Corporate Culture and CEO Turnover
January 31st, 2013
Abstract
We study the effect of corporate culture on the relationship between firm performance
and CEO turnover. Utilizing a measure of cultural dimension developed in
Organization Behavior, we quantify corporate culture by assessing corporate
documents using a text analysis approach. We employ this quantification to examine
the impact of culture on the shareholders’ decision to fire the CEO, especially in the
case of poor firm-specific performance. We show that the probability of a CEO change
in the case of poor performance is greater for companies with a culture oriented toward
control. Irrespective of performance, a culture oriented toward competition or
innovation is positively related to the frequency of management turnover, whereas the
contrary occurs for a culture oriented toward collaboration.
Keywords: Corporate culture, Text analysis, Corporate governance, CEO
JEL classification: G14, G21, G34, M14
Acknowledgements: We thank Arnoud Boot, Olivier DeJonghe, Alessandra Ferrari, Giorgio Gobbi,
Emmanuel Mamatzakis, Roman Matousek, Phil Molyneux, Nikolas Papanikolaou, Enrico Sette, and
Amine Tarazi for helpful comments. We are also thankful to participants at the seminars of the
Financial Intermediation of European Studies (FINEST) at the University of Rome III, and the Free
University of Bozen. We would like to give special thanks to Anjan Thakor for his continuous
support and great suggestions. Franco Fiordelisi also wishes to acknowledge the support of the
Fulbright Commission and the Olin Business School of the Washington University in St. Louis, U.S.
We alone are responsible for any remaining errors.
___________________________
* Corresponding author: Via S. D’Amico 77, 00145 Rome, Italy, tel. +39 06 57335672; fax. +39 06
57335797; e-mail: fiordelisi@uniroma3.it .
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1. Introduction
The threat of CEO change after poor performance is one of the main instruments
available to shareholders to align managers’ interests to their goals. It is widely
believed that corporate culture plays an important moderating role in linking corporate
culture and past performance. Surprisingly, we are not aware of any large-sample
empirical evidence to show whether and how corporate culture influences the link
between firm performance and the probability of CEO change. This lack of research is
perhaps because the notion of culture is somewhat nebulous and raises several
measurement issues in empirical research (Guiso et al., 2006). Nonetheless, recent
research has begun to explore the empirical link between culture and various economic
phenomena using novel approaches to measuring culture (Fang, 2001; Guiso et al.,
2006; Bernhardt et al., 2006; Guiso et al., 2009; Luttemer and Singhal, 2011), but this
research has not addressed CEO turnover.
What role does corporate culture play in the decision to fire a CEO after bad
performance? Is there a specific firm culture that increases (decreases) the probability
of changing a CEO after bad performance? These questions are critical for assessing
the credibility of the CEO change threat as a corporate governance instrument. The
purpose of this paper is to empirically address these questions by focusing on a large
sample of US listed companies between 1994 and 2011. Our approach involves
obtaining a quantitative measurement of corporate culture by assessing corporate
financial statements (e.g., 10-K reports). Text analysis has recently been used in
various finance papers (e.g., Antweiler and Murray., 2004; Tetlock, 2007; Li, 2008;
Tetlock et al., 2008; Loughran and McDonald, 2011). This method allows us to link the
probability of a CEO change to the extent of various corporate culture orientations.
Our main result is that corporate culture influences the probability of a CEO
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change. Specifically, the probability of CEO change increases in the case of a
corporate culture oriented toward enhancing competitiveness and emphasizing
organizational effectiveness and fast response. In the case of bad performance, the
probability of a CEO turnover increases in companies with a high control-oriented
culture, i.e., companies focusing on internal improvements in efficiency through the
implementation of better processes. These results are consistent with different
definitions of CEO change, different time periods (bad performance are 1 and 2 years
before the CEO change) and performance indicators (ROA and ROS).
The rest of this paper is organized as follows. Section 2 provides a literature
review, and Section 3 illustrates our definitions of corporate culture and formulates our
research hypotheses. The econometric framework appears in Section 5. Section 6
discusses the empirical results and robustness checks, and Section 7 concludes.
2. Related literature
To be considered a valuable corporate governance instrument, CEO change must to be
credible in the sense that the shareholders’ decision to fire the CEO is negatively
related to firm performance. Early papers (Coughlan and Schmidt, 1995; Warner et al.,
1988) find a negative link between firm performance and CEO change.
The relationship between performance and CEO change is not a simple and
direct one. Since the 1990s, various authors (e.g., Zajac, 1990) have noted that neither
the strategic management nor the financial economic literature offer a unified
theoretical or empirical framework for topics related to CEO succession.
Furthermore, these studies have relied exclusively on archival data, with no attempt
to collect or analyze primary data provided by the CEOs themselves. Consequently,
the results are not always consistent, and past performance often explains only a very
3
low portion of the turnover phenomenon (Pitcher et al., 2000). More recent papers
(e.g., Wiersema and Zhang (2011) recognize that research has advanced our knowledge
of the firm performance – CEO turnover linkage, but the relationship continues to
appear complex and somewhat ambiguous amid the existence of several variables that
may play a moderating role (e.g., CEOs’ influence on boards through direct ownership,
as outlined in Easterwood et al., 2012).
Jenter and Kanaan (2012) have recently proposed a novel approach that splits
firm performance into two components (systematic and firm specific), showing that
CEOs are significantly more likely to be dismissed from their jobs after bad industry or
bad market performance. Although there appears to be convergent evidence that CEO
change is credible, there are no studies assessing the reason for this link, as noted by
Jenter and Kanaan (2012, page 4) “more research is needed to identify the root cause
of the peer performance effect on CEO turnover”.
The main contribution of our paper is that it is the first to provide empirical
evidence (based on a large sample) that the relationship between performance and CEO
turnover is strongly influenced by corporate culture. Although this finding is certainly
logical and intuitive, there have been no studies documenting whether and how
corporate culture influences the relationship between firm performance and
shareholders’ decision to fire the CEO. We build a unique dataset of all US listed
companies between 1994 and 2011 by obtaining a quantitative measurement (at the
company level) of corporate culture by assessing financial statements. Our approach is
based on text analysis (recently used in such finance papers as Antweiler and Murray,
2004; Tetlock, 2007; Li, 2008; Tetlock et al., 2008; Loughran and McDonald, 2011),
which provides us an objective assessment of corporate culture.
As suggested by Jenter and Kanaan (2012), we use a two-stage regression
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approach to assess the sensitivity of CEO turnover to firm-specific performance. Jenter
and Kanaan’s (2012) approach enables us to decompose firm performance into a
systematic component (caused by peer group performance) and a firm-specific
component that should reflect CEO ability. This approach fits our research needs very
well. First, this approach is an effective instrumental variable estimation, with peer
group performance serving as an instrument for firm performance (Jenter and Kanaan,
2012)1. Second, CEOs should be evaluated based on the firm-specific component of
firm performance only and not on the performance of its reference group (e.g., the
industry).
3. Theory and Hypotheses
Culture is a broad concept and represents the implicit and explicit contracts that govern
behavior within an organization (Benabou and Tirole, 2002 and 2011; Tabellini, 2008).
Corporate culture is traditionally considered to have an important influence on an
organization’s effectiveness (Deal and Kennedy, 1982; Peters and Waterman, 1982;
Schein, 1992; Wilkins and Ouchi, 1983), and, in a recent review of the literature,
Sackmann (2010) suggests that some culture orientations have a positive effect on
performance measures.
A first necessary step for our analysis is to define culture in a sufficiently
narrow way within this framework so that it is possible to identify its influence on the
relationship between CEO turnover and company performance change. We adopt the
definition proposed by Cameron et al. (2006) who identify the following four types of
corporate cultures (labeled as culture-dimensions): control, compete, collaborate and
The tests treat peer performance as a plausibly exogenous instrument for the “luck” that has aided or hampered
the CEO’s running of the firm. For peer group performance to be a valid instrument, it is necessary that (i) peer
performance be exogenous and (ii) peer performance itself not have an effect on CEO dismissals.
1
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create.
3.1 Collaboration-oriented culture
A collaboration-oriented culture focuses internally on its employees and attempts to
develop human competencies and to strengthen organizational culture by building
consensus. The goal of this culture is to develop cooperative processes and attain
cohesion through consensus and broad employee involvement, e.g., clarifying and
reinforcing organizational values, norms, and expectations, developing employees and
cross-functional work groups, implementing programs to enhance employee retention,
and fostering teamwork and decentralized decision making. Companies with this
culture usually succeed because they hire, develop, and retain their human resource
base.
We posit that as the collaboration-orientation of corporate culture increases,
there is a smaller probability of a CEO change. A greater collaboration-orientation of
corporate culture implies that boards care about the development of human
competencies and consensus on a daily basis rather than organizational effectiveness,
resulting in a situation where CEO change is not used as a corporate governance tool
and is less likely to occur relative to other companies.
Hypothesis 1 (H1): Firms with a greater collaboration-oriented corporate
culture have a smaller probability of experiencing CEO change compared to
other firms.
We also posit that as firm-specific profits decrease in a company with a greater
collaboration-orientation culture, there is a smaller probability of a CEO change. In
companies with greater collaboration-orientation of corporate culture, CEO change in
the case of poor performance is less probable, as shareholders care less about poor
6
performance than in other companies and will not punish the CEO for bad
performance.
Hypothesis 2 (H2): In the case of poor performance, firms with a greater
collaboration-orientation of corporate culture have a lower probability of
experiencing CEO change compared to other firms.
3.2 Competition-oriented culture
A competition-oriented culture focuses on the organization’s external effectiveness by
pursuing enhanced competitiveness and emphasizing organizational effectiveness, fast
response, and customer focus. These companies usually attach the highest priority to
customers and shareholders and judge success on the basis of indicators such as market
share, revenues, meeting budget targets, and profitability growth. We posit that as the
competition-orientation of corporate culture increases, there is a greater probability of a
CEO change. A greater competition-orientation of corporate culture implies that boards
care on a daily basis about the competitiveness and organizational effectiveness; thus,
CEO change (i.e., a last-resort measure) is more likely than in other companies.
Hypothesis 3 (H3): Firms with a greater competition-orientation of corporate
culture have a higher probability of experiencing CEO change compared to
other firms.
Second, we assume that as the firm-specific profits decrease in a company
with a greater competition-orientation culture, there is a greater probability of a CEO
change. In companies with greater competition-orientation of corporate culture, a CEO
change (i.e., a last-resort measure) in the case of poor performance is more probable
because shareholders will punish the CEO for poor performance more than in other
companies where competition is less important (H4).
Hypothesis 4 (H4): In the case of poor performance, firms with a greater
competition-orientation of corporate culture have a higher probability of
experiencing CEO change compared to other firms.
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3.3 Control-oriented culture
A control-oriented culture refers to one that focuses on creating value through internal
improvements in efficiency, the implementation of better processes (e.g., by the
extensive use of processes, systems and technology) and quality enhancements (such as
statistical process control and other quality control processes). Companies that have
this culture usually make extensive use of standardized procedures and emphasize rule
reinforcement and uniformity.
We posit that as the control-orientation of corporate culture increases, there is
a greater probability of a CEO change. A greater control-orientation of corporate
culture implies that boards care on a daily basis about the implementation of better
production processes and thus that CEO change (i.e., a last-resort measure) is more
probable than in other companies.
Hypothesis 5 (H5): Firms with a greater control-orientation of corporate
culture have a higher probability of experiencing CEO change compared to
other firms.
Second, we believe that as the firm-specific profits decrease in a company with
a greater control-orientation culture, there is a greater probability of a CEO change. In
companies with greater control-orientation of corporate culture, the CEO change (i.e., a
last-resort measure) in the case of poor performance is more likely because
shareholders will punish the CEO for poor performance more than in other companies
where control is less important.
Hypothesis 6 (H6): In the case of poor performance, firms with a greater
control-orientation of corporate culture have a higher probability of
experiencing CEO change compared to other firms.
3.4 Creation-oriented culture
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A creation-oriented culture focuses on creating future opportunities in the marketplace
through innovation in the organization’s products and services of the organization. The
organization encourages entrepreneurship, vision and constant change, e.g., allowing
for freedom of thought and action among employees so that rule breaking and reaching
beyond barriers are common characteristics of the organization’s culture. These
companies usually aim to include innovative product-line extensions, radical new
process breakthroughs, innovations in distribution and logistics that redefine entire
industries and to develop new technologies.
We posit that as the creation-orientation of corporate culture increases, there is
a smaller probability of a CEO change. A greater creation-orientation implies that
boards care about products and service innovations on a daily basis more than they do
organizational effectiveness and thus that CEO change is not used as corporate
governance tool and is less likely than in other companies.
Hypothesis 7 (H7): Firms with a greater creation-oriented corporate culture
have a smaller probability of experiencing CEO change compared to other
firms.
We also assume that as firm-specific profits decrease in a company with a
greater creation-orientation of corporate culture, there is a smaller probability of a CEO
change. In companies with a greater creation-orientation, the probability that the CEO
will be fired in the case of poor performance (i.e., a last-resort measure) is lower than
in other companies, as shareholders care more about vision and constant change than
about performance.
Hypothesis 8 (H8): In the case of poor performance, firms with a greater
creation-orientation of corporate culture have a lower probability of
experiencing CEO change compared to other firms.
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Table 1 summarizes the attributes of the corporate culture orientations
proposed by Cameron et al. (2006).
<<< INSERT TABLE 1 >>>
4. Empirical Approach
This section describes the data we use in our analysis, the econometric approach and
the testable hypotheses.
4.1 Data
To answer our research questions, we build a unique dataset by collecting information
from three different sources to have a comprehensive and careful profile of each
company.
First, we obtained information on top executive officers (and specifically, on
CEOs’) from the Execucomp database. Data were available for the period 1992-2011,
resulting in 209,840 year-observations. We excluded all cases of inconsistent or
missing data (i.e., if the CEO annual flag was in conflict with the dates when the
interested manager joined or left the company or if the information about the identity
of the CEO was available only for a specific year but not for the previous one,
rendering it impossible to know whether there was a turnover).
Second, we obtained financial data by extracting 247.796 simplified balance
sheets from Compustat between 1990 and 2011. As in the previous case, we removed
all companies with missing data.
Third, we obtained 128.489 company filings by downloading the 10-K reports
(available from 1994 to 2011) from the SEC’s Edgar database, and for each of these
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filings, we ran a text analysis to estimate each cultural dimension identified by
Cameron et al. (2006) (513,956 texts analyzed overall).
Our final sample includes all US listed companies between 1994 and 2011 for
which it was possible to a) collect information on top managers, b) determine
accounting-based performance, and c) measure the relevant cultural dimensions.
Information relative to the same company and drawn from different databases was
matched using the GVKEY code. As a result, we have a unique dataset of 19.453 yearobservations, combining managerial, accounting and cultural information. A CEO
change occurs at a frequency of approximately 10% (see Table 2). We distinguished
financial from non-financial companies on the basis of the SPINDEX code and the
industry group definitions: observations for financial and non-financial companies
represent, respectively, approximately 15% and 85% of our sample. We considered the
following industry groups as financial groups: Asset Management and Custody banks;
Consumer finance; Diversified banks; Diversified REITs; Industrial REITs; Insurance
brokers; Investment banking and brokerage; Life and health insurance; Mortgage
REITs; Multi-line insurance; Office REITs; Other diversified financial services;
Property and casualty insurance; Real investment trust; Regional banks; Reinsurance;
Residential REITs; Retail REITs; Specialized finance; Specialized REITs; and Thrifts
and mortgage finance. Companies without an assigned industry group were excluded
from the sample.
<<< INSERT TABLE 2 >>>
4.2. Corporate culture estimation
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We now describe our corporate culture variables. First, we outline our text analysis
approach to estimate Cameron et al.’s (2006) corporate culture dimensions and then
present our variables to measure culture homogeneity and heterogeneity.
To quantitatively measure Cameron et al.’s (2006) four dimensions of
corporate culture, we use text analysis. Text analysis is a technique used to examine, in
a systematic and objective manner, the characteristics specific to a text (Stone et al.,
1966). The idea underlying our approach is based on the assumption that the words and
expressions used by the members of an organization (labeled “vocabulary”) represent
the outcome of the culture they develop over time (Levinson, 2003).
We posit that the distinctive features of any organization are mirrored in its
written documents. Text analysis methodology (Stone et al., 1966) is instrumental in
measuring the semantic content of firms’ publicly available official documents
(namely, annual reports to shareholders). This technique provides us with measures that
are less prone to the subjectivity of our opinion as researchers in interpreting the data.
Recently, the text analysis approach has been exploited in various finance and
management papers (e.g., Antweiler and Murray, 2004; Tetlock, 2007; Li, 2008;
Tetlock et al., 2008; Hoberg and Phillips, 2010; Hoberg and Hanley 2010; Loughran
and McDonald, 2011).
To estimate Cameron et al.’s (2006) culture dimensions (i.e., collaborate,
compete, control, and create, as defined in Table 1), we identify a large set of
synonyms for each of these aspects. Following Carretta et al. (2011), synonyms are
selected using a two-step procedure that minimizes subjectivity in the selection
process. First, we selected synonyms suggested by authors (Cameron et al., 2006) to
identify each culture dimension. Second, all words selected in the first step have been
included within in the Harvard-IV-4 Psycho Social Dictionary to identify other
12
synonyms. Loughran and McDonald (2011) have noted that the Harvard-IV-4 PsychoSocial Dictionary2 is a commonly used source of word classification, in part because its
composition is beyond the control of the researcher and the possible impact of
researcher subjectivity is significantly diminished. For example, words such as
“capabilities, collective, cooperation, etc.” are associated with the word “collaborate,”
and a relatively high frequency of their use in corporate documents suggests that the
company has a collaboration-oriented culture. Words such as “achievement,
performance, excellence, etc.” are associated with the word “compete,” and a relatively
high frequency of their use in corporate documents suggests that the company has a
competition-oriented culture. Words such as “boss, efficiency, caution, etc.” are
considered synonyms for “control,” and a relatively high frequency of their use in
corporate documents suggests that the company has a control-oriented culture. Words
such as “dream, begin, elaborate, etc.” are associated with the word “create,” and a
relatively high frequency of their use in corporate documents suggests that the
company has a creation-oriented culture.
We estimated the four corporate culture dimensions for each listed US firm
between 1994 and 2011 by determining the number of times that our synonyms occur
in each annual report, using percentages to measure cultural emphasis. For example, if
the estimate for a “competition-oriented” dimension is equal to 5, the synonyms used
to capture this culture dimension (reported in Table 1) represent 5% of the entire
document.
One possible difficulty of our approach is that listed companies may tend to
write official documents to “cater” for investors’ expectations and, consequently, most
Although Loughran and McDonald (2011) show that the Harvard IV Psycho-Social Dictionary
misclassifies words that are not likely to be correlated with the variables under consideration (e.g.,
taxes and liabilities), we use this dictionary because the list of synonyms used to capture Cameron et
al.’s (2006) four dimensions of corporate culture are directly correlated with the variables and do not
suffer from the problems shown by Loughran and McDonald (2011).
2
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official documents exhibit significant similarity. This phenomenon may prevent us
from detecting any cultural differences in the cross-section. Nonetheless, we document
in Table 3 that there is significant cross-section heterogeneity among companies along
the four corporate culture dimensions proposed by Cameron et al. (2006).
<<< INSERT TABLE 3 >>>
4.3 Measuring CEO turnover and firm performance
Starting from the information contained in the Execucomp database, we define three
different turnover variables. First, we define the CEOT variable, a dummy variable
taking the value of 1 if the company has changed its CEO with respect to the previous
year and 0 otherwise. When we have more than one top manager in charge of the
company in the same financial year, we choose the person who has been the CEO for
the longest period (e.g., if there is a change in 2008, but the entrant CEO is in charge
only from October to December, i.e., only for 3 months, we consider the 2008 CEO to
be the predecessor and register a CEO change in 2009).
To compute the other two measures of CEO change, we consider the
difference between the case of an internal and an external turnover, a distinction that
has proven to be fundamental in several previous papers (e.g., Karaevli, 2007; Zhang
and Rajagopalan, 2010). The selection of an outsider successor may be the signal of a
stronger desire for change, implying that the link between poor performance and
succession should be stronger than in the case of an internal turnover (e.g., Datta and
Guthrie, 1994). We construct the variables linked to an external turnover (External
CEO Turnover 1 and External CEO Turnover 2), based on two different definitions of
an internal successor as follows: a) someone who has been among the top managers at
14
least for two years before the nomination or b) someone who joined the company at
least 2 years before the nomination. Furthermore, we do not consider turnovers in
which the predecessor was older than 63, as they most likely reflect a retirement
situation and not shareholders’ decision to change the company’s top management.
Regarding the measurement of firm performance, we use accounting-based
performance measures because, as noted by Jenter and Kanaan (2012), these are shortterm profit measures that are better predictors of CEO turnover than stock market
returns if the market incorporates future benefits after replacing an underperforming
CEO. First, we focus on the Return on Assets (ROA), obtained by the ratio between the
Earnings Before Interest, Tax and Depreciation and Amortization (EBITDA) on total
assets. We focus on EBITDA, rather than net income, as EBITDA is a good means of
comparing companies within and across industries. In our sample, we have companies
in different industries, and the amount of fixed effect (which is subject to depreciation
charges) may be subject to distorted accounting and financing effects on company
earnings, which is not the case in EBIDTA. We also focus on Return on Sales (ROS)
as robustness check. ROA may be decomposed into two parts: ROS (i.e.,
ROS=EBITDA/total sales) and asset turnover (i.e., total sales /total assets). ROS is a
useful indicator of the firm’s profitability and operational efficiency in particular, as it
provides an insight into how much profit is being produced per dollar of sales. As a
robustness check, we also estimate the industry-adjusted ROA (IAROA), which is the
difference between ROA and the average ROA for companies in the same industry
group for every financial year (the industry group is identified on the basis of the
SPINDEX code).
5. Results
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The test of the credibility of the CEO change threat essentially aims to verify whether
the poor performance of a company is related to a future change in the CEO. We test
this assumption using both a univariate and a multivariate approach.
In the univariate approach, we measure CEO turnover frequencies by past
performance quintiles, defined in terms of both ROA and IAROA. We distinguish
internal and external turnovers, analyzing both the case of one-year and two-year time
intervals. We also drop observations for which the predecessor was older than 63,
assuming that in this case, the change is due to retirement and not to the decision to
remove a bad performer. As shown in Table 5, there is a very strong relationship
between past poor performance and subsequent CEO turnover. The turnover frequency
in the lowest quintile (worst performers) is always greater than in the highest quintile
(best performers), for both t-1 and t-2. When we disentangle turnovers in internal and
external, we observe that the inverse relationship between past performance and
change decisions is mostly explained by external turnover. The Q1-Q5 difference is
statistically significant (at the confidence level) for both ROA and IAROA with
reference to the one-year lag, whereas for the two-year interval, it is statistically
significant (at the 5% confidence level) only when we consider the unadjusted
performance. The results are the same irrespective of the chosen definition of external
and internal successors and are consistent with the idea that when a company uses CEO
turnover to remove a bad performer, it will most likely choose someone who is not
linked to the previous CEO.
<<< INSERT TABLE 5 >>>
In the multivariate approach, we borrow the empirical strategy used by Jenter
16
and Kanaan (2012) to test the strong-form relative performance evaluation hypothesis
(i.e., industry and market performance have no predictive power for the likelihood of
forced CEO turnovers: see also Bertrand and Mullainathan (2001), Wolfers (2002),
Garvey and Milbourn (2006), and Jenter and Kanaan (2012)).
Following Jenter and Kanaan (2012), we use a two-stage regression approach
to assess the sensitivity of CEO turnover to peer performance. In the first stage, we
decompose firm performance into a systematic component caused by peer group
performance and a firm-specific component that should, in part, reflect CEO ability.
We use accounting-based performance measures (specifically, the ROA and ROS)
because these are short-term profit measures that are better predictors of CEO turnover
than stock market returns if the market incorporates future benefits for replacing an
underperforming CEO. In the second stage, we predict the probability of CEO turnover
using both the estimated peer group component and the estimated residual component
of firm performance. As noted by Jenter and Kanaan (2012), this approach is an
effective instrumental variable estimation, with peer group performance serving as an
instrument for firm performance3:
(First stage)
𝑃𝑖,𝑡−1 = 𝛽0 + 𝛽1 𝑃𝑝𝑒𝑒𝑟𝑔𝑟𝑜𝑢𝑝,𝑡−1 + 𝑣𝑖,𝑡−1
(1)
(Second stage)
𝑃𝑟(𝐶𝐸𝑂 𝑐ℎ𝑎𝑛𝑔𝑒𝑖,𝑡 ) = 𝛾0 + 𝛾1 𝑃̂𝑖,𝑡−1 + 𝛾2 𝑣̂𝑖,𝑡−1 + 𝛾3 𝐶𝑂𝐿𝑖,𝑡−1 + 𝛾4 𝐶𝑂𝐿𝑖,𝑡−1 ∗ 𝑣̂𝑖,𝑡−1 +
𝛾5 𝐶𝑂𝑀𝑖,𝑡−1 + 𝛾6 𝐶𝑂𝑀𝑖,𝑡−1 ∗ 𝑣̂𝑖,𝑡−1 + 𝛾7 𝐶𝑂𝑁𝑖,𝑡−1 +
𝛾8 𝐶𝑂𝑁𝑖,𝑡−1 ∗ 𝑣̂𝑖,𝑡−1 + 𝛾9 𝐶𝑅𝐸𝑖,𝑡−1 + 𝛾10 𝐶𝑅𝐸𝑖,𝑡−1 ∗ 𝑣̂𝑖,𝑡−1 +
𝜍𝑖,𝑡−1
(2)
where 𝑃̂𝑖,𝑡−1 = 𝛽̂0 + 𝛽̂1 𝑃𝑝𝑒𝑒𝑟𝑔𝑟𝑜𝑢𝑝,𝑡−1, 𝑃̂𝑖,𝑡−1 is the estimated exogenous component of
The tests treat peer performance as a plausibly exogenous instrument for the “luck” that has aided or hampered
the CEO’s running of the firm. For peer group performance to be a valid instrument, it is necessary that (i) peer
performance be exogenous and (ii) peer performance itself not have an effect on CEO dismissals.
3
17
firm performance common to the peer group and not attributable to CEO actions, and
𝑣̂𝑖,𝑡−1 is the estimated firm-specific performance component. Following previous
studies (e.g., Jenter and Kanaan, 2012), we estimate a common peer performance beta
for all firms in the first-stage regression. Table 4 summarizes all variables used in the
empirical investigation.
<<< INSERT TABLE 4 >>>
Compared to previous studies (e.g., Jenter and Kanaan, 2012), we added our
four measures of corporate culture (COL, COM, CON, and CRE) and their interaction
with the estimated firm-specific performance component, as we posit that the corporate
culture plays a moderating role in the relationship between firm performance and CEO
turnover. Regarding the prediction of estimated coefficients, we expect that 1) 𝛾1 = 0.
We expect that the exogenous performance component will not affect CEO turnover,
consistently with the prediction of strong-form relative performance evaluation; that is,
2) 𝛾2 < 0. To be a credible corporate governance instruments, the probability of CEO
turnover must be inversely related to firm-specific performance, so we expect to find a
negative coefficient; that is, 3) 𝛾3,5,7,9 ≠ 0. Consistently with our research hypotheses
(Section 1), we expect that the probability of a CEO change is positively related to
control- and competition-oriented corporate cultures (respectively, 𝛾5 > 0, 𝛾7 > 0) and
negatively related to collaboration- and creation-oriented cultures (respectively, 𝛾3 <
0, and 𝛾9 < 0); and 4) 𝛾4,6,8,10 ≠ 0. We expect corporate culture to be a mediator of
the relationship between CEO turnover and firm-specific performance: specifically, we
posit that companies with different cultures react differently to poor firm-specific
performance and thus that the probability of observing a CEO turnover depends on the
interaction of firm-specific performance and corporate culture. Consistently with our
18
research hypothesis (Section 1), we expect that the probability of a CEO change will be
negatively related to the interactions between firm-specific performance and the
control- and competition-oriented corporate cultures (respectively, 𝛾6 < 0, 𝛾8 < 0) and
positively related to the interactions between the firm-specific performance and the
collaboration- and creation-oriented cultures (respectively, 𝛾4 > 0, and 𝛾10 > 0).
As outlined by Powers (2005), the interpretation of the interaction term
coefficients in logit and probit models requires great care. Specifically, in logit and
probit models of the type turnover=f(performance, firm type, firm type*performance,
controls), Power (2005) shows that the significance of the interaction term coefficient
may be misleading. One possibility is that the true difference in the relationship could
be stronger than is indicated by the estimated coefficient, potentially generating Type 1
errors. Alternatively, the true difference in the relationship could be weaker than is
indicated by the estimated coefficient, potentially generating Type 2 errors. To
overcome this problem, we use the methodology developed by Norton et al. (2004) to
compute correct marginal effects and their standard errors (as in Lel and Miller, 2010;
Bushman et al., 2010; Loureiro, 2010).
The two-stage estimation is repeated for the probability of a CEO change and
the probability of an external turnover (with two possible definitions of an internal
successor as follows: a) someone who has been among top managers for at least two
years before the nomination or b) someone who joined the company at least 2 years
before the nomination). In addition to this, the company performance is analyzed with
respect to both ROA and ROS. As a robustness check, we also conduct a set of simple
logit models in which the past performance of the company is not disentangled from
specific and systematic components but measured in terms of industry-adjusted ROA.
The other regressors are our four measures of corporate culture (COL, COM, CON, and
19
CRE) and their interaction with the firm performance, as we posit that corporate culture
plays a moderating role in the relationship between firm performance and CEO
turnover. Our predictions for the signs of the estimated coefficients are absolutely
similar to those elaborated for the two-stage model.
<<< INSERT TABLE 6 >>>
First, we examine the results from the two-stage multivariate analysis of the
relationship between past ROA and CEO change for both one-year and two-year lags
(see Table 6). Consistent with prior papers, we find a strong negative relationship
(statistically significant at the 1% confidence level) between the firm-specific ROA
and the probability of a CEO turnover in all models without the introduction of cultural
variables (1, 4, and 7) and in both time horizons considered (panel A, and B).
Regarding the exogenous component of the ROA, the estimated coefficient is always
positive but statistically significant (at least at the 5% confidence level) only for the
two-year period (Table 6, panel B). This finding should represent a signal that a
management change is more probable when the rest of the industry has been doing
well, making bad performance more evident and demanding an intervention.
The most interesting results are that we show that different corporate cultures
have a different impact on the probability of changing a CEO. With respect to our four
cultural dimensions, the results are highly consistent with our predictions. The sign of
this coefficient is negative for the collaboration-oriented culture (consistently with H1),
although not highly statistically significant, for both t-1 and t-2. The estimated
coefficients for the competition-oriented corporate cultures are always positive and
also statistically significant (at the 1% confidence level). As predicted by H3, we find
20
statistically significant evidence that the probability that shareholders fire a CEO
increases as it increases the culture orientation toward competition. Contrary to our
expectations (H7), the sign for the creation-oriented culture is positive, even if
statistically significant only for the model predicting CEO change from firm
performance in t-1. Estimated coefficients for the control-oriented culture are never
statistically significant at the 10% level or less, so we do not find support for H5.
Regarding the interactions measuring the moderating role of corporate culture,
we find that the product of control-oriented culture and firm-specific performance
always has a negative coefficient (significant at the 1% confidence level), as predicted
by H6. This finding suggests that when firm-specific performance is positive, the
probability of a CEO turnover is decreased by a control-oriented culture, whereas when
firm-specific performance is negative, the probability of a CEO turnover is increased.
Therefore, a control-oriented culture amplifies the magnitude of the inverse
relationship between past performance and CEO turnover.
We also find that the interaction with the creation-oriented culture always has
a positive coefficient. However, this finding does not provide a statistically significant
indication that this type of culture reduces the size of the inverse relationship between
past performance and CEO turnover, as predicted by H8. The correct marginal effects
calculated following Norton et al. (2004) show that the effect is generally positive (as
predicted by H8), but we do not observe this positive link to be statistically significant
at the 10% or less. As for the other cultural dimensions, collaborate and compete, the
interactions are never statistically significant, rejecting H2 and H4.
The results from the logit models predicting CEO change or external turnover,
for both t-1 and t-2 (reported in the Table 6, in panel A and B, respectively) are always
consistent with one another.
21
To test the robustness of our results, we perform two additional analyses. First,
we repeat the analysis measuring firm performance focusing on ROS (rather than
ROA). We find very similar results regarding the role of culture (Table 7). The only
relevant difference is the sign of the coefficient for the exogenous component of the
firm performance, which is negative and strongly significant.
<<< INSERT TABLE 7 >>>
Second, we run a one-stage logit model in which we consider the industryadjusted ROA without distinguishing between specific and exogenous components. As
shown in Table 8, there is evidence of a strong inverse relationship between past
performance and CEO change, confirming that turnover is a credible corporate
governance instrument. The results also corroborate the hypothesis that corporate
culture influences turnover decisions, signaling a positive relationship between a
competition-oriented culture and CEO change. However, this simpler model does not
allow us to highlight the moderating role of culture with interaction terms that are
never statistically significant.
<<< INSERT TABLE 8 >>>
7. Conclusions and discussion
CEO change must be credible to be considered a valuable corporate governance
instrument. Although there is convergent evidence that CEO change is credible, there
are no studies assessing the reason for this link. In this paper, we show that corporate
culture plays a moderating role in the relationships between CEO turnover and
22
performance pre-CEO change. Our paper is the first to provide empirical evidence
using a large dataset (US listed companies from 1994 to 2010) that corporate culture
influences the decision to fire the CEO.
Specifically, we find a strong negative relationship between the firm-specific
ROA (i.e., the ROA component not caused by peer group performance) and the
probability of a CEO turnover. We also find that corporate culture is related to CEO
change: the collaboration-oriented culture has a negative connection with the
probability of a CEO change, whereas the other three dimensions of corporate culture
(i.e., creation-, control- and competition-oriented corporate cultures) display a positive
link with the probability of a CEO change. Overall, our results support that different
cultures have a different impact on the probability of a CEO change. Once we consider
what happens to the probability of a CEO change in the case of poor performance
among companies with different cultures, we find strong, statistically significant
evidence that the interaction between control-oriented culture and firm-specific
performance has a negative link with the probability of a CEO change. CEO turnover
is more credible in companies with a high control-oriented culture (i.e., when the firmspecific performance is negative, the probability of CEO turnover increases). We thus
show that the control-oriented culture amplifies the magnitude of the inverse
relationship between past performance and CEO turnover. We do not find statistically
significant evidence that the other cultural dimensions have a moderating role. These
results are consistent with different definitions of CEO change, different time periods
(1 and 2 years before the CEO change) and performance indicators (ROA and ROS).
Our results are robust to various specifications and obtained by focusing on
firm performance that are related to the CEO responsibility (rather than sectors).
Specifically, we disentangled firm performance by splitting it into two components
23
(systematic and firm specific), as in Jenter and Kanaan, (2012).
We acknowledge some limitations of our analysis that suggest some
interesting directions for future research. First, we have very limited information on
both the predecessor and the new CEO, e.g., data on their origin, education, and
previous experience. This type of data might help to better explain the moderating role
of corporate culture. In addition to this limitation, we believe that cultural variables are
likely crucial not only in determining the probability of dismissal for bad results but
also in influencing opinions on the current CEO, irrespective of performance and in
directing the choice for a successor to the candidate that best fits the corporate
orientation. Further investigations should be devoted to enriching the database and
exploring these issues. Second, it would be interesting to study the moderating role of
culture not only on the relationship between CEO turnover and past firm performance
(as in our paper) but also on the performance effects of succession to assess both CEO
credibility and CEO effectiveness. Third, it is probable that a cultural approach is able
to offer interesting results if applied to top managers other than the CEO, as the upper
echelons perspective suggests.
In conclusion, this study represents a first step toward opening a new
perspective in the strategic literature, contributing to the studies on both the
antecedents and the consequences of top management change.
24
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Table 1:
The corporate culture dimensions investigated
This table illustrates Cameron et al.’s (2006) corporate culture dimensions investigated in our study. Panel A defines the four culture dimensions,
and Panel B reports the bag of words used in the text analysis to capture each culture dimension. The word banks were obtained in two steps:
first, we selected synonyms suggested by Cameron et al. (2006) to identify each culture dimension. Second, all words selected in
the first step were cross-referenced with the Harvard-IV-4 Psycho Social Dictionary to identify other synonyms. Words ending
with an “*” mean that we accept all suffixes for them. In this way, we are able to count as many words as possible with close
meaning without reporting all of them.
Panel A)
Culture dimensions definition
Control
(CON)
Control-oriented cultures pursue improvements in efficiency by implementing better processes (e.g., by the
extensive use of processes, systems and technology) and quality enhancements (such as statistical process
control and other quality control processes). These banks usually make great use of standardized procedures
and emphasize rule-reinforcement and uniformity.
Compete
(COM)
Competition-oriented cultures are aggressive and forceful in the pursuit of competitiveness by focusing on
organizational effectiveness, fast response, and customer focus. These banks usually set customers as the
highest priority and are defined as the ultimate objective of being in business (e.g., success is judged on the
basis of indicators such as market share, revenues, meeting budget targets, and profitability growth).
Collaborate
(COL)
Collaboration-oriented cultures address building human competencies, developing people, and solidifying an
organizational culture by following a consensual and cooperative process rule to build cohesion through
consensus and satisfaction through involvement (e.g., clarifying and reinforcing organizational values,
norms, and expectations; developing employees and cross-functional work groups; implementing programs
to enhance employee retention; and fostering teamwork and decentralized decision making). These banks
usually succeed because they hire, develop, and retain their human resource base.
Create
(CRE)
Creation-oriented cultures engage innovation in the products and services the organization produces by
effectively focusing on entrepreneurship, vision and constant change (e.g., allowing for freedom of thought
and action among employees so that rule breaking and reaching beyond barriers are common characteristics
of the organization’s culture). These banks usually aim to include innovative product-line extensions, radical
new process breakthroughs, innovations in distribution and logistics that redefine entire industries and
developing new technologies.
Source: Adapted from Cameron et al. (2006)
Panel B)
Bag of words
Control
(CON)
capab*, collectiv*, commit*, competenc*, conflict*, consens*, control*, coordin*, cultur*, decentr*,
employ*, empower*, engag*, expectat*, facilitator*, hir*, interpers*, involv*, life*, long-term*, loyal*,
mentor*, monit*, mutual*, norm*, parent*, partic*, procedur*, productiv*, retain*, reten*, skill*,
social*,tension*, value*
Compete
(COM)
achiev*, acqui*, aggress*, agreem*, attack*, budget*, challeng*, charg*, client*, compet*, customer*,
deliver*, direct*, driv*, excellen*, expand*, fast*, goal*, growth*, hard*, invest*, market*, mov*, outsourc*,
performanc*, position*, pressur*, profit*, rapid*, reputation, result*, revenue*, satisf*, scan*, succes*
signal*, speed*, strong, superior, target*, win*
Collaborate
(COL)
boss*, burocr*, cautio*, cohes*, certain*, chief*, collab*, conservat*, cooperat*, detail*, document*,
efficien*, error*, fail*, help*, human*, inform*, logic*, method*, outcom*, partner*, people*, predictab*,
relation*, qualit*, regular*, solv*, share*, standard*, team*, teamwork*, train*, uniform*, work group*
Create
(CRE)
adapt*, begin*, chang*, creat*, discontin*, dream*, elabor*, entrepre*, envis*, experim*, fantas*, freedom*,
futur*, idea*, init*, innovat*, intellec*, learn*, new*, origin*, pioneer*, predict*, radic*, risk*, start*,
thought*, trend*, unafra*, ventur*, vision*
28
Table 2:
Sample descriptive statistics
This table reports the main characteristics of the sample selected. Our sample included all listed companies in the US
between 1994 and 2011 for which both financial and culture information was available. Data have been obtained
from Compustat and Execucomp. We consider as financial all firms operating in the following industry groups:
Asset Management and Custody banks; Consumer finance; Diversified banks; Diversified REITs; Industrial
REITs; Insurance brokers; Investment banking and brokerage; Life and health insurance; Mortgage REITs; Multiline insurance; Office REITs; Other diversified financial services; Property and casualty insurance; Real
investment trust; Regional banks; Reinsurance; Residential REITs; Retail REITs; Specialized finance; Specialized
REITs; Thrifts and mortgage finance. Companies without an assigned industry group were excluded from the
sample.
Overall
Year
Financial
Non-financial
No. obs
No. CEO changes
No. obs
No. CEO changes
No. obs
No. CEO changes
1994
772
69
98
6
674
63
1995
695
63
78
4
617
59
1996
767
78
98
7
669
71
1997
963
84
112
7
851
77
1998
1003
107
130
10
873
97
1999
945
95
128
5
817
90
2000
930
129
125
13
805
116
2001
915
126
126
20
789
106
2002
1080
99
150
10
930
89
2003
1511
166
234
27
1277
139
2004
1517
138
232
15
1285
123
2005
1310
151
220
18
1090
133
2006
1579
149
271
19
1308
130
2007
1528
146
283
28
1245
118
2008
1604
160
275
25
1329
135
2009
1574
146
271
28
1303
118
2010
586
52
62
7
524
45
2011
174
14
6
2
168
12
Total
19453
1972
2899
251
16554
1721
Source of data: Compustat and Execucomp
29
Table 3:
Corporate culture dimension estimates
Panel A reports the descriptive statics for the four culture dimensions proposed by Cameron et al. (2006) investigated in our
study. Data have been obtained from 10-K reports in the SEC’s Edgar database.
Industry
Overall
Time
Financial
Non-Financial
Collaborate
Mean
1.386
1.620
1.346
1.315
1.264
1.332
1.504
1.461
(COL)
St. Deviation
0.440
0.509
0.413
0.629
0.427
0.395
0.495
0.349
0
0
0
0.43
0
0
0
0.83
Min
1994
1998
2002
2006
2011
Max
8.60
5.36
8.60
4.580
4.290
3.220
4.320
4.360
Compete
Mean
4.844
4.521
4.901
4.887
4.904
4.914
5.089
5.076
(COM)
St. Deviation
1.184
1.103
1.189
1.989
1.045
1.133
1.700
0.885
Min
0
0
0
1.22
0
0
0
3.44
Max
25.72
18.04
25.72
19.82
9.57
8.85
15.80
9.76
Control
Mean
1.410
1.342
1.422
1.460
1.328
1.324
1.589
1.451
(CON)
St. Deviation
0.509
0.363
0.530
0.629
0.329
0.926
0.524
0.303
Min
0
0
0
0.58
0
0
0
0.77
Max
33.33
5.76
33.33
6.500
3.740
28.570
5.620
3.100
Create
Mean
1.081
1.262
1.049
0.877
0.932
1.082
1.266
1.214
(CRE)
St. Deviation
0.387
0.381
0.379
0.429
0.343
0.372
0.416
0.267
Min
0
0
0
0
0.16
0
0
0.69
Max
20.00
5.16
20.00
3.03
3.71
7.14
5.48
2.3
30
Table 4:
Variables description
This table defines the variables used in the paper. Data have been obtained by Compustat.
Variable
Symbol
Definition and calculation method
CEO Turnover
CEOT
CEOT is a dummy variable taking the value 1 if the company has changed
its CEO with respect to the previous year and 0 otherwise
External CEO Turnover 1
ECT1
EXT1 is a dummy variable taking the value 1 if the company has changed
its CEO with respect to the previous year with an external successor (i.e.,
someone who has not been among the top managers at least for two years
before the nomination) and 0 otherwise
External CEO Turnover 2
ECT2
EXT2 is a dummy variable taking the value 1 if the company has changed
its CEO with respect to the previous year with an external successor (i.e.,
someone who did not join the company at least 2 years before the
nomination) and 0 otherwise
Control-Oriented Culture
CONi,t
CONi,t is the control-oriented corporate culture estimate of the company i at
the time t obtained using text analysis.
Competition-Oriented Culture
COMi,t
COMi,t is the competition-oriented corporate culture estimate of the bank i
at the time t obtained using text analysis.
Collaboration-Oriented
Culture
COLi,t)
COLi,t is the collaboration-oriented corporate culture estimate of the bank i
at the time t obtained using text analysis.
Creation-Oriented Culture
CREi,t
CREi,t is the creation-oriented corporate culture estimate of the bank i at the
time t obtained using text analysis.
Return on Assets
ROA
ROA is obtained by the ratio between Earnings Before Interest, Tax and
Depreciation and Amortization (EBITDA) and Total Assets.
Industry-adjusted ROA
IAROA
IAROA is the difference between ROA and the average ROA for companies
in the same industry group for every financial year (the industry group is
identified on the basis of the SPINDEX code).
ROA firm-specific
performance component
𝑣̂𝑖,𝑡−1
𝑣̂𝑖,𝑡−1 is the estimated firm-specific performance component. We follow
Jenter and Kanaan (2012) to disentangle the peer group component and the
estimated residual component of ROA.
ROA exogenous component
𝑃̂𝑖,𝑡−1
𝑃̂𝑖,𝑡−1 is the estimated exogenous component of firm performance common
to the peer group and not attributable to CEO actions. We follow Jenter and
Kanaan (2012) to disentangle the peer group component and the estimated
residual component of ROA.
Return on Sales
ROS
ROS is obtained by the ratio between Earnings Before Interest, Tax and
Depreciation and Amortization (EBITDA) and Total Sales.
31
Table 5:
CEO change credibility: the univariate analysis
This table presents CEO turnover frequency by performance quintile in US companies over the period 1994-2011.
Performance is measured in terms of ROA and industry-adjusted ROA (i.e., for every financial year, the difference
between the single-company ROA and the average ROA for companies in the same industry group, considering
the SPINDEX code in the Execucomp database). Quintiles are calculated with respect to two different periods: one
year and two years before the turnover. Companies without an assigned industry group are not considered. Internal
and external turnover are classified on the basis of two different definitions of an insider successor: 1) someone
who has been among top managers for at least two years before the nomination (Panel A) or 2) someone who
joined the company at least 2 years before the nomination (Panel B). CEO turnovers in which the predecessor was
older than 63 were not considered. ***,**,* indicate statistical significance at the 1%, 5% and 10% levels,
respectively, for the t-test of differences in turnover likelihood between the top and bottom performance quintiles.
Data have been obtained from Compustat and Execucomp
Panel A
Q1
Q2
Q3
Q4
Performance quintiles based on ROA in t-1
P (Ceo Turnover in t)
9.9%
8.8%
7.4%
7.4%
P (External Turnover in t)
5.4%
4.0%
3.2%
2.6%
P (Internal Turnover in t)
4.6%
4.8%
4.2%
4.8%
Performance quintiles based on industry-adjusted ROA in t-1
P (Ceo Turnover in t)
9.1%
8.0%
7.1%
6.9%
P (External Turnover in t)
4.5%
3.3%
2.8%
2.9%
P (Internal Turnover in t)
4.5%
4.7%
4.3%
4.1%
Performance quintiles based on ROA in t-2
P (Ceo Turnover in t)
9.4%
9.1%
8.3%
8.1%
P (External Turnover in t)
4.7%
4.1%
3.5%
2.7%
P (Internal Turnover in t)
4.8%
5.0%
4.8%
5.4%
Performance quintiles based on industry-adjusted ROA in t-2
P (Ceo Turnover in t)
P (External Turnover in t)
P (Internal Turnover in t)
8.7%
3.9%
4.8%
8.6%
4.0%
4.6%
8.6%
3.2%
5.4%
Q5
Q1 vs Q5
6.7%
2.5%
4.2%
***
***
7.5%
2.9%
4.6%
8.2%
3.3%
4.9%
8.1%
2.9%
5.2%
8.5%
3.5%
5.0%
Q1
Q2
Q3
Q4
Performance quintiles based on ROA in t-1
Q5
***
**
Panel B
Q1 vs Q5
P (Ceo Turnover in t)
9.9%
8.8%
7.4%
7.4%
P (External Turnover in t)
5.2%
4.1%
3.2%
2.6%
P (Internal Turnover in t)
4.7%
4.8%
4.2%
4.8%
Performance quintiles based on industry-adjusted ROA in t-1
6.7%
2.4%
4.3%
P (Ceo Turnover in t)
P (External Turnover in t)
P (Internal Turnover in t)
7.5%
2.8%
4.6%
***
P (Ceo Turnover in t)
9.4%
9.1%
8.3%
8.1%
P (External Turnover in t)
4.6%
4.1%
3.5%
2.6%
P (Internal Turnover in t)
4.8%
5.0%
4.8%
5.5%
Performance quintiles based on industry-adjusted ROA in t-2
8.2%
3.3%
4.9%
**
P (Ceo Turnover in t)
P (External Turnover in t)
P (Internal Turnover in t)
8.5%
3.5%
5.0%
9.1%
8.0%
7.1%
6.9%
4.5%
3.3%
2.7%
2.8%
4.6%
4.7%
4.4%
4.1%
Performance quintiles based on ROA in t-2
8.7%
3.7%
5.0%
8.6%
4.1%
4.5%
8.6%
3.2%
5.4%
8.1%
2.9%
5.2%
***
***
32
Table 6:
CEO change credibility: The two-stage multivariate analysis
This table reports the results for the two-stage logit regression of CEO turnover on firm performance reported in model 2. The first stage is an OLS regression of individual ROA on contemporaneous industry ROA. Predicted
values and errors from the first stage are used to disentangle firm performance in specific and systematic components. The second-stage logit models predict CEO turnover or External Turnover, classified on the basis of two
different definitions of an insider successor: a) someone who has been among top managers for at least two years before the nomination or b) someone who joined the company at least 2 years before the nomination. CEO
turnovers in which the predecessor was older than 63 are not considered. 𝑃̂𝑖,𝑡−1 is the estimated exogenous component of firm performance common to the peer group and not attributable to CEO actions, and 𝑣̂𝑖,𝑡−1 is the
estimated firm-specific performance component. COL, COM, CON and CRE are the four corporate culture dimensions proposed by Cameron et al. (2006). Panel A reports the results for the second-stage logit regression of CEO
turnover on firm performance in t-1. Panel B reports the results for the second-stage logit regression of CEO turnover on firm performance in t-2. The mean interaction effects and the corresponding level of significance are
estimated following Norton et al. (2004). The graphs displaying the interaction effects and the corresponding z-statistics are reported in Appendix A.1, Figure 1. ***, **, * indicate statistical significance at the 1%, 5% and
10% levels, respectively. Source: Authors’ elaboration of Execucomp data.
Panel A
Ceo Turnover
𝑣̂𝑖,𝑡−1
𝑃̂𝑖,𝑡−1
-0.1807***
(0.0348)
0.0565*
(0.0327)
COL
COM
CON
CRE
𝑣̂𝑖,𝑡−1 * COL
𝑣̂𝑖,𝑡−1 * COM
𝑣̂𝑖,𝑡−1 * CON
𝑣̂𝑖,𝑡−1 * CRE
Constant
-2.4947***
(0.0331)
-0.1864***
(0.0359)
0.0547
(0.0346)
-0.0722*
(0.0373)
0.1082***
(0.0343)
-0.0033
(0.0403)
0.0694**
(0.0351)
-0.0065
(0.0372)
0.0025
(0.0294)
-0.0927**
(0.0469)
0.043
(0.0269)
-2.499***
(0.0335)
External Turnover a
-0.1879***
(0.0362)
0.042
(0.0343)
-0.051
(0.0365)
0.0956***
(0.0346)
0.0179
(0.0396)
0.0843**
(0.0348)
-0.0051
(0.0367)
0.0025
(0.0295)
-0.0932**
(0.0454)
0.0409*
(0.0233)
-2.5896***
(0.3273)
-0.3601***
(0.0496)
0.0681
(0.0476)
-3.4579***
(0.0519)
-0.3797***
(0.0495)
0.0475
(0.0514)
-0.0937
(0.0586)
0.1878***
(0.0472)
0.0288
(0.057)
0.0454
(0.0526)
-0.0244
(0.0525)
0.0415
(0.0412)
-0.1264**
(0.0642)
0.1026***
(0.0363)
-3.4812***
(0.0529)
External Turnover b
-0.3831***
(0.0498)
0.0447
(0.0523)
-0.1013*
(0.0607)
0.2051***
(0.0501)
0.0407
(0.0577)
0.0235
(0.0566)
-0.0256
(0.0538)
0.0383
(0.045)
-0.1335**
(0.0656)
0.1041***
(0.0368)
-2.9948***
(0.3814)
-0.3543***
(0.05)
0.072
(0.0481)
-3.4684***
(0.0522)
-0.374***
(0.0499)
0.051
(0.0521)
-0.089
(0.0588)
0.1945***
(0.0474)
0.0248
(0.0579)
0.0352
(0.0533)
-0.0284
(0.0527)
0.043
(0.0419)
-0.1224*
(0.0651)
0.1011***
(0.0371)
-3.4925***
(0.0531)
-0.3773***
(0.0503)
0.0493
(0.0532)
-0.0962
(0.0608)
0.2127***
(0.0502)
0.0354
(0.0586)
0.0118
(0.0575)
-0.0291
(0.054)
0.0401
(0.0456)
-0.1296*
(0.0666)
0.1027***
(0.0375)
-2.9943***
(0.3812)
Year dummies
No
No
Yes
No
No
Yes
No
No
Yes
Observations
13,186
13,186
13,186
13,186
13,186
13,186
13,186
13,186
13,186
-0.00001
(0.002)
-0.0006
(0.002)
-0.0043**
(0.0021)
0.003
(0.0021)
0.00004
(0.002)
-0.0009
(0.0021)
-0.0047**
(0.0023)
0.0033
(1.4335)
Mean interaction effects (on the basis of the model with year dummies)
𝑣̂𝑖,𝑡−1 * COL
𝑣̂𝑖,𝑡−1 * COM
𝑣̂𝑖,𝑡−1 * CON
𝑣̂𝑖,𝑡−1 * CRE
0.0003
(0.0026)
-0.001
(0.0027)
-0.0066**
(0.0027)
0.0023
(0.0027)
0.0002
(0.0026)
-0.0009
(0.0026)
-0.0069**
(0.0028)
0.002
(0.0026)
0.0002
(0.002)
-0.0006
(0.002)
-0.0046**
(0.0021)
0.003
(0.0021)
0.0002
(0.002)
-0.0009
(0.0021)
-0.005**
(0.0024)
0.0033
(0.0023)
33
Panel B
Ceo Turnover
𝑣̂𝑖,𝑡−2
𝑃̂𝑖,𝑡−2
-0.1231***
(0.0363)
0.088***
(0.0342)
-2.4263***
(0.0343)
-0.1212***
(0.037)
0.079**
(0.0362)
-0.0135
(0.0359)
0.0942***
(0.0348)
-0.0517
(0.0406)
0.0101
(0.0367)
-0.0139
(0.0368)
0.0016
(0.031)
-0.0955**
(0.0436)
0.0125
(0.0329)
-2.8134***
(0.3877)
No
11,489
No
11,489
Yes
11,489
COL
COM
CON
CRE
𝑣̂𝑖,𝑡−2 * COL
𝑣̂𝑖,𝑡−2 * COM
𝑣̂𝑖,𝑡−2 * CON
𝑣̂𝑖,𝑡−2 * CRE
Constant
Year dummies
Observations
External Turnover a
-0.1248***
(0.0368)
0.0822**
(0.036)
-0.028
(0.0366)
0.1001***
(0.0347)
-0.0457
(0.0406)
-0.0156
(0.0383)
-0.0168
(0.0377)
0.0005
(0.0319)
-0.1009**
(0.045)
0.018
(0.0398)
-2.4287***
(0.0347)
-0.293***
(0.0562)
0.1557***
(0.0498)
External Turnover b
-3.3756***
(0.0533)
-0.2843***
(0.0562)
0.1633***
(0.0526)
-0.0028
(0.0522)
0.1395***
(0.0484)
-0.0487
(0.0589)
0.0404
(0.0471)
0.0051
(0.0532)
-0.0222
(0.0407)
-0.0732
(0.0691)
0.0886**
(0.0446)
-3.382***
(0.0537)
-0.2816***
(0.0564)
0.1793***
(0.0534)
-0.0093
(0.0534)
0.1542***
(0.0496)
-0.0776
(0.0616)
0.0311
(0.0511)
0.0023
(0.0546)
-0.0207
(0.0408)
-0.0735
(0.07)
0.0904*
(0.0472)
-3.3881***
(0.5065)
-0.2949***
(0.0565)
0.1732***
(0.0507)
-3.3834***
(0.0536)
No
11,489
No
11,489
Yes
11,489
No
11,489
-0.2867***
(0.0565)
0.1762***
(0.0535)
-0.0063
(0.0524)
0.1302***
(0.0487)
-0.0471
(0.0589)
0.023
(0.0483)
0.0146
(0.0534)
-0.0158
(0.0409)
-0.0662
(0.0688)
0.0879*
(0.0468)
-3.3884***
(0.0539)
No
11,489
-0.2834***
(0.0567)
0.1936***
(0.0544)
-0.0133
(0.0536)
0.1468***
(0.05)
-0.0764
(0.0615)
0.0134
(0.0532)
0.0125
(0.055)
-0.0145
(0.0409)
-0.066
(0.0697)
0.0905*
(0.0503)
-3.3809***
(0.5065)
Yes
11,489
Mean interaction effects (on the basis of the model with year dummies)
𝑣̂𝑖,𝑡−2 * COL
𝑣̂𝑖,𝑡−2 * COM
𝑣̂𝑖,𝑡−2 * CON
𝑣̂𝑖,𝑡−2 * CRE
-0.001
(0.0027)
-0.0007
(0.0028)
-0.0072**
(0.0028)
0.0015
(0.0027)
-0.0009
(0.0027)
-0.0006
(0.0027)
-0.0067**
(0.0028)
0.0009
(0.0027)
0.0002
(0.0018)
-0.002
(0.0019)
-0.0021
(0.0018)
0.0027
(0.0019)
0.0002
(0.0018)
-0.002
(0.0019)
-0.0019
(0.0019)
0.0029
(1.3928)
0.0006
(0.0018)
-0.0017
(0.0019)
-0.0019
(0.0018)
0.0029
(0.0019)
0.0005
(0.0018)
-0.0016
(0.0019)
-0.0019
(0.0019)
0.003
(0.002)
34
Table 7:
CEO change credibility: The two-stage analysis focusing on Return on Sales
This table reports the results for the two-stage logit regression of CEO turnover on firm performance in t-1 reported in model 2. We decompose the ROA into two components, Return on Sales (ROS=EBITDA/Total Sales) and
Asset Turnover (AT=Total Sales/Total Assets), and we report the results for ROS. The first stage is an OLS regression of individual ROS on contemporaneous industry ROS. Predicted values and errors from the first stage are
used to disentangle firm performance in specific and systematic components. The second-stage logit models predict CEO turnover or External Turnover, classified on the basis of two different definitions of an insider
successor: a) someone who has been among top managers for at least two years before the nomination or b) someone who joined the company at least 2 years before the nomination. CEO turnovers in which the predecessor
was older than 63 are not considered. 𝑃̂𝑖,𝑡−1 is the estimated exogenous component of firm performance common to the peer group and not attributable to CEO actions, and 𝑣̂𝑖,𝑡−1 is the estimated firm-specific performance
component. COL, COM, CON and CRE are the four corporate culture dimensions proposed by Cameron et al. (2006). The results for logit regression of CEO turnover on firm performance in t-2 are available on request from the
authors. Mean interaction effects and the corresponding level of significance are estimated following Norton et al. (2004). The graphs displaying the interaction effects and the corresponding z-statistics are reported in
Appendix A.1, Figure 2. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Source: Authors’ elaboration of Execucomp data.
.
Ceo Turnover
𝑣̂𝑖,𝑡−1
𝑃̂𝑖,𝑡−1
-0.1371***
(0.0326)
-0.1014***
(0.0331)
-2.5016***
(0.0331)
-0.1588***
(0.0328)
-0.0988***
(0.0336)
-0.0832**
(0.0374)
0.1037***
(0.035)
-0.0209
(0.0403)
0.0703**
(0.0339)
0.0118
(0.0319)
0.0008
(0.0264)
-0.0903*
(0.0493)
0.0367
(0.0232)
-2.5096***
(0.0335)
-0.1589***
(0.0332)
-0.115***
(0.0344)
-0.0574
(0.0364)
0.0863**
(0.0353)
-0.0028
(0.0397)
0.0928***
(0.0335)
0.0118
(0.0312)
-0.0004
(0.0269)
-0.0887*
(0.0484)
0.0402*
(0.0213)
-2.6262***
(0.327)
No
No
Yes
COL
COM
CON
CRE
𝑣̂𝑖,𝑡−1 * COL
𝑣̂𝑖,𝑡−1 * COM
𝑣̂𝑖,𝑡−1 * CON
𝑣̂𝑖,𝑡−1 * CRE
Constant
Year dummies
External Turnover a
Observations
13,186
13,186
13,186
Mean interaction effects (on the basis of the model with year dummies)
𝑣̂𝑖,𝑡−1 * COL
𝑣̂𝑖,𝑡−1 * COM
𝑣̂𝑖,𝑡−1 * CON
𝑣̂𝑖,𝑡−1 * CRE
0.0016
(0.0027)
-0.0009
(0.0027)
-0.0063**
(0.0028)
0.002
(0.0028)
0.0014
(0.0026)
-0.0008
(0.0027)
-0.0063**
(0.0028)
0.002
(0.0027)
-0.2629***
(0.0609)
-0.2087***
(0.049)
-3.4629***
(0.0519)
-0.3116***
(0.0455)
-0.207***
(0.0505)
-0.1054*
(0.0582)
0.1796***
(0.0474)
-0.0132
(0.0595)
0.0525
(0.0535)
0.0105
(0.0487)
0.0404
(0.0342)
-0.1702**
(0.0678)
0.0489
(0.0397)
-3.4904***
(0.0528)
External Turnover b
-0.317***
(0.0452)
-0.2141***
(0.0521)
-0.1081*
(0.0599)
0.191***
(0.0506)
-0.0043
(0.061)
0.0415
(0.0574)
0.0122
(0.0491)
0.0381
(0.0348)
-0.1686**
(0.0681)
0.046
(0.0412)
-3.4904***
(0.0528)
-0.2592***
(0.0609)
-0.2038***
(0.0491)
-3.4734***
(0.0521)
-0.3082***
(0.0457)
-0.2015***
(0.0508)
-0.100*
(0.0583)
0.1865***
(0.0478)
-0.0165
(0.0603)
0.0427
(0.0546)
0.0093
(0.0486)
0.0399
(0.034)
-0.1677**
(0.0682)
0.0542
(0.042)
-3.5017***
(0.0531)
-0.3136***
(0.0455)
-0.2076***
(0.0524)
-0.1026*
(0.06)
0.199***
(0.0508)
-0.0089
(0.0617)
0.0297
(0.0588)
0.0108
(0.0491)
0.0378
(0.0345)
-0.1664**
(0.0685)
0.0513
(0.0433)
-3.0591***
(0.383)
No
No
Yes
No
No
Yes
13,186
13,186
13,186
13,186
13,186
13,186
0.0013
(0.002)
-0.0002
(0.0021)
-0.0055**
(0.0022)
0.0011
(0.002)
0.0014
(0.0021)
-0.0004
(0.0021)
-0.0055**
(0.0025)
0.0011
(0.0021)
0.0012
(0.002)
-0.0003
(0.002)
-0.0053**
(0.0022)
0.0014
(0.002)
0.0013
(0.0021)
-0.0005
(0.0021)
-0.0053**
(0.0025)
0.0014
(0.0021)
35
Table 8:
CEO change credibility: A robustness check using a one-stage approach
This table reports the results for the one-stage logit regression of CEO turnover on firm performance measured as industry-adjusted ROA (IAROA). The logit models predict CEO turnover or External Turnover, classified on
the basis of two different definitions of an insider successor: a) someone who has been among top managers for at least two years before the nomination or b) someone who joined the company at least 2 years before the
nomination. CEO turnovers in which the predecessor was older than 63 are not considered. 𝐼𝐴𝑅𝑂𝐴 is the industry-adjusted performance (i.e., for every financial year, the difference between the single company ROA and the
average ROA for companies in the same industry group, considering the SPINDEX code in the Execucomp database) . COL, COM, CON and CRE are the four corporate culture dimensions proposed by Cameron (1993). We
report the results for logit regression of CEO turnover on firm performance in t-1. The results for logit regression of CEO turnover on firm performance in t-2 are available on request from the authors. Mean interaction
effects and the corresponding level of significance are estimated following Norton et al. (2004). The graphs displaying the interaction effects and the corresponding z-statistics are not reported in the Appendix because the
interactions are never statistically significant at the 10% confidence level or less. These graphs are available from the authors upon request. ***, **, * indicate statistical significance at the 1%, 5% and 10% levels,
respectively. Source: Authors’ elaboration of Execucomp data.
Ceo Turnover
𝐼𝐴𝑅𝑂𝐴𝑖,𝑡−1
-0.0682**
(0.0315)
-2.4864***
(0.0327)
-0.0964***
(0.0356)
-0.0545
(0.036)
0.1097***
(0.0344)
0.0125
(0.0388)
0.068**
(0.0326)
-0.0166
(0.0331)
0.0063
(0.0302)
-0.0305
(0.0327)
0.0326
(0.022)
-2.6032***
(0.3273)
No
No
Yes
COL
COM
CON
CRE
𝐼𝐴𝑅𝑂𝐴𝑖,𝑡−1 * COL
𝐼𝐴𝑅𝑂𝐴𝑖,𝑡−1 * COM
𝐼𝐴𝑅𝑂𝐴𝑖,𝑡−1* CON
𝐼𝐴𝑅𝑂𝐴𝑖,𝑡−1* CRE
Constant
Year dummies
Observations
External Turnover a
-0.0869***
(0.0327)
-0.0791**
(0.037)
0.1237***
(0.0341)
-0.0096
(0.0394)
0.0478
(0.0332)
-0.02
(0.0336)
0.0093
(0.0299)
-0.0259
(0.0329)
0.0296
(0.0244)
-2.4952***
(0.0336)
13,186
13,186
13,186
-0.1602***
(0.0472)
-0.1602***
(0.0472)
No
13,186
-0.1978***
(0.0469)
-0.0881
(0.0567)
0.1986***
(0.0461)
0.0177
(0.0561)
0.0103
(0.0535)
0.0091
(0.0493)
0.0311
(0.0442)
-0.0598
(0.047)
0.0756*
(0.045)
-0.1978***
(0.0469)
No
External Turnover b
-0.2133***
(0.0531)
-0.0925
(0.0581)
0.2128***
(0.0492)
0.0347
(0.0566)
-0.0021
(0.0569)
0.0048
(0.0509)
0.0329
(0.0472)
-0.0674
(0.0487)
0.0782*
(0.0449)
-0.2133***
(0.0531)
Yes
13,186
13,186
0.0008
(0.0017)
-0.00012
(0.0025)
-0.002
(0.0017)
0.0023
(0.0017)
0.0007
(0.0018)
-0.00022
(0.0018)
-0.0024
(0.0019)
0.0025
(0.0018)
-0.155***
(0.0479)
-3.4266***
(0.0501)
-0.1939***
(0.0475)
-0.0838
(0.0568)
0.2051***
(0.0465)
0.0147
(0.0569)
-0.0002
(0.0543)
0.003
(0.0497)
0.0321
(0.0445)
-0.0574
(0.0479)
0.0721
(0.0465)
-3.4497***
(0.0515)
-0.2073***
(0.0537)
-0.0879
(0.0583)
0.2201***
(0.0495)
0.0302
(0.0573)
-0.0141
(0.058)
-0.0015
(0.0512)
0.0339
(0.0474)
-0.0641
(0.0494)
0.0743
(0.0465)
-3.0083***
(0.3828)
No
No
Yes
13,186
13,186
13,186
Mean interaction effects (on the basis of the model with year dummies)
𝑣̂𝑖,𝑡−1 * COL
𝑣̂𝑖,𝑡−1 * COM
𝑣̂𝑖,𝑡−1 * CON
𝑣̂𝑖,𝑡−1 * CRE
-0.001
(0.0026)
0.00002
(0.0025)
-0.0018
(0.0025)
0.0019
(0.0025)
-0.0009
(0.0026)
-0.00018
(0.0025)
-0.0022
(0.0026)
0.0019
(0.0025)
0.0006
(0.0016)
-0.0001
(0.0017)
-0.0019
(0.0017)
0.0023
(0.0017)
0.0005
(0.0018)
-0.0002
(0.0018)
-0.0022
(0.0018)
0.0024
(0.0018)
36