An Empirical Investigation on the Choices of Supply Chain and
Operations Management
Vinod Singhal
College of Management
Georgia Tech
Paper for University of Illinois Proseminar Presentation
October 28, 2011
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An Empirical Investigation on the Choices of Supply Chain and Operations Management
Executives
Many firms are changing the composition of their top management team (TMT) to include a Supply
Chain and Operations Management Executive (SCOME).
The SCOME as part of the TMT has
responsibility for all or a broad spectrum of the Supply Chain and Operations Management functions.
This paper empirically examines three issues related to the appointment of SCOMEs. First, we examine
the stock market reaction to announcements of appointments of SCOMEs. Second, we shed light on the
timing of the decision to appoint a new SCOME by examining whether appointments of SCOME are
preceded by poor stock price performance. Third, we identify and investigate the factors that can affect
the likelihood of whether the SCOME is appointed from inside or outside the firm.
Our empirical analysis is based on a sample of 496 SCOME appointments that are publicly
announced during 2000-2009 period. In examining the stock market reaction, we find that on the day of
the announcement, the market reaction is positive. This reaction is more positive for newly created
positions than appointments in existing positions. The stock market also reacts more positively when the
SCOME is an outsider than an insider. Additionally, we find evidence of poor stock price performance
during the year preceding the appointment of SCOMEs. Much of this poor stock price performance is
observed in the subsample where the firm replaces the current SCOME with an outsider. We use probit
models to examine the factors that drive the choice of SCOMEs and find that the likelihood of the
SCOME being an outsider is higher for (i) smaller firms, (ii) for firms that operate in more concentrated
industries, and (iii) for firms that have experienced poor prior performance.
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1.0 Introduction
Over the last decade, the complexity and challenges of managing global and outsourced supply chain
networks in an intensely competitive environment has increased the prominence and importance of the
Supply Chain and Operations Management (SCOM) function. The ability to develop SCOM strategies
and execute them effectively can significantly influence the performance of firms. To ensure that the
SCOM function is effectively executed, many firms are changing the composition of their top
management team (TMT) by creating positions to appoint Supply Chain and Operations Management
Executives (SCOMEs). The SCOME, as part of the TMT, has responsibility for all or a broad spectrum
of the SCOM functions including operations, procurement, logistics, and distribution. SCOME positions
have been created at the Senior Vice President and Executive Vice President levels and these senior
executives are commonly referred as Chief Supply Chain Officers (CSCOs). In several firms, SCOMEs
now report directly to the Chief Executive Officers (CEOs) or Chief Operating Officers (COOs), a sharp
contrast from 15 to 20 years ago when such SCOME positions were not part of the TMT and rarely
reported to CEOs or COOs (Grosyberg et al. (2011)).
Articles by practitioners provide some evidence about the frequency of SCOMEs appointments to top
management teams. An analysis of a survey by CSCO Insights (2008) of 125 manufacturers, retailers,
wholesalers from the Fortune 500 list indicates that 25% of these 125 firms have an executive in the
firm’s TMT who is in charge of the entire supply chain. More recently, a survey by Aberdeen Group
(2010) finds that 36% of the 215 respondents indicate that their firm has an executive who plays the role
of the CSCO.
While there is an extensive body of analytical and empirical literature in both academic and
practitioner literature on design, development, and implementation of strategies in SCOM, we know little
about executives who have the responsibility to manage all or a broad spectrum of the SCOM functions.
For example, we know little about the background of individuals who are appointed as SCOMES, how
investors react to the appointment of SCOMES, when firms choose to replace existing SCOMES or create
new SCOME positions, and what factors influence the choices made by the firm to appoint SCOMEs by
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promoting internally within the firm or by hiring from outside the firm. This gap is surprising given that
SCOM strategy is a key part of corporate strategy and the TMT is the main driver of corporate strategy.
Furthermore, the effectiveness of a CEO is associated with the executives who are part of the TMT
(Marcel (2009)). Appointing individuals to the TMT is among the most critical decisions made by a
CEO. As an initial step towards developing more systematic knowledge about SCOMEs, this paper
empirically examines three issues.
First, we examine the stock market (or investor) reaction to the announcements of appointments of
SCOMEs. Our sample consists of announcements of replacements of existing SCOME positions as well
as appointments of newly created SCOME positions.
We hypothesize that newly-created SCOME
positions will be viewed positively by investors and test whether the stock market reaction is consistent
with this prediction.
We also examine whether the stock market reacts differently to hiring SCOMEs
from outside (referred to as outsider) or promoting internally within the firm (referred to as insider).
Hiring an outsider or insider has their own advantages and disadvantages and the stock market reaction
provides an indication of which strategy is valued more by investors.
Our motivation to examine the stock market reaction to appointments of SCOMEs is to provide an
assessment of the value creation potential of the change in composition and governance structure of the
TMT by the inclusion of SCOMEs. More importantly, it also provides an assessment of whether the
stock market values the top-level focus placed on the SCOM function.
Furthermore, our work
complements research that examines the stock market reaction to appointments in TMT of other senior
executives. Much of this research has focused on CEO appointments (see Kind and Schlapher (2010) and
Huson et al. (2004) for a review of the literature). However, studies on stock market reaction to non-CEO
appointments are limited and include the study of Chief Financial Officers (CFOs) (Mian (2001)), Chief
Information Officers (CIOs) (Chatterjee et al. (2001), and Chief Marketing Officers (CMOs) (Nath and
Mahajan (2008), and Boyd et al. (2010)). We contribute to this limited literature and compare and
contrast the stock market reaction to appointments of SCOMEs with appointments of non-CEO top
management appointments in other functional areas.
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Second, by examining whether appointments of SCOME are preceded by poor stock price
performance this paper sheds light on the timing of the decision to appoint a new SCOME. Several
studies have shown that CEOs are replaced following weak financial performance (see for example Denis
and Denis (1995), Parrino (1997), Furtado and Rozeff (1987), Warner et al (1988)). Collectively, these
studies suggest that performance related disciplinary mechanisms are at work in replacing CEOs. A
natural question that arises is whether similar performance related disciplinary mechanisms pertain to
appointment of executives to non-CEO top management positions.
Third, we examine the factors that drive the choice of SCOMEs. In appointing SCOMEs, firm could
choose to promote an executive from within the firm or hire an executive from outside the firm. We
provide evidence on factors that affect the likelihood of whether the SCOME is an insider or outsider.
More specifically, we hypothesize why factors such as firm size, homogeneity of the firm’s industry, level
of concentration of the firm’s industry, and performance of the firm in the period prior to the appointment
of the SCOME affect the choice of appointments from inside or outside the firm. This analysis provides
empirical evidence on the practices followed by firms with respect to appointment of SCOMEs. These
findings also provide guidance and suggestions to firms on some factors to consider in making SCOME
appointments.
Our empirical analyses are based on a sample of public announcements of 496 SCOME appointments
over a ten year period from 2000 to 2009. Our overall results on the stock market reaction show that on
the day of the announcement, the mean (median) reaction is 0.25% (0.05%). The stock market reaction is
more positive for newly created positions. Specifically, the mean (median) market reaction is 0.68%
(0.22) for newly created positions and 0.01% (0.01%) for old positions. Further, the stock market reacts
more positively when the SCOME is an outsider than an insider. The mean (median) market reaction for
is 0.41% (0.05) for outsider appointments and -0.11% (0.03%) for insider appointments. We find
evidence of poor stock price performance during the year preceding the appointment of SCOMEs. The
mean (median) stock price performance is -8.61% (-3.74%) during the year preceding the appointment of
SCOMEs by outsiders. We use probit models to analyze the factors that influence the choice of an insider
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versus outsider SCOME. We find that the likelihood of the SCOME being an outsider is greater for firms
that are smaller, operate in more concentrated industries, and have experienced poor prior performance.
The rest of the paper is organized as follows. The next section develops our hypotheses on the stock
market reaction and factors that affect the likelihood of hiring from inside or outside the firm. Section 3
describes the sample collection. Section 4 presents the empirical results on the stock market reaction to
appointments of SCOMEs. Section 5 presents the findings from the probit analysis on the factors that
affect the choice of appointments from inside the firm or outside the firm. Section 6 concludes the paper.
2.0 Hypotheses
2.1 Hypotheses about the stock market reaction to SCOME appointments
Creating a new SCOME position that is part of the TMT or reports to the TMT sends a signal to
investors about the importance and prominence a firm places on its SCOM activities. It elevates the role
of SCOM in the firm and indicates to the market that the firm is making a commitment to improving the
effectiveness of SCOM. Besides this external signal, internally the creation of a new SCOME position
can help inform other members of the TMT about the challenges, opportunities, and potential of SCOM
activities, which in turn can help build awareness and cooperation among various functions. Since
corporate strategy is largely driven by the TMT, appointments of SCOMEs provide an opportunity to
articulate the vision, role, and contribution of SCOM in the development and execution of corporate
strategy. This link between SCOM activities and corporate strategy is particularly important as SCOM
decisions can affect a significant portion of the firm resources. For example, SCOM has a direct impact
on cost of goods sold, which can often account for 40% or more of revenues in many firms. The
effectiveness of corporate strategy is also influenced by the integration and alignment of various
functional strategies. The presence of SCOMEs in the TMT can ensure that the relationships of SCOM
strategies with other functional strategies are discussed, and adjustments are made to ensure integration
and alignment of various functional strategies. As part of the TMT, SCOMEs have the power to
influence corporate decisions as it relates to SCOM, and can make the business case for investments and
resources to build capabilities that increase the effectiveness of SCOM.
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In short, given that the importance and prominence of SCOM has increased in the recent years and
the above discussion on newly-created SCOME positions, our first hypothesis, stated in alternate form, is:
H1. The stock market will react positively to announcements of newly created SCOME positions.
Our second hypothesis examines whether the stock market reacts differently to hiring outsider or
insider SCOMEs. To the best of our knowledge, there does not exist any literature that provides theory
and evidence directly related to SCOMEs that we can use to develop this hypothesis. However, there is
literature on executive appointments, successions and turnover that we can use to build our hypothesis.
There are several benefits of hiring an outsider.
The outside SCOME brings new experience,
knowledge, and perspective of how SCOM is managed in other firms and industries, which could provide
new insights and best practices about SCOM (Boeker (1997), Guthrie and Datta (1997)).
He\she can
more objectively assess the situation since he\she does not have a stake in the decisions made in the past.
Moreover, he\she is not likely to be obligated or committed to follow the past policies and investments
decisions, and may be more willing to challenge the status quo and change existing strategies and
practices (Finkelstein and Hambrick (1996), Peteraf and Shanley (1997)). Being new to the firm, the
outside SCOME can also start fresh in building relationships with other senior executives without the
burden of past conflicts and disagreements, if any. Additionally, hiring an outsider may send a strong
signal to investors and other internal and external stakeholders that the firm is serious about SCOM
functions and activities (Friedman and Singh (1989)).
Alternatively, there are also a number of benefits of hiring an insider. These benefits are associated
with the SCOME having firm-specific knowledge about the markets, technology, systems, and internal
networks (Harris and Helfat (1997) and Vancil (1987)). Internal promotions can also facilitate loyalty
and boost employee morale (Howard 2001) as the existing employees may view internal promotions as
rewards for good performance and opportunities for advancing in the firm. Firms have better information
about the capabilities and performance of their internal candidates (Harris and Helfat (1997) and Zajac
(1990)), which reduces the chances of hiring an individual not suited for the position. Internal promotions
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may be also better if the value of continuity and stability of existing strategies is high and if the value of
existing social networks is high (Ocasio 1999).
The empirical evidence on the stock market reaction to outsider or insider executive successions is
somewhat mixed. Kind and Schlapfer (2010) summarize the results of 10 studies that examine the stock
market reaction to CEO successions. In seven of these 10 studies, the stock market reaction is more
positive (or less negative) for outsiders than insiders. Mian (2001) in his study of CFOs and Chatterjee et
al. (2001) in their study of CIOs also find that the stock market reacts more positively when the new
appointee is an outsider than an insider.
Considering the above discussion and the empirical evidence on the stock market reaction to top
management changes, we take the position that
H2. The stock market will react more positively when the new SCOME is an outsider than an insider.
2.2 Hypotheses on the likelihood of insider or outsider SCOME
Next we develop our hypotheses on the effects of firm size, industry homogeneity, industry
concentration, and prior performance of the firm on the choice to appoint an insider or outsider SCOME.
Larger firms are likely to have a more global, dispersed and complex supply chains than smaller
firms. To coordinate these supply chains, larger firms are also more likely to have mid-level and junior
executives who have experience in managing various aspects of supply chains. For example, larger firms
may have directors or vice presidents managing different parts of the supply chains such as
manufacturing, distribution, sourcing, purchasing, and warehousing. These executives provide a pool of
potential candidates for the top-level SCOME position, and some of them may have the qualification and
experience to be appointed as the SCOME. Given this internal pool, the benefits of hiring an outsider
must be balanced with the search costs involved in scouting for an outsider as well as the risk that the
outsider may not mesh well with the culture of the firm. Any outside appointee must acquire firmspecific human capital. This will be more challenging and time consuming in larger firms given the
greater scope, complexity, and idiosyncrasies of supply chains in larger firms. Furthermore, when a
qualified pool of internal candidates is available within the firm (more likely in the case of larger firms
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than smaller firms), hiring an insider not only ensures a smooth transition to the new role but it also sends
a signal to mid-level and junior executives that opportunities for advancement and promotions exist
within the firm. This signal can motivate and encourage junior executives to stay with the firm, thereby
reducing the cost and risk associated with turnovers. Finally, larger firms are more likely to pay attention
to succession planning (Shen and Cannella (2003)). They may have designated a group of candidates as
potential heirs to be appointed as the top SCOME in the future or serve as a backup in case the incumbent
SCOME needs to be replaced. Given this, larger firms are likely to give preference to an insider over an
outsider.
Research on appointments of CEOs provides evidence that larger firms select a greater
proportion of insider CEOs than smaller firms (Warner et al. (1988) and Furtado and Rozeff (1987)).
Given the above discussion we hypothesize that:
H3. The likelihood of hiring outside SCOMEs will be negatively related to firm size.
Even if a firm has pool of potential inside candidates for appointments as SCOMEs, it is likely to
consider potential outside candidates before appointing a SCOME. However, outsiders are likely to have
less firm-specific human capital than insiders. Compared to outsiders, it is easier for the firm to judge
how well insiders have performed in their careers as firms have better and more reliable information to
judge the performance of insiders. Given these issues, firms may be more inclined to hire insiders.
However, Parrino (1997) argues that the importance of firm-specific human capital and the
performance measurement issues in the decision to hire outsiders will vary significantly across industries
and can depend on the homogeneity of the industry. Parrino’s (1977) argument of industry homogeneity
applies to hiring an outsider but within the same industry.
Firms operating in a more homogenous
industry are likely to compete in similar product markets, using similar inputs and technologies, and
similar supply chain structures. Thus, hiring outsiders in more homogenous industries can mitigate the
concern about the lack of firm-specific human capital associated with outsiders. Performance evaluation
of outsiders in a more homogenous industry can be more reliable because changes in factors such as input
prices, technology, and competitive environment are likely to have similar effect on all firms in a more
homogenous industry. Thus, the errors in judging the relative performance of outsiders are likely to be
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less of a concern in a more homogenous industry.
The above discussion leads to the following
hypothesis:
H4. The likelihood of hiring outside SCOMEs will be positively related to industry homogeneity.
A firm’s choice to hire an outsider SCOME, particularly outside its own industry, is likely to be
influenced by the level of industry concentration. A highly concentrated industry is one where a single
firm or few firms dominate and account for a significant portion of the industry sales. To augment the
internal pool of candidates, a dominant firm in a highly concentrated industry is likely to consider
candidates from other dominant firms in the same industry as such firms are often similar in terms of
structure and operating environment.
Since the number of such firms may be few in a highly
concentrated industry, the pool of potential applicants from the same industry will be quite limited. Given
this, a dominant firm may broaden the pool by considering candidates outside their industry.
In a highly concentrated industry the SCOM expertise of the industry is likely to reside among the
few firms that dominate the industry. This may pose a hiring challenge for the non-dominant firms if they
plan to appoint a SCOME from their own industry. This challenge for a non-dominant firm in the
industry to hire from a dominant firm in the same industry arises because of the prestige, visibility, and
perks associated with being part of a dominant firm in the industry. Given this, a non-dominant firm may
broaden the pool by considering candidates outside the industry.
Based on the above discussion our hypothesis is:
H5. The likelihood of hiring outside SCOMEs will be positively related to industry
concentration.
The choice of hiring an insider or outsider SCOME can be affected by the performance of the firm in
the period prior to hiring the new SCOME. Poor prior performance could suggest that the existing
strategies and policies in SCOM are not delivering the expected performance, and continuation with the
status quo will not help.
Reversing the trend in poor performance may require a change in SCOME
leadership. Even if a firm has pool of internal candidates who may be capable of assuming this leadership
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role, a firm may not want to appoint an insider as he/she helped develop and implement the strategies and
policies that have contributed to the poor performance. In such cases, hiring an outsider might be more
attractive. An outsider can bring new and fresh perspectives on running the SCOM function, and will
have no vested interest in continuing the past strategies and policies; he/she can be more objective in
evaluating the value of past strategies and policies. On the other hand, good prior performance indicates
that existing strategies and policies are working well and the firm should not change its course. Hiring an
insider will ensure continuation of existing strategies and policies. It may also be viewed by mid-level
and junior executives as a reward for good performance, which can help the morale and retention of these
executives. Our next hypothesis is:
H6. The likelihood of hiring outside SCOMEs will be negatively related to performance in the
period prior to hiring.
3.0 Data and sample characteristics
The sample consists of firms that appoint SCOMEs to existing or new positions. Sample firms are
collected from announcements made in business publications and newswires. To generate our sample, we
use a preliminary set of key words to collect a small sample of announcements about SCOME
appointments. We read these announcements to identify additional phrases and words that are commonly
used to announce SCOME appointments and the proximity of the key words to each other. The final set
of key words includes chief or president or director or head within ten words of supply or procurement
or sourcing or manufacturing or logistics or distribution or purchasing. We search the headlines and
lead paragraphs of all announcements in the The Wall Street Journal, Dow Jones News Service, PR
Newswire, and Business Wires during 2000-2009 and download announcements that meet the search
criteria. For an announcement to be included in the sample, we use the following criteria.
(1) The appointment in the announcement is related to some aspects of the SCOM function such as
manufacturing, operations, supply chain, procurement, purchasing, logistics, or distribution.
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(2)
If an announcement includes two or more simultaneous personnel changes, then the SCOME
appointment in the announcement was not included in the sample. For example, if the announcement
mentions that in addition to the SCOME appointment the firm is also appointing a new CIO, this SCOME
appointment will not be included in our sample. The reason for this exclusion is to reduce the possibility
of conflicting events driving the stock market reaction.
(3) Only those appointments where the SCOME is part of the TMT or where the SCOME reports to a
member of the TMT are included in the sample. This criterion attempts to focus the sample to critical
high-level appointments that are also likely to be of interest to investors. Some announcements clearly
indicate that the appointee is part of the TMT or reports to a member of the TMT. In cases where such
information is not available from the announcement, we examine the 10-K reports, annual reports, and
other filings with the Securities and Exchange Commission to determine whether the appointee is part of
the TMT or reports to a member of the TMT.
(4) To be included in the sample, the firm must have stock returns information available on the University
of Chicago’s Center for Research in Security Prices (CRSP).
Based on the above criteria, the sample consists of 496 SCOME appointments. Examples of some
appointments are:

The Talbots, Inc. today announced that it has named Gregory Poole Executive Vice President and
Chief Supply Chain Officer. In this newly created position, Mr. Poole will oversee the global
manufacturing, sourcing, transportation and distribution centers for the Talbots and J. Jill brands,
reporting directly to Talbots President and Chief Executive Officer, Trudy F. Sullivan. Business
Wire, 17 June 2008.

Under Armour, Inc. today announced the appointment of James Calo as the company's first Chief
Supply Chain Officer. The new position is designed to further strengthen Under Armour's
executive management team as the company continues its expansion in international and
domestic markets. Mr. Calo will report to Kevin Plank, Chairman and Chief Executive Officer of
Under Armour, Inc. In his role as Chief Supply Chain Officer for Under Armour, Mr. Calo will
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be responsible for managing the global functions of production planning, sourcing, development,
logistics and the distribution house. Business Wire, 24 October 2006.

Dean Foods Company announced today the appointment of Gregg A. Tanner, Executive Vice
President and Chief Supply Chain Officer, effective November 5. Reporting directly to Gregg
Engles, Chairman and Chief Executive Officer, Tanner will lead the effort to optimize Dean
Foods manufacturing and other supply chain systems. PR Newswire, 26 October 2007.
The title of Chief Supply Chain Officer (CSCO) is quite extensively used in the practitioner literature
to generically refer to the executive responsible for all or nearly all of the SCOM functions. While some
SCOMEs in our sample have the title of CSCO, many have other titles such as Senior Vice President,
Executive Vice President, Vice president, and President. Some also hold the additional title of Chief
Procurement Officer or Chief Purchasing Officer. SCOMEs who are part of the TMT generally report to
the CEOs or COOs. SCOMEs who report to a member of the TMT generally report to COOs, CFOs,
Senior Vice Presidents, or Executive Vice Presidents.
Panel A of Table 1 presents statistics on the sample based on the most recent fiscal year completed
before the date of the SCOME appointment. The median observation represents a firm with market value
of equity of $990.2 million, total assets of $1199.5 million, sales of $1543.9 million, and net income of
$19.8 million. Panel B of Table 1 gives the number of appointments by year. Nearly 18% of the
appointments are announced in 2001 and 4% in 2009, with each of the remaining years accounting for
about 10% of the appointments. The sample is diverse in terms of industry representations with firms
from 51 (167) distinct two-digit (three-digit) standard industrial classification (SIC) codes.
In
approximately 81% of the sample, the SCOME is part of the TMT and in the remaining 19% of the
sample the SCOME reports to a member of the TMT.
Announcements often provide background information on the new SCOME and the nature of the
appointment. In 93% of the appointments (463 of the 496 appointments), the new SCOME is a male. 71%
of the sample (352 of the 496 appointments) contains information about the educational background of
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the new SCOME. The highest education degree for 38% of the new SCOMEs is a Bachelor’s degree, for
54% it is a Master’s degree, and for 8% it is a Ph.D. 47% have an MBA and 65% have a science, math,
and/or engineering degree. The mean (median) work experience of the new SCOME at the time of
appointment is 23 years (22 years).
The evidence indicates that firms have a strong preference to appoint outsiders as SCOMEs. In 70%
of our sample (346 appointments) the SCOME is an outsider. The preference for outside SCOMEs is
much greater than that for outside CEOs. Based on CEO turnover studies, the percent of new CEOs that
are outsiders ranges from 11% to 36% with an average of 22% (see the studies mentioned in Table 1 of
Kind and Schlapher (2010)). Preference for outside SCOMEs is also greater than that for outside Chief
Financial Officers (CFOs) but is similar when compared to the preference for outside Chief Marketing
officers (CMOs). In his study of 2,227 CFO successions during 1984-1997, Mian (2001) finds that 50%
of the new CFOs are outsiders. Based on a study of 88 CMO successions during 1996-2005, Boyd et al.
(2010) find that 73% of the new CMOs are outsiders.
In nearly 35% of the sample (or 171 cases), the SCOME is appointed to a newly created position.
Mian (2001) reports that 12% of the CFO positions are newly created in the sample of 2,227 CFO
successions during 1984-1997, whereas Boyd et al. (2010) report that 66% of the CMO positions are
newly created in the sample of 88 CMO successions during 1996-2005.
4.0 Stock market reaction to SCOME appointments
This section describes the methods used to estimate the stock market reaction to SCOME
appointments, presents the results of the market reaction, and compares the results to appointments to
other non-CEO top management appointments.
4.1 Methods for estimating the stock market reaction – abnormal returns
We use event study methodology to estimate the stock market reaction to announcements of SCOME
appointments. The stock market reaction is commonly referred to as abnormal returns. Event study
methodology offers a rigorous approach to estimate abnormal returns associated with specific events by
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controlling for market-wide and other factors that influence stock returns (see Brown and Warner 1985,
and MacKinlay 1997 for a review of this methodology). The abnormal returns are an estimate of the
percent change in stock price associated with an event.
All announcements that we use in creating our sample first appear in Dow Jones News Service, or PR
Newswire, or Business Wires. These announcements indicate the time when the information is publicly
released. We use the time of release of information to determine a one-day event period. If the SCOME
announcement is released after 4:00 PM Eastern Standard Time (EST), then the announcement date is set
to the next trading day to account for the fact that investors cannot act until the next trading day on the
information contained in the announcement. If the announcement is made before 4:00 PM EST, then no
adjustment is necessary to the announcement date. The announcement day is the one-day event period
that we use for measuring abnormal returns. Calendar days are translated into event days where the
announcement date is Day 0 in event time, the next trading day is Day 1, the trading day before the
announcement is Day -1, etc.
We estimate abnormal returns using two different models: the Four-Factor Model and the Market
Model. The Four-Factor Model is based on the three factors identified by Fama and French (1993) and
the momentum factor identified by Carhart (1997). The Four-Factor Model posits a linear relationship
between the stock return and the four factors over a given time period as:
(1)
where Rit is the return of stock i on Day t,
is the intercept of the relationship for stock i, Rft is the risk-
free return on day t, Rmt is the market return on Day t (i.e., the return on the market portfolio), SMBt is the
small-minus-big size portfolio return on day t, HMLt is the high-minus-low book-to-market portfolio
return on day t, UMDt is the past one-year winners-minus-losers stock portfolio return on day t. and
is
the error term.
The Market Model is a single factor model and posits a linear relationship between the stock return
and Rmt, the market return, over a given time period as:
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(2)
where
is the slope of the relationship for stock i with respect to the market return.
We estimate the expected return for each sample firm using data from a 200 day estimation period
that begins on Day −210 and ends on Day −11. We end the estimation period two weeks (10 trading
days) prior to the event day to shield the estimates from the effects of the announcement and to ensure
that any non-stationarity in the estimates is not an issue. In estimating the parameters we require that a
firm must have a minimum of 40 stock returns during the estimation period of 200 trading days. Similar
estimation periods are common in the literature (e.g., Hendricks and Singhal 1997, Sharma and Lacey
2004). Using ordinary least squares regression over the estimation period of 200 trading days we
estimate the parameters
,
,
,
Model (equation (1)) and parameters
The abnormal return
,
, and
(the variance of the error term
) for the Four-Factor
,
, and
for the Market Model (equation (2)).
for firm i on Day t is the difference between the actual and the expected
return. For the Four-Factor Model:
(3)
For the Market Model:
(4)
The mean abnormal return for Day t is given by:
(5)
where N is the number of announcements in the sample. To test the statistical significance of the mean
abnormal return in equation (5), each abnormal return Ait is divided by its estimated standard deviation
to yield a standardized abnormal return. Since the abnormal returns are assumed to be independent across
events, we have from the central limit theorem that the sum of the N standardized abnormal returns is
approximately normal with mean 0 and variance N. Thus, the test statistic TSt for Day t is calculated as:
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(6)
The cumulative abnormal return (CAR) for a given time period [t1, t2] is:
(6)
The test statistic TSe for a multiple day period is derived in a manner similar to that for a single day.
(8)
We use both parametric t-tests to determine the statistical significance of the mean abnormal return as
well as non-parametric Wilcoxon signed-rank tests on the median abnormal return. Unless otherwise
noted, we report one-tailed p-values for all tests.
4.2 Event period abnormal returns
Table 2 presents the abnormal returns using the Four-Factor Model and the Market Model for the
announcement day (day 0 or the event period). The results are reported for the full sample and various
subsamples. We are unable to compute the abnormal returns for 40 out of the 496 firms because some of
the firms did not have the minimum 40 days of stock returns data during the estimation period and/or did
not have stock returns on the day of the announcement. Since the results from the two models are very
similar, we focus our discussion on the results from the Four-Factor Model. Where possible, we also
compare the stock market reaction to appointment of SCOMES to non-CEO top management
appointments such as CFOs, CMOs, and CIOs. To make it easier to compare the stock market reaction to
announcement of non-CEO top management appointments across different functions, Table 3 summarizes
the results for SCOMEs, CFOs, CMOs, and CIOs, which we use in our discussion below.
Panel A of Table 2 presents the abnormal returns for the full sample of new SCOME appointments.
The mean abnormal return for the event period is 0.25%, significantly different from zero at the 2.5%
level. The median abnormal return is 0.05%, insignificantly different from zero. The stock market seems
to react positively to appointment of new SCOMEs. The mean abnormal return of 0.25% is higher than
15
the mean abnormal return for non-CEO top management appointments in other functions. Mian (2001)
reports that the mean abnormal return for new CFO appointments is -0.05%, insignificantly different from
zero, and Boyd et al. (2010) reports that the mean abnormal returns for new CMO appointments is
0.003%, insignificantly different from zero.
Panel B of Table 2 presents the abnormal returns for the newly created and existing positions. The
mean (median) abnormal return for the newly created position is 0.68% (0.22%), significantly greater
than zero at the 2.5% (10%) level. This is consistent with our hypothesis that the stock market will react
positively to newly created positions. In contrast, when the new SCOME is appointed to an existing
position, the mean (median) market reaction is 0.01% (0.01%), both insignificantly different from zero.
The difference in the stock market reaction to newly created and existing positions is significantly
different from zero at the 10% level.
It is useful to compare our results for appointments of new and existing SCOMES with other nonCEO top appointments (see Table 3). In the case of CFOs, the mean stock market reaction to newly
created (existing) positions is 0.41% (-0.11%). Chatterjee et al. (2001) study the stock market reaction for
only newly created Chief Information Officers (CIOs). They find that the mean stock market reaction
based on a sample of 96 positions is 1.16%. Boyd et al. (2010) do not report results for newly created or
existing CMO positions separately.
Panel C of Table 2 presents the abnormal returns for outsider and insider SCOMEs. As mentioned
earlier, about 70% of SCOMEs are outsiders. The mean (median) abnormal return for outsider SCOMEs
is 0.41% (0.05%), significantly different from zero at the 2.5% (10%) level. In the case of insider
SCOMEs, the mean (median) abnormal return is -0.11% (0.06%), insignificantly different from zero.
The difference in the stock market reaction to outsider and insider SCOMEs is significantly different from
zero at the 10% level. This provides support for our hypothesis that the stock market will react more
positively to appointment of outsider SCOMEs when compared to insider SCOMEs. This is consistent
with the results for CFO appointments. Mian (2001) reports that the mean stock market reaction for
outsider (insider) new CFO is 0.40% (-0.23%). In their study of newly-created CIO positions, Chatterjee
16
et al. (2001) find the mean stock market reaction for appointment of outsider (insider) CIO is 1.02%
(1.37%).
Panel D provides results for the breakdown of new and old positions into outsiders and insiders. In
terms of magnitude, newly created SCOME positions staffed by outsiders have the most positive reaction
with a mean abnormal return of 0.86%. This result in terms of magnitude is followed by new positions
and insider with a mean abnormal return of 0.28%, existing positions and outsider with a mean abnormal
return of 0.17%, and existing positions and insider with a mean abnormal return of -0.34%.
To summarize, overall the stock market reacts positively to appointments of SCOMEs. Consistent
with our hypothesis, the market reaction is positive for newly created SCOME positions.
Although
hiring outsiders versus insiders each have their advantages and disadvantages, the stock market reacts
more positively to outsider appointments rather than insider appointments. This finding is not only
consistent with our hypothesis but also with the evidence provided on other non-CEO top management
appointments such as CFOs and CIOs.
4.3 Abnormal returns in the period prior to appointment of new SCOMEs
To develop additional insights into the relation between stock price performance and SCOME
appointments, this section analyzes the abnormal stock price performance in the period prior to SCOME
appointments. There are two reasons for presenting this analysis. First, nearly 65% of the SCOMEs in
our sample are appointed to existing positions and 35% to newly created positions. The need to replace
an existing SCOME could be because the existing SCOME is retiring, promoted, fired for poor
performance, leaving for personal and family reasons, or joining another firm, among others. The
creation of a new SCOME position could be because of reorganization, promotion, the need to improve
performance, or the need to attract a SCOME from another firm, among others. 88% of the
announcements did not give information about the reasons for replacing existing SCOMEs or creating
new positions. Given this lack of information, we explore the more general issue of whether prior stock
price performance is a driver for replacing existing SCOMEs or creating new SCOME positions. Second,
we also analyze whether the stock market reaction to announcements of appointment of new SCOMEs
17
(see Section 4.2) is related to prior stock price performance. One could conjecture that if the prior
performance is good (poor) then the announcement to appoint a new SCOME could be bad (good) news
as a good performer (poor performer) is being replaced.
Our measure of prior stock price performance is the cumulative abnormal return (CAR) over a 250
day preannouncement period that starts on day -270 and ends on day -21. Since a month typically has 21
trading days, days -270 to -21 spans a year that starts 13 months before the announcement and ends one
month before the announcement. The reason we end one month before the announcement is to reflect the
possibility that firms are likely to have made the decision to find a new SCOME sometime before the
announcement. The estimation period used to compute the CAR is from Day -480 to day -281. We
estimate the CAR using the Four-Factor Model and the Market Model. Since 64 firms did not have
sufficient data during the estimation period and/or the pre-announcement period to compute CARs, the
results for CARs are based on 432 firms. Table 4 presents these results. Although the magnitudes of the
abnormal returns are different across the two models, there are no substantive differences in the
inferences. Given this, we focus our discussion on the results from the Four-Factor Model.
The results suggest that new SCOME appointments are preceded by poor stock price performance.
Panel A of Tables 4 indicates that for the full sample the abnormal returns are negative during the 250 day
pre-announcement period. The mean prior-period abnormal return is -8.61%, significantly different from
zero at the 1% level. The median abnormal return is -3.74%, significantly different from zero at the 2.5%
level. Panel B suggest that the much of this negative prior-period stock price performance is driven by
the subsample of new SCOMEs in existing positions. The mean (median) prior-period abnormal return is
-13.57% (-6.45%), significantly different from zero at the 1% (1%) level.
For newly-created SCOME
positions, the prior-period abnormal returns are insignificantly different from zero.
This evidence
suggests that many firms may be replacing existing SCOMEs after a period of poor performance.
Panel C indicates that when prior performance is poor, firms are more likely to appoint outsider
SCOMEs. The mean (median) prior-period abnormal return for outsider SCOMEs is -12.94% (-9.51%),
significantly different from zero at the 1% (1%) level. On the other hand, the prior-period abnormal
18
returns for insider SCOMEs are insignificantly different from zero.
This result provides some
preliminary evidence to support our hypothesis that the likelihood of hiring outsider SCOMEs is
negatively related to prior performance.
Panel D provides results for the breakdown of new and old positions into outsiders and insiders. The
results indicate that the worst prior performance is for the subsample where outsiders are appointed to
existing positions. The mean (median) prior-period abnormal return is -18.95% (-14.40%), significantly
different from zero at the 1% (1%) level. Overall, the evidence presented in Table 4 suggests that
SCOMEs are replaced when the prior performance is weak and firms tend to appoint outsiders when the
prior performance is weak. This suggests that performance related disciplinary mechanisms are at work
in appointments of SCOMEs.
To shed light on the issue of whether the stock market reaction to announcement of appointment of
new SCOMEs is related to prior stock price performance, we estimate the correlation between the
abnormal return on the announcement day and the CAR over a 250 day preannouncement period. The
Pearson correlation coefficient is -0.07 and the Spearman rank correlation coefficient is -0.03. Both these
correlation coefficients are insignificantly different from zero suggesting that the stock market reaction on
announcement day is not influenced by the stock price performance during the preannouncement period.
5.0 Results on factors that affect the choice of an insider or outsider as the new SCOME
In this section, we test our hypotheses on whether factors such as firm size, industry homogeneity,
industry concentration, and prior performance affect the likelihood of the SCOME being an outsider or
insider. We first describe how the various independent variables are measured and then present the
results from the probit regression analysis. The independent variables are measured as follows:
 Firm Size: measured as the natural logarithm of sales in the most recent fiscal year completed before
the date of the announcement of the appointment of the new SCOME.
 Industry Homogeneity: Recall that we posited that firms operating in a more homogenous industry
are likely to compete in similar product markets, using similar inputs and technologies, and similar
supply chain structures. Therefore, changes in factors such as input prices, technology, and competitive
19
environment are likely to have similar effects on firms in a more homogenous industry.
This
observation also suggests that the cash flows and therefore the stock prices of firms in a more
homogenous industry are likely to be more positively correlated. Following Parrino (1997), we use a
stock price based proxy for measuring industry homogeneity. We use the industry homogeneity in the
month before the announcement of the new SCOME in our probit analysis.
For a given month X, the proxy for industry homogeneity is calculated in three steps described as
follows:
1.
We identify all firms that belong to a particular three-digit SIC code in month X. For each firm in
a three-digit SIC code, we estimate the monthly abnormal returns using the Market Model for 60
months. This 60 month period includes the month X and 59 months before month X. For a firm to
be included in this estimation, it must have at least 30 months of data over the 60 month period.
The monthly abnormal returns are the residuals from the following regression equation:
RiM = αi + βiRmM + εiM
where RiM is the return of stock i in month M,
is the intercept of the relationship for stock i,
(9)
is
the slope of the relationship for stock i with respect to the market return, and RmM is the market
return in month M, and
, the error term, is the portion of the return that cannot be explained by
the market and is the abnormal return of stock i in month M.
2. For each firm Y in a three-digit SIC code, we correlate Y’s monthly abnormal returns that is
estimated in step 1 with an equally-weighted portfolio of monthly "industry" abnormal returns of all
other firms that are in the same three-digit SIC code as Y. This gives one value of the correlation
coefficient. We exclude Y’s abnormal returns in computing the equally weighted portfolio of
monthly "industry" abnormal to prevent any bias if the number of firms in the industry is small. If
there are N firms in the industry, we repeat this for each firm in the industry, and estimate N
correlation coefficients, one for each firm.
3. The average of the N correlation coefficients estimated in step 2 is our proxy of industry
homogeneity. This average can be interpreted as the average correlation between the firm and its
20
industry after the effect of the overall stock market movements is removed. The higher the average
correlation, the more homogenous is the industry.
 Industry Concentration:
We use the Herfindahl index to measure industry concentration.
The
Herfindahl index for an industry is defined as the sum of the squared fraction of industry sales of each
firm that is in the industry. For each sample firm, we compute the Herfindahl index of its industry
using sales of all firms with the same primary three-digit SIC code as that of the firm sample. We use
the sales in the most recent fiscal year completed before the date of the announcement of the
appointment of the new SCOME.
A higher (lower) value of Herfindahl index means a more
concentrated (fragmented) industry.
 Prior Performance: We measure prior performance in a number of ways including stock price,
profitability, and inventory performance. The specific measures used are the following:
1. Cumulative Abnormal Returns from Day-270 to Day -21: We use the Four-Factor Model described
in Section 4 to compute the abnormal returns.
2. Return on assets (ROA) of the Firm: ROA is the ratio of operating income (sales less the cost of
goods sold, and selling, general, and administration expenses), divided by the average of beginning
and ending period book value of assets to get return on assets (ROA). We estimate ROA for the
most recent fiscal year completed before the date of the announcement of the appointment of the
new SCOME.
3. Median Industry ROA: Industry ROA includes all firms that have the same three-digit SIC code
and is based on the most recent fiscal year completed before the date of the announcement of the
appointment of the new SCOME.
4. Industry-adjusted ROA of the Firm: This measure is the difference between the ROA of the firm
and Median Industry ROA of the firm, where industry is defined at the three-digit SIC code level.
5. Industry-adjusted Days of inventory of the Firm: To ensure comparability, we convert all inventory
and cost of goods sold (COGS) data to First-In-First-Out (FIFO) basis by using the Last-In-FirstOut (LIFO) reserves. Days of inventory is calculated as 360*(FIFO Inventory/FIFO COGS). We
21
also compute the industry median days of inventory where industry includes all firms that have the
same three-digit SIC code. The industry adjusted days of inventory for a sample firm is the
difference in the sample firm’s days of inventory and the industry median days of inventory divided
by the industry median days of inventory. This measure estimates by how much the sample firm is
above or below the industry median in percentage terms, and makes this measure comparable
across different industries. A positive (negative) number indicates that the firm has higher (lower)
days of inventory than the industry median.
5.1 Results from the probit regression analyses
Since the dependent variable (hiring insider or outsider) is binary, we employed probit regression
using the following specification:
Pr( Y  1 / X )   (  0  1X )
(10)
The probability that the hired SCOME will be an outsider is represented by Y=1 and 0 if an insider. The
conditional expectation of Y given firm size, industry homogeneity, industry concentration, and
performance of the firm prior to hiring the SCOME scope is captured as vector X and  is the cumulative
distribution function of the standard normal distribution.
Table 5 presents results from four different probit regression models.
Firm size, industry
homogeneity, and industry concentrations are included in all four models. Where the models differ from
each other is in the proxy used for prior performance. In Model 1 prior performance is measured as the
CAR from days -270 to -21. Model 2 uses the ROA of the sample firm as the performance measure.
Model 3 breaks down the sample firm’s ROA into two components: industry adjusted ROA of the firm
and the median industry ROA. Model 4 augments the ROA measures in Model 3 to include industry
adjusted days of inventory of the firm.
Table 5 reports the regression coefficients as well as the Z-statistics in parentheses. To provide a
sense of the magnitude of the effect on the likelihood of hiring an outsider, Table 5 also reports the
estimated effect that each variable has on the likelihood of hiring an outsider. For each variable, this is
22
the derivative of the probability of hiring an outsider calculated at the mean values of all variables times
one standard deviation in the variable. In estimating the likelihood, we are assuming that the derivative
for each variable remains constant over one standard deviation from the mean.
The results in Table 5 provide support for three of the four factors that we hypothesized as affecting
the likelihood of whether the new SCOME is insider or outsider. The evidence is consistent with our
hypothesis that larger firms are less likely to hire an outsider as the new SCOME. In all four models, the
regression coefficient of firm size is negative and significantly different from zero at the 1% level in a
one-tail test. The estimates of the effect on likelihood suggests that for a firm with natural log of sales
one standard deviation more than the sample average, the likelihood of hiring an outsider drops by 6.95%
in Model 3 to 11.59% in Model 1.
We had predicted that the more homogenous the industry, the higher the likelihood of hiring an
outsider SCOME. Although the industry homogeneity coefficients in all four models are positive, none
of the coefficients are significantly different from zero. The evidence does not support our hypothesis on
the relationship between industry homogeneity and the likelihood of hiring an outsider.
As predicted, the more concentrated the industry, the higher is the likelihood of hiring an outsider
SCOME. The coefficients of industry concentration are positive and range from 1.18 to 1.63, depending
on the models, and are significantly different from zero at the 2.5% level in one-tail tests. Depending on
the model, a one standard deviation increase in industry concentration from the mean of the sample
increases the likelihood of hiring an outsider range from 5.96% on Model 1 to 8.45% in Model 4.
Our results indicate that poor prior performance increases the likelihood of hiring an outsider
SCOME. Model 1 uses the CAR from days -270 to -21 as the proxy for prior performance. The
coefficient of CAR is -0.151, significantly different from zero at the 5% level in a one-tail test. Thus,
poor stock performance increases the likelihood of hiring an outsider SCOME. For a firm with CAR one
standard deviation above the mean CAR (better performance than the average firm), the likelihood of
hiring an outsider drops by 4.29%.
23
For Model 2, we use an accounting measure of performance instead of a stock return measure. We
measure performance as the ROA of the sample firm for most recent fiscal year completed before the date
of the announcement of the appointment of the new SCOME. The coefficient of ROA is –1.190,
significantly different from zero at the 1% level in a one-tail test. For a firm with ROA one standard
deviation above the mean ROA (better performance than the average firm), the likelihood of hiring an
outsider drops by 9.29%.
A firm’s ROA will be a function of the ROA of its industry as well as the performance of the firm
relative to its industry. Model 3 reflects this by replacing the ROA of the firm by industry adjusted ROA
of the firm and median industry ROA. The coefficient of industry adjusted ROA is –0.974, significantly
different from zero at the 5% level in a one-tail test. A one standard deviation increase in the industry
adjusted ROA decreases the likelihood of hiring an outsider drops by 6.91%.
Model 4 extends Model 3 by including the industry adjusted days of inventory to the variables
included in Model 3. Industry adjusted days of inventory is a non-financial measure of performance and
captures a firm’s inventory performance relative to its industry. The coefficient of this variable is positive
but is insignificantly different from zero.
6.0 Conclusion, implications, and future research directions
In recent years, SCOME positions have been created at the Senior Vice President and Executive Vice
President levels where such positions typically report directly to the Chief Executive Officers (CEOs) or
Chief Operating Officers (COOs). This paper uses a sample of 496 public announcements of SCOME
appointments over the 2000-2009 period to empirically address two issues related to the appointments of
SCOMEs.
We examine the stock market reaction announcements of appointments of SCOMEs. On the day of
the announcement, the mean (median) stock market reaction is 0.25% (0.05%). The stock market
reaction is more positive for newly created positions. The mean (median) market reaction for newly
created positions is 0.68% (0.22%) and is 0.01% (0.01%) for existing positions. The stock market reacts
24
more positively when the SCOME is an outsider versus an insider. The mean (median) market reaction
for outsider appointments is 0.41% (0.05) and is -0.11% (0.03%) for insider appointments.
We also find evidence of poor stock price performance during the year preceding the appointment of
SCOMEs. The mean (median) cumulative abnormal return is -8.61% (-3.74%) during the year preceding
the appointment of SCOMEs. Much of this poor stock price performance is observed in the subsample
where the firm replaces the current SCOME with an outsider SCOME.
We provide evidence on factors that affect the likelihood of whether the SCOME is from inside or
outside the firm. More specifically, we hypothesize why factors such as firm size, the homogeneity of the
firm’s industry, the level of concentration of the firm’s industry, and the performance of the firm in the
period prior to the appointment of the SCOME affect the choice of appointments from inside or outside
the firm. The evidence is consistent with our hypothesis that larger firms are less likely to hire an outsider
the new SCOME. The estimates of the effect on likelihood suggests that for a firm with natural log of
sales one standard deviation more than the sample average, the likelihood of hiring an outsider drops by
6.95% to 11.59%. As predicted, the more concentrated the industry, the higher is the likelihood of hiring
an outsider SCOME. A one standard deviation increase in industry concentration from the mean of the
sample increases the likelihood of hiring an outsider by 5.96% to 8.45%. Our results indicate that poor
prior performance increases the likelihood of hiring an outsider SCOME. The evidence does not support
our hypothesis on the relationship between industry homogeneity and likelihood of hiring an outsider.
Our evidence has key implications for the management of the SCOM function in firms. First, the
stock market reacts positively to appointments of SCOMES and in particular to outsider appointments in
newly created positions. Creating new TMT positions can be risky as it raises issues of realignment of
decision making rights, governance structure, and power among members of TMT. Hiring an outsider
also raises issues about fit and ability to work as a team. The positive stock market reaction to such
appointees suggest that on average such decisions are viewed positively by the market and should be
reassuring to the CEO and other members of the TMT as they contemplate giving SCOM a seat on the
TMT. Second, the evidence that replacement of existing SCOMEs is preceded by poor stock price
25
performance and that existing SCOMEs are replaced by outsiders suggest that performance related
disciplinary mechanisms are at work in the selection of SCOMEs. This suggests that firms not only
believe that the SCOM function is an important contributor to the overall performance of the firm, but
that SCOMEs are also under pressure to deliver. This is similar to the pressures faced by CEOs and
consistent with the inverse relation between performance and CEO turnover that has been documented in
several studies. Finally, the results of the probit analyses suggest certain practices that firms have
followed in appointing SCOMEs. This could serve as guidelines for firms in their search for SCOMEs
There are a number of directions for future research. An interesting extension would to see to
examine the performance changes following the appointment of a new SCOME. Performance could be
measured over the long-term using stock price, accounting, and operational/inventory measures. It would
also be insightful to track the firms that have appointed new SCOMES to identify the changes they have
made in SCOM strategies and practices, when these changes are made, and link these changes to
performance. Another interesting research issue would be to link the characteristics of the individual
SCOME to performance. Such characteristics may include the firms where the SCOME had prior
experience, diversity of experience, the number of years of experience, education, and other relevant
factors.
This research would help firms identify the profile of the SCOME that is likely to deliver
improved performance.
Finally, it would be of interest to see what proportion of the SCOMES are
promoted to CEOs, and in turn how this promotion affects the firm’s strategy and performance.
26
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29
Table 1: Sample description
Panel A: Descriptive statistics for the sample of 496 SCOME appointments. Sample statistics
are based on the most recent fiscal year completed before the date of appointment.
___________________________________________________________________________
Measure
Mean
Median
Std. Dev.
Maximum
Minimum
___________________________________________________________________________
Market Value of equity
6880.0
990.2
21809.3
269532.6
0.82
(Million $)
Total Assets (Million $)
7842.8
1199.5
25232.8
303100.0
0.06
Sales (Million $)
7411.5
1543.9
17644.3
180557.0
0.01
1493.1
22876.0
-12613.0
Net Income (Million $)
220.5
19.8
____________________________________________________________________________
Panel B: Distribution of the announcement year for the 496 SCOME appointments.
____________________________________________________________________________
Year
Number of appointments
% of appointments
____________________________________________________________________________
2000
90
18.14%
2001
56
11.29%
2002
49
9.88%
2003
53
10.68%
2004
48
9.67%
2005
46
9.27%
2006
37
7.46%
2007
52
10.48%
2008
45
9.07%
2009
20
4.03%
496
100.00%
2000-2009
30
Table 2: Abnormal returns for the full sample and various subsamples on the day of the announcement of the SCOME appointment.
Sample
Size
Announcement day abnormal return
using the Four-Factor Model
Mean
t-statistic
Median Z-statistic
Announcement day abnormal return
using the Market Model
Mean
t-statistic
Median Z-statistic
Panel A: Full sample
All firms
456
0.25%
1.98 b
0.05%
1.06
0.29%
2.28 b
0.05%
1.25
New positions
164
0.68%
2.26 b
0.22%
1.34 d
0.68%
2.18 b
0.09%
1.43 d
Existing positions
292
0.01%
0.78
0.01%
0.33
0.07%
1.23
0.02%
0.51
Outsider
318
0.41%
2.07 b
0.05%
1.28 d
0.43%
2.09 b
0.06%
1.32 d
Insider
138
-0.11%
0.46
0.06%
-0.01
-0.03%
0.98
1.00%
0.31
New position and outsider
114
0.86%
1.91 c
0.07%
1.14
0.86%
1.74 c
0.08%
1.19
New position and insider
50
0.28%
1.22
0.27%
0.82
0.27%
1.31 d
0.11%
0.91
Existing position and outsider
204
0.17%
1.16
0.02%
0.81
0.19%
1.31 d
0.03%
0.75
Existing position and insider
88
-0.34%
-0.35
-0.04%
-0.59
-0.19%
-0.04%
-0.21
Panel B: New vs. existing positions
Panel C: Outsider vs. insider
Panel D: New, existing, insider, outsider
All tests are one-tailed: a -1% level, b – 2.5% level, c – 5% level, d – 10 % level
31
0.24
Table 3: Comparison of the stock market reaction to appointments of Supply Chain and Operations Management Executive (SCOME),
Chief Financial officer (CFO), Chief Marketing Officer (CMO), and Chief Information officer (CIO).
Sample
Sample Size
SCOME
Mean Abnormal returns
CFO
CMO
CIO
496
2227
88
96
All Firms
0.25% (456)
-0.05% (1530)
0.003% (88)
NA
New positions
0.68% (164)
0.41% (164)
NA
1.16% (96)
Existing positions
0.01% (292)
-0.11% (1366)
NA
NA
Outsider
0.41% (318)
0.40 (736)
NA
NA
Insider
-0.11% (138)
-0.23% (648)
NA
NA
New position and outsider
0.86% (114)
NA
NA
1.02% (61)
New Positions and insider
0.28% (50)
NA
NA
1.37% (35)
Old position and outsider
0.17% (204)
NA
NA
NA
Old Positions and insider
-0.34% (88)
NA
NA
NA
Information about the stock market reaction to appointments of Chief Financial officer (CFO) is from Mian (2001), Chief Marketing Officer
(CMO) is from Boyd et al. (2010), and Chief Information officer (CIO) is from Chatterjee et al. (2001). Sample sizes of individual segments are
in parentheses.
NA – information about this sample or subsample is not available in the cited literature.
32
Table 4: Cumulative abnormal returns from days -270 to -21 for the full sample and various subsamples of SCOME appointments.
Sample
Cumulative abnormal returns from days -270 to -21
using the Four-Factor Model
Mean
t-statistic
Median
Z-statistic
Size
Cumulative abnormal returns from days -270 to -21
using the Market Model
Mean
t-statistic
Median
Z-statistic
Panel A: Full sample
All firms
432
-8.61%
-3.16 a
-3.74%
-1.97 b
-4.98%
-1.75 c
-0.92%
-1.38 d
New positions
153
0.45%
0.11
1.57%
0.21
4.66%
0.97
5.23%
0.81
Existing positions
279
-13.57%
-4.22 a
-6.45%
-2.61 a
-10.27%
-3.07 a
-6.33%
-2.29 b
Outsider
299
-12.94%
-3.81 a
-9.51%
-2.83 a
-7.51%
-2.16 b
-3.94%
-1.91 c
Insider
133
1.15%
0.26
8.56%
0.98
0.69%
0.15
3.42%
0.45
New position and outsider
105
-1.85%
-0.31
-4.14%
-0.24
3.33%
0.55
5.27%
0.34
New position and insider
48
5.48%
0.72
12.42%
0.76
7.56%
0.97
4.42%
1.05
Existing position and outsider
194
-18.95%
-4.88 a
-14.40%
-3.33 a
-13.38%
Existing position and insider
85
-1.29%
0.64
-3.19%
Panel B: New vs. existing positions
Panel C: Outsider vs. insider
Panel D: New, existing, insider, outsider
-0.28
7.29%
All tests are one-tailed: a -1% level, b – 2.5% level, c – 5% level, d – 10 % level
33
-3.38 a
-0.63
-10.47%
3.43%
-2.63 a
0.14
Table 5: Coefficient estimates for probit models using the sample of SCOME appointments. The dependent variable equals one if the SCOME is
an outsider and zero if the SCOME is an insider. Effect on the likelihood of hiring an outsider is the derivative of the probability of hiring an
outsider calculated at the mean values of all variables times one standard deviation in the variable of interest.
Independent variables
INTERCEPT
Size (Natural Log of sales)
Model 1
Coefficeint
Effect on
Estimates
Likelihood
1.337
(5.312)
-0.140
(-4.488)
-11.59%
a
0.025
(0.048)
Industry Concentration
1.181
(2.274)
b
-0.151
(-1.777)
c
Cumaltive abnormal returns
0.980
(4.015)
a
Industry Homogeniety
Model 2
Coefficeint
Effect on
Estimates
Likelihood
-0.085
(-2.566)
0.11%
0.265
(0.532)
5.96%
1.284
(2.510)
Model 3
Coefficeint
Effect on
Estimates
Likelihood
0.957
(3.900)
a
-7.42%
a
-0.080
(-2.388)
-6.95%
a
0.290
(0.583)
6.36%
1.502
(2.775)
b
-0.974
(-1.942)
c
a
-1.190
(-2.396)
0.339
(0.660)
7.43%
1.626
(2.949)
-2.068
(-2.721)
a
Industry adjusted inventory days
1.57%
8.45%
b
-6.91%
-1.022
(-1.870)
-6.76%
-8.06%
-2.203
(-2.632)
-7.91%
0.052
(0.746)
1.83%
413
448
448
417
-242.301
-262.344
-261.108
-248.412
0.00
a
-9.29%
Median Industry ROA
p-value of Chisquare Test
1.29%
-7.11%
a
Industry adjusted ROA
Log Likelihood
-0.090
(-2.574)
a
-4.29%
ROA
Number of Observations
0.995
(3.793)
a
1.18%
Model 4
Coefficeint
Effect on
Estimates
Likelihood
a
0.00
a
All tests are one-tailed: a -1% level, b – 2.5% level, c – 5% level, d – 10 % level
34
0.00
a
0.00
a