value
Variable

IND_Q

CEO_PTNR

CEO_NOT_PTNR

CEO_INT

CEO_PTNR

0.025*

CEO_NOT_PTNR-0.021-0.012

CEO_INT-0.007-0.040***-0.025*TA-0.081***-0.059***-0.0120.050***

LEV-0.045***-0.029**-0.0210.029**

0.332***

IA

0.032**-0.033**-0.039***-0.021

0.007

0.023*

SEG-0.080***-0.027*0.008

0.028*

0.331***

0.098***-0.055***CS

0.148***-0.0220.001-0.037***0.018-0.131***-0.195***-0.005CAP-0.037***-0.017-0.0160.017

0.069***

0.064***

0.296***-0.074***-0.470***

INDROA0.181***-0.040***-0.012-0.042***0.116***-0.027**0.104***

0.014

0.484******, **, * denote significance at the 0.01, 0.05, and 0.10 level, respectively.
All variables are defined in Table 2.

48

TA

LEV

IA

SEG

CS

CAP

-0.188***

Table 4
CEO Succession Planning and Audit Fees
Dependent Variables: AUD_FEE
Variables
Constant
CEO_PTNR (λ1)
CEO_NOT_PTNR (λ2)
CEO_INT (λ3)
SIZE
LOSS
ROA
INVREC
LEV
RET
GOCON
BIG
NEWAUD
MERGER
INSTOWNINDSPECNONAUD_FEE
Year and Industry Fixed Effects
F-value: λ1=λ2
F-value: λ2=λ3
F-value of model
Adj R-squareN

Pred. Sign
＋
＋
＋
＋
＋
－
＋
＋
－
＋
＋
－
＋
？
＋
＋

Coefficient
9.966
0.174
-0.091
0.046
0.524
0.215
-0.361
0.904
0.199-0.0130.195
0.166-0.2130.024-0.253
0.096
0.031
Included
9.919***
14.459***314.710
0.814
5,367

***, **, * denote significance at the 0.01, 0.05, and 0.10 level, respectively.
All variables are defined in Table 2.

49

p-value＜0.001***＜0.001***
0.887
0.022**＜0.001***＜0.001***＜0.001***＜0.001***＜0.001***0.084*
0.036**＜0.001***＜0.001***
0.068*＜0.001***0.005***＜0.001***

Table 5
CEO Succession Planning and Firm Value
Dependent Variables: IND_Q
Variables
Constant
CEO_PTNR (π1)
CEO_NOT_PTNR (π2)
CEO_INT (π3)
TA
LEV

Pred. Sign
＋
?
＋
?
－

Coefficient
3.881
0.418
-0.4770.163-0.160-0.112

IA

＋

0.779

p-value＜0.001***0.071*
0.149
0.130＜0.001***0.301＜0.001***SEG＋-0.070＜0.001***

CS
CAP

＋

2.018

＜0.001***

＋

0.246

INDROA＋

3.773
Included
2.790**
1.800*
19.5420.054
4,536

0.060*＜0.001***

Year Fixed Effects
F-value: π1=π2F-value: π2=π3F-value of model
Adj R-squareN***, **, * denote significance at the 0.01, 0.05, and 0.10 level, respectively.
All variables are defined in Table 2.

50

Table 6
Additional Analysis to Address Auditor Change
Dependent Variables: AUD_FEE
Variables
Constant
CEO_PTNR (λ1)
CEO_NOT_PTNR (λ2)
CEO_INT (λ3)
SIZE
LOSS
ROA
INVREC
LEV
RET
GOCON
BIG
MERGER
INSTOWNINDSPECNONAUD_FEE
Year and Industry Fixed Effects
F-value: λ1=λ2
F-value: λ2=λ3
F-value of model
Adj R-squareN

Pred. Sign
＋
＋
＋
＋
＋
－
＋
＋
－
＋
＋
＋
？
＋
＋

Coefficient
9.965
0.158
-0.022
0.037
0.526
0.205
-0.3960.891
0.190-0.014
0.214
0.177
0.026
-0.234
0.091
0.029
Included
4.103**
12.178***326.981
0.824
5,168

***, **, * denote significance at the 0.01, 0.05, and 0.10 level, respectively.
All variables are defined in Table 2.

51

p-value＜0.001***＜0.001***
0.612
0.051*＜0.001***＜0.001***＜0.001***＜0.001***＜0.001***0.086*
0.027**＜0.001***
0.052*＜0.001***0.006***＜0.001***