A psychometric network approach to organizational culture: opening the can of worms

Introduction

For more than 40 years, since the publication of major culture frameworks (e.g., Quinn and Rohrbaugh, 1983), practitioners and scholars have been drawn to the concept of organizational culture in their attempts to explain and/or change organizational phenomena. Supporting its popularity, reviews have shown how culture relates to different aspects of organizational effectiveness (Hartnell et al., 2011), even when other variables (e.g., strategy, structure, and leadership) are considered (Hartnell et al., 2019). These culture dimensions are represented as abstract and higher-order values held by members of an organization (e.g., Schein, 1990). Although useful, the strong focus on separate dimensions restricts our understanding of what cultures are exactly, how they form, and what explains their essential features such as their resistance to change. The purpose of this paper is to provide an alternative perspective on organizational culture by looking under the hood of higher-order dimensions. Specifically, we employ psychometric network theory (Borsboom and Cramer, 2013) to reconceptualize organizational culture as a web or system of interconnected elements. As we will demonstrate, this conceptual extension can shed new light on old questions that pertain to cultural drivers and cultural inertia.

Organizational culture can be broadly defined as the shared pattern of assumptions, values, and beliefs held by members of an organization (Schein, 1990, 2010). Current operationalizations of culture strongly rely on a relatively restricted set of predefined culture dimensions (e.g., clan, adhocracy, market, and hierarchy; Quinn and Rohrbaugh, 1983) which are operationalized in terms of concrete underlying culture elements reflecting, for instance, desired and/or rewarded behaviors, dominant management styles, and strongly emphasized values (Helfrich et al., 2007). Following this approach, the focus lies on assessing the degree to which these higher-order dimensions are present and/or on analyzing the relationships between these dimensions and outcomes of interest (e.g., innovation; Büschgens et al., 2013). Yet, this approach neglects a crucial aspect of an organizational culture, namely the fact that the elements underlying these dimensions are interconnected in a particular and meaningful way.

Indeed, seminal work describes organizational culture as an interconnected pattern (Schein, 1990), system (Chatman and O'Reilly, 2016), or web (Schall, 1983) of elements shared by members of an organization. Organizational culture is therefore an emergent construct; because employees agree about the presence of certain culture elements, it becomes a shared higher-level construct. This means that culture can not only be understood by assessing the degree to which separate culture dimensions are present or agreed upon, but also by the shared contingencies between culture elements on an organizational level. After all, because these elements organically interact, perceptual agreement about them will be reflected in contingencies between them. Yet, such patterns of interconnection remain untapped when lumping elements together and reducing them to scores on predefined higher-order culture dimensions. To address this gap, we employ and extend psychometric theory (Borsboom and Cramer, 2013) to reconceptualize organizational culture as an emergent web of interrelated elements, or what we coin culture networks. In doing so, this work responds to calls to examine the “synergistic interaction among the values that define an organization's culture” (Hartnell et al., 2011, p. 687) by making such interactions explicit and measurable.

This paper is structured in two parts. The first part is conceptual and, drawing on a review of current practices in organizational culture research, outlines the basic ideas behind the network approach. Network theory and analysis are used for this purpose, given their propensity to deconstruct psychological phenomena into mutually interacting elements (Borsboom and Cramer, 2013; Schmittmann et al., 2013). The second part of this paper presents three empirical demonstrations to illustrate how culture networks can be used to study organizational culture. The first demonstration focuses on the operationalization of culture networks and the assessment of their psychometric quality. Because organizational culture is inherently comparative, this demonstration compares culture networks from two organizations, a public service organization and a health care organization. The second demonstration uses the public service data to explore the roots of a culture by identifying those culture elements that have more and/or stronger connections with other elements in the network (centrality). Finally, using the same public service dataset, the third demonstration examines the extent to which the overall strength of interconnections in the network (connectivity) can be used to assess resistance to cultural change.

Toward a network conceptualization of organizational culture

The concept of organizational/corporate culture is often traced back to early work in the field of anthropology (e.g., Gardner, 1945). However, it was not until the work of Schein (1985) that culture became a prominent topic in management and organizational psychology (Ostroff et al., 2012). Although many definitions have been proposed, Schein's (1990), 1996) description is among the most cited, defining an organizational culture as “(a) a pattern of basic assumptions, (b) invented, discovered, or developed by a given group, (c) as it learns to cope with its problems of external adaptation and internal integration, (d) that has worked well enough to be considered valid and, therefore (e) is to be taught to new members as the (f) correct way to perceive, think, and feel in relation to those problems” (Schein, 1990, p. 111).

This seminal definition serves as a good example because it includes a common denominator of most definitions: the assumed interconnectedness of the elements that constitute an organizational culture (i.e., the pattern). The different elements that make up an organizational culture are in fact assumed to overlap and connect with one another, thereby forming a dynamic web or interrelated system (Schall, 1983). This is also reflected in Schein's (1985), 1990) culture framework that classifies culture elements into three layers: artifacts (e.g., dress-codes or office lay-outs), espoused values (e.g., mission statements), and underlying assumptions (e.g., unconscious beliefs). Importantly, these layers are presumed to organically interact and—through this process—shape each other (Ostroff et al., 2012; Schneider et al., 2017). For instance, values give rise to certain practices and observable artifacts, which in turn reinforce and legitimize those values over time.

Measuring organizational culture

Culture measurement is marked by considerable heterogeneity. Jung et al. (2009) reviewed 70 organizational culture instruments and concluded that no single instrument has emerged as a standard and that the “best” instrument depends on the purpose and context of its use. This reflects a broader pattern for culture research. For instance, Taras et al. (2009) identified more than 100 instruments to measure national culture. In this line of research, culture is modeled using multiple culture types that each capture a specific dimension such as collaboration, innovation, or hierarchy. The extent to which these dimensions are present is then measured using a set of elements or items (reflecting values, practices, attitudes, etc.) that are conceptually related to that dimension. In operational terms, group members are asked to indicate the degree to which they agree with items, after which conceptually related items are combined into an overall score for the underlying dimension. This underscores that there is no universally accepted model of organizational culture (Jung et al., 2009; Tadesse Bogale and Debela, 2024). Rather, culture has been operationalized in multiple ways each relying on its own set, depending on theoretical perspective, level of analysis, and research purpose (Jacobs, 2023).

Following this tradition, the Competing Values Framework (CVF; Quinn and Rohrbaugh, 1983) is among the most widely used models to measure organizational culture (Hartnell et al., 2019). It is composed of two axes that each represent a continuum between two poles. The first axis (“focus”) ranges from an internal focus on factors such as wellbeing and development to an external focus on opportunities and competitors. The second axis (“structure”) distinguishes flexible cultures emphasizing decentralization and change from control cultures emphasizing stability. The combination of these two axes results in four culture types: clan, adhocracy, market, and hierarchy (Helfrich et al., 2007). Importantly, research has shown that these culture types can coexist within organizations (Hartnell et al., 2011, 2019), which allows them to be labeled and treated as separate culture dimensions in empirical research.

The measurement approach described above represents a reflective measurement model (Borsboom and Cramer, 2013; Edwards, 2011; Epskamp and Fried, 2018). Here, the underlying and unobserved higher-order factor is taken to be the cause of the observed items or indicators (Costantini et al., 2015). Scores on items that coincide conceptually are modeled as a function of this unobserved construct and an item-specific error variance (Schmittmann et al., 2013). Likewise, each culture dimension of the CVF can be modeled as a higher-order factor that causes item scores. After conditioning on that factor, dependencies between culture elements are assumed to disappear (also referred to as local independence). For instance, an example item of the CVF from the hierarchy culture dimension is “My facility emphasizes permanence and stability—efficient, smooth operations are important” (Shortell et al., 1995). The reflective model thus postulates that the presence of a hierarchy culture causes facilities to emphasize permanence and stability.

This measurement approach has proven useful for researchers and practitioners for several reasons. First, combining item scores into higher-order culture dimensions is a concise and comprehensible method of assessing culture. Second, it also allows tracking potential changes in such dimension scores over time, for instance before and after an intervention. Finally, reflective measurement approaches have facilitated the study of relationships between organizational culture dimensions and outcomes of interest, both at the individual (e.g., innovation; Büschgens et al., 2013), team (e.g., knowledge sharing; Witherspoon et al., 2013), and organizational level (e.g., organizational effectiveness; Hartnell et al., 2011).

Interconnected nature of culture elements

Despite these advantages, the above approach also comes with limitations that restrict our understanding of an organizational culture. As noted earlier, culture can be understood as a pattern of “synergistic interactions” (Hartnell et al., 2011, p. 687) between different culture elements, which is disregarded when lumping these elements together into a higher-order dimension score. Consider the following two items that belong to the clan dimension of the CVF: (1) “Managers in my facility are warm and caring—they seek to develop employees' full potential and act as their mentors or guides”; and (2) “The glue that holds my facility together is loyalty and tradition—commitment to this facility runs high” (Shortell et al., 1995). Being a reflective model, the CVF attributes covariance between caring leader behaviors and loyalty to the higher-order clan culture dimension. However, rather than forcing this covariance to arise from a common cause, it also makes sense to search for causal connections between these elements. Indeed, there is meta-analytic research showing that caring leader behaviors engender loyalty from followers (Bono and Judge, 2004; Eisenberger et al., 2019). Hence, rather than turning to an invisible common cause, delving into the interconnections between culture elements can help to get a better understanding of how an organizational culture functions. In the preceding example, social exchange theory was used to explain how warm and caring leaders install a reciprocity norm that evokes loyal attitudes and commitment among followers.

In a similar vein, it can be fruitful to examine connections between elements that conceptually belong to different higher-order culture dimensions. Consider the following example items of the CVF: (1) “The glue that holds my facility together is formal rules and policies” (hierarchy); (2) “The glue that holds my facility together is commitment to innovation and development” (adhocracy); (3) “My facility distributes rewards based on rank” (adhocracy). When a reflective model is applied, formal rules and policies and commitment to innovation are considered interchangeable indicators of their higher-order hierarchy dimension. As a result, any covariance between those two and commitment to innovation and development not accounted for by their respective higher-order culture dimensions is considered nuisance (De Schryver et al., 2015; Schmittmann et al., 2013). However, research does not support this exchangeability of culture dimension measures. For instance, whereas the detrimental effect of bureaucracy on innovation is well-established (Hirst et al., 2011), the effect of incentive structure on innovation is still up for debate (Kim et al., 2022). Instead of treating divergent relationships between elements across dimensions as nuisance, we argue that they are indicative of theoretically plausible associations. Allowing and actively exploring unique relationships between culture elements within and across dimensions, allows us to examine relationships that would go unnoticed when modeling culture only as a set of higher-order dimensions.

Both examples illustrate that culture elements rarely adhere to simple structures that are built around common causes. Rather, they are inherently intertwined and form an emergent pattern of mutually interacting elements. It is this particular feature that has led authors to frame organizational culture as “a can of worms” (Laurent, 1990, p. 12). Specifically, attempts to understand a specific cultural element gets one into the whole “messy pack” of all other elements. However, rather than turning away from this complexity, we argue that exploring these meaningful patterns -as highlighted in seminal culture definitions- contributes to our understanding of an organizational culture. However, this requires an alternative conceptualization of culture, and this is where networks can offer added value.

Network conceptualizations

Networks or graphs are in essence a set of elements connected through a set of relations (Borsboom and Cramer, 2013; Bringmann et al., 2019). Psychometric network analysis conceptualizes psychological constructs as networks of interconnected variables. These variables are considered to be autonomous entities that mutually reinforce each other, instead of passive indicators of a higher-order construct (Schmittmann et al., 2013). Nodes represent variables such as single items, sub-scales, or composite scales; the specific choice depends upon the questions being addressed (Hevey, 2018). Edges represent the unknown statistical relationships between nodes and reflect a homeostatic or logical association (Cramer et al., 2012). This is an important difference with social networks, where edges reflect the relationship between known entities such as individuals or organizations (Buso et al., 2019; Epskamp and Fried, 2018).

Network analysis is fundamentally different from reflective and formative models (Schmittmann et al., 2013). Reflective models consider latent culture dimensions (e.g., innovative culture) to be the cause of culture elements. Elements hang together because they are caused by the latent dimension (i.e., local independence). Conversely, formative models consider culture dimensions to be the joint effect of different culture elements. Here again, a latent culture dimension is assumed to account for the covariance between elements. Both reflective and formative measurement models depart from unilinear causality between indicators and constructs and, as a result, disregard reciprocal (causal) relationships among the indicators themselves. Network models, conversely, abandon the idea of latent constructs and instead model the unique interactions between nodes.

This application of network theory gained popularity in clinical psychology where it has been used to study symptoms or states and their interactions, rather than focusing on higher-order disorders (e.g., Borsboom and Cramer, 2013; Bringmann et al., 2019). Similarly, the field of personality psychology has successfully adopted this approach to clarify the complex patterns of interrelationships between different personality building blocks (e.g., Papageorgiou et al., 2019). Conversely, network analysis has only recently been introduced in organizational psychology, to examine the structure of work attitudes (e.g., Carter et al., 2019; Letouche and Wille, 2022) and job performance (Lowery et al., 2021).

Organizational culture networks

Applying the network approach to organizational culture involves an extension of network theory in psychological research. Previous applications have typically focused on modeling associations between psychological variables by making individuals report on themselves (e.g., symptoms, personality, and attitudes). In culture networks, by contrast, individuals report on aspects or elements of an external entity to which they belong, namely an organization. At the measurement level, edges are estimated from between-person covariation in individuals' responses within a focal organization, and interpreted as collective, shared culture structure. Thus, whereas individual-level symptom networks in clinical psychology represent patterns between symptoms drawn from within-person data, organizational culture networks represent between-person covariance among members of the same organization. The network is therefore computed from individual-level responses, but interpreted at the collective level as a shared pattern of culture elements.

When conceptualizing organizational culture as a system of interconnected elements, every culture element functions as a unique component in the sense that it exhibits distinctive patterns of causes and effects in relation to other elements (Cramer et al., 2012). Figure 1 presents a hypothetical network of an organizational culture, hereafter referred to as culture network. Nodes (circles) represent four culture elements of the CVF. The example includes two items of the hierarchy dimension (yellow nodes): “My facility emphasizes permanence and stability—efficient, smooth operations are important”; and “Managers in my facility are rule-enforcers—they expect employees to follow established rules, policies, and procedures.” Green nodes represent sample items of the adhocracy dimension: “My facility is a very dynamic and entrepreneurial place—people are willing to stick their necks out and take risks”; and “Managers in my facility are risk-takers—they encourage employees to take risks and be innovative” (Shortell et al., 1995). Edges (arrows) between these nodes represent their partial correlation, thus controlling for their relations with all other nodes in the network (Costantini et al., 2015). Blue arrows indicate positive relations while red ones indicate negative relations. Finally, the thickness of an edge corresponds to the relative strength of the association.

Culture networks allow exploring the complex and organization-specific relationships between culture elements without regarding these elements as (passive) indicators of a latent construct. In Figure 1, the two leadership behaviors are connected. Instead of relying on a latent leadership styles to account for this shared variance, the culture network approaches this association logically (Cramer et al., 2012): it is difficult for leaders to expect employees to follow rules and procedures, while at the same time stimulating risk-taking. Figure 1 also illustrates that in culture networks, elements from the same higher-order dimension (e.g., Hierarchy) can demonstrate unique patterns of associations with other elements in the network. In this example, for instance, perceptions of rule-enforcement but not environmental stability are associated with risk-taking behavior.

Thus, culture networks revisit seminal work that emphasizes its inherent emergent and shared nature (Ostroff et al., 2012). Emergent phenomena reside at the individual level, but develop into a higher-level construct through interaction (Carter et al., 2018). Likewise, culture is the aggregate of perceptions or attitudes at the individual-level. Culture networks, therefore, focus on the contingencies between culture elements. Since these elements organically interact, edges reflect shared perceptual agreement on the presence or absence of culture elements. Importantly, in this approach this shared perception should not be seen as a consequence of the culture; rather, the culture exists because of these shared perceptions. Culture networks allow operationalizing and studying the shared mental models that employees hold of (certain aspects of) their organization.

Three empirical demonstrations

Having outlined the rationale underlying culture networks, the second part of this paper presents three empirical demonstrations of this approach to illustrate (1) how culture networks are built, and which parameters can be used to evaluate their psychometric quality, (2) how node centrality informs about the relative importance of culture building blocks, or network drivers, and (3) how network connectivity can be used to study cultural inertia.

Demonstration 1: Operationalizing culture networks

The first demonstration serves to illustrate the operationalization of culture networks using the survey data collected in both organizations. Having presented and inspected the two resulting networks, network centrality stability and edge-weight accuracy are assessed after which network differences between both organizations are tested.

Methods

For this purpose, culture data were collected in two organizations. The first dataset comprises 4,270 responses on a culture survey conducted in a large Belgian public service organization. Participation to this survey was high (response rate = 98%) given that this project was part of a larger and mandatory strategic exercise. Respondents' age was 47.76 years on average (SD = 10.00) and 27% identified as male. The average organizational tenure of this sample was 16.13 years (SD = 10.86). The second dataset comprised culture data collected in a Belgian health care organization where all 800 employees were invited to voluntarily participate in an online survey study. This resulted in complete surveys from 338 employees (response rate = 42%), of which 19% identified as male. The average age in this sample was 46.22 years (SD = 12.87). The average organizational tenure of this sample was 11.00 years (SD = 9.13).

Organizational culture was measured in both samples using a Dutch translation of the 16-item CVF scale developed by Shortell et al. (1995). The scale is designed to capture the four original CVF culture dimensions defined by Quinn and Rohrbaugh (1983), namely clan, adhocracy, market, and hierarchy. All four dimensions were each measured using four elements (see Table 1). The CVF was selected because it provides a parsimonious yet comprehensive set of culture elements that are commonly assessed in organizational surveys, making it particularly suitable for illustrating the proposed network approach. Participants indicated the degree to which they agreed with each item using a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree).

Table 1. Overview of all culture items and their corresponding label and culture dimension.

Analyses were performed using the open-source R software and the packages “bootnet” (Epskamp et al., 2018), “networktools” (Jones, 2020), and “qgraph” (Epskamp et al., 2012). In line with von Stockert et al. (2018), missing values were handled using listwise deletion. In all three demonstrations, analyses were performed on the item-level and topological overlap was evaluated to prevent overlapping items from distorting the network (Jones, 2020). Specifically, the goldbricker function was used to examine the degree to which nodes demonstrate similar correlations, which points to item redundancy. However, across both datasets, no problematic items were identified (threshold of 0.25, p < 0.05) and all items could be retained. Finally, in each network, a regularization procedure (i.e., graphical lasso algorithm) with a parameter of 0.5 was used to increase interpretability (Hevey, 2018). Ethical approval for this study was granted by the Ethics Committee (EC) of the Faculty of Psychology and Educational Sciences at Ghent University.

Results

Figure 2 shows the two culture networks. Nodes (circles) reflect culture elements from the CVF and are color-coded to indicate the dimension to which they belong. Edges (lines) denote their association (blue = positive, red = negative). Wider edges correspond to stronger relationships. This culture network shows the interconnections, both within and across culture dimensions, that would go unnoticed when analyzing them at the higher-order level. For instance, hierarchy leader and hierarchy emphasis are positively related in both the public service company (0.59) as the health care organization (0.16). Leaders who ensure that their employees follow rules and procedures might lead their employees to perceive their environment as emphasizing stability, but environments that emphasize stability could equally evoke rule-enforcing leader behaviors. Culture networks can then be used to show how culture arises from the interactions between elements. For example, the network of the health care organization (right-side panel) suggests that leaders are perceived as risk-stimulating (adhocracy leader) in organizations that, among others, emphasize innovation and development (adhocracy emphasis; 0.22) and are performance-oriented (market orientation; 0.16).

A direct comparison of the two networks shows that several associations—both within and across culture dimensions—differ in strength and even in sign between organizations. For instance, adhocracy leader and adhocracy orientation are related in both organizations. However, whereas they exhibit a positive relationship in the health care organization (0.34), this relationship becomes negative in the public service organization (−0.04). In the latter, risk-taking leader behaviors do not create the perception of a dynamic environment or -given the cross-sectional nature- dynamic environments do not necessarily evoke risk-stimulation among leaders. This finding goes unnoticed when analyzing the higher-order adhocracy dimension. Likewise, differences between both networks are observed when focusing on associations between elements from different culture dimensions. For instance, clan leader and market emphasis are positively associated in the public service organization (0.12), but slightly negatively related in the health care organization (−0.05). This implies that, in the latter organization, warm and caring leader behaviors are accompanied by perceptions of reduced competition. Conversely, in the public service organization, those leader behaviors go hand in hand with perceptions of the environment as being achievement oriented.

The accuracy and stability of the networks are examined using bootstrapping (1,000 iterations: α = 0.05). Accuracy refers to variability of edge weights due to sampling variation and is typically evaluated by inspecting the confidence intervals (CIs). If these CIs are large, then it becomes hard to interpret edge weights. Accuracy analyses revealed small to moderate CIs for both organizations. Centrality stability is an index which expresses the extent to which the rank order of node centrality (see further) remains stable after subsetting observations. This form of stability is expressed as a coefficient that represents the maximum proportion of cases that can be dropped, while maintaining a correlation between the original centrality indices and the subset of 0.7 or higher (Epskamp et al., 2018). Higher centrality stability implies greater confidence that conclusions about the most central nodes are not an artifact of sampling variability in this dataset. Results showed stability coefficients 0.75 (public service organization) and 0.67 (health care organization), respectively, which is well-above the recommended cut-off of 0.5 (Hevey, 2018).

In sum, this first demonstration serves to illustrate how culture networks can be operationalized and verified. Further, it shows how interconnections between individual culture elements, within and across dimensions, clarify specific processes within organizations. Importantly, these patterns of interconnections shed a new light on organizational culture differences, where the presence or absence of rules, procedures, and leader behaviors influence the conditional dependencies between all individual culture elements.

Demonstration 2: Identifying cultural drivers

Having explained the rationale behind culture networks and shown how this can be operationalized, a central aim of the current article is to illustrate how this approach can yield new insights into this phenomenon. In this regard, demonstration 2 zooms in on the fundamental question of cultural drivers: what are the roots of a specific organizational culture and where can we intervene to facilitate culture change? In network terminology, this boils down to identifying the most central nodes in the network.

What various culture models have in common is that they typically depart from a limited set of dimensions that are each manifested through more specific elements. In the CVF, each of the four dimensions is realized by averaging out its respective four elements. Importantly, by taking the mean of the observed scores, it is assumed that they are all equally important in determining the culture. This is, however, not necessarily the case. Schein (2010), for instance, highlighted the importance of leadership elements relative to other culture elements in creating, changing, and maintaining an organizational culture. Leader behaviors have also empirically been shown to function as an important driver of other culture elements: leaders establish norms for social conduct (e.g., conflict management; Gelfand et al., 2012), clarify which behavior is valued in the organization (e.g., ethical leadership; Bavik et al., 2018), and establish priorities by allocating resources (e.g., stimulating innovation; Szczepańska-Woszczyna, 2015). Hence, rather than assuming that elements are equally important in shaping culture, a relevant approach is to investigate empirically what elements are the most important drivers of that culture. Knowing these elements helps to understand where cultures come from and how it can be changed.

Using network analysis, this question can be addressed by examining the centrality of nodes in the culture network: elements with more and/or stronger connections with other nodes will be more important for the overall network structure (Hevey, 2018). As a result, interventions aimed at changing these central elements can be assumed to have a larger impact than those changing peripheral nodes (Carter et al., 2019). Building on theoretical and empirical work that stresses the relative importance of leadership, we expect leadership culture elements to be among the most central nodes in the culture network.

Methods

For this demonstration, we rely on data from the large public service organization. Node centrality was operationalized using the expected influence index (EI; Robinaugh et al., 2016). Because EI takes into account the sign of edge values, it is more appropriate in networks with both positive and negative edges, as opposed to strength centrality. Culture elements with many negative edges diminish activation in other elements, making them less important than elements that increase activation in other nodes.

Results

Figure 3 shows the centrality of all nodes in the culture network, presented as standardized z-scores. Nodes are arranged in increasing order of EI centrality. Market leader (EI = 1.12), hierarchy emphasis (EI = 1.08), and hierarchy leader (EI = 1.06) emerged as the three most central nodes. Two leadership elements were among the three most influential nodes. Culture elements with EI values greater than one standard deviation above the mean were considered influential in the network (95% CIs for EI were generated using non-parametric bootstrapping with 1,000 iterations). Following this procedure, market leader emerged as particularly influential. Results suggest that the degree to which leaders formulate challenging objectives is highly influential for employees' perceptions of the organizational culture.

Demonstration 3: Assessing cultural inertia

Besides identifying cultural drivers, which can serve as focal points of attention when designing culture change interventions, network parameters can also be used to inform about the dynamic property of a culture network. Specifically, organizational cultures have been described as notoriously difficult to change (Schein, 1990), and conceptualizing cultures from a network perspective helps to understand why—an organizational culture is an interlocked set of values, practices, and assumptions. According to this view, these elements fit together as a mutually reinforcing system and combine to prevent any attempt to change it. Knowledge about the strength of this system thus informs about the difficulty of changing the system.

The term cultural inertia has already been coined to describe the reluctance of social systems (including organizations) to adopt a different culture (Carrillo and Gromb, 2006). In culture networks, cultural inertia can be formally inferred by assessing the overall level of connectivity between culture elements. Connectivity or density reflects how densely culture elements are connected (“interlocked”) on average and is determined by calculating the extent to which networks contain a lot of (strong) connections between nodes. This is illustrated in Figure 1 which displays a dense culture network. Rule_force and stability are strongly connected (i.e., they have a strong causal impact on each other). Suppose that these two are both highly present in an organization: managers strongly expect employees to follow rules and the facility is seen as a very stable environment. Now suppose the organization decides upon a policy change that no longer urges leaders to enforce rules and, because of this external force, the score on rule_force decreases. The result is an unstable equilibrium: rather than varying independently, their strong connection forces them to align their states when either of the two is changed (Carter et al., 2019). To recover a stable culture network, employees can either (a) undo the perception change (i.e., the decrease of rule_force), or (b) change the perception of all the other culture elements connected to rule_force. With increasing connectivity, the latter option becomes less likely (Dalege et al., 2019). Dense culture networks typically change in an all-or-none fashion, making them more resistant to change. Therefore, we argue that this application of network theory to organizational culture provides a formal operationalization of cultural inertia.

Interestingly, drawing on attitude strength theory (Carter et al., 2019; Dalege et al., 2017), this operationalization of cultural inertia also enables predictions about when (or why) differences in inertia can be observed. Specifically, Carter et al. (2019) showed that “job attitude strength”—operationalized as connectivity—was greater for employees with higher job tenure. The explanation was that, as employees gain experience in a certain job, they are more frequently exposed to specific job attributes, leading to higher density. In a similar vein, we expect connectivity (i.e., cultural inertia) to be stronger for high-tenure employees because: (a) knowledge about organizational culture increases with tenure (Lee et al., 2018), (b) accessibility of information about culture in one's memory increases (Carter et al., 2019), and (c) fit with the culture typically increases (Carrillo and Gromb, 2006; Lee et al., 2018). These factors increase the degree to which people perceive elements present or absent in their culture, causing the associations between them to become clearer, as shown in higher levels of connectivity. We thus expect network connectivity to be higher for employees with higher organizational tenure.

Methods

This demonstration again relies on data from the large public service organization. The network comparison test (NCT; van Borkulo et al., 2023) was used to compare the culture network of low tenure employees with that of high tenure employees. Employees were categorized as low tenure if they had worked less than 5 years in the organization (N = 1,002) and high tenure if they had worked there for more than 20 years (N = 1,349). This resulted in two roughly equal groups which makes the NCT more accurate (von Stockert et al., 2018). The NCT is a permutation test that compares (a) the network structure (i.e., the maximum difference in any edge weight); and (b) global network strength (i.e., connectivity or the sum of all node strengths) based on 1,000 iterations.

Results

The first test states that the overall level of connectivity (i.e., the weighted absolute sum of all edges) is the same across subpopulations. Contrary to our expectations, no significant differences between high tenure and low tenure employees were found (S = 0.56, p = 0.217). The second test examined if the structure of both networks is identical by comparing the maximum difference between edge weights. The null hypothesis (i.e., all edges are equal across both groups) was rejected since significant structural differences were found between both networks (M = 0.18, p = 0.016). This indicates that at least one edge between both networks is different. We thus conclude that the network structure for high-tenure employees is not identical to the structure of low tenure employees. Differences between all the edges were therefore tested. Considering the exploratory nature of this demonstration, no correction for multiple testing was applied. An example is the edge between hierarchy leader and hierarchy emphasis, which was significantly stronger (p = 0.001) for high-tenure employees. Another difference was found with hierarchy leader and clan emphasis, which was stronger for low-tenure employees. Because age and tenure are naturally correlated, we controlled for age by regressing it out of each culture item before estimating the networks, and compared these results to the analysis without the correction. The same pattern emerged in both cases: no significant difference in connectivity but a significant difference in network structure.

Discussion

Organizational culture is a cornerstone concept in organizational psychology, yet new insights into this phenomenon seem to have stagnated (Chatman and O'Reilly, 2016). The current paper proposed reconceptualizing culture in terms of a network of mutually interacting elements, hence the term culture networks. Importantly, this conceptualization aligns better with seminal definitions of the concept which departed from the idea that cultures function as a pattern or system of interconnected norms, values and behaviors that are shared in an organization. Accompanying this alternative conceptualization, network analysis was introduced as a methodological shift to operationalize cultures. We coined the term culture networks and formally tested some of these ideas in three empirical demonstrations using culture survey data collected in two organizations.

Our findings are the first to demonstrate the untapped potential of modeling culture as an interconnected system. First, we modeled 16 culture elements from the Competing Values Framework (CVF) in two organizations. Looking closer at the connections between individual culture elements, the added value of this approach becomes apparent. Specifically, whereas traditional approaches typically focus on higher-order culture dimensions (e.g., clan, adhocracy, market, and hierarchy), culture networks help to understand the organization-specific interactions between the concrete building blocks underneath these abstract dimensions. At this level, it becomes clear for instance how hierarchical leader behaviors are positively related to the perception of bureaucratic environments (in both organizations); or how warm and caring manager behaviors are negatively related to the perception of a competitive organization. Such micro-level associations go unnoticed when focusing solely on culture dimensions, while clearly they can help to better understand the mechanisms and processes underlying culture perceptions.

Moreover, our research illustrates how networks can complement latent dimension-based comparisons when examining cultural differences between organizations. Dimension scores remain useful for summarizing culture in a digestible way and for benchmarking across organizations, whereas networks are more informative when the research question concerns how culture elements relate to each other within an organization. By modeling the (regularized) partial association structure, culture networks provide a “within-culture” view that can be compared across organizations in terms of structure and salient connections. In the current example, risk-taking manager behaviors were positively associated with perceptions of a dynamic work environment in the health care organization, whereas this association was reversed in the public service organization—an organization-specific pattern that would be obscured when aggregating these elements into a single adhocracy score.

Having demonstrated how cultures can be adequately represented as networks of culture building blocks, our second demonstration aimed at showing how network parameters can be leveraged to clarify ongoing debates about culture. Specifically, demonstration 2 focused on centrality as an index informing about the relative importance of nodes in culture networks. Focal questions in culture research and interventions are typically about the origin of a certain culture (“Where does this come from?”) and/or about the potential for interventions (“How can we change it?”). In this regard, centrality has the potential to identify cultural drivers (i.e., building blocks of the interconnected system). Confirming our expectations, the results showed that leadership behaviors were among the most central nodes in this network, with market leader emerging as the most influential node in our analysis. This suggests that, in this specific culture, formulating challenging objectives for employees is an influential element driving employees' perceptions of the organizational culture. Similarly, this also suggests that a change in this node (e.g., through an external intervention) has the strongest effect on other nodes in the network.

Finally, we explored within-organization differences in network connectivity to assess potential differences in cultural inertia. Based on attitude strength theory, we expected the culture network as reported by high tenure employees to be more strongly connected—and, as a result, more resistant to change—as compared to the network for low tenure employees. The results showed, however, that the networks of both groups did not significantly differ in terms of overall connectivity. Instead, more subtle differences were found regarding the network structure and node centrality, indicating significant differences in more specific edge weights both groups.

At first glance, Demonstrations 2 and 3 may seem to be in tension: Demonstration 2 suggests that a change introduced at a central node propagates widely, whereas Demonstration 3 suggests that dense connections make a network more resistant to change. We argue that centrality and connectivity index different properties of the same network rather than opposing predictions. Centrality is a local, node-level property: it identifies which element would propagate furthest through the existing pattern of connections. Connectivity is a global, system-level property that reflects how strongly the network as a whole pulls back toward its prior configuration. Dense connections are therefore double-edged: the same conditional dependencies that allow an intervention on a central node to reach many other elements (Demonstration 2) are also what pulls the system back toward its pre-existing equilibrium once the intervention pressure is removed (Demonstration 3). Put differently, centrality tells us where a change would spread if it takes hold, while connectivity tells us how much sustained force is needed to make it take hold in the first place. This mirrors a well-documented dynamic in networks of mutually reinforcing elements more broadly, where densely connected systems are simultaneously the most responsive to well-targeted intervention and the most prone to reverting to their original state once that intervention is withdrawn (Borsboom and Cramer, 2013; Cramer et al., 2012).

Implications

The ideas and findings presented in this paper have implications for theory, research and practice. Most importantly, we believe the time is ripe for a new conceptualization of culture which pays greater attention to the individual building blocks of a culture and their specific pattern of interconnection. Similar evolutions have already taken place in other disciplines (e.g., research on psychopathology, personality, and work attitudes), thereby providing a new way of thinking about the complex nature of these—often intangible—psychological phenomena. Although not without limitations (e.g., Bringmann et al., 2019), the network approach introduces a new way of thinking about constructs, and in the case of culture networks, this way of thinking actually reflects “going back to the roots.” A defining feature of the network approach is that it relaxes assumptions about narrow behaviors, attitudes or perceptions (as “indicators”) being the result of a limited set of predefined higher-order dimensions. This way, the network approach encourages exploring interconnections using a more exploratory approach, in turn fostering new insights (Lowery et al., 2021). In this regard, the network approach also has an important hypothesis-generating function.

The three empirical demonstrations presented in this paper can be considered first steps in showing the potential of this approach for culture researchers. To further encourage research on culture networks, several promising directions can be outlined. First, this study examined culture in two different organizations. Future research expanding the number and type of organizations will enhance our understanding of how these culture networks are impacted by external factors. Indeed, organizations are embedded in larger, multilayered cultural contexts where shared values on the level of both industries (Ehrhart et al., 2013) and geographical barriers (Van Muijen and Koopman, 1994) have been shown to influence organizational culture. Taking such effects into account could further explain differences across organizations. Similarly, in addition to mapping potential differences between organizations, future research could extend our work by differentiating subcultures within organizations. In a network approach, subcultures are subsets of organizational members with a similar position, job type, or demographic profile that share a unique set of interconnections between culture elements.

Second, culture networks build on the assumption that the interconnected system of elements is the organizational culture. Differences in network structures therefore correspond to meaningful differences with respect to the organizational culture itself. This study applied expected influence centrality and density, but future research could apply other network indices to organizational culture. For instance, clustering refers the extent to which a node and its neighbors form a community within the network (Hevey, 2018). Communities are considered to share underlying mechanisms and potential targets for interventions aimed at changing certain attitudes, values, or behaviors.

Another theoretical implication concerns the relationship between our network findings and the structure of the CVF. Although the CVF is often presented in terms of four distinct culture types and “competing” value orientations, our network results show substantial interconnections across elements, with many associations being positive. This pattern suggests that the CVF elements may co-occur in practice and that “competing” values are better interpreted as ideal-typical tensions or trade-offs rather than as mutually exclusive opposites. Our analyses therefore do not support the CVF's theoretical mapping itself.

Instead, the present study illustrates how culture networks can be used as an empirical lens to examine whether (and how strongly) culture elements align with predefined culture typologies within a given organization. Put differently, this study showed that the fuzzy boundaries (Borsboom and Cramer, 2013) across and within culture dimensions are not necessarily the result of methodological limitations (e.g., items demonstrating poor factor loadings), but they can instead reflect inherent properties of organizational culture. Future research could expand culture networks and include antecedents and outcomes such as innovation, turnover or job satisfaction as nodes. Including external constructs might show direct and indirect pathways through which these phenomena interact with different layers of culture.

Similarly, for reasons of clarity the demonstrations in the current study were restricted to analyses using the CVF as an operationalization of culture perceptions. The CVF originates as a theory-driven typology of organizational effectiveness rather than as an empirically extracted factor structure derived from survey data. This is not a limitation for the purposes of this study, because our goal is not to validate the CVF measurement model, but to use its concrete culture elements as an illustrative and widely recognized item set to demonstrate how culture can be modeled as a network of interrelated elements. However, network analysis is flexible such that future culture networks can be constructed using a variety of employee perceptions.

Culture networks also have several implications for practitioners working for instance in the field of organizational development. It was shown how culture networks can be compared across but also within organizations, and how network parameters such as centrality (pointing to cultural drivers) and connectivity (reflecting cultural inertia) provide useful insights and handles. An additional asset of the network approach is that it generates visual information that can be translated into practical insights in a very straightforward manner. Hence, the networks can be intuitively presented to professionals as culture maps, providing an intuitive way for understanding cultures and for guiding change interventions. As this overview shows, this alternative conceptualization is not intended to replace more traditional approaches, which often focus on profiles of dimension scores. Rather, both approaches seem highly complementary, each one looking at culture from a different angle and providing unique information for researchers and practitioners to work with.

Limitations

The present work has several limitations that need to be acknowledged. First, all analyses in this paper are based on cross-sectional data. Although networks constructed from cross-sectional data reveal group-level conditional independence relationships (Hevey, 2018), conclusions regarding the causality of these relationships remain putative. Longitudinal research is needed to study the causal patterns between cultural elements and examine how culture networks change over time. A related limitation concerns the organizational contexts from which the results were drawn. This paper relied on public service and health care firms, and therefore not the typical for-profit firms that constitute a substantial part of the context in which the CVF was originally developed. As a result, the observed associations between culture elements may reflect institutional logics or professional norms that differ from those commonly found in commercial organizations. Consequently, caution is warranted when generalizing the specific network structures reported here. Future research should examine culture networks across a broader range of organizational contexts to determine the extent to which these patterns replicate.

A second limitation of this work is that all demonstrations relied on the same measurement of organizational culture (i.e., CVF). Given that the purpose of this paper is to introduce culture networks as a new tool, it was a deliberately choice to employ one of the most prominent culture instruments available (Hartnell et al., 2011). Further research could explore networks based on other culture typologies (for an overview, see Chatman and O'Reilly, 2016). Doing so, future studies could abandon the idea of a universal set of culture elements entirely and take an emic approach (Taras et al., 2009) where culture maps are tailored to an organization's specific set of culture elements.

Moreover, the CVF measures each culture dimension along four domains (i.e., leadership, orientation, value, and emphasis). We believe that this connects this research to recent work by Hartnell et al. (2019), who studied culture in the larger organizational system to which it belongs. Indeed, it may be argued that culture's boundaries with leadership, structure, and strategy are ill-defined and/or highly interdependent at the very least. Reciprocal effects between these elements also likely exist. For example, an explorative strategy is likely to create the perception of an innovative culture. Exploring such associations was highlighted by (Chatman and O'Reilly 2016) as a critical direction for culture research. An important contribution of this study is that it provides a new tool to examine these associations.

Conclusion

Organizational culture has always been high on the agenda of researchers and practitioners. Yet, over the course of years, conceptualizations of this concept have moved away from a description of culture elements—and the web or system that they form—to a narrower description of culture profiles based solely on average dimension scores. The current paper aimed to change this conversation by introducing a conceptualization that presents cultures as an emergent system of nodes that are interconnected in a specific—but not predefined—manner. Moreover, as we explained and showed, characteristics of the culture network such as centrality and connectivity provide a new and quantifiable approach to ongoing debates about cultural drivers and cultural inertia, respectively. Researchers and practitioners can build on these ideas to further develop our understanding of organizational cultures and how they can be managed.

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Footnotes

Edited by: Timo Kortsch, IU Internationale Hochschule, Germany

Reviewed by: Carolin Graßmann, SRH University Berlin, Germany

Michael Schachner, VU Amsterdam, Netherlands