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Organizations allocate considerable resources in surveys aimed at assessing how employees perceive certain job aspects. These perceptions are often modeled as latent constructs (e.g., job satisfaction) measured by multiple indicators. This approach, although useful, has several drawbacks such as a strong reliance on local independence and a lower performance in exploratory contexts with many variables. In this paper, we introduce psychological network analysis (PNA) as a novel method to examine organizational surveys. It is first argued how the network approach allows studying the complex patterns of attitudes, perceptions, and behaviors that make up an organizational survey by modeling them as elements in an interconnected system. Next, two empirical demonstrations are presented showcasing features of this technique using two datasets. The first demonstration relies on original organizational survey data (N = 4270) to construct a network of attitudes and behaviors related to innovative work behavior. In the second demonstration, drawing on archival leadership data from an organization (N = 337), the focus lies on comparing structural properties of leadership attitude networks between subsamples of supervisors and non-supervisors. We conclude this paper by discussing how PNA constitutes a promising avenue for researching organizational phenomena which typically constitute a set of interconnected elements.
Content may be subject to copyright.
fpsyg-13-838093 April 27, 2022 Time: 15:22 # 1
published: 03 May 2022
doi: 10.3389/fpsyg.2022.838093
Edited by:
Prasanta Panigrahi,
Indian Institute of Science Education
and Research Kolkata, India
Reviewed by:
Ansar Abbas,
Airlangga University, Indonesia
Shashankaditya Upadhyay,
Indian Institute of Science Education
and Research Kolkata, India
Mayukha Pal,
ABB, Switzerland
Senne Letouche
Senne Letouche
Bart Wille
Specialty section:
This article was submitted to
Organizational Psychology,
a section of the journal
Frontiers in Psychology
Received: 17 December 2021
Accepted: 31 March 2022
Published: 03 May 2022
Letouche S and Wille B (2022)
Connecting the Dots: Exploring
Psychological Network Analysis as
a Tool for Analyzing Organizational
Survey Data.
Front. Psychol. 13:838093.
doi: 10.3389/fpsyg.2022.838093
Connecting the Dots: Exploring
Psychological Network Analysis as a
Tool for Analyzing Organizational
Survey Data
Senne Letouche*and Bart Wille
Department of Developmental, Personality and Social Psychology, Ghent University, Ghent, Belgium
Organizations allocate considerable resources in surveys aimed at assessing how
employees perceive certain job aspects. These perceptions are often modeled
as latent constructs (e.g., job satisfaction) measured by multiple indicators. This
approach, although useful, has several drawbacks such as a strong reliance on local
independence and a lower performance in exploratory contexts with many variables.
In this paper, we introduce psychological network analysis (PNA) as a novel method
to examine organizational surveys. It is first argued how the network approach allows
studying the complex patterns of attitudes, perceptions, and behaviors that make
up an organizational survey by modeling them as elements in an interconnected
system. Next, two empirical demonstrations are presented showcasing features of this
technique using two datasets. The first demonstration relies on original organizational
survey data (N= 4270) to construct a network of attitudes and behaviors related
to innovative work behavior. In the second demonstration, drawing on archival
leadership data from an organization (N= 337), the focus lies on comparing structural
properties of leadership attitude networks between subsamples of supervisors and non-
supervisors. We conclude this paper by discussing how PNA constitutes a promising
avenue for researching organizational phenomena which typically constitute a set of
interconnected elements.
Keywords: psychological networks, organizational surveys, employee perceptions, leadership attitudes,
innovative work behavior
Organizations spend a great deal of time, energy and money on assessing attitudes, behaviors
and/or perceptions of their employees (Rogelberg and Stanton, 2007). Organizational surveys are
widely used for this purpose, systematically collecting employees’ perceptions about a broad range
of psychological constructs including (but not limited to) employee engagement, job satisfaction
and turnover intentions. This assessment can then be used to observe trends in the perceptions of
employees (Lang et al., 2018), serve as input for future decisions (Nieva, 2003), or design attitude
change interventions (Huebner and Zacher, 2021).
Organizational surveys are typically conducted in a similar way: Respondents are presented with
a set of items or indicators each belonging to one of the overarching psychological constructs
of interest. Indicators are statements that refer to behaviors, attitudes, or perceptions. Next,
respondents are asked to indicate the degree to which they agree with each statement. Related
indicators are subsequently combined into an overall score that represents the construct. By doing
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Letouche and Wille Network Analysis for Organizational Surveys
so, organizational surveys all share the same underlying
assumption: The psychological construct causes the item scores.
This is consistent with causal latent variable theory: Items hang
together because they are caused by the same underlying latent
construct (Costantini et al., 2015a). For example, innovative work
behavior (IWB) - a popular construct in organizational research
and surveys (Anderson et al., 2014) - is typically measured
by combining aspects of idea generation, idea promotion, and
idea realization (Janssen, 2000). The scale thus assumes that
higher levels of IWB is what causes individuals to engage in the
generation, promotion, and/or realization of ideas.
Several drawbacks of this approach have been noted. First,
grouping items under latent constructs builds on the premise
that no direct causal relationships exist between indicators both
within and across constructs (Schmittmann et al., 2013). The
latent construct accounts for the covariance between indicators.
This covariance is assumed to disappear after controlling for
the latent construct, also referred to as local independence
(Schmittmann et al., 2013). Going back to innovation, the
common cause (i.e., IWB) accounts for the covariance between
idea generation, idea promotion, and idea realization. Similarly,
covariance with external variables is entirely attributed to the
latent construct since indicators are considered interchangeable.
However, meaningful relationships between indicators are likely
to exist within and across many organizational phenomena.
For instance, research suggests that idea generation often
precedes idea realization (Baer, 2012;Škerlavaj et al., 2019),
thereby violating the assumption of local independence. Likewise,
idea generation and idea realization have been found to be
distinct activities, requiring different resources (Škerlavaj et al.,
2019) and being differently related to other constructs (e.g.,
communication; Hülsheger et al., 2009).
Second, conducting surveys can require a considerable
amount of time and effort (Kavanaugh et al., 2012). To ensure
an efficient use of resources, it is vital to get the most out of a
survey. As a result, a broad range of employee attitudes, behaviors,
and perceptions toward work-related issues is typically assessed.
This also makes sense from a theoretical perspective since
the relationship between different organizational phenomena
is usually influenced by many situational variables (Johns,
2006). However, traditional approaches (e.g., structural equation
modeling or SEM) typically perform less well to study such
complex interplays with large sets of variables (Hevey, 2018;
Letina et al., 2019).
Finally, many employee attitudes and behaviors are
dynamically intertwined. Research by Newman et al. (2010)
showed substantial overlap between different employee attitudes
and bidirectional effects are also observed in many organizational
phenomena (e.g., innovation; Anderson et al., 2014). As a result,
when assessing attitudes and/or behaviors in surveys, it is
often difficult to pinpoint in advance the specific elements that
will relate to one another (and those that will not). However,
studying large sets of variables in an exploratory manner is
precisely where contemporary methods have the tendency to
fall short (Epskamp et al., 2017;Ito, 2020) since they are more
appropriate in confirmatory situations (Costantini et al., 2015b;
Letina et al., 2019).
Building upon recent developments in psychological research
methods, this paper examines employee attitudes, perceptions,
and behaviors from a psychological network perspective.
Rather than interpreting survey elements as a function of
a set of underlying latent constructs, psychological network
analysis (PNA) conceptualizes items as autonomous entities
(Borsboom and Cramer, 2013;Schmittmann et al., 2013). These
entities mutually exert causal force on one another, thereby
forming a complex system. In this regard, PNA helps to
circumvent the aforementioned shortcomings by exploratorily
analyzing a greater number variables at once (Letina et al.,
2019) and by focusing on the interplay between indicators.
Additionally, and importantly, the approach also comes with
a range of indices that convey information about structural
properties of these complex systems (i.e., network centrality
and connectivity) that reflect aspects of the phenomenon
under study that are both conceptually and empirically
distinct from metrics provided by contemporary methods
(Costantini et al., 2015a).
This paper makes two contributions to the field of
organizational survey research. First and foremost, this is, to
the best of our knowledge, the first application of psychological
network theory and analysis to a broad range of organizational
measures. Building on the work of Carter et al. (2019) and
Lowery et al. (2021), who successfully applied PNA to job
satisfaction and job performance, respectively, the current
paper focuses on a wider set of employee perceptions and
attitudes. Thereby, this study answers to the explicit call for
studies to examine many job attitudes and their complex
interactions (Carter et al., 2019). As we will argue below,
exploring the connections between employee attitudes using
PNA is a hypothesis generating endeavor with the potential
to further our conceptual understanding of organizational
phenomena. Second, this paper also makes a practical
contribution. Network analysis yields statistical measures
(i.e., centrality, connectivity) that traditional methods cannot
provide (Hevey, 2018). It is shown how these measures can
be used as complementary tools for those working with
organizational surveys.
This paper proceeds by first outlining the general ideas behind
PNA. Next, the approach is applied to organizational survey
data collected in two organizations. In Demonstration 1, the
conceptual basis of the network approach is illustrated after
which we empirically demonstrate the relevance of network
centrality in particular. In demonstration 2, the focus lies on
examining structural differences in network properties between
subgroups of employees within an organization (i.e., supervisors
versus non-supervisors).
Networks analysis is a method to analyze the interrelations
between elements or nodes which are mutually interconnected
through edges (Bringmann et al., 2019). Importantly, the meaning
and nature of these nodes and edges depends on the phenomenon
being studied. Perhaps the most common application in the field
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Letouche and Wille Network Analysis for Organizational Surveys
of organizational psychology is social network analysis (SNA),
which models the edges between identifiable nodes such as
employees, departments, or organizations. The network structure
can subsequently be related to aspects of the phenomenon
under study. For instance, prior work has investigated how one’s
position in a SNA relates to one’s status (Agneessens and Wittek,
2012), or how edges between nodes facilitate the formation of
friendships (Lowery et al., 2021).
The current study focusses on a different application of
network analysis called psychological network analysis (PNA).
PNA conceptualizes constructs and their interrelations as
complex systems of interconnected elements. Nodes represent
psychological variables that differ across people (e.g., symptoms,
attitudes, and/or behaviors). Edges represent the unknown
statistical relationship between two nodes and need to be
estimated. An important distinction with traditional approaches
to study the relationship between these elements is that nodes
in PNA are treated as autonomous entities with causal power
(Schmittmann et al., 2013;De Schryver et al., 2015). For instance,
symptom-level depression networks are a popular application of
PNA wherein depression is conceptualized as “a causal interplay
of symptoms” (Borsboom and Cramer, 2013, p. 91). Studies have
subsequently demonstrated how depression networks emerge
and behave as a whole (Bringmann et al., 2019). PNA received
rather modest attention so far in the context of organizational
behavior, namely for investigating job satisfaction (Carter et al.,
2019) and job performance (Lowery et al., 2021). However, it can
be argued that such causal interplays are likely to function at the
core of many other organizational phenomena and that PNA can
therefore be deployed to model the relations between a range of
variables often included in organizational surveys.
First, it allows for a better understanding of organizational
phenomena by focusing on patterns of relationships between
behaviors, perceptions, and attitudes that might go unnoticed
when analyzing surveys at the construct level. Consider the
following items of the IWB scale: “I generate original solutions for
problems” (idea generation), and “I put effort in the development
of new things” (idea realization). A network approach treats these
items as autonomous entities that hang together for a causal,
logical reason (i.e., it might be easier to develop new things if
one has previously generated original solutions for problems).
Moreover, a network approach to organizational survey data is
well-suited to study differential relationships between indicators
and one or more variables of interest (e.g., communication).
Whereas traditional approaches would establish an overall
relationship between IWB and communication, the network
approach provides a more fine-grained insight into which
specific innovative behaviors and which communication aspects
incite each other. Examining how attitudes incite each other,
cluster, and/or emerge therefore has the potential of deepen our
understanding of organizational survey data.
Second, by focusing on the interrelations between a
broader range of variables simultaneously, PNA better enables
exploratory research aimed at examining complex systems of
patterns of employee attitudes. Hypotheses following these
patterns can subsequently be tested in confirmatory settings. For
example, there is an extensive line of research which supports the
positive impact of job autonomy on job satisfaction (Griffin et al.,
2001). However, contextual variables such as job characteristics
(e.g., skill variety; Park, 2016) and leadership (e.g., feedback;
Dodd and Ganster, 1996) have been shown to influence this
relationship. Network analysis allows exploring these unique
patterns across and within organizations without the need to
make strong assumptions about their direction or causality.
Another final advantage of this approach is that network
analysis offers several unique statistical metrics that inform
about structural properties of the network. Specifically, the
current study explores both network centrality and connectivity
characteristics. Centrality refers to the relative importance of
a node in a network and has previously been used to study
networks in many areas, ranging from transportation (Liu
et al., 2019) to gene research (Siddani et al., 2013;Hindumathi
et al., 2014). In psychological networks, central nodes (i.e.,
survey elements) have more or stronger connections with
other nodes, making them more important for the network
than other peripheral elements. These structurally important
nodes are particularly useful candidates for interventions within
organizations since a change in these elements is more likely
to affect other elements (Carter et al., 2019). Additionally,
network connectivity or density reflects the degree to which
nodes are connected to each other. In the context of attitude
networks, densely connected networks are more resistant to
change. In the context of organizational surveys, connectivity
informs whether changing job attitudes will be more difficult
for specific groups (e.g., functional departments, geographical
regions, or hierarchical levels). We argue that this approach has
the potential to provide organizations with practical tools that
allow for future decisions or interventions to be tailored to the
specific needs of relevant groups based on topological features of
their network structure.
In sum, we argue that PNA offers several features that
are complementary to more traditional methods for analyzing
organizational survey data. To this end, the paper is built around
two empirical demonstrations, in which we illustrate the meaning
of (a) centrality in attitude networks (i.e., demonstration 1) and
(b) within-organization differences in network structures (i.e.,
demonstration 2).
In psychological network theory, centrality refers to the
importance of a particular node relative to other nodes in
the network and is generally determined by the number of
connections. Several centrality indices have been specified with
strength centrality being the most common one (Costantini et al.,
2015a;Epskamp et al., 2018;Bringmann et al., 2019). Expected
influence, an extension of strength centrality that considers
both the magnitude and the direction of the connected edges
(Robinaugh et al., 2016) is used as the centrality metric in this
study. Expected influence is the summed weight of the edges
that a node shares with the remaining nodes in the network.
Formula 1 presents the mathematical expression of expected
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Letouche and Wille Network Analysis for Organizational Surveys
influence: aij represents an adjacency matrix with binary elements
(1 = edge present, 0 = edge absent) between node i and node j;
wij represents an adjacency matrix whose elements range from
1 to 1, indicating the edge weight between node i and node j
(Robinaugh et al., 2016).
aijwij (1)
In terms of interpretation, highly central nodes are more
important for determining the dynamics and the structure of
the network, and as a result, the psychological phenomenon
under investigation (Bringmann et al., 2019). In the context of
job attitudes, central nodes in a network of job satisfaction have
been shown to be more likely to ‘ripple through’ or affect change
in other elements of job satisfaction (Carter et al., 2019). The
purpose of this first demonstration is to show how centrality can
ameliorate our understanding of a broad network of employee
attitudes related to aspects of innovation.
Participants and Procedure
Data was collected in a large Belgian service organization using an
online survey conducted June 2021. This demonstration is built
around IWB as a focal construct since it lends itself well for PNA.
A selection of four additional variables were also included in the
analyses given their potential relevance for IWB: job autonomy,
communication, team cohesion, and workload. A total of 4,336
employees were instructed to complete the survey, which was
done by 4,270 participants. This translates into a response rate of
98%. The average age of participants was 47.76 years (SD = 10.00)
and 27% identified as male. The average career tenure was
25.64 years (SD = 10.86), and the average organizational tenure
was 16.13 years (SD = 10.86). Ethical approval for this study
was granted by the Ethical Committee of Ghent University. Item
descriptions of the nodes are detailed in Table 1.
Job Autonomy
Job autonomy was measured using the four-item scale developed
by Janssen (2000). An example item is “I can decide for myself
how to get a job done.” The reliability of the scale was adequate
(Cronbach’s α= 0.71, McDonald’s ω= 0.71). The response format
ranged from 1 = Strongly disagree to7=Strongly agree.
Innovative Work Behavior
IWB was measured using the nine items developed by Janssen
(2000). This IWB scale foresees three items for each of the
three subdimensions: idea generation, idea promotion, and idea
realization. Participants were asked to indicate how often they
performed a range of innovative work behaviors. The response
format ranged from 1 = Never to 7 = Always. An example of an
item for idea generation is “creating new ideas for difficult issues.”
Cronbach’s alpha was 0.95, McDonald’s omega was 0.97.
Team Cohesion
Team cohesion was measured using the three-item Perceived
Cohesion Scale adopted by Chin et al. (1999). An example item
TABLE 1 | Description of the items and their label.
Element Item label Item content
Innovative work
Idea generation Generating, developing, and
communicating ideas
Innovative work
Idea promotion Mobilizing support for/championing
Innovative work
Idea realization Transforming innovative ideas into
Autonomy Autonomy process Autonomy over how the work is
Autonomy Autonomy planning Autonomy over the planning
Autonomy Autonomy pace Autonomy over the work pace
Autonomy Autonomy monitor Supervisor does not monitor
Team cohesion Cohesion member Feeling as a member of the
Team cohesion Cohesion morale Happy to belong to the organization
Team cohesion Cohesion belong Seeing one as part of the
Communication Online formal Meeting colleagues online formally
Communication Offline formal Meeting colleagues face-to-face
Communication Online informal Meeting colleagues online informally
Communication Offline informal Meeting colleagues face-to-face
Workload Workload pace Having to work fast
Workload Workload amount Having to work much
Workload Workload extra Having to work extra
is “I see myself as part of this group.” Cronbach’s alpha was 0.84,
McDonald’s omega was 0.85.
Following McAlpine (2018), communication was measured by
capturing communication differences in the mode (offline vs.
online) and content (formal vs. informal). The combination of
these two aspects resulted in four items which were presented
to participants on a seven-point Likert scale ranging from
1 = Strongly disagree to 7 = Strongly agree. An example
item for online formal communication is “Employees regularly
communicate with each other in an online, formal setting (e.g.,
online meeting).”
In line with Dubbelt et al. (2016), workload was measured using
three items that assessed aspects of quantitative demands of the
job. A sample item is “I have to work fast” (i.e., work pace).
Items are scored on a seven-point Likert-scale, ranging from
1 = Strongly disagree to 7 = Strongly agree. The Cronbachs alpha
of the items in this sample was 0.91, McDonald’s omega was 0.91.
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). Missing
data was handled using pairwise deletion. The network was
estimated using Gaussian Markov random field estimation and
implementing graphical LASSO set to 0.5. Edges represent
partial correlations (i.e., controlling for the other nodes in
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Letouche and Wille Network Analysis for Organizational Surveys
FIGURE 1 | Network of employee perceptions, attitudes, and behaviors related to innovative work behavior. Blue edges represent positive relationships, red edges
represent negative relationships. The thickness is proportional to the magnitude of the relationship.
the network). The graphical LASSO penalization causes small
edges to reduce to exactly zero, resulting in a sparser network
(Costantini et al., 2015a). R-code for the analysis is available as
Supplementary Material.
Network analysis assumes that nodes represent unique
entities. Two nodes that were to measure the same underlying
construct would display an inflated correlation. Therefore,
IWB items were combined to form a single node for each
subscale. Furthermore, the goldbricker function was used to
examine if nodes constitute a similar construct or process (Jones,
2020). This function considers two nodes to be indicative of
a similar construct or process if they elicit similar correlations
with other nodes (less than 25% of divergent correlations,
p= 0.01). Results indicated that there were no nodes with similar
correlation patterns.
Figure 1 presents the network structure constructed from the
organizational survey. Nodes represent single items or scales
from the survey and edges represent their statistical relationship
(i.e., partial correlations). The thickness of an edge corresponds
to the strength of that relationship. For instance, autonomy over
how the work is done (autonomy process) is more strongly related
to feelings of being a member of the organization (cohesion
member) than to meeting colleagues face-to-face in an informal
manner (offline informal). The color of edges reflects the sign of
the relationship (blue = positive; red = negative).
This visualization immediately illustrates one the benefits of
the network approach. Instead of having to look at 91 statistics
describing the associations between 17 elements in complex
matrices, networks offer an intuitive representation of the same
information (Costantini et al., 2015a). Importantly, because of the
cross-sectional nature of this dataset, no conclusions can be made
regarding the direction of the relationships between nodes. As
such, the negative edge between online informal communication
(online informal) and idea generation (idea generation) suggests
that higher online informal communication results in less idea
generation but generating more ideas could also decrease the
amount of informal offline communication. We return to this
point in the discussion of the paper.
Once a network has been constructed, its structure provides
information on how different elements influence each other on
a group-level (i.e., group-level conditional independence; Hevey,
2018). For example, turning innovative ideas into applications
(idea realization) hangs together with, among others, generating
ideas (idea generation), mobilizing support for those ideas (idea
promotion), the pace of work (workload pace) and autonomy
over how one’s work is done (autonomy process). In turn, the
pace of work negatively relates to autonomy of one’s work
pace (autonomy pace), the amount of work (workload extra)
and having to work extra (workload extra). This suggests that
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Letouche and Wille Network Analysis for Organizational Surveys
FIGURE 2 | Expected influence centrality of the employee perceptions, attitudes and behaviors related to innovative work behavior. Standardized expected influence
centrality of the attitudes and behaviors related to IWB.
idea realization occurs in a work environment characterized by
limited workload, no hierarchical control, and an emphasis on
earlier stages of the innovation process.
Importantly, the main difference with traditional techniques
to visualize and/or analyze the relationships between indicators
within and across constructs is that the selected indicators now
function as unique entities. For instance, traditional techniques
would explain the negative edge between autonomy over one’s
work pace (autonomy pace) and the pace of one’s work (workload
pace) by relying on the latent constructs job autonomy and
workload. The network approach suggests the latter edge is
indicative of the causal, logical explanation that if employees have
autonomy over their work pace, they will less likely experience
pace workload and vice versa. Thereby, it is not necessarily
assumed that autonomy over the planning of one’s work is equally
related to the pace of one’s work (as would be the case in
latent approaches).
Network Accuracy and Stability
As mentioned earlier, psychological networks are constructed on
sample data. Therefore, investigating the accuracy of the sample-
estimates is required before interpreting structural properties
of the network (e.g., centrality, connectivity). Specifically, the
following features are examined here: (a) the stability of the
centrality indices; (b) the accuracy of the edge weights.
Centrality Stability
Centrality stability informs about the accuracy of centrality
indices. It expresses how well the order of centralities remains
after subsetting the data. Centrality stability is estimated with
the CS-coefficient, which represents the maximum proportion
of cases that can be dropped, such that with 95% probability
the correlation between original centrality indices and centrality
of the subsets is 0.70 or higher (Epskamp et al., 2017).
Several authors note that this coefficient should exceed the
threshold of 0.25 (Glück et al., 2017) and preferably above
0.50 (Hevey, 2018). Following a 1,000-sample bootstrap, the
CS-coefficient of the current network was 0.75 for expected
influence, which suggests that expected influence is a stable
indicator of centrality.
Edge-Weight Accuracy
Edge-weight accuracy assesses the accuracy of the connections in
the estimated network by calculating confidence intervals (CIs)
using non-parametric bootstrapping (i.e., using a 1,000-sample
bootstrap, a 95% confidence bootstrapped CI was calculated for
the edge weights). If these CIs are large, then it becomes hard
to interpret the edge weights. As a result, fewer nodes and/or
more observations will result in more reliable edges. The accuracy
assessment of the edges shows small to moderate CIs overlap
around the edge weight estimates, indicating stable results (i.e.,
little variability in edge weight estimation in each sample). We
refer the reader to Epskamp et al. (2018) for a broader discussion
of edge weight accuracy as well as centrality stability.
Centrality in Organizations
The expected influence of the nodes of the organizational
survey is plotted in Figure 2. The y-axis displays the nodes,
and the x-axis shows the corresponding expected influence as
standardized z-values. Being happy to belong to the organization
(1.15; cohesion morale), mobilizing support for ideas (1.13;
idea promotion) and the amount of work one must do (1.06;
workload amount) were the three nodes with the highest
expected influence. Conversely, online formal communication
with colleagues (online formal) was the node with the lowest
expected influence. This suggests future interventions aimed
at increasing IWB could focus on these central attitudes or
behaviors and their interactions. After all, changes in these
elements should have more influence than interventions focusing
on peripheral nodes.
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The results of organizational surveys can also be used to identify
possible differences between subgroups within organizations,
for instance depending on the department where one works,
the kind of job one occupies, or one’s demographic group.
However, no research up to date has compared subgroups in
organizations on structural properties of psychological networks.
We explore how connectivity and centrality of job attitudes
might be different across different groups of employees within
one organization.
Whereas centrality focuses on single nodes, connectivity
or density applies to the entire network. Connectivity is
calculated by the average correlation between all the nodes
in the network. Greater connectivity typically implies the
tendency of nodes in the network to be more resistant
to change (Dalege et al., 2017). For instance, connectivity
differences in attitude networks of two groups reveals which
group would be more easily persuaded to change its behavior
(Zwicker et al., 2020).
With regard to the factors influencing network connectivity,
Carter et al. (2019) used the notion of attitude strength to
explain how increased exposure to attitude objects increases
connectivity of the attitude network. Specifically, using job
tenure as a proxy for exposure, Carter et al. (2019) demonstrated
that certain aspects of the job satisfaction network became
gradually more dense or strongly connected with higher job
tenure. Drawing from this work, we explore whether higher
leadership exposure results in more densely connected leadership
attitude networks. Holding supervisory responsibilities could
facilitate a more strongly connected leadership attitude network
because this position is likely to (a) increase the volume of
knowledge one has about leadership, (b) heighten awareness of
what constitutes ‘good’ or ‘bad’ leadership, and (c) facilitate
the accessibility of information about what constitutes
leadership. As a result of this increased interaction with the
attitude object, dependencies between different leadership
behaviors can be expected to become clearer, resulting in
a denser network.
For this purpose, we gathered data on perceptions toward
leadership of both employees with and without supervisory
responsibilities (further referred to as ‘supervisors’ and ‘non-
supervisors’) within an organization. All the nodes in our
model therefore concern perceptions or attitudes that people
hold toward their supervisors in the organization. Specifically,
each perception was considered a key leadership behavior
that the participating organization identified as important.
In addition to the general connectivity of the leadership
attitude network, we also explore how centrality of different
leadership nodes might differ for supervisors and non-
supervisors. Greater experience with leadership responsibilities
could lead to a shift in the relative importance of certain
leadership aspects, as shown by more or fewer associations
with other aspects of leadership. No specific expectations
are formulated with respect to the centrality of particular
leadership nodes.
Participants and Procedure
This demonstration uses archival survey data collected in 2018
in a sample of employees (N= 337; 44% male) from a Belgian
department of an international business advisory firm. The
survey assessed perceptions of employees toward their leaders.
Participants were on average 32.79 years old (SD = 9.23).
Employees were considered leaders if supervisory responsibilities
were part of their job role. Of the 337 employees, 181 were
identified as supervisors versus 156 non-supervisors. Missing
data was handled using pairwise deletion. All analysis were
performed using the R software (R Core Team, 2020) and the
packages ‘qgraph’ (Epskamp et al., 2012), ‘bootnet’ (Epskamp
et al., 2018), and ‘NetworkComparisonTest’ (van Borkulo et al.,
2017). The research was conducted according to the ethical
rules presented in the General Ethical Protocol of the Faculty
of Ghent University. R-code for the analysis is available as
Supplementary Material.
The survey covered ten theoretically distinct leadership attitudes
assessing a key leadership behavior that the firm identified as
important (see Table 2). Example items include control (“I feel
controlled by my supervisor”), and trust (“I can count on my
supervisor to be trustworthy”). Employees presented with these
items indicated the degree to which their supervisor displayed
these behaviors (1 = Strongly disagree to7=Strongly agree).
General Network Structures
Similar to Demonstration 1, this network was constructed using
partial correlations and graphical LASSO regularization with a
tuning parameter set to 0.5. This makes the interpretation of the
two group networks identical to the demonstration 1. Stability
TABLE 2 | Description of the items and their label.
Element Item label Item description
“I can engage in an open conversation
with my supervisor if I want to”
Good terms Good terms “I have a good relationship with my
Trust Trust “I can count on my supervisor to be
Support Support “I receive adequate support from my
Autonomy Autonomy “My supervisor entrusts me to work
Feedback Gives
“My supervisor provides me with
sufficient feedback”
“My supervisor is open to my feedback”
Control Control “I feel controlled by my supervisor”
Awareness Awareness “My supervisor is alert to the
atmosphere of group members”
Development “My supervisor provides sufficient
opportunity to develop myself”
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Letouche and Wille Network Analysis for Organizational Surveys
FIGURE 3 | Survey networks of leadership attitudes of both non-supervisors (Left) and supervisors (Right). Blue edges represent positive relationships, red edges
represent negative relationships. Thicker edges represent stronger associations.
assessments showed moderate confidence intervals around the
edge weights. The CS-coefficients for the networks of non-
supervisors and supervisors were 0.36 and 0.52, respectively,
which is well-above the 0.25 cut-off (Glück et al., 2017). This
indicates that the strength centrality index is rather stable and
can be interpreted.
The resulting two networks (see Figure 3) display both
similarities and differences. Of note are the strong connections
between providing feedback (gives feedback) and being open
to feedback (receives feedback) in both groups. Being aware
of the atmosphere (awareness) and providing subordinates
opportunities to develop (development) also display a
similar association. However, receiving adequate support
from supervisors (support) and providing subordinates the
opportunity to develop themselves (development) is strongly
associated for supervisors, but not for non-supervisors. Similarly,
a good relationship with one’s supervisor (good terms) and
receiving adequate support (support) were only strongly
connected in non-supervisors.
Network Comparisons
To test for potential differences between the two networks of
leadership attitudes, we used the Network Comparison Test
(NCT; van Borkulo et al., 2017). The NCT examines the
invariance of three different network aspects: (a) global network
structure; (b) edge strength; and (c) global network strength
or connectivity.
The first test examines whether the structure of the
network as a whole is identical across the two subpopulations
based on examining connection strength between edges
from subpopulations. It informs about differences in the
similarity of edge weight distributions. Results were not
significant (M= 0.273, p= 0.334), indicating that the
network structure is identical across the two subpopulations.
Because the network structure test yields no significant
results, testing group-level differences for specific edges is not
recommended as this increases the likelihood of Type 1 errors
(van Borkulo et al., 2017).
Second, we compared global network strength or connectivity
across the two subpopulations using the NCT, by comparing the
absolute sum of all the edges between groups. The test statistic S
(i.e., the difference in global strength) was 0.66 (p<0.05), which
suggests that the attitude network was denser for supervisors
compared to non-supervisors.
Finally, we tested for potential differences in the strength
centrality between both networks. Out of the ten nodes in the
network, only the quality of given feedback (‘Gives Feedback’)
differed significantly between the two networks (p<0.01). The
amount of given feedback was less central for non-supervisors
than for supervisors.
The management and improvement of positive employee
perceptions is vital for organizations. Understanding how
employees feel about different aspects of their work environment
and how these perceptions influence each other is therefore
paramount to science and practice. Organizational surveys are a
popular means to achieve this, often assessing a broad range of
variables with the goal of initiating change processes to improve
organizational effectiveness (e.g., Huebner and Zacher, 2021).
Building on recent developments in job attitude research (Carter
et al., 2019) and network methodology (van Borkulo et al., 2017;
Epskamp et al., 2018), this study is the first to explore a broad
range of employee perceptions collected in surveys through the
lens of PNA. PNA conceptualizes perceptions, attitudes, and
behaviors as autonomous entities in a dynamic system. Items are
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Letouche and Wille Network Analysis for Organizational Surveys
“part of the construct instead of indicators of the construct” (De
Schryver et al., 2015, p. 5), and can exert causal force on other
employee attitudes.
In demonstration 1, 17 attitudes and behaviors related to
innovation were modeled as a complex system in which they
hang together for causal, logical reasons. The resulting network
demonstrated good edge-weight accuracy and stable expected
influence. Cohesion morale,idea promotion, and workload
amount showed the strongest expected influence centrality.
This suggests that, relative to all other elements, these three
nodes are the most influential employee perceptions in this
network of innovation attitudes and behaviors. Changes in
these elements cause proportionally greater changes to the rest
of the network compared to peripheral attitudes. Although
the results of this study need to be interpreted in their
organization-specific context, taking into regard the centrality
of these organizational survey elements might prove useful for
determining starting points for interventions aimed at improving
employee attitudes related to IWB.
In addition, demonstration 2 illustrates how potential
differences between subgroups within organizations can be
examined. Specifically, the Network Comparison Test (NCT)
was used to compare leadership attitudes of supervisors and
non-supervisors on three network properties: network structure,
connectivity, and centrality. The overall structure of the network
was found to be identical since the distributions of edge
weights did not significantly differ between supervisors and
non-supervisors. However, differences did emerge with regard
to connectivity and centrality. Consistent with theoretical
expectations, the leadership attitudes of supervisors were more
densely connected compared to non-supervisors. The two
networks thus significantly differed with respect to global
strength. This is consistent with the idea that higher exposure
to an attitude object –in this case the leadership role– fosters
conditional dependencies between leadership attitudes (Carter
et al., 2019). Finally, the centrality of the node gives feedback
differed significantly between the two groups, in the sense that
it was more central for supervisors. It could be that supervisors’
experience with leadership results in a higher accessibility of
information on what constitutes ‘good’ or ‘bad’ leadership
behaviors. This could, in turn, cause that element (i.e., the
amount of given feedback) to become more influential. This
result would be consistent with traditional research findings on
leadership, highlighting that supervisor feedback is crucial for
employees (Su et al., 2019).
Our findings have several practical and theoretical implications.
From a practical point of view, the results of our study
provide practitioners with several powerful tools to both examine
and –ultimately– improve employee perceptions. Centrality can
be applied to examine trends in the relative importance of
organizational survey elements. Next, centrality might prove
useful for determining starting points for interventions aimed at
changing the broader attitudinal network (von Klipstein et al.,
2021). In addition, we illustrated how the network approach
can also be used to compare different subpopulations within
organizations. Specifically, connectivity informs about the ease
with which change will occur, whereas differences in centrality
inform about the relative importance of nodes across groups.
These findings can prove useful for organizations who wish to
allocate their resources in the most efficient way (i.e., tailored to
the specific needs of groups).
In addition to these practical implications, PNA can also foster
theoretical developments. Outside organizational psychology,
this approach has already dramatically changed how researchers
look at important phenomena such as clinical disorders (e.g.,
van Borkulo et al., 2015), physical health (e.g., Nudelman et al.,
2018) or personality traits (e.g., Costantini et al., 2015b). More
recently, Carter et al. (2019) and Lowery et al. (2021) were
among the first to argue how network analysis can further our
understanding of important organizational constructs such as
job satisfaction and performance, respectively. The current study
extends these advancements in two ways. First, by considering a
broader set of employee attitudes and perceptions, it illustrates
how the network approach can also lead to new insights into how
different organizational phenomena co-occur or co-develop. Our
study shows that improving belonging might boost IWB (and
covariates) since changes in this perception create the strongest
ripple effect to other perceptions and behaviors. This study also
underpins that reinforcing autonomy requires the consideration
of unique aspects of autonomy and workload. The pace of work
was related to feelings of autonomy over one’s pace but far less
with other aspects of autonomy. This finding would go unnoticed
when analyzing these variables as latent constructs. Second, when
investigating between-group comparisons (i.e., demonstration
2), the current study was also the first to apply PNA to the
leadership domain in particular. There is a long tradition of
leadership research aimed at differentiating between various
styles or behavioral categories, subsequently modeled as (latent)
factors. Here, leadership perceptions are studied at a lower level
as causal forces mutually influencing each other in a dynamic
system. This has the potential to shed new light on leadership
in organizations. Specifically, we showed that leaders’ attitude
networks showed a higher connectivity, perhaps due to their
increased interaction with the focal construct (i.e., leadership). It
was also shown that feedback was more influential for supervisors
compared to non-supervisors.
Limitations and Future Research
This study has some limitations. First, the cross-sectional
nature does not allow for conclusions about directionality of
effects between the different employee perceptions. Nevertheless,
both networks reveal group-level conditional independency in
complex systems of a great number of variables simultaneously
(Hevey, 2018). This exploratory feature leads to the generation
of hypotheses of causal dynamics (von Klipstein et al., 2021). For
instance, following the results of demonstration 1, future research
could examine why formal online meetings are associated with
less informal offline communication, but more informal online
communication. Holding formal online meetings perhaps installs
a norm where people opt for online events more frequently.
Future longitudinal studies could investigate how network
structures as a whole change over time. Similarly, future research
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Letouche and Wille Network Analysis for Organizational Surveys
can investigate other organizational phenomena, beyond IWB
and leadership, using PNA. To give only one example, the
concept of organizational culture is typically defined as a pattern
of shared values, attitudes, and thoughts (Schein, 1990) which
hang together as an interconnected system or web (Hartnell
et al., 2019). Yet, this fundamental principle of interconnections
between culture elements has received little attention so far in
culture research, and PNA opens the way for new insights in
this area as well.
Finally, future research can also study additional network
properties in organizational survey data, such as clustering
(Hevey, 2018). Clustering is a network feature which has proven
useful in the field of clinical psychology, for instance by showing
how different PTSD symptoms form communities (De Schryver
et al., 2015). In the context of organizations, detecting clusters
reveals which job attitudes or behaviors have the tendency
to closely relate to each other and thus share information
in a sensible way.
The current study is one of the first to apply psychological
network analysis to organizational survey data. By modeling a
broad array of employee perceptions, attitudes, and behaviors
as an interconnected set of causal forces, this approach allows
for a new view on important phenomena within organizations
(e.g., innovation, leadership), and how they hang together. Given
the burgeoning attention for network approaches in various
disciplines of psychology, we foresee a growing number of
applications in organizational contexts as well. Future research
can build on the current work to further explore the ways in
which network analysis can enhance our understanding of people
in work settings.
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
The studies involving human participants were reviewed
and approved by the Ethical Committee of the Faculty of
Psychology and Educational Sciences of Ghent University. The
patients/participants provided their written informed consent to
participate in this study.
Both authors listed have made a substantial, direct, and
intellectual contribution to the work, and approved it
for publication.
This research was funded by the Flemish Agency
for Innovation and Entrepreneurship under contract
agreement HBC.2019.2621.
The Supplementary Material for this article can be found
online at:
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Full-text available
Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to compare network structures across populations. However, the statistical tools to do so are lacking. In this paper, we present the Network Comparison Test (NCT), which uses resampling-based permutation testing to compare network structures from two independent, cross-sectional data sets on invariance of 1) network structure, 2) edge (connection) strength, and 3) global strength. Performance of NCT is evaluated in simulations that show NCT to perform well in various circumstances for all three tests: the Type I error rate is close to the nominal significance level, and power proves sufficiently high if sample size and difference between networks are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed.
Full-text available
Objective Within the network approach to psychopathology, cross-sectional partial correlation networks have frequently been used to estimate relationships between symptoms. The resulting relationships have been used to generate hypotheses about causal links between symptoms. In order to justify such exploratory use of partial correlation networks, one needs to assume that the between-subjects relationships in the network approximate systematic within-subjects relationships, which are in turn the results of some within-subjects causal mechanism. If this assumption holds, relationships in the network should be mirrored by relationships between symptom changes; if links in networks approximate systematic within-subject relationships, change in a symptom should relate to change in connected symptoms. Method To investigate this implication, we combined longitudinal data on the Borderline Personality Disorder Severity Index from four samples of borderline personality disorder patients ( N = 683). We related parameters from baseline partial correlation networks of symptoms to relationships between change trajectories of these symptoms. Results Across multiple levels of analysis, our results showed that parameters from baseline partial correlation networks are strongly predictive of relationships between change trajectories. Conclusions By confirming its implication, our results support the idea that cross-sectional partial correlation networks hold a relevant amount of information about systematic within-subjects relationships and thereby have exploratory value to generate hypotheses about the causal dynamics between symptoms.
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Employee surveys are commonly used tools in organizations for the purpose of organizational development. We investigated the post-survey action planning process in 3,091 organizational units (OUs) of one large company in Germany. We expected action planning to lead to improvements on subsequent employee survey scores, with OUs that continuously and repeatedly planned actions showing the greatest improvements. Results suggest that the development of action plans can lead to improvements on subsequent survey scores, but effect sizes were generally small. Furthermore, managers who initiated action planning in the previous year were more likely to do so again the following year. Overall, these findings contribute to the literature on employee surveys by investigating effects of post-survey action planning.
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This study investigates the effect of a network of general trust on the willingness to communicate in English among Japanese people. Previous studies have shown that general trust positively affects the willingness to communicate in English for Japanese people. However, the network structure of general trust and its effects have not yet been revealed. The present study conducted a network analysis with 761 Japanese university students and 601 Japanese social survey participants, for 1362 participants total. Four variables regarding general trust positively affected the willingness to communicate in English for all participants, whereas one variable had a negative effect if each network was estimated for only university students or social survey participants. Centrality indices, such as node strength, closeness, and expected influence, revealed the centrality of several variables in the network of all participants. Bootstrapping methods showed the trustworthiness of the estimated edges and centrality indices. Contrary to the regression analysis, the network analysis can help us understand the profound effect of general trust on the willingness to communicate in a second language, which will prove useful for intervention studies.
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In a time of rapid climate change, understanding what may encourage sustainable consumer behaviour is a vital, but difficult challenge. Using an attitude network approach, we investigated which associations people have toward conventional and bio-based plastic in order to develop an empirically-based approach to initiate attitude- and behaviour change. With a qualitative study (N = 97), we distilled 25 evaluative reactions (i.e. beliefs, emotions, and behaviours) that encompass people's attitude toward using (bio-based) plastic. These reactions were used to create a new scale, which was subsequently tested among 508 online participants. The resulting data was then used to build a network displaying relationships between participants' evaluative reactions regarding plastic use. Analyses on this network indicated that guilt was most strongly connected to people's willingness to pay more for bio-based plastic products. Based on this, we conducted another study (N = 285) in which we experimentally manipulated guilt (general guilt, personal guilt, and control condition) to determine its effects on people's willingness to pay for a sustainable cause. Results indicate that manipulating guilt can lead participants to donate more to a sustainable cause. This effect was fully mediated by self-reported guilt. Determining which factors influence consumers to change their buying behaviour towards sustainability is the first step in creating a demand for more sustainable products amongst the public and investors.
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Job satisfaction researchers typically assume a tripartite model, suggesting evaluations of the job are explained by latent cognitive and affective factors. However, in the attitudes literature, connectionist theorists view attitudes as emergent structures resulting from the mutually-reinforcing causal force of interacting cognitive evaluations. Recently, the causal attitudes network (CAN; Dalege et al., 2016) model was proposed as an integration of both these perspectives with network theory. Here, we describe the CAN model and its implications for understanding job satisfaction. We extend the existing literature by drawing from both attitude and network theory. Using multiple datasets and measures of job satisfaction, we test these ideas empirically. First, drawing on the functional approach to attitudes, we show the instrumental-symbolic distinction in attitude objects is evident in job satisfaction networks. Specifically, networks for more instrumental features (e.g., pay) show stable, high connectivity and form a single cluster, whereas networks regarding symbolic features (e.g., supervisor) increase in connectivity with exposure (i.e., job tenure) and form clusters based on valence and cognitive-affective distinction. We show these distinctions result in "small-world" networks for symbolic features wherein affective reactions are more central than cognitive reactions, consistent with the affective primacy hypothesis. We show the practical advantage of CAN by demonstrating in longitudinal data that items with high centrality are more likely to affect change throughout the attitude network, and that network models are better able to predict future voluntary turnover compared with structural equation models. Implications of this exciting new model for research and practice are discussed.
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Centrality indices are a popular tool to analyze structural aspects of psychological networks. As centrality indices were originally developed in the context of social networks, it is unclear to what extent these indices are suitable in a psychological network context. In this article we critically examine several issues with the use of the most popular centrality indices in psychological networks: degree, betweenness, and closeness centrality. We show that problems with centrality indices discussed in the social network literature also apply to the psychological networks. Assumptions underlying centrality indices, such as presence of a flow and shortest paths, may not correspond with a general theory of how psychological variables relate to one another. Furthermore, the assumptions of node distinctiveness and node exchangeability may not hold in psychological networks. We conclude that, for psychological networks, betweenness and closeness centrality seem especially unsuitable as measures of node importance. We therefore suggest three ways forward: (a) using centrality measures that are tailored to the psychological network context, (b) reconsidering existing measures of importance used in statistical models underlying psychological networks, and (c) discarding the concept of node centrality entirely. Foremost, we argue that one has to make explicit what one means when one states that a node is central, and what assumptions the centrality measure of choice entails, to make sure that there is a match between the process under study and the centrality measure that is used. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Previous scholars have recognized the critical role of supervisors in stimulating employee innovative behavior, although it is still unclear whether and how supervisor developmental feedback impacts employee innovative behavior. To resolve this issue, the present study develops and verifies a moderated mediation model to explore the positive influence of supervisor developmental feedback on employee innovative behavior via creative self-efficacy, as well as the moderating role of a supervisor’s organizational embodiment in this process. Analyses of the multi-time data from 375 employees indicate that supervisor developmental feedback is positively associated with employee innovative behavior via his/her creative self-efficacy. Moreover, a supervisor’s organizational embodiment moderates the influence of supervisor developmental feedback on employee creative self-efficacy and the mediating role of creative self-efficacy. From these analyses, the present study not only further develops several views of pervious research in the field of supervisor feedback and employee innovation, but also provides a potential managerial way to promote employee innovative behavior from the perspective of supervisor feedback.
Organizational citizenship behaviors (OCBs) and counterproductive work behaviors (CWBs) are recognized as two primary dimensions of the job performance domain, each contributing to crucial individual and organizational outcomes. However, an incomplete understanding of the OCB-CWB interplay persists due to a predominant focus on their between-person interrelation and a lack of consistent empirical support for the few within-person OCB-CWB studies. Here we elucidate the dynamic OCB-CWB interplay by integrating moral licensing and moral cleansing theories to form an overarching framework that explains previous inconsistency in findings. Specifically, we argue that performing OCBs can lead individuals to perceive a license to perform subsequent CWBs, whereas performing CWBs can lead individuals to compensate for their undesirable behavior by performing OCBs. In a 10-day daily-diary survey, we utilize the cutting-edge technique of directed-graph psychometric network analysis to test our hypotheses. Results suggest that although there may be a negative or null OCB-CWB relationship due to individuals’ general consistency in behavior, integrating moral licensing and cleansing can explain instances of positive relations wherein OCBs predict subsequent CWBs and vice versa. Our findings demonstrate distinct relations at the between- and within-person levels of this phenomenon, as well as individual differences in the specific behavioral patterning of the OCB-CWB interplay. Notably, the current findings constitute a contribution to both our theoretical understanding of the dynamic OCB-CWB interplay, an empirical test of two major theories of moral behavior applied to the workplace, and points to the practical potential of the psychometric network approach within research and practice.
We explore the impacts of high-speed rail (HSR) development on airport-level traffic by considering not only the availability of air-HSR intermodal linkage between the airport and HSR station but also the position of the airport’s city in the HSR network. The latter is measured by both the degree centrality (to reflect connectivity) and the harmonic centrality (to reflect accessibility). Using a sample of 46 airports in China and a sample of 16 airports in Japan over the period of 2007–2015, we conduct regression analysis and compare the effects of HSR network development on airports in these two Northeast Asian countries. We find that as HSR connectivity or accessibility increases, there is, on average, a decline in airports’ domestic and total traffic in China but little change in Japan. Meanwhile, we observe a strong complementary effect of HSR to feed international flights with the presence of air-HSR intermodal linkage. As a result, some airports may experience a total traffic increase. In China, hub airports tend to gain traffic regardless the availability of air-HSR linkage, while non-hub airports are likely to lose. In Japan, on the other hand, airports with air-HSR linkage tend to gain traffic regardless the hub status. Our analysis also reveals some differentiated impacts of HSR connectivity and accessibility in China. An important policy implication is that the investment in air-HSR intermodal linkage at busy airports may not help with realizing the benefit of congestion mitigation and emission reduction. Rather, policy makers may invest air-HSR linkage at regional airports which have the potential to be converted into international gateway hubs.