Examination of CEO-CFO Social Interaction through Language...

Examination of CEO-CFO Social Interaction through Language Style Matching: Outcomes for the CFO and the

Organization

Journal: Academy of Management Journal

Manuscript ID AMJ-2016-1062.R3

Manuscript Type: Revision

Keywords:

CEO/TMT decision making < Upper Echelons/Corporate Governance < Business Policy and Strategy < Topic Areas, Executive compensation < Upper Echelons/Corporate Governance < Business Policy and Strategy < Topic Areas, Upper echelons/corporate governance (General) < Upper Echelons/Corporate Governance < Business Policy and Strategy < Topic Areas

Abstract:

This study proposes that CEO-CFO language style matching (LSM)—a form of unconscious verbal mimicry based on function words—can provide

insights into social interaction processes between CEOs and CFOs. We argue and empirically verify that high CEO-CFO LSM reflects CFOs’ strong attempts to ingratiate CEOs. Because ingratiation of superiors can lead to the superiors’ positive evaluations of subordinates, CFOs who exhibit higher LSM with CEOs will receive higher compensation and are more likely to become board members of the associated firms. In addition, the proposed relationships will be stronger when CEOs are more powerful. Yet, in the presence of high CEO-CFO LSM, CFOs are less likely to voice different viewpoints and challenge CEOs in strategic decision processes. As a result, firms tend to undertake more mergers and acquisitions (M&As), and such M&As will be paid with a low percentage of cash (vs. stock) and realize lower announcement returns. Using a sample of over 2,000 U.S.

firms in 2002-2013, we find empirical support for these predictions.

Academy of Management Journal

Examination of CEO-CFO Social Interaction through Language

Style Matching: Outcomes for the CFO and the Organization

Wei Shi

Miami Business School University of MiamiF [email protected]

Yan Zhang

Jones Graduate School of Business Rice University [email protected]

Robert E. Hoskisson

Jones Graduate School of Business Rice University

[email protected]

Acknowledgements: This study is based on the third essay of the first author’s dissertation. We are grateful to AMJ action editor Sucheta Nadkarni’s guidance and three anonymous reviewers’ valuable suggestions. We also would like to thank seminar participants at Georgia State University, Tulane University, and University of Miami for their helpful feedback. The first author would like to thank his dissertation committee members for their feedback on earlier versions of the paper.

Page 1 of 63 Academy of Management Journal

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EXAMINATION OF CEO-CFO SOCIAL INTERACTION THROUGH LANGUAGE

STYLE MATCHING: OUTCOMES FOR THE CFO AND THE ORGANIZATION

ABSTRACT

This study proposes that CEO-CFO language style matching (LSM)—a form of unconscious verbal mimicry based on function words—can provide insights into social interaction processes between CEOs and CFOs. We argue and empirically verify that high CEO-CFO LSM reflects CFOs’ strong attempts to ingratiate CEOs. Because ingratiation of superiors can lead to the superiors’ positive evaluations of subordinates, CFOs who exhibit higher LSM with CEOs will receive higher compensation and are more likely to become board members of the associated firms. In addition, the proposed relationships will be stronger when CEOs are more powerful. Yet, in the presence of high CEO-CFO LSM, CFOs are less likely to voice different viewpoints and challenge CEOs in strategic decision processes. As a result, firms tend to undertake more mergers and acquisitions (M&As), and such M&As will be paid with a low percentage of cash (vs. stock) and realize lower announcement returns. Using a sample of over 2,000 U.S. firms in 2002-2013, we find empirical support for these predictions.

Upper echelons research suggests that social interactions among top managers are critical

to strategic decision quality (Finkelstein, Hambrick, & Cannella, 2009). Given that top

management team (TMT) social interaction processes typically are not directly observable to

external constituents, most scholars have relied on TMT demographic characteristics to infer the

nature and quality of TMT social interactions. For instance, scholars have argued that TMT

demographic heterogeneity can give rise to cognitive conflict (Bantel & Jackson, 1989;

Wiersema & Bantel, 1992) but to low group cohesiveness (Michel & Hambrick, 1992). Despite

the popularity of demographic heterogeneity in TMT research, scholars have questioned the

underlying meaning of such data and call for studying TMT social interactions using more

substantive constructs (Hambrick, 2007; Lawrence, 1997; Priem, Lyon, & Dess, 1999).

This study moves beyond prior research relying upon top managers’ demographic

characteristics to infer their social interactions and proposes that behavioral mimicry between

CEOs and CFOs can provide new insights into their social interaction processes. Behavioral

mimicry is a critical component of human social interactions (Chartrand & Lakin, 2013;

Page 2 of 63Academy of Management Journal

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Chartrand & van Baaren, 2009) and can be manifested in various ways from facial mimicry to

verbal mimicry. We focus on verbal mimicry that occurs when individuals exhibit speech

characteristics and patterns similar to others during social interactions (Chartrand & van Baaren,

2009). Although behavioral mimicry tends to take place unconsciously, given that mimicry can

lead to partiality, individuals with an affiliation goal are more likely to engage in mimicry with

interaction partners (Chartrand & van Baaren, 2009).

Furthermore, we focus on verbal mimicry between CEO and another senior executive, i.e.,

CFO, instead of verbal mimicry of the whole TMT because examining interactions and dynamics

within “subteams” of TMTs who are relevant in certain decision making situations can improve

the predictive strength of upper echelons theory (Hambrick, 2007). Specifically, we choose the

CEO-CFO dyad because CEOs are the primary corporate decision makers (Finkelstein et al.,

2009) and almost all the U.S. public firms have the CFO position (Zorn, 2004). Also, due to

corporate scandals in the early 2000s (e.g., Enron and WorldCom) and the implementation of the

Sarbanes-Oxley legislation, the responsibility of CFO within the executive team has dramatically

increased (Geiger & Taylor, 2003). While the CEO is still expected to take overall responsibility

for firm performance and strategy, the CFO is shouldering an increasingly important role in

corporate financial as well as strategic decisions (Datta & Iskandar-Datta, 2014; Tulimieri &

Banai, 2010; Zorn, 2004; Zorn, Dobbin, & Kwok, 2004). In addition, in many public companies,

the CFO—instead of other non-CEO top managers—tends to appear publically with the CEO to

communicate with investors and security analysts (Tulimieri & Banai, 2010), providing a

feasible empirical setting to study CEO-CFO verbal mimicry.


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We rely on CEO-CFO language style matching (LSM) in conference calls with investors

and security analysts1 to capture CEO-CFO verbal mimicry. LSM reflects the degree to which

two people match each other’s use of function words (Ireland & Pennebaker, 2010; Ireland et al.,

2011; Richardson, Dale, & Kirkham, 2007). Function words (e.g., pronouns, propositions, and

articles) differ from content words in that function words reflect how people speak whereas

content words (e.g., nouns, regular verbs, and adjectives) capture what people say. People’s use

of content words tends to be conscious and context-specific (Pennebaker & King, 1999) whereas

their use of function words is often unconscious and independent from contexts (Pennebaker,

2011). Due to this important feature of function word use, CEO-CFO LSM in one setting (i.e.,

conference calls) can provide an insight into their social interactions in other settings, which are

not directly observable.2

Building on prior behavioral mimicry research (Chartrand & Lakin, 2013; Chartrand &

van Baaren, 2009), we argue that CEO-CFO LSM in conference calls, a form of behavioral

mimicry, reflects the CFO’s ingratiation of the CEO and can predict the CFO’s personal and the

firm’s organizational outcomes. CEOs play an important role in evaluating the CFOs’

performance and deciding the latter’s compensation (Gore, Matsunaga, & Yeung, 2011) and

influencing board member nomination (Westphal & Khanna, 2003). Meanwhile, the ingratiation

of superiors by subordinates can help the latter achieve career advancement (Judge & Bretz,

1994). High CEO-CFO LSM signals that the CFO has a strong attempt to affiliate with and

ingratiate the CEO. Thus, CFOs who exhibit high LSM with CEOs will receive higher

compensation and are more likely to become board members of the associated firms. This is

1 For the sake of brevity, “CEO-CFO LSM in conference calls with investors and security analysts” is referred to as “CEO-CFO LSM” throughout the paper unless noted otherwise. 2 In our empirical models testing the effects of CEO-CFO LSM in function words, we control for their language matching in content words to rule out alternative explanations.


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particularly true when CEOs are more powerful since such CEOs have a greater say in executive

compensation and board nomination decisions.

When low-power individuals (e.g., CFOs in the CFO-CEO dyads) ingratiate high-power

individuals (e.g., CEOs), the former tend to agree with the latter and would be less willing to

play the role of a “devil’s advocate” as opinion conformity is one of the most important

ingratiation tactics (Jones, 1965). Therefore, when CEO-CFO LSM is high, CFOs are less likely

to challenge CEOs in decision making and thus constructive debates may not occur, which can

undermine the quality of firm strategic decisions.

We choose firm mergers and acquisitions (M&As) as our empirical context to examine

the relationship between CEO-CFO LSM and firm strategic decisions because M&As require

active participation from both CEOs and CFOs. Although CEOs typically make final M&A

decisions (Graham, Harvey, & Puri, 2015), CFOs play a critical role in identifying acquisition

targets, conducting due diligence, arranging financing, and engaging in post-deal execution

(Altman, 2002; Huyett & Koller, 2011). If CEO-CFO LSM reflects ingratiation of CEOs by

CFOs, CFOs who show high LSM with the CEOs will be less willing to voice different opinions

and play the devil’s advocate role in decision-making (Schwenk, 1990). Consequently, CEOs

may face less constraint in decision making and overestimate their judgments and capabilities

(Park, Westphal, & Stern, 2011), thereby undertaking intensive M&A programs (Malmendier &

Tate, 2008).

In addition, acquirers tend to use stock payment when they are not confident about

synergy with target firms (Schijven & Hitt, 2012). If firms rush into M&As in the presence of

high CEO-CFO LSM, such M&As will likely be paid with a lower (higher) percentage of cash

(stock). Meanwhile, investors react positively to deals paid with a high percentage of cash


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because such deals are associated with low risk and have a higher chance of success (Hansen,

1987; Martin, 1996). Thus, CEO-CFO LSM may be negatively associated with investor reactions

to M&A announcements, and such an association is mediated by payment methods.

This study attempts to make three important contributions. First, prior research has

largely relied on top managers’ demographic characteristics to infer their social interaction

processes and implications on decision-making quality (Williams & O'Reilly, 1998). Findings

from this study show that CEO-CFO LSM, an inconspicuous indicator of their social interactions,

can represent a new approach to understand social interaction processes between top executives.

Second, existing upper echelon research has focused either on the whole TMTs or on CEOs

(Finkelstein et al., 2009; Hambrick & Mason, 1984). Our focus on CEO-CFO dyads in the M&A

context enriches our understanding into the role of TMT “subteams” in shaping firm strategic

decisions. Third, although the role of CEOs in shaping firm M&A decisions has been extensively

documented in prior research (Haleblian et al., 2009), our focus on CEO-CFO dyads sheds light

on the important role that CFOs play in M&A decisions.

THEORETCAL BACKGROUND AND RESEARCH HYPOTHESES

TMT Social Interactions

A key topic in upper echelon research is to examine what factors influence TMT decision

making (Carpenter, Geletkanycz, & Sanders, 2004; Finkelstein et al., 2009). Given that top

managers are social and political beings, studying TMT social interactions helps inform the

processes via which firms’ strategic decisions are made (Westphal & Zajac, 2013). Because

TMT social interactions are oftentimes not directly observable to outsiders (Pitcher & Smith,

2001), most scholars have relied on TMT demographic characteristics to infer TMT social

interaction processes. TMT demographic heterogeneity has gained high popularity primarily


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because of its accessibility, objectivity, and the reliability of demographic data (Hambrick &

Mason, 1984). Past research has shown that TMT demographic heterogeneity can provide

insights into cognitive heterogeneity and social cohesion among top managers (Finkelstein et al.,

2009).

TMT demographic heterogeneity reflects cognitive heterogeneity in that top managers

with different demographic backgrounds can enhance TMT innovativeness (Bantel & Jackson,

1989), problem solving abilities (Nemeth, 1986), information diversity (Wiersema & Bantel,

1992), and openness to change (Glick, Miller, & Huber, 1993). Yet, TMT demographic

heterogeneity can be detrimental to TMT social cohesion because demographic heterogeneity

can increase social conflict (Amason, 1996; Amason & Sapienza, 1997; Barsade et al., 2000;

Ferrier, 2001) and coordination costs (Pfeffer, 1985; Smith, Carson, & Alexander, 1984), reduce

communication frequency (Roberts & O' Reilly, 1979; Wagner, Pfeffer, & Oreilly, 1984), and

harm group cohesiveness (Ancona & Caldwell, 1992; Zander, 1977).

Despite the popularity of the demographic approach in TMT research, scholars have

questioned the underlying meaning of such data and call for studying more substantive

constructs to capture and understand TMT social interactions (Hambrick, 2007; Lawrence, 1997;

Priem et al., 1999). In addition, upper echelon research has focused on how demographic

heterogeneity of the whole TMTs shapes firm strategic decisions and performance. Yet, TMT as

a unit of analysis has been questioned (Arendt, Priem, & Ndofor, 2005; Hambrick, 2007; Jackson,

1992). This is because not all the top executive positions are created equal, and if all top

executives do not “collectively engage in information processing or decision making, then what

is the point in trying to use their collective characteristics … to predict company strategy or

performance?” (Hambrick, 2007, p. 336). Hambrick (2007) proposes that the next frontier of


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upper echelons research should be TMT subteams as investigating social interactions within the

subteams who are relevant in certain decision making can improve the predictive strength of

upper echelons theory.

Behavioral Mimicry and Language Style Matching

Behavioral mimicry regards the adoption of mannerisms, postures, gestures, and

movements of one’s interacting partner (Chartrand & Dalton, 2009). Although there are many

forms of human mimicry such as the facial expressions of others (Chartrand & Bargh, 1999;

Zajonc et al., 1987) and emotion mimicry where observing another person’s emotional

expression elicits a congruent affective state in an observer (Neumann & Strack, 2000), this

study focuses on a specific type of interpersonal behavioral mimicry, namely verbal mimicry.

Verbal mimicry occurs when people imitate each other’s vocal behavior, including syntax

(Levelt & Kelter, 1982), speech rates (Webb, 1969), and accents (Giles, Coupland, & Coupland,

1991).

Behavioral mimicry can give rise to both individual and social consequences. At the

individual level, behavioral mimicry can give rise to effective persuasion (Chartrand & Lakin,

2013). For instance, research in marketing suggests that customers being mimicked by

salespersons stated a higher likelihood of purchasing a product than those not being mimicked

(Tanner et al., 2008). In addition, being mimicked can influence people’s cognitive processing.

This is evidenced by that participants in an experiment who exhibit high behavioral mimicry

detect more similarities when shown to distantly related images (van Baaren et al., 2009). The

consequences of mimicry can also be social. Behavioral mimicry can generate liking, empathy,

and affiliation between interaction partners, and is thus called “social glue” (Lakin & Chartrand,

2003). Consistent with such arguments, mimicking negotiation partners can facilitate negotiation


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success in that mimicry can create trust and liking with such partners (Maddux, Mullen, &

Galinsky, 2008). Similarly, servers who exhibited a higher level of verbal mimicry with

customers receive bigger tips than those who did not (Van Baaren et al., 2003).

In addition to research on consequences of behavioral mimicry, research has identified

two key antecedents to behavioral mimicry. First, behavioral mimicry can be affected by

interpersonal cohesion between interaction partners (Chartrand & Lakin, 2013). There is no

surprise that interacting partners exhibit high behavioral mimicry when they have high

interpersonal cohesion (Gonzales, Hancock, & Pennebaker, 2010; Lafrance & Broadbent, 1976;

Tickle-Degnen & Rosenthal, 1987). The results of a meta-analysis study (Tickle-Degnen &

Rosenthal, 1990) indicate that group cohesion is positively associated with behavioral mimicry.

Because people sharing similar demographic backgrounds tend to categorize themselves as part

of a group and act together to safeguard their shared identity (Hogg, 1992), members of an in-

group are mimicked more than people who belong to an out-group (Bourgeois & Hess, 2008).

Second, interpersonal behavioral mimicry can be affected by the goal of affiliation

because mimicry can lead to liking and rapport (Chartrand and Lakin, 2013). In an experimental

study, Stel and Vonk (2010) find that mimicry of the interaction partner can create stronger

bonding with the mimicker. As noted above, mimicry can create liking and rapport; thus,

mimicry can be functional and adaptive in creating bond between people (Chartrand & van

Baaren, 2009). When an interaction partner has a strong goal of affiliating with the other partner,

the two will exhibit a high level of behavioral mimicry (Lakin & Chartrand, 2003). In contrast,

when a person does not want to affiliate with an interaction partner, they will exhibit less

behavioral mimicry (Johnston, 2002).


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We use language style matching (LSM) in conference calls to capture CEO-CFO verbal

mimicry (Ireland & Pennebaker, 2010; Niederhoffer & Pennebaker, 2002). LSM derives from

function word usage between interaction partners (Ireland & Pennebaker, 2010; Ireland et al.,

2011). Function words (e.g., articles, pronouns, auxiliary verbs, and conjunctions), generally

short and frequently used, capture language style, but have little semantic content outside the

context of a sentence. Although function words largely reflect a speakers’ cognitive and

psychological states, the speakers rarely pay attention to function words (Chung & Pennebaker,

2007). In other words, function words are used without conscious awareness and are less likely

to be purposefully manipulated. More important, the use of function words is independent from

contexts and consistent across different communication scenarios, from informal communication

to task-based communication (Gonzales et al., 2010). This is important because CEO-CFO LSM

in conference calls offers a psychometrically useful tool to capture their social interaction

processes in other contexts that are not directly observable to scholars (Ireland & Pennebaker,

2010; Ireland et al., 2011; Niederhoffer & Pennebaker, 2002).

Unlike function words, content words (e.g., nouns, regular verbs, adjectives, and adverbs)

carry meanings on their own and reflect what individuals say. The use of content words is highly

conscious and context-specific (Pennebaker & King, 1999). Therefore, when CEOs and CFOs

exhibit a certain level of language content matching (LCM) in conference calls, we cannot infer

much about their LCM in other contexts. When CFOs exhibit high LCM with CEOs, this

indicates that CFOs share similar opinions as CEOs. Opinion conformity in public is an

important impression management tactic (Bolino et al., 2008). Thus, CFOs may engage in LCM

with CEOs in public to consciously show solidarity while such solidarity may not exist in other

contexts. Because function words do not carry real meanings, investors and analysts may not pay


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attention to CEO-CFO LSM. Accordingly, CFOs are unlikely to engage in high LSM with CEOs

for the sake of conscious impression management in public.

Prior research has typically focused on LSM between two (directly) interacting

individuals (Gonzales et al., 2010; Ireland & Pennebaker, 2010). In question-and-answer sections

of conference calls, CEOs and CFOs take turns to address questions raised by security analysts

and investors. In this context, CEOs and CFOs do not engage in frequent direct communications

with each other, but they do observe their respective interactions with security analysts and

investors. The presence of third-parties (e.g., other top executives) is common for CEO-CFO

dyads in strategic decision making processes. In this sense, the presence of third parties (i.e.,

security analysts and investors) for the CEO-CFO dyad in conference calls is similar to the

presence of third parties (e.g., other senior executives) for the CEO-CFO dyad in strategic

decision making contexts. Although security analysts and investors in conference calls are

outsiders and other top executives in decision making scenarios are insiders, we do not have

reasons to believe that CEOs’ and CFOs’ language styles would differ significantly given that

function words do not carry actual meanings and the use of such words is unconscious.

While LSM between interaction partners can be shaped by social cohesion and goals of

affiliation, we argue that in the CEO-CFO dyad, their LSM is mainly driven by the CFO’s goal

of affiliating with the CEO. Social motives are crucial determinants of whether and to what

extent mimicry occurs (Dalton, Chartrand, & Finkel, 2010) and implicit schemas can guide

unconscious behavioral mimicry in a given circumstance of social interactions (Dalton et al.,

2010). According to mimicry schemas, individuals are likely to mimic others with higher power

than those with lower power (Cheng & Chartrand, 2003). As a result, individuals interacting with


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more powerful others may not expect to be mimicked whereas individuals interacting with less

powerful others may anticipate being mimicked (Chartrand & van Baaren, 2009).

For the CEO-CFO dyad, because the CEO has higher structural power and authority than

the CFO, the CFO is more likely to mimic the CEO to create rapport. Yet, CFOs may differ in

terms of their goals of affiliation with CEOs. Lakin and Chartrand (2003) find that high self-

monitors (i.e., people with strong ability to regulate behavior to accommodate social situations)

engage in more mimicry with powerful others than low self-monitors. In this sense, CFOs with

strong goals of ingratiating CEOs will exhibit a higher level of LSM with CEOs than those with

weak goals.3

In the following sections, building on behavioral mimicry research, we develop specific

hypotheses on how CEO-CFO LSM in conference calls may predict CFO personal outcomes and

organizational outcomes.

CEO-CFO LSM and CFO Compensation/Board Directorship

As CFOs are becoming increasingly important in crafting and executing corporate

strategy (Datta & Iskandar-Datta, 2014), management research has started to examine the drivers

of CFO compensation (Datta & Iskandar-Datta, 2014; Gore et al., 2011). For example, Datta and

Iskandar-Datta (2014) find that CFOs’ background characteristics (generalists versus specialists)

are related to their compensation levels. Although CFOs’ compensation is typically determined

by boards’ compensation committees, CEOs can also influence CFOs’ compensation. CEOs,

who typically serve on their firms’ boards, can make CFO compensation recommendations to

compensation committees. In addition, CEOs, as CFOs’ direct superior, evaluate CFOs’

performance, and such evaluations can serve as a significant input to compensation committees’

3 In the methods section, we will provide comprehensive evidence to show that CEO-CFO LSM is mainly driven by

CFOs’ ingratiation of CEOs rather than by CEO-CFO social cohesion.


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decisions on CFO pay. As evidence to show CEOs’ influence on CFO compensation, Gore et al.

(2011) find that CFOs’ compensation structures are associated with CEOs’ functional

backgrounds.

Ingratiatory behavior toward a superior can increase the likelihood of gaining positive

evaluations from the superior (Judge & Bretz, 1994; Westphal & Stern, 2006). For instance,

CEOs engage in ingratiatory behaviors toward independent directors and such behaviors can

neutralize board independence and lead to higher CEO compensation (Westphal, 1998). As noted,

CEO-CFO LSM reflects CFOs’ ingratiation of CEOs. Meanwhile, such ingratiation of CEOs by

CFOs can help the CFOs gain positive evaluations from the CEOs, which can help the CFOs

receive higher compensation. Thus, we expect CFOs who exhibit a higher level of LSM with

CEOs to receive a higher level of compensation.

Hypothesis 1: The level of language style matching between the CEO and the CFO of a

firm is positively associated with the CFO’s compensation level.

CFOs’ ingratiation of CEOs, reflected by CEO-CFO LSM, can help the CFOs not only

attain higher compensation but also may help them gain board seats in their firms. Unlike CEOs,

who are typically sitting on their firms’ boards, CFOs are less frequently granted board seats in

their firms (Bedard, Hoitash, & Hoitash, 2014). CFOs tend to care about obtaining board seats

for two reasons. First, board seats provide top managers with additional power and influence

(Finkelstein, 1992). CFOs on their firms’ boards can directly participate in key strategic

decision-making and vote on important strategic issues of their firms. Second, board seats in

focal firms or other firms also reflect top managers’ attractiveness and visibility in the external

labor market (Kaplan & Reishus, 1990) and their social status (Avery, Chevalier, & Schaefer,

1998; Westphal, 1999). A recent study shows that non-CEO executives who obtain directorships


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in home firms or other firms have a higher likelihood of becoming a CEO (Boivie et al., 2016).

Thus, directorship may pave the way for the CFO to being promoted to a CEO position.

A CEO can play a critical role in deciding whether a CFO receives a board appointment

in their firm because the CEO’s recommendation of the CFO to the firm’s board nominating

committee can significantly increase the likelihood that the CFO will be appointed as a board

member (Westphal & Stern, 2006, 2007). We argue that CEOs are more likely to recommend

CFOs who exhibit higher CEO-CFO LSM to become board directors. As argued earlier, CEO-

CFO LSM reflects ingratiation of CEOs by CFOs. Meanwhile, ingratiation can give rise to

positive biases in performance evaluation (Judge & Ferris, 1993; Latham, Wexley, & Pursell,

1975). Thus, CFOs who exhibit higher LSM with CEOs are more likely to be perceived as

capable board member candidates by the CEOs and recommended by the CEOs. In addition,

CEOs are more likely to recommend CFOs with strong attempts of ingratiation, reflected by

LSM, because such CFOs may defer to CEOs and having them on the boards can strengthen the

CEOs’ coalition on the boards.

Hypothesis 2: The level of language style matching between the CEO and the CFO is

positively related to the likelihood that the CFO becomes a board member at his or her

focal firm.

Hypotheses 1 and 2 are based upon the assumption that the CEO can influence CFO

compensation and the likelihood of a CFO becoming a board member at the focal firm. However,

a CEO’s ability to influence CFO-related personal outcomes may hinge on the power that the

CEO can exercise relative to board members (Haleblian & Finkelstein, 1993) in that powerful

CEOs can impose strong influences not only on firm strategic decisions but also on board

composition. Golden and Zajac (2001) find that powerful CEOs are more likely to affect

strategic change, and Haynes and Hillman (2010) argue that the influence of boards on firm


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strategic change can be attenuated by powerful CEOs. Relatedly, research (Adams, Almeida, &

Ferreira, 2005) has shown that powerful CEOs have a stronger influence on firm performance

than less powerful ones.

What is more relevant to our study is that powerful CEOs can exert a strong influence on

a firm’s board composition. Findings by Westphal and Zajac (1995) suggest that powerful CEOs

have a greater say in new director selection. Lynall, Golden, and Hillman (2003) argue that CEO

power relative to important external stakeholders can influence board composition such that

boards will reflect the social network of the CEO when the CEO has dominant power. In sum,

powerful CEOs not only have a strong influence on firm strategic decision and firm performance

but also can shape governance practices (Boyd, 1994; Dalton & Kesner, 1987). If CFOs who

exhibit higher LSM with CEOs tend to receive a higher level of compensation and are more

likely to become board members at the focal firms, these relationships would be particularly

strong for powerful CEOs who have a greater say in executive compensation and board

nomination decisions. In contrast, when the CEO has less power, he or she may not be able to

influence CFO compensation decisions or the chance of a CFO becoming a board member. To

further verify the underlying assumption of Hypotheses 1 and 2, we propose that the

relationships between CEO-CFO LSM and CFO-related personal outcomes will be stronger for

more powerful CEOs.

Hypothesis 3a: The positive relationship between the level of language style matching

between the CEO and the CFO and the CFO’s compensation level is stronger when the

CEO has greater power.

Hypothesis 3b: The positive relationship between the level of language style matching

between the CEO and the CFO and the likelihood that the CFO becomes a board

member at his or her focal firm is stronger when the CEO has greater power.

CEO-CFO LSM and Firm M&A Decisions


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While CFOs’ ingratiation of CEOs, reflected in their LSM, may be beneficial to the

CFOs, as discussed above, it may have an adverse impact on their firms. Role theory suggests

that roles in formal organizations connote identified social positions and normative expectations

(Biddle, 1986; Gross, Mason, & McEachern, 1958; Kahn et al., 1964). Among senior executives

of a firm, the CEO plays the role of leader, who looks for new business opportunities and sets the

agenda and direction for organizational growth (Mintzberg, 1973a, b, c), while other senior

executives play supporting roles by providing critical inputs for the CEO’s decisions. In the

CEO-CFO dyad, the CEO is expected to look over the horizon in search of next great ideas, and

the CFO is anticipated to urge caution, voice dissent, and make the CEO aware of risks

(Tulimieri & Banai, 2010, p. 240).

As noted, CEO-CFO LSM reflects CFOs’ desires to ingratiate CEOs. A high CEO-CFO

LSM indicates that the CFO attempts to ingratiate the CEO and thus s/he is less likely to voice

dissent and different opinions, engage in debates with the CEO, or play the role of a “devil’s

advocate” in decision making processes. Recent research suggests that the level of opinion

conformity that CEOs receive from subordinates can foster their self-enhancing cognitions and

lead CEOs to overestimate their judgments and abilities (Park et al., 2011). Meanwhile, given

that M&As are associated with high levels of risks, CEOs who tend to overestimate their

strategic judgments and capabilities are more likely to undertake intensive M&As (Malmendier

& Tate, 2008). In addition, CFOs can play an important role in bottom-up monitoring (Landier et

al., 2012). In the presence of CFOs who are unwilling to voice different opinions, CEOs may not

receive constructive opposition from the CFOs and may face less constraint in decision making.

Consequently, these CEOs are more enabled to engage in intensive M&A activities.

Hypothesis 4: The level of language style matching between the CEO and the CFO of a

firm is positively associated with (a) the firm’s number of M&As and (b) value of M&As.


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Although CEOs tend to make final M&A decisions (Graham et al., 2015), CFOs play an

important role in identifying acquisition targets and conducting due diligence (Altman, 2002). In

particular, CFOs play a crucial role in ensuring that the firm has the financial resources to

finance M&A deals (Huyett & Koller, 2011). Thus, the presence of a CFO who is unwilling to

challenge the CEO will not only lead the firm to undertake intensive M&As but also can affect

how the M&A deals are financed.

An acquiring firm typically has two ways to pay a target firm: cash and stock. Payment

with stock implies that any risk involved in the deal is shared with the target firm’s investors

(Hansen, 1987; Martin, 1996). When acquirers pay targets with a high percentage of cash, this

provides a signal to investors that managers have strong beliefs in the quality of the transaction

and have high expectations for post-acquisition performance (King et al., 2004). In contrast,

stock payment is associated with a heightened degree of risk and mirrors the lack of acquirers’

confidence on creating synergistic value from a deal (Schijven & Hitt, 2012). Thus, compared to

deals paid largely with stock, deals paid largely with cash are perceived to be of a higher quality

by investors and are associated with higher announcement returns (Loughran & Vijh, 1997;

Travlos, 1987).

The decision making literature suggests that the expression of dissent is critical to

decision quality (Dooley & Fryxell, 1999) because exposure to minority dissent produces higher

cognitive conflict (Gruenfeld, Thomas-Hunt, & Kim, 1998) and cognitive conflict helps senior

executives make high-quality decisions (Amason, 1996). In addition, research attests to the

importance of devil’s advocacy in effective decision-making under uncertainty (Schweiger,

Sandberg, & Ragan, 1986). Devil’s advocacy involves active and intense debates about different

and opposing viewpoints and employs conflict as the primary mechanism in encouraging


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different views (Schweiger et al., 1986). Exposure to devil’s advocacy can improve the quality of

group decision-making (Schwenk, 1990).

Therefore, when CEO-CFO dyads exhibit high LSM, the CFO is less likely to voice

contrasting opinions and more likely to conform to the CEO’s viewpoint in M&A decision

making. In the absence of the CFO’s constructive “opposition,” the CEO tends to overestimate

his or her abilities and acquire targets even if synergistic value from such targets may be

uncertain. Consequently, acquirers are more likely to pay targets with a high percentage of stock.

In contrast, when CEO-CFO dyads demonstrate low LSM, the CFO is more willing to voice

different opinions and to provoke the CEO to carefully evaluate the synergistic value and

organizational fit between acquirers and targets. Accordingly, CEO-CFO LSM tend to be

negatively associated with the percentage of cash payment in M&A deals.

In sum, effective CFOs can act as a counteracting power imposing discipline on CEOs

through bottom-up monitoring (Landier et al., 2012). In the presence of low CEO-CFO LSM, the

CFO tends to play a more effective monitoring role. In such a case, firms will not make a deal

unless they are certain about potential value creation and choose to pay targets with cash,

resulting in higher announcement returns. In contrast, a high CEO-CFO LSM indicates that the

CFO has a strong intention to ingratiate the CEO and will be ineffective in bottom-up monitoring.

Thus, firms may acquire targets whose synergistic value may be ambiguous and pay targets with

a high percentage of stock, giving rise to lower announcement returns.

Hypothesis 5: CEO-CFO LSM has a negative relationship with investor reactions to

M&A announcements and the percentage of cash payment partially mediates this

negative relationship.

METHOD


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Data and Sample

The sample selection of this study starts with all the conference call transcript data from

the Thomson Reuters StreetEvents database over the period of 2002-2013. StreetEvents offers

the largest available archive of global transcripts, briefs, events, guidance, and filings. We

include U.S. firms’ corporate disclosure transcripts, which contain quarterly earnings conference

calls, corporate conference calls, conference presentations, and analyst conference calls. We

obtain firm accounting data from Compustat, stock return data from the Center for Research in

Security Prices (CRSP), and CEO and CFO demographic characteristic data from Capital IQ

People Intelligence, MorningStar Historical Governance, and BoardEx. M&A data are from the

Securities Data Corporation (SDC) Mergers and Acquisitions Database. We start our sample

selection with all the U.S. firms covered by StreetEvents. After matching all the variables needed

for this study, we have a total number of 2,384 firms, 1,406 of which belong to S&P 1500 Index.

Prior research has used firms’ Letters to the Shareholders in their annual reports to

capture top managers’ communication and cognitions (Fanelli, Misangyi, & Tosi, 2009;

Gamache et al., 2015; Kaplan, 2008; Loomis & Meyer, 2000). Compared with Letters to the

Shareholders, CEOs’ and CFOs’ speeches during conference calls are more spontaneous and can

capture their personalized communication styles (Bowen, Davis, & Matsumoto, 2002; Francis,

Hanna, & Philbrick, 1997).

Conference call transcripts typically include three sections: the list of participants, the

prepared remarks section, and the question-and-answer (Q&A) section. Because information

disclosed in conference calls can have a direct influence on stock market performance (Frankel,

Johnson, & Skinner, 1999), top managers devote great attention to preparing for conference calls.

Thus, CEOs’ and CFOs’ prepared remarks during the prepared remarks section are highly


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polished and may not reflect their natural language use. During the Q&A section, the CEO and

CFO address questions raised by investors and security analysts; thus, the Q&A section can

better represent CEOs’ and CFOs’ spontaneous language styles. We therefore use Q&A section

transcripts to measure CEO-CFO LSM.

Independent Variable

The key variable in this study is CEO-CFO language style matching (LSM). As noted

above, we use CEOs’ and CFOs’ speeches during the Q&A sections of conference calls to

measure CEO-CFO LSM. Because we attempt to capture CEO-CFO LSM, we include transcripts

for events during which both CEOs and CFOs spoke during the Q&A sections.

We use the measure developed by Ireland et al. (2011) to measure CEO-CFO LSM for

each included transcript. To do so, we extract all the words spoken by the CEO and the CFO

during the Q&A section and save them as two separate files. We then context-analyze the CEO

and the CFO files separately using Linguistic Inquiry and Word Count (LIWC) (Pennebaker,

Booth, & Francis, 2007). LIWC is a word count program with over 70 pre-defined grammatical

dictionaries, including function word categories and psychological dictionaries. The output from

LIWC is based on the percentage of words from each category used during the Q&A section. We

focus on the percentage of total words in a text that fall into nine basic-level function word

categories. Table 1 reports the nine categories of function words used to calculate LSM between

CEOs and CFOs.

[Insert Table 1 here]

We calculate separate LSM for each category using the following formula (prepositions

are used in the following example) (Ireland & Pennebaker, 2010; Ireland et al., 2011):

prep CEO CFO CEO CFOLSM =1-[(|preps -preps |)/(preps +preps +0.001)] (1)


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In this formula, CEOpreps is the percentage of prepositions used by the CEO and CFOpreps

is the percentage used by the CFO. In the denominator, 0.001 is added to prevent empty sets. The

nine category-level language similarity scores are added to yield a composite LSM score. A

larger number represents a higher level of LSM between the CEO and the CFO.

We have argued that CEO-CFO LSM is mainly driven by CFOs’ ingratiation of CEOs

instead of CEO-CFO social cohesion because of the power asymmetry between CEOs and CFOs.

To empirically verify this argument, we conduct a battery of analyses. First, we compare changes

in CEOs’ and CFOs’ language style (LS) during their tenure overlap. We find that the change in

CEOs’ LS is 2.9 percent whereas the change in CFOs’ LS is 5.9 percent and the difference is

statistically significant (t=38, p<.01). Such findings suggest that CEOs’ LS is relatively stable

while CFOs’ LS experiences significant change during their tenure overlap, providing evidence

that CEO-CFO LSM is more likely to be driven by CFOs’ adjustments than CEOs’ and CFOs’

mutual adjustments.

Second, we conduct regression analyses to investigate whether CEO (CFO) LS can

predict CFO (CEO) LS. In our first regression, we use CFO LS at the end of CEO-CFO tenure

overlap as the dependent variable. The predictors include CEO LS, CFO LS, firm size (natural

logarithm of total assets), firm performance (ROA), R&D intensity, CFO gender, and CFO age,

and two-digit SIC industry dummy variables. All the predictors are measured at the beginning of

CEO-CFO tenure overlap. We also include CEO-CFO tenure overlap as a predictor. In

unreported results, the coefficient estimate of CEO LS (beginning) is positive and statistically

significant (β=0.046, p<.05) with CFO LS (end) as the dependent variable. In contrast, with CEO

LS at the end of CEO-CFO tenure overlap as the dependent variable, the coefficient estimate of

CFO LS (beginning) is not statistically significant (β=-0.018, n.s.). These results further support


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that during their tenure overlap, CFOs adjust their LS toward their respective CEOs’ LS, but not

vice versa.

Third, we examine what factors affect the change in CEO-CFO LSM within each CEO-

CFO dyad. For this purpose, we conduct CEO-CFO dyad fixed-effects regressions. If CEO-CFO

LSM is mainly driven by CFOs’ ingratiation of CEOs, we expect that CEO power should predict

CEO-CFO LSM since CFOs would have stronger incentives to ingratiate CEOs when the CEOs

are more powerful. In comparison, if CEO-CFO LSM is mainly driven by CEO-CFO social

cohesion, CEO-CFO LSM should increase as CEO-CFO tenure overlap extends. In unreported

results, we find that CEO power rather than CEO-CFO tenure overlap predicts CEO-CFO LSM.

This combined evidence provides strong empirical support indicating that CEO-CFO LSM is

mainly driven by CFO’s ingratiation of CEOs rather than CEO-CFO social cohesion.

Because our first two dependent variables are at the CFO level and the unit of analysis

accordingly is CFO-year, we calculate CEO-CFO LSM at the CFO-year level. We weight each

transcript with the ratio of the total number of words spoken by the CEO and the CFO during the

Q&A section of each conference call to the total number of words spoken by the CEO and the

CFO during the Q&A sections of all the conference calls in a CFO-year.

Because the dependent variable for Hypothesis 4 is M&A intensity, and the unit of

analysis is firm-year, we weight each transcript with the ratio of the total number of words

spoken by the CEO and the CFO during the Q&A sections of each conference call to the total

number of words spoken by the CEO and the CFO during the Q&A sections of all the conference

calls in a firm-year to measure CEO-CFO LSM at the firm-year level. Also, because the

dependent variable for Hypothesis 5 is stock market reactions which are at the deal level, we

measure CEO-CFO LSM at the firm-year level when testing this hypothesis.


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Dependent Variables

The first dependent variable is CFO compensation. We obtain CFO compensation data

from Capital IQ People Intelligence and MorningStar Historical Governance and measure CFO

compensation as the natural logarithm of the CFO’s total compensation, including salary, bonus,

options granted, restricted stock, and other pay. The second dependent variable is CFO director.

As we will discuss later, we use event history analysis to examine the likelihood that a CFO

becomes a director on the focal firm’s board. The variable of CFO director receives a value of “1”

in the year when a CFO becomes a board member of the focal firm and “0” otherwise. All the

observations after a CFO becomes a board member of the focal firm are excluded in the event

history analysis.

We use the number of M&As and the value of M&As (Sanders & Hambrick, 2007; Shi,

Hoskisson, & Zhang, 2017) to measure M&A intensity. We constrain the sample to deals with a

transaction value over $1 million (Moeller, Schlingemann, & Stulz, 2005) so that our sample

includes only meaningful transactions from the acquirer’s perspective. Consistent with prior

research (Iyer & Miller, 2008; Wang & Xie, 2009), we exclude M&As with missing transaction

values. In addition, we require a deal should be in the form of “acquisition of majority interests,”

“acquisitions of assets,” “acquisitions” or “mergers” based on SDC’s classification. Number of

M&As is measured as the total number of majority-ownership M&As announced in a year, which

will have been completed subsequently, as reported in SDC data. Our results are similar if we

count deals with missing transaction values in measuring the number of M&As. Value of M&As

is measured as the total transaction value of these M&As in a year. Firm years with no M&As

receive a value of “0” for both the number and the value of M&As.


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The dependent variable of Hypothesis 5 is investor reactions to M&A announcements.

We follow the standard event study methodology to measure this variable (Brown & Warner,

1985). We calculate cumulative abnormal returns (CARs)—the returns over the event window

minus the normal returns. To calculate CARs, we first compute abnormal returns (ARs) for an

M&A announcement. Next, we accumulate the ARs for a period of three days surrounding the

announcement date of the deal [-1, +1] (Campbell, Sirmon, & Schijven, 2016). This event

window incorporates the day before and the day after the announcement. Hypothesis 5 also

proposes the mediating effect of payment method. We measure this mediating variable using the

percentage of cash (as opposed to stock) used to pay for the focal deal (Campbell et al., 2016).

Moderator

We create a formative index consisting of five variables to measure CEO power: (1) CEO

tenure, (2) CEO duality, (3) board independence, (4) CEO equity ownership, and (5) percentage

of directors appointed by CEOs (Finkelstein, 1992; Haynes & Hillman, 2010; Pollock, Fischer,

& Wade, 2002). CEO tenure is measured as the number of years that a CEO has been at the CEO

position. CEO duality receives a value of “1” if a CEO holds the board chairman position and “0”

otherwise. Board independence is measured as the ratio of the number of non-executive directors

to board size. We reverse code this variable so that a larger value of this variable (i.e., a smaller

percent of non-executive directors) will correspond to a greater level of CEO power. CEO equity

ownership is measured as the ratio of total shares owned by the CEO to total shares outstanding.

Percentage of directors appointed by CEO is measured as the ratio of the number of board

members appointed after a CEO took office to board size. We standardize these five variables

and combine them to create a formative index of CEO power (Briscoe, Chin, & Hambrick, 2014;

Haynes & Hillman, 2010).


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Control Variables

CEO-CFO LCM. Language style match (LSM) is based on the styles of CEOs’ and

CFOs’ language usages whereas CEO-CFO language content matching (LCM) is based on the

contents of CEOs’ and CFOs’ language usages. Unlike CEO-CFO LSM, which is unconscious

and context independent, CEO-CFO LCM is highly dependent on contexts and takes place

consciously. Therefore, CEO-CFO LCM in conference calls may not provide much insight into

their LCM in other settings. Yet, high CEO-CFO LCM in conference calls may indicate the

presence of a “strategic” CFO who is willing to demonstrate solidarity with a CEO in public.

Therefore, we control for CEO-CFO LCM in conference calls, which helps us discriminate

between unconscious and conscious language matching between CEOs and CFOs.

We rely on the dictionaries of positive words and negative words developed by Loughran

and McDonald (2011) to capture CEO-CFO LCM. Loughran and McDonald (2011) develop

dictionaries of positive and negative words by adapting the General Inquirer Harvard IV-4

dictionary, and address the issue that words generally classified as negative, such as “cost” or

“liability,” do not carry negative connotations in a business setting. We rely on positive and

negative words instead of other types of content words to measure LCM because positive and

negative words are more likely to capture CEOs’ and CFOs’ assessments of business prospects

and opinions. We follow the same procedure used in measuring LSM to capture LCM. We

control for CEO-CFO LCM in all the regressions.

Control variables for CFO compensation/CFO director as dependent variables. We

control for a number of firm and executive characteristics that have been found to affect the level

of executive pay and the probability of having the CFO on the board. We first control firm size

(natural logarithm of total assets) and firm performance (return on assets or ROA in short)


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because large firms and good performing firms tend to offer their top executives a higher level of

pay. In addition, CEOs of large firms are more likely to share responsibilities with CFOs,

increasing the likelihood of CFOs’ becoming board members. Firms with desirable performance

may face lower scrutiny from external stakeholders, increasing the possibility of having inside

executives (e.g., CFOs) on such boards. CFO compensation and the promotion of CFOs as board

members may also be related to firm slack resources. We therefore control for firm cash holding

ratio. Cash holding ratio is measured as the ratio of cash and short-term investments to total

assets. In addition, CFOs may play a more critical role in highly leveraged firms, which may

influence CFO compensation and the chance of CFOs’ becoming board members. We thus

control for debt ratio, which is measured as the ratio of summated long-term debt and debt in

current liabilities to total assets.

The proportion of independent directors has been found to affect executive compensation

(Boyd, 1994); thus, we control for board independence, which is measured as the ratio of the

number of non-executive board members to board size. In addition, if a firm has high board

independence, the chance of having an insider director (e.g., CFO) sitting on board will be

reduced.

We include the following controls related to CFOs. We first control for CFO age because

age is associated with executives’ human and social capital and has been found to affect

executive pay (Gray & Cannella, 1997; Hill & Phan, 1991). We control for CFO gender because

male and female executives of publicly traded firms have compensation differences (Mohan &

Ruggiero, 2003). CFO gender receives a value of “1” if a CFO is male and “0” otherwise. We

also control for CFO tenure as this may capture a CFO’s firm-specific human capital, which is

measured as the number of years since becoming the firm’s CFO. As CFO educational level may


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directly affect CFOs’ human capital, we control for CFO educational level. We classify

educational background into three levels: undergraduate degrees (“1”), master degrees (“2”), and

doctoral degrees (“3”). These CFO factors may also influence firms’ evaluations of CFOs’

candidacy for directorship. Therefore, we control for CFO age, CFO gender, CFO tenure, and

CFO educational level when predicting CFO board membership. In addition, CFOs who are

board members may receive higher compensation; thus, we control for CFO director

membership in the focal firm when predicting CFO compensation.

We include four variables relating to CEO-CFO demographic background similarity,

which may affect CFO compensation and CFO’s directorship. The first variable is CEO-CFO

age difference, which we measure as the natural logarithm of the absolute value of the difference

between CEO age and CFO age plus one. The second variable refers to CEO-CFO same gender,

which receives a value of “1” if CEOs and CFOs have the same gender and “0” otherwise. We

control for this variable because CEO-CFO same gender can influence CEO-CFO social

interactions, which can confound the relationships between CEO-CFO LSM and our dependent

variables. The third variable captures CEO-CFO tenure overlap, which is measured as shared

tenure between CEOs and CFOs. The fourth variable captures whether CEOs and CFOs have the

same educational level. If CEOs and CFOs have the same educational level, a CEO-CFO dyad

receives a value of “1” and “0” otherwise. In addition, we control for whether there is a CEO

turnover in a year. This variable receives a value of “1” if there is a CEO turnover in a year and

“0” otherwise. Lastly, we control for year fixed-effects when predicting CFO compensation. We

do not control for year fixed-effects in testing Hypothesis 2 because we use event history

analysis that directly incorporates time effects.


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Control variables for M&A intensity as dependent variables. We draw on the M&A

literature and include the following firm-level, CEO-level, CFO-level, board-level, and CEO-

CFO dyadic-level variables that may affect firm M&A intensity. At the firm level, we first

include firm size and firm performance because large firms and firms with better financial

performance may have more resources to undertake acquisitions (Carper, 1990). We also control

for firm cash holding ratio and debt ratio, whose measures have been discussed earlier, because

these two variables influence the financial resources available for firms to undertake M&As

(Duchin, 2010).

In addition to firm-level control variables, we include the following CEO-level, CFO-

level, and board-level control variables. We first control for CEO duality because CEOs holding

chair positions can have higher levels of discretion in influencing their firms’ strategic decisions

(Finkelstein, 1992). CEO duality is coded as “1” if CEOs hold board chair positions and “0”

otherwise. CEO appointed directors (the firm’s directors appointed during the CEO’s tenure) is

controlled for because directors appointed by CEOs are less willing to exert a monitoring role

(Coles, Daniel, & Naveen, 2014), which may increase firm M&A intensity. We control for CEO

option pay ratio and CEO equity ownership because these two variables may influence firm

M&A decisions (Sanders, 2001; Sanders & Hambrick, 2007). CEO option pay ratio is measured

as the ratio of the total value of annual option awards to the total value of compensation, and

CEO equity ownership is measured as the ratio of the number of shares owned by the CEO to the

total number of shares outstanding.

We control for CFO option pay ratio and CFO equity ownership because CFOs’

compensation structure also influences corporate policies and decisions (Chava & Purnanandam,

2010). Furthermore, we include the four CEO-CFO dyadic-level variables: CEO-CFO age


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difference, CEO-CFO same gender, CEO-CFO tenure overlap, and CEO-CFO same educational

level. Yet, the unit of analysis for M&A intensity is at the firm-year level and there can be

multiple CEO-CFO dyads in a firm year. Thus, CEO same gender and same educational level

receive a value of “1” if any CEO-CFO dyad in a firm year has the same gender and educational

level and “0” otherwise. We also calculate average age difference and tenure overlap for all the

CEO-CFO dyads in a firm year to measure CEO-CFO age difference and tenure overlap. We

also control for whether there is a CEO or CFO turnover in a year, which receives a value of “1”

if there is a CEO or CFO turnover in a year and “0” otherwise.

Control variables for M&A announcement returns as dependent variable. In testing

the relationship between LSM and M&A announcement returns, we include the following deal-

level control variables. We first control for related deals. Prior research suggests that acquiring

related firms is associated with positive market reactions (King et al., 2004). This control

variable receives a value of “1” if the acquirer and the target firms operate in the same industry,

specified by the two-digit SIC code, and “0” otherwise. The market may perceive hostile

takeovers differently from friendly takeovers (Jarrell, Brickley, & Netter, 1988); thus, we control

for whether a deal is hostile, with hostile deals coded as “1” and “0” otherwise. We classify a

deal as a hostile takeover if the SDC records the bid as “hostile” or “unsolicited.” Research

suggests that acquisitions of public and private firms are associated with different stock market

reactions (Capron & Shen, 2007). We therefore control for public target firms, which receive a

value of “1” if the target firms are public and “0” if the target firms are private. Research finds

that relative size between acquiring firms and target firms influences M&A announcement

returns (Moeller, Schlingemann, & Stulz, 2004). We control for relative size using the ratio of

transaction value to acquirers’ market value. We control for acquisition experience because


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acquisition experience may shape firm acquisitiveness (Haleblian & Finkelstein, 1999).

Acquisition experience is measured as the number of completed M&As made by the acquiring

firm during the preceding four years (Bruton, Oviatt, & White, 1994). We take the natural

logarithm of this variable plus one to address skewness.

In addition to these deal-level control variables, we include all the firm-level, CEO-level,

CEO-CFO dyadic level control variables used in testing Hypothesis 4. We use a cross-sectional

dataset to test Hypothesis 5 and cannot conduct firm fixed-effects regressions; thus, we control

for Fama-French 48 industry fixed effects (Fama & French, 1997) and year fixed-effects. Table

2A shows descriptive statistics for variables used in testing Hypotheses 1-4 (at the firm-year

level) and Table 2B shows descriptive statistics for variables used in testing Hypothesis 5 (at the

deal level).

[Insert Table 2A and Table 2B here]

In all our regressions except for Hypothesis 5, we measure the dependent variables at t

and all the other variables at t-1. Given that Hypothesis 5 examines M&A announcement returns,

the deal-level variables are measured at t.

MODELS AND RESULTS

Table 3 reports models used to test Hypotheses 1-3. Model 1 (with total CFO

compensation as the dependent variable) is used to test Hypothesis 1, which proposes a positive

relationship between CEO-CFO LSM and CFO compensation. We use firm fixed-effects OLS

regressions to test Hypothesis 1, in order to control for the influence firm-level time-invariant

heterogeneity on CFO compensation. Given that Hypothesis 1 regards CFO compensation, we

cluster standard errors by CFOs to address potential residual correlations for the same CFOs.


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The coefficient estimate of CEO-CFO LSM is positive and statistically significant

(β=0.032, p<.01), supporting Hypothesis 1. In terms of economic magnitude, one unit increase in

CEO-CFO LSM will be associated with a 3.25% increase in CFO total compensation, holding

other variables constant. In unreported results, we also examine the relationships between CEO-

CFO LSM and CFO short-term compensation and long-term compensation, respectively. CFO

short-term compensation is measured as the sum of salary and bonus whereas CFO long-term

compensation is measured as the sum of stock options and restricted stocks. In unreported results,

we find that the coefficient estimate of CEO-CFO LSM is positive and statistically significant

(β=0.026, p<.01) with CFO short-term compensation as the dependent variable. With CFO long-

term compensation as the dependent variable, the coefficient estimate of CEO-CFO LSM is

negative but not statistically significant. Note that CEOs may play a more important role in

shaping CFO short-term compensation than in influencing CFO long-term compensation because

short-term compensation is in cash payments whereas long-term compensation is affected by

stock price movements. These additional results are consistent with our argument that CFOs’

ingratiation of CEOs, reflected by CEO-CFO LSM, can result in positive evaluations by CEOs

and give rise to higher CFO short-term compensation, which can be directly influenced by CEOs.


Hypothesis 2 predicts that CEO-CFO LSM is positively associated with the likelihood of

CFOs’ becoming board members of the focal firm. We use the event history analysis to test this

hypothesis. Such analysis focuses on the length of time that passes until a CFO becomes a board

member of his/her own firm. We choose Cox proportional hazard models for the following

reasons. First, an event history model like the Cox model incorporates the fact that a CFO can be

at “risk” of becoming his/her firm’s board member in a given year and yet has not become a


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board member. The hazard function estimates the likelihood of becoming a board member in a

particular year conditional on the fact that the CFO has yet become a director on his/her firm’s

board up to that point. Second, the model uses the time series of information of a CFO in

estimating the hazard of becoming a board member. Lastly, the Cox model is semiparametric and

makes no assumption about the particular shape or nature of the survival distribution (Cox, 1972).

We cluster observations at the CFO level and apply the robust variance estimator option to

account for potential correlation of observations.

Model 2 of Table 3 reports results from the Cox model, in which the coefficient estimate

of CEO-CFO LSM is positive and statistically significant (β=0.215, p<.01), supporting

Hypothesis 2. In terms of economic magnitude, one unit increase in CEO-CFO LSM increases

the “risk” of becoming a board member by 23.99%. Some research suggests that Cox models are

not appropriate when there are a lot of tied events in the same time period (Yamaguchi, 1991).

For each year in our sample, we have multiple CFOs’ becoming board members (i.e., tied

events). Therefore, we also use discrete-time event history analysis to test Hypothesis 2. Results

from discrete-time event history analysis based on a logistic model are reported in Model 3. The

coefficient estimate of CEO-CFO LSM is positive and statistically significant (β=0.269, p<.01),

also supporting Hypothesis 2. In terms of economic magnitude, one unit increase in CEO-CFO

LSM is associated with a 30.87% increase in the likelihood of becoming a director at the focal

firm.

Hypothesis 3a suggests that the positive relationship between CEO-CFO LSM and CFO

compensation should be stronger when CEOs have higher power. In Model 4 of Table 3, the

coefficient estimate of CEO-CFO LSM × CEO power is positive and marginally significant

(β=0.005, p<.10, two-tailed test), consistent with Hypothesis 3a. We graph the interaction effect


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in Figure 1. In Figure 1, CEO-CFO LSM takes values from its 25th percentile value to 75th

percentile value and CEO power takes its low value (mean minus one S.D.) versus its high value

(mean plus one S.D.). We find a stronger positive relationship between CEO-CFO LSM and

CFO compensation when CEO power is high (the dotted line) than when CEO power is low (the

solid line), consistent with Hypothesis 3a’s prediction.

[Insert Figure 1 here]

Hypothesis 3b suggests that the positive relationship between CEO-CFO LSM and

CFO’s likelihood of becoming board member at the focal firm should be stronger when CEOs

have higher power. In Model 5 of Table 3, the coefficient estimate of CEO-CFO LSM × CEO

power is positive and statistically significant (β=0.031, p<.05), consistent with Hypothesis 3b.

We graph the interaction effect in Figure 2. As shown in Figure 2, the positive relationship

between CEO-CFO LSM and the hazard ratio of CFO becoming board member is stronger when

CEO power is high (the dotted line) than when CEO power is low (the solid line), supporting

Hypothesis 3b.

[Insert Figure 2 here]

Across all the models in Table 3, the coefficient estimates of CEO-CFO language content

matching (LCM) are not statistically significant, indicating that CEO-CFO LCM in conference

calls may not provide much insight into CEO-CFO LCM in other settings and cannot predict

CFO-related personal outcomes.

The dependent variable of Hypothesis 4 is firm M&A intensity. We use two variables to

proxy firm M&A intensity. The first is the number of M&As, which is a count variable. Two

methods commonly used to analyze count data are negative binomial regressions and Poisson

regressions. We do not use fixed-effects negative binomial regressions because such regressions


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do not provide a true fixed-effects analysis (Allison, 2005; Allison & Waterman, 2002). In

addition, fixed-effects Poisson regressions can only include firms that have time-variant

dependent variables into regressions (Allison, 2005). Around one-third of the final sample firms

did not announce any acquisition during the sample frame. In this sense, using fixed-effects

Poisson regressions can give rise to sample selection bias. We therefore choose a firm fixed-

effects OLS regression to test the relationship between CEO-CFO LSM and the number and

value of M&A. We take the natural logarithm of the number and value of M&As plus one to

address skewness associated with these two dependent variables and cluster standard errors by

firms to address potential correlations among residuals (Petersen, 2009).

Results in Table 4 are used to test Hypothesis 4. In Model 1, the number of M&As is the

dependent variable. The coefficient estimate of CEO-CFO LSM is positive and statistically

significant (β=0.023, p<.05) in Model 1, supporting Hypothesis 4. In terms of economic

magnitude, one unit increase in CEO-CFO LSM will lead to a 2.3% increase in the number of

M&As. Model 2 presents results from a firm fixed-effects Poisson regression. The coefficient

estimate of CEO-CFO LSM is also positive and statistically significant (β=0.107, p<.05). The

dependent variable of Model 3 is the value of M&As. The coefficient estimate of CEO-CFO

LSM is positive and statistically significant (β=0.111, p<.05). In terms of economic magnitude,

one unit increase in CEO-CFO LSM will lead to a 11.7% increase in the value of M&As.


We use pooled OLS regressions to test Hypothesis 5 given that we have a cross-sectional

dataset. As Hypothesis 4 proposes, firms’ M&A activities may not be random. This can lead to

sample selection bias: unobservable factors that drive whether a firm conducts M&As can also

affect stock reactions to the firm’s M&A announcements, if M&As are made. We use the


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Heckman selection model to address potential sample selection bias (Heckman, 1979). In the

first-stage probit regression, we use the panel dataset (used to test Hypothesis 4) to predict

whether a firm engages in at least one M&A in a year. The variables used in the first-stage probit

regression are all the variables used in testing Hypothesis 4 as well as industry and year fixed-

effects. The instrument that we use in the first-stage probit regression is the number of M&As

conducted by firms located in the same state in the same year (excluding focal firms). Research

shows that other firms located in the same community can influence firms’ strategies (Marquis &

Battilana, 2009; Rao, Davis, & Ward, 2000). In this sense, firms are more likely to undertake

M&As when their peer firms in the same state engage in M&As. However, same-region peer

firms’ M&A activities are unlikely to influence how investors and analysts react to the focal

firms’ M&A announcements.4 Based upon the first-stage probit regression result, we calculate

the inverse Mills ratio and control for it in the second-stage regression. In the unreported first-

stage regression, we find that the coefficient estimate of our instrument is positive and

statistically significant (β=0.047, p<.01), consistent with our conjecture.

Models 1-3 of Table 5 report results used to test Hypothesis 5 on the mediating effect of

the percentage of cash payment on the relationship between CEO-CFO LSM and investor

reactions to M&A announcement returns. The dependent variable of Model 1 is the percentage

of cash payment and the coefficient estimate of CEO-CFO LSM is negative and statistically

significant (β=-0.0526, p<.01), indicating that acquirers tend to pay deals with a lower

percentage of cash when CEOs and CFOs exhibit higher LSM. The dependent variable of Model

2 is CARs for an event window of [-1, +1]. The coefficient estimate of Percentage of cash

4 One may argue that M&A activities of industry peers may affect whether a firm undertakes mergers and

acquisitions in a year. However, M&A activities of industry peers may also affect stock market reactions to the focal

firm’s M&As because the focal firm and its industry peers tend to consider a potential bid on the same target firms

(McNamara, et al., 2008).


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payment is positive and statistically significant (β=0.0054, p<.01). In Model 3, we introduce both

CEO-CFO LSM and percentage of cash payment. The coefficient estimate of Percentage of cash

payment is positive and statistically significant (β=0.0053, p<.01) and the coefficient estimate of

CEO-CFO LSM is negative and statistically significant (β=-0.0050, p<.05). Following Baron and

Kenny (1986), we conduct a Sobel test to examine the mediating effect of the percentage of cash

payment. The Sobel test statistic = -2.05 (p<.05), supporting the mediating effect of the

percentage of cash payment. Our results indicate that the percentage of cash payment partially

mediates the relationship between CEO-CFO LSM and CARs.


In Model 4 and Model 5, we report results with CARs with an event window of [-2, +2].

We continue to find that the coefficient estimate of Percentage of cash payment is positive and

statistically significant (β=0.0062, p<.01) in Model 4. In Model 5, the coefficient estimate of

Percentage of cash payment is positive and statistically significant (β=0.0060, p<.01) and the

coefficient estimate of CEO-CFO LSM is negative and statistically significant (β=-0.0083,

p<.01). Sobel test statistic = -2.46 (p<.05). Results in Table 5 thus provide consistent support for

Hypothesis 5.

As CARs are reflections of investors’ perceptions of M&As and may not necessarily

capture deal quality (Schijven & Hitt, 2012; Zollo & Meier, 2008), we use ROA change around

M&A announcements as an alternative measure of M&A deal quality. We measure ROA change

by calculating the difference between ROA at t+1 and ROA at t-1 (t refers to M&A

announcement years). If CEO-CFO LSM are associated with firms’ undertaking low quality

M&As, LSM should be negatively associated with ROA change. In Model 6, the coefficient


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estimate of CEO-CFO LSM is negative and statistically significant (β=-0.0115, p<.01),

consistent with our argument.

Supplementary Analyses

Propensity score weighted regressions. Dominant CEOs may select CFOs who have

strong desires to affiliate with and ingratiate CEOs. Meanwhile, these CEOs are more likely to

undertake intensive M&A activities. In other words, CEO-CFO dyads with different levels of

LSM are not randomly assigned across firms. To mitigate bias from the non-random assignment

of CEO-CFO LSM, we conduct additional analyses using the inverse-probability-of-treatment-

weighted (IPTW) methodology—a two-stage selection-on-observables estimation technique

(Robins, Hernan, & Brumback, 2000). IPTW has been used in prior observational research to

mitigate selection bias and adjust for non-random treatment (Almandoz & Tilcsik, 2016; Yue,

Luo, & Ingram, 2013). This weighting approach is akin to simulating what would occur if all

firms have both high and low levels of CEO-CFO LSM, addressing the possibility that specific

types of firms have a high level of LSM.

To implement IPTW, we first estimate a probit model predicting the probability of a firm

having a high or low level of CEO-CFO LSM. Following Almandoz and Tilcsik (2016), we use

the median value of CEO-CFO LSM to code whether a CEO-CFO dyad has a high or low level

of LSM. We include all the firm-level and CEO-CFO dyadic level variables used in predicting

CEO-CFO LSM in the first-stage probit regression. In addition, we include the ratio of the total

number of words spoken by the CFO during the Q&A section to the total number of words

spoken by the CEO in a firm year as an additional predictor. If the CFO attempts to ingratiate the

CEO, the CEO may delegate more speaking opportunities to the CFO. Yet, we would not expect

the ratio of words spoken by the CFO to the words spoken by the CEO to directly influence our


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outcome variables. We use the inverse of the propensity calculated from the first-stage probit

regression as a weight in the second-stage regressions. Results from the IPTW methodology are

reported in Table 6.

[Insert Table 6 about here]

The dependent variable of Model 1 is CFO compensation, and the coefficient estimate of

CEO-CFO LSM is positive and marginally significant (β=0.025, p<.10, two-tailed test),

consistent with Hypothesis 1. The dependent variable of Model 2 (logistic regression)5 is the

likelihood of CFO becoming a board member at the focal firm, and the coefficient estimate of

CEO-CFO LSM is positive and statistically significant (β=0.404, p<.01), consistent with

Hypothesis 2. The dependent variable of Model 3 is the number of M&As, and the coefficient

estimate of CEO-CFO LSM is positive and statistically significant (β=0.024, p<.05), consistent

with Hypothesis 4. The dependent variable of Model 4 is the value of M&As, and the coefficient

estimate of CEO-CFO LSM is positive and statistically significant (β=0.110, p<.05), also

supporting Hypothesis 4.

Subgroup analyses based on CEO-CFO tenure difference. To further rule out the

possibility that our results are driven by the alternative explanation that CEOs select similar

and/or submissive CFOs, we test our hypotheses using firm-year observations that CEO tenure

starts after CFO tenure starts (around one third of all firm-year observations in the sample).

When CEO tenure starts after CFO tenure starts, it is unlikely that the CFO is selected by the

CEO. If our results hold using this subsample, this would indicate that CEO-CFO LSM is less

likely to be driven by this alternative selection explanation.

5 We do not use the Cox model because it requires the weight to be constant for the same CFO.


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In unreported results, using the subsample in which CEO tenure starts after CFO tenure

starts, the coefficient estimate of CEO-CFO LSM is positive but not statistically significant with

CFO compensation as the dependent variable. Using the subsample in which CEO tenure starts

before CFO tenure, the coefficient estimate of CEO-CFO LSM is positive but not statistically

significant either. Using the subsample in which CEO tenure starts after CFO tenure, we conduct

a logistic regression to examine whether CEO-CFO LSM is positively associated with the

likelihood of a CFO being appointed as a board member. The coefficient estimate of CEO-CFO

LSM is positive and statistically significant. We do not use event history analyses to conduct

subgroup analyses because the subgroup can artificially create time gaps for the same CFO,

which can bias estimation. With the number of M&As and value of M&As as the dependent

variables, the coefficient estimates of CEO-CFO LSM are positive and statistically significant

using the sample that CFO tenure starts before CEO tenure. These findings further mitigate the

concern that CEO selection of CFO drives our findings.

Moderating Effects of CEOs on Important Committees. Hypotheses 1-2 suggest that

CEOs play a role in shaping CFO compensation and the likelihood of a CFO becoming a board

member at the focal firm. If this is the case, CEOs who are members of compensation

committees are more likely to play a significant role in setting CFO compensation level and

CEOs who are members of nominating committees can exert a stronger influence in making

CFOs as board members at focal firms. We test such arguments by examining the moderating

effect of CEO compensation committee membership on CFO compensation and the moderating

effect of CEO nominating committee membership on CFO board directorship. In unreported

results, the coefficient estimate of CEO-CFO LSM × CEO compensation committee member is

positive and statistically significant (β=0.088, p<.05) with CFO compensation as the dependent


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variable. The coefficient estimate of CEO-CFO LSM × CEO nominating committee member is

positive but not statistically significant with CFO becoming a board member at the focal firm.

Hybrid regressions. To rule out the influence of firm-level heterogeneity on CFO

compensation and M&A intensity, we have conducted our analyses using firm fixed-effects

regressions. Such regressions capture how change in CEO-CFO LSM is related to change in the

dependent variables. Yet, fixed-effects regressions do not allow us to evaluate whether between-

firm variance drives our findings. Although random-effects regressions can capture between-firm

variance, such regressions are often subject to omitted variable bias (Certo, Withers, & Semadeni,

2017). To assess whether within- or between-firm variance drives our findings, we use a hybrid

approach (Allison, 2005) that combines the advantages both within-and between-firm

approaches. The hybrid approach separates predictors into two variables (1) a group-centered

variable -X Xit i (2) a variable representing the group mean (Xi ). Afterwards, we use random-

effects regressions to estimate coefficients representing both the within- and between-firm

effects of each predictor.

In unreported results, we find that both within- and between-firm variance in CEO-CFO

LSM are significantly related to CFO compensation, indicating that within-firm change in CEO-

CFO LSM and between-firm variation in CEO-CFO LSM can predict CFO compensation. In

addition, only within-firm variance in CEO-CFO LSM is significantly related to the number of

and value of M&As, indicating that only within-firm change in CEO-CFO LSM can predict firm

M&A intensity. Within-firm variation captures how changes in CEO-CFO LSM reflect changes

in CFOs’ ingratiation intentions. Such variation, we believe, can be mainly driven by CFOs’

differential intentions to ingratiate CEOs within each firm. In contrast, between-firm variation

captures cross-sectional differences between CEO-CFO LSM, which can be driven by similar


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demographic backgrounds between CEOs and CFOs instead of CFOs’ differential attempts to

ingratiate CEOs.

DISCUSSION

This study proposes that LSM between top executives can provide important cues into

understanding their social interaction processes. Empirically, we focus on CEO-CFO LSM and

find that CEO-CFO LSM is positively associated with CFO compensation and the probability of

CFO’s becoming a board member on the focal firm’s board. These effects are particularly strong

when CEOs are powerful. We also find that firms with high CEO-CFO LSM undertake more

M&As, but their M&As are paid with a lower percentage of cash payment and associated with

lower announcement returns. Our findings collectively demonstrate that CFOs’ goals of

affiliating with and ingratiating CEOs, reflected by high CEO-CFO LSM, can benefit CFOs’

personal interests yet undermine firms’ value-creation from M&As.

This study can make several important theoretical contributions. First, different from

prior research that has mainly relied on senior executives’ demographic characteristics to capture

their social interactions (Finkelstein et al., 2009), this study proposes that LSM between senior

executives can offer a new, and probably more accurate channel to understand the nature and

effectiveness of their social interactions. Unlike the use of content words, the use of function

words is context independent. While ingratiation of CEOs by CFOs, reflected by CEO-CFO

LSM, can benefit CFOs’ personal interest in terms of higher compensation and a higher

likelihood of becoming a director on the focal firm’s board, such personal benefits may be traded

off against the firm’s organizational interest as the CFO is less likely to voice dissent and play

the role of “devil’s advocate” in strategic decision making process. In the absence of CFOs’

constructive opposition, CEOs are more inclined to undertake M&As, but such M&As tend to be


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associated with lower announcement returns. In comparison, we do not find any significant

relationship between CEO-CFO LCM and the dependent variables examined in this study. As we

discussed earlier, different from function word usage that is unconscious and context-free,

content word usage is conscious and context-specific and thus CEO-CFO language content

match in conference calls does not reflect their social interactions in other settings.

Second, different from prior research that has either focused on the individual level of

CEOs or the whole TMTs, this study takes an intermediate approach and focuses on an important

subteam made up of two key executives—CEOs and CFOs. Adopting such an approach is

important because not all the executives are involved in all strategic decisions. Studying

subteams of TMTs enables us to model the influence of TMTs on firm strategic decisions more

accurately and accordingly increases the predictive power of upper echelons theory (Hambrick,

2007).

Third, this study sheds new light on corporate governance research by demonstrating the

important governance role of subordinate executives to their supervisors. Corporate governance

research suggests that monitoring by board members and proper incentive compensation are

critical to refraining CEOs of public firms from taking actions detrimental to shareholders’

interests (Dalton et al., 2007; Shi, Connelly, & Hoskisson, 2017). Yet, not much research has

been devoted to the governance role played by executives who report to CEOs (i.e., subordinate

executives). Although subordinate executives may not be in a position to monitor CEOs directly,

their social interactions with CEOs can have an impact on firm governance quality. Subordinate

executives who are unwilling to challenge CEOs are unlikely to play an active role in bottom-up

governance, giving rise to strategic decisions that may harm shareholders.

In addition, this study offers new insights into determinants of executive compensation


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and board appointments. Prior literature has shown that executives’ demographic backgrounds

and working experiences are important determinants of executive compensation (Devers et al.,

2007). Scholars have also examined board directors’ social comparison (O' Reilly, Main, &

Crystal, 1988; Shi, Connelly, & Sanders, 2016) and executives’ power in shaping executive

compensation (Westphal & Zajac, 1995). Our findings imply that an executive’s (CFO in our

context) social interaction with the one at the center of power of a firm (i.e., CEO), can have

important implications for the former’s compensation and the likelihood of board appointment.

This study also contributes to the M&A literature. M&A research has attested to the

important role of CEOs in firm M&A decisions (Haleblian et al., 2009). This stream of research

has shown that CEOs’ aspirations for higher compensation (Seo et al., 2015) and social standing

(Shi, Zhang, & Hoskisson, 2017) and their personality attributes (Gamache et al., 2015; Hayward

& Hambrick, 1997) shape firm M&A decisions. Although CFOs are shouldering an increasingly

important role in firm strategic decisions, M&A research has largely been silent on their role in

firm M&A decisions. Our findings imply that CFOs can exert their influence on firm M&A

decisions. When CFOs attempt to ingratiate CEOs, the CEOs are more likely to increase M&A

intensity, but such M&A activities tend to be low quality.

This paper also has important practical implications. Given that processes of social

interactions between senior executives are difficult to be observed by people outside the TMTs

including the independent directors of the firms’ boards, it may be challenging for the firms’

independent board members and other internal and external key constituents to assess their social

interactions. This study provides a practical way for boards of directors and other internal and

external constituents to evaluate social interactions between a dyad of senior executives through

their LSM. Our results suggest that the board of directors, as well as other internal and external


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key constituents, should be concerned if the CEO and the CFO (or any other executive) of the

firm are too similar to each other in their language style use, or other behavioral styles.

Our findings also offer a useful tool for CEOs to evaluate whether they are matched with

constructive CFOs. If CFOs are showing high LSM with CEOs, CEOs may need to encourage or

provoke the CFOs to voice contrasting opinions in strategic decision making. In addition, CEOs

may consider introducing programmed conflicts to decision making processes (Cosier &

Schwenk, 1990). CFOs need be forced to voice dissent regardless of their true feelings. In

addition, our findings have implications for other top executives. When CFOs are unwilling to

shoulder the role of a “devil’s advocate,” other top executives may need to step in and avoid the

tendency of agreeing with CEOs. Instead, these executives should debate different viewpoints

with CEOs to avoid group thinking.

While our study focuses on the relationship between CEO-CFO LSM and firm M&As, a

logical extension of our findings is that social interactions reflected by high levels of LSM

between CEO and other non-CEO executives, while benefiting the personal interests of these

executives, probably undermine the quality of a firm’s other critical strategic decisions. For

example, ineffective social interactions captured by high LSM between CEO and chief

technology officer (CTO) may undermine the quality of the firm’s R&D decisions and high LSM

between CEO and chief marketing officer (CMO) may hurt the firm’s marketing decisions.

Likewise, future research may examine whether LSM at the TMT level can predict strategic

decision quality and firm performance. Moreover, future research may examine whether and how

LSM between non-CEO executives, such as COO-CFO LSM, matters given that these non-CEO

executives have a low level of power asymmetry and thus neither side has a strong incentive to

ingratiate the other. The bottom line is that the board should consider hiring a CFO (or another


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non-CEO executive) who is willing to voice dissent and provoke the CEO to think through the

potential risks associated with strategic decisions, instead of thinking and “sounding” just alike

as the CEO.

As with any study, this one has limitations that may provide avenues for future research.

First, building on behavioral mimicry research, we contend that CEO-CFO LSM mainly reflects

the CFO’s ingratiation of the CEO. Our empirical evidence also suggests so. However, future

research can collect CEO-CFO behavioral data through survey instruments and further

disentangle whether CEO-CFO social cohesion or the CFO’s ingratiation of the CEO mainly

affects CEO-CFO LSM. Relatedly, our study has focused on CEO-CFO unconscious verbal

mimicry and does not examine other aspects of behavioral mimicry. Future research may

measure CEO-CFO facial mimicry during public interviews and examine whether facial mimicry

can also provide insights into their social interactions.

In addition, prior research on leadership communication has focused on how language

use affects the audience’s reactions (Gao, Yu, & Cannella, 2016; Pan et al., in press). Our

research departs from such research by examining how we can infer TMT social interactions

through their LSM. Yet, it is also important to examine how LCM between top managers can

affect the third-party’s reactions. For instance, future research can investigate whether CEO-CFO

LCM can influence analysts’ evaluation of CEO-CFO dyads. Relatedly, future research can use

experiments to examine whether CEO-CFO LCM in public can affect CEOs’ positive

evaluations of CFOs.

Finally, future research can explore whether LSM between CEOs and independent board

chairs is related to board effectiveness. Previous research (Westphal, 1998; Westphal & Bednar,

2008) finds that CEOs can neutralize board independence and shareholder influence through


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ingratiation of board members and institutional investors. Findings from this study imply that

high CEO-board chair LSM might lead to less effective board monitoring. With a high level of

CEO-board chair LSM present, the CEO may intend to ingratiate the board chair, mitigating the

latter’s monitoring role. Extending this study to not only other senior executives, but also to

CEO-board relations, seems a natural extension of this research.

In summary, this study takes a novel approach to examine social interactions of the CEO-

CFO dyad by focusing on their LSM in conference calls. Our results suggest that a high level of

CEO-CFO LSM, reflecting the CFO’s ingratiation of the CEO, can benefit the CFO personally

by increasing his/her compensation and likelihood of becoming a board member of the focal firm,

it harms the firm by allowing the firm to make a larger number/value of M&As that result in

lower returns. More broadly, we believe that our study can make important contributions to

upper echelons theory by moving away from the widely adopted demographic approach and by

focusing on subteams among key executives. We hope that our study encourages more research

to explore this important issue.

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physical appearance of spouses. Motivation and Emotion, 11(4): 335-346. Zander, A. F. 1977. Groups at work. San Francisco, CA: Jossey-Bass. Zollo, M., & Meier, D. 2008. What is M&A performance? Academy of Management

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Zorn, D. M. 2004. Here a chief, there a chief: The rise of the CFO in the American firm. American Sociological Review, 69(3): 345-364.

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TABLE 1 Word Categories Used in Calculating LSM

Category Examples

Personal pronouns I, his, their

Impersonal pronouns it, that, anything

Articles a, an, the

Conjunctions and, but, because

Prepositions in, under, about

Auxiliary verbs shall, be, was

High-frequency adverbs very, rather, just

Negations no, not, never

Quantifiers much, few, lots


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TABLE 2A Descriptive Statistics for Variables Used in Testing Hypotheses 1-4

Variable Mean S.D. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

1 Log CFO compensation 13.99 0.90 1.00

2 CFO director 0.57 0.50 0.29 1.00 3 Number of M&As 0.49 1.05 0.17 0.10 1.00 4 Log value of M&As 1.47 2.55 0.27 0.14 0.76 1.00 5 CEO-CFO LSM 7.89 0.43 0.19 0.15 0.04 0.07 1.00 6 CEO-CFO LCM 1.28 0.34 0.18 0.15 0.02 0.05 0.55 1.00

7 CEO power 0.22 2.90 -

0.01 0.23 0.02 0.01 0.00 0.01 1.00 8 Firm size 7.50 1.93 0.70 0.43 0.18 0.28 0.17 0.19 0.01 1.00 9 Firm performance 0.07 0.13 0.20 0.19 0.09 0.12 0.14 0.13 0.09 0.24 1.00

10 Cash holding ratio 0.17 0.19 -

0.18 -

0.16 -

0.06 -

0.08 -

0.08 -

0.10 0.01 -

0.43 -

0.30 1.00

11 Debt ratio 0.22 0.20 0.14 0.04 0.03 0.03 0.01 0.00 0.00 0.23 0.00 -

0.34 1.00

12 CEO duality 0.53 0.50 0.12 0.15 0.04 0.05 0.01 0.04 0.64 0.16 0.09 -

0.12 0.06 1.00

13 Board independence 0.69 0.13

-0.09

-0.63

-0.06

-0.06

-0.10

-0.09

-0.42

-0.16

-0.16 0.01 0.00

-0.10 1.00

14 CEO appointed director 0.53 0.31

-0.09

-0.03 0.01

-0.01

-0.05

-0.05 0.70

-0.09

-0.01 0.06 0.01 0.36

-0.06 1.00

15 CEO option pay 0.15 0.24 0.21 0.02 -

0.04 0.01 0.02 0.02 -

0.07 0.09 -

0.05 0.13 -

0.03 -

0.05 0.02 -

0.06 1.00

16 CEO equity ownership 0.01 0.03

-0.15

-0.10

-0.04

-0.06

-0.01

-0.03 0.53

-0.19 0.01 0.08

-0.06 0.15 0.00 0.21

-0.05 1.00

17 CFO option pay 0.12 0.17 0.14 -

0.05 -

0.04 -

0.03 0.02 0.00 -

0.04 -

0.03 -

0.09 0.19 -

0.07 -

0.05 0.04 -

0.02 0.65 -

0.01 1.00

18 CFO equity ownership 0.00 0.00

-0.07

-0.12

-0.04

-0.06 0.00

-0.01 0.08

-0.23

-0.01 0.03 0.00 0.01 0.08 0.07

-0.09 0.16

-0.08 1.00

19 CEO turnover 0.20 0.40 0.16 0.15 0.10 0.14 0.05 0.06 -

0.08 0.23 0.03 -

0.05 -

0.01 -

0.02 -

0.08 -

0.15 0.00 -

0.06 -

0.03 -

0.07 1.00

20 CEO-CFO turnover 0.28 0.45 0.12 0.10 0.08 0.10 0.04 0.05 -

0.08 0.20 0.01 -

0.04 0.00 -

0.01 -

0.04 -

0.12 0.01 -

0.05 -

0.01 -

0.06 0.79 1.00

21 CEO-CFO age difference (Log) 1.92 0.79

-0.08

-0.01 0.03 0.01

-0.03

-0.02 0.01

-0.06

-0.02 0.02

-0.01

-0.03

-0.04

-0.01

-0.03 0.03

-0.02

-0.01 0.02 0.01 1.00

22 CEO-CFO same gender 0.87 0.34 0.01 0.01 0.01 0.01 0.05 0.04 0.09 0.00

-0.02

-0.02 0.02 0.07

-0.05 0.08

-0.03 0.06

-0.03 0.05

-0.02

-0.02 0.04 1.00

23 CEO-CFO same education level 0.42 0.49 0.06 0.02 0.02 0.03

-0.02 0.00

-0.02 0.03 0.02 0.00 0.02 0.00

-0.01

-0.01 0.01

-0.04 0.01

-0.01 0.01 0.01

-0.01 0.06 1.00

24 CEO-CFO tenure overlap 2.88 2.95 0.01

-0.04 0.00 0.00

-0.01

-0.03 0.34

-0.03 0.04

-0.04 0.00 0.14 0.03 0.26

-0.04 0.12

-0.06 0.14

-0.10

-0.16

-0.02 0.03

-0.02 1.00

25 CFO age 57.61 7.14 0.02 0.13 0.03 0.02 0.02 0.02 0.05 0.07 0.05 -

0.06 -

0.02 0.06 -

0.07 0.00 -

0.08 -

0.03 -

0.11 0.00 0.04 0.00 0.15 0.10 -

0.01 0.21 1.00

26 CFO gender 0.91 0.29 -

0.01 0.00 0.00 0.00 0.04 0.04 0.03 -

0.02 -

0.03 -

0.02 0.02 0.02 0.00 0.02 -

0.04 0.04 -

0.04 0.05 0.00 -

0.01 0.04 0.80 0.03 0.02 0.15 1.00

27 CFO tenure 4.15 3.76 0.00 -

0.09 -

0.02 -

0.02 0.00 -

0.02 0.08 -

0.05 0.05 -

0.03 -

0.03 0.01 0.08 0.05 -

0.02 0.04 -

0.06 0.15 -

0.03 -

0.10 0.04 0.04 -

0.04 0.73 0.31 0.03 1.00

28 CFO educational level 1.51 0.51 0.11 0.06 0.04 0.06 0.01 0.04

-0.02 0.08

-0.01 0.06 0.01

-0.01

-0.03

-0.02 0.04

-0.03 0.03

-0.04 0.07 0.06

-0.01 0.05 0.21

-0.06 0.10 0.06

-0.09 1.00

Note: Absolute value of correlations greater than .03 statistically significant at p<.05 level at both tails.


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TABLE 2B Descriptive Statistics for Variables Used in Testing Hypothesis 5

Variable Mean S.D. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 CAR [-1, +1] 0.01 0.05 1.00

2 Percent of cash payment 0.55 0.46 0.05 1.00

3 CEO-CFO LSM 7.90 0.43 -

0.05 -

0.01 1.00

4 CEO-CFL LCM 1.29 0.34 -

0.04 0.04 0.55 1.00

5 Firm size 7.95 1.85 -

0.08 -

0.03 0.12 0.18 1.00

6 Firm performance 0.04 0.09 0.03 0.12 0.06 0.05 0.10 1.00

7 Cash holding ratio 0.15 0.17 0.00 0.13

-0.02 0.02

-0.26

-0.04 1.00

8 Debt ratio 0.24 0.21 0.00 -

0.15 0.01 -

0.03 0.14 -

0.13 -

0.42 1.00

9 CEO duality 0.58 0.49 0.03 0.01 0.03 0.00 0.12 0.05

-

0.10 0.04 1.00

10 CEO appointed director 0.56 0.31 0.01 0.01

-0.02

-0.05

-0.07 0.01 0.01 0.02 0.29 1.00

11 CEO option pay 0.13 0.22 -

0.01 0.07 0.05 0.06 0.11 0.06 0.16 -

0.08 -

0.04 -

0.06 1.00

12 CEO equity ownership 0.01 0.03 0.05 0.01 0.01

-0.05

-0.22 0.01 0.08

-0.04 0.14 0.20

-0.04 1.00

13 CFO option pay 0.10 0.16 -

0.01 0.10 0.02 0.03 0.06 0.08 0.16 -

0.09 -

0.03 -

0.04 0.70 -

0.02 1.00

14 CFO equity ownership 0.00 0.00 0.05

-0.02 0.01

-0.05

-0.28

-0.01

-0.02 0.08 0.03 0.03

-0.08 0.14

-0.08 1.00

15 CEO-CFO turnover 0.22 0.42

-0.04 0.03 0.04 0.06 0.22 0.02 0.04

-0.02 0.08

-0.07 0.03

-0.04 0.02

-0.09 1.00

16 CEO-CFO age difference 1.98 0.80 0.02 0.00

-0.02

-0.01

-0.06

-0.04 0.01

-0.02

-0.05

-0.03

-0.05 0.05

-0.06 0.03

-0.04 1.00

17 CEO-CFO same gender 0.74 0.44

-0.04 0.02 0.09 0.08 0.13 0.05

-0.03 0.00 0.05 0.04 0.03 0.01 0.01

-0.04 0.08 0.01 1.00

18

CEO-CFO same educational level 0.42 0.49

-0.03

-0.02 0.04 0.03 0.12 0.01

-0.01 0.01 0.08 0.01 0.03

-0.03 0.03

-0.08 0.08

-0.06 0.16 1.00

19 CEO-CFO tenure overlap 3.54 3.80 0.03 0.01

-0.05

-0.05

-0.08 0.03

-0.05 0.02 0.13 0.29

-0.06 0.10

-0.04 0.09

-0.15

-0.01

-0.26 0.00 1.00

20 Related deal 0.45 0.50 0.03 0.00 -

0.02 -

0.01 -

0.10 -

0.01 0.06 -

0.04 -

0.05 0.03 0.03 0.06 0.06 0.04 -

0.01 0.02 0.01 -

0.05 -

0.01 1.00

21 Hostile takeover 0.01 0.07 0.01 0.00 0.01 0.02 0.02 0.01 0.02 0.01

-

0.01 0.04

-

0.01 0.03

-

0.01

-

0.01 0.03 0.01 0.01

-

0.01

-

0.01 0.03 1.00

22 Relative size 0.12 0.22 0.08 -

0.03 -

0.05 -

0.09 -

0.15 -

0.08 -

0.04 0.10 -

0.05 -

0.02 -

0.03 0.02 -

0.03 0.08 -

0.04 0.02 -

0.06 -

0.03 0.01 0.05 0.09 1.00

23 Target public firm 0.35 0.48

-0.02 0.05 0.01 0.04 0.26

-0.02

-0.06

-0.01 0.01

-0.06 0.00

-0.07

-0.01

-0.10 0.11

-0.06 0.04 0.03

-0.05 0.06 0.10 0.14 1.00

24

Acquisition experience (Log) 1.31 0.95

-0.05 0.01 0.03 0.06 0.41

-0.02

-0.13 0.10 0.08 0.04

-0.04

-0.11

-0.04

-0.11 0.13 0.03 0.10 0.09

-0.02

-0.07 0.02

-0.17 0.02 1.00

Note: Absolute value of correlations greater than .03 statistically significant at p<.05 level at both tails.


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TABLE 3 CEO-CFO LSM and CFO Compensation and Directorship

Model 1 Model 2 Model 3 Model 4 Model 5 Variable OLS Cox Logit OLS Cox

CEO-CFO LSM 0.032*** 0.215*** 0.269*** 0.025*** 0.192*** [0.012] [0.045] [0.078] [0.010] [0.046]

CEO-CFO LSM x CEO power 0.005* 0.031** [0.003] [0.014]

CEO-CFO LCM 0.007 0.078 0.090 0.020 0.078 [0.014] [0.056] [0.101] [0.012] [0.056]

CEO power -0.044* -0.223** [0.024] [0.108]

Firm size 0.238*** 0.141*** 0.528*** 0.208*** 0.143*** [0.018] [0.013] [0.027] [0.015] [0.013]

Firm performance 0.429*** 0.604*** 0.656* 0.369*** 0.587*** [0.080] [0.221] [0.392] [0.062] [0.221]

Cash holding ratio 0.072 -0.702*** -0.406 -0.007 -0.702*** [0.069] [0.146] [0.254] [0.056] [0.146]

Debt ratio -0.182*** -0.036 -0.378* -0.136*** -0.030 [0.058] [0.120] [0.217] [0.051] [0.119]

Board independence 0.134 -7.253*** -11.464*** -7.124*** [0.085] [0.232] [0.382] [0.247]

CFO age 0.004*** 0.046*** 0.027*** 0.046*** [0.001] [0.003] [0.005] [0.003]

CFO gender 0.021 0.107 0.700*** 0.133 [0.040] [0.114] [0.217] [0.114]

CFO tenure 0.004 -0.043*** -0.039** -0.039*** [0.002] [0.010] [0.017] [0.010]

CFO education level 0.018 -0.133*** -0.213*** -0.136*** [0.018] [0.042] [0.076] [0.042]

CEO turnover -0.021 0.325*** 0.009 0.327*** [0.016] [0.053] [0.100] [0.052]

CFO director 0.055** 0.027* [0.023] [0.016]

CEO-CFO age difference -0.001 0.067*** 0.017 0.001 0.067*** [0.008] [0.026] [0.047] [0.006] [0.026]

CEO-CFO same gender 0.016 -0.213** -0.642*** 0.009 -0.237** [0.034] [0.100] [0.193] [0.013] [0.100]

CEO-CFO same educational level 0.025 0.026 0.039 0.015 0.032 [0.016] [0.042] [0.075] [0.013] [0.042]

CEO-CFO tenure overlap 0.001 0.028** -0.016 0.006*** 0.017 [0.003] [0.012] [0.021] [0.001] [0.013]

Constant 10.817*** 0.374 11.425*** [0.190] [0.883] [0.131]

Observations 12,546 7,877 8,915 16,182 7,877 Firm FE YES YES Year FE YES YES Industry FE YES YES YES Adjusted R-squared 0.810 0.808 Chi-squared 2344 1619 7502 Log pseudolikelihood -16739 -2288 -16735

Note: Standard errors clustered by CFOs in brackets.*** p<0.01, ** p<0.05, * p<0.1. Two-tailed tests.


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TABLE 4 CEO-CFO LSM and M&A Intensity

Model 1 Model 2 Model 3 Variable OLS Poisson OLS

CEO-CFO LSM 0.023** 0.107** 0.111** [0.010] [0.047] [0.053]

CEO-CFO LCM -0.014 -0.067 -0.019 [0.012] [0.057] [0.066]

Firm size -0.038*** -0.035 -0.210*** [0.013] [0.054] [0.068]

Firm performance 0.269*** 2.228*** 1.696*** [0.043] [0.303] [0.240]

Cash holding ratio 0.301*** 1.409*** 1.752*** [0.044] [0.229] [0.229]

Debt ratio -0.206*** -0.986*** -1.171*** [0.042] [0.196] [0.226]

CEO duality -0.012 -0.054 -0.012 [0.013] [0.057] [0.069]

CEO appointed director 0.001 -0.026 0.010 [0.017] [0.079] [0.096]

CEO option pay 0.013 0.067 0.174 [0.025] [0.105] [0.151]

CEO equity ownership -0.011 0.295 -0.779 [0.137] [0.791] [0.694]

CFO option pay 0.011 0.119 -0.179 [0.031] [0.140] [0.170]

CFO equity ownership -2.782 -15.751 -4.840 [2.615] [12.188] [11.853]

CEO-CFO turnover 0.007 0.037 0.046 [0.011] [0.043] [0.061]

CEO-CFO age difference 0.010* 0.045* 0.026 [0.006] [0.026] [0.032]

CEO-CFO same gender -0.008 -0.010 -0.037 [0.011] [0.048] [0.057]

CEO-CFO same educational level 0.012 0.039 0.082 [0.011] [0.049] [0.061]

CEO-CFO tenure overlap 0.000 0.003 -0.000 [0.001] [0.006] [0.007]

Constant 0.308*** 1.482** [0.116] [0.601]

Observations 15,984 11,753 15,984 Firm FE YES YES YES Year FE YES YES YES Adjusted R-squared 0.285 0.243 Chi-squared 587.8 Log-likelihood -7898

Note: Standard errors clustered by firms in brackets.*** p<0.01, ** p<0.05, * p<0.1. Two-tailed tests.


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TABLE 5 CEO-CFO LSM and Investor Reactions/ROA Change

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Percent of cash [-1, +1] [-1, +1] [-2, +2] [-2, +2] ROA change

CEO-CFO LSM -0.0526*** -0.0050** -0.0083*** -0.0115*** [0.0187] [0.0024] [0.0029] [0.0039]

Percent of cash payment 0.0054*** 0.0053*** 0.0062*** 0.0060*** -0.0057** [0.0018] [0.0018] [0.0021] [0.0021] [0.0026]

CEO-CFO LCM 0.0516** -0.0050** -0.0017 -0.0068** -0.0013 0.0044 [0.0230] [0.0024] [0.0028] [0.0030] [0.0034] [0.0044]

Firm size -0.0285*** -0.0042** -0.0044*** -0.0041** -0.0045** 0.0021 [0.0097] [0.0016] [0.0017] [0.0021] [0.0021] [0.0023]

Firm performance 0.2694*** 0.0043 0.0038 -0.0011 -0.0019 [0.0877] [0.0148] [0.0148] [0.0180] [0.0180]

Cash holding ratio 0.2176*** 0.0066 0.0067 -0.0037 -0.0036 -0.0133 [0.0472] [0.0067] [0.0067] [0.0085] [0.0085] [0.0117]

Debt ratio -0.0865** 0.0007 0.0007 0.0012 0.0012 0.0253*** [0.0401] [0.0051] [0.0051] [0.0059] [0.0059] [0.0082]

CEO duality 0.0146 0.0027 0.0027 0.0023 0.0024 -0.0001 [0.0145] [0.0017] [0.0017] [0.0020] [0.0020] [0.0026]

CEO appointed director -0.0043 -0.0032 -0.0034 -0.0044 -0.0048 0.0027 [0.0250] [0.0032] [0.0032] [0.0039] [0.0039] [0.0050]

CEO option pay -0.0289 0.0012 0.0014 -0.0005 -0.0002 0.0074 [0.0444] [0.0045] [0.0045] [0.0053] [0.0053] [0.0085]

CEO equity ownership 0.0146 0.0395 0.0444 0.0584 0.0666 0.0341 [0.2443] [0.0418] [0.0422] [0.0473] [0.0477] [0.0396]

CFO option pay 0.1208** -0.0003 -0.0001 0.0008 0.0011 -0.0242** [0.0588] [0.0067] [0.0067] [0.0080] [0.0080] [0.0113]

CFO equity ownership -3.3113 0.1285 0.1308 0.9106 0.9143 -1.9795*** [4.2001] [0.6703] [0.6697] [0.7927] [0.7903] [0.7517]

CEO-CFO turnover -0.0145 -0.0049* -0.0051** -0.0070** -0.0073** -0.0027 [0.0177] [0.0026] [0.0026] [0.0031] [0.0031] [0.0034]

CEO-CFO age difference -0.0087 -0.0002 -0.0003 -0.0012 -0.0014 0.0040** [0.0088] [0.0011] [0.0011] [0.0013] [0.0014] [0.0017]

CEO-CFO same gender 0.0126 -0.0026 -0.0024 -0.0007 -0.0005 -0.0043 [0.0159] [0.0020] [0.0020] [0.0024] [0.0024] [0.0030]

CEO-CFO same educational level -0.0236* -0.0019 -0.0019 -0.0035* -0.0034* -0.0034 [0.0135] [0.0017] [0.0017] [0.0020] [0.0020] [0.0025]

CEO-CFO tenure overlap 0.0028 0.0002 0.0002 0.0002 0.0002 -0.0008** [0.0019] [0.0003] [0.0003] [0.0003] [0.0003] [0.0004]

Related deal -0.0148 0.0031* 0.0031* 0.0042** 0.0042** 0.0034 [0.0138] [0.0017] [0.0017] [0.0021] [0.0021] [0.0025]

Hostile takeover -0.1043 0.0087 0.0088 0.0109 0.0112 -0.0192 [0.0827] [0.0100] [0.0100] [0.0118] [0.0118] [0.0251]

Relative size -0.0083 0.0162** 0.0162** 0.0177** 0.0178** -0.0316*** [0.0295] [0.0067] [0.0067] [0.0076] [0.0076] [0.0075]

Target public firm 0.0859*** -0.0011 -0.0011 -0.0027 -0.0027 0.0018 [0.0144] [0.0018] [0.0018] [0.0022] [0.0022] [0.0025]

Acquisition experience 0.0118 -0.0006 -0.0007 -0.0011 -0.0012 0.0051*** [0.0083] [0.0010] [0.0010] [0.0013] [0.0013] [0.0015]

Inverse Mills ratio -0.3238*** -0.0371* -0.0402** -0.0429* -0.0480* 0.0170 [0.0942] [0.0197] [0.0201] [0.0242] [0.0245] [0.0255]

Constant 1.3648*** 0.0883** 0.1290*** 0.1023** 0.1698*** 0.0516 [0.2465] [0.0386] [0.0475] [0.0475] [0.0573] [0.0588]

Observations 4,828 4,828 4,828 4,828 4,828 4,775 Industry FE YES YES YES YES YES YES Year FE YES YES YES YES YES YES Adjusted R-squared 0.0874 0.0233 0.0241 0.0202 0.0220 0.0785

Note: Robust standard errors reported in brackets.*** p<0.01, ** p<0.05, * p<0.1. Two-tailed tests.


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TABLE 6 IPTW Regression Results

Model 1 Model 2 Model 3 Model 4 Variable OLS Logit OLS OLS

CEO-CFO LSM 0.025* 0.404*** 0.024** 0.110** [0.015] [0.105] [0.010] [0.050]

CEO-CFO LCM 0.020 0.131 -0.006 0.006 [0.017] [0.127] [0.012] [0.063]

Firm size 0.226*** 0.546*** -0.032** -0.173*** [0.018] [0.028] [0.013] [0.065]

Firm performance 0.318*** 0.220 0.203*** 1.228*** [0.081] [0.395] [0.044] [0.251]

Cash holding ratio 0.079 -0.262 0.250*** 1.442*** [0.071] [0.265] [0.043] [0.213]

Debt ratio -0.149** -0.364 -0.182*** -0.978*** [0.059] [0.232] [0.040] [0.214]

Board independence 0.144* -12.138*** [0.085] [0.398]

CFO age 0.004*** 0.029*** [0.001] [0.006]

CFO gender 0.013 0.804*** [0.042] [0.226]

CFO tenure 0.002 -0.042** [0.003] [0.018]

CFO education level 0.013 -0.229*** [0.018] [0.079]

CEO turnover -0.019 -0.038 [0.017] [0.105]

CFO director 0.057** [0.023]

CEO-CFO age difference 0.002 -0.027 0.009 0.019 [0.008] [0.049] [0.006] [0.031]

CEO-CFO same gender 0.021 -0.697*** -0.004 -0.022 [0.036] [0.194] [0.011] [0.055]

CEO-CFO same educational level 0.022 0.065 0.011 0.064 [0.016] [0.080] [0.011] [0.057]

CEO-CFO tenure overlap 0.003 -0.020 0.001 0.002 [0.003] [0.022] [0.001] [0.007]

CEO duality -0.019 -0.044 [0.013] [0.070]

CEO appointed director 0.005 0.025 [0.017] [0.091]

CEO option pay 0.020 0.182 [0.025] [0.148]

CEO equity ownership 0.016 -0.659 [0.146] [0.754]

CFO option pay 0.018 -0.137 [0.030] [0.160]

CFO equity ownership -1.286 0.681 [2.550] [11.402]

CEO-CFO turnover 0.006 0.046 [0.011] [0.058]

Constant 10.896*** -0.530 0.220** 1.055* [0.197] [1.042] [0.108] [0.562]

Observations 12,546 8,915 15,982 15,982 Firm FE YES YES YES Year FE YES YES YES Industry FE YES Adjusted R-squared 0.811 0.293 0.256 Chi-squared 1591 Log pseudolikelihood -4964

Note: Standard errors clustered by CFOs in Models 1-2 and by firms in Models 3-4 in brackets.*** p<0.01, ** p<0.05, * p<0.1. Two-tailed tests.


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13.

98

13.99

14

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01

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14.03

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CFO

com

pen

sation

7.5 7.7 7.9 8.1 8.3 8.5CEO-CFO LSM

Low CEO power

High CEO power

Figure 1. Moderating Effect of CEO Power on CFO Compensation

22.2

2.4

2.6

2.8

CFO

Direc

tor H

azar

d R

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7.5 7.7 7.9 8.1 8.3 8.5CEO-CFO LSM

Low CEO power

High CEO power

Figure 2. Moderating Effect of CEO Power on CFO Directorship


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Wei Shi ([email protected]) is Associate Professor at University of Miami. He obtained his Ph.D. in strategic management from Rice University. His current research focuses on the intersection of corporate governance and corporate strategy and corporate governance in the international context. Yan “Anthea” Zhang ([email protected]) holds the Fayez Sarofim Vanguard Chair of Strategic Management at the Jones Graduate School of Business, Rice University. Her areas of specialization include CEO succession and corporate governance, as well as foreign direct investment and technology entrepreneurship in emerging markets. She has served as an associate editor of the Academy of Management Journal (2010-2013) and is an associate editor of Strategic Management Journal (2017-2020). Robert E. Hoskisson ([email protected]) holds the George R. Brown Emeritus Chair of Strategic Management at Rice University’s Jones School of Business. His research topics include: corporate and international strategy; corporate governance; strategic entrepreneurship; acquisitions and divestitures; business groups; and IPOs. He is a Fellow of the Academy of Management and Strategic Management Society.


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