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sustainability
Article
Effect of Cross-Departmental Collaboration on Performance:
Evidence from the Federal Highway Administration
Warit Wipulanusat 1, * , Jirapon Sunkpho 2and Rodney Anthony Stewart 3
Citation: Wipulanusat, W.; Sunkpho,
J.; Stewart, R.A. Effect of
Cross-Departmental Collaboration on
Performance: Evidence from the
Federal Highway Administration.
Sustainability 2021,13, 6024. https://
doi.org/10.3390/su13116024
Academic Editor: Katarzyna
Sienkiewicz-Małyjurek
Received: 4 May 2021
Accepted: 22 May 2021
Published: 27 May 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
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iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Logistics and Business Analytics Center of Excellence, School of Engineering and Technology,
Walailak University, Nakhon Si Thammarat 80160, Thailand
2Thammasat University AI Center, College of Innovation, Thammasat University, Bangkok 10200, Thailand;
jirapon@tu.ac.th
3School of Engineering and Built Environment, Griffith University, Gold Coast, QLD 4222, Australia;
r.stewart@griffith.edu.au
*Correspondence: wwarit@wu.ac.th
Abstract:
Cross-departmental collaboration, one of the most salient administrative reforms, has been
promoted to resolve cross-jurisdictional administration issues over the previous three decades. Nearly
all previous empirical studies have examined the direct impact of cross-departmental collaboration
on organizational performance without accounting for the indirect effects of managerial practices.
Using data from the Federal Highway Administration, this study develops an integrated structural
equation modeling and Bayesian network model used to examine both direct and indirect impacts
of cross-departmental collaboration on organizational performance. The structural model indicates
that cross-departmental collaboration has a direct effect on organizational performance and indirect
effects through its influence on resource acquisition and knowledge creation. The scenario-based
simulation suggests the optimal integration of managerial actions to improve agency performance,
which is achieved by encouraging cross-departmental collaboration and supporting the knowledge
creation process. Finally, implications are provided to present practical managerial actions from the
Federal Highway Administration as an exemplar for other highway agencies.
Keywords:
cross-departmental collaboration; organizational performance; structural equation mod-
eling; Bayesian network
1. Introduction
Highway agencies confront long-term governance issues and challenges under the
federalist system due to cross-jurisdictional networks and organizational silos [
1
]. Since
1990, federal transport law has changed focus from highway-centered planning governed
by state transportation authorities to a more collaborative approach and intermodal net-
work. Federal regulations also mandate state agencies to collaboratively develop transport
improvement plans identifying approved projects in each fiscal year [
2
]. Highway planning
includes networks of transportation departments participating in collaborative decision-
making to resolve cross-jurisdictional administration issues. The interdepartmental net-
works are usually a formal structure with designated members operating within the current
governance structure [
2
,
3
]. Multijurisdictional coordination can solve multi-sectoral issues
in highway management, especially megaregional planning [
4
], disaster recovery [
5
], and
operational resiliency [6].
Cross-departmental collaboration, one of the most salient administrative reforms, is
being adopted to enhance the federal departments’ organizational performance [
7
]. Be-
cause highway systems transverse jurisdictional boundaries and modal networks, highway
agencies’ cross-departmental collaboration is multi-directional coordination through ge-
ographic and political boundaries, consisting of horizontal and vertical collaboration [
8
].
Horizontal collaboration is coordination between agencies on the same scale concerning
Sustainability 2021,13, 6024. https://doi.org/10.3390/su13116024 https://www.mdpi.com/journal/sustainability
Sustainability 2021,13, 6024 2 of 22
geographical adjacency and proximity. In contrast, vertical collaboration entails multi-level
connections between federal, state, regional, and local jurisdictions. Cross-departmental
collaborations in both horizontal and vertical approaches develop collaborative networks
for highway management and operation. These networks could respond to unexpected
accidents, manage highway assets, and integrate highway policies, ultimately resulting in
improving organizational performance.
Nearly all previous empirical studies have examined the direct impact of cross-
departmental collaboration on organizational performance [
3
,
9
–
11
]. However, there has
been limited research that accounts for the indirect effects of cross-departmental collab-
oration through the role of managerial practices. This study enables the investigation
of the causal pathways between resource acquisition and knowledge creation through
which cross-departmental collaboration leads to organizational performance improve-
ments. Resource acquisition is grounded on the resource dependence theory (RDT) and
the resource-based view (RBV), which are well-established organizational theories. The
RDT was adopted to empirically investigate mutual dependence between organizations,
resulting in valuable insights and leveraging more understanding of the organization [
12
].
The RDT states that organizations opt to collaborate in exchange relationships due to the
requirement for critical resources [
13
]. The RBV was used as a theoretical lens to under-
stand resource acquisition mechanisms to sustain performance. The RBV highlights that
an organization’s sustainable advantage is derived from the strategic resources that the
organization possesses [
14
]. Nonaka’s theory was adopted to frame knowledge creation.
From a process viewpoint, knowledge creation is defined as an organization’s ability to
create new knowledge, transfer it within the organization, and incorporate it into products,
services, and processes [15].
Furthermore, no research to date has empirically established the causal relationships
between these constructs using a sample from a highway agency. It is vital to highlight that
a lack of theoretical and empirical study on causality among these organizational constructs
provides the scientific problem investigated in this research. Therefore, the purpose of this
paper is to address this research gap by exploring the direct impacts of cross-departmental
collaboration on organizational performance and unraveling the organizational mechanism
of indirect effects through resource acquisition and knowledge creation in the Federal
Highway Administration (FHWA).
2. Conceptual Model
2.1. Theory
2.1.1. Cross-Departmental Collaboration
Cross-departmental collaboration is defined as any joint action across diverse de-
partments that is intended to increase public value and address difficult public chal-
lenges through coordination, partnering, conflict resolution, and cooperation [
3
,
11
]. In
rapidly changing public services, cross-departmental collaboration has become an es-
sential activity for each agency to implement. Cross-departmental collaboration is a
process that encourages other related departments to work together in joint activities to
increase public value and improve governance. In this study, the authors use the terms
“cross-departmental collaboration” and “interdepartmental collaboration” interchangeably
throughout the manuscript.
The characteristics of interdepartmental collaboration can be either dyads or two-
party interactions between department levels within government agencies or at the inter-
organizational level [
9
]. To solve wicked problems that cannot be solved through the
traditional approach, collaboration is crucial to deal with these complicated social problems.
In the past, cross-departmental collaboration was considered an informal or coincidental
activity, and at best, it was an administrative experiment [
10
]. Currently, cross-departmental
collaboration has been developed and accepted as a common concept. For example, in
the United States, interdepartmental collaboration has become the gold standard in public
Sustainability 2021,13, 6024 3 of 22
management. Furthermore, federal, state, and local departments need to coordinate under
collaborative governance to receive financial support for the public service program [9].
2.1.2. Resource Acquisition
Resource acquisition refers to how an organization acquires tangible and intangible
resources from internal and external environments [
16
]. This study adopts two concepts of
resource acquisition: resource dependence theory (RDT) and resource-based view (RBV).
The RDT proposes that, to succeed, organizations depend on critical resources from the
external environment to maintain their existence and development [
13
]. The solution to
reducing dependency is to collaborate with other organizations to secure the needed critical
resource. From another perspective, the RBV theory states that a subset of the resources
enables the organization to achieve effectiveness and contributes to long-term efficiency.
The RBV theory highlights the significance of the ability to exchange the information and
knowledge systems with collaborative partners [
17
]. Organizations should sustain the
critical resources of tangible assets (e.g., capital, machinery, and workforce) and intangible
assets (e.g., intellectual capital, social resources, technology, capabilities, and competencies).
Several methods can be applied to acquire resources, including resource attraction, resource
purchase, and external resource sharing [16].
2.1.3. Knowledge Creation
Knowledge creation is defined as a dialectical process, where various contradictions
are integrated through dynamic and interactive synergy among employees, the organi-
zations, and the working environment [
15
]. Knowledge creation is a generative process
and involves accumulating organizational knowledge stock through collecting knowledge
from internal and external sources through individual learning. Each member interacts
with other members by synthesizing existing knowledge and sharing knowledge and
lessons learned through collaborative networks. Knowledge creation can be principally
categorized into two perspectives. The first perspective, called a stock view, means the
activities and initiatives employees engage in accumulate their organizational knowledge
stock [
18
]. Another perspective is defined as a process in which an organization can col-
lectively generate novel knowledge; distribute it within the organization; and integrate
it with products, systems, and services. In the knowledge-creating theory, Nonaka and
Toyama [
15
] divide knowledge into two categories: explicit and tacit. Explicit knowledge
is the documented knowledge in textbooks, procedural manuals, and contract documents
(e.g., drawings, specifications, and conditions of contract). This type of knowledge is easy
to codify, capture, and disseminate [
19
]. In contrast, tacit knowledge is the skill, expertise,
intuition, and judgment of an individual accumulated from work experience, which is
not simple to exchange among the organization members. Knowledge is created through
a synthesized process, occurring from repeated interactions between explicit and tacit
knowledge.
2.1.4. Organizational Performance
Delivering efficient and effective public service is a critical organizational outcome
of government agencies. The organizational outcomes can be measured by perceived
organizational performance in terms of an organization’s ability to effectively achieve its
objectives and missions through work quality [
7
]. Measuring perceived performance is
appropriate for this study because the perception of an employee, acting as an internal
constituency, can further the understanding of actual organizational performance as they
have direct experience with their organization’s mission accomplishments. Since the
administration’s goal is to enhance organizational performance, operating results can
provide guidance for executives to improve public service. Therefore, many government
agencies have deployed organizational performance as the focus of the agencies’ agenda.
Sustainability 2021,13, 6024 4 of 22
2.2. Hypothesis Development
We ask the following research question: what is the interplay between cross-departmental
collaboration, resource acquisition, and knowledge creation in improving organizational
performance in the FHWA? Hypotheses have been formulated to answer this research
question and to confirm the conjectured relationships.
2.2.1. Cross-Collaboration and Resource Acquisition
Highway agencies strive to receive funding through the mechanism of annual budgets
and the authorization of permanent legislation [
20
]. Contracts are a common and formal
tool for highway agencies to obtain necessary resources. They are intended to minimize
future risks that may exist in the process of an exchange agreement. Nevertheless, high-
way agencies’ success is also directly or indirectly related to their capability to acquire
essential resources from collaborative partners. The informal approach of applying more
collaborative strategies, like alliance, co-optation, and integration, can help agencies seek
more profound commitment from partners [
21
]. From the RDT perspective, resource
dependence between highway agencies and their partners has formed a self-reinforcing
structure of mutual power and increased pragmatic benefits, creating network ties to
improve resource acquisition.
Because the highway agencies’ public services are policy, planning, operation, and
maintenance of highway networks, both tangible and intangible resources are primary
factors for organizations’ efficiency and effectiveness [
22
]. Organizations possess a different
set of tangible and intangible resources that uniquely present their distinctive identities.
These organizations can acquire intangible assets through network ties based on joint
activities between agencies, so they do not usually need a contribution of tangible resources
(e.g., finance, machines, and materials). Practice-based collaboration through social and
informal networks, supported by the leaders, results in an accumulation of intangible
resources such as technology transfer between agencies [
16
]. The outside-in capabilities of
agencies also include exchanging diverse competencies and skill sets between informal
networks, so these networks are important channels for agencies to acquire resources [
23
].
Thus, based on previous findings, cross-departmental collaboration supports resource
acquisition from mutual relationships between departments.
Hypothesis 1 (H1).
Promoting cross-departmental collaboration will increase resource acquisition.
2.2.2. Cross-Departmental Collaboration and Knowledge Creation
Interdepartmental collaboration is a primary factor in supporting employees’ access
to diverse ideas, various information sources, and broader knowledge [
24
]. Such collabo-
rations equip the employee with in-depth knowledge and experience-based skill. These
activities help create knowledge repositories and improve job-related skills, leading to
the accumulation of innovative knowledge that spans across inter-organizational bound-
aries [
25
,
26
]. Knowledge sharing also occurs through collaboration between departments,
enabling improved organizational learning capability.
Because highway planning involves multi-jurisdictional cooperation, highway agen-
cies are required to expand their duties beyond a geographic proximity, making their
boundaries increasingly blurred [
7
,
27
]. Therefore, the collaboration between organizational
boundaries is an effective learning channel for knowledge creation among the various
parties in the provision of highway services [
19
]. This interaction encourages engineers to
acquire knowledge and skills related to the duties for which they are responsible. Previous
literature has led to the assumption that a high level of cross-departmental collaboration
supports knowledge creation in an organization.
Hypothesis 2 (H2).
Promoting cross-departmental collaboration will increase knowledge creation.
Sustainability 2021,13, 6024 5 of 22
2.2.3. Resource Acquisition and Knowledge Creation
RDT posits that organizations need to garner necessary resources from their collabora-
tive networks to maintain operation and development [
13
]. Ding and Huang [
28
] revealed
that sufficient resource sharing positively impacts collaborative knowledge creation. The
success of collaborative knowledge creation depends on adequate and timely allocation
and sharing of resources. Knowledge creation is recognized as a resource-sharing pro-
cess through interdepartmental collaboration between individuals and divisions and as
extra-organizational entities formed as dyads or networks [14].
In the built environment, Goh and Loosemore [
29
] asserted that the critical intangible
asset is social resources, which are agencies’ connections to their collaborative networks.
Thus, agencies’ connections with other federal authorities, state agencies, and municipali-
ties are crucial channels for engineers to engage in the knowledge creation process [
17
,
20
].
Engineers can create technical knowledge of highway planning based on legislation rele-
vant to federal, state, and local standards [
30
]. Because engineers can acquire knowledge
from collaborative networks through highway agencies’ connections, they can create new
knowledge based on highway agencies’ existing knowledge [
19
]. The newly created knowl-
edge can be tacit (e.g., experience, judgment) or codified (e.g., drawings, specifications,
and manuals). This mechanism can help them share a lesson learned from projects, which
leads to the development of engineers’ technical abilities [
14
]. According to these studies,
resource acquisition is hypothesized to be directly related to knowledge creation.
Hypothesis 3 (H3). Resource acquisition has a positive impact on knowledge creation.
2.2.4. Cross-Departmental Collaboration and Organizational Performance
Many benefits arising from cross-departmental collaboration support the achievement
of public policy goals. The collaborative network has access to resources among various
organizations; thus, they can collaborate to overcome budget constraints. Another advan-
tage of collaboration is risk sharing, especially in highway construction projects, which
inherently have high-impact risk factors [
27
]. The benefits of departmental collaboration
could significantly impact the performance of public organizations, as the transition would
be more radical than in less bureaucratic and hierarchical private companies [7].
Cross-departmental collaboration is considered a means to enhance an organization’s
effectiveness and efficiency, leading to improved organizational performance [
10
]. To
present transportation evidence, Margerum and Parker [
2
] examined a multilevel net-
work of Area Commissions on Transportation, established for transportation planning
and management in Oregon. These collaborative networks encouraged cooperation and
communication to solve interjurisdictional issues in transportation investment, which in
turn improved agencies’ performance.
Hypothesis 4 (H4).
Cross-departmental collaboration has a positive effect on organizational
performance.
2.2.5. Knowledge Creation and Organizational Performance
There are several empirical studies on the positive impact of knowledge creation on
organizational performance in the construction industry. Based on a survey of small- and
medium-sized construction enterprises in Malaysia, knowledge creation, contributing to
inter-organizational learning through the established external links of inter-organizational
relationships, had a significant impact on these enterprises’ performance [
31
]. In a study of
Singaporean construction companies, Pheng [
32
] also examined the relationships between
organizational learning, construction productivity, and performance. They found that
organizational learning significantly impacts construction productivity, which increases
competitive advantages and improves company performance.
Sustainability 2021,13, 6024 6 of 22
In highway agencies, significant construction delays are caused by external issues,
such as the unsolved acquisition of land, increased prices of construction materials, limited
coordination and knowledge sharing among parties, and public utility relocation [
19
,
33
].
Engineers who participate in the knowledge creation process tend to possess more devel-
oped skills and an increased depth and range of knowledge, equipping them with the
critical thinking necessary to effectively solve such delays [
19
,
34
]. Alashwal et al. [
19
] con-
ducted a case study of highway projects in Malaysia to develop the knowledge utilization
process that can be applied to manage project delay. The empirical research presented how
engineers create and utilize new knowledge to develop solutions to resolve delay issues,
consecutively improving project performance. In the public sector context, Ngah, Tai,
and Bontis [
35
], in a study of the Roads and Transport Authority of Dubai, indicated that
knowledge creation, as the component of knowledge management capability, is the critical
antecedent variable of organizational learning and improves organizational performance.
Thus, knowledge creation is expected to positively enhance organizational performance.
Hypothesis 5 (H5). Knowledge creation has a positive impact on organizational performance.
2.2.6. Resource Acquisition and Organizational Performance
The RBV argues that resources are acknowledged as a primary driver for competitive
advantage if they are unique, valuable, inimitable, and company-specific [
36
]. Dzeng and
Wen [
37
] revealed construction firms should highlight the significance of the acquisition of
tangible resources assets (e.g., plant or construction equipment) and intangible resources
(e.g., patented construction methods, in-house geographic data). Both assets interact in spe-
cific ways to develop construction companies’ core competencies and distinct identities [
29
].
Barrett and Sexton [
38
] argued that construction innovation occurred because of how firms
utilized their unique resources to generate innovation. In the proactive resource-push view,
which applies the RBV as a theoretical lens, construction firms develop more innovation
capability if they innovate because they have sufficient abilities. This approach provides a
more solid cornerstone for construction innovation than market-pull orientation, where
clients demand innovation implementation [
39
]. Therefore, acquiring unique and sufficient
resources is essential to survive the competition, innovate, and generate high profit, which
is crucial for improving firm performance.
Hypothesis 6 (H6). Resource acquisition has a positive impact on organizational performance.
The theorizing process is based on a deductive approach, which involves a devel-
opment of hypotheses based upon literature and theories, then followed by hypothesis
testing [
40
]. Prior empirical studies and theoretical literature were reviewed to develop
these six hypotheses. The conceptual model was proposed based on the hypothesized
relationships, as displayed in Figure 1.
Sustainability 2021,13, 6024 7 of 22
Figure 1. Conceptual model.
3. Research Methodology
3.1. Study Design and Participants
The survey approach is used because the study aims to assess the causal relationships
between organizational constructs by using a questionnaire tool to address a scientific prob-
lem. This study used the collected data from the Office of Personnel Management, which
conducted an annual employee census, known as the Federal Employee Viewpoint Survey
(FEVS). The survey was organized to collect data from officials of diverse backgrounds in
the US federal departments. The FEVS employed a stratified sampling method to provide
survey results, representing the entire workforce of federal departments and staff within
individual departments [
41
]. The samples are full-time, part-time, permanent, non-seasonal
employees of large departments and small or independent agencies. The online question-
naire was administered to measure aspects of attitudes, opinions, and perceptions toward
the organization’s life. The questions related to the employees themselves, as well as their
managers and their organizations, covering diverse topics regarding work collaboration,
resource allocation, knowledge management, and organizational performance. The FEVS
covers various aspects of public administration and provides generalizability and repre-
sentativeness for the US federal agencies. The FEVS dataset has also been used for analysis
to publish more than 40 articles related to organizational research studies [42].
The study used the data drawn from the Federal Highway Administration (FHWA),
which is an agency under the United States Department of Transportation. The agency
manages federal budgets for the National Highway System and supports state and local
governments in the highway’s design, construction, and maintenance. The FHWA is
headquartered in Washington, DC, and has one division office in each state. There are
approximately 3000 employees nationwide, among whom civil engineer is by far the most
typical profession, followed by transportation experts [
43
]. A sample of the 2019 FEVS
dataset was selected for data analysis because this dataset was the most recent published
for public use. For analysis, responses with missing values in any one of the research
items were excluded from the sample, leaving a final sample size of 1535. To briefly
summarize respondents’ characteristics, about 66% of the sample were male, while 34%
were female. Regarding education level, employees were well educated: 94.4% held a
tertiary degree (bachelor’s degree and beyond). As for employment length, 35.7% had job
tenure fewer than ten years, 33.1% were between 10 and 20 years, and 31.2% were longer
than 20 years. In terms of position, 19.8% were in supervisory positions and 80.2% reported
a non-supervisory role. According to the ethnicity, 75.9% were white.
Sustainability 2021,13, 6024 8 of 22
3.2. Measurement
The authors selected the questionnaire items used in this study after an extensive
review of all the questions in the 2019 FEVS. Eight questionnaire items were selected and
grouped according to the literature of each latent construct. The questions were measured
with a 5-point Likert scale (i.e., 1 = “strongly disagree”, 3 = “neutral”, and 5 = “strongly
agree”). The exogenous construct is the practice of cross-departmental collaboration (CC).
The first question was “Managers promote communication among different work units”.
The second item was “Managers support collaboration across work units to accomplish
work objectives”. These two items were used to measure interdepartmental collaboration
by Lee [7]. Cronbach’s alpha (α) was 0.934.
The first endogenous construct, resource acquisition (RA), was measured using the
two questions: “I have sufficient resources (for example, people, materials, and budgets)
to get my job done”, and “I have enough information to do my job well”. The reliability
alpha for this construct was 0.719.
The second endogenous construct, knowledge creation (KC), was measured using
the following two questions: “The workforce has the job-relevant knowledge and skills
necessary to accomplish organizational goals”, and “The skill level in my work unit has
improved in the past year” (Cronbach alpha = 0.702).
The dependent construct is organizational performance (OP), measured by two ques-
tionnaire items: “How would you rate the overall quality of work done by your work
units? ” and “My agency is successful at accomplishing its mission”. In prior studies,
these questionnaire items were administered to measure organizational performance [
7
,
44
].
Cronbach’s alpha was 0.721. All the constructs have Cronbach’s alpha values greater than
0.70, indicating that the item questions are reliable and present uni-dimensionality within
their measurement scale [
45
]. Harman’s single-factor test was conducted to detect the
possibility of common source bias in the self-reported survey method [
46
]. The result
indicates that the first factor accounted for less than 50% of the variance. Thus, the common
source bias did not suggest any serious concern.
3.3. Research Design
This research adopted a hybrid approach by integrating structural equation modeling
(SEM) with the Bayesian network (BN). SEM is a confirmatory technique that examines
casual relationships in a conceptual model, and it is appropriate to explain established
theoretical relationships from pre-existing knowledge [
47
]. In contrast, BN is an exploratory
technique to provide theoretical explanations by learning the quantitative probabilities
from the data [
47
,
48
]. This novel approach combines a theoretical construction based on
an empirically validated structural model with a graphical interaction’s BN. A conceptual
model was formulated to test the hypothesized relationships between the constructs using
SEM. Subsequently, the BN, developed based on the structural model’s causal relationships,
was utilized as a decision support model.
3.3.1. Structural Equation Modeling
Structural equation modeling is a multivariate statistical technique combining factor
analysis and path analysis. This technique allows researchers to develop, test, and confirm
causal relationships among constructs, represented by multiple indicators that help address
the problem of measure-specific errors [
49
]. This study adopted a two-step approach that
consisted of measurement model validation and structural model assessment, analyzed
using AMOS 22.0.
A measurement model is formed by two linear equations that identify the relationship
between the latent construct and the observed variable, with terms denoted as follows:
x=Λxξ+δ(1)
y=Λyη+ε(2)
Sustainability 2021,13, 6024 9 of 22
where
xis a column vector of exogenous, or independent, variables
Λxis the coefficient matrix of exogenous factor loadings of xon ξ
ξis a vector of the independent latent variables, exogenous variables
δis a column of measurement errors in x
yis a column vector of endogenous variables
Λyis the coefficient matrix of endogenous factor loadings of yon η
ηis a vector of latent dependent, or endogenous, variables
εis a column vector of measurement errors in y.
The form of the structural model is expressed by the following equation:
η=βη +Γξ+ζ(3)
where
βis a coefficient matrix of direct effects between endogenous variables
Γis a coefficient matrix of regression effects of the exogenous variables
ζis a column vector of the residual error.
3.3.2. Bayesian Network Modeling
Bayesian networks, also called belief networks, are probabilistic graphical models
representing joint probability distribution over a set of random variables. The network
topology of a BN is represented by a directed acyclic graph (DAG), which is a pair G = (V, E).
A set of nodes that represents variables or attributes is denoted by V, and a set of directed
edges, represented by E, connects the nodes representing causal relations [
50
]. The DAG is
a graphical representation of a set of nodes and directed edges. In the DAG, when there
is a directed edge from node Vi to node Vj, node Vi is called the parent node of node Vj,
and node Vj is the descendent of node Vi, also known as a child node [
51
]. A conditional
probability table (CPT) describes each child node’s joint probability distribution, expressing
the relationships’ strengths conditioned by combining the parent nodes’ values. If a node
has no parent node, it is expressed by a prior probability. The following formula expresses
the joint probability distribution of the BN [50]:
P(V1,V2,..., Vn) = ∏N
i=1P(Vi|Parent(Vi)) (4)
where nodes are random variables denoted as
V1
,
V2
,
. . .
,
Vn
. A DAG consists of
n
number
of nodes. The set of all random variables
Vi
with an arc connect node
i
and
j
is represented
by Parent(Vi).
4. Results
4.1. Structural Model
The first step is to examine the measurement model using confirmatory factor analysis
(CFA). The purpose of this step is to consider whether the observable variables (i.e., indica-
tors) combine to represent the latent variables (i.e., construct) and confirm that indicators
are hypothesized to measure each construct [
52
]. The study applied the maximum likeli-
hood approach to conducting the CFA. Furthermore, the study adopted several descriptive
goodness-of-fit indices to establish the validity of the model: goodness-of-fit index (GFI),
comparative fit index (CFI), Tucker-Lewis index (TLI), incremental-fit index (IFI), standard-
ized root mean square residual (SRMR), and root mean square error of approximation
(RMSEA). The fit indices are categorized as absolute fit indices (i.e., Chi-square, GFI, SRMR,
and RMSEA) and incremental fit indices (i.e., CFI, TLI, and IFI). Absolute fit indices are a
direct measure of the degree to which the hypothesized model fits the observed data [
49
].
In contrast, comparative fit indices assess how well a hypothesized model fits, comparing
with a null model [
53
]. The Chi-square statistic calculates the difference between a hypoth-
esized model and observed data. The GFI evaluates the proportion of the variance in the
sample variance-covariance matrix. The SRMR is calculated from covariance residuals,
Sustainability 2021,13, 6024 10 of 22
describing the difference between observed data and the hypothesized model, while the
RMSEA assesses the lack of fit to the saturated model. The CFI compares the proportionate
improvement in the model fit of the hypothesized model over a null model. The TLI
determines a correlation for model complexity. The IFI calculates the chi-square difference
between the hypothesized model and a baseline model with uncorrelated variables [54].
To be an acceptable model, all six indices need to pass the following criteria: GFI,
CFI, TLI, and IFI > 0.90; SRMR < 0.05; and RMSEA < 0.08 [
49
,
55
]. Since chi-square is
sensitive to large sample size, and chi-square and degree of freedom (df) were used for
descriptive information [
56
]. The CFA results revealed that the measurement model
presented acceptable fit indices (
χ2
= 189.2, df = 14, GFI = 0.97, CFI = 0.98, TLI = 0.95,
IFI = 0.98, SRMR = 0.03, and RMSEA = 0.08). As presented in Table 1, all the constructs of the
measurement model had R2 values greater than 0.50, confirming convergent validity. The
composite reliability values (CR in Table 1), ranging from 0.71 to 0.94, exceed the suggested
values of 0.70, demonstrating that these indicators have sufficient internal consistency and
represent the respective constructs. The average variance extracted values (AVE in Table 1)
of all constructs also passed the suggested 0.50 cut-off, indicating that questionnaire items
for each construct captured more variance in the underlying construct than the amount of
variance caused by measurement error [
49
,
55
]. After validation, the measurement model
was subsequently adopted for structural model assessment.
Table 1. Indicators of measurement model.
Construct αR2CR AVE
Cross-Departmental Collaboration 0.934 0.94 0.94 0.88
Resource Acquisition 0.682 0.83 0.71 0.55
Knowledge Creation 0.719 0.70 0.74 0.59
Organizational Performance 0.721 0.78 0.72 0.57
In the second step, SEM was conducted to test the theoretical relationship and esti-
mate the paths’ strength between constructs simultaneously. Because indicators measure
underlying latent constructs, SEM can be used to correct the measurement error [
55
,
57
].
In model comparison, SEM reports fit indices, which can be used as a criterion to select
the best-fitted model. Thus, SEM is methodologically appropriate to test the conceptual
model’s hypotheses.
The fit indices for the structural model in this study are presented in Table 2. After
considering the fit indices, the RMSEA value was 0.09, greater than the acceptable level
of 0.08; therefore, the conceptual model might not have been the best-fitted model. The
post hoc modification was applied using model trimming to delete the path, which results
in a best-fitted model. Model trimming was conducted by deleting the path in which the
standardization residual was higher than
|4|
[
54
] and then determining improvements
in the fit indices. Complying with the criteria of model trimming, the revised model
was developed by deleting the hypothesized path from the resource acquisition to the
organizational performance.
Table 2. Fit indices of structural model.
Model
Fit Indices
χ2df GFI CFI TLI IFI SRMR RMSEA BIC
Conceptual model 197.82
16
0.968 0.975 0.956 0.975
0.027 0.086
237.81
Revised model 189.14
15
0.970 0.976 0.955 0.976
0.027 0.079
231.14
The revised model presented an acceptable level of model fit (
χ2
= 189.14, df = 15,
GFI = 0.97, CFI = 0.98, TLI = 0.96, IFI = 0.98, SRMR = 0.03, and RMSEA = 0.08). Additionally,
the Bayesian information criterion (BIC), appropriate to compare models with large sample
Sustainability 2021,13, 6024 11 of 22
sizes, was used to assess parsimony for model comparison [
58
]. The model with lower BIC
is more parsimonious than the compared model. The BIC of the revised model was less
than the value of the conceptual model. Consequently, the revised model was considered a
parsimonious model and thus was accepted as the final structural model, as displayed in
Figure 2.
Figure 2. Final structural model.
Using a deductive approach, structural equation modeling has been employed to
empirically test the hypotheses because deductive reasoning establishes firm criteria for
verifying hypotheses [
40
]. The standardized regression coefficients are presented in Table 3.
Cross-departmental collaboration was considered an exogenous factor (
γ
), while the other
remaining constructs were considered endogenous factors (
β
). The critical ratio (C.R.) is
calculated by dividing a path parameter’s regression weight by its standard error. The
C.R. is interpreted similarly to the Z-test, whereby the path with a C.R. greater than 3.29
is significant at the 0.001 level [
54
]. Cross-departmental collaboration exerted a strong
and positive influence on resource acquisition (0.692, p< 0.001), supporting H1. Cross-
departmental collaboration had a moderate and positive impact on knowledge creation
(0.274, p< 0.001), thus H2 is supported. The association between resource acquisition
and knowledge creation was strong and positive (0.576, p< 0.001), providing support for
H3. Cross-departmental collaboration is positively related to organizational performance
(0.111, p< 0.001), supporting H4. Knowledge creation exerted a highly positive impact on
organizational performance (0.942, p< 0.001), accepting H5. Finally, the proposed path
from resource acquisition to organizational performance was removed, which implies the
rejection of H6.
Table 3. Standardized path coefficients and structural equations.
Paths Structural Equations Coefficient S.E. C.R.
CC →RA ZRA = 0.692(ZCC)γ= 0.692 0.026 19.542 ***
CC →KC ZKC = 0.274(ZCC) + 0.576(ZRA )γ= 0.274 0.032 6.540 ***
RA →KC β= 0.576 0.050 11.770 ***
CC →OP ZOP = 0.111(ZCC) + 0.942(ZKC )γ= 0.111 0.035 20.165 ***
KC →OP β= 0.942 0.020 3.164 ***
Note: *** p< 0.001; S.E., standard error; C.R., critical ratio.
Sustainability 2021,13, 6024 12 of 22
4.2. Bayesian Networks
4.2.1. Bayesian Network Construction
The first step to develop a BN model is structure learning, which is qualitative. Typi-
cally, the structure of a BN can be learned by expert knowledge and data learning. However,
the expert judgments cannot confirm the objectivity and accuracy of the results, which may
lead to spurious relationships, while it is difficult for the simple data-driven technique to
learn the order among the nodes and critical information concealed in the investigation
reports [
50
]. This study applies the integrated approach to address these weaknesses in
structure learning. The integrated approach connects the structural model to Bayesian
networks by constructing the DAG based on an empirically validated structural model.
The DAG was developed by deriving the causal relationships between latent constructs in
the structural model, as shown in Figure 3.
Figure 3. Directed acyclic graph.
The second step is parameter learning to specify joint distributions, which is quantita-
tive. The parameter learning calculates the CPT of each node in the BN. The parameter
learning of the BN
G=(A,ψ,P)
calculates the parameter values
Θ
relating to the
DAG
’
ψ
’
and distribution from the data set ‘
D
’. Generally, if
G=(A,ψ,P)
is a Bayesian net-
work with parameter values
Θ={Θi}
where
Θi=Θij
and
Θij =nΘi jk o
such
that
Θijk =P(Ai=k|parent(Ai)=j)
;
∀i,j,k
. Accordingly, the parameter learning is to
calculate the parameter of Θijk from the data set ‘D’ [59].
The CPT can be calculated through expert knowledge or learning from sample data.
The expert knowledge could be incomplete and subjective, which might affect the network’s
accuracy [
50
]. Therefore, this study calculated the conditional probability distribution for
each node by learning from sample data. The most straightforward method to learn
the parameter is the counting algorithm. In addition, the counting algorithm should
be applied in all possible circumstances because it is acknowledged as a true Bayesian
learning algorithm [
48
]. Thus, this study adopted the counting algorithm to calculate the
CPT from sample data. The total aggregation method was applied by averaging responses
to questionnaire items in each construct to form a single variable as a node. By determining
the occurrence frequency, the numerical value of each node was discretized to transform
the scale into three states: [1–2.5] as low, [2.5–4] as medium, and [4–5] as high [
48
]. This
study used the commercially available software package Netica to develop the BN. The
DAG was drawn as a cognitive map, and then the CPTs were automatically learned from
the training dataset containing 1535 cases. Table 4shows an example of CPT presenting the
probability distribution of the organizational performance node. In this example, when the
cross-departmental collaboration and knowledge creation nodes are both in the low state,
the probabilities of low, medium, and high states of organizational performance node are
41.0%, 46.2%, and 12.8%, respectively.
Sustainability 2021,13, 6024 13 of 22
Table 4. Conditional probability table for organizational performance node.
Cross-Departmental Collaboration Knowledge Creation Organizational Performance
Low Medium High
Low Low 41.0 46.2 12.8
Medium Low 16.7 44.4 38.9
High Low 9.1 54.5 36.4
Low Medium 2.9 64.7 32.4
Medium Medium 0.6 38.9 60.5
High Medium 0.5 15.0 84.5
Low High 2.6 23.7 73.7
Medium High 0.6 11.3 88.1
High High 0.1 1.3 98.6
After learning the CPTs, the probabilistic inference was conducted using the ‘Compile
the net’ function in Netica. Then, the overall graphical representation of the proposed
BN was automatically developed in the form of a belief bar, as depicted in Figure 4. The
BN presents the existing condition of the organizational performance, impacted by the
antecedent variables as evidence from the sample data. The BN is used to explain the effect
of different predictor variables on the outcome variable in the workplace environment. In
the current scenario, high cross-departmental collaboration (70%), high knowledge creation
(68%), and high resource acquisition (62%) are likely to occur. These three predictor
variables result in a high organizational performance node (85%), corresponding to the
mean value of 4.28. Although most employees perceived organizational performance to be
high, there is still an opportunity for improvement in this outcome variable.
Figure 4. Bayesian network.
4.2.2. Sensitivity Analysis
Sensitivity analysis is a diagnostic method used to assess input variables that have
significant impacts on the output variable. Sensitivity analysis is applied to identify the
most influential input variables that can minimize uncertainty in predicting the output
variable. The diagnostic results present the influence of related nodes on the mean value of
the target node.
Sustainability 2021,13, 6024 14 of 22
This study applied the variance reduction method to analyze the sensitivity analy-
sis. The aim is to present which varying nodes can reduce the uncertainty of the query
node, as expressed by the degree of reduction in variance. This method calculates the
variance reduction of a query node G caused by varying node H. The variance of the query
node G given the input node H, denoted as V(G/h), can be calculated by the following
equation [51]:
V(G/h)=∑
g
pg
hXg−EG
h2
(5)
where gis the state of query node G,his the state of input node H,p(g/h) is the conditional
probability of ggiven h,E(G/h) is the expected real value of Gdue to finding hfor node H,
and Xg is the numeric value relating to state g.
Table 5presents the sensitivity analysis of the query node, which is organizational
performance. The indicators consist of a variance reduction, a percentage of variance
reduction, and a normalized variance reduction for each input node. The normalized
variance reduction presents the relative sensitivity resulting from the percent variance
reduction. The normalized variance reduction is the relative sensitivity resulting from
the percent variance reduction of each node divided by the highest percent variance
reduction [48].
Table 5. Sensitivity analysis of organizational performance node.
Factor Variance
Reduction
Percent Variance
Reduction
Normalized
Variance Reduction
Cross-Departmental Collaboration 0.1039 23.8 1.00
Knowledge Creation 0.0819 18.7 0.79
Resource Acquisition 0.0365 8.34 0.35
Sensitivity analysis ranked the critical factors as per their explanatory power on a
target node. If the input node’s variance reduction is comparatively high, this input node
exerts relatively high explanatory power on the target node. The critical factors of organi-
zational performance are cross-departmental collaboration and knowledge creation, with
variance reductions of 23.8% and 18.7%, respectively. Cross-departmental collaboration and
knowledge creation have the greatest explanatory power over organizational performance
node and thus are significant factors in improving agency’s performance.
4.2.3. Scenario Analysis and Discussion
This study applied a what-if analysis for scenario-based simulation to explore pos-
sible consequences based on changing factor(s). In the first scenario, the impact of cross-
departmental collaboration on other nodes can be considered when the chance of 100%
occurrence was conditioned at a high state, as presented in Figure 5. The chance of high
knowledge creation increased from 68.4% to 80.0%, reflecting an increase of 17.0%. Con-
sequently, the chance of high organizational performance increased from 85.3% to 95.3%,
indicating an improvement of 11.7%.
Sustainability 2021,13, 6024 15 of 22
Figure 5. The effect of cross-departmental collaboration.
Encouraging cross-departmental collaboration among highway agencies is necessary
because highway networks extend throughout geographical and political boundaries. The
transportation agencies are also confronted with budget constraints and conflicting de-
mands due to overlapping jurisdictions and ambiguous boundaries [
60
]. Transportation
agencies’ decision-making is characterized by dynamic multi-jurisdictional issues that
are meant to be resolved by collaborative networks. To solve these issues, transporta-
tion agencies develop a mutual relationship and policy coalition through collaborative
networks [27].
For example, the Oregon Transportation Commission has chartered Area Commissions
on Transportation (ACTs), consisting of government and non-government organizations [
2
].
The ACTs are characterized as multi-level collaboration networks, in which representatives
are nominated from the state, regional, and local agencies [8]. The purpose of the ACTs is
to improve collaboration at the regional level. Furthermore, the ACTs also act as advisory
bodies in developing the Statewide Transportation Improvement Program, allocating and
prioritizing projects for funding. Collaboration within the ACTs structure is a prime driver
in facilitating joint decision-making among the participating parties. Therefore, agencies
coordinate in these collaborative networks to help solve complicated inter-jurisdictional is-
sues due to the limited budget and increased travel demand, thus contributing to successful
statewide decision-making in transportation planning and policy.
The most optimistic scenario in improving organizational performance can be achieved
by enhancing both critical factors that have the most significant explanatory factors. This
scenario is simulated by entering the opportunity of 100% occurrence of high state for
both cross-departmental collaboration and knowledge creation, as depicted in Figure 6.
As a result, the probability of high organizational performance rises from 85.3% to 98.6%,
representing an increase of 15.6%.
The scenario analysis emphasizes the significance of knowledge creation to improve
organizational performance. Knowledge creation begins with socialization, which is
an effective process of exchanging and creating tacit knowledge within a construction
project. Because tacit knowledge is difficult to codify and is workplace-specific, it can be
accumulated by directly sharing team members’ experiences in social interactions. Junior
engineers can develop tacit knowledge through observation and informal discussion, as
well as working together with senior engineers to acquire hands-on experience in day-to-
day construction activities [31]. Converting tacit knowledge to explicit knowledge occurs
in the externalization stage. Storytelling and dialogue are efficient methods to articulate
Sustainability 2021,13, 6024 16 of 22
a hidden concept of tacit knowledge and then codify tacit knowledge into formalized
documents [
15
]. The project team uses brainstorming sessions to create new explicit
knowledge by developing a written and understandable standard operating procedure for
the new construction method. The combination is the process that systemizes and applies
explicit knowledge from inside and outside the organization. During the combination
process, the explicit knowledge is gathered, edited, and integrated to formulate more
practical and systematic knowledge. The new explicit knowledge is transferred among
project members for future usage [
35
]. Team members convert explicit knowledge into
tacit knowledge to develop a shared mental model through the internalization stage. For
instance, junior engineers can embody explicit knowledge, such as construction methods
and specifications, by reading and understanding these written documents during action
and practice [
19
]. Subsequently, they can enrich their tacit knowledge through learning-by-
doing in their professional practices. The knowledge creation process is a spiral movement
that continuously stimulates the conversion between explicit and tacit knowledge. The four
modes of knowledge creation amplify a new spiral of knowledge conversion; therefore,
more knowledge can be created in an organization.
Figure 6. The effect of cross-departmental collaboration and knowledge creation.
5. Discussion
5.1. Theoretical Contributions
These empirical results offer insights into how cross-departmental collaboration in-
fluences organizational performance through the role of resource acquisition and knowl-
edge creation. Cross-departmental collaboration indirectly impacts organizational per-
formance through resource acquisition and knowledge creation. The results reveal that
cross-departmental collaboration has a positive and sizable effect on resource acquisition,
which in turn positively impacts knowledge creation. As hypothesized, the results indi-
cate that the effect of cross-departmental collaboration on knowledge creation is positive.
Knowledge creation, in turn, has a positive and substantial effect on organizational per-
formance. This finding is consistent with previous research from transportation agencies,
presenting that collaborative networks among metropolitan planning organizations (MPOs)
and other related partners are formulated to develop and implement active transportation
policies in the United States [
61
]. The research revealed that cross-departmental collabora-
tion was significantly related to resource sharing and knowledge creation around active
transportation policies. Organizing MPOs as collaborative intermediaries played a crucial
role in creating explicit and tacit knowledge and sharing tangible and intangible resources
Sustainability 2021,13, 6024 17 of 22
among actors and organizations. Therefore, MPOs could solve complicated transportation
planning problems that arise from metropolitan areas’ silo effect and bureaucratic culture,
thus improving the performance of transportation agencies.
The empirical results also reveal that cross-departmental collaboration directly im-
pacts organizational performance, but the effect size is relatively small. Although cross-
departmental collaboration through multijurisdictional schemes has been promoted over
the previous three decades, functional fragmentation between transportation agencies is
still a longstanding and complex issue across jurisdictional boundaries and modal net-
works [
62
,
63
]. The complexity of organizational collaboration may be another reason
cross-departmental collaboration has a relatively small direct impact on performance.
Sanders [
64
] studied the inherent complexity of collaboration and revealed an organiza-
tional mechanism that interdepartmental collaboration impacts intradepartmental collabo-
ration, ultimately improving organizational performance. Collaboration is usually effective
after the working relationship has been developed. Therefore, it may take substantial
time, particularly in the early stage of developing a collaborative network, to facilitate
interdepartmental collaboration and engage a high level of intradepartmental collaboration
to be synergistic for reaping performance benefits.
This empirical result does have a significant implication concerning the influence
of resource acquisition. Samaddar and Kadiyala [
18
] argue that sufficient resource ac-
quisition had a statistically significant impact on the success of collaborative knowledge
creation. This finding is consistent with the resource-based view (RBV) that highlights the
significance of the critical resource of outside-in capabilities when engaging with strategic
alliances [
17
]. As an example, sharing lessons learned from projects with external partners
leads to a sustainable advantage for organizations by creating their technical and manage-
rial knowledge [
14
]. Besides, this finding is in line with the main argument of the resource
dependence theory (RDT) that organizations need to enter into collaborative networks to
maintain critical intangible resources [
12
]. Consequently, they can coordinate to develop
professional expertise to manage the technical complexity of highway mega-projects [13].
This study also provides theoretical contributions to the knowledge management liter-
ature by investigating how knowledge creation can improve organizational performance.
Knowledge creation has a positive and substantial impact on organizational performance.
This finding is in line with the knowledge-based theory that knowledge is a strategic
resource used by the organization to develop organizational ability leading to improved
performance [
65
]. Through this theoretical lens, organizations are regarded as integrated
repositories of tacit and explicit knowledge, in which diverse knowledge bases serve as
main antecedents of long-term sustainable organizational performance [66].
Further highlighting the policy reliance of this research, cross-departmental collabora-
tion has emerged as a prime mover for organizational performance. Notably, it improved
performance by promoting resource acquisition and knowledge creation required for task
accomplishment. The two variables capture most of the positive effects on performance,
which means that these variables are very significant for organizational success. Remark-
ably, the degree of knowledge-intensive activities is substantial for the FHWA as a research
and advisory organization for state and local highway departments, so the improved
performance gain through knowledge creation is very critical.
5.2. Practical Implications
Cross-departmental collaboration, such as collaborative networks of transportation
agencies, is considered a critical approach to solving increasingly complex inter-jurisdictional
and wicked issues with no solutions, such as budget constraints and increased demands
and climate change [
2
,
10
]. The highway agencies should promote cross-departmental
collaboration across multi-jurisdictional agencies. For instance, the FHWA recognizes
the necessity of highway planning and management from a megaregional perspective.
The US megaregions are a network of metropolitan areas combined through similar char-
acteristics and mutual interests of social, economic, topographic, political, climatic, and
Sustainability 2021,13, 6024 18 of 22
infrastructural issues [
67
]. Because the US highway systems transcend many jurisdictional
boundaries, highway planning and management is an inherently megaregional issue. The
FHWA has continuously funded the collaborative design, construction studies, and project
works at the megaregional scale for a decade. Since 2016, the FHWA has been actively
promoting collaborative research, convening seminars, and sponsoring peer-to-peer work
in large-scale highway issues of megaregions [
43
]. These initiatives reveal that the FHWA
embraces cross-departmental collaboration as a crucial priority.
Additionally, to improve organizational performance, highway agencies should ac-
knowledge the benefit of knowledge creation, presuming that the most significant assets
are skills and knowledge. For example, the FHWA organizes knowledge disciplines around
technical specialty, occupation, and profession. Examples of disciplines are design, finance,
planning, and safety. A discipline champion is appointed to lead each discipline and
execute mandatory activities that each discipline needs to accomplish [
68
]. The FHWA
adopted the Discipline Support System, an integrated knowledge management system
created to transfer knowledge across the agency, share best practices, and network within
the discipline [
69
]. With geographically dispersed offices across the country, a virtual envi-
ronment for knowledge creation provided by the Discipline Support System is essential
to sustain and grow the knowledge discipline. The discipline champion also facilitates
mentoring programs to capture and transfer explicit and tacit knowledge from senior
engineers to the younger workforce, which helps solve the issue of knowledge loss because
of retirement. The evidence illustrates the effort that the FHWA has exerted to inculcate
knowledge creation into the organizational culture.
5.3. Limitation and Future Directions
Using secondary data from the FEVS has some limitations that researchers should
acknowledge and may address in future research. First, the survey data is from federal
departments in the United States, exposed to Anglo-Saxon culture. As a result, some of the
findings may not be generalizable to eastern countries in which the patronage system dom-
inates public sector organizations. Future studies should be conducted to test the benefits
and costs of cross-departmental collaboration in these eastern countries, in which collabora-
tion activities are impeded by significant barriers from bureaucratic culture, the silo effect,
jurisdictional fragmentation, and an autocratic leadership style. Second, the study did
not investigate the impact of demographic variables that could also affect organizational
performance. Analyzing innovation activities in the Australian Public Service, Demir-
cioglu [
70
] revealed that demographic variables, including education, experience, and
managerial position, are positively related to innovation implementation, thus improving
federal departments’ performance. Therefore, future research should include demographic
variables in a causal model to explore their effects on organizational performance.
Finally, because the FEVS data come from a cross-sectional design, we cannot dismiss
the possibility of reverse causation, which excludes inference for any causal claims. Further-
more, cross-departmental collaboration takes considerable time to develop a trusted working
relationship, so it will not lead to immediate benefits in improving performance. Additionally,
collaboration continuously facilitates resource acquisition and knowledge creation in the long
term; therefore, the ultimate results on organizational performance should be measured in the
long run. Thus, future work can expand our study by applying a longitudinal research design,
which collects data at several time points. Latent growth modeling can be used to analyze
the time-lagged panel data [
71
]. The longitudinal model can examine whether dynamic
organizational attributes increase the level and growth rate of organizational performance.
This longitudinal model can also explore the dynamic nature of organizational attributes
and the relationships among their dynamics (i.e., rates of change) [
72
]. This research would
theoretically contribute to the literature of time-phased organizational studies and investigate
the role of cross-departmental collaboration as a significant determinant of the dynamics and
longitudinal organizational performance trajectories.
Sustainability 2021,13, 6024 19 of 22
6. Conclusions
A hybrid SEM-BN approach is proposed based on an integrated method that connects
an empirically validated structural model to Bayesian networks. As a confirmatory tool,
the SEM is performed to identify causal relationships between organizational constructs
and empirically validate the conceptual model. The BN is used as an exploratory technique
to identify the critical organizational variables and analyze the influence of improvement
in organizational attributes on organizational performance.
Using the data from the FHWA, this SEM was conducted to validate an organizational-
centric structural model. This study examined the direct effect of cross-departmental
collaboration on organizational performance and explored indirect effects through man-
agerial practices of resource acquisition and knowledge creation. The results indicate
that cross-departmental collaboration positively impacts resource acquisition (H1) and
knowledge creation (H2). Resource acquisition is also positively related to knowledge
creation (H3). Cross-departmental collaboration has a positive impact on organizational
performance (H4). Meanwhile, knowledge creation is substantially associated with organi-
zational performance (H5). Finally, we removed the hypothesized path between resource
acquisition and organizational performance, implying the rejection of H6. The post hoc
modification was conducted using the model trimming approach, which removed the
direct path from resource acquisition to organizational performance, implying that re-
source acquisition by itself did not directly improve organizational performance. Instead,
agencies need to acquire resources, which are indispensable to prepare the foundations for
knowledge creation.
The BN models are applied for scenario-based simulation to highlight critical path-
ways to enhance organizational performance. The simulation was conducted using sensitiv-
ity analysis to identify critical factors that significantly impact organizational performance.
The most critical factors are cross-departmental collaboration and knowledge creation. Two
scenarios were analyzed using what-if analysis to demonstrate how the cross-departmental
collaboration practice and knowledge creation process directly and indirectly impact or-
ganizational performance. The scenario-based simulation provides recommendations for
cross-departmental collaboration practice and knowledge creation processes in improving
highway agencies’ performances.
Author Contributions:
W.W.; Investigation, W.W.; Methodology, W.W. and R.A.S.; Software, J.S.;
Supervision, W.W.; Validation, W.W.; Visualization, W.W.; Writing—original draft, W.W.; Writing—
review and editing, R.A.S. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by Logistics and Business Analytics Center of Excellence. The
APC was funded by Institute of Research and Innovation, Walailak University.
Data Availability Statement: Publicly available datasets were analyzed in this study. This data can
be found here: https://www.opm.gov/fevs/public-data-file/.
Conflicts of Interest: The authors declare no conflict of interest.
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