Corporate Culture and CEO Turnover

January 31st, 2013

AbstractWe 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

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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

create.

1 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.

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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

performance than in other companies and will not punish the CEO for bad

performance.

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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.

3.3 Control-oriented culture

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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

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

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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.

Table 1 summarizes the attributes of the corporate culture orientations

proposed by Cameron et al. (2006).

<<< INSERT TABLE 1 >>>

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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

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

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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 year-

observations, 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

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

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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

synonyms. Loughran and McDonald (2011) have noted that the Harvard-IV-4 Psycho-

Social 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

2 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).

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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

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).

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<<< 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

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.

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Regarding the measurement of firm performance, we use accounting-based

performance measures because, as noted by Jenter and Kanaan (2012), these are short-

term 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

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

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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

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)).

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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+v i ,t−1 (1)

(Second stage)

Pr (CEO change¿¿ i , t)¿γ 0+γ 1 P̂i ,t−1+γ2 v̂ i , t−1+γ 3COLi , t−1+γ 4 COLi ,t−1∗v̂i , t−1+γ 5COM i , t−1+γ 6 COM i ,t−1∗v̂ i ,t−1+γ7 CON i , t−1+γ 8CON i ,t−1∗v̂ i , t−1+γ 9CRE i ,t−1+γ 10CRE i ,t−1∗v̂ i ,t−1+ςi , t−1¿ (2)

where P̂i ,t−1¿ β̂0+ β̂1 Ppeergroup ,t−1, 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. 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.

3 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.

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<<< 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

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

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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

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.

19

<<< 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

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

20

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.

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

21

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 industry-

adjusted 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

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.

22

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 firm-

specific 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

(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

23

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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27

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; 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; Thrifts and mortgage finance. Companies without an assigned industry group were excluded from the sample.

Year Overall Financial Non-financialNo. 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.

OverallIndustry Time

Financial Non-Financial 1994 1998 2002 2006 2011

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

Min 0 0 0 0.43 0 0 0 0.83

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 AQ1 Q2 Q3 Q4 Q5 Q1 vs Q5

Performance quintiles based on ROA in t-1P (Ceo Turnover in t) 9.9% 8.8% 7.4% 7.4% 6.7% ***P (External Turnover in t) 5.4% 4.0% 3.2% 2.6% 2.5% ***P (Internal Turnover in t) 4.6% 4.8% 4.2% 4.8% 4.2%

Performance quintiles based on industry-adjusted ROA in t-1P (Ceo Turnover in t) 9.1% 8.0% 7.1% 6.9% 7.5%P (External Turnover in t) 4.5% 3.3% 2.8% 2.9% 2.9% ***P (Internal Turnover in t) 4.5% 4.7% 4.3% 4.1% 4.6%

Performance quintiles based on ROA in t-2P (Ceo Turnover in t) 9.4% 9.1% 8.3% 8.1% 8.2%P (External Turnover in t) 4.7% 4.1% 3.5% 2.7% 3.3% **P (Internal Turnover in t) 4.8% 5.0% 4.8% 5.4% 4.9%

Performance quintiles based on industry-adjusted ROA in t-2

P (Ceo Turnover in t) 8.7% 8.6% 8.6% 8.1% 8.5%P (External Turnover in t) 3.9% 4.0% 3.2% 2.9% 3.5%P (Internal Turnover in t) 4.8% 4.6% 5.4% 5.2% 5.0%

Panel BQ1 Q2 Q3 Q4 Q5 Q1 vs Q5

Performance quintiles based on ROA in t-1

P (Ceo Turnover in t) 9.9% 8.8% 7.4% 7.4% 6.7% ***P (External Turnover in t) 5.2% 4.1% 3.2% 2.6% 2.4% ***P (Internal Turnover in t) 4.7% 4.8% 4.2% 4.8% 4.3%

Performance quintiles based on industry-adjusted ROA in t-1

P (Ceo Turnover in t) 9.1% 8.0% 7.1% 6.9% 7.5%P (External Turnover in t) 4.5% 3.3% 2.7% 2.8% 2.8% ***P (Internal Turnover in t) 4.6% 4.7% 4.4% 4.1% 4.6%

Performance quintiles based on ROA in t-2

P (Ceo Turnover in t) 9.4% 9.1% 8.3% 8.1% 8.2%P (External Turnover in t) 4.6% 4.1% 3.5% 2.6% 3.3% **P (Internal Turnover in t) 4.8% 5.0% 4.8% 5.5% 4.9%

Performance quintiles based on industry-adjusted ROA in t-2

P (Ceo Turnover in t) 8.7% 8.6% 8.6% 8.1% 8.5%P (External Turnover in t) 3.7% 4.1% 3.2% 2.9% 3.5%P (Internal Turnover in t) 5.0% 4.5% 5.4% 5.2% 5.0%

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 ACeo Turnover External Turnover a External Turnover b

v̂i , t−1-0.1807***

(0.0348)-0.1864***

(0.0359)-0.1879***

(0.0362)-0.3601***

(0.0496)-0.3797***

(0.0495)-0.3831***

(0.0498)-0.3543***

(0.05)-0.374***

(0.0499)-0.3773***

(0.0503)

P̂i ,t−10.0565* (0.0327)

0.0547 (0.0346)

0.042 (0.0343)

0.0681 (0.0476)

0.0475 (0.0514)

0.0447 (0.0523)

0.072 (0.0481)

0.051 (0.0521)

0.0493 (0.0532)

COL -0.0722* (0.0373)

-0.051 (0.0365)

-0.0937 (0.0586)

-0.1013* (0.0607)

-0.089 (0.0588)

-0.0962 (0.0608)

COM 0.1082*** (0.0343)

0.0956*** (0.0346)

0.1878*** (0.0472)

0.2051*** (0.0501)

0.1945*** (0.0474)

0.2127*** (0.0502)

CON -0.0033 (0.0403)

0.0179 (0.0396)

0.0288 (0.057)

0.0407 (0.0577)

0.0248 (0.0579)

0.0354 (0.0586)

CRE 0.0694** (0.0351)

0.0843** (0.0348)

0.0454 (0.0526)

0.0235 (0.0566)

0.0352 (0.0533)

0.0118 (0.0575)

v̂i , t−1 * COL

-0.0065 (0.0372)

-0.0051 (0.0367)

-0.0244 (0.0525)

-0.0256 (0.0538)

-0.0284 (0.0527)

-0.0291 (0.054)

v̂i , t−1 * COM

0.0025 (0.0294)

0.0025 (0.0295)

0.0415 (0.0412)

0.0383 (0.045)

0.043 (0.0419)

0.0401 (0.0456)

v̂i , t−1 * CON

-0.0927** (0.0469)

-0.0932** (0.0454)

-0.1264** (0.0642)

-0.1335** (0.0656)

-0.1224* (0.0651)

-0.1296* (0.0666)

v̂i , t−1 * CRE

0.043 (0.0269)

0.0409* (0.0233)

0.1026*** (0.0363)

0.1041*** (0.0368)

0.1011*** (0.0371)

0.1027*** (0.0375)

Constant -2.4947*** (0.0331)

-2.499*** (0.0335)

-2.5896*** (0.3273)

-3.4579*** (0.0519)

-3.4812*** (0.0529)

-2.9948*** (0.3814)

-3.4684*** (0.0522)

-3.4925*** (0.0531)

-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 13,186Mean interaction effects (on the basis of the model with year dummies)

v̂i , t−1 * COL

0.0003 (0.0026)

0.0002 (0.0026)

0.0002 (0.002)

0.0002 (0.002)

-0.00001 (0.002)

0.00004 (0.002)

v̂i , t−1 * COM

-0.001 (0.0027)

-0.0009 (0.0026)

-0.0006 (0.002)

-0.0009 (0.0021)

-0.0006 (0.002)

-0.0009 (0.0021)

33

v̂i , t−1 * CON

-0.0066** (0.0027)

-0.0069** (0.0028)

-0.0046** (0.0021)

-0.005** (0.0024)

-0.0043** (0.0021)

-0.0047** (0.0023)

v̂i , t−1 * CRE

0.0023 (0.0027)

0.002 (0.0026)

0.003 (0.0021)

0.0033 (0.0023)

0.003 (0.0021)

0.0033 (1.4335)

34

Panel B

Ceo Turnover External Turnover a External Turnover b

v̂i , t−2-0.1231***

(0.0363)-0.1248***

(0.0368)-0.1212***

(0.037)-0.293*** (0.0562)

-0.2843*** (0.0562)

-0.2816*** (0.0564)

-0.2949*** (0.0565)

-0.2867*** (0.0565)

-0.2834*** (0.0567)

P̂i ,t−20.088*** (0.0342)

0.0822** (0.036)

0.079** (0.0362)

0.1557*** (0.0498)

0.1633*** (0.0526)

0.1793*** (0.0534)

0.1732*** (0.0507)

0.1762*** (0.0535)

0.1936*** (0.0544)

COL -0.028 (0.0366)

-0.0135 (0.0359)

-0.0028 (0.0522)

-0.0093 (0.0534)

-0.0063 (0.0524)

-0.0133 (0.0536)

COM 0.1001*** (0.0347)

0.0942*** (0.0348)

0.1395*** (0.0484)

0.1542*** (0.0496)

0.1302*** (0.0487)

0.1468*** (0.05)

CON -0.0457 (0.0406)

-0.0517 (0.0406)

-0.0487 (0.0589)

-0.0776 (0.0616)

-0.0471 (0.0589)

-0.0764 (0.0615)

CRE -0.0156 (0.0383)

0.0101 (0.0367)

0.0404 (0.0471)

0.0311 (0.0511)

0.023 (0.0483)

0.0134 (0.0532)

v̂i , t−2 * COL-0.0168 (0.0377)

-0.0139 (0.0368)

0.0051 (0.0532)

0.0023 (0.0546)

0.0146 (0.0534)

0.0125 (0.055)

v̂i , t−2 * COM

0.0005 (0.0319)

0.0016 (0.031)

-0.0222 (0.0407)

-0.0207 (0.0408)

-0.0158 (0.0409)

-0.0145 (0.0409)

v̂i , t−2 * CON-0.1009**

(0.045)-0.0955** (0.0436)

-0.0732 (0.0691)

-0.0735 (0.07)

-0.0662 (0.0688)

-0.066 (0.0697)

v̂i , t−2 * CRE0.018

(0.0398)0.0125

(0.0329)0.0886** (0.0446)

0.0904* (0.0472)

0.0879* (0.0468)

0.0905* (0.0503)

Constant -2.4263*** (0.0343)

-2.4287*** (0.0347)

-2.8134*** (0.3877)

-3.3756*** (0.0533)

-3.382*** (0.0537)

-3.3881*** (0.5065)

-3.3834*** (0.0536)

-3.3884*** (0.0539)

-3.3809*** (0.5065)

Year dummies No No Yes No No Yes No No Yes

Observations 11,489 11,489 11,489 11,489 11,489 11,489 11,489 11,489 11,489

Mean interaction effects (on the basis of the model with year dummies)

v̂i , t−2 * COL-0.001

(0.0027)-0.0009 (0.0027)

0.0002 (0.0018)

0.0002 (0.0018)

0.0006 (0.0018)

0.0005 (0.0018)

v̂i , t−2 * COM

-0.0007 (0.0028)

-0.0006 (0.0027)

-0.002 (0.0019)

-0.002 (0.0019)

-0.0017 (0.0019)

-0.0016 (0.0019)

v̂i , t−2 * CON-0.0072** (0.0028)

-0.0067** (0.0028)

-0.0021 (0.0018)

-0.0019 (0.0019)

-0.0019 (0.0018)

-0.0019 (0.0019)

v̂i , t−2 * CRE0.0015

(0.0027)0.0009

(0.0027)0.0027

(0.0019)0.0029

(1.3928)0.0029

(0.0019)0.003

(0.002)

35

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 External Turnover a External Turnover b

v̂i , t−1-0.1371***

(0.0326)-0.1588***

(0.0328)-0.1589***

(0.0332)-0.2629***

(0.0609)-0.3116***

(0.0455)-0.317*** (0.0452)

-0.2592*** (0.0609)

-0.3082*** (0.0457)

-0.3136*** (0.0455)

P̂i ,t−1-0.1014***

(0.0331)-0.0988***

(0.0336)-0.115*** (0.0344)

-0.2087*** (0.049)

-0.207*** (0.0505)

-0.2141*** (0.0521)

-0.2038*** (0.0491)

-0.2015*** (0.0508)

-0.2076*** (0.0524)

COL -0.0832** (0.0374)

-0.0574 (0.0364)

-0.1054* (0.0582)

-0.1081* (0.0599)

-0.100* (0.0583)

-0.1026* (0.06)

COM 0.1037*** (0.035)

0.0863** (0.0353)

0.1796*** (0.0474)

0.191*** (0.0506)

0.1865*** (0.0478)

0.199*** (0.0508)

CON -0.0209 (0.0403)

-0.0028 (0.0397)

-0.0132 (0.0595)

-0.0043 (0.061)

-0.0165 (0.0603)

-0.0089 (0.0617)

CRE 0.0703** (0.0339)

0.0928*** (0.0335)

0.0525 (0.0535)

0.0415 (0.0574)

0.0427 (0.0546)

0.0297 (0.0588)

v̂i , t−1 * COL0.0118

(0.0319)0.0118

(0.0312)0.0105

(0.0487)0.0122

(0.0491)0.0093

(0.0486)0.0108

(0.0491)v̂i , t−1 * COM

0.0008 (0.0264)

-0.0004 (0.0269)

0.0404 (0.0342)

0.0381 (0.0348)

0.0399 (0.034)

0.0378 (0.0345)

v̂i , t−1 * CON-0.0903* (0.0493)

-0.0887* (0.0484)

-0.1702** (0.0678)

-0.1686** (0.0681)

-0.1677** (0.0682)

-0.1664** (0.0685)

v̂i , t−1 * CRE0.0367

(0.0232)0.0402* (0.0213)

0.0489 (0.0397)

0.046 (0.0412)

0.0542 (0.042)

0.0513 (0.0433)

Constant -2.5016*** (0.0331)

-2.5096*** (0.0335)

-2.6262*** (0.327)

-3.4629*** (0.0519)

-3.4904*** (0.0528)

-3.4904*** (0.0528)

-3.4734*** (0.0521)

-3.5017*** (0.0531)

-3.0591*** (0.383)

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 13,186Mean interaction effects (on the basis of the model with year dummies)

v̂i , t−1 * COL 0.0016 (0.0027)

0.0014 (0.0026)

0.0013 (0.002)

0.0014 (0.0021)

0.0012 (0.002)

0.0013 (0.0021)

v̂i , t−1 * COM

-0.0009 (0.0027)

-0.0008 (0.0027)

-0.0002 (0.0021)

-0.0004 (0.0021)

-0.0003 (0.002)

-0.0005 (0.0021)

v̂i , t−1 * CON -0.0063** (0.0028)

-0.0063** (0.0028)

-0.0055** (0.0022)

-0.0055** (0.0025)

-0.0053** (0.0022)

-0.0053** (0.0025)

36

v̂i , t−1 * CRE 0.002 (0.0028)

0.002 (0.0027)

0.0011 (0.002)

0.0011 (0.0021)

0.0014 (0.002)

0.0014 (0.0021)

37

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 External Turnover a External Turnover b

IAROA i ,t−1-0.0682** (0.0315)

-0.0869*** (0.0327)

-0.0964*** (0.0356)

-0.1602*** (0.0472)

-0.1978*** (0.0469)

-0.2133*** (0.0531)

-0.155*** (0.0479)

-0.1939*** (0.0475)

-0.2073*** (0.0537)

COL -0.0791** (0.037)

-0.0545 (0.036)

-0.0881 (0.0567)

-0.0925 (0.0581)

-0.0838 (0.0568)

-0.0879 (0.0583)

COM 0.1237*** (0.0341)

0.1097*** (0.0344)

0.1986*** (0.0461)

0.2128*** (0.0492)

0.2051*** (0.0465)

0.2201*** (0.0495)

CON -0.0096 (0.0394)

0.0125 (0.0388)

0.0177 (0.0561)

0.0347 (0.0566)

0.0147 (0.0569)

0.0302 (0.0573)

CRE 0.0478 (0.0332)

0.068** (0.0326)

0.0103 (0.0535)

-0.0021 (0.0569)

-0.0002 (0.0543)

-0.0141 (0.058)

IAROA i ,t−1 * COL

-0.02 (0.0336)

-0.0166 (0.0331)

0.0091 (0.0493)

0.0048 (0.0509)

0.003 (0.0497)

-0.0015 (0.0512)

IAROA i ,t−1 * COM

0.0093 (0.0299)

0.0063 (0.0302)

0.0311 (0.0442)

0.0329 (0.0472)

0.0321 (0.0445)

0.0339 (0.0474)

IAROA i ,t−1* CON

-0.0259 (0.0329)

-0.0305 (0.0327)

-0.0598 (0.047)

-0.0674 (0.0487)

-0.0574 (0.0479)

-0.0641 (0.0494)

IAROA i ,t−1* CRE

0.0296 (0.0244)

0.0326 (0.022)

0.0756* (0.045)

0.0782* (0.0449)

0.0721 (0.0465)

0.0743 (0.0465)

Constant -2.4864*** (0.0327)

-2.4952*** (0.0336)

-2.6032*** (0.3273)

-0.1602*** (0.0472)

-0.1978*** (0.0469)

-0.2133*** (0.0531)

-3.4266*** (0.0501)

-3.4497*** (0.0515)

-3.0083*** (0.3828)

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

Mean interaction effects (on the basis of the model with year dummies)

v̂i , t−1 * COL-0.001 (0.0026)

-0.0009 (0.0026)

0.0008 (0.0017)

0.0007 (0.0018)

0.0006 (0.0016)

0.0005 (0.0018)

v̂i , t−1 * COM0.00002 (0.0025)

-0.00018 (0.0025)

-0.00012 (0.0025)

-0.00022 (0.0018)

-0.0001 (0.0017)

-0.0002 (0.0018)

v̂i , t−1 * CON-0.0018 (0.0025)

-0.0022 (0.0026)

-0.002 (0.0017)

-0.0024 (0.0019)

-0.0019 (0.0017)

-0.0022 (0.0018)

v̂i , t−1 * CRE0.0019 (0.0025)

0.0019 (0.0025)

0.0023 (0.0017)

0.0025 (0.0018)

0.0023 (0.0017)

0.0024 (0.0018)

38

39