Under Pressure: Culture and Structure as Antecedents of Organizational Misconduct

AUTHORS

Andrea Cavicchini (acavicchini@iese.edu)IESE Business School

Fabrizio Ferraro (fferraro@iese.edu)IESE Business School

Samspa Samila (ssamila@iese.edu)IESE Business School

Keywords: Organizational misconduct; Organizational Culture; Goal Setting; Incentives;

Organizational Structure

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Under Pressure: Culture and Structure as Antecedents of Organizational Misconduct

Abstract

Does a toxic organizational culture focused on performance lead to misconduct? To address this

question, we build on Merton's strain theory and theorize the relationship between organizational

culture, structure and misconduct. We first theorize that organizations with cultures

characterized by strong performance pressure are more likely to engage in misconduct. Then we

consider how organizational structure moderates this relationship, which we hypothesize is

weaker in more formalized organizations and stronger in more decentralized organizations. To

test these hypotheses, we analyzed the regulatory and law violations of 880 publicly traded firms

in the United States and measured organizational culture and structure through a natural

language processing (NLP) analysis of the firms’ employee reviews on Glassdoor. The empirical

results lend support to our hypotheses. Organizations with high performance pressure are 68

percent more likely to be fined for misconduct than organizations with low performance

pressure. The moderation effect of organizational structure is fully supported for

decentralization, but only partially supported for formalization.

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Introduction

Virtually every analysis of corporate misconduct in the last two decades, including Enron (Sims

and Brinkmann, 2003; Kuliks, 2005), Wells Fargo (Independent Directors of the Board of Wells

Fargo, 2017), Boeing (Bretton Putter, 2019), and Deutsche Bank (Enrich, 2020), points to toxic

organizational cultures with strong performance pressure as a key antecedent of misconduct. Yet

this evidence tends to be anecdotal, mostly originating from journalistic investigations of high-

profile scandals, and thus lacks generalizability. Building on Merton’s (1938) foundational strain

theory of social deviance and Sutherland’s (1939, 1949) work on white-collar crimes, the

theoretical foundations of the association between culture and misconduct were first developed

by sociologists and organization theorists (Merton, 1936; Sutherland, 1949; Vaughan, 1999). Yet,

as organization theorists moved from an earlier emphasis on intra-organizational processes

(incentives, structure, culture) towards broader institutional and network mechanisms, they all

but abandoned this topic in the misconduct literature (Greve, Palmer, and Pozner, 2010; Cooper,

Dacin, and Palmer, 2013) prematurely, in our view. As a consequence, the relationship between

organizational culture and misconduct remains undertheorized and its empirical support mixed.

In this paper, we build on Merton's (1938) strain theory to theorize the relationship between an

organization’s specific cultural and structural features specifically, the extent of performance

pressure, formalization, and decentralization—and organizational misconduct.

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In his influential 1938 article on "social structure and anomie," Robert Merton directed

sociologists’ attention away from examining individual-level antecedents of deviance towards

discovering "how some social structures exert a definite pressure upon certain persons in society

to engage in nonconformist rather than conformist conduct" (Merton 1938:672). Society, he

suggested, defines aspirational references (goals, purpose, interests) that are not attainable by all

members. Individuals in lower social classes who do not have access to the same educational and

economic resources as individuals in more privileged ones are still expected to achieve wealth

and fame. This situation creates frustration and might translate into deviant and criminal

behavior. This theory was developed at the societal level, and specifically referred to class as the

relevant social structure shaping individual behavior, yet the logic of the theory extended to

other levels of analysis, including the organization and the individual (Agnew, 1992; Langton

and Piquero, 2007; Agnew, Piquero, and Cullen, 2009). Notably, in criminology Agnew’s

General Strain Theory (1992, 2001) defined and widened the range of possible sources of

pressure and strain in society, and has been employed to explain white-collars crimes (for

example, Langton and Piquero 2007) but this theory tend to be more focused at the individual

level, and does not clearly outline how organizational factors can lead to misconduct.

Furthermore, even in this field the empirical evidence for the theory is very thin (Agnew,

Piquero, and Cullen 2009:55).

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In organization theory, and building on March and Simon (1958), Staw and Szwajkowski

(1975) first showed that organizations operating in less munificent environments were more

likely to commit illegal actions in the process of gaining resources. Other studies showed that

firms affected by declining profitability or competitive threats are more likely to engage in

misconduct (Wheeler and Mody, 1992; Vaughan, 1999). Yet, well performing firms are also

under constant pressure to maintain their performance, as their aspirations might be more

difficult to achieve, and failure to do so may translate into more risk taking (Cyert and March,

1963; Tversky and Kahneman, 1991; Audia and Greve, 2006) and even misconduct. Indeed,

research on the relationship between performance expectations and misconduct found that both

organizational results below (Harris and Bromiley 2007) or above expectations (Mishina et al.,

2010) were associated with a greater likelihood of misconduct. However, at the organizational

level, empirical evidence on the relationship between performance pressure and misconduct is

mixed (Hill, Kelley, and Agle 1992). At a more micro level, the literature on goal setting theory

had documented, both in experimental and field settings, the positive performance consequences

of setting challenging goals (Locke and Latham 1990; Locke and Latham 2013; 2002). However,

studies also have yielded evidence of how these goals can lead to unethical behavior

(Schweitzer, Ordóñez, and Douma, 2004; Ordóñez et al., 2009a; Ordóñez and Welsh, 2015). As

such, scholars have suggested that the interplay between organizational cultures and goal setting

can play an important role in influencing organizational outcomes (Locke and Latham, 2009;

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Ordóñez et al., 2009b; Yip et al., 2021). Yet, the relationship between goal setting and

organizational culture remains undertheorized.

In this paper, we reconceptualize strain theory at the organizational level by teasing out its

cultural and structural mechanisms. At the societal level, strain theory leverages both a cultural

mechanism (i.e., the widespread adoption of cultural goals of success and the meritocratic ideal)

and a structural one (i.e, a lack of resources preventing some groups from achieving these

ideals). In this paper, we theorize strain at the organizational level as the result of both

organizational culture and organizational structure. We first develop the hypothesis that

organizations with a culture characterized by strong performance pressure are more likely to

engage in misconduct. This cultural hypothesis is consistent with the nature of the mechanism

suggested by Merton's strain theory and Agnew’s general strain theory. Yet, testing this cultural

hypothesis beyond individual case studies has not been not possible so far, given the lack of

systematic data on organizational culture and misconduct (for a recent exception on the

consequences of misconduct, see a paper on fraudulent firms’ investment behavior by Wang,

Stuart, and Li, 2020).

Building on the literature on bureaucracy (Weber 1922/2019; Merton 1940; Gouldner 1954;

Blau 1955; Adler and Borys 1996), and the literature on monitoring in economics (Nagin et al.,

2002; Duflo, Hanna, and Ryan, 2012; Pierce, Snow, and McAfee, 2015), we hypothesize that

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more formalized organizations, which arguably have more controls in place, have fewer negative

effects of performance pressure. Likewise, more decentralized organizations, which tend to have

less effective monitoring mechanisms, provide more fertile ground for performance pressure to

have negative effects (McKendall and Wagner, 1997).

We test these hypotheses using data from a panel of 880 firms in the United States on

420,000 civil and criminal cases resolved with total penalty amounts of $616 billion between

2008 and 2019, as well as 2.6 million employer reviews gathered from Glassdoor.com, which we

used to peek inside corporations and unobtrusively observe the digital footprints (Golder and

Macy, 2014) of their cultures. Not surprisingly, this resource has enabled organization theorists

to reignite research on organizational culture (Luo, Zhou, and Shon 2016; Corritore, Goldberg,

and Srivastava 2020; Marchetti 2019). To disentangle the different topics and dimensions of

employee reviews, we used natural language processing (NLP) tools—specifically, unsupervised

topic modeling (i.e., latent Dirichlet allocation [LDA]; Campbell, Hindle, and Stroulia, 2015)

and word embedding (Mikolov et al., 2013).

Our findings show that, on average, performance pressure increases the likelihood of

organizational misconduct by up to 68 percent. At the same time, the number and dollar amounts

of organizational penalties are significantly higher (averaging $2.8 million) when performance

pressure is high. The negative effect of performance pressure is stronger for companies that rely

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on a more decentralized structure, while the results for companies that rely on a more formalized

structure are mixed. Less formalized organizations with high performance pressure have a higher

likelihood of misconduct and a higher average number of violations per year than companies

with low performance pressure.

This article makes several contributions to literature on the antecedents of organizational

misconduct and strain theory (Agnew, 1992; Vaughan, 1999; Greve, Palmer, and Pozner, 2010),

as well as literature on the consequences of goal setting in organizational psychology

(Schweitzer, Ordóñez, and Douma, 2004; Ordóñez and Welsh, 2015). First, we provide

systematic, empirical evidence on the relationship between organizational culture and

misconduct in a large sample of organizations. To the best of our knowledge, we are the first to

empirically test this correlation systematically. From a theoretical point of view, we contribute to

the development of Merton's strain theory at the organizational level, articulating the role of

culture and structure in predicting misconduct. We show that decentralization creates the

conditions for misconduct only when mediated by organizational culture, and that formalization

might decrease the likelihood of misconduct, but also lead to higher penalties. Our findings also

contribute to the literature on the dark side of goal setting by showing the importance of culture

and structure in linking stretch goals with misconduct (Ordóñez et al., 2009b:86). Finally, our

methodological approach offers researchers new tools to revive research on organizational

culture and structure and offers practitioners a forensic tool to predict organizational misconduct.

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THEORY AND HYPOTHESES

Organizational Misconduct and Strain Theory

Following Greve et al. (2010), we define organizational misconduct as "behavior in or by an

organization that a social control agent judges to transgress a line separating right from wrong"

(Greve et al. 2010: 56). Although our empirical test focuses on behaviors deemed illegal by U.S.

regulatory agencies or the Department of Justice, our theorizing need not be limited to illegal

activity. Corporate misconduct is widespread, with survey data, for instance, showing that 20%

of CFOs in U.S. public companies engage in earnings management practices (Dichev et al.,

2013), despite the increasing sophistication of internal and external control mechanisms.

Organization theorists have long sought to understand misconduct, as it is foundational to

the discipline's mandate to explore how organizations affect the societies in which they are

embedded (Boulding 1958; Stern, and Barley 1996). Under different labels (misconduct,

wrongdoing, illegality, white-collar crime, fraud, unethical behavior), scholars have explored the

antecedents and consequences of misconduct for decades (see Szwajkowski 1985; Vaughan

1999; Simpson and Weisburd 2009; Greve, Palmer, and Pozner 2010; Cooper, Dacin, and Palmer

2013; and more specifically on CEO misconduct, Schnatterly, Gangloff, and Tuschke 2018). Yet,

despite decades of scholarly inquiry, the development of research at the meso and macro levels

has lagged behind research at the individual level (Palmer, Smith-Crowe, and Greenwood 2016;

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See Treviño, Den Nieuwenboer, and Kish-Gephart 2014 for a review of the micro literature).

Most recent advances in the macro level literature, furthermore, have drawn on institutional

theory (Gabbioneta et al., 2013) and theories based on the concepts of embeddedness and social

networks (Yenkey, 2018) to explain interorganizational processes of diffusion of misconduct

(Mohliver, 2019), devoting less attention to intra-organizational dynamics.

In a review of the literature on organizational misconduct, Diane Vaughan (1999: 273)

suggested: "Merton's thinking is the foundation of any consideration of the dark side of

organizations. He observed that any system of action inevitably generates secondary

consequences that run counter to its objectives." In this quote, she referred to his work on strain

theory (Merton, 1938) and the dysfunctional consequences of bureaucracy (Merton, 1940),

framing both within his general theoretical insight that the same mechanisms and processes that

generate positive outcomes can lead to negative consequences under certain conditions (Merton,

1936). Together with others (primarly Perrow 2011), she translated this insight into a research

program on the normalization of accidents, deviance and mistakes, stemming from her rich

analysis of the NASA Challenger disaster (Vaughan 1996). On the other hand, in assessing the

literature on misconduct based on strain theory, she found that the evidence on the role of

performance pressure for organizations as the antecedent of misconduct is not very conclusive.

She suggested that given the inherent competitiveness of business activity, “all organizations

may be structurally induced to violation, regardless of ranking in the organizational stratification

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system” (Vaughan, 1999: 289). Ten years later, in another review, Greve et al. (2010) still

lamented this state of affairs but suggested that conflicting empirical evidence might be

explained by organizational expectations, which translate external pressures and aspirations into

specific goals for teams and individuals.

In line with this insight, we suggest a novel reconceptualization of Merton’s strain theory

that distinguishes the role of two different mechanisms: a cultural mechanism (performance

pressure perceived by employees), and a structural mechanism (the degree to which this pressure

is counterbalanced by formalization and centralization, two key components of organizational

structure). This conceptualization is consistent with the original theoretical emphasis on both a

cultural/symbolic mechanism (i.e., the widespread adoption of cultural goals of success and the

meritocratic ideal) and the role of broader structural features of society (i.e., stratification and a

lack of resources which prevents some groups from achieving those goals). This

reconceptualization is also consistent with the accounting literature on the “fraud triangle”

(Cressey, 1950, 1953), a conceptual framework used to understand the antecedents of financial

statement fraud based on three elements: pressure (i.e. the necessity to commit wrongdoing),

opportunity (i.e. the environmental conditions that leads to misconduct), and rationalization (i.e.

the moral justification for the fraud). This model had been applied to predict many cases of

organizational fraud, especially by professionals (American Institute of Certified Public

Accountants (AICPA), 2002) but academic attention on its effectiveness had shown mixed

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results (for example, Skousen, Smith, and Wright 2009), and had been questioned on its absence

for individual and group-level explanations (Donegan and Ganon, 2008; Cooper, Dacin, and

Palmer, 2013; Power, 2013; Morales, Gendron, and Guénin-Paracini, 2014; Lokanan, 2015).

Organizational Culture, Performance Pressure and Organizational Misconduct

In line with strain theory (Merton, 1938), organizations might establish very ambitious formal or

informal goals for their employees, who might not have the resources to achieve them. Thus,

they feel the pressure to cut corners and ignore ethical guidelines, and sometimes engage in

misconduct. This pressure might operate at different levels of the organization: from top

managers feeling the pressure of shareholders and competitors (Bartov, Givoly, and Hayn 2002;

Bergstresser and Philippon 2006; Zhang et al. 2008; Harris and Bromiley 2007; Mishina et al.

2010), to line employees working under ambitious supervisors and aggressive goal-setting

programs.

As high expectations for performance become part of employees' learning experience (i.e.,

their routine), the performance orientation of the organization will become part of its culture

(Schein, 1996; Chatman and O’Reilly, 2016), defined as "a system of shared values (that define

what is important) and norms that define appropriate attitudes and behaviors for organizational

members (how to feel and behave)" (O’Reilly and Chatman 1996: 160). Indeed, several studies

based on the Organizational Culture Profile (OCP) survey tool, have identified results

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orientation as one of six key dimensions of organizational culture (together with adaptiveness or

innovation, detail-orientation, collaboration or teamwork, customer-orientation, and integrity

(O’Reilly, Chatman, and Caldwell, 1991; Chatman and O’Reilly, 2016). Results-orientation is

defined as an organization's tendency to have high expectations for achievement, goals and

individual performance (O'Reilly, Chatman, and Caldwell, 1991). In line with goal setting

theory, an organizational culture focused on results-orientation can have positive effects on

individual and organizational performance. Indeed, in decades of research in goal setting theory,

scholars have documented how ambitious goals can increase individual effort and performance

(Locke and Latham, 2002, 2013). However, considerable evidence has documented the negative

consequences of these aggressive goals on employee’s mental stress and anxiety (Karasek, 1979;

Demerouti et al., 2001; Fox, 2016; Yip et al., 2021), and unethical behavior (Schweitzer,

Ordóñez, and Douma, 2004; Ordóñez and Welsh, 2015).

Scholars studying these negative consequences suggest that the same psychological

mechanisms that explain the benefits of performance-oriented cultures (i.e., increased

performance), might also lead to unethical behavior under specific conditions (see Ordoñez, et

al. 2015 for a review). For instance, experimental evidence on goal-setting across multiple

rounds shows that people with unmet goals were more likely to engage in unethical behavior

(Schweitzer, Ordóñez, and Douma, 2004), and that the depletion of self-regulatory resources

mediates the relationship between goal structures (high-low) and unethical behavior (Ordoñez, et

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al. 2015). Furthermore, individuals might engage in misconduct to restore their positive self-

evaluation: after experiencing a threat to their abilities, individuals who misrepresent their

performance as better than it is experience increased feelings of competence (Wakeman, Moore,

and Gino, 2019). These psychological mechanisms might translate into group norms that might

dominate broader moral concerns, as demonstrated in a series of studies showing that group

activities such as bribing officials, lying to investors, or cheating are compensated in various

ways (Wiley Wakeman and Moore, 2018), contributing to a general culture of moral

disengagement (Moore, 2008; Moore et al., 2012).

At the organizational level, several case studies, ethnographies, and journalistic

investigations have confirmed the insight that “toxic” performance cultures can create conditions

that facilitate misconduct. In a study of Enron's accounting fraud, Simms and Brinkmann (2003)

described how the adoption of performance rankings to evaluate employees created a culture

based on pressure and competition with peers. However, performance pressure can arise even in

the absence of rankings and performance bonuses, as the manager of the equity derivatives

trading room studied by Beunza (2019) explained: “Even if you tell a guy, ‘I’m going to pay you

$2 million no matter what,’ he will still like to show a $50 million profit and not $10, because

$50 million means he’s a rooster. He can walk through the hen house as the biggest rooster.”

(2019:229). Another recent example comes from the Wells Fargo accounting fraud of 2016,

whereby bank employees created millions of fraudulent savings and checking accounts on behalf

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of clients. Findings from investigations show that individual workers and branches charged fees

and issued unexpected credit or debit cards to customers to meet the company's incentives to sell

multiple financial products. The company's strong emphasis on its sales goals "distorted the sales

model" and translated into a performance culture that fostered "an atmosphere that prompted

low-quality sales and improper and unethical behavior" (Shearman and Sterling LLP, 2017).

Organizational cultures might also convey limited concerns for following rules and regulations,

and turn a blind eye to unethical behavior. For instance, Kweku Adoboli, the UBS rogue trader

ultimately sentenced to 7 years in prison for illegal trading activities, revealed that his supervisor

had told him: "You don't know you are pushing the boundaries hard enough until you get a slap

on the wrist" (Fortado, 2012). On several occasions where he had pushed the boundaries through

lucrative trades, he had been congratulated rather than penalized (Moore et al., 2019).

Beyond case studies, the relationship between culture and misconduct has been established

at the macro level of analysis. Starting from national culture, Fisman and Miguel (2007) studied

parking violations of United Nations officials in Manhattan and found that officials from

countries with high levels of corruption accumulated significantly more unpaid parking

violations. Similarly, DeBacker, Heim, and Tran (2015) used the level of perceived corruption in

owners’ countries of origin and Internal Revenue Service (IRS) audit data to study corporate tax

evasion among U.S. companies with owners from foreign countries, and found that owners from

countries with higher corruption norms were more likely to engage in tax evasion. Similarly,

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using U.S. Census data, Liu (2016) found that firms with executives whose ancestors came from

countries where corruption is less frowned upon were more likely to engage in fraud,

opportunistic insider trading, earnings management, and option backdating. Periods of economic

booms might also contribute to shaping a culture of overconfidence and risk taking among

executives, which in turn might translate into unethical behavior and misconduct. Bianchi and

Mohliver (2016) found that CEOs were more likely to backdate their stock options (an illegal

practice that maximizes financial gains), during prosperous economic times. They also showed

an imprinting effect, which hints at a cultural mechanism, as CEOs who began their careers in

prosperous times were also more likely to engage in misconduct. A similar socialization

mechanism, albeit in a different direction, was suggested in a study showing that CEOs who

served in the military were less likely to engage in financial misconduct (Koch-Bayram and

Wernicke, 2018).

The literature in finance, accounting, and management has documented the relationship

between aggressive financial targets and organizational misconduct (O’Connor et al. 2006;

Zhang et al. 2008; Johnson et al. 2009). For instance, executives who committed fraud had

greater financial incentives to do so (Johnson, Ryan, and Tian, 2009). CEOs are more likely to

misrepresent company earnings if their financial incentives are linked to company performance

(Bergstresser and Philippon, 2006; Zhang et al., 2008). Also, aggressive financial targets can be

the result of managerial aspiration levels, whether to outperform their peers (Harris and

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Bromiley 2007), or to exceed shareholders’ expectations (Mishina et al.. 2010). In addition to

executives, middle managers can play a crucial role in translating high-level abstract

performance objectives into lower-level ones that induce deceptive performance, as shown in an

ethnographic study of sales at a telecom firm (den Nieuwenboer, da Cunha, and Treviño, 2017).

Regardless of whether misconduct originates from the top or lower levels of an organization

(Palmer, 2008), the literature on the diffusion of misconduct within organizations concur that

misconduct tends to become institutionalized in routines and embedded in organizational culture

(Brief, Buttram, and Dukerich, 2001; Ashforth and Anand, 2003). Thus, environmental pressures

on organizations, and specific incentive design decisions can translate into a culture of

performance for all employees, and in turn lead to unethical behavior and organizational

misconduct.

Another mechanism through which performance pressure can lead to misconduct is by

driving more risk taking. This mechanism is entirely consistent with strain, goal setting, and

prospect theories. Specifically, challenging goals serve as reference points that create a region of

perceived losses for outcomes below established thresholds (Kahneman & Tversky, 1979;

Tversky & Kahneman, 1992), which can cause employees to increase risk taking, as also

demonstrated in experimental settings (i.e., Larrick, Heath, and Wu 2009). For instance, banks

with more aggressive competitive cultures are more likely to engage in riskier lending practices

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by extending more loans to borrowers with poorer ratings (Nguyen, Nguyen, and Sila, 2019).

Therefore, performance pressure likely increases risk taking among employees, which may

increase the likelihood of misconduct.

Building on these arguments, we expect:

Hypothesis 1. Organizations with cultures characterized by strong performance

pressure for employees are more likely to engage in misconduct.

Organizational Structure and Misconduct: Formalization

Despite its central role in the early development of the field (Talor, Weber, Blau, Gouldner,

March and Simon, Pugh et al), formal organizational structure has been neglected by

organization theorists for many years, in what some scholars refer to as a period of “collective

amnesia” in the field (McEvily, Soda, and Tortoriello 2014: 302). However, as predictions

regarding the disappearance of bureaucracy have been proven wrong, we have experienced a

(small) revival of studies of bureaucratic organizational structure (Adler and Borys 1996; Adler

2012; Du Gay 2005; Clement and Puranam 2018; Sandhu and Kulik 2019).

In line with the traditional Columbia approach to bureaucracy, which explored the dark side

of bureaucracy (Merton, Gouldner and Blau), sociologists initially tended to consider

bureaucratic organizational structure as a factor that makes organizations more prone to

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misconduct or accidents (Vaughan, 1999; Perrow, 2011). However, empirical evidence on this

relationship remains flimsy and is limited to individual case studies. Furthermore, more recent

theorizing on bureaucracy has reinvigorated a more complex—and more positive—view of

bureaucracy, emphasizing its enabling properties (Adler and Borys, 1996) by developing a

hitherto underappreciated insight from the Columbia school that formal bureaucratic rules might

have different effects depending on the informal organizational and cultural practices used to

introduce them (Gouldner, 1954; Blau, 1995). Adler and Borys (1996) reconceptualized the

Toyota Production System as a form of enabling bureaucracy, given that formal rules were

coupled with workers’ control of the process. This enabling form contrasted with traditional

coercive ones wherein workers have no control over rules, perceive them as a control device,

and exhibit typical signals of alienation.

Building on Adler and Borys (1996), we theorize the relationship between organizational

structure and misconduct by distinguishing an organization’s level of formalization - “the extent

to which rules, procedures, instructions, and communications are written” (Pugh et al. 1968: 75)

- from its degree of decentralization and considering how each contributes to shaping the

organization’s culture by moderating the effect of performance pressure. In line with this more

positive view of bureaucratic organizing, more formalization should reduce the likelihood of

misconduct by making work processes more transparent; in turn, higher efficiency might reduce

pressure to engage in malfeasance. Notice that this does not necessarily mean that employees

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have a positive perception of formal rules. Indeed, even when formalization is designed in

enabling ways that can lead to individual and collective improvements in productivity, individual

workers might still express ambivalence about the rules, as revealed in Adler’s (1993, 2012)

interviews with Nummi workers.

This expectation is also consistent with the economics literature on crime and deviance,

which models deviant behavior as the result of rational calculations wherein individuals weigh

the benefits of engaging in misconduct against the costs of sanctions and likelihood of getting

caught (Becker, 1968; Milgrom and Roberts, 1988). Empirical research on the topic has

confirmed this basic insight. A field experiment at a telephone call center, for instance, found

that employees engaged in malfeasance at a higher rate under reduced monitoring (Nagin et al.,

2002). Likewise, in a field experiment in Indian schools, teacher absenteism dropped from 43

percent to 21 percent in schools that introduced a system of attendance control and incentives

(Duflo, Hanna, and Ryan, 2012). Whereas higher formalization is not limited to control

mechanisms, it is likely that more formal organizations adopt stricter and more extensive

management control practices. For instance, restaurant chains that invest in technology-based

employee monitoring experience less theft and higher employee productivity (Pierce, Snow, and

McAfee, 2015). In line with our theoretical argument and the empirical evidence we described,

we expect:

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Hypothesis 2. Formalization of organizational structure and processes negatively

moderates the relationship between performance pressure and organizational

misconduct.

Organizational Structure and Misconduct: Decentralization

Decentralization is another key structural feature of organizations and affects the locus of

decision-making power: in more decentralized organizational structures, employees participate

more in decisions, and have more autonomy in their jobs (Hage and Aiken, 1967; Pugh et al.,

1969). A decentralized, or flattened, structure can increase the benefits of a performance-oriented

culture by enhancing market responsiveness, morale, and employee accountability (Rajan and

Wulf, 2006). However, precisely the increased autonomy of employees in a decentralized

structure can also increase the likelihood of misconduct.

Decentralization fosters an entrepreneurial environment that enables quicker responses to

local demands. Although this autonomy, coupled with performance pressure, may yield

performance benefits (Hoskisson and Hitt, 1988; Williamson, 1975), it might also lead to

increased risk-taking by local managers to meet goals and expectations established by the

company. Furthermore, higher decentralization is usually more common in larger and more

diversified organizations. In these more complex organizations monitoring is usually more

difficult, and this might facilitate misconduct (Jamieson, 1994).

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Evidence from previous studies on the effect of organizational structure on misconduct

suggests this structural feature of the organization is not a sufficient condition to explain

misconduct (Hill et al. 1992; Simpson and Koper 1997). Hill et al. (1992) studied misconduct in

the manufacturing sector by analyzing a range of different factors, from firm size to financial

strain and decentralization, using Environmental Protection (EPA) and Occupational Safety and

Health Administration (OSHA) data. They found that decentralization could not explain

misconduct in either category. Focusing on violations of environmental laws in the

manufacturing industry, McKendall and Warner (1997) found that while decentralization alone

could not explain an increased likelihood of misconduct, the interactions between structure and

organizational culture (i.e., ethical climate), and to a lesser extent, industry profitability,

decreased the likelihood of serious environmental violations.

Even if decentralization might not have an independent effect on misconduct, we propose

that decentralization would facilitate the development of a toxic performance culture in the

organization, and thus strengthen the positive relationship between performance pressure and

misconduct. For instance, in the Wells Fargo accounting fraud scandal of 2016, a decentralized

organizational structure coupled with aggressive sales targets at the regional level led to illegal

sales practices in multiple branches of the bank. Results of an independent investigation of Wells

Fargo show that the company's "decentralized corporate structure gave too much autonomy to

the Community Bank's senior leadership, who were unwilling to change the sales model or even

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recognize it as the root cause of the problem" (Shearman and Sterling LLP, 2017: 2). Flatter

organizations might reduce the effectiveness of hierarchical control. At Wells Fargo, for instance,

the control function at the company was "constrained by the decentralized organizational

structure and culture of substantial deference to the business unit" (Shearman and Sterling LLP,

2017: 2).

Thus, in line with our theoretical arguments, and the evidence emerging from multiple case

studies of misconduct, we hypothesize:

Hypothesis 3. Decentralization of organizational authority positively moderates the

relationship between performance pressure and organizational misconduct.

DATA AND METHODS

Sample

We tested our hypotheses using a panel dataset of yearly firm observations for a sample of firms

that were covered in both Compustat and evaluated on Glassdoor to which we subsequently

added the variables gathered from Good Jobs First.

The variables measuring organizational misconduct were gathered from Good Jobs First

and, specifically, by the “Violation Tracker” dataset which covers 71 types of legal and

regulatory violations addressed by more than 50 federal regulatory agencies, including 412,000

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civil and criminal cases with total penalties of $616 billion and a minimum penalty of $5,000. In

our sample, we restricted our sample to publicly traded companies with matched CIK codes in

Compustat, resulting in 1,293 companies affected by penalties for 34,203 violations.

We used Glassdoor employee reviews to construct our measures of performance pressure,

formalization, and decentralization. Glassdoor aggregates millions of reviews and company

ratings, CEO approvals, salary and benefits reports, and interview reviews. The service counts

more than 70 million reviews and covers 250 thousand companies only in the US. We provide an

example of a Glassdoor employee review in Figure 1 (see Online Appendix A for more

information on Glassdoor). The initial sample included 2.6 million reviews of 3,423 companies

with performance data on Compustat. Companies were matched through a Python fuzzy name-

matching algorithm.

------------------------------------------

Insert Figure 1 about here.

-------------------------------------------

We used Compustat as the source of corporate financial information and as the starting point for

the matching process of both Glassdoor and Good Job First datasets.

Matching the three datasets resulted in an initial sample of 3,688 companies. Following recent

literature using Glassdoor data (i.e., Corritore et al., 2019) and using a similar filtering approach,

24

we excluded all observations with less than 100 reviews per year, resulting in a sample of 925

firms, between 2008 and 2019, yielding an unbalanced panel dataset of 4,296 firm-year

observations.

Dependent Variable

We operationalized organizational misconduct using official resolutions from U.S. courts

provided by Good Jobs First. Due to the nature of these data, our examination is limited to cases

of misconduct discovered by official agencies. Nevertheless, our data cover a broader spectrum

of illegal activities, have not yet been used in academic research, and are more accurate than data

from media coverage, which tends to be focused only on the largest cases of misconduct. We

measured organizational misconduct in three ways. For each firm-year observation, we: created

a binary variable, penalties, assigning a value of 1 if the company received a penalty due to

misconduct, and 0 otherwise; counted the number of penalties; and calculated the total dollar

amount of penalties.

Independent Variables

The dataset includes all employer reviews written by employees in the United States from 2008

to 2019 on the website Glassdoor.com. Glassdoor reviews are divided into two sections: a

quantitative section where users are asked to rate their firms on some attributes, and a qualitative

25

open textual section divided into two sentiment-coded categories: pros (i.e., positive aspects of

the company), and cons (i.e., negative aspects of the company), and an additional text section

(optional) in which employees can submit suggestions regarding how to improve the company

(advice to management). Since our analysis focuses on the negative perception of the

performance orientation norm, we only focused our analyses on the cons section of the reviews.

To grasp the specific dimension that might refer to the performance orientation norm in a

wide sample of reviews, we used NLP tools and, specifically, unsupervised topic modeling

(LDA; Blei, Ng, and Jordan 2003). Our model was based on 26 topics which resulted from

coherence score metrics (Newman et al., 2010). For technical details on the number of topics and

the methodology, see Online Appendix B. Prior studies in management research used topic

modelling in multiple areas. For example, Kaplan and Vakili (2015) applied LDA on patent text

to identify inventions that originated new topics in the body of knowledge, Corritore, Goldberg,

and Srivastava (2020) used topic modelling on Glassdoor reviews to create measures of cultural

heterogeneity, and, Di Maggio et al. (2013) used this methodology to study the evolution of

press coverage regarding public funding of the arts.

Following Giorgi and Weber (2015), we assigned unique labels to the topics resulting from

the LDA process to better contextualize them, as shown in Table 2. Topic 1 is the topic under

26

study, defined by words that connote a negative perception of the company’s pressure on

performance (i.e., sale, goal, number, expectation, pressure, commission).

------------------------------------------

Insert Table 1 about here.

-------------------------------------------

The final stage of the textual analysis involved inferring a score representing the

standardized probability from 0 to 100 for every topic discussed across the entire corpus of data.

Thus, the higher the score, the higher the probability that the topic would be discussed in an

employee’s review. Finally, we created a binary variable (performance pressure), assigning a

value of 1 if the weight of the topic was in the 95 th percentile or above among firm-year

observations. This value is arbitrary and represents approximately 3 standard deviations from the

mean (Mean = 3.13; SD = 1.61; Max = 14.85). We used this arbitrary value because it is a more

stable representation of a year in which the performance pressure topic is one of the most

discussed by employees. Because the computed model includes 26 topics, a single topic is

unlikely to appear in more than 50% of reviews in a given year. The values found for the topic

under study follow a distribution that is roughly similar to those of the other 25 topics.

Therefore, we posit that analyzing the tail of the semantic distribution (i.e., 95 th percentile)

assures that the topic under study is prevalent in the focal year.

27

Organizational Structure

Researchers have studied organizational structure primarily by analyzing proprietary data

(Guadalupe and Wulf, 2010), interview and observational data (Soderstrom and Weber, 2020), or

survey data (i.e. World Management Survey (WMS); Bloom and Van Reenen, 2008). In this study

we measured formalization and decentralization through an unobtrusive NLP technique: linear

text classification of words in employee discourse (i.e., company reviews on Glassdoor.com). To

do so, we used word embedding and FastText as a pre-trained dictionary (see Appendix B for

details on the methods). This pre-trained dictionary enabled us to classify every word of a review

into a semantic space and identify word similarities between target vectors (i.e., words related to

formalization and decentralization) in our dataset. FastText enable us to identify and analyze less

common and more specific topics that would not appear as often and were discarded by our topic

modelling analysis. Our use of this tool is similar to Cao, Koning, and Nanda (2020), who

studied biased sampling of early users in entrepreneurial experiments and used FastText to train

a sematic model to measure product’s focus on female customers on a large sample descriptions

of new technology products.

We derived our target vectors through additive compositionality (Gittens, Achlioptas, and

Mahoney, 2017), whereby arithmetic operations are applied to vectors to refine their word

28

compositions. For example, the nearest vector to the word “king” can be identified via an

arithmetic operation (Mikolov et al., 2013):

vman+vroyal=vking,

with v representing the vector with the related word. In the same way, the target vectors used in

our study for formalization and decentralization, respectively, are represented by:

v target−formalization=vbureaucratic+v formalization+vred−tape ; and

v target−decentralization=vdecentralization+vdecentralized−vcentralization.

Table 2 shows the list of words most similar to our target vectors. Finally, we calculated cosine

similarity between our target vectors and every word of each review, and retained the maximum

score for each vector. Thus, for every review, we obtained two scores (from -1 to 1) representing

the maximum similarity of the review to our target vectors for formalization and

decentralization.

------------------------------------------

Insert Table 2 about here.

-------------------------------------------

We considered only the cons section of each review to measure the high formalization

variable as the words in the target vector related to formalization tend to have negative

29

connotations, and thus are less likely to appear in the pro section of the review. We treated high

formalization as a binary variable, assigning a value of 1 when the value of formalization for the

company exceeded the industry (by two-digits SIC) median, and 0 otherwise. We analyzed both

the pros and cons sections of each review to measure the high decentralization variable, as the

words in the target vector are neutral. We treated high decentralization as a binary variable,

assigning a value of 1 if the value of decentralization exceeded the median for of our sample, 0

otherwise. Decentralization does not include industry, as we expected decentralized structure to

be more concentrated in some industries (i.e., manufacturing) more than others (i.e., mining).

Control Variables

We included several control variables in our analysis. First, we controlled for the number of

reviews in the focal year (in logarithmic form). Second, we operationalized firm size as the

number of employees reported in Compustat for the focal year, using natural logarithm values to

reduce the potential effects of extreme values. Third, we included sales growth (%), and ROA

(both winsorized at the 1% and 99% level) to account for firm performance in the focal year as a

possible explanation that could increase performance pressure on employees. Fourth, we

measured market competition as the Hirschman-Herfindahl index representing the distribution of

sales among firms in a given industry (based on 2-digit SIC code), as a higher concentration of

sales could lead to misconduct whether the thread represented by competition brings to a decline

30

in status for lower-performing firms (Wilmot and Hocker, 2001), or to lose customers to

competitors (Bennett et al., 2013). Finally, we controlled for other quantitative variables

measured in the reviews on Glassdoor. We controlled for CEO approval by assigning a value of -

1 (negative), 0 (neutral), or 1 (positive) representing average employee ratings for each firm-year

observation. We also controlled for senior management approval, compensation and benefits,

and work-life balance using average employee ratings on a 1 to 5 scale for each firm-year

observation.

Estimation techniques

Because the dependent variable is penalties incurred for organizational misconduct, we

calculated all independent and control variables using values from the end of the prior year. We

specified various models to test our hypotheses using three dependent variables representing

organizational misconduct to offer wider evidence for the proposed effects.

First, we used fixed-effects logistic regression analysis for the penalty variable because the

variable is dichotomous and takes the value of 1 if the company received a penalty, and 0

otherwise. Second, we used a fixed-effects Poisson regression analysis for the number of

penalties variable as it represents a count of events, accounting for both year and industry (2-

digit SIC code) fixed-effects. Third, we used a fixed-effects linear regression analysis for the

amount of penalties, using the dollar amount as a continuous variable. We specified fixed effects

31

on both industry (2-digit SIC code) and year, and transformed the variable to represent millions

of dollars. For all of the models, we excluded outliers in the dependent variable (i.e. winsorized

at the 1st and 99th percentiles of the distribution) to eliminate the potential for coefficients to be

influenced by a small group of observations with a high number and/or amount of penalties.

Also, we clustered standard errors by organization to control for potential heteroscedasticity and

provide a more conservative test of the hypotheses (White and White, 1980).

To test Hypotheses 2 and 3, we ran three separate analyses, dividing the sample by high/low

formalization and high/low decentralization. By using different subsets in our models we

described formalization and decentralization (whether high or low) as different environments,

therefore assuming that the effect of performance pressure would differ among them. As

Venkatraman (1989) put it, these types of analyses reflect the strength of moderation. A test of

differences confirmed that subgroup analysis is appropriate, as the two groups are significantly

different for all the variables under study. Finally, we ran collinearity diagnostics to check for the

presence of multicollinearity in our models, and all values were below the threshold of 30

(Bollinger et al., 1981). Finally, as the analyses uses fixed-effects to estimate the results, the

number of observations will not strictly follow the initial specification of observations as fixed-

effects models drops all the observations without within-group variance of the dependent

variable.

32

RESULTS

Table 3 provides pairwise correlations and descriptive statistics for each of the variables in our

study. The predicted likelihood of receiving a penalty for misconduct, of $5000 or more, is 28.6

percent for our sample. The probability of receiving a penalty with a dollar amount over the

mean is 8.6 percent; this likelihood is similar to that reported in other studies on corporate

misconduct (e.g., 15 percent in Mishina et al., 2010). As shown in Table 3, the correlation

between performance pressure and the dependent variable is not high, and is actually negative

between the binary penalty variable and number of penalties, although the correlation is very

close to zero.

------------------------------------------

Insert Table 3 about here.

-------------------------------------------

The regressions in Table 4A show that performance pressure increases both the likelihood of

a company incurring a penalty in the subsequent year (model 1). The result for performance

pressure holds adding financial controls (model 2) and the quantitative measures of the reviews

(model 3), thus supporting Hypothesis 1. Companies with result pressure have a probability to

receive a penalty to subsequent year around 68 percent. In addition, Tables 4b and 4c showing,

respectively, the effect of results pressure on the number of penalties and the amount in dollars,

33

provide more support for Hypothesis 1: companies with results pressure receive a higher number

of penalties and pay 2.8 million dollars more for their misconduct the subsequent year compared

to firms without such pressure.

-----------------------------------------

Insert Table 4 about here.

-------------------------------------------

Hypothesis 2 is only partially supported. The sub-sample analyses for the full models 1 and

2 in Tables 5A and 5B show that performance pressure affects companies with low formalization

by increasing both the likelihood of misconduct (83 percent higher than companies with low

performance pressure) and the number of violations. However, even though the effect of

performance pressure is stronger in the low formalization subsample, model 2 of Table 5A and

5B show that performance pressure is significant also in the high formalization subsample.

Furthermore, results for the dollar amount of penalties in Table 5C (model 1) show that

performance pressure does not have a significant effect on misconduct in companies with low

formalization (p=0.110).

Finally, Hypothesis 3 is supported. The results for models 3 and 4 in Table 5A show that

when performance pressure is high, the likelihood of a company with a decentralized structure

incurring a penalty the subsequent year is 86 percent; In centralized companies, the effect of

34

performance pressure is not significant. The results for models 3 and 4 in Table 5B reveal the

effect of performance pressure on the number of penalties incurred by companies with different

levels of decentralization. Companies with high performance pressure in the decentralized

subsample receive on average 1 penalty more than companies in the same group without high

performance pressure. Finally, the results for models 3 and 4 in Table 5C support the hypothesis

that the effect of performance pressure is stronger in companies with a decentralized structure,

which receive higher average fines of about 5 million dollars.

------------------------------------------

Insert Table 5 about here.

-------------------------------------------

These results suggest that, overall, performance pressure increases the likelihood of

organizational misconduct. When dividing the companies by structure, the result holds for

companies with decentralized structures, but are mixed for companies with highly formalized

structures.

ROBUSTNESS CHECKS

We conducted 3 additional analyses to assess the robustness of our findings. First, to test whether

the results might be driven by CEO-specific characteristics, we replicate our analyses including

35

CEO compensation, tenure, and gender finding nearly identical results. These analyses are

presented in Online Appendix D.

Second, our organizational misconduct variable measured corporate illegal actions that had

been identified and sanctioned by the relevant authorities. To test whether performance pressure

might affect broader measures of misconduct, we replicate our analyses using negative media

coverage on environmental, social, and governance (ESG) issues as our dependent variable

(Online Appendix E). We measure this variable with RepRisk, a data provider that collect news

articles from various national and local sources that criticize organizations on ESG issues. The

results of the analyses show that the likelihood to receive negative media coverage is positively

correlated with performance pressure, though the result is not significant (p= 0.124). The sub-

sample analysis divided by type of organizational structure shows that the likelihood to receive

negative media coverage is higher for low formalized and decentralized organizations. Overall,

the results for the analyses on negative ESG media coverage show that the effect of performance

pressure is consistent with our theory.

Finally, our performance pressure variable only focused on the tail of the distribution for the

performance pressure topic. We operationalized the variable in this way to identify the

companies where performance pressure characterizes the culture of the organization.

Nevertheless, we tested whether our results are consistent with different cutoffs for our measure

36

of performance pressure. Specifically, we replicated the analysis at the 50th, 75th, 90th percentile

of the continuous variable for performance pressure resulting from our topic analysis (not shown

here). The distribution for the relationship between performance pressure on the dollar amount

of penalties shown in Figure F1 of Online Appendix F shows that our results are only confirmed

at the 95th percentile.

DISCUSSION

In this study, we reconceptualized Merton’s strain theory at the organizational level and

theorized that organizations with cultures characterized by strong performance pressure are more

prone to engage in misconduct. Furthermore, in line with prior research on the contingency

effects of organizational structure on misconduct (Hill et al., 1992; McKendall and Wagner,

1997), we argued that formalization and decentralization, two key structural features of

organizations, can dampen or amplify this effect by reducing the effectiveness of control

systems. Unlike other studies in which researchers measured culture through surveys (e.g.,

McKendall and Wagner 1997; Bloom, Sadun, and Van Reenen 2010), we used machine learning

and NLP tools to measure employees’ perceptions of the culture (i.e., high vs. low performance

pressure) and structure (formalization and decentralization) of each organization in our sample.

Results of our empirical analysis generally support our hypotheses. Companies with high

performance pressure are 68 percent more likely to receive a penalty in the subsequent year than

37

companies with low performance pressure. We also found that performance pressure has a

positive effect on both the number and dollar amount of penalties incurred by a firm in the

subsequent year. On average, companies with high performance pressure paid $2.8 million more

per year in penalties for misconduct. The results on the relationship between formalization and

performance pressure provide limited support for our hypotheses. On the one hand, our results

confirm that when performance pressure is high, organizations with low formalization are more

likely to engage in misconduct and incur more penalties. On the other hand, results for the

monetary value of penalties were not found to be significant.

The results on decentralization, in contrast, completely support our hypotheses. Consistent

with the idea that the delegation of responsibilities in decentralized organizations leads to a lack

of control compared to centralized organizations, we found that decentralized organizations

experience harsher consequences when performance pressure is high, with an increased

likelihood to commit misconduct, as well as a greater number and average amount of penalties.

Taken together, our results show that a decentralized organizational structure can create

conditions that enable the development of a toxic culture, with negative consequences for the

organization. This result extends McKendall and Warner’s (1997) findings that an ethical climate

negatively mediates the effects of decentralization on environmental misconduct by

demonstrating that performance-oriented cultures could create conditions that increase the

likelihood for misconduct.

38

Limitations

Given the nature of the data we analyzed, this study has some limitations that present

opportunities for future research. Our measures of organizational culture and structure were

based on employee reviews. Although our method enabled us to measure performance pressure,

formalization and decentralization, using a corpus of data consisting entirely of employee

reviews might have biased our results in ways we could not fully control. For instance,

performance pressure is obviously much more salient than an organization’s structural features

when writing a review as an employee. Thus, our approach might have led to oversampling the

former and under sampling the latter. Nevertheless, we would expect these effects to hold across

types of companies and industries. In the case of organizational decentralization, we confirmed

our measure in a random sample of companies via a media search of newspaper articles

describing the companies’ organizational structures, and were satisfied by the results (not shown

here). The wider range of terms and sentiments associated with formalization, however, make it

difficult to confirm these data in a similar way. In future studies, scholars could validate these

measures through, for example, surveys or interviews. Despite these limitations, our method

could help researchers revive empirical research on organizational structure, a key concept for

organization theorists that until now, has been very difficult to study empirically. For instance,

drawing on Adler and Borys’s (1996) conceptualization of formalization as coercive or enabling,

39

researchers could explore the difference between these two concepts empirically in large sample

of organizations, thereby advancing our understanding of their antecedents and consequences.

Another limitation of our paper stems from two types of selection bias inherent in our data.

First, like other studies that build on voluntary employee reviews (e.g., Corritore et al., 2019) our

results might have been affected by the different propensities of employees from different firms

to contribute reviews. To address this limitation, researchers could apply the same NLP methods

to executive discourse (i.e., quarterly earnings calls) to identify references to organizational

decision-making processes and control mechanisms. Second, because we studied official legal

penalties, we did not observe any misconduct cases that were not discovered by official

agencies. This is a common limitation in the organizational misconduct research (Vaughan,

1999). Yet, given the scope of our data (which include many different types of violations) and

our methods, we are confident that our study offers what we believe to be the first empirical

evidence of links between performance pressure, structure, and misconduct over a large sample

of organizations.

Contributions

Our study makes several contributions to the literature on antecedents of organizational

misconduct (e.g., Greve, Palmer, and Pozner 2010; Agnew 1992; Vaughan 1999b). First, from a

theoretical point of view, we contribute to the operationalization of strain theory at the

40

organizational level, and suggest that the strain generated by aggressive performance cultures

can be dampened or amplified by structural features of the organization. Second, from an

empirical point of view, whereas it is not novel to point to culture as an antecedent of

misconduct (Vaughan, 1999), to our knowledge we are the first to empirically test this

relationship systematically with a large sample of organizations. Similar to Corritore et al.’s

(2020) approach, we collected data on organizational culture—specifically, performance

pressure—by analyzing employee discourse. This approach enabled us to measure culture in a

large sample of organizations and control for a wide range of potential confounders. Finally,

whereas in prior studies researchers focused on strain or performance expectations for top

managers (e.g. Harris and Bromiley 2007; Mishina et al. 2010), we studied performance pressure

as experienced by employees.

This study also contributes to research in organizational psychology on the dark side of goal

setting (Ordóñez et al., 2009a; Ordóñez and Welsh, 2015) and on the relationship between goal

setting and organizational culture (Ordóñez et al., 2009b). Beyond the existing evidence on the

dark side of goal setting (Ordóñez et al., 2009a; Ordóñez and Welsh, 2015), our results suggest

that aggressive, ambitious targets can translate into durable characteristics of organizational

culture. Although goal setting strategies can be easily adjustable, organizational culture as a

socially constructed reality (Berger and Luckmann 1967) is resistant to change. Cultural change

can be a slow-moving process (Meyerson and Martin, 1987), and toxic norms may persist many

41

years after incentives have changed, as demonstrated lately by the process of culture change at

Wells Fargo after the cross-selling scandal (Flitter and Cowley, 2019).

Finally, we also hope our study contributes to a revival of research on culture and

organizational structure from a methodological perspective. By studying formalization and

decentralization through unobtrusive observation and measurement of employee discourse (via

NLP and LDA), we suggest a novel approach (and methods) to identify structural properties of

organizations for larger samples of companies. Our methodological approach could also be

fruitfully employed by practitioners to predict the risk of organizational misconduct, either from

the inside (e.g., compliance officers, internal auditors) or from the outside (e.g., auditors,

investors, members of the media, regulators, law enforcement officers).

42

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FIGURE 1

Example of a Glassdoor Review

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TABLE 1

LDA Analysis of Glassdoor Reviews – Main Topics

The following table shows the keywords, and labels, for the topic modelling analysis on Glassdoor reviews. To generate the topics, we started from our complete sample of Glassdoor reviews (2.6 millions) and selected a random 20% of the reviews (CONS section) for the LDA analysis. Please see Appendix A for more details on the method.

Topic Topic label Top topic words1 Performance pressure sale, goal, number, expectation, push, pressure, meet, set, commission2 Senior management manager, store, department, supervisor, depend, manage, associate3 Corporate strategy change, make_things_right, decision, policy, direction4 Type of position job, worker, full_time, position5 Leadership culture, leadership, corporate, communication6 Teamwork team, lead, leader, performance, individual, role7 Work hours work, day, hour, schedule, shift, family, holiday8 Details of the position year, month, first, end

9 Respectful treatment of employees employee, treat, respect, value, good, better, fair

10 Job opportunities opportunity, growth, grow, advancement, career, promotion, advance, limited

11 Work hours hour, long, pay, time, horrible, terrible12 Pay and benefits pay, benefit, salary, raise, increase, bonus, compensation13 Open communications communication, open, idea, meeting, bring, information14 Technology system, issue, technology, old, back, side, tech, software15 N.D. sell, follow, break, rule16 Listen to employees employee, listen, talk, feedback, concern, review, hear, speak17 Career opportunities hire, care, people, promote, experience, talent, position, attention18 Training training, staff, need, train, learn, support, provide, improve, better,19 Customer relationship customer, time, help, deal, call, ask, rude, task20 Stressful work conditions environment, anything, stressful, negative, create, stress

21 Product/service quality focus, product, quality, market, service, customer, long_term, build, profit

22 Employee turnover people, stop, leave, let, start, fire, stay, look, quit, rid23 Company characteristics company, big, large, lose, layoff, location, due, industry, future, go24 Work-life balance work, life_balance, place, none, life, appreciate, time, home, load25 Management management, level, upper, senior, middle, advice, need, top, executive26 Work processes process, project, lot, organization, move, bit, engineer, internal, time

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TABLE 2

Target vectors’ most similar words (FastText)

Target: formalization Similarity1 Target: decentralization Similarity2

bureaucratic 0.8804816 decentralized 0.8884735bureaucracy 0.80162853 decentralised 0.7864013bureacratic 0.78376913 de-centralized 0.6914408beaurocratic 0.77073193 decentralization 0.6866374redtape 0.73887277 Decentralized 0.6521732bureacracy 0.73699331 decentral 0.6317272beauracracy 0.72877342 non-centralized 0.6103989beurocratic 0.71758479 decentralizing 0.6012831red-tapism 0.71712649 decentralize 0.5883033beauracratic 0.71091759 de-centralised 0.5882939beauracracy 0.67835552 decentralizes 0.5851191beurocracy 0.6729213 Decentralization 0.5676084burocratic 0.66317499 decentralisation 0.5537003burocracy 0.64025646 Decentralizing 0.5480962Bureaucratic 0.64011562 nonhierarchical 0.5362982bureaucracies 0.63829845 Decentralised 0.5333222hoop-jumping 0.60826135 self-organizing 0.5269514bureacracies 0.60292083 non-hierarchical 0.5261458tapism 0.59983265 self-organized 0.5193331paper-pushing 0.58549082 decentralism 0.5161116bureaucratically 0.58070523 censorship-resistant 0.5156702Bureaucracy 0.56742257 self-governed 0.5148664bureaucratization 0.56587136 self-organised 0.511211bureaucratism 0.56382871 bottom-up 0.5067362paper-work 0.55870563 participatory 0.5027277inefficiency 0.55810237 Decentral 0.502153over-regulation 0.55697292 decentralise 0.4997308paper-shuffling 0.55400884 decentralising 0.499221bureaucratisation 0.55029672 centrally-controlled 0.4977726bureaucratized 0.54976827 peer-production 0.4971926overregulation 0.54810959 decentralist 0.4971693formalization 0.54752803 citizen-centered 0.4952511rigmarole 0.54262245 self-organising 0.4930755bureaucrats 0.54181415 participative 0.4858297micro-management 0.53989881 micro-grids 0.4832599bureaucratese 0.5398289 community-driven 0.4828814bureacrats 0.53723311 non-authoritarian 0.4798779

1 Refers to cosine similarity with our target vector. 2 Refers to cosine similarity with our target vector.

59

TABLE 3

Descriptive Statistics and Correlation Table

60

Variables Mean s.d. 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1.Organizational Misconduct: Penalty (1/0) 0.285871 0.451859 -

2.Organizational Misconduct: No of Penalties 0.965328 2.29512 0.631

8 -

3.Organizational Misconduct: Penalty ($) 3.043791 12.12639 0.365

2 0.4014 -

4.Performance pressure n-1 0.051604 0.221257 -0.006 -0.023 0.022

8 -

5.Decentralized n-1 0.547559 0.497802 -0.119 -0.089 0.054

6 -0.057 -

6.Formalized n-1 0.458577 0.498351 -0.031 -0.066 0.013

3 0.0255 0.1856 -

7.Number of reviews (log) n-1 5.585789 0.833584 0.221

6 0.2213 0.1622 0.0045 -0.197 -0.033 -

8.Number of employees (log) n-1 3.442483 1.4669 0.391 0.4258 0.217

6 -0.053 -0.141 -0.043 0.4451 -

9.ROA n-1 0.13468 0.098014 0.0681 0.0148 -0.064 -0.002 -0.125 -0.064 0.1013 0.211 -

10.Sales growth n-1 0.063432 0.187245 -0.06 -0.05 -0.034 0.0102 0.0112 -0.053 -0.058 -0.127 -0.078 -

11.Market Competitionn-1 0.102186 0.094971 0.162

7 0.24 0.0022 0.0581 -0.244 -0.046 0.1607 0.153 0.0627 -0.026 -

12.Sen. MGMT Approval n-1 3.026037 0.582549 0.044

4 0.0786 0.0633 -0.042 0.2649 -0.174 -0.014 0.084 -0.009 0.138

5 0.0154 -

13.Compensation & Benefitsn-1 3.298355 0.485537 0.006 0.0386 0.092

3 -0.045 0.3113 -0.048 0.0241 0.01 -0.114 0.0953 -0.238 0.463 -

14.CEO Approval n-1 3.844842 0.441321 -0.033 -0.052 0.014

9 -0.005 0.1343 -0.205 0.1373 -0.016 -0.008 0.2048 -0.089 0.568 0.609 -

15.Work-Life Balance n-1 3.262898 0.431379 -0.132 -0.161 0.025

9 -0.089 0.4134 -0.064 0.0001 -0.109 -0.151 0.0687 -0.221 0.589 0.605 0.595

TABLE 4A

Regressions on Penalties (Likelihood of Organizational Misconduct)

VariablePenalties

(1) (2) (3)Performance pressure n-1 0.619* 0.762** 0.783**

(0.343) (0.367) (0.375)Number of reviews (log)n-1 0.138 0.0286 -0.00488

(0.207) (0.213) (0.216)Number of employees (log)n-1 0.824** 1.106*** 1.121***

(0.337) (0.365) (0.378)ROA n-1 1.453 1.454

(2.211) (2.221)Sales growth n-1 -0.929 -0.961

(0.599) (0.628)Market competitionn-1 -1.782 -2.213

(4.348) (4.441)Senior management approval n-1 0.0192

(0.271)Compensation and benefits n-1 0.0734

(0.557)CEO approval n-1 -0.0590

(0.319)Work-life balance n-1 0.409

(0.592)

Observations 1,680 1,436 1,436Number of firms 289 254 254Year FE YES YES YESRobust standard errors in parentheses.*** p < 0.01, ** p < 0.05, * p < 0.1

61

TABLE 4B

Regressions on Number of Penalties

VariableNumber of penalties

(1) (2) (3)Performance pressure n-1 0.222* 0.257* 0.287**

(0.120) (0.131) (0.130)Number of reviews (log)n-1 -0.0195 -0.0252 -0.0511

(0.0748) (0.0794) (0.0798)Number of employees (log)n-1 0.550*** 0.570***

(0.0880) (0.0860)ROA n-1 0.856** 0.908**

(0.431) (0.457)Sales growth n-1 -0.167 -0.178

(0.160) (0.158)Market competitionn-1 0.162 0.152

(1.043) (1.035)Senior management approval n-1 -0.0258

(0.0473)Compensation and benefits n-1 -0.152

(0.163)CEO approval n-1 -0.183*

(0.100)Work-life balance n-1 0.221

(0.210)Constant -0.618 -1.136** -0.486

(0.533) (0.473) (0.753)

Observations 2,281 1,995 1,995Number of firms 393 359 359Year FE YES YES YESIndustry FE YES YES YESRobust standard errors in parentheses.*** p < 0.01, ** p < 0.05, * p < 0.1

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TABLE 4C

Regressions on Amount of Penalties (in Dollars)

VariableAmount of penalties (in dollars)

(1) (2) (3) Performance pressure n-1 2.663* 2.882* 2.894*

(1.498) (1.574) (1.588)Number of reviews (log)n-1 1.430 1.606* 1.397

(0.883) (0.888) (0.881)Number of employees (log)n-1 1.754 2.255* 2.310**

(1.126) (1.170) (1.176)ROA n-1 -1.758 -0.530

(6.199) (6.124)Sales growth n-1 -1.555 -1.289

(2.351) (2.351)Market competitionn-1 -14.59 -14.83

(13.43) (13.22)Senior management approval n-1 -1.130

(1.277)Compensation and benefits n-1 5.108**

(2.074)CEO approval n-1 -2.163*

(1.240)Work-life balance n-1 0.811

(2.057)Constant -9.169 -10.42* -17.35**

(5.769) (6.002) (8.271)

Observations 3,094 2,986 2,985Number of firms 663 637 637 R-squared 0.464 0.424 0.426Year FE YES YES YESIndustry FE YES YES YESRobust standard errors in parentheses.*** p < 0.01, ** p < 0.05, * p < 0.1

63

TABLE 5A

Regressions on Penalties (Likelihood of Organizational Misconduct), by Type of

Organizational Structure

Variable

Penalties(1) (2) (3) (4)

Low formalization

High formalization

Low decentralization

High decentralization

Performance pressure n-1 1.645* 1.271* 0.406 1.816**

(0.857) (0.766) (0.365) (0.712)Number of reviews (log)n-1 -0.101 0.285 0.288 -0.318

(0.317) (0.241) (0.231) (0.360)Number of employees (log)n-1 0.565 1.640** -0.0835 2.147***

(0.733) (0.713) (0.624) (0.547)ROA n-1 6.627* 0.930 3.235 5.536

(3.631) (4.284) (2.920) (3.921)Sales growth n-1 -2.161* -1.856 0.368 -3.555***

(1.148) (1.271) (0.625) (1.106)Market competitionn-1 -2.788 -0.258 -10.09 3.365

(9.381) (8.346) (6.280) (9.373)Senior management approval n-1 1.509* 0.103 0.760 -0.522

(0.898) (0.300) (0.540) (0.465)Compensation and benefits n-1 -0.596 0.109 -0.483 -0.0672

(1.111) (0.849) (0.927) (0.818)CEO approval n-1 -0.263 -0.225 -0.553 -0.0340

(0.613) (0.527) (0.528) (0.597)Work-life balance n-1 -0.466 1.628 0.135 1.731*

(1.186) (0.994) (0.950) (0.955)

Observations 453 488 691 492Number of firms 149 143 152 99Year FE YES YES YES YESRobust standard errors in parentheses.*** p < 0.01, ** p < 0.05, * p < 0.1

64

TABLE 5B

Regressions on Number of Penalties, by Type of Organizational Structure

Variable

Number of Penalties(1) (2) (3) (4)

Low Formalization

High Formalization

Low Decentralization

High Decentralization

Performance pressure n-1 0.329** 0.256* 0.290 1.113***

(0.159) (0.147) (0.204) (0.135)Number of reviews (log)n-1 0.00698 -0.172 -0.0264 0.0399

(0.114) (0.132) (0.122) (0.117)Number of employees (log)n-1 0.421* 0.880*** 0.160 1.091***

(0.241) (0.299) (0.142) (0.239)ROA n-1 1.203** 0.745 0.255 3.093**

(0.566) (1.059) (0.524) (1.309)Sales growth n-1 -0.0155 -0.494** -0.0266 -0.287

(0.191) (0.228) (0.178) (0.233)Market competitionn-1 0.768 -0.885 -0.420 -1.040

(1.208) (2.246) (1.491) (1.706)Senior management approval n-1 0.0331 -0.103 -0.0911 -0.0739

(0.0513) (0.0895) (0.0716) (0.112)Compensation and benefits n-1 0.0419 -0.607* 0.151 -0.188

(0.211) (0.321) (0.227) (0.241)CEO approval n-1 -0.362** 0.0139 -0.209 -0.331*

(0.167) (0.177) (0.132) (0.189)Work-life balance n-1 0.349 0.308 0.0369 0.682**

(0.244) (0.372) (0.237) (0.294)Constant -0.783 -0.265 1.368 -4.214***

(1.497) (1.770) (1.032) (1.484)

Observations 966 823 919 900Number of firms 217 204 209 194Year FE YES YES YES YESIndustry FE YES YES YES YESRobust standard errors in parentheses.*** p < 0.01, ** p < 0.05, * p < 0.1

65

TABLE 5C

Regressions on Amount of Penalties (in Dollars), by Type of Organizational Structure

Variable

Amount of penalties (in dollars)(1) (2) (3) (4)

Low formalization

High formalization

Low decentralization

High decentralization

Performance pressure n-1 7.271 0.293 1.125 5.402**

(4.536) (1.533) (2.428) (2.685)Number of reviews (log)n-1 -0.329 1.685 1.667 1.954*

(1.210) (1.505) (1.650) (1.081)Number of employees (log)n-1 4.904*** -0.339 1.103 6.347***

(1.683) (2.013) (1.899) (2.299)ROA n-1 4.378 -7.975 -17.33* 13.30

(8.603) (10.08) (9.855) (9.552)Sales growth n-1 -2.791 3.323 1.804 -2.199

(2.729) (5.162) (3.830) (4.008)Market competitionn-1 -20.85 -24.50 0.175 -46.78

(20.00) (18.29) (16.29) (30.30)Senior management approval n-1 1.739 -5.615*** 1.508 -0.441

(1.538) (1.983) (2.075) (2.046)Compensation and benefits n-1 5.166 3.566 3.605 5.436*

(3.317) (3.518) (3.349) (2.844)CEO approval n-1 -1.305 -1.954 -0.788 -5.442***

(1.945) (1.768) (2.040) (2.041)Work-life balance n-1 -0.742 5.253 2.094 1.715

(2.888) (3.942) (2.639) (3.810)Constant -24.61** -4.701 -26.08* -26.23*

(11.43) (14.01) (13.76) (14.44)

Observations 1,494 1,242 1,278 1,507Number of firms 381 347 328 379R-squared 0.463 0.488 0.450 0.469Year FE YES YES YES YESIndustry FE YES YES YES YESRobust standard errors in parentheses.*** p < 0.01, ** p < 0.05, * p < 0.1

66

ONLINE APPENDIX

Appendix A: Glassdoor

Glassdoor is a job board service that can both help employees look for new job positions and,

especially, gather information on the organization’s culture, climate, and job conditions. To

access content on the website, users have to anonymously rate their employers in what is called a

give-to-get model3, a service through which the user can have free access to the service by

leaving a review on his/her actual, or former, jobs. This mechanism had been shown to reduce

polarization in evaluations (Marinescu et al., 2018).

Glassdoor reviews are divided into two sections: the quantitative section asks users to rate

their firms on a scale ranging from 1 to 5 on a series of organizational attributes (i.e. general

evaluation, culture and values, senior management, work-life balance, career opportunities, and

compensation and benefits). Glassdoor users can also optionally state opinions ranging from -1

to 1 about the CEO, the outlook (i.e. how do the short-term future of the organization will be),

and whether they would recommend the job to a friend.

The qualitative section is divided into two sentiment-coded categories: pros (i.e., positive

aspects of the company), and cons (i.e., negative aspects of the company). Furthermore,

3 See https://help.glassdoor.com/article/Using-Glassdoor/en_US/Glassdoor_Basics

67

Glassdoor reviews provide an additional text section in which employees can submit suggestions

on how to improve the company (advices to management).

References

Marinescu, I., N. Klein, A. Chamberlain, and M. Smart.

2018 “Incentives can reduce bias in online employer reviews”. Academy of Management

Proceedings Vol. 2018. Cambridge, MA. doi:10.5465/ambpp.2018.11189abstract

68

Appendix B: Detecting Organizational Culture Topics Using Latent Dirichlet Allocation

(LDA)

The Latent Dirichlet Allocation (LDA) algorithm starts from the assumption that a document

has its own topic distribution from which a topic is randomly selected. Moreover, each randomly

selected topic has its own word distribution, from which a word is randomly selected. Repeating

these two steps word by word generates a document. The LDA algorithm discovers the topic

distribution for each document and the word distribution of each topic iteratively by fitting this

two-step generative model to the observed words in all documents until it finds the best set of

variables to describe the topic and word distributions. Essentially, like cluster analysis or

principal component analysis, LDA reduces the extraordinary dimensionality of linguistic data

from words to topics based on word co-occurrences in the same document, and provides a

reliable and replicable classification of topics.

Like any other generative model, LDA adopts a training model to infer the words contained

in the topics and their distributions. For computational reasons, we created the training model

starting from a random sample comprising 20% of the full sample of reviews (cons section

only). Other researchers (Corritore, Goldberg, and Srivastava, 2020) created a training model

using all sentences containing the word “culture” or synonyms (i.e., atmosphere, attitude,

climate, value, philosophy, belief) to reduce the number of “non-culture” topics that might come

69

up in the reviews. Unlike these researchers who analyzed similarity and heterogeneity using all

topics from the training model, we focused on a single topic: performance pressure.

LDA is an unsupervised model. The only thing the researcher must choose is the number of

topics. To select the optimal number of topics, we followed the computational linguistics

literature (Newman et al., 2010) and calculated the coherence score of the LDA model computed

with different numbers of topics (i.e. from 2 to 200). Topic coherence is a standardized measure

that evaluates the cohesion or accuracy of a set of topics in a model using a co-occurrence

measure based on pointwise mutual information. Figure A1 shows the distribution of cohesion

scores based on the number of topics selected. We decided to use the topic model with the

highest coherence score, which was the 26 topics model.

As suggested by DiMaggio et al. (2013) even though tests like log-perplexity or coherence

scores help to find the best model that “fits” the data, there is no statistical test yet for the

semantically optimal number of topics. Another concern related to topic modeling regards the

semantic validation of the topics (Chang et al., 2009; DiMaggio, 2015) as models that might

have high coherence scores, for example, might be semantically confusing (Hannigan et al.,

2019). To address this concern we coded the topics focusing on the words that were both

frequent and exclusive for every topic, which, as demonstrated by Bischof and Airoldi (2012), is

a more rigorous way to characterize topical content. Figure A2 shows the words related to our

70

topic of interest (performance pressure) in terms of both frequency and exclusivity using

PyLDA, a Python package that permits us to visualize topics in a two-dimensional space. This

tool helps researchers identify semantic similarities between topics and macro categories inside

the topic model. In figure A2, we report the words that best characterize the topic (Figure A2).

71

FIGURE B1

Coherence Scores for the Topic Models

0 20 40 60 80 100 120 140 1600

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Number of Topics

72

FIGURE B2

Performance Pressure Topic Words Filtered on Exclusivity

73

References

Bischof, J. M., and E. M. Airoldi

2012 “Summarizing topical content with word frequency and exclusivity.” Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 1: 201–208.

Chang, J., J. Boyd-Graber, S. Gerrish, C. Wang, and D. M. Blei

2009 “Reading tea leaves: How humans interpret topic models.” Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference, 288–296.

Corritore, M., A. Goldberg, and S. B. Srivastava

2020 “Duality in Diversity: How Intrapersonal and Interpersonal Cultural Heterogeneity Relate to Firm Performance.” Administrative Science Quarterly, 65: 359–394.

DiMaggio, P.

2015 “Adapting computational text analysis to social science (and vice versa).” Big Data and Society, 2: 1–5.

DiMaggio, P., M. Nag, and D. Blei

2013 “Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding.” Poetics, 41: 570–606. Elsevier B.V.

Newman, D., J. H. Lau, K. Grieser, and T. Baldwin

2010 “Automatic evaluation of topic coherence.” NAACL HLT 2010 - Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference, 100–108.

74

Appendix C: Word Embedding and FastText

Word embedding (Turian, Ratinov, and Bengio, 2010; Mikolov et al., 2013) is a set of modeling

techniques for mapping words or sentences to a vectorial space. Whereas other methods (e.g.,

LDA) use a similar operation to transform words inside a vectorial space, word embedding

methods maintain syntactic and semantic information from the text. Unlike LDA, however, word

embedding methods are supervised: to create a neural network with the ability to classify text

into vectorial spaces, word embedding needs to “learn” from training inputs based on a series of

ideal examples. Training a dictionary in machine learning is a long and computationally

intensive process that requires various iterations to find the best fit for the semantic model that

represents the data.

In this paper, we decided to skip the training process by using FastText to represent the text in

vectorial space. FastText (Joulin et al., 2017) is a library that increases the efficiency of prior

word representation models and sentence classifications developed by Facebook AI Research

and trained using the entire Wikipedia dataset. Joulin et al. (2017) calculated that FastText had

better performances regarding accuracy, scaling, and prediction compared to any other existing

text classifiers. In a nutshell, FastText is a pre-trained dictionary that offers researchers that

adopt machine learning operations such as text tagging, or sentiment analysis, to quickly set up a

baseline semantic model. Furthermore, by using a pre-trained dictionary we reduce the concerns

related to the replicability of the semantic model.

75

References

Joulin, A., E. Grave, P. Bojanowski, and T. Mikolov.

2017 “Bag of tricks for efficient text classification.” 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference, 2: 427–431.

Turian, J., L. Ratinov, and Y. Bengio.

2010 Word representations: A simple and general method for semi-supervised learning. Aclweb.Org. Association for Computational Linguistics.

Mikolov, T., K. Chen, G. Corrado, and J. Dean.

2013 “Efficient estimation of word representations in vector space.” 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings.

76

Appendix D: Robustness Checks - Regressions with CEO controls

In this robustness check, we control for various characteristics of CEOs that have been

associated with higher pressure on results. Executive compensation, for instance, can lead he

CEO to exert more pressure on results, and thus might be leading to misconduct (Zhang et al.

2008; Bergstresser and Philippon 2006). CEO tenure can also lead to misconduct as the length of

tenure might lead to more decision-making autonomy, as demonstrated by Altunbaş, Thornton,

and Uymaz (2018) in the banking industry, while Simpson and Koper (1997) had shown that

new CEOs significantly decreased the probability of misconduct. Finally, women CEOs have

been associated with more conservative earnings management and ethical leadership (Ho et al.,

2015), and women employees have a lower propensity for white-collar crimes (Daly, 1989).

We gathered data on CEOs from Execucomp and created two variables: CEO Salary

measuring CEO’s Total Compensation (Salary + Bonus + Other Annual + Restricted Stock

Grants + LTIP Payouts + Value of Option Grants), and CEO Bonus Fraction measuring how

much of the CEO’s salary is paid as a bonus (bonus/salary).

Including the controls for CEO compensation, tenure and gender did not materially change

our results as shown by tables D1, D2, D3, and D4. Hypothesis 1 is mostly confirmed by the

analyses. One exception is presented by model 1 of D1 in which the inclusion of these controls

altered the significance of results pressure. We believe this result depends on the significant

77

restriction in the sample due to the inclusion of the controls (i.e. a drop of 500 observations).

Hypothesis 2 was rejected as results pressure is significant for low formalization organizations

only in the case of Table D3 regarding the number of violations. Hypothesis 3 was confirmed

even after adding CEO controls: all the models for high decentralization firms show significant

effects for performance pressure on organizational misconduct. Overall, despite the data

limitations. we believe that the results from our analyses with CEO controls is consistent with

our our main models.

78

TABLE D1

Robustness Checks - Regressions with CEO controls

VARIABLESPenalties Number of

Penalties

Amount of penalties

(in dollars)(1) (2) (3)

Performance pressure n-1 0.634 0.269** 3.836**

(0.413) (0.136) (1.932)Number of reviews (log)n-1 -0.0466 0.00240 1.862

(0.242) (0.0786) (1.241)Number of employees (log)n-1 1.068** 0.551*** 2.550

(0.427) (0.0819) (1.647)ROA n-1 2.745 0.926* -1.302

(2.392) (0.478) (9.712)Sales growth n-1 -0.651 -0.0863 0.126

(0.629) (0.159) (3.555)Market competitionn-1 -0.264 0.411 -27.14

(5.641) (1.084) (19.01)Senior management approval n-1 0.0542 0.00622 -0.923

(0.301) (0.0443) (1.370)Compensation and benefits n-1 0.509 0.0252 7.899***

(0.655) (0.169) (2.647)CEO approval n-1 -0.0216 -0.186* -2.608*

(0.374) (0.103) (1.466)Work-life balance n-1 0.0741 0.222 1.235

(0.706) (0.228) (2.458)CEO Salary n-1 9.50e-06 1.97e-06 -2.10e-05

(8.00e-06) (2.40e-06) (3.70e-05)CEO Bonus Fraction n-1 -0.000235 -0.000357*** 0.00152

(0.000232) (6.59e-05) (0.00141)CEO Gender n-1 -0.171 0.202 6.587**

(0.534) (0.153) (3.114)CEO Tenure n-1 -0.0231 -0.00219 -0.0288

(0.0202) (0.00430) (0.0741)Constant - -1.444** -28.96**

- (0.704) (12.01)

Observations 1,171 1,669 2,181Number of firms 205 295 438R-squared - - 0.394Year FE YES YES YESIndustry FE NO YES YESRobust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

79

TABLE D2

Robustness Checks - Regressions with CEO controls on Penalties (Likelihood of

Organizational Misconduct), by Type of Organizational Structure

Variable

Penalties(1) (2) (3) (4)

Low High Low High

formalization formalization decentralization decentralization

Performance pressure n-1 0.476 0.912 0.551 1.457*(0.713) (0.648) (0.374) (0.762)

Number of reviews (log)n-1 0.0716 -0.310 0.165 -0.320(0.233) (0.235) (0.247) (0.429)

Number of employees (log)n-1 0.960 0.906 0.0900 2.250***(0.584) (0.580) (0.644) (0.654)

ROA n-1 1.303 2.829 4.034 7.543*(3.342) (3.088) (2.995) (4.464)

Sales growth n-1 -1.503 -0.147 -0.131 -3.721***(1.126) (0.714) (0.631) (1.340)

Market competitionn-1 7.113 -23.50*** -14.41* 5.843(6.586) (8.056) (8.406) (10.45)

Senior management approval n-1 0.0181 -0.0701 0.515 -0.544(0.401) (0.407) (0.586) (0.582)

Compensation and benefits n-1 -0.227 0.142 0.522 0.274(0.880) (1.080) (1.160) (1.007)

CEO approval n-1 -0.0792 -0.273 -0.305 0.369(0.672) (0.409) (0.598) (0.745)

Work-life balance n-1 -0.0886 1.430 -0.712 1.465(0.952) (1.068) (1.126) (1.107)

CEO Salary n-1 5.52e-06 1.25e-05 4.72e-05* -2.75e-05(2.17e-05) (1.04e-05) (2.45e-05) (2.37e-05)

CEO Bonus Fraction n-1 -0.000256 -0.155 0.189 -0.000150(0.000248) (0.150) (0.186) (0.000318)

CEO Gender n-1 -1.478 0.0809 0.664 -1.314(1.104) (0.629) (0.740) (1.390)

CEO Tenure n-1 -0.0341 -0.0107 -0.0318 -0.0255(0.0315) (0.0257) (0.0308) (0.0344)

Observations 468 478 593 378Number of firms 102 109 130 78Year FE YES YES YES YESRobust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

80

TABLE D3

Robustness Checks - Regressions with CEO controls on Number of Penalties, by Type of

Organizational Structure

Variable

Number of Penalties(1) (2) (3) (4)

Low High Low Highformalization formalization decentralization decentralization

Performance pressure n-1 0.315* 0.185 0.313 1.064***(0.170) (0.136) (0.206) (0.120)

Number of reviews (log)n-1 0.0534 -0.0781 0.0386 0.120(0.114) (0.153) (0.118) (0.118)

Number of employees (log)n-1 0.383 0.950*** 0.152 1.164***

(0.243) (0.302) (0.145) (0.238)ROA n-1 1.297** 0.816 0.428 3.467**

(0.550) (1.105) (0.541) (1.368)Sales growth n-1 0.0463 -0.464* 0.125 -0.311

(0.190) (0.253) (0.157) (0.260)Market competitionn-1 0.486 -0.137 0.0462 -0.610

(1.218) (2.482) (1.634) (1.778)Senior management approval n-1 0.0469 -0.133 -0.0609 -0.0373

(0.0520) (0.0891) (0.0664) (0.116)Compensation and benefits n-1 0.0501 -0.245 0.380 -0.0128

(0.208) (0.338) (0.251) (0.272)CEO approval n-1 -0.361** 0.112 -0.185 -0.474***

(0.176) (0.165) (0.137) (0.179)Work-life balance n-1 0.345 0.191 -0.0428 0.664**

(0.255) (0.394) (0.234) (0.333)CEO Salary n-1 6.08e-07 2.38e-06 7.05e-07 -3.79e-07

(3.42e-06) (4.78e-06) (3.54e-06) (4.69e-06)CEO Bonus Fraction n-1 -0.000245** 0.00583 0.0789 -2.28e-05

(0.000114) (0.0612) (0.0698) (0.000155)CEO Gender n-1 0.287 0.198 0.751** -0.0103

(0.365) (0.258) (0.319) (0.206)CEO Tenure n-1 0.00757* -0.0145 0.00461 0.0101

(0.00459) (0.0121) (0.00349) (0.0119)Constant -0.944 -2.401 0.162 -5.311***

(1.522) (1.836) (0.923) (1.636)

Observations 844 667 782 731Number of firms 189 165 175 157Year FE YES YES YES YESIndustry FE YES YES YES YESRobust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

81

TABLE D4

Robustness Checks - Regressions with CEO controls on Amount of Penalties (in Dollars),

by Type of Organizational Structure

Variable

Amount of penalties (in dollars)(1) (2) (3) (4)

Low High Low Highformalization formalization decentralization decentralization

Performance pressure n-1 8.021 -0.502 2.102 8.064**(5.081) (2.073) (2.702) (3.531)

Number of reviews (log)n-1 0.606 1.110 3.145 2.524(1.653) (2.122) (2.188) (1.605)

Number of employees (log)n-1 6.001*** -0.909 -0.488 9.645***(2.296) (2.447) (2.217) (3.707)

ROA n-1 6.612 -9.432 -19.76* 19.82(12.87) (14.00) (11.82) (17.75)

Sales growth n-1 -4.027 6.126 4.971 -1.809(4.266) (6.926) (5.390) (6.294)

Market competitionn-1 -55.46* -20.44 5.903 -53.13(30.54) (21.75) (26.81) (38.41)

Senior management approval n-1 1.889 -6.899*** 2.284 -0.239(1.616) (2.167) (2.139) (2.340)

Compensation and benefits n-1 8.835** 3.205 3.524 8.866**(3.847) (4.755) (4.360) (4.234)

CEO approval n-1 -2.809 -1.725 -1.711 -6.344**(2.644) (2.132) (2.313) (2.635)

Work-life balance n-1 -0.0224 7.585 3.829 2.283(3.166) (5.084) (3.455) (4.961)

CEO Salary n-1 2.60e-05 -1.62e-05 -6.21e-05 -3.47e-05(7.30e-05) (5.13e-05) (0.000102) (6.01e-05)

CEO Bonus Fraction n-1 0.00178 -1.403** -0.928 0.00230(0.00204) (0.550) (0.871) (0.00238)

CEO Gender n-1 12.43 -0.0576 2.973* 4.514(9.014) (2.797) (1.548) (4.301)

CEO Tenure n-1 0.0825 -0.229 -0.0514 -0.0915(0.114) (0.159) (0.0939) (0.139)

Constant -41.24** 38.95 -31.76* -52.84**(16.82) (23.98) (17.75) (24.04)

Observations 1,142 886 1,000 1,037Number of firms 240 281 244 254R-squared 0.464 0.455 0.446 0.428Year FE YES YES YES YESIndustry FE YES YES YES YESRobust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

82

References

Altunbaş, Y., J. Thornton, and Y. Uymaz

2018 “CEO tenure and corporate misconduct: Evidence from US banks.” Finance Research Letters, 26: 1–8. Elsevier Ltd.

Bergstresser, D., and T. Philippon

2006 “CEO incentives and earnings management.” Journal of Financial Economics, 80: 511–529.

Daly, K.

1989 “Gender and Varieties of White‐Collar Crime.” Criminology, 27: 769–794.

Ho, S. S. M., A. Y. Li, K. Tam, and F. Zhang

2015 “CEO Gender, Ethical Leadership, and Accounting Conservatism.” Journal of Business Ethics, 127: 351–370. Kluwer Academic Publishers.

Simpson, S. S., and C. S. Koper

1997 The Changing of the Guard: Top Management Characteristics, Organizational Strain, and Antitrust Offending. Journal of Quantitative Criminology Vol. 13.

Zhang, X., K. M. Bartol, K. G. Smith, M. D. Pfarrer, and D. M. Khanin

2008 “Ceos on the edge: Earnings manipulation and stock-based incentive misalignment.” Academy of Management Journal, 51: 241–258. Academy of Management.

83

Appendix E: Robustness Checks - Regressions with Negative ESG Media Coverage

Our analysis of misconduct relies on data on official penalties received after legal and regulatory

proceedings. An alternative approach to measure misconduct is to rely on media’s account of

potential misconduct, which might both capture misconduct that does not lead to legal penalty,

but might also be noisier and more skewed towards larger, more visible companies. In any case,

in this set of analyses, we created two new dependent variables based on data provided by

RepRisk, a business intelligence provider specialized in environmental, social, and governance

(ESG) risk analytics and metrics. RepRisk identifies news items that criticize companies for

misconduct on ESG issues such as corruption, fraud, environmental degradation, and human

rights abuses. In this analysis, we will follow prior literature that used RepRisk data (e.g. Kölbel,

Busch, and Jancso, 2017) and use a similar scope of consideration of the ESG issues.

RepRisk search methodology is guided by a scope of 28 pre-defined issues, divided into 5

categories: environmental footprint, community relations, employee relations, corporate

governance, general. Issues in the last category are only used in conjunction with another

category. RepRisk analysts collect and code this media information as a professional service for

banks and investors, and thus we believe they provide a good level of consistency and reliability.

We define negative ESG media coverage as the number of articles covering the

organization for their misconduct on ESG issues. The variable was winsorized (1st and 99th

84

percentile) and, as it represents media coverage which is different from official penalties coming

after a trial, we did not use lagged independent variables. The analyses represent panel OLS

regressions with year and industry (SIC 2-digits) fixed-effects.

The results in Table E1 show the analyses testing the effects of performance pressure on

negative ESG media coverage. The result of model 1 is consistent with our main models on

misconducts regarding the direction of the result for performance pressure, though the

coefficient is not significant (p= 0.124).

Table E2 show the split sample analyses by organizational structure. The results of Table E3

are consistent with the logic of both Hypothesis 2 on the increased effect of performance

pressure regarding low formalized organizations and of Hypothesis 3 on the higher negative

effects of performance pressure for decentralized organizations.

Overall, these results are broadly consistent with the logics of our hypotheses, even if the

main effect of performance pressure on ESG media converage was not significant.

85

TABLE E1

Robustness Checks - Regressions with Reputational Risk

Variable

Negative ESG Media Coverage

(1) Performance pressure n-1 1.290

(0.836)Number of reviews (log)n-1 0.340

(0.619)Number of employees (log)n-1 3.498***

(0.648)ROA n-1 -1.008

(4.485)Sales growth n-1 -2.173**

(1.011)Market competitionn-1 21.86***

(6.815)Senior management approval n-1 0.431

(0.478)Compensation and benefits n-1 -0.188

(1.441)CEO approval n-1 -0.373

(0.738)Work-life balance n-1 0.450

(1.283)Constant -13.00**

(5.605)

Observations 1,145Number of firms 370R-squared 0.738Year FE YESIndustry FE YESStandard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

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TABLE E2

Robustness Checks - Regressions with Number of Articles, by Type of Organizational

Structure

Variable

Negative ESG Media Coverage(1) (2) (3) (4)

Low High Low Highformalization formalization decentralization decentralization

Performance pressure n-1 0.984** -0.299 0.104 0.529*(0.479) (0.331) (0.395) (0.280)

Number of reviews (log)n-1 0.882*** 0.198 0.697** 0.150(0.265) (0.393) (0.351) (0.280)

Number of employees (log)n-1 0.714** 1.303** 0.358 1.375**(0.349) (0.530) (0.506) (0.590)

ROA n-1 -0.872 -2.010 -3.797** -1.370(1.370) (1.709) (1.745) (1.848)

Sales growth n-1 -1.425*** -0.976** -0.681* -1.127**(0.514) (0.457) (0.352) (0.500)

Market competitionn-1 8.421** 6.291 4.148 12.27***(3.605) (3.927) (4.786) (4.316)

Senior management approval n-1 0.0843 -0.209 0.341 0.207(0.441) (0.427) (0.354) (0.528)

Compensation and benefits n-1 -0.544 0.650 -1.595** 1.102(0.689) (0.737) (0.776) (0.686)

CEO approval n-1 0.348 0.743* 1.672*** -0.184(0.388) (0.421) (0.485) (0.417)

Work-life balance n-1 -0.203 -0.732 -0.132 -0.654(0.621) (0.643) (0.708) (0.550)

Constant -5.623** -6.121* -5.603* -5.613**(2.564) (3.165) (3.287) (2.819)

Observations 1,812 1,530 1,726 1,665Number of firms 406 306 276 276R-squared 0.693 0.690 0.658 0.753Year FE YES YES YES YESIndustry FE YES YES YES YESStandard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

87

References

Kölbel, J. F., T. Busch, and L. M. Jancso

2017 “How Media Coverage of Corporate Social Irresponsibility Increases Financial Risk.” Strategic Management Journal, 38: 2266–2284.

88

Appendix F: Robustness Checks – Distribution of Performance Pressure on Dollar Amount

of Penalties

FIGURE F1

Robustness Checks – Distribution of Performance Pressure (Continuous variable) on Amount of Penalties (in Dollars)

89

FIGURE F2

Robustness Checks – Distribution of Performance Pressure (Continuous variable) on Amount of Penalties (in Dollars) by Type of Organizational Structure

90