The chief human resource officer in the C-suite: peer prevalence and environmental uncertainty

Abstract

The chief human resource officer (CHRO) role elevates people-related matters to the apex of the firm. Why do some companies’ leading management teams place so much emphasis on human resources while others do not? The present study argues that CHROs’ presence in the C-suite is driven by firms’ imitation of industry peers’ leadership structures as a response to uncertainty. The investigation also sheds light on the moderating role of environmental factors that can influence mimetic isomorphism in HR leadership. Through a longitudinal analysis of large listed firms between 2006 and 2020, the study shows a positive relationship between the prevalence of the CHRO position among firms’ peers and a focal firm having a CHRO in its top management. The results demonstrate that certain types of uncertainty serve as boundary conditions for such copying actions: Industry growth strengthens mimicking behavior while industry dynamism weakens it. There is no clear evidence for the moderating role of industry competition. The findings contribute a neo-institutional view of human resource structures in the top management and strengthen the bond between the strategy and human resource literature.

Keywords:

• Chief human resource officer
• CHRO
• upper echelons
• mimetic isomorphism

Introduction

Now, across the industry, we’re seeing the most successful HR leaders and teams as enablers with a critical role in making growth possible and expanding leadership impact. It’s shifted from an underlying foundational layer of an executive team to a standalone innovator truly leading the way in unlocking all aspects of the business.—Gupta (2022) in Forbes

Research on top management continues to thrive in the strategy realm (e.g. Gamache et al., 2020; V. K. Gupta et al., 2020), yet a noticeable gap remains in the exploration of human resource management (HRM) within leadership structures. According to Boada-Cuerva et al. (2019, p. 64), ‘despite their relevance, top management have been largely absent from the HRM literature’.

Thus, the integration of the corporate strategy and human resource (HR) disciplines faces two challenges. First, although extant research underscores that including HR in strategic decisions can contribute to competitive advantages (cf. Saridakis et al., 2017, for a meta-analysis), personnel professionals still struggle to be seen as equal to their functional peers at the leadership table (Charan et al., 2015; Groysberg et al., 2011; Jacoby et al., 2005). The top management team (TMT) tends to be dominated by figures such as chief financial officers (CFOs) with a shareholder value orientation (Shi et al., 2019). As HR’s highest representative, the chief human resource officer (CHRO) can be an important counterpart to other top leaders, yet we need to learn more about the CHRO role to grasp the full potential of the strategy–HR nexus.

Second, although advocates for enhancing the expertise in people matters within firm leadership often cite economic motives (Caldwell, 2003; Florentine, 2018), strategy scholars suggest that uncertainty and group identity considerations can also influence a firm’s decisions (Dess & Beard, 1984; A. Gupta & Misangyi, 2018; Peteraf & Shanley, 1997). HR-related management structures are likely not installed exclusively for economic reasons, such as an increase in efficiency; emulating HR structures can also be a response to an ambiguous environment and the lack of understanding of other organizations. Although initial research underscores the importance of industry leaders as role models for HR leadership’s presence on the TMT (Abt & Knyphausen-Aufseß, 2017), we aim to increase our understanding of why firms generally imitate their industry peers’ leadership structures and what industry boundary conditions affect this relationship.

Considering these issues and guided by Boada-Cuerva et al.’s (2019) suggestions, we present a neo-institutional examination of the relationship between CHROs’ prevalence among industry contemporaries and a focal firm’s presence of the CHRO role. Recent studies have revealed that the imitation of firm structures driven by uncertainty—so-called mimetic isomorphism—occurs at the top leadership level (Roh et al., 2016; Wiedeck & Engelen, 2018); thus, we argue that frequency-based imitation can occasionally elevate HR leaders to the C-suite. We integrate the external uncertainty indicators of the organization’s task environment into our framework, as outlined by Dess and Beard (1984), which allows us to investigate the moderating roles of industry dynamism, growth, and competition.

Our empirical approach relies on a longitudinal study of 6,124 U.S. firm-years ranging from 2006 to 2020 and uses both manager profile data and financial statement data. The results provide support for the mimetic isomorphism regarding CHRO presence in the C-suite. They further indicate that environmental dynamism and growth act as boundary conditions for the main relationship.

Ultimately, this article contributes to existing research in two significant ways. First, we enhance the nascent but growing strand on HR’s positioning in the C-suite and the HR–strategy interface (Steffensen et al., 2019). Building on initial CHRO-related findings (Abt & Knyphausen-Aufseß, 2017; Smith, 2015), we delineate how industry imitation actions and uncertainty factor into the structural decision to promote HR leaders to top management. Our efforts shed light on the mechanisms that bring the person responsible for strategic HRM closer to the CEO.

Second, we strengthen the notion that neo-institutional sociology can help understand HR authority in the firm (Boada-Cuerva et al., 2019). We apply Wiedeck and Engelen’s (2018) copycat logic to the HR domain to provide a fresh lens on how imitative behavior shapes organizational structures. In addition to affirming the significance of uncertainty in organizational modeling considerations (DiMaggio & Powell, 1983), we uncover both reinforcing and attenuating elements of industry uncertainty for mimetic isomorphism.

Theoretical background

HR in the C-suite

According to the Society for Human Resource Management (SHRM), the CHRO is the most senior member of a firm’s TMT and is in charge of employee recruitment, development, compensation, restructuring, and related policies and services (Society for Human Resource Management, 2021). Such HR-related upper echelons interpret environmental information based on their own views and make strategic personnel decisions based on them (Hambrick & Mason, 1984). Synonyms for the CHRO role include, among others, chief people/personnel/talent/human capital officer, (group) head of people matters, and senior vice president HR. Managers responsible for employee matters have been an integral component of management since the advent of professional businesses, but their relevance and scope have continuously evolved. In the early twentieth century, labor managers primarily served as supervisors, overseeing employee-related transactional tasks, such as payroll and hiring (Bottger & Vanderbroeck, 2008). Over time, these managers developed into personnel administrators as their responsibilities grew to include labor union relations, apprenticeships, and workforce allocation (Bottger & Vanderbroeck, 2008; Mahoney & Deckop, 1986).

A shift from personnel administration to HRM occurred in the 1970s, driven by changes in business school curricula and corporate elite practices, leading TMTs to include HR representatives for the first time (Bottger & Vanderbroeck, 2008; Mahoney & Deckop, 1986). The prevalence of CHROs increased in the 1980s and 1990s, with a growing emphasis on this role’s alignment with business strategy (Wright et al., 2011). By 2005, approximately 41% of U.S. firms included a CHRO in their TMT (Kabst & Giardini, 2009). In recent decades, the discourse has mainly focused on HR–IT alignment, outsourcing, and the relevance of the CHRO role among corporate elites (Bottger & Vanderbroeck, 2008; Charan et al., 2015). Building on this discussion, our study aims to contribute to the ongoing dialogue about why some firms include a CHRO in their inner management circle while others do not.

Thus far, discussions on the presence of HR at the strategy table have been based on firms’ logical internal needs. Both academics and practitioners argue that the HR function and the CHRO deserve more power to help firms cope with organizational complexity (Aldrich et al., 2015; Caldwell, 2003; Charan et al., 2015). In the twenty-first century, given large multinational organizations and demographic shifts in the workforce, specialized HR expertise is needed (Groysberg et al., 2011). Smith (2015) demonstrated that the hierarchical rank of the CHRO in a firm is positively driven by the number of employees (i.e. in larger firms), but negatively driven by international revenues. He further observed that this hierarchical level of the CHRO can positively influence the firm’s recognition as a good place to work. Abt and Knyphausen-Aufseß (2017) concluded that complexity—in terms of unionization, changing workforce, outsider CEOs, and new CEOs—positively relates to the CHRO presence. Furthermore, past CHRO appointment has a positive relation while TMT HR experience has a negative relation to CHRO presence.

Overall, empirical research on senior HR executives’ role remains scarce and ambiguous. Welbourne and Cyr (1999) found no direct relationship between the presence of a senior HR manager and the stock price of initial public offering firms; however, they identified a positive relationship between senior HR presence and stock price for firms with high growth. This finding indicates the relevance of how a firm is positioned against its competitive environment. Engels et al. (2022) showed that CHRO tenures affect social firm performance—an outcome traditionally attributed to the HR realm. In their literature review, Steffensen et al. (2019) observed that HR-related management research rarely emphasizes HR managers themselves and their own potential for strategic change. Research on other functional top executives may offer more insights as marketing scholars have found that peer behavior can drive functional presence in top management (Germann et al., 2015; Wiedeck & Engelen, 2018). Abt and Knyphausen-Aufseß (2017) observed that the presence of a CHRO in the three largest firms in an industry positively relates to a CHRO presence in a focal firm in the same industry. This finding raises the question of whether firms seek homogeneity (in contrast to differentiation) with the structures of their whole industry or if they mainly mimic industry leaders when it comes to HR leadership.

Institutional isomorphism

Boada-Cuerva et al. (2019) proposed that neo-institutional theory holds the key to solidifying the connection between top management and HR research. Neo-institutionalism elucidates how industry-related norms guide the choices of individuals and organizations in those industries (DiMaggio & Powell, 1983; Meyer & Rowan, 1977). According to neo-institutionalists, firms not only implement certain organizational structures but also replicate them, even in the absence of evidence for their economic superiority (Alvesson & Spicer, 2019). This situation leads to institutional isomorphism: Firms imitate the HR management structures and processes prevalent in their environment, thereby leading to a gradual homogenization of organizations within an industry over time (Alvesson & Spicer, 2019; DiMaggio & Powell, 1983).

DiMaggio and Powell (1983) identified three mechanisms of institutional isomorphism: coercive, normative, and mimetic isomorphism. Coercive isomorphism summarizes adaption forms driven by sanctions from organizations on which the focal institution depends (DiMaggio & Powell, 1983). Antecedents of coercive isomorphism are often described as governmental mandates, regulations and laws, and expectations from society (DiMaggio & Powell, 1983). Meanwhile, normative isomorphism encapsulates assimilation pressures based on professionalization (Martínez-Ferrero & García-Sánchez, 2017). Managers sharing similar educational backgrounds (e.g. the Wharton CHRO program) or memberships in professional collectives (e.g. SHRM) are likely to generate similar solutions to organizational challenges. Finally, mimetic isomorphism describes institutional mimicry caused by external uncertainty in the organizational environment. We posit that this uncertainty-driven isomorphism is the primary factor influencing the presence of CHROs and, thus, focus on this form of imitation behavior. ‘Market uncertainty stems from volatility in firms’ environments from unforeseeable variations in customers’ preferences, product demand, and/or competitor behavior’ (Wiedeck & Engelen, 2018, p. 639). Therefore, we examine the external uncertainty indicators of the organization’s task environment that may sway a firm’s decision to mimic the inclusion of CHROs in the C-suite.

In addition, a neo-institutional strand argues that isomorphism is economically driven, as organizations imitate their peers’ HR practices that they perceive to be valuable to attain superior economic performance (Ketokivi & Schroeder, 2004). This viewpoint aligns with the notion that imitators seek economic efficiency and follow a profit-maximization rationale (Haunschild & Miner, 1997). Ketokivi and Schroeder (2004) provided the example of ‘a plant manager [who] wishes to benchmark another plant’s production planning process in an attempt to improve efficiency’ (p. 66).

We focus on mimetic isomorphism to understand the CHRO presence, given that several studies have observed mimetic isomorphism in top management selection. For instance, Williamson and Cable (2003) showed that firms are more likely to hire top managers from sources that previously provided TMT executives to other Fortune 500 firms. They noted that this mimicry is largely influenced by the hiring practices of large firms that frequently hire new employees. Roh et al. (2016) indicated that early adopters of the chief supply chain officer (CSCO) position were driven by economic motives, while late adopters seem to have merely copied their peers. Germann et al. (2015) explained the positive relationship between chief marketing officer (CMO) industry prevalence and focal firm CMO presence in the TMT with similar market conditions and expectations among industry peers. Wiedeck and Engelen (2018) supported the notion that uncertainty-driven mimicking behavior drives the presence of CMOs in the C-suite. The authors further revealed that firm uncertainty amplifies this relationship, whereas inference uncertainty diminishes it. This latter finding emphasizes the importance of uncertainty as a contingency factor in neo-institutional frameworks. For instance, in an environment with high uncertainty regarding competitive behavior, imitation may present a low-effort, viable solution (DiMaggio & Powell, 1983). Based on this understanding, we examine the moderating role of uncertainty factors in an organization’s task environment, following the framework presented by Dess and Beard (1984), in the relationship between CHRO peer prevalence and focal firm CHRO presence. Inspired by Wiedeck and Engelen (2018), our research is depicted in Figure 1.

Figure 1. Research model.

Impact of CHRO peer prevalence

Based on our argument from a neo-institutional view on HR leadership, we investigate a CHRO presence in the C-suite, the inner circle of firm leadership where strategies are formulated and the firm’s most powerful actors exert their influence (Hambrick, 2007; Kelly & Gennard, 2007). This TMT is an inner circle of limited size, and having a CHRO implies opportunity costs as there is only limited room for other positions (Carpenter et al., 2004; Dezsö & Ross, 2012). To address uncertainty, we posit that firms opt for CHRO positions when their peers do.

When many incumbent organizations in a field hold a specific position, the visibility of this position is strong (DiMaggio & Powell, 1983; Fernhaber & Li, 2010), and frequency-based imitation becomes common. Frequency-based imitation is the assumption of visible behaviors by a large number of other actors (Haunschild & Miner, 1997). Firms naturally scrutinize the structures and behaviors of their peers (Williamson & Cable, 2003). Top management structures are easily observable to outsiders, and Wiedeck and Engelen (2018) demonstrated that they are susceptible to replication. Adhering to the HR trends set by a large number of peers caters to actors’ desire to copy their peers’ success without decrypting the underlying reasons for the competitive advantage. By simply duplicating the CHRO position, firms minimize their search costs (Cyert & March, 1963; Haveman, 1993). Mimickers follow a convenient solution that can be implemented even if time and information for thorough decision-making processes are scarce. Although firms cannot fully delve into their peers’ inner decision-making paths, they can still consciously or unconsciously imitate their competitors’ HR standards (DiMaggio & Powell, 1983). Paauwe and Boselie (2003) concluded that copying peers’ HR blueprints reduces the fear of falling behind the competition. Once HR leadership is installed in many firms, an industry becomes increasingly homogeneous. CHROs interviewed by Abt and Knyphausen-Aufseß (2017) detected the presence of neo-institutionalism in the HR domain, as evidenced in statements like ‘our competitors do it that way’ (p. 58).

Although the convenience of a limited search may be an underlying motive, the official line of argumentation may evoke financial advantages and reflect the appearance of rationality. CEOs tend to justify bringing HR to the apex of the firm by emphasizing costs and benefits. As many competitors install CHROs, focal firms are likely to follow suit, which may be perceived as a best practice even without conducting an in-depth assessment. A large number of peers with CHROs may give the impression that the practice offers a high technical value and its presence increases the quality of strategic decision-making (Haunschild & Miner, 1997; Williamson & Cable, 2003). We propose:

• H1: A positive relationship exists between the prevalence of the CHRO position among the focal firm’s peers and the focal firm having a CHRO.

Environmental uncertainty

DiMaggio and Powell’s (1983) concept of mimetic isomorphism argues that change behavior is influenced by environmentally constructed uncertainties—that is, organizations model themselves after others perceived as successful or legitimate in navigating these uncertainties. In situations of high uncertainty, it is often unclear what the best course of action is. In this context, imitating others can seem like a safer bet than trying to innovate and forge a unique path. Mimetic behavior may offer a solution when environmental uncertainty constrains targeted decision-making. Dess and Beard (1984) suggested that the competitive environment can be described using the three dimensions of (1) dynamism, (2) munificence, and (3) competition. We tailor the three dimensions regarding relevant uncertainties for our HR management context. (1) External dynamism refers to the unpredictability of external shifts (Dess & Beard, 1984). (2) We use munificence, ‘the extent to which the environment can support sustained growth’ (Dess & Beard, 1984, p. 55), by analyzing industry growth. (3) We utilize industry concentration as a measure of competition, ‘with greater levels of competitive intensity equating to lower levels of market concentration’ (Eroglu & Hofer, 2014, p. 351). As shown in Figure 1, we propose that these three factors influence the relationship between the prevalence of CHROs in an industry and the presence of the CHRO position in the focal firm.

Industry dynamism

Dynamic market settings are characterized by hard-to-predict outside actors and changing environmental activities (Ghosh & Olsen, 2009; Tosi et al., 1973). Firms cannot rely on their sales forecasts, and their HR needs are unclear. In such dynamic contexts, mimetic isomorphism, in the form of imitating the HR structures of one’s peers, may seem a safe and cost-efficient decision (DiMaggio & Powell, 1983). Managers engage in less risky activities when industry demand instability is high, making mimicry a seemingly secure alternative (Han et al., 2017). Fluctuating customer needs are likely to be accompanied by adjustments to human capital and workforce planning. Current experts may become obsolete, and new talent may be needed. The presence of a CHRO role and alignment with peer standards become even more tempting when the pressure to find flexible workforce solutions is high. Hence, we predict that firms acting in a dynamic industry will be subject to more pronounced CHRO mimetic isomorphism.

Although we argue that firms tend to want their HR management structure to resemble that of their peers, this urge is likely amplified in industries with volatile customer demand. Finding the optimal design for the HR function in alignment with market needs becomes costlier with increasing market fluctuations. Dynamic settings limit managerial judgment, which makes firms receptive to external information on HR structures, promoting a propensity for mimetic isomorphism as firms look to their industry peers for guidance (Bikhchandani et al., 1992). We propose:

• H2: Industry dynamism strengthens the relationship between the prevalence of the CHRO position among the focal firm’s peers and the focal firm having a CHRO.

Industry growth

Strategic deviance, such as having no CHRO in the C-suite while other firms in the industry have one, is likely to incur search costs for understanding why an organization deviates from others (e.g. in benchmarking competitor organizations). This situation raises the question of how CHRO-related mimetic isomorphism differs in a munificent industry environment that allows for broad growth (Dess & Beard, 1984). Munificent environments, characterized by industry growth, necessitate firms effectively managing increasing customer demands and abundant growth opportunities, thereby increasing the need for a firm to have the capacity for talent management and strategic human resource planning, typically spearheaded by a CHRO. One could argue that, in a munificent environment, it is easier to bear the burden of the search costs to explore why competitors have CHROs and whether the same makes sense for one’s own organization. In this case, industry growth would weaken the relationship between the prevalence of a CHRO among the focal firm’s peers and the focal firm having a CHRO.

In contrast, we claim the opposite. A fast-growing environment requires paying attention to increasing customer demand and increasing sales opportunities. Although positive, a growing environment is ambiguous and characterized by many diverse decision-making paths. For example, Yasai-Ardekani (1989) found that firms might change their structure based on the degree of environmental munificence. Accordingly, the attention of the firm’s board and CEO is likely drawn to how they can keep up with demand and use the abundant resources to participate in growth opportunities. A munificent environment requires additional HR expertise among top management to satisfy the increased need for talent to cater to the rising customer demand. The presence of a CHRO among successful peers can be seen as a sign of a winning formula, leading to a greater likelihood of adopting a similar structure. At the same time, growth dynamics are likely to accelerate the pressure to find quick and easy solutions, which may in turn increase homogenization among peers (Haveman, 1993). Therefore, firms in high-growth industries are more likely to exhibit mimetic isomorphism in response to these uncertainties, implementing a CHRO role if it is prevalent among their peers. Replicating similar HR leadership structures may offer a convenient answer, and Chandler (2015) argued that imitation is a successful product strategy during the industry growth phase. As industries stabilize or even start to decline, there is less enthusiasm, and questioning peers’ structures becomes more likely, which may, in turn, reduce the need to address uncertainty through a CHRO presence in the C-suite via mimetic isomorphism.

Moreover, although pronounced growth is perceived to be a positive outcome, it is still accompanied by uncertainty regarding exact workforce needs and HR budgets. It may be uncertain how long the growth will last and how fast it will occur. Focal firms are likely to respond with mimetic behavior regarding CHRO positions in the absence of reliable forecasts and development targets (DiMaggio & Powell, 1983). Weighing all these arguments together, we predict:

• H3: Industry growth strengthens the relationship between the prevalence of the CHRO position among the focal firm’s peers and the focal firm having a CHRO.

Industry competition

Industry competition is considered a key dimension of environmental uncertainty (Azadegan et al., 2013; Eroglu & Hofer, 2014; Simerly & Li, 2000; Swamidass & Newell, 1987). Following industrial organization logic (e.g. Waldman & Jensen, 2016), the competitive structure of the environment influences firm-level decisions with regard to the power of relationships with industry peers, suppliers, and customers (Eroglu & Hofer, 2014; Qian et al., 2013). In industries with higher concentration, rival firms’ actions are harder to predict and understand, which is known as competitive uncertainty (Kor et al., 2008). As shifts in stakeholder behavior, resources, and technology are tougher to assess in high competition conditions, it becomes more challenging for managers to obtain information, synthesize it, and derive decisions (Qian et al., 2013). Mimetic isomorphism, driven by uncertainty, can cause firms to reduce their search costs for the right HR leadership by mimicking the structure of their peers. As resources in competitive boundary conditions are needed to facilitate product differentiation (Shaked & Sutton, 1982), fewer resources and managerial attention are available to ponder whether strategic deviance in terms of not having a CHRO in the C-suite is useful or not.

In contrast, in industries characterized by low concentration (i.e. high competition), where numerous firms vie for market share, the unpredictability of rivals’ actions and the dynamism of stakeholder shifts heighten uncertainty. This situation is reinforced by the growing restricted knowledge collection and alertness of decision-makers in competitive situations (Qian et al., 2013; Staw et al., 1981). When more CHROs operate in an industry, the focal firm’s leadership perceives more such CHROs and their actions, whether consciously or unconsciously. Organizations face pressure to perform and differentiate themselves in a highly competitive industry. However, the high degree of competition exacerbates uncertainty, as predicting rivals’ actions and stakeholder shifts in behavior, resources, and technology becomes harder. In such conditions, firms may resort to mimetic isomorphism as a risk-averting strategy, copying peer organizations’ HR structures to reduce their search costs for optimal structures and decisions (Haunschild & Miner, 1997).

We recognize the potential curvilinearity of the concentration–uncertainty relationship, where very high and low concentrations could increase uncertainty. However, such extremes are less likely in our sample of large U.S. companies. Our hypothesis focuses on significant levels of competition. In this context, we posit that higher industry concentration weakens and lower concentration strengthens the mimetic tendency to adopt a CHRO position. Hence, we expect that firms will exhibit mimetic isomorphism in highly competitive markets, aligning their strategies with the strategies of peer firms when introducing a CHRO into their C-suite. We predict:

• H4: Industry competition strengthens the relationship between the prevalence of the CHRO position among the focal firm’s peers and the focal firm having a CHRO.

Methodology

Data and sample

To better understand the CHRO presence in the TMT, we conducted an empirical study based on archival data. Our sample comprises all firms in the Standard & Poor’s (S&P) 500 index listed between the years 2006 and 2020. This index covers about 80% of the U.S. stock market value and tracks the performance of large-cap stocks in the U.S. market (Banton, 2020). We focus on the largest publicly listed firms to yield representative insights for large parts of the U.S. economy.

Our study builds on a comprehensive dataset from two main sources: TMT data collected from firms’ annual filings as requested by the Securities and Exchange Commission (SEC) and financial statements from Compustat and Compustat Segments. We excluded firm-years with missing values and lagged relevant variables, leading to a sample of 6,124 firm-year observations with 732 firms for the 15 years from 2006 to 2020.

Measures

Dependent variable

CHRO firm presence is the dependent variable of our study. Following a common approach applied in prior TMT research (Abt & Knyphausen-Aufseß, 2017; Nath & Bharadwaj, 2020; Nath & Mahajan, 2008), we define a firm’s TMT as the Executive Officers of the Registrant listed in the firm’s SEC filings (10-K or DEF 14 A). According to Nath and Bharadwaj (2020), these listed executive officers include the CEO and all executives in charge of a principal business unit or other policy-making function; defining the TMT in this way has the advantage of being an objective and inclusive measure that is reported consistently over time.

To identify the CHRO’s presence in the TMT, we manually collected TMTs from the SEC filings (10-K or DEF 14 A) and initially searched for titles that included keywords such as ‘human’, ‘personnel’, ‘people’, ‘staff’, and ‘talent’ (Abt & Knyphausen-Aufseß, 2017; Smith, 2015). Every executive in the database was manually checked and, if an executive’s function was unclear, the information was cross-checked with firm websites and managers’ career profiles. This approach allowed us to identify CHROs with similar functions but different titles (e.g. recruiting director). Our variable, CHRO firm presence, is a dichotomous measure, equaling one if the position is included in the firm’s TMT and zero otherwise.

Independent and interaction variables

CHRO peer prevalence, or the presence of a C-level HR leader among a focal firm’s sector population, is the primary independent variable of our study. We follow Germann, Ebbes, and Grewal’s (2015) approach building on three main characteristics. First, the basis for our sample firm’s peers was the two-digit Standard Industrial Classification (SIC) code. Second, we generated the CHRO prevalence for each two-digit SIC code in our sample. Therefore, we initially derived the firm-specific prevalence per two-digit SIC code for firm i by dividing the number of firms with a CHRO position in firm i’s primary SIC code j by the number of other firms in this primary SIC code j excluding firm i. Third, as most firms are assigned to multiple primary two-digit SIC codes, we use all primary two-digit SIC codes allocated to a firm by the Compustat Segments database to capture all peers for our final measure, CHRO firm prevalence. Thus, we calculate a weighted average of CHRO prevalence among the firm’s peers using the number of sample firms in each primary two-digit SIC code as the weight. Our main relationship also holds in an unreported analysis when we use the alternative measure applied by Wiedeck and Engelen (2018), modeled as market share weighted average based on the singular primary SIC code.

Inspired by Dess and Beard (1984) and including insights from more recent literature (e.g. Eroglu & Hofer, 2014; Firk et al., 2021; Li & Wang, 2019; Nielsen & Nielsen, 2013), we use three industry characteristics as our moderator variables—namely, industry dynamism, industry growth, and industry competition—and utilize Compustat as the primary source for data derivation.

Industry dynamism

We use the methodology outlined by Dess and Beard (1984) to measure industry dynamism. This involves calculating the mean of the natural logarithm of annual firm revenues within an industry, classified by a two-digit SIC code, for the past five years. These means are then regressed over time. Industry dynamism is calculated by dividing the average of the antilogarithms of the standard errors from this regression by the mean of the yearly natural logarithms of revenue. The values are then multiplied by −1 to increase interpretability, where lower scores indicate lower industry dynamism and higher scores indicate higher dynamism (Keats & Hitt, 1988; Schilke, 2014; Simerly & Li, 2000). A higher standard error indicates greater revenue instability or uncertainty, which suggests increased dynamism and uncertainty in market demand (Azadegan et al., 2013; Pagell & Krause, 2004).

Industry growth

Our Compustat-based measure of industry growth is calculated by regressing the annual average industry growth on the two-digit SIC industry sales over five years. The focal year is included as the midpoint. The respective regression slope coefficient is divided by the average firm sales for this timeframe (Dess & Beard, 1984; Nielsen & Nielsen, 2013). We utilize the growth over a five-year period to capture the market size changes originating from the long-term growth within an industry.

Industry competition

The moderator variable industry competition is determined using the four-firm concentration ratio (CR4) in each industry. We calculate the variable as the portion of total industry sales by the four largest firms based on two-digit SIC industry sales. The values are then multiplied by −1, where lower scores indicate lower competitive intensity and higher scores indicate higher intensity (Eroglu & Hofer, 2014).

Control variables

We accounted for additional firm- and TMT-level characteristics within our regression as applied by previous CHRO-related research (Abt & Knyphausen-Aufseß, 2017; Smith, 2015). We employed financial and non-financial data from Compustat and firms’ annual statements with the SEC. Table 1 summarizes the variables we used in the study. We included time-fixed effects to account for year-dependent economic fluctuations in our sample period (i.e. 2006 to 2020). To consider CHRO-presence specifics across industries, we controlled for industry-fixed effects. Inspired by Chung and Low (2017), we winsorized continuous variables at the 1% and 99% levels to reduce the impact of extreme and outlier observations.

Table 1. Variables and data sources.

Variable	Description	Data source
(1) CHRO firm presence	Binary variable taking a value of one if there is a CHRO among the senior executives mentioned in the firm’s proxy statements and zero otherwise	SEC filings from EDGAR
(2) CHRO peer prevalence	Weighted average of firms with a CHRO among the senior executives within an industry based on each primary two-digit SIC code. Firms with several primary SIC codes are considered in each of them (Germann et al., 2015).	SEC filings from EDGAR; Compustat segments
(3) Industry dynamism	The natural logarithm of annual firm revenue, classified by two-digit SIC codes, is calculated and then averaged over the past five years. These logarithmic averages undergo a time-series regression to analyze changes over time. The average of the antilogarithms of the standard errors is then divided by the average logarithmic revenue. To enhance interpretability, the values are multiplied by −1. In this context, lower scores indicate lower industry dynamism, while higher scores indicate greater industry dynamism (Dess & Beard, 1984; Keats & Hitt, 1988; Schilke, 2014; Simerly & Li, 2000).	Compustat
(4) Industry growth	Regression of annual average industry growth based on two-digit SIC industry sales over five years, including the focal year as the midpoint. The respective regression slope coefficient is divided by the average firm sales for this timeframe (Dess & Beard, 1984).	Compustat
(5) Industry competition	Portion of total industry sales by the four largest firms based on two-digit SIC industry sales. Values are multiplied by −1, where lower scores indicate lower competitive intensity and vice versa (Eroglu & Hofer, 2014)	Compustat
(6) Firm total assets	Natural logarithm of total firm assets of a firm	Compustat
(7) Firm operating cashflow	Sum of the income before extraordinary items and depreciation and amortization cash flow of a firm	Compustat
(8) Firm profitability change	Return on equity divided by the previous year’s return on equity of a firm	Compustat
(9) Firm return on sales	Ratio of net income to net sales of a firm	Compustat
(10) Firm employee growth	Employee count divided by the previous year’s employer count of a firm	Compustat
(11) CEO HR background	Binary variable receiving a value of one if a CEO has previous work experience in HR-related functions and zero otherwise	Biographies and public profiles; SEC filings from EDGAR
(12) TMT HR background	Relative number of TMT members with prior work experience in HR-related functions	Biographies and public profiles; SEC filings from EDGAR
(13) TMT size	Total number of members in the TMT	Biographies and public profiles; SEC filings from EDGAR
(14) TMT education level	Average TMT education level (1 = none; 2 = bachelor’s degree; 3 = master’s degree; 4 = MBA/Ph.D.; 5 = professor), simplified based on Rajagopalan and Datta (1996)	Biographies and public profiles; SEC filings from EDGAR
(15) TMT gender heterogeneity	Degree of gender diversity within the TMT. The Blau index quantifies the diversity of gender within TMTs using the formula${\mathit{i}}_{\mathit{\rho }}^2$, where ρ represents the proportion of TMT members that are either male or female, and i indicates the number of distinct gender categories (Ozer, 2010; Wu et al., 2022). To improve interpretability, we multiply the measure of TMT gender diversity by two to set the range between zero and one.	Biographies and public profiles; SEC filings from EDGAR

Analysis and results

Estimation procedure

To analyze our binary dependent variable, CHRO firm presence, we used a generalized estimating equation (GEE) approach, representing a quasi-likelihood method based on generalized linear models (Ballinger, 2004). We used GEE due to improvements within the estimation of standard errors as it accounts for firm heterogeneity by incorporating correlations between observations within one cluster (Quintana-García & Benavides-Velasco, 2008; C. J. W. Zorn, 2001). GEE requires the definition of three criteria: distribution, link function, and within-group correlation structure (Cui, 2007). We used the binomial distribution with a logit link due to our binary dependent variable as well as an exchangeable correlation structure corresponding to an equal-correlation model. We lagged all independent, moderator, and control variables by one year to account for reverse causality. Table 2 displays the descriptive statistics and correlations of the variables, excluding year and industry effects.

Table 2. Sample summary statistics and Pearson correlation matrix.

	Variable	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.	12.	13.	14.	15.
1.	CHRO firm presence	1.00
2.	CHRO peer prevalence	0.15*	1.00
3.	Industry dynamism	0.13*	0.25*	1.00
4.	Industry growth	−0.03*	−0.05*	0.01	1.00
5.	Industry competition	−0.05*	−0.13*	−0.28*	−0.05*	1.00
6.	Firm total assets^a	0.08*	0.00	0.34*	−0.11*	0.07*	1.00
7.	Firm operating cashflow^a	0.04*	0.01	0.30*	−0.04*	−0.05*	0.64*	1.00
8.	Firm profitability change	0.00	0.03*	0.00	0.01	−0.01	0.00	0.07*	1.00
9.	Firm return on sales	−0.04*	−0.01	−0.10*	0.08*	0.10*	0.05*	0.21*	0.22*	1.00
10.	Firm employee growth	−0.03*	0.01	0.01	0.13*	−0.04*	−0.09*	−0.01	0.03*	0.16*	1.00
11.	CEO HR background	−0.02	−0.03*	0.00	0.02	0.01	−0.02	−0.02	0.01	0.00	0.01	1.00
12.	TMT HR background	−0.02	0.00	0.03*	0.03*	−0.06*	−0.01	−0.03*	0.00	−0.04*	0.00	0.09*	1.00
13.	TMT size	0.50*	0.12*	0.15*	−0.04*	−0.06*	0.09*	0.10*	0.01	−0.01	−0.03*	0.02	0.07*	1.00
14.	TMT education level	−0.03*	0.07*	0.01	−0.03*	0.19*	0.12*	0.09*	0.00	0.08*	−0.02	0.02	0.01	−0.06*	1.00
15.	TMT gender heterogeneity	0.26*	0.04*	0.09*	−0.07*	−0.01	0.10*	0.04*	0.00	0.03*	−0.04*	0.00	0.04*	0.19*	−0.02	1.00
	Mean	0.55	0.55	9.57	0.03	−0.69	62.18	2.95	−0.11	0.10	0.64	0.03	0.03	5.68	2.99	0.22
	Standard deviation	0.50	0.11	0.75	0.05	0.23	211.03	5.89	2.26	0.11	0.48	0.17	0.09	1.77	0.50	0.19
	Min	0.00	0.00	7.72	−0.22	−1.00	0.36	−29.42	−12.96	−0.40	0.00	0.00	0.00	2.00	1.20	0.00
	Max	1.00	1.00	11.74	0.22	−0.22	2,687.4	91.86	11.65	0.43	1.00	1.00	0.67	13.00	4.50	0.55

Note: n = 6,124 firm-year observations. Number of firms: 732. * p < .05. a. Descriptive figures reported in thousands without log.

Following the recommendations of Kalnins (2018) and Kalnins and Praitis Hill (2023), we assessed the pairwise correlations among all combinations of independent, moderator, and control variables. We used a correlation coefficient threshold of |0.3|. The correlations that exceeded this threshold included those between CHRO firm presence and TMT size, industry dynamism and firm total assets, as well as between firm total assets and operating cash flow. To mitigate concerns of multicollinearity and potential Type 1 errors (i.e. false positives), we followed Kalnins’s (2018) evaluation criteria. We conducted a hierarchical regression by individually adding each variable and interaction term to the model. As shown in Table 3, the results indicate that the overall model specifications were robust, supporting the conclusion that multicollinearity and associated false positives may not significantly affect the analysis (Kalnins, 2018).

Table 3. Results of the GEE with CHRO firm presence as the dependent variable.

	Model 1			Model 2			Model 3			Model 4			Model 5			Model 6			Model 7
	Coef.	SE	p	Coef.	SE	p	Coef.	SE	p	Coef.	SE	P	Coef.	SE	p	Coef.	SE	p	Coef.	SE	p
CHRO peer prevalence				1.371	0.321	0.000***	1.331	0.324	0.000***	9.189	4.016	0.022*	0.963	0.354	0.007**	1.561	1.189	0.189	9.685	4.063	0.017*
Industry dynamism							0.098	0.084	0.245	0.523	0.236	0.027*	0.111	0.085	0.189	0.096	0.084	0.254	0.580	0.241	0.016*
CHRO peer prevalence × industry dynamism										−0.813	0.413	0.049*							−0.897	0.420	0.033*
Industry growth							−0.047	0.558	0.933	−0.043	0.560	0.938	−6.041	2.292	0.008**	−0.043	0.559	0.938	−6.435	2.310	0.005**
CHRO peer prevalence × industry growth													11.386	4.215	0.007**				12.133	4.240	0.004**
Industry competition							0.100	0.287	0.728	0.131	0.289	0.650	0.075	0.288	0.794	−0.038	0.747	0.960	0.071	0.766	0.926
CHRO peer prevalence × industry competition																0.276	1.374	0.841	0.083	1.398	0.953
Firm total assets	0.095	0.053	0.071+	0.097	0.053	0.064+	0.078	0.055	0.156	0.075	0.055	0.174	0.081	0.055	0.144	0.078	0.055	0.156	0.078	0.055	0.158
Firm operating cashflow	0.001	0.001	0.621	0.001	0.001	0.631	0.001	0.001	0.658	0.001	0.001	0.660	0.001	0.001	0.740	0.001	0.001	0.660	0.001	0.001	0.750
Firm profitability change	0.002	0.010	0.798	0.002	0.010	0.814	0.003	0.010	0.788	0.003	0.010	0.765	0.003	0.010	0.779	0.003	0.010	0.788	0.003	0.010	0.751
Firm return on sales	−0.337	0.260	0.195	−0.323	0.261	0.215	−0.340	0.264	0.198	−0.365	0.266	0.169	−0.261	0.266	0.327	−0.339	0.265	0.200	−0.282	0.268	0.291
Firm employee growth	−0.005	0.049	0.921	−0.008	0.049	0.867	−0.011	0.050	0.827	−0.013	0.050	0.799	−0.008	0.050	0.874	−0.011	0.050	0.825	−0.010	0.050	0.845
CEO HR background	−0.622	0.198	0.002**	−0.623	0.198	0.002**	−0.625	0.198	0.002**	−0.616	0.199	0.002**	−0.646	0.199	0.001**	−0.626	0.198	0.002**	−0.638	0.199	0.001**
TMT HR background	−1.796	0.330	0.000***	−1.843	0.331	0.000***	−1.872	0.333	0.000***	−1.869	0.334	0.000***	−1.855	0.333	0.000***	−1.871	0.333	0.000***	−1.845	0.334	0.000***
TMT size	0.476	0.022	0.000***	0.474	0.022	0.000***	0.473	0.022	0.000***	0.476	0.022	0.000***	0.474	0.022	0.000***	0.473	0.022	0.000***	0.477	0.022	0.000***
TMT education level	−0.097	0.068	0.153	−0.111	0.068	0.102	−0.112	0.068	0.101	−0.119	0.069	0.084+	−0.111	0.068	0.105	−0.113	0.068	0.100	−0.118	0.069	0.087+
TMT gender heterogeneity	1.352	0.160	0.000***	1.375	0.160	0.000***	1.379	0.161	0.000***	1.394	0.161	0.000***	1.385	0.161	0.000***	1.378	0.161	0.000***	1.400	0.162	0.000***
Constant	−2.374	1.176	0.043*	−3.101	1.189	0.009**	−3.855	1.382	0.005**	−7.848	2.488	0.002**	−3.864	1.386	0.005**	−3.950	1.465	0.007**	−8.296	2.508	0.001***
Industry effects	Yes			Yes			Yes			Yes			Yes			Yes			Yes
Time effects	Yes			Yes			Yes			Yes			Yes			Yes			Yes
Observations	6,124			6,124			6,124			6,124			6,124			6,124			6,124
Number of firms	732			732			732			732			732			732			732
Degrees of freedom	31			32			35			36			36			36			38
Chi^2	633.7***			644.2***			641.6***			645.7***			645.7***			641.7**			650.7***

Note. Unstandardized coefficients. All explanatory and control variables are lagged by one year and winsorized at the 1% and 99% levels. + p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001; all two-tailed.

Hypothesis tests

Table 3 presents the results of our hierarchically conducted hypothesis tests estimating the relationship between the peer prevalence of a CHRO within an industry and the presence of one in a focal firm. Model 1 introduces the control variables, while Models 2 to 6 sequentially add the independent variables, the moderators, and the interaction terms. Model 7 displays the full model, integrating all main effects and moderations. All models consider time and industry effects.

Hypothesis 1 predicted a positive relationship between the prevalence of the CHRO position among a firm’s peers and the presence of a CHRO in the focal firm. We found support for this hypothesis (Model 2—H1: β = 1.371, p = 0.000). This is consistent across model specifications except in Model 6, where the main effect cannot be observed. Next, we analyze whether industry conditions act as interacting variables. Hypothesis 2 predicted that industry dynamism strengthens the association between the prevalence of the CHRO position among a firm’s peers and the likelihood that the firm exhibits a CHRO. Our results did not support Hypothesis 2 but showed that industry dynamism significantly weakens the relationship between the sector prevalence of the CHRO and the focal firm’s CHRO presence (Model 4—H2: β = −0.813, p = 0.049). Hypothesis 3 states that industry growth positively influences the CHRO industry–CHRO firm relationship. We found support for Hypothesis 3, with a positive and statistically significant interaction term between CHRO prevalence and industry growth (Model 5—H3: β = 11.386, p = 0.007). Finally, Hypothesis 4 predicted that industry competition positively influences our main relationship. We found a positive but statistically insignificant interaction term between CHRO prevalence and industry competition (Model 6—H4: β = 0.276, p = 0.841), providing no support for Hypothesis 4.

Figure 2 illustrates the interaction effects of our moderations. It shows the predicted values of CHRO presence at low and high values of CHRO prevalence, separated by low and high values of each moderator variable. The high (low) values are derived by adding (deducting) one standard deviation to (from) the mean. The graphs confirm our results as the slopes of each statistically significant moderation differ for high and low values of the moderator (Dawson, 2014). Furthermore, we analyzed the moderated relationships by computing marginal effects, as shown in Figure 3, in line with Busenbark et al. (2022). We applied this approach as it enhances the simple slope approach by considering changes in the relationship between dependent and independent variables for different moderator values (Busenbark et al., 2022). We observed that the relationship between CHRO peer prevalence and CHRO presence in the focal firm decreases when industry dynamism is high. In contrast, it increases with higher levels of industry growth. The marginal effects reflect the significant positive relationship between CHRO peer prevalence and CHRO presence for the first, second, and third quartiles of industry dynamism. For industry growth, the relationship is significant for the second through fourth quartiles.

Figure 2. Simple slope analysis of interaction effects.

Figure 3. Marginal effects analysis of moderation effects.

Robustness tests

We conducted additional analyses to probe the robustness of our estimations, which are presented in Table 4. First, we employed a logit logic within our main GEE estimation for our binary dependent variable. An alternative is to apply the probit logic. The difference between the two estimation methods lies in their underlying assumptions regarding the distribution of the error terms (Borooah, 2002). We re-estimated our regressions utilizing the probit link function displayed in Model 1 within Table 4. The results were consistent regarding the significance levels and direction of our hypothesized variables.

Table 4. Results of robustness checks with CHRO firm presence as the dependent variable.

	Model 1: Alternative probit link function			Model 2: Peer status based on firm assets			Model 3: Peer status based on firm sales			Model 4: Industry top 3 largest firms with CHRO
	Coef.	SE	p	Coef.	SE	p	Coef.	SE	p	Coef.	SE	p
CHRO peer prevalence	5.679	2.409	0.018*	9.621	4.065	0.018*	9.678	4.062	0.017*	11.403	4.632	0.014*
Industry dynamism	0.331	0.143	0.021*	0.582	0.241	0.016*	0.581	0.241	0.016*	0.822	0.273	0.003**
CHRO peer prevalence × industry dynamism	−0.513	0.249	0.039*	−0.892	0.420	0.034*	−0.898	0.420	0.032*	−0.966	0.482	0.045*
Industry growth	−4.078	1.382	0.003**	−6.476	2.312	0.005**	−6.423	2.311	0.005**	−5.684	2.776	0.041*
CHRO peer prevalence × industry growth	7.651	2.537	0.003**	12.237	4.245	0.004**	12.122	4.240	0.004**	10.459	5.113	0.041*
Industry competition	−0.034	0.463	0.942	0.074	0.766	0.923	0.076	0.767	0.921	0.369	0.857	0.666
CHRO peer prevalence × industry competition	0.181	0.845	0.830	0.065	1.399	0.963	0.069	1.400	0.961	1.204	1.571	0.443
Firm total assets	0.055	0.034	0.102	0.088	0.058	0.129	0.076	0.057	0.182	0.009	0.058	0.871
Firm operating cashflow	0.001	0.001	0.917	0.001	0.000	0.706	0.001	0.000	0.753	0.001	0.001	0.147
Firm profitability change	0.001	0.006	0.830	0.003	0.010	0.747	0.003	0.010	0.751	0.002	0.011	0.875
Firm return on sales	−0.152	0.163	0.350	−0.291	0.268	0.278	−0.283	0.268	0.290	−0.157	0.296	0.595
Firm employee growth	−0.010	0.030	0.744	−0.010	0.050	0.837	−0.010	0.050	0.845	−0.021	0.057	0.706
CEO HR background	−0.395	0.120	0.001**	−0.638	0.199	0.001**	−0.638	0.199	0.001**	−0.531	0.217	0.014*
TMT HR background	−1.118	0.202	0.000***	−1.843	0.334	0.000***	−1.845	0.334	0.000***	−1.685	0.373	0.000***
TMT size	0.286	0.013	0.000***	0.477	0.022	0.000***	0.477	0.022	0.000***	0.466	0.025	0.000***
TMT education level	−0.073	0.042	0.079+	−0.120	0.069	0.082+	−0.118	0.069	0.087+	−0.054	0.076	0.477
TMT gender heterogeneity	0.855	0.098	0.000***	1.402	0.162	0.000***	1.400	0.162	0.000***	1.447	0.181	0.000***
CHRO peer prevalence in similar firms				−0.271	0.481	0.573	0.079	0.542	0.884
Industry top 3 largest firms with CHRO										2.056	0.149	0.000***
Constant	−4.919	1.489	0.001***	−8.269	2.508	0.001***	−8.319	2.514	0.001***	−10.774	2.900	0.000***
Industry effects	Yes			Yes			Yes			Yes
Time effects	Yes			Yes			Yes			Yes
Observations	6,124			6,124			6,124			6,124
Number of firms	732			732			732			732
Degrees of freedom	38			39			39			39
Chi^2	721.0***			650.7***			650.7***			734.9***

Note. Unstandardized coefficients. All explanatory and control variables are lagged by one year and winsorized at the 1% and 99% levels. + p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001; all two-tailed.

Second, we used alternative measures for CHRO peer prevalence by approximating peer similarity based on both asset and sales sizes in addition to industry similarity. This approach broadens our comparison criteria to include companies with comparable sales and assets, providing a more comprehensive analysis of peer status. The results, reported in Models 2 and 3 in Table 4, exhibit consistency in terms of significance and direction for industry similarity, further supporting the robustness of our results. We observe no effect for sales- and asset-based peer status.

In addition, we added a variable describing the industry’s three largest firms with a CHRO to the set of control variables based on Abt and Knyphausen-Aufseß (2017). This variable takes a value of one if there is a CHRO among the senior executives mentioned in the three largest firms by total assets per industry based on the two-digit SIC code and zero otherwise. The variable captures the effect of the largest firms significantly driving CHRO presence. The estimation is presented in Model 4 in Table 4. Overall, these additional analyses support the robustness of our results and increase the overall confidence in the underlying model.

In this paper, we follow neo-institutionalists’ view on industry-related norms guiding the choices of organizational structures. According to this view, firms implement specific structures in their organization not to achieve the objective of economic superiority, but instead to minimize their search costs. We transfer this mimetic isomorphism to a firm’s choice to include a CHRO in their C-suite. However, there is a neo-institutional view that isomorphism may be economically driven (Ketokivi & Schroeder, 2004). We believe it is critical to consider managerial discretion when investigating the firm’s introduction of a CHRO. Thus, accounting for the economic motivation is crucial for separating the economic effect from the imitation effects in an industry.

We conducted a supplementary analysis to capture the economic motivation and understand whether only successful firms with a CHRO are being imitated. Firm performance is a suitable proxy for success as it is comparable across firms and industries. We looked at the moderating role of the average industry performance of firms with a CHRO on the primary relationship between CHRO presence in the focal firm and CHRO prevalence among industry peers. Industry performance is measured using a firm’s returns on equity (ROE) per industry based on the two-digit SIC code. The results are presented in Table 5. The relationship between the prevalence of the CHRO position among the focal firm’s peers and the presence of a CHRO in the focal firm remains significantly positive (Model 4: β = 1.039, p = 0.004). However, we did not find support for an economic argument. The interaction term of industry performance of firms with a CHRO in their TMT and CHRO peer prevalence on an individual’s CHRO firm prevalence is statistically insignificant (Model 4: β = −0.047, p = 0.951). While only correlational, these results point toward mimetic isomorphism rather than economic motivations.

Table 5. Robustness test of the moderating effect of the average financial performance of firms with a CHRO per industry, measured by ROE, with CHRO firm presence as the dependent variable.

	Model 1			Model 2			Model 3			Model 4
	Coef.	SE	p	Coef.	SE	p	Coef.	SE	p	Coef.	SE	p
CHRO peer prevalence				1.033	0.342	0.002**	1.033	0.342	0.003**	1.039	0.358	0.004**
Industry performance of firms w/CHRO							−0.022	0.103	0.829	0.002	0.410	0.996
CHRO peer prevalence × industry performance of firms w/CHRO										−0.047	0.763	0.951
Firm total assets	0.091	0.051	0.074+	0.094	0.051	0.065+	0.094	0.051	0.065+	0.094	0.051	0.066+
Firm operating cashflow	0.001	0.001	0.994	0.001	0.001	0.978	0.001	0.001	0.972	0.001	0.001	0.971
Firm profitability change	0.006	0.010	0.538	0.006	0.010	0.555	0.006	0.010	0.551	0.006	0.010	0.551
Firm return on sales	−0.297	0.258	0.250	−0.295	0.258	0.254	−0.287	0.261	0.271	−0.288	0.261	0.270
Firm employee growth	0.002	0.051	0.967	0.001	0.051	0.990	0.001	0.051	0.986	0.001	0.051	0.985
CEO HR background	−0.595	0.192	0.002**	−0.596	0.191	0.002**	−0.596	0.191	0.002**	−0.596	0.191	0.002**
TMT HR background	−1.804	0.326	0.000***	−1.818	0.325	0.000***	−1.816	0.325	0.000***	−1.817	0.325	0.000***
TMT size	0.476	0.022	0.000***	0.474	0.022	0.000***	0.474	0.022	0.000***	0.474	0.022	0.000***
TMT education level	−0.106	0.068	0.117	−0.116	0.068	0.088+	−0.116	0.068	0.088+	−0.116	0.068	0.088+
TMT gender heterogeneity	1.460	0.158	0.000***	1.464	0.158	0.000***	1.463	0.158	0.000***	1.463	0.158	0.000***
Constant	−2.342	1.155	0.043*	−2.885	1.172	0.014*	−2.885	1.172	0.014*	−2.886	1.172	0.014*
Industry effects	Yes			Yes			Yes			Yes
Time effects	Yes			Yes			Yes			Yes
Observations	6,124			6,124			6,124			6,124
Number of firms	732			732			732			732
Degrees of freedom	38			39			39			39
Chi^2	721.0***			650.7***			650.7***			734.9***

Note. Unstandardized coefficients. All explanatory and control variables are lagged by one year and winsorized at the 1% and 99% levels. + p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001; all two-tailed.

The COVID-19 pandemic affected firms’ strategic HR decisions (Collings et al., 2021): ‘At its core the COVID-19 pandemic is a human crisis’ (p. 1378). In a post hoc analysis reported in the Appendix, we assessed whether the pandemic influenced our analysis in the fiscal year 2020. This post hoc analysis did not reveal any significant effect of the COVID-19 pandemic on the firms’ strategic HR decisions or other HR-related matters.

Discussion

The present study proposed a neo-institutional framework to explain how HR structures in the top management evolve. Based on a large-scale secondary data investigation, we showed that CHRO prevalence in an industry relates to the presence of HR in the C-suite of the focal firm. This relationship is moderated by external environmental uncertainty factors. Industry dynamism weakens the relationship, while industry growth strengthens it. We did not observe a clear and statistically significant moderating role of industry competition. The findings offer novel implications for HR theory and practice.

Theoretical implications

In this study, we merged neo-institutional theory with upper echelons research to provide a framework for its application in strategic HRM. First, our findings address the argument made by scholars who believe that the role of HR in firm leadership is generally underexplored and that the HR-strategy interface requires greater attention (Boada-Cuerva et al., 2019; Steffensen et al., 2019). Surprisingly, CHRO research is less developed than other strategic disciplines’ thriving research on upper echelons, such as marketing (Germann et al., 2015; Nath & Mahajan, 2017) and finance (V. K. Gupta et al., 2020; Shi et al., 2019). Understanding strategic decision-making requires an encompassing view of the entire leadership team, including the CHRO. By exploring how the prevalence of CHROs among peers correlates to a focal firm’s CHRO presence in the TMT, we strive to provide a more comprehensive understanding of how HR and C-suites are organized as well as which factors drive the formation of related structures. We picked up the recent discussion on functional imitators (Germann et al., 2015; Wiedeck & Engelen, 2018) and transferred it to strategic HR structures. We offered a fresh perspective on HR’s historical and ongoing struggle to be acknowledged as a strategic function on par with its top management peers (Charan et al., 2015; Jacoby et al., 2005), illustrating that considerations of search costs play a crucial role in this context.

Second, our research supports neo-institutionalism and particularly mimetic isomorphism research in HRM. We found that neo-institutionalism can indeed link HR with upper echelons research (Boada-Cuerva et al., 2019). Our framework lays the groundwork for future research exploring how uncertainty shapes HR structures within a firm, and it seeks to integrate more fully the types of imitation defined by Haunschild and Miner (1997). Building on the work of Björkman and Gooderham (2012), Abt and Knyphausen-Aufseß (2017) argued that firms tend to mimic the HR structures of industry leaders. This conclusion implies that isomorphism relies on trait-based imitation of a subset of the industry population (Haunschild & Miner, 1997). We enhanced this view by suggesting that imitation occurs across whole industries in frequency-based imitation. This notion aligns with the neo-institutionalist perspective that firms may accept one another’s HR structures as standard and replicate them.

We also contributed to the discourse on whether isomorphism is driven by uncertainty or economic considerations. We constructed a framework that elucidates the role of uncertainty and search costs, demonstrating that uncertainty contingencies can influence the presence of a CHRO. However, our additional tests did not provide evidence for economically-driven mimicry. The main relationship was not moderated by the average firm performance of companies with a CHRO in their C-suite. This result indicates that the convenience of imitating industry peers’ HR leadership structures is more likely than direct economic effects, such as an increase in efficiency due to having a CHRO.

Third, we elaborated on how environmental uncertainty influences the mimicry of HR structures. Although the notion of environmental uncertainty as a pivotal factor dates back to the origins of neo-institutionalism (DiMaggio & Powell, 1983), our research showed that different types of uncertainty exert diverse impacts. We found an unexpected negative and statistically significant moderating effect of industry dynamism. This finding is noteworthy as it challenges DiMaggio and Powell’s (1983) assertion that mimetic isomorphism is a standard response to uncertainty. Comparing our finding to Wiedeck and Engelen’s (2018) finding that market uncertainty does not significantly moderate marketing mimicry, we postulate that demand instability, as a boundary condition of imitative action, depends on the functional context. In environments with fluctuating sales, the focus might shift away from HR toward marketing and innovation functions. This result also corresponds to our observation that industry competition as another dimension of environmental uncertainty does not exhibit the expected significant implications. Although higher levels of competition may impact other firm-level characteristics (Eroglu & Hofer, 2014), mimetic isomorphism regarding the CHRO does not appear to be affected.

We observed that industry growth dynamics can indeed reinforce endeavors’ imitation of CHRO presence. The results indicate that firms are most likely to exhibit a CHRO in high-growth contexts, lending support to voices arguing for the relevance of the CHRO in the war to attract talent to realize growth ambitions (Aldrich et al., 2015; Charan et al., 2015).

Implications for practice

The importance of HR at the strategy table has significantly evolved (Wright et al., 2011). Despite the enduring debate about how the business impact of HR and the rise of the shareholder value paradigm have increased HR’s visibility in leadership (Jacoby et al., 2005), our study emphasizes that it might sometimes be imitation behavior that facilitates HR experts’ presence in the highest management tier. This insight can be valuable for leadership nomination committees and HR leaders themselves. They can acknowledge that decision uncertainty and limited information trigger the CHRO’s presence.

Our findings can assist those responsible for deciding about the composition of top management, such as CEOs and leadership nomination committees, by challenging their decision patterns when decision ambiguity is high. Nomination committees can use consultancies to provide insights into why peers have the CHRO role and whether the same boundary conditions apply to their own focal firm. They can also initiate working groups and discussion forums in advocacy organizations, where the rationale for installing CHROs is examined in more detail. It is vital for HR leaders and nomination committees to stay informed about industry trends. Understanding these trends can provide valuable contexts for decision-making processes, helping to align the firm’s practices with industry standards while maintaining its unique identity.

HR leaders may learn from our study that they should raise their leadership colleagues’ attention regarding the relevance of HR issues. Industry dynamism signals a heightened level of change, and HR can help address related challenges, such as the need for retention and development programs and an overall increased demand for employee well-being.

Limitations and directions for future research

Although our study provides meaningful insights, its design has limitations that open opportunities for future research. First, like most studies aimed at hard-to-reach top executives, ours relied on archival data to find representative results (Hamori & Koyuncu, 2015). Consequently, we could only speculate about the underlying motivations prompting firms to imitate their peers. Future investigations could enhance our understanding by interviewing nomination committees and CEOs about their reasoning behind the elevation or non-elevation of HR experts to the C-suite. Extending the dataset of this study to further pandemic and post-pandemic years presents another potential avenue for future studies. As we did not find any effects of the first pandemic year (i.e. 2020) on HR-related characteristics, an extended dataset might reveal potential lagged effects due to the COVID-19 pandemic.

Second, although we confirmed the overall relationship between peer prevalence and focal firm CHRO presence in the TMT, future research could disaggregate the underlying mechanisms regarding the maturity of HR policies in an industry. Some pioneering firms may initially elevate HR experts to the C-suite to achieve superior performance (Roh et al., 2016). At later stages, herding mechanisms may accelerate the firm’s assimilation into an industry (Wiedeck & Engelen, 2018).

Finally, our research focused exclusively on the C-suite of U.S. firms, providing insights into this specific segment of the global economy. Future studies should delve deeper into other areas, exploring potential industry trends in middle HR management and investigating how small firms may mimic the HR processes of larger firms.

Acknowledgment

David Bendig would like to thank the State of North Rhine-Westphalia’s Ministry of Economic Affairs, Industry, Climate Action and Energy as well as the Exzellenz Start-up Center. NRW program at the REACH – EUREGIO Start-Up Center for their kind support of our work.

Data availability statement

Data subject to third-party restrictions.

Appendix

Post hoc analysis in COVID-19 pandemic condition

We conducted a post hoc analysis to assess whether the pandemic influenced our analysis in the fiscal year 2020. First, we estimated a probit regression for the primary relationship between the prevalence of the CHRO position among a firm’s peers and the focal firm having a CHRO considering only the year 2020. We assumed that the first year of the COVID-19 pandemic represented a shock and would affect HR-related firm strategies. As presented in Table A1, the main relationship remains positive and statistically significant (β = 1.423; p = 0.016). Furthermore, we present a line plot of the average CHRO firm presence in low- and high-volatile industries over time for the underlying dataset presented in Figure A1. The plot reveals a considerable increase (decrease) in the average CHRO firm presence in industries with a high (low) dynamism between 2009 and 2013 in opposing directions. However, for the year 2020, there is no apparent trend that the pandemic may cause. In an unreported analysis, we analyzed the average CHRO firm presence per industry based on the one-digit SIC codes over time for the underlying dataset. Although the average CHRO presence per industry may alternate over time, we did not observe an effect of the pandemic on employees or HR-related subjects in firms. In sum, this post hoc analysis did not reveal any significant effect of the COVID-19 pandemic on the firms’ strategic HR decisions or other HR-related matters. Maybe other effects will emerge as time progresses further.

Figure A1. Line chart of the average CHRO firm presence in low- and high-dynamism industries over time for 6,124 firm-year observations from 2006 to 2020.

Table A1. Probit regression for the year 2020 with CHRO firm presence as the dependent variable.

	Coef.	SE	p
CHRO peer prevalence	1.423	0.591	0.016*
Firm total assets	0.030	0.076	0.696
Firm operating cashflow	0.001	0.001	0.705
Firm profitability change	0.035	0.028	0.213
Firm return on sales	−0.428	0.461	0.353
Firm employee growth	−0.217	0.158	0.170
CEO HR background	−0.601	0.434	0.167
TMT HR background	−1.184	0.822	0.150
TMT size	0.517	0.050	0.000***
TMT education level	0.304	0.149	0.042*
TMT gender heterogeneity	2.150	0.411	0.000***
Constant	−5.112	0.948	0.000***
Observations	450
Number of firms	450
Degrees of freedom	11
Chi^2	230.9***

Note: Unstandardized coefficients. All explanatory and control variables are winsorized at the 1% and 99% levels. + p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001; all two-tailed.