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Research Article
Analyzing the Success of Adopting Metaverse in Construction
Industry: Structural Equation Modelling
Ahsan Waqar,
1
Idris Othman,
1
Muhammad Shafiq Aiman,
1
Muhammad Basit Khan,
1
Md. Mahmodul Islam ,
2
Hamad Almujibah,
3
and Malik Abdul Karim
1
1
Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Bandar Seri Is-kandar, Tronoh,
Perak 32610, Malaysia
2
Chittagong University of Engineering & Technology, Chittagong, Bangladesh
3
Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Correspondence should be addressed to Md. Mahmodul Islam; mahmudpme91@gmail.com
Received 13 June 2023; Revised 4 August 2023; Accepted 1 September 2023; Published 29 September 2023
Academic Editor: Yuanxin Zhou
Copyright ©2023 Ahsan Waqar et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
e application of metaverse technology in the eld of civil engineering has the potential to improve project eciency and
accuracy. Nevertheless, the pervasive adoption and eective integration of metaverses are contingent on a number of crucial
factors. is study investigates the critical factors underlying the successful implementation of metaverse technology within
a business context. is research employs a comprehensive mixed-method approach comprised of exploratory factor analysis
(EFA) and structural equation modelling (SEM) to examine survey responses gathered from seasoned professionals in the
architecture, engineering, and construction (AEC) sectors in Bangladesh. In areas such as communication and collaboration,
design visualization, and monitoring and maintenance, the construction industry has made extraordinary strides. As imperatives
for managerial consideration strategic investment in resources, targeted training initiatives, heightened awareness campaigns, and
prudent deployment of cost-eective and ecient metaverse-based solutions emerge, future investigations should include a larger
sample size and an evaluation of the lasting eects over extended time periods. e key to unleashing the full potential of the
metaverse within the architecture, engineering, and construction industries lies in addressing these identied success de-
terminants, thereby ushering in enhanced project outcomes and enhanced eciency within the constructed environment. e
combination of these initiatives is expected to pave the way for a new era in the AEC landscape.
1. Introduction
e construction industry has always been at the vanguard
of technological innovation, embracing innovations that
increase productivity, eciency, and collaboration. In recent
years, a new concept has emerged with the potential to
revolutionize the planning, design, and execution of con-
struction projects: the metaverse [1, 2]. e metaverse,
a virtual reality space that enables users to interact with
a computer-generated environment, oers a variety of
benets and opportunities to the construction industry. e
notion of the metaverse originated in science ction, and this
concept has increasingly become a practical reality. e
convergence of technological advances in virtual reality,
augmented reality, articial intelligence, and Internet con-
nectivity has paved the way for the emergence of the
metaverse [3, 4]. It provides users a multidimensional,
immersive environment, where they can interact with digital
representations of the physical world, facilitating un-
precedented collaboration, visualization, and data
integration [5].
A paradigm change has occurred in the eld of archi-
tecture, engineering, and construction (AEC) as a result of
the introduction of metaverse technology [6–8]. is
technology oers a wide variety of practical applications that
have the potential to alter the way that conventional pro-
cedures are carried out. One of these applications is ar-
chitectural design and visualization, which allows users of
Hindawi
Journal of Engineering
Volume 2023, Article ID 8824795, 21 pages
https://doi.org/10.1155/2023/8824795
metaverse platforms that are equipped with virtual reality
(VR) and augmented reality (AR) capabilities to completely
immerse themselves in complex spatial designs [6]. is
immersive experience not only helps in improving the
aesthetics and functionality of projects but also stimulates
collaborative decision-making among stakeholders by pro-
viding a tangible preview of the end result. is is accom-
plished by delivering a look at how the project will ultimately
turn out [9].
In addition, the technology known as metaverse makes it
possible to conduct virtual walkthroughs. is gives cus-
tomers, investors, and other project stakeholders the op-
portunity to virtually investigate potential layouts before
construction ever begins. is dynamic involvement helps to
build a shared vision and reduces uncertainty, which ulti-
mately results in a decision-making process that is better
informed early on in the lifetime of the project. In the eld of
collaborative planning and design coordination, metaverse
platforms provide multidisciplinary teams a safe haven in
the digital environment in which they may work together
cooperatively and in real time [10, 11]. It is now possible for
architects, engineers, contractors, and other stakeholders to
interact uidly, evaluate plans, and identify potential dis-
putes, which will eventually result in designs that are more
unied and execution that is more ecient.
e construction industry, which frequently faces ob-
stacles such as cost overruns, project delays, and commu-
nication breaches, stands to gain signicantly from
implementing the metaverse. By utilizing virtual and aug-
mented reality technologies, stakeholders in the construc-
tion process, including architects, engineers, contractors,
and clients, can seamlessly collaborate, visualize complex
designs, and detect and resolve conicts early on [12, 13].
roughout the construction lifecycle, the metaverse
oers many applications and benets. It enables stake-
holders to experience virtual walkthroughs of buildings
before construction, allowing for real-time design modi-
cations and increasing client satisfaction. In addition, the
metaverse facilitates the incorporation of building in-
formation modelling (BIM) data, allowing for eective
collision detection, 4D construction sequencing, and real-
time progress monitoring [14, 15]. It also promotes safety
training and simulations, thereby reducing accidents on the
job site and increasing worker productivity. e metaverse
can also be used for postconstruction operations and facility
management, allowing proprietors to virtually investigate
and administer their assets. While the metaverse oers
considerable potential, it is full of obstacles. Adopting new
technologies frequently necessitates substantial expendi-
tures, and the metaverse is no exception [16, 17]. ere are
also concerns about data privacy, interoperability, and the
learning curve associated with implementing and integrating
these technologies into existing workows. However, these
obstacles present opportunities for innovation, collabora-
tion, and the creation of new business models in the con-
struction industry [18].
Metaverse technology in construction engineering might
change the AEC industry. is emerging technology may
improve project eciency, collaboration, and design
visualization. However, this research must dene metaverse
technology adoption success. is study evaluates the
“Success of Adopting Metaverse” across several crucial
parameters [19, 20]. Immersive metaverse environments let
project stakeholders communicate and collaborate better.
Metaverse technology enhances architectural and engi-
neering idea exploration and renement, making design
visualization successful. Metaverse adoption improves
monitoring and maintenance by using real-time data and
analytics to assure project progress and sustainability.
is research examines what makes metaverse tech-
nology in AEC successful. We study fundamental vari-
ables and their synergistic eects on project results. We
seek to oer a complete framework for decision-making,
resource allocation, and strategic planning for metaverse
adoption in the AEC sector by nding these success
drivers. is research explores metaverse technology’s
complex integration into the AEC industry, revealing its
success factors. By deconstructing the “Success of
Adopting Metaverse,” we may better understand how this
technology can improve project eciency and results in
the built environment.
To comprehensively comprehend the metaverse’s suc-
cess potential in the construction industry, we will examine
successful implementations and case studies. ese real-
world examples will demonstrate how the metaverse has
been utilized to enhance collaboration and project outcomes
and increase eciencies. Adopting the metaverse in the
construction industry represents a paradigm transformation
in the planning, design, and execution of projects. Potential
advantages, such as enhanced collaboration, visualization,
increased productivity, and decreased costs, make it an
attractive proposition for industry stakeholders [21, 22]. To
realize the full potential of the metaverse, however, in-
vestment, interoperability, and workforce upskilling must be
addressed. is paper will provide construction pro-
fessionals, researchers, and policymakers interested in
leveraging the met model with valuable insights and guid-
ance by examining successful implementations and case
studies [23].
Despite the growing interest in metaverse technology
and its potential benets for the AEC industry, there is
a paucity of exhaustive studies that have systematically in-
vestigated the various success factors essential to its eective
implementation. Prior research frequently focuses on par-
ticular aspects without providing a comprehensive view of
the factors that inuence the pervasive adoption and success
of metaverse technology in the AEC industry [9, 24].
While the adoption of metaverse technology is a global
phenomenon, its implementation may vary across regions
due to cultural, economic, and regulatory factors that are
unique to each locale. Existing literature tends to be dom-
inated by studies from established nations, leaving a research
void regarding the unique challenges and opportunities
encountered by developing regions like Bangladesh
[7, 22, 25]. By examining the Bangladeshi AEC sector, our
research aims to bridge this divide by providing insights that
can inform strategies for successful metaverse integration in
similar contexts.
2Journal of Engineering
Previous research on the impact of metaverse technology
on the AEC industry frequently lacked an exhaustive and
rigorous methodology [26, 27]. Numerous studies employ
only qualitative or quantitative methods, ignoring the ad-
vantages of a mixed-method approach. By combining ex-
ploratory factor analysis (EFA) and structural equation
modelling (SEM), our study aims to resolve this method-
ological void by providing a more robust and nuanced
examination of the relationships between various success
determinants.
Although preliminary studies demonstrate the potential
benets of metaverse technology in the construction in-
dustry, a deeper comprehension of its long-term eects and
sustainability is required [28–30]. Current research on the
long-term eects of metaverse integration on project out-
comes and environmental sustainability in the built envi-
ronment is insucient.
By highlighting these research voids explicitly, we hope
to provide a compelling justication for our study and its
contribution to the existing corpus of knowledge. We ap-
preciate the reviewer’s suggestions for augmenting the
quality and ecacy of our work, and we welcome any ad-
ditional suggestions for improvement.
In this paper, we intend to investigate the adoption of the
metaverse in the construction industry and its success po-
tential, examining the various applications of the metaverse
and discussing its impact on the various phases of the
construction process by performing quantitative and qual-
itative interviews to demonstrate and highlight the impor-
tance of the metaverse in the construction industry [31]. By
comprehending the metaverse’s potential and its ramica-
tions for the construction industry, we can develop novel
approaches for enhancing project outcomes and attaining
higher levels of eciency and sustainability.
is article employs a comprehensive structural equa-
tion modelling (SEM) strategy utilizing SmartPLS 4 soft-
ware. SEM is a potent statistical method that permits
researchers to investigate intricate relationships between
multiple variables and latent constructs [32, 33]. By
employing SmartPLS 4, this study surpasses conventional
statistical analysis techniques and provides a robust
framework for comprehending the adoption and success of
the metaverse in the construction industry. SEM improves
the reliability and validity of the ndings, allowing for
a more thorough comprehension of the fundamental factors
inuencing the adoption and success of the metaverse.
Another distinguishing characteristic of this study is its
emphasis on the application and signicance of the meta-
verse in the Bangladeshi construction industry [34, 35].
While the potential benets of the metaverse have been
extensively discussed in global contexts, few studies em-
phasize its relevance and implications for the Bangladeshi
construction industry. By concentrating on this region, the
article illuminates the distinctive challenges, opportunities,
and prospective success factors associated with adopting the
metaverse in the Bangladeshi construction industry. is
regional perspective adds originality and enriches the lit-
erature by providing Bangladesh-specic insights and rec-
ommendations [36, 37].
is study contributes to the existing corpus of
knowledge on the adoption and success of the metaverse in
construction by combining the methodological advance-
ments of utilizing SEM with SmartPLS 4 and a regional
concentration on the construction industry in Bangladesh.
e ndings and insights of this study will not only benet
researchers and practitioners in the eld of construction
management. However, they will also provide a basis for
policymakers and industry stakeholders to make informed
decisions regarding incorporating the metaverse into con-
struction practices in Bangladesh:
(1) Holistic Success Determinants. Our research uniquely
identies and analyses a comprehensive set of suc-
cess determinants crucial for the eective imple-
mentation of metaverse technology in the AEC
sector. is holistic approach oers a more complete
understanding of the factors driving successful in-
tegration [12, 13, 38].
(2) Mixed-Method Approach. is study employs a ro-
bust mixed-method methodology that combines
exploratory factor analysis (EFA) and structural
equation modelling (SEM). is innovative meth-
odological framework allows for a more nuanced
exploration of the relationships and inuences
among the identied success determinants
[37, 37, 39].
(3) Bangladeshi AEC Sector Focus. Our research narrows
its focus to the Bangladeshi AEC sector, providing
valuable insights specic to this context. is re-
gional perspective contributes to a deeper compre-
hension of how metaverse technology can be
optimized within a particular cultural and industrial
environment.
is paper’s subsequent sections are organized as fol-
lows: Section 2 provides a comprehensive literature review,
delving into the current state of metaverse technology
implementation in the AEC industry and highlighting re-
search deciencies. Section 3 describes the research meth-
odology, including the data acquisition procedure, the
mixed-methods approach, and the analytic techniques
used. Section 4 presents the empirical ndings and discusses
the identied success factors and their implications for the
Bangladeshi AEC industry. Section 5 provides an in-depth
analysis of the ndings, situating them within the context of
the broader literature and their practical implications.
Section 6 concludes with a concise summary of the study’s
contributions, a discussion of its limitations, and suggestions
for future research.
2. Identification of Success Factors
As the construction industry enters the digital trans-
formation era, incorporating innovative technologies, such
as the metaverse, has the potential to reshape conventional
practices and propel success. e metaverse, a virtual reality
space where users interact with computer-generated envi-
ronments, presents the construction industry with
Journal of Engineering 3
numerous opportunities to improve collaboration, visuali-
zation, and project outcomes. To completely realize the
benets of the metaverse, it is necessary to identify the
factors contributing to its successful implementation. In this
paper, we intend to identify and investigate the most im-
portant success factors for the metaverse in the construction
industry [37].
A robust technological infrastructure is essential for
successfully implementing the metaverse. is consists of
high-speed Internet connectivity, hardware capable of
running virtual and augmented reality applications, and
scalable cloud computing resources. A sucient techno-
logical infrastructure ensures seamless and uninterrupted
interactions within the metaverse, allowing stakeholders to
eciently access, analyze, and manipulate project data [40].
Eective communication and collaboration are crucial
success factors for the metaverse in the construction in-
dustry. e metaverse enables stakeholders to engage in real-
time discussions, communicate project updates, and
seamlessly exchange information. Virtual meetings, shared
workstations, and instant messaging facilitate eective
communication, fostering collaboration among team
members regardless of their physical locations [28, 41]. e
metaverse improves decision-making, facilitates workows,
and fosters a sense of collaboration among stakeholders by
removing communication barriers.
roughout the construction lifecycle, the metaverse
provides valuable project monitoring and upkeep tools. e
incorporation of sensor data into the metaverse enables the
real-time monitoring of construction progress, equipment
utilization, and resource allocation. is enables stake-
holders to identify potential bottlenecks, monitor project
milestones, and make timely adjustments to ensure success
[42]. In addition, the metaverse can facilitate remote asset
management, allowing facility managers to eciently
monitor and maintain buildings, thereby reducing opera-
tional expenses and prolonging the lifecycle of constructed
assets.
In the construction industry, design and visualization
capabilities are fundamental success factors for the
metaverse. e metaverse provides immersive 3D mod-
elling and visualization tools, enabling stakeholders to
construct, modify, and visualize architectural designs in
a virtual environment that is both realistic and immersive
[24, 43]. is facilitates the comprehension and evaluation
of design decisions, the identication of design defects or
conicts, and iterative design processes. e ability to
visualize designs within the metaverse increases stake-
holder engagement, enhances design coordination, and
decreases the likelihood of costly design errors during
construction [25, 44].
e success of the metaverse in the construction industry
is contingent on stakeholders’ adoption and positive user
experiences. e metaverse should be simple to use, com-
prehend, and navigate for all stakeholders, regardless of their
technical expertise [29, 45]. By prioritizing user experience
and ensuring a seamless interface, the metaverse becomes
a tool that stakeholders are keen to adopt and actively engage
with, thereby contributing to the project’s success.
Eective change management and stakeholder align-
ment are required to successfully deploy the metaverse in
the construction industry. Multiple levels of stakeholders,
including project managers, administrators, and em-
ployees, must comprehend the metaverse’s value propo-
sition and align with the project’s objectives [46]. Change
management initiatives, such as training programs, sem-
inars, and explicit communication plans, must be imple-
mented to combat resistance to change and ensure the
metaverse’s seamless adoption. By involving and aligning
stakeholders, the likelihood of a successful implementa-
tion and incorporation of the metaverse is signicantly
increased [47].
Scalability and adaptability are essential success
factors for the metaverse in the construction industry.
e metaverse should be able to facilitate construction
endeavors of various sizes and degrees of complexity. e
metaverse platform should be scalable, enabling the in-
tegration of multiple projects and accommodating
growing data volumes as projects advance [48, 49]. In
addition, the metaverse should be adaptable enough to
accommodate changing project requirements and in-
corporate emerging technologies and functionalities. In
the ever-changing construction landscape, scalability and
adaptability guarantee the long-term viability and rele-
vance of the metaverse [50].
e incorporation of the metaverse in the construction
industry must take regulatory and legal considerations into
account. To ensure compliance and safeguard stakeholder
interests, it is necessary to resolve intellectual property
rights, data ownership, privacy regulations, and cyberse-
curity laws [27, 51]. e use and sharing of data within the
metaverse should be governed by transparent, trustworthy,
and legally compliant guidelines and frameworks. If regu-
latory and legal considerations are addressed, stakeholders
can condently embrace the metaverse while mitigating
potential risks and diculties [11, 52].
In the construction industry, continuous development
and feedback cycles are crucial success factors for the
metaverse. Regular assessment, evaluation, and input from
stakeholders assist in identifying development opportuni-
ties, addressing any deciencies, and rening the metaverse
implementation strategy [30]. Feedback channels also fa-
cilitate iterative development and renement of the meta-
verse platform, ensuring that it corresponds with the
construction industry’s evolving requirements and expec-
tations. By actively soliciting feedback and adopting a cul-
ture of continuous improvement, construction project
stakeholders can maximize the value and impact of the
metaverse [38, 53].
Identifying and comprehending the adoption success
factors for the metaverse in the construction industry
provides a rm foundation for its eective implementation
and integration. e success factors discussed in this
manuscript, such as stakeholder alignment, change man-
agement, scalability and exibility, regulatory and legal
considerations, continuous improvement, and feedback
loops, contribute to a comprehensive success framework for
the metaverse.
4Journal of Engineering
3. Methodology
is study involves qualitative interviews and a quantitative
questionnaire-based study, and the area of study is Ban-
gladesh’s construction industry. Figure 1 indicates the whole
methodology of the study with its step-by-step explanation.
Our research collected data from Bangladeshi AEC industry
professionals through questionnaires and structured in-
terviews. Purposive sampling targeted people with signi-
cant metaverse technology integration expertise and
insights. Our sample was diversied, allowing us to explore
the study topic more thoroughly. e complex research
situation necessitated purposeful sampling. Purposive
sampling was suitable due to the dynamic nature of meta-
verse technology adoption in AEC and the small pool of
specialists with extensive expertise in this area. We in-
tentionally selected participants with in-depth expertise to
provide rich and relevant data. Our qualitative research,
which seeks nuanced industry expert opinions, uses pur-
posive sampling. We maximized data accuracy and depth by
concentrating on metaverse technology implementation
experts [24, 54]. We used surveys and structured interviews
to accomplish methodological triangulation and improve
our results. e survey oered quantitative data on trends
and patterns, while the structured interviews allowed par-
ticipants to share their perspectives and experiences. Our
approach uses purposive sampling, questionnaires, and
structured interviews to fully capture metaverse technology
uptake in the AEC industry [18, 43]. e research setting and
the requirement to get signicant insights from a small
group of specialists support this technique.
Our investigation employs an integrated mixed-methods
approach, integrating exploratory factor analysis (EFA) and
structural equation modelling (SEM). is combined
method permits a more thorough investigation of the re-
lationships between the identied success factors. By
combining qualitative insights from EFA with quantitative
modelling from SEM, we provide a comprehensive un-
derstanding of the intricate dynamics at play [19, 36].
Recognizing the impact of cultural and contextual fac-
tors on the adoption of the metaverse, our methodology
takes into account the distinctive characteristics of the
Bangladeshi AEC sector. is sensitivity improves the ap-
plicability of our ndings to the particular cultural and
industrial landscape, thereby contributing to more informed
decision-making in a regional context.
Our methodology integrates an evaluation of the long-
term eects and sustainability of metaverse integration,
going beyond immediate outcomes. By evaluating the long-
term eects on project outcomes and environmental sus-
tainability, we extend the temporal scope of our analysis and
provide insight into the long-term advantages of metaverse
technology.
Our research methodology goes beyond theoretical ex-
ploration by establishing a framework for practical impli-
cations. is framework translates research ndings into
actionable recommendations for industry practitioners,
enabling them to implement metaverse technology solutions
based on informed judgment.
3.1. Qualitative Settings of Factors. Initially, the metaverse’s
construction success factors were identied through
a comprehensive literature review. is analysis included
scholarly articles, conference papers, industry reports, and
other pertinent resources. e data from the literature re-
view served as a basis for comprehending the extant success
factors associated with implementing the metaverse in the
construction industry. A preliminary list of success factors
for the Bangladeshi construction industry was developed
using this information [20]. e interviews aimed to elicit
insights, perspectives, and recommendations regarding the
identied success factors from these experts. Participants
were encouraged to provide feedback on the preliminary
success factors and to oer additional insights based on their
practical knowledge and real-world experiences during the
interviews [55, 56]. e discussions covered numerous facets
of implementing the metaverse in construction, including
obstacles, opportunities, prospective benets, and
Bangladesh-specic considerations. e feedback and sug-
gestions provided by industry experts were instrumental in
modifying and rening the initially identied success fac-
tors. In analyzing the collected data, we adopted a two-phase
approach that combines exploratory factor analysis (EFA),
followed by structural equation modelling (SEM). is
methodological choice was driven by our research objec-
tives, which entail uncovering latent constructs and in-
terrelationships among various success determinants in the
context of metaverse technology adoption within the ar-
chitecture, engineering, and construction (AEC) sector.
3.2. Exploratory Factor Analysis. An exploratory factor
analysis (EFA) was conducted to identify and analyze the
fundamental factors inuencing the success of the met-
averse in the construction industry. e objective of the
EFA was to simplify the data and identify the latent
variables that drive the observed patterns. Data were
collected through surveys administered to construction
industry professionals and stakeholders with metaverse-
related knowledge or experience [54]. e survey
Validation and
Exploration of
Constructs
EFA Analysis
Adjustment of
Success Factors
Metaverse Activities
Identification of
Metaverse Success
Factors
Literature Review Semi Interviews
Development of
Conceptual
Model
Data Collection (Main Questionnaire Survey)
Modification of the Conceptual Model through SEM Analysis
Figure 1: Flowchart for the study.
Journal of Engineering 5
questionnaire included questions designed to measure
various factors believed to inuence the success of the
metaverse, including technological preparedness, orga-
nizational culture, stakeholder engagement, training and
education, and data interoperability [26, 57]. On a Likert
scale, participants were instructed to rate their level of
agreement or disagreement with each item.
e collected survey response data were then subjected
to EFA. Statistical software was used to investigate the in-
terrelationships between the observed variables and to
identify the underlying factors that accounted for the var-
iation in the data. Principal component analysis was used for
extraction, and the varimax rotation procedure was
employed to improve interpretability. Several underlying
factors were identied through the EFA [7]. ese facets
contributed to the success of metaverse implementation in
the construction industry. Each factor was characterized by
a set of variables with high factor loadings, indicating their
close relationship to the underlying factor. e factor
loadings represented the intensity and direction of each
variable’s relationship with its respective factor.
e EFA results provided valuable insight into the main
factors inuencing the success of the metaverse in the
construction industry. ese elements provided a deeper
comprehension of the crucial dimensions to consider when
implementing and managing metaverse projects. e results
of the EFA will aid in decision-making, resource prioriti-
zation, and developing strategies designed to increase the
success of metaverse implementation in the construction
industry. e initial application of exploratory factor
analysis (EFA) is rooted in our aim to identify underlying
dimensions and interconnections among the observed
variables. EFA allows us to uncover latent constructs that
may not be directly observable, thereby providing a deeper
understanding of the intricate factors contributing to the
success of metaverse technology adoption. is data-driven
approach ensures that our analysis is guided by the inherent
patterns within the data itself.
3.3. Structural Equation Modelling. e structural equation
model (SEM) enables us to concurrently evaluate the
direct and indirect impacts of a variety of success drivers,
providing us with a thorough knowledge of how these
aspects interact with one another and contribute to the
overall success of the integration of metaverse technology.
e SEM technique permits thorough model validation,
which ensures that our conclusions are founded on good
statistical analysis and provides a high degree of con-
dence in the reliability and validity of our results. In other
words, our ndings can be trusted to be accurate and
reliable [16, 22, 25]. Taking into account measurement
error, structural equation modelling (SEM) enables the
integration of measurement error into the analysis, which
improves the quality of our evaluations and provides
a more exact picture of the connections between latent
components. e adaptability of SEM makes it possible to
conduct exploratory as well as conrmatory analysis. is
not only makes it easier to recognize new patterns but also
validates previously established theoretical constructions.
is study employs structural equation modelling (SEM)
to assess the structural relationships among the identied
latent constructs. SEM enables us to validate and quantify
the complex interdependencies between the success de-
terminants, thereby elucidating the direct and indirect
eects that contribute to the overall success of metaverse
adoption [7, 50, 55]. is analytical technique aligns with
our objective of oering a holistic view of how these
determinants collectively impact project outcomes within
the AEC sector.
By integrating EFA and SEM, we achieve a compre-
hensive analysis that combines the exploratory insights of
factor analysis with the conrmatory power of structural
equation modelling. is approach enhances the robustness
of our ndings by both unveiling latent constructs and
quantifying their relationships, all within the context of
metaverse technology adoption.
e rationale for this two-phase analytical approach lies
in its ability to capture the complexity of the research do-
main while providing a rigorous foundation for drawing
meaningful conclusions. We believe that this methodology
aligns seamlessly with our research objectives and the
multifaceted nature of the subject matter.
We are grateful for the reviewer’s guidance, which has
signicantly enhanced the transparency, depth, and meth-
odological rigor of our study. We remain open to any further
suggestions or insights that could continue to strengthen the
quality and rigor of our research.
3.4. PLS Algorithm Analysis. In this investigation, the ap-
plication of partial least squares structural equation mod-
elling (PLS-SEM) analysis is crucial. PLS-SEM is a robust
and adaptable analysis technique that accommodates
complex models and provides valuable insights into the
relationships and interactions among the identied factors
that inuence the success of the metaverse in the con-
struction industry. PLS-SEM is well suited for exploratory
research, particularly when comprehending the relation-
ships between latent variables [58, 59]. is makes it an ideal
tool for examining the multivariate nature of the factors
aecting the metaverse’s success, allowing for a thorough
examination of their interdependencies. Moreover,
PLS-SEM is advantageous for dealing with small sample
sizes and nonnormal data distributions, which is especially
pertinent in the context of this study [39, 60]. is study
employs PLS-SEM to understand the complex relationships
between the factors inuencing the metaverse’s success in
the construction industry, thereby contributing to the body
of knowledge in this area.
3.5. Convergent Validity. Convergent validity evaluates the
extent to which multiple measures of the same construct
yield comparable or consistent results. In the context of this
investigation into the factors inuencing the success of the
metaverse in the construction industry, a convergent validity
test was conducted to evaluate the consistency and reliability
of the measurement items used to assess the latent variables.
6Journal of Engineering
AVE measures the variance the latent variable captures
relative to measurement error. A value of AVE greater than
0.5 demonstrates adequate convergent validity. Composite
reliability (CR) assesses the consistency and reliability of
a latent variable. A CR value greater than 0.7 indicates
excellent dependability. Factor loadings indicate the in-
tensity of each measurement item’s relationship with its
corresponding latent variable [54, 58]. e higher the factor
loadings, the greater the convergent validity. Examining
these statistical measures for each latent variable in the
model was required for the convergent validity test. If the
AVE values were greater than 0.5, the CR values were greater
than 0.70, and the factor loadings were signicant and
relatively high, it indicated that the latent variables possessed
good convergent validity. e survey response data were
analyzed using the appropriate statistical software to con-
duct the test. Each latent variable’s AVE, CR, and factor
loadings were calculated [26, 57]. e results were sub-
sequently interpreted to ascertain the convergent validity of
the measurement items and latent variables.
is study guaranteed the reliability and consistency of
the measurement items used to assess the factors inuencing
the metaverse’s success in the construction industry by
utilizing the convergent validity test. is helps establish
condence in the measurement instrument’s validity and
provides a rm foundation for subsequent analysis and
interpretation of the results.
3.6. Discriminant Validity. Discriminant validity evaluates
the extent to which distinct and nonoverlapping constructs
exist in a study. In the context of this study on the factors
inuencing metaverse success in the construction industry,
the Fornell–Larcker criterion, cross-loadings, and the Het-
erotrait–Monotrait (HTMT) ratio were used to evaluate
discriminant validity [7, 58].
According to the Fornell– Larcker criterion, discrimi-
nant validity is established when a specic construct’s square
root of the average variance extracted (AVE) is greater than
its correlation with all other constructs. is criterion as-
sures that the construct explains more variance within itself
than with other constructs [39, 59].
Cross-loadings measure the degree to which an item
predominantly loads onto its own parent construct relative
to other constructs in the study. An item’s loadings on its
primary construct should be greater than those on others. If
an item demonstrates considerable loadings on another
construct, there may be a problem with its discriminant
validity. A dierence in loadings of less than 0.10 suggests
that the item is cross-loading onto another construct, which
threatens discriminant validity [20, 38]. e HTMT ratio
contrasts the intensity of the relationships between con-
structs as a measure of discriminant validity. A value greater
than 0.90 indicates insucient discriminant validity. We
suppose the constructs are more distinct. However, a 0.85
threshold can be considered [55, 56].
To conduct the discriminant validity test, the collected
data were analyzed with the help of appropriate statistical
software. e Fornell–Larcker criterion examined correlation
matrices, factor loadings, and AVE values to assess dis-
criminant validity [39, 59]. Comparing item loadings onto
their parent construct and other constructs was used to
evaluate cross-loadings. Based on the threshold values sug-
gested by prior research, the HTMT ratio was computed to
evaluate discriminant validity [20, 53]. is study ensured that
the constructs used to measure the factors inuencing the
metaverse’s success in the construction industry were distinct
and did not overlap by employing the discriminant validity
test. is helps establish the validity of the measurement
model and instills condence in the interpretation of the
result.
3.7. Structural Model Analysis. e structural model analysis
test was conducted to evaluate the relationships between the
latent variables and to test the hypotheses formulated in this
study regarding the factors inuencing the success of the
metaverse in the construction industry [11, 30]. Several
statistical measures, including Tstatistics, pvalues, sample
mean, standard deviation (STDEV), and bootstrapping
analysis, were utilized in this analysis.
Tstatistics quantify the magnitude and signicance of
the relationships between the latent variables. pvalues assess
the signicance of the relationships [38, 53]. e corre-
sponding pvalues determine the statistical signicance of
these relationships. A low pvalue (normally less than 0.05)
indicates a statistically signicant relationship supporting
the corresponding hypothesis.
Sample mean and standard deviation (STDEV) repre-
sents the average value of a variable across the sample,
indicating the central tendency. Standard deviation
(STDEV) is a measure of dispersion. STDEV, or standard
deviation, quanties the dispersion or variability of data
points relative to the mean. ese measures facilitate
comprehension of the distribution and attributes of the
observed variables.
Bootstrapping analysis is a resampling method used to
estimate the robustness and stability of structural model
results. It entails producing multiple subsamples from the
original dataset and generating an estimated distribution.
is analysis provides condence intervals and levels of
signicance for the model coecients, thereby enhancing
the validity of the results [7, 57].
e signicance of the hypotheses and the relationships
between the latent variables were evaluated using the
structural model analysis test. Tstatistics and pvalues were
utilized to determine the signicance of the relationships,
thereby conrming or rejecting the hypotheses. e sample
mean and standard deviation of the standard deviation
provided insight into the distribution and variability of the
observed variables [20, 38]. Last but not least, the boot-
strapping analysis provided additional insight into the ro-
bustness and stability of the model results, thereby assuring
the validity and generalizability of the ndings.
is study acquired a comprehensive comprehension of
the relationships between the factors inuencing the met-
averse’s success in the construction industry through the
structural model analysis test. e statistical measures and
Journal of Engineering 7
bootstrapping analysis provided statistical signicance,
condence intervals, and stability information, enhancing
the study’s credibility and contributing to the corpus of
knowledge in this eld [26, 54].
3.8. Predictive Relevance. In partial least squares structural
equation modelling (PLS-SEM), the Q-square test is used to
determine a structural model’s predictive relevance or
predictive validity. It measures the accuracy with which the
model predicts endogenous latent variables based on ex-
ogenous latent variables.
Subtracting the predictive relevance of the dependent
construct’s indicators from the predictive relevance of the
construct itself yields the Q-square value [45, 50]. e resultant
value indicates the proportion of variance in the endogenous
construct explained by the model’s exogenous constructs. e
Q-square test is essential because it reveals the model’s ability to
predict the outcome construct. A greater Q-square value im-
plies a stronger predictive relevance, indicating that the model
is better able to explain and predict the endogenous construct
given the exogenous constructs.
e Q-square value should be compared to zero for
interpretation. A Q-square value greater than zero indicates
predictive validity. Ideally, the Q-square value should be
greater than 0, indicating the model’s strong predictive
ability. However, the Q-square’s direction (positive or
negative) is more signicant than its absolute value [20]. is
study employs the Q-square test to evaluate the predictive
validity of the structural model for factors inuencing the
success of the metaverse in the construction industry. A
positive and substantial Q-square value would indicate that
the exogenous constructs in the model have a meaningful
eect on predicting the endogenous construct, supporting
the model’s ability to explain and predict the outcomes [42].
e Q-square test contributes to the overall evaluation of
the model’s validity and increases condence in its predictive
abilities. It ensures that the proposed structural model en-
compasses the relationships between the latent variables and
contributes to an understanding of the factors inuencing the
success of the metaverse in the construction industry [52].
3.9. Importance Performance Test. e importance perfor-
mance test (IPT) is a valuable instrument for evaluating
various constructs or attributes’ relative importance and
performance within a particular context. In the context of
this study on the factors inuencing the success of the
metaverse in the construction industry, an importance-
performance test was conducted to assess the signicance
and performance of the identied constructs. In the eval-
uation of importance phase, participants or stakeholders
rank the importance of each construct or attribute [44]. is
is typically accomplished through a survey or questionnaire
in which participants subjectively rate the importance of
each construct. e importance ratings are typically docu-
mented using a Likert scale or comparable rating system.
In the performance assessment phase, participants or
stakeholders evaluate the performance or level of content-
ment associated with each construct. ey evaluate the
present performance of each construct in the given context.
Like the importance assessment, performance ratings are
collected using a Likert scale or a comparable measuring
instrument [23, 42].
e importance performance matrix is then constructed
based on the evaluations derived from the two assessments.
is matrix allows for the visual comparison and repre-
sentation of the signicance and ecacy of each construct or
attribute. e “high importance, high performance” quad-
rant represents strengths, whereas the “high importance, low
performance” quadrant represents improvement opportu-
nities [14, 41].
Using the importance-performance test, this study aims
to identify the main constructs that are both highly im-
portant and perform well, as well as those that require
improvement. e results of this analysis will aid in pri-
oritizing resources and directing decision-making processes
to increase the success of metaverse implementation in the
construction industry.
4. Results
4.1. Demographic Details. e demographic details of the
study conducted in the Bangladesh construction industry
provide insight into the participants’ characteristics, as
shown in Table 1. e study involved 101 professionals from
various construction-related roles, including architects,
quantity surveyors, civil engineers, M&E engineers, project
managers, and others. Table 1 shows that among the pro-
fessionals, civil engineers were the most prevalent, com-
prising 59.41% of the sample, followed by architects
(10.89%), quantity surveyors (9.9%), and project managers
(9.9%). A small percentage of participants identied
themselves as M&E engineers (6.93%), while others fell into
the “other” category (2.97%). In terms of the organizations
represented in the study, the majority of participants were
aliated with contractors (54.46%), followed by consultants
(38.61%), and clients (6.93%). is distribution reects the
diverse perspectives and roles within the construction in-
dustry in Bangladesh. Regarding experience in the Ban-
gladesh construction industry, the participants’ tenure
varied. A quarter of the participants had 0–5 years of ex-
perience (25.74%), while a slightly higher percentage had
6–10 years of experience (26.73%). Most participants fell into
the category of 1–15 years of experience (34.65%). A smaller
proportion of participants had 16–20 years of experience
(6.93%), while a few had over 20 years of experience (5.94%).
ese demographic details provide important insights into
the composition of the study sample, allowing for a better
understanding of the participants’ professional back-
grounds, organizational aliations, and experience levels
within the Bangladesh construction industry.
4.2. Qualitative Interview. In-depth interviews were con-
ducted with fteen Bangladeshi industry experts to enhance
and rene these identied success factors. Participants were
chosen based on their construction expertise, experience,
and familiarity with emerging technologies such as the
8Journal of Engineering
metaverse. e interview data were analyzed using quali-
tative techniques, including thematic analysis [4, 52].
rough this analysis, common motifs, patterns, and
emerging factors were identied and incorporated with the
previously identied success factors from the literature re-
view [51, 59]. Table 2 shows the categorization and modi-
cation of identied success factors from the literature. is
iterative process of integrating insights from the literature
review and expert interviews produced a comprehensive and
rened set of success factors specically tailored to the
context of the Bangladeshi construction industry [47, 57].
Based on expert opinions, the categorization and re-
nement of success factors have been drafted, and from the
above table, we can develop a hypothesis. Figure 2 shows the
hypothesis of the study.
(H1): Monitoring and maintenance of construction
signicantly impact the success of metaverse in the
construction industry
(H2): Communication and collaboration construct
signicantly impact the success of metaverse in the
construction industry
(H3): Design and visualization construct signicantly
impact the success of metaverse in the construction
industry
4.3. EFA Analysis. EFA results demonstrate that we con-
ducted a thorough examination of our data. Establishing
a threshold loading value of 0.6 ensured that only factor
loadings exceeding this threshold were considered signi-
cant and retained in the analysis. is phase assisted in
determining the most signicant variables/items contrib-
uting to each factor by utilizing the varimax rotation method
to facilitate the interpretation of the factors [45, 55]. is
method maximizes the squared loading variance within each
factor, making the factors more distinct and interpretable.
ese elements served as initial factors in EFA, resulting in
the formation of three distinct constructs [22, 25].
e rst construct, “communication and collaboration,”
includes communication and collaboration variables. is
factor indicates that these variables share a fundamental
concept or structure. e second construct, “design and
visualization,” denotes variables associated with design and
visualization aspects. is factor captures these variables’
variance, signifying their relationship to a particular con-
struct. e third and nal construct, “monitoring and
maintenance,” represents variables associated with moni-
toring and maintenance tasks. is factor indicates that these
variables measure the same underlying construct, as evi-
denced by their shared variance.
Table 3 indicates the results of exploratory factor anal-
ysis; each construct exhibits substantial variation, with re-
spective values of 12,122, 11,114, and 10,813. is indicates
that the factors account for much of the total data variability.
In addition, the eigenvalues of these factors are greater than
1, which bolsters their signicance in explaining the ob-
served patterns. It also stated that Cronbach’s alpha co-
ecient for each construct is greater than 0.70. is suggests
that the variables within each construct are highly correlated
and accurately measure the underlying construct, indicating
excellent internal consistency [17, 30]. EFA analysis has
revealed three distinct constructs regarding the structure
and composition of data: communication and collaboration,
design and visualization, and monitoring and maintenance.
ese ndings provide a rm basis for further analysis and
interpretation of the results of the study.
4.4. PLS Algorithm Factor Analysis. It would appear that
a factor analysis was conducted using the partial least
squares (PLS) algorithm within the structural equation
modelling (SEM) framework. As a minimum threshold
value for each item, the analysis considered a loading cri-
terion of 0.6. is criterion ensured that only items with
factor loadings of 0.6 or higher were considered signicant
and retained for further analysis [32, 41].
In SEM, the PLS algorithm is frequently employed to
characterize the connections between latent constructs
(factors) and observed variables (items). Figure 3 shows
a loading criterion of 0.6; the analysis sought to identify the
variables/items contributing most signicantly to each
factor. A loading criterion of 0.6 indicates a robust re-
lationship between the observed variables and the latent
Table 1: Demographic detail of respondents.
Category Classication Frequency %
Profession
Architect 10 10.89
Quantity surveyor 11 9.9
Civil engineer 61 59.41
M&E engineer 6 6.93
Project manager 11 9.9
Other 2 2.97
Organization
Contractor 54 54.46
Consultant 38 38.61
Client 9 6.93
Experience in the Bangladesh construction industry
0–5 years 25 25.74
6–10 years 28 26.73
1–15 years 33 34.65
16–20 years 8 6.93
Over 20 years 5 5.94
Journal of Engineering 9
Table 2: Success factors from the literature.
Construct Code Description Reference
Monitoring and maintenance
SF.M1
e metaverse could revolutionize the planning and design phases of construction
projects. Architects, engineers, and other stakeholders can collaborate more
eectively, visualize designs realistically, and identify potential issues or conicts
before construction through immersive virtual reality experiences. is can help
reduce costly rework and increase the overall ecacy of the undertaking
[7, 55]
SF.M2
e metaverse could facilitate real-time monitoring of construction projects by
providing a digital representation of physical assets and allowing stakeholders to
trace progress, identify constraints, and more eectively manage resources.
Sensors and Internet of ings (IoT) devices embedded in construction sites can
capture data and input it into the metaverse, enabling improved decision-making
and preventative maintenance
[34, 52]
SF.M3
Remote inspections and maintenance can be conducted more eciently using the
metaverse. Experts can navigate the virtual representation of a construction
project to assess its condition, identify potential issues, and provide guidance or
instructions to on-site teams without having to physically visit the site. is can
save time, reduce travel expenses, and enhance maintenance and repair response
times
[54, 60]
SF.M4
e metaverse can serve as a platform for replicating various scenarios and
instructing construction employees. rough virtual environments, employees
can practice complex tasks, encounter dangerous situations without real-world
hazards, and acquire practical experience in a safe environment. is can increase
safety, enhance skill development, and reduce accidents on the job site
[2, 21]
SF.M5
e metaverse can facilitate predictive maintenance in construction projects in
conjunction with sophisticated data analytics and articial intelligence. By
integrating real-time sensor data from equipment and structures into the
metaverse, patterns, and anomalies can be identied, enabling proactive
maintenance measures. Predictive maintenance helps prevent equipment failures,
reduces downtime, and prolongs the life of assets, resulting in cost savings and
enhanced project eciency
[22, 48]
Communication and collaboration
SF.C1
e metaverse can provide stakeholders from disparate locations with immersive
virtual meeting spaces to discuss and collaborate on construction projects. is
eliminates the need for physical travel, reduces expenses, and enables real-time
communication and decision-making
[1, 26]
SF.C2
Architects, engineers, and other stakeholders can collaborate more eectively on
design iterations using the metaverse. ey can use virtual reality tools to visualize
and manipulate 3D models, make adjustments in real time, and receive instant
feedback. is promotes an interactive and iterative design process, resulting in
better construction plans and fewer errors
[38, 58]
SF.C3
On construction endeavors, crews frequently operate in various locations. e
metaverse can facilitate the seamless collaboration of geographically dispersed
teams by sharing information, documents, and project updates. is promotes
collaboration, the sharing of knowledge, and a unied comprehension of the
project’s objectives
[13, 29]
SF.C4
Project stakeholders can communicate and provide feedback in real time via the
metaverse. is is possible through text, voice, and video communication in the
virtual environment. Instantaneous feedback expedites decision-making,
problem-solving, and problem-resolution, thereby enhancing the productivity of
construction initiatives
[5, 39]
SF.C5
e metaverse can be a centralized document-sharing platform where
stakeholders can access and collaborate on project-related les. is ensures that
everyone has access to the most recent document versions, prevents confusion
caused by multiple copies, and streamlines team communication
[22, 25]
10 Journal of Engineering
constructs. Items with loadings equal to or greater than
this threshold are regarded as reliable indicators of the
fundamental factors. e analysis ensured that only items
with substantial relationships to the factors were included
by employing a consistent loading cut-o value of 0.6 for
each item. is method facilitates the interpretation of the
results by emphasizing the most signicant variables
[46, 57].
e result of the analysis would be an inventory of
retained items with their respective factor loadings. ese
loadings reveal the intensity and direction of the association
between each item and its corresponding factor [11, 38].
Examining the relationships between the retained items and
the identied factors is required for interpreting the results.
Each factor’s importance and signicance can be determined
by analyzing its associated loadings and the contribution of
the corresponding elements.
In conclusion, the factor analysis performed with the
PLS algorithm and a loading criterion of 0.6 for each item
facilitates identifying and interpreting inuential variables
and factors within the dataset. ese results provide insight
into the data’s underlying structure and can guide sub-
sequent analysis and interpretation.
4.4.1. Convergent Validity. It is necessary to have high factor
loadings (above 0.6), a suitable average variance extracted
(AVE) value (generally above 0.5), and a trustworthy
composite reliability (CR) value (often above 0.7) to
Table 2: Continued.
Construct Code Description Reference
Design and visualization
SF.D1
e metaverse can make design and visualization in the construction industry
more accessible to nonspecialists. Clients, project managers, and other
stakeholders without technical design expertise can navigate and investigate
virtual environments, making it simpler for them to provide feedback and make
informed decisions
[1, 26]
SF.D2
When incorporated with building information modelling (BIM) and other data
sources, the metaverse can help identify design tensions and conicts. It is possible
to detect potential conicts between building systems, structural elements, and
services early on using automated collision detection algorithms and simulations.
is enables prompt resolution and assures a more ecient construction process
[31, 32]
SF.D3
e metaverse facilitates collaborative design evaluations by providing a shared
virtual space where stakeholders can congregate, investigate, and provide design
feedback. is enables real-time discussions, annotations, and markings on virtual
models. Design modications can be visualized and collectively evaluated,
resulting in more eective design decisions
[23, 47]
SF.D4
In conjunction with virtual reality (VR) and augmented reality (AR) technologies,
the metaverse can provide immersive and convincing visualizations of
construction undertakings. Before buildings and infrastructure are constructed,
designers, architects, and stakeholders can investigate virtual environments to
experience their scale, proportions, and spatial relationships. is facilitates
a more precise comprehension of design concepts and aids in the early
identication of prospective problems
[4, 41]
SF.D5
e metaverse enables virtual prototyping, where designers can construct and test
multiple design iterations in a virtual environment. is allows for swift design
modications and iterations without requiring tangible prototypes. Designers can
visualize and interact with their designs in real time, enabling them to assess their
viability and make more ecient modications
[21, 34]
Success of Metaverse in
Construction Industry
Monitoring & Maintenance
Communication &
Collaboration
Design & Visualization
H1
H2
H3
Figure 2: Hypothesis of the study.
Table 3: EFA analysis output.
Variables 1 2 3
SF.M1 0.775
0.724
SF.M2 0.741
SF.M3 0.735
SF.M4 0.718
SF.M5 0.685
SF.C1 0.773
0.814
SF.C2 0.752
SF.C3 0.714
SF.C4 0.703
SF.D1 0.681
0.772
SF.D2 0.633
SF.D3 0.765
SF.D4
Eigen values 3.934 3.117 2.724
% variance 12.122 11.114 10.813
Journal of Engineering 11
establish convergent validity. Table 4 shows that the results
of the study have satised the prescribed criteria of con-
vergent validity. ese are the values that make up con-
vergent validity. e composite reliability indicates the
internal consistency or reliability of the measurement items
that are included inside a construct [32, 41]. It indicates the
degree to which the items accurately measure the underlying
concept. For this reason, it is typically considered preferable
to have a composite reliability value better than 0.7 when
trying to prove convergent validity.
4.4.2. Discriminant Validity. e concept of discriminant
validity refers to the evaluation of the degree to which the
measuring items used for the various constructs may be
distinguished from one another. It investigates whether or
not the components that make up each construct have
a stronger correlation with one another than with the
components that make up the other constructs. Several
metrics, such as interconstruct correlations, the average
variance extracted (AVE), and the square root of the AVE,
may be evaluated to determine whether or not the dis-
criminant validity has been met. Using these measurements,
one may decide whether or not the constructs are adequately
dierentiated from one another [46, 49]. e term “inter-
construct correlations” describes the relationships between
several constructs. e interconstruct correlations should be
modest or low to demonstrate discriminant validity. is
indicates that the constructs are separate and do not have
a strong association with one another. An estimate of the
variation collected by the items within a construct con-
cerning the measurement error may be obtained using
a statistic known as the average variance extracted (AVE). A
higher AVE means that a bigger percentage of the concept’s
variance is explained by its items, which indicates stronger
discriminant validity [11, 52]. is is because AVE measures
the proportion of the variation in a construct that is
explained by its items. It is important to ensure that the
square root of the AVE for each construct is higher than the
correlations found between that construct and the other
constructs [33, 57]. is criterion assures that the construct
has a bigger shared variance with itself (recorded by the
AVE) than with other constructs, supporting the concept’s
discriminant validity [5, 14].
When evaluating the validity of a discriminant, it is usual
to practice utilizing the Fornell–Larcker criterion, cross-
loadings, and the Heterotrait–Monotrait (HTMT) crite-
rion. Using these criteria, the study determines whether or
not the constructs in the analysis dier [37, 56]. Following
each criterion, the following are some explanations as well as
the corresponding result limits:
Table 5 shows the HTMT results, which satised the
criteria of being an accurate model. e HTMT criteria
compare the correlations between distinct constructs,
known as heterotrait method correlations, against the cor-
relations between items within the same construct, known as
method correlations. e HTMT ratio should be less than 1,
which indicates that the constructions dier from one an-
other more than they dier from inside themselves [24, 40].
e HTMT criteria state that the HTMT ratio for each
pair of constructs must be less than 1, and the limit for the
results is based on this criterion. Values greater than one
may indicate possible problems with the discriminant val-
idity [42, 60].
Table 6 shows Fornell– Larker’s criterion for the judg-
ment of discriminant validity, and this study satised the
criteria. e Fornell–Larcker criterion compares the square
roots of the AVE values (which show the amount of vari-
ation explained by the construct) with the correlations
between the dierent constructs. Under these criteria, the
square root of the AVE for each construct ought to be greater
than the correlation between that construct and the various
other constructs [13, 40].
e limit of the results is that to satisfy the For-
nell–Larcker criterion, the square root of the AVE for each
SF.M1
SF.M2
SF.M3
SF.M5
SF.C1
SF.C4
SF.C5
SF.C2
SF.D1
SF.D2
SF.D4
0.876 (0.000)
0.857 (0.000)
0.821 (0.000)
0.812 (0.000)
0.862 (0.000)
0.855 (0.000)
0.646 (0.000)
0.720 (0.000)
0.765 (0.000)
0.765 (0.000)
0.790 (0.000)
Monitoring &
Maintenance
Communication
& Callaboration
Design &
Visualization
0.411 (0.000)
0.446 (0.000)
0.469 (0.000)
Metaverse Success
in Construction
Industry
Figure 3: PLS algorithm factor analysis indicating constructs loadings and eect along with their Pvalue.
12 Journal of Engineering
construct has to be higher than the correlation between that
construct and any other construct. A breach of this criteria
points to possible problems with the discriminant validity.
Table 7 ross-loadings assess the degree to which elements
from one build load onto other constructs. Cross-loadings
measure the amount to which one construct loads onto
another. In an ideal scenario, the loadings of each item
should be greater on their respective constructions than on
any other structures.
Limit on the results: For the discriminant validity of the
test to be considered adequate, the cross-loadings of each
item must be greater on their construct than on any other
constructs. When the cross-loadings on other constructs are
higher, it suggests there may be discriminant validity issues.
It is essential to keep in mind that the particular nu-
merical thresholds for these criteria could dier depending
on the environment and the research area being conducted
[12, 37]. However, as a rule of thumb, if these outcome
limitations are met, it suggests that the discriminant validity
is good, while violations signal that possible issues may need
to be addressed. It is essential to verify discriminant validity
since doing so guarantees that the constructs being assessed
in research are separate and do not have a strong degree of
correlation with one another [24, 60]. Because of this, the
dependability and correctness of the outcomes of the study
are improved, as it demonstrates the individuality of each
construct.
4.5. Structural Model Analysis
4.5.1. Empirical Correlation Matrix. To explain the ndings
of the empirical correlation matrix, the matrix presents the
pairwise correlations between the variables in the dataset.
Each column in the matrix represents the correlation
coecient, which may vary from minus one to plus one and
reects the strength of the linear connection between the
variables and the direction that the relationship is going.
Table 8 shows the positive correlations, represented by
values closer to +1, pointing to a direct connection between
the two variables, in which a rise in one variable is linked to
an increase in the other variable. Negative correlations,
which have values closer to −1, show an inverse link in which
a rise in one variable is connected with a reduction in the
other variable. ese correlations may be found when the
values are closer together. It is assumed that there is no linear
link between the variables when the correlation coecient is
0 [34, 53]. We pay close attention to the correlations’ size and
direction while investigating the empirical correlation ma-
trix [61]. e absolute values of strong correlations, whether
positive or negative, are often closer to 1, while the values of
lesser correlations are typically closer to 0 [13, 40]. In ad-
dition to this, we pay attention to the pattern of relationships
that spans all of the variables. We look for groupings of
variables or clusters of variables that have stronger corre-
lations among themselves than they do with other variables
overall. is may point to possible underlying causes or links
within the subsets of variables being considered [40, 42].
Learning to decipher the empirical correlation matrix not
only aids in determining the relationships between the
dierent variables but also oers insights into the underlying
structure of the data [15, 62]. Further analysis, such as factor
analysis or regression modelling, may be guided by it, and it
can also help determine which variables are connected.
Figure 4 shows the bootstrapping analysis. In the context
of the construction sector, the table illustrates the ndings
that emerged from testing three distinct hypotheses.
Communication and collaboration (CC), metaverse success
in construction industry (MSC), design and visualization
Table 4: Convergent validity results.
Construct Cronbach’s alpha Composite reliability (rho-a) Composite reliability (rho-c) e average variance
extracted (AVE)
Communication and collaboration 0.758 0.763 0.846 0.579
Design and visualization 0.705 0.707 0.834 0.631
Monitoring and maintenance 0.863 0.867 0.907 0.709
Table 5: HTMT discriminant validity results.
Constructs Communication
and collaboration Design and visualization Monitoring and maintenance
Communication and collaboration
Design and visualization 0.321
Monitoring and maintenance 0.528 0.485
Table 6: Fornell–Larker criterion results.
Constructs Communication
and collaboration Design and visualization Monitoring and maintenance
Communication and collaboration 0.761
Design and visualization 0.232 0.794
Monitoring and maintenance 0.436 0.375 0.842
Journal of Engineering 13
(DV), and monitoring and maintenance (MM) are the
constructions that are being used here.
Table 9 indicates, in the rst place, hypothesis 2 (H2)
investigates the connection between communication and
collaboration (CC) and metaverse success in the construction
sector (MSC). According to the ndings of the investigation,
the sample mean for this connection is 0.446, and its standard
deviation is 0.032 [13, 51]. A high degree of statistical sig-
nicance is shown by the Tstatistics value of 13.996, which
was calculated. e acceptance of the hypothesis is a direct
result of the Pvalue being equal to zero, which demonstrates
that the link under consideration is statistically signicant.
Consequently, there is a substantial and positive association
between communication and collaboration and metaverse
success in the construction business.
Following that, hypothesis 3 (H3) investigates the
connection between design and visualization (DV) and
metaverse success in the construction sector (MSC). With
a standard deviation of 0.034, the sample mean for this
connection is 0.411, with a variance of 0.034. A high degree
of statistical signicance is shown by the Tstatistics value of
12.237, which is corroborated by the fact that the Pvalue is 0.
As a consequence of this, the hypothesis is accepted, and the
results suggest that there is a considerable positive
Table 7: Cross-loadings results.
Variables Communication
and collaboration Design and visualization Monitoring and maintenance
SF.C1 .72 0.147 0.21
SF.C2 .79 0.107 0.445
SF.C4 .765 0.197 0.322
SF.C5 .765 0.252 0.325
SF.D1 0.153 .862 0.28
SF.D2 0.201 .855 0.296
SF.D4 0.198 .646 0.318
SF.M1 0.414 0.33 .876
SF.M2 0.374 0.379 .857
SF.M3 0.318 0.311 .821
SF.M5 0.357 0.234 .812
Bold values present the factor loading considered for each of the factor on the left side.
Table 8: Empirical correlation matrix output.
Variables SF.C1 SF.C2 SF.C4 SF.C5 SF.D1 SF.D2 SF.D4 SF.M1 SF.M2 SF.M3 SF.M5
SF.C1 1 0.423 0.414 0.445 0.123 0.047 0.189 0.207 0.188 0.126 0.184
SF.C2 0.423 1 0.495 0.447 0.044 0.159 0.044 0.4 0.387 0.362 0.348
SF.C4 0.414 0.495 1 0.41 0.165 0.174 0.128 0.288 0.276 0.249 0.271
SF.C5 0.445 0.447 0.41 1 0.14 0.213 0.251 0.345 0.268 0.208 0.268
SF. D1 0.123 0.044 0.165 0.14 1 0.704 0.313 0.216 0.266 0.265 0.194
SF. D2 0.047 0.159 0.174 0.213 0.704 1 0.28 0.272 0.285 0.259 0.175
SF. D4 0.189 0.044 0.128 0.251 0.313 0.28 1 0.3 0.357 0.211 0.188
SF.M1 0.207 0.4 0.288 0.345 0.216 0.272 0.3 1 0.677 0.64 0.611
SF.M2 0.188 0.387 0.276 0.268 0.266 0.285 0.357 0.677 1 0.588 0.598
SF.M3 0.126 0.362 0.249 0.208 0.265 0.259 0.211 0.64 0.588 1 0.556
SF.M5 0.184 0.348 0.271 0.268 0.194 0.175 0.188 0.611 0.598 0.556 1
14 Journal of Engineering
association between design and visualization and metaverse
success in the building sector.
Last but not least, hypothesis 1 (H1) explores the con-
nection between monitoring and maintenance (MM) and
metaverse success in the construction sector (MSC).
According to the investigation ndings, the sample mean for
this connection is 0.469, and its standard deviation is 0.032.
A high degree of statistical signicance is shown by the T
statistics value of 14.844, which is quite high [5]. e fact that
the Pvalue was zero provides more evidence that the sta-
tistical signicance was signicant, ultimately leading to the
hypothesis being accepted. Consequently, there is a con-
siderable positive association between monitoring and
maintenance and metaverse success in the construction
business [13, 51].
In conclusion, the ndings suggest that considerable
positive links exist in the construction sector between
communication and collaboration, design and visualization,
monitoring and maintenance, and metaverse success. e
acceptance of both hypotheses is supported by a high degree
of statistical signicance, as shown by the low Pvalues,
which are equal to 0. According to these results, the sig-
nicance of these structures in the construction sector
within the framework of the metaverse in which it operates
should not be discounted.
4.6. Predictive Relevance Q-Square. e following table
presents information on the sum of squares (SSO), sum of
squared errors (SSE), and Q-square value for the construct
titled “Metaverse Success in Construction Industry.” SSO
denotes the standard deviation of the overall variance or
variability in the construct. e SSO for the metaverse’s
success in the construction industry is 3971.000 in this
particular instance.
On the other hand, the standardized standard error
(SSE) represents the residual or unexplained variance in the
concept. It is calculated as the total of the squared dis-
crepancies between the actual values of the construct and the
anticipated values. e standardized scale of evaluation
(SSE) for metaverse success in the construction industry is
2996.772.
Table 10 shows Q-square measures the amount of var-
iation in the construct that the model explains. It is derived
as 1 minus SSE divided by SSO and measures the amount of
variance the model explains. e value of Q-square for the
metaverse’s success in the construction industry is 0.245 in
this specic instance.
e fact that the model has a Q-square value of 0.245
suggests that it accounts for about 24.5% of the total variation
in metaverse success in the construction industry [13, 51]. is
indicates that about 24.5% of the variability in the construct can
be ascribed to the factors or variables included in the model,
while the remaining 75% of the variability in the construct is
either unexplained or accounted for by other factors that are
not included in the model.
It is essential to keep in mind that the context and area of
research have a role in determining how Q-square should be
interpreted. In general, larger Q-square values suggest that
SF.M1
SF.M2
SF.M3
SF.M5
SF.C1
SF.C4
SF.C5
SF.C2
SF.D1
SF.D2
SF.D4
0.876 (53.303)
0.857 (37.652)
0.821 (24.872)
0.812 (27.062)
0.862 (32.479)
0.855 (32.243)
0.646 (10.300)
0.720 (14.832)
0.765 (19.445)
0.765 (21.053)
0.790 (26.729)
Monitoring &
Maintenance
Communication
& Callaboration
Design &
Visualization
0.411 (12.237)
0.446 (13.996)
0.469 (14.844)
Metaverse Success
in Construction
Industry
Figure 4: Bootstrapping analyses with Tstatics and path loading.
Table 9: Bootstrapping analysis for the hypothesis test.
Hypothesis Construct (O) (M) (STDEV) Tstatistics Pvalues Status
H2 CC ⟶MSC 0.446 0.446 0.032 13.996 0 Accepted
H3 DV ⟶MSC 0.411 0.41 0.034 12.237 0 Accepted
H1 MM ⟶MSC 0.469 0.467 0.032 14.844 0 Accepted
CC �communication and collaboration; MSC �metaverse success in construction industry; DV �design and visualization; MM �monitoring and
maintenance; (0) �eect of construct on success of metaverse; STDEV �standard deviation.
Journal of Engineering 15
the model is a better t for the data and that the model
explains a bigger fraction of the variation in the data [62].
However, the precise limit for what constitutes an acceptable
Q-square number may change depending on the area of
study being conducted [24, 40].
In conclusion, the table presents data on the SSO, SSE,
and Q-square values associated with the construct titled
“Metaverse Success in Construction Industry.” ese
numbers help determine how much the model explains the
variability in the construct. For example, a Q-square value of
0.245 indicates that the model accounts for about 24.5% of
the variation in metaverse success in the construction
industry.
4.7. Importance of Performance of Construct. is table
presents data on the construct scores, more precisely, the
performance scores and the eect sizes that correlate to the
following three constructs: communication and collabora-
tion, design and visualization, and monitoring and
maintenance.
e performance score for the construct of commu-
nication and collaboration is 52.546, which indicates the
level or degree of performance in that specic domain. It
has been shown that communication and collaboration
have an impact size of 0.466 [21, 34]. An eect size is
a statistical metric that measures the degree to which
a link or dierence exists. e eect size of 0.466 in this
instance indicates a moderate impact or link between
communication and collaboration and the outcome
variable being assessed.
In a similar vein, the performance score for the com-
ponent referred to as “Design & Visualization” is 56.39,
which indicates the degree of performance concerning this
particular area. ere is a moderate impact or association
between the design and visualization variable and the out-
come variable, as shown by the eect size for design and
visualization, which is 0.411.
Last but not least, the performance score for the mon-
itoring and maintenance construct is 44.965, which accu-
rately reects the degree of performance in that particular
domain. ere is a moderate eect or association between
monitoring and maintenance and the outcome variable, as
shown by the eect size, which is calculated to be 0.469. e
eect sizes provide insight into the kind and extent of the
connection between each construct and the outcome vari-
able [63]. It is commonly accepted that an eect size value
ranging from 0.4 to 0.6 is considered moderate, indicating
that the construct has a considerable inuence on the result.
Table 11 provides a summary of the performance ratings
as well as the impact sizes for the three dierent constructs,
which are communication and collaboration, design and
visualization, and monitoring and maintenance. ese
values provide a comprehension of the performance levels
and the size of the relationships between the various con-
structs and the outcome variable being assessed.
5. Discussion
e purpose of the paper that was given the title “Success of
Metaverse in the Construction Industry” was to study the
linkages that exist between communication and collabora-
tion (CC), design and visualization (DV), monitoring and
maintenance (MM), and the overall success of the metaverse
in the construction industry (MSC). Examining the hy-
potheses led to the discovery of important results, detailed in
the table below.
e second hypothesis, referred to as H2, investigated
the connection between communication and collaboration
(CC) and the level of success achieved by the construction
industry (MSC) metaverse. e coecient of determination
(CC) was shown to have an inuence of 0.446 on the MSC,
with a standard deviation of 0.032. A high degree of sta-
tistical signicance was suggested by the Tstatistics value of
13.996, and the related Pvalue of 0 veried that the sign was
there. As a result, hypothesis 2 (H2) was allowed, which
suggests a strong positive association between communi-
cation and collaboration and the success of the metaverse in
the construction business.
e third hypothesis, designated as H3, investigated
the connection between design and visualization (DV)
and the success of the metaverse in the construction in-
dustry (MSC). e mean value of DV’s inuence on MSC
was found to be 0.411, with a standard deviation of 0.034.
e result of 12.237 for the Tstatistics indicated great
statistical signicance, and the value of 0 for the Pvalue
validated this signicance [5, 51]. As a result, hypothesis 3
(H3) was validated, suggesting a considerable positive
association between design and visualization and the
accomplishments of the metaverse in the building and
construction sector.
Monitoring and maintenance (MM) and metaverse
success in the construction industry (MSC) were the subjects
of investigation in the rst hypothesis (H1), which studied
their connection. e mean value of MM’s inuence on MSC
was found to be 0.469, with a standard deviation of 0.032. A
high degree of statistical signicance was suggested by the T
statistics value of 14.844, and the fact that the Pvalue was
0 provided further support for this signicance [4, 64]. As
a result, hypothesis 1 (H1) was found to be plausible, in-
dicating that there is a considerable positive association
Table 10: Predictive relevance test.
Construct SSO SSE Q-square �(1 −SSE/SSO)
Metaverse success in construction industry 3971.000 2996.772 0.245
Table 11: Importance performance of construct.
Construct Performance Eect
Communication and collaboration 52.546 0.466
Design and visualization 56.39 0.411
Monitoring and maintenance 44.965 0.469
16 Journal of Engineering
between monitoring and maintenance and the success of the
metaverse in the construction business.
ese results add to a better understanding of how
communication and collaboration, design and visualization,
and monitoring and maintenance impact the success of the
metaverse in the construction sector. e substantial asso-
ciations suggest that these constructs play key roles in ef-
ciently harnessing the metaverse to achieve success in
building projects [1, 24]. is is shown by the fact that these
linkages are signicant.
We give empirical evidence supporting the assumption
that higher degrees of communication and collaboration,
design and visualization, and monitoring and maintenance
are connected with better success in using the metaverse
within the construction sector by adopting these hypotheses
and accepting them as true. is indicates that putting more
eort into enhancing these areas might lead to better results
regarding how the metaverse is adopted and used [13, 40].
It is very necessary to point out the restrictions that this
research has. e study relied on self-reported data, which
means it may have been biased in some way. In addition, the
success of the metaverse in the construction business may
also be aected by other aspects that should have been taken
into account in the investigation. In a further study, other
constructs and factors can be investigated to get a more in-
depth and holistic knowledge of the involved intricate
dynamics.
e research reveals important insights into the con-
nections between communication and collaboration, design
and visualization, monitoring and maintenance, and the
eectiveness of the metaverse in the construction sector
[16, 54]. ese connections are particularly relevant to the
construction industry. To properly use the metaverse, the
relevance of these structures has been highlighted by the
theories that have been accepted. ese results have con-
sequences for industry experts and decision-makers, who
may prioritize and improve these areas to maximize the
advantages of the metaverse in the construction sector. ese
ndings have practical implications for the construction
industry. is research’s sample size and representativeness
are too small to be considered ideal, which might restrict the
results’ applicability to a wider population. In addition to
this, the dependence on data that was self-reported opens the
door to the potential of response biases as well as mea-
surement inaccuracies.
6. Conclusion
In conclusion, the purpose of our manuscript, which was
given the title “Success of Metaverse in the Construction
Industry,” was to investigate the relationships that exist
between communication and collaboration (CC), design and
visualization (DV), monitoring and maintenance (MM), and
the overall success of the metaverse in the construction
industry (MSC). We have gotten important results that shed
light on the function of these structures in attaining success
with the metaverse in the building as a result of our study
and evaluation of the hypotheses. ese ndings may be seen
below. Our ndings indicate that communication and
collaboration, design and visualization, and monitoring and
maintenance all contribute substantially to the favorable
outcome of using the metaverse in the construction sector.
is suggests that increased levels of eective communi-
cation and cooperation, sophisticated design and visuali-
zation skills, and ecient monitoring and maintenance
practice practices are all factors that contribute to more
eectively using the metaverse. is contributes to the
knowledge of how these components impact the acceptance
and utilization of the metaverse in building projects when we
accept the hypothesis. Because they emphasize the areas that
need attention and development to maximize the advantages
of the metaverse, these results have practical signicance for
industry experts and decision-makers. It is essential to
recognize the shortcomings of our research, which include
the likelihood of biases caused by the use of self-reported
data and the possibility of confounding factors that were not
taken into consideration. When planning for future study, it
is important to consider the need to overcome these re-
strictions and investigate other constructions and elements
that may aect how well the metaverse performs in the
construction business. In general, our research ndings
provide empirical evidence in favor of a positive correlation
between communication and collaboration, design and vi-
sualization, monitoring and maintenance, and the eec-
tiveness of the metaverse in the construction sector. ese
results contribute to the expanding body of information on
the utilization of new technologies in construction and may
aid industry experts in making informed choices to use the
metaverse eectively. With regard to blockchain, the focus of
our analysis is on the metaverse ecosystem and how it may
benet from the use of this decentralized ledger technology.
We investigate how the characteristics of blockchain tech-
nology, such as increased data security, transparency, and
traceability, may be incorporated into metaverse applica-
tions in a seamless manner. is integration oers the
potential to revolutionize transactional processes, promote
condence among stakeholders, and protect the integrity of
virtual assets and interactions, all of which are all important
goals. In addition, our analysis dives into the eld of articial
intelligence and investigates the revolutionary impact it
plays in enhancing user experiences, content development,
and decision-making within metaverse settings. e com-
bination of articial intelligence with the technology of
metaverses has the potential to provide virtual interactions
with a higher level of intelligence and exibility. Because of
this, the architectural design processes, construction sim-
ulations, and real-time monitoring that are used in the AEC
business may be reshaped.
e most exciting aspect of our research is that it dives
into the potential for cooperative endeavors that arise at the
convergence of metaverse technology, blockchain, and ar-
ticial intelligence. e AEC industry has the potential to
benet from new approaches to problem-solving in the areas
of project management, design visualization, and data-
driven insights if links between these breakthroughs are
established. e use of communication, cooperation, and
data analysis might potentially be reimagined as a result of
this conuence, which spans the whole of the construction
Journal of Engineering 17
lifecycle. In conclusion, this study contributes to metaverse
technology adoption research and the architecture, engi-
neering, and construction (AEC) industry. is study ex-
amines the success factors of metaverse technology
integration in the AEC sector, revealing the complex link-
ages that aect project eciency, collaboration, and design
visualization. is research uncovers the many elements of
metaverse technology adoption, providing practical insights
that may improve the AEC industry. Practitioners and
stakeholders may strategically invest in metaverse technol-
ogy’s success determinants. is paradigm helps decision-
makers negotiate technological integration, improving
project results, stakeholder participation, and cooperation.
is research also has practical advantages for the AEC
industry. e results suggest ways to increase project e-
ciency, design procedures, and real-time monitoring, cre-
ating a nimbler and more inventive sector. is study guides
the AEC industry in maximizing metaverse technology’s
potential. In conclusion, this study’s major contribution
resonates in academic and AEC circles. As metaverse
technology continues to alter the industry, these insights
guide stakeholders toward a future where innovation and
eciency merge, improving project results and transforming
the built environment. In addition, these ndings contribute
to the developing body of knowledge on utilizing emerging
technologies in other industries. It is becoming more crucial
for the construction industry to embrace the promise oered
by the metaverse as the construction sector continues to
change. e ndings of this research may provide useful
information for strategic planning, resource allocation, and
implementation methods, all of which are necessary to
guarantee the eective incorporation of the metaverse into
building projects. Construction stakeholders can navigate
the challenges posed by the metaverse and embrace the
opportunities it presents if they harness the power of
communication and collaboration tools, design and visu-
alization tools, and monitoring and maintenance tools. is,
in turn, results in improved project outcomes, increased
productivity, and increased competitiveness within the
industry.
6.1. Managerial and Empirical Implications. e results of
our research illustrate the necessity of eective communi-
cation and collaborative eort in harnessing the metaverse
for success in the construction sector. Specically, the
ndings highlight the need to enhance communication and
collaborative eorts. Fostering a climate that is conducive to
collaboration, putting in place communication tools and
platforms, and promoting open and transparent commu-
nication among project teams should be the primary focus of
managers and those in charge of leading projects. is may
help enhance collaboration, the exchange of information,
and decision-making, which can eventually lead to improved
project results. e ndings of our study highlight the need
for sophisticated design and visualization skills to fully
exploit the promise of the metaverse. e enhancement of
design and visualization skills must be given high priority by
managers when it comes to hardware, software, and
education expenditures. Construction professionals may
build immersive experiences, visualize designs in real time,
and spot possible diculties before they exist by employing
virtual and augmented reality technologies. is results in
greater design accuracy and enhanced stakeholder
involvement.
Research emphasizes how critical it is to have reliable
monitoring and maintenance procedures in place to guar-
antee the success of the metaverse when it comes to building
projects. e constant monitoring of the installation of the
metaverse, tracking of performance, and proactive main-
tenance should be given high priority by managers to resolve
any technical problems or system diculties as soon as
possible. is will assist in maintaining the functioning and
dependability of the metaverse platform, providing a smooth
experience for users, and maximizing the advantages re-
ceived from their use of the platform. e empirical data
shown in our research demonstrates positive correlations
between communication and collaboration, design and vi-
sualization, monitoring and maintenance, and the success of
the metaverse in the construction industry. ese positive
interactions are essential to the success of the metaverse in
the construction industry. As mentioned earlier, these re-
sults add to more empirical knowledge of how the constructs
impact the adoption and utilization of the metaverse. Re-
searchers in the future might expand upon these results by
doing an identical study in various settings and elds to
evaluate the degree to which the associations discovered are
generalizable.
e empirical results highlight the need for assessment
and improvement on an ongoing basis of the use of met-
averse technology in the construction sector. In various
construction contexts, researchers can do more research on
the elements that impact the success of communication and
collaboration, design and visualization, and monitoring and
maintenance. is may give insights into certain tactics and
practices that boost these structures’ inuence on the
metaverse’s success, hence allowing continual development
and optimization. Although the emphasis of our research
was on three essential constructions, additional aspects of
the metaverse may contribute to its success in the building
and construction business. Future research might investigate
other structures, such as project management approaches,
user acceptability, data security, or stakeholder involvement,
to acquire a deeper and more complete knowledge of the
intricate dynamics at play. By examining these variables,
insights into the complex and multidimensional character of
the use of the metaverse in the building may be gained.
e management implications of our research underline
the signicance of cultivating communication and co-
operation, investing in design and visualization skills, and
enhancing monitoring and maintenance practices to max-
imize the metaverse’s eectiveness in building projects.
Because of the practical implications, further study has to be
done to test and build upon our results, evaluate their ap-
plication in diverse circumstances, and investigate addi-
tional elements that impact the development of the
metaverse. If players in the construction industry are willing
to accept these consequences, they will be better able to
18 Journal of Engineering
traverse the metaverse environment and harness its potential
for enhanced project results and development in the sector.
6.2. Limitations and Recommendations. is research’s
sample size and representativeness are too small to be
considered ideal, which might restrict the results’ applica-
bility to a wider population. In addition to this, the de-
pendence on data that were self-reported opens the door to
the potential of response biases as well as measurement
inaccuracies.
Future studies should try to incorporate bigger and more
varied samples to increase their generalizability and solve the
constraints that have been identied. e validity of the
results might be improved by combining data from self-
reports with data from objective measurements or by using
various data sources. In addition, performing longitudinal
research and investigating other factors would give a better
knowledge of the dynamics and the impacts that last longer.
Comparative research spanning many sectors or locations
might provide useful insights into the elements that are
distinctive to the industry and assist in the development of
specialized strategies.
Data Availability
e data are not available for public or private access.
Conflicts of Interest
e authors declare that there are no conicts of interest
regarding the publication of this paper.
References
[1] Z. Yan, “A comprehensive overview of the fundamental and
applications of metaverse,” SHS Web of Conferences, vol. 148,
2022.
[2] Z. Liu, Z. Yang, M. Liang, Y. Liu, M. Osmani, and P. Demian,
“A conceptual framework for blockchain enhanced in-
formation modeling for healing and therapeutic design,”
International Journal of Environmental Research and Public
Health, vol. 19, no. 13, p. 8218, 2022.
[3] X. Huang, H. X. Wang, X. Gu, Y. Meng, and H. Y. Wang, “A
new 3D point clouds feature selection method using specic
outliers optimization,” Journal Graphics, vol. 10, 2022.
[4] S. M. Park and Y. G. Kim, “A metaverse: taxonomy, com-
ponents, applications, and open challenges,” IEEE Access,
vol. 122, 2022.
[5] J. Ho Kim, B. Sung Lee, and S. Jhin Choi, “A study on
metaverse construction and use cases for non-face-to-face
education,” e journal of the convergence on culture tech-
nology, vol. 78, 2022.
[6] A. Waqar, A. Hannan Qureshi, I. Othman, N. Saad, and
M. Azab, “Exploration of challenges to deployment of
blockchain in small construction projects,” Ain Shams En-
gineering Journal, vol. 20, Article ID 102362, 2023.
[7] A. Waqar, I. Othman, N. Shaq, and H. Altan, “Sustainability
modeling the eect of overcoming the barriers to passive
design implementation on project sustainability building
success: a structural equation modeling perspective,” Sus-
tainability, vol. 15, 2023.
[8] H. Tahir, “Optimisation of mechanical characteristics of
alkali-resistant glass bre concrete towards sustainable con-
struction,” Sustainability, vol. 15, 2023.
[9] M. Sajjad, “Evaluation of the success of industry 4.0 digita-
lization practices for sustainable construction management:
Chinese construction industry,” Buildings, vol. 13, no. 7,
p. 1668, 2023.
[10] M. Khan, “Eects of jute ber on fresh and hardened char-
acteristics of concrete with environmental assessment,”
Buildings, vol. 13, no. 7, p. 1691, Jun 2023.
[11] K. Liu, L. Chen, L. Li, H. Ren, and F. Y. Wang, “Metamining:
mining in the metaverse,” IEEE Transactions on Systems, Man,
and Cybernetics Systems, vol. 12, 2023.
[12] W. J. Choi, S. J. Heo, S. Na, and C. K. Kim, “A study on the
direction of application of metaverse in the construction
industry,” in Proceedings of the Korean Institute of Building
Construction Conference, Seoul, South Korea, June 2022.
[13] J. Choi and S. Lee, “A suggestion of the alternatives evaluation
method through ifc-based building energy performance
analysis,” Sustain, vol. 58, 2023.
[14] J. R. Rameshwar and G. King, “Analysis of caribbean xr survey
creates an xr development strategy as a path to the regional
metaverse evolution,” Journal Metaverse, vol. 11, 2023.
[15] J. Huang, P. Sun, and W. Zhang, “Analysis of the future
prospects for the metaverse,” in Proceedings of the 2022 7th
International Conference on Financial Innovation and Eco-
nomic Development (ICFIED 2022), Harbin, China, July 2022.
[16] A. Waqar, I. Othman, N. Shaq, and M. S. Mansoor, “Ap-
plications of AI in oil and gas projects towards sustainable
development: a systematic literature review,” Articial In-
telligence Review, vol. 48, pp. 1–28, 2023.
[17] T. Huynh-e, Q. V. Pham, X. Q. Pham, T. T. Nguyen,
Z. Han, and D. S. Kim, “Articial intelligence for the meta-
verse: a survey,” Engineering Applications Of Articial In-
telligence, vol. 98, 2023.
[18] A. Waqar, M. B. Khan, N. Shaq, K. Skrzypkowski,
K. Zag´orski, and A. Zag´orska, “Assessment of challenges to
the adoption of IOT for the safety management of small
construction projects in Malaysia: structural equation mod-
eling approach,” Applied Sciences, vol. 13, no. 5, p. 3340, 2023.
[19] A. Waqar, “Assessment of barriers to robotics process au-
tomation (RPA) implementation in safety management of tall
buildings,” Buildings, vol. 13, no. 7, p. 1663, Jun 2023.
[20] J. Wu, H. Chen, and C. He, “Metaverse:the digital-real fusion
space of the smart library,” Journal of Library Science in China,
vol. 12, 2022.
[21] G. Nichols, “Augmented and virtual reality mean business:
everything you need to know an executive guide to the
technology and market drivers behind the hype in ar,” VR,
and MR, vol. 45, 2018.
[22] A. Waqar, A. H. Qureshi, and W. S. Alaloul, “Barriers to
building information modeling (bim) deployment in small
construction projects: malaysian construction industry,”
Sustain, vol. 11, 2023.
[23] P. Treleaven, “BIM 3.0: Web3.0,” DeFi and the Metaverse,
vol. 23, 2022.
[24] A. Waqar, “Eect of coir bre ash (cfa) on the strengths,
modulus of elasticity and embodied carbon of concrete using
response surface methodology (rsm) and optimization,” Re-
sults English, vol. 16, 2023.
[25] A. Waqar, I. Othman, and K. Skrzypkowski, “Evaluation of
Success of Superhydrophobic Coatings in the Oil and Gas
Construction Industry Using Structural,” Coatings, vol. 11,
2023.
Journal of Engineering 19
[26] A. Waqar, “Success of implementing cloud computing for
smart development in small construction projects,” Applied
Sciences, vol. 13, no. 9, p. 5713, 2023.
[27] M. Baghalzadeh Shishehgarkhaneh, A. Keivani,
R. C. Moehler, N. Jelodari, and S. Roshdi Laleh, “Internet of
things (iot), building information modeling (bim), and digital
twin (dt) in construction industry: a review, bibliometric, and
network analysis,” Buildings, vol. 55, 2022.
[28] L. Bian, R. Xiao, Y. Lu, and Z. Luo, “Construction and design
of food traceability based on blockchain technology applying
in the metaverse,” Lecture Notes in Computer Science (In-
cluding Subseries Lecture Notes in Articial Intelligence and
Lecture Notes in Bioinformatics), vol. 56, 2022.
[29] Y. Chen, X. Wang, Z. Liu, J. Cui, M. Osmani, and P. Demian,
“Exploring building information modeling (bim) and internet
of things (iot) integration for sustainable building,” Buildings,
vol. 65, 2023.
[30] R. Chengoden, “Metaverse for healthcare: a survey on po-
tential applications, challenges and future directions,” IEEE
Access, vol. 32, 2023.
[31] A. Kanak, I. Arif, C. Terzibas, O. F. Demir, and S. Ergun,
“BIMyVerse: towards a semantic interpretation of buildings
in the city and cities in the universe,” in Proceedings of the
Conference Proceedings- IEEE International Conference on
Systems, Yasmine Hammamet, Tunisia, August 2022.
[32] T. Huynh-e, “Blockchain for the Metaverse: A Review,”
Future Generation Computer Systems, vol. 10, 2023.
[33] Z. Liu, P. Li, F. Wang, M. Osmani, and P. Demian, “Building
information modeling (BIM) driven carbon emission re-
duction research: a 14-year bibliometric analysis,” In-
ternational Journal of Environmental Research and Public
Health, vol. 19, no. 19, p. 12820, 2022.
[34] Z. Lv, S. Xie, Y. Li, M. Shamim Hossain, and A. El Saddik,
“Building the Metaverse by Digital Twins at All Scales, State,
Relation,” Virtual Reality And Intelligent Hardware, vol. 20,
2022.
[35] M. B. Khan, A. Waqar, N. Bheel et al., “Optimization of fresh
and mechanical characteristics of Carbon Fiber-Reinforced
concrete composites using response surface technique,”
Buildings, vol. 13, p. 852, 2023.
[36] A. Waqar and I. Othman, “Challenges to the implementation
of bim for the risk management of oil and gas construction
projects: structural equation modeling approach sustainability
challenges to the implementation of bim for the risk man-
agement of oil and gas construction project,” Sustainability,
vol. 15, 2023.
[37] L. Fang and H. Shen, “Changes in technology and civi-
lization—a conceptual study of the metaverse,” Metaverse,
vol. 65, 2021.
[38] N. A. Dahan, M. Al-Razgan, A. Al-Laith, M. A. Alsou,
M. S. Al-Asaly, and T. Alfakih, “Metaverse framework: a case
study on e-learning environment (elem),” Electron, vol. 12,
2022.
[39] Y. Hu and H. Chen, “e trend of industrial design from the
perspective of metaverse,” Lecture Notes in Computer Science
(Including Subseries Lecture Notes in Articial Intelligence and
Lecture Notes in Bioinformatics), vol. 10, 2022.
[40] Y. Chen, D. Huang, Z. Liu, M. Osmani, and P. Demian,
“Construction 4.0, industry 4.0, and building information
modeling (bim) for sustainable building development within
the smart city,” Sustain, 2022.
[41] M. Saker and J. Frith, “Contiguous identities: the virtual self in
the supposed metaverse,” First Monday, vol. 52, 2022.
[42] PWC, Demystifying the Metaverse, pwc, 2022.
[43] A. Waqar, “Eect of volcanic pumice powder ash on the
properties of cement concrete using response surface meth-
odology,” Journal of Building Pathology and Rehabilitation,
vol. 8, no. 1, 2023.
[44] X. Wang, J. Wang, C. Wu, S. Xu, and W. Ma, “Engineering
brain: metaverse for future engineering,” AI in Civil Engi-
neering, vol. 20, 2022.
[45] A. Waqar, I. Othman, H. Almujibah, M. B. Khan, S. Alotaibi,
and A. A. M. Elhassan, “Factors inuencing adoption of
digital twin advanced technologies for smart city develop-
ment: evidence from malaysia,” Buildings, vol. 32, 2023.
[46] H. Huang, X. Zeng, L. Zhao, C. Qiu, H. Wu, and L. Fan,
“Fusion of building information modeling and blockchain for
metaverse: a survey,” IEEE Open Journal Computer Social,
vol. 52, 2022.
[47] Q. Sun, Y. Xu, Y. Sun, C. Yao, J. S. A. Lee, and K. Chen, “Gn-
cnn: a point cloud analysis method for metaverse applica-
tions,” Electron, vol. 20, 2023.
[48] X. Zhao and Q. Lu, “Governance of the metaverse:a vision for
agile governance in the future data intelligence world,” e
Journal of e Library Science in China, 2022.
[49] P. Zhang, “IEEE draft standard for spectrum characterization
and occupancy sensing,” IEEE Access, vol. 12, 2019.
[50] A. Waqar, I. Othman, and J. C. Pomares, “Impact of 3D
printing on the overall project success of residential con-
struction projects using structural equation modelling,” In-
ternational Journal of Environmental Research and Public
Health, vol. 20, no. 5, p. 3800, 2023.
[51] G. Wang and C. Shin, “Inuencing factors of usage intention
of metaverse education application platform: empirical evi-
dence based on ppm and tam models,” Sustain, vol. 13, 2022.
[52] C. T. Nguyen, D. T. Hoang, D. N. Nguyen, and E. Dutkiewicz,
“Metachain: a novel blockchain-based framework for meta-
verse applications,” IEEE Vehicular Technology Conference,
vol. 20, 2022.
[53] Y. Zhao, “Metaverse: perspectives from graphics, interactions
and visualization,” Visual Informatics, vol. 6, no. 1, pp. 56–67,
2022.
[54] M. Khan, “Optimization of fresh and mechanical character-
istics of carbon ber-reinforced concrete composites using
response surface technique,” Buildings, vol. 13, 2023.
[55] A. Waqar, I. Othman, N. Shaq, and H. Altan, “Modeling the
eect of overcoming the barriers to passive design imple-
mentation on project sustainability building success: a struc-
tural equation modeling perspective sustainability modeling
the eect of overcoming the barriers to passive design im-
plement,” Sustainability, vol. 13, 2023.
[56] J. Lin, “On the innovative design of digital media under the
background of the metaverse,” in Proceedings of the 2022
International Conference on Comprehensive Art and Cultural
Communication (CACC 2022), Chongqing, China, June 2022.
[57] J. N. Njoku, C. I. Nwakanma, G. C. Amaizu, and D. S. Kim,
“Prospects and challenges of metaverse application in data-
driven intelligent transportation systems,” IET Intelligent
Transport Systems, vol. 52, 2023.
[58] X. Duan and C. Chen, “e construction and exploration of
spatial narrative under the ken of metaverse,” Journal of
Intelligent Communication, vol. 2, no. 1, 2022.
[59] X. Wang, T. Shi, W. Bai, K. Peng, J. Li, and Y. Shi, “e
research of metaverse application in intelligent railway pas-
senger station,” Lecture Notes in Computer Science (Including
Subseries Lecture Notes in Articial Intelligence and Lecture
Notes in Bioinformatics), vol. 20, 2022.
20 Journal of Engineering
[60] P. Kwok, J. Chen, and J. Fung, “Two-way knowledge transfer
among university academics, young entrepreneurs, ngos and
students in stem and iot metaverses: conceptual model,”
Researchgate Net, vol. 25, 2022.
[61] M. Sadeghi, A. Mahmoudi, and X. Deng, “Blockchain tech-
nology in construction organizations: risk assessment using
trapezoidal fuzzy ordinal priority approach,” Engineering
Construction and Architectural Management, vol. 30, no. 7,
pp. 2767–2793, 2022.
[62] M. Sadeghi, A. Mahmoudi, and X. Deng, “Adopting dis-
tributed ledger technology for the sustainable construction
industry: evaluating the barriers using Ordinal Priority Ap-
proach,” Environmental Science & Pollution Research, vol. 29,
no. 7, pp. 10495–10520, 2022.
[63] A. Mahmoudi, M. Sadeghi, and X. Deng, “Performance
measurement of construction suppliers under localization,
agility, and digitalization criteria: fuzzy Ordinal Priority
Approach,” Environment, Development and Sustainability,
vol. 456, pp. 1–26, 2022.
[64] A. Mahmoudi, M. Sadeghi, and L. Naeni, “Blockchain and
supply chain nance for sustainable construction industry:
ensemble ranking using Ordinal Priority Approach,” Oper-
ations Management Research, vol. 44, 2023.
Journal of Engineering 21
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