Investigating Users’ Acceptance of the Metaverse with an Extended Technology Acceptance Model

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Investigating Users’ Acceptance of the Metaverse
with an Extended Technology Acceptance Model
Rong Wu & Zhonggen Yu
To cite this article: Rong Wu & Zhonggen Yu (06 Aug 2023): Investigating Users’ Acceptance
of the Metaverse with an Extended Technology Acceptance Model, International Journal of
Human–Computer Interaction, DOI: 10.1080/10447318.2023.2241295
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Investigating Users’Acceptance of the Metaverse with an Extended Technology
Acceptance Model
Rong Wu and Zhonggen Yu
Faculty of Foreign Studies, Beijing Language and Culture University, Beijing, China
ABSTRACT
The Metaverse, characterized as an interactive and immersive 3D virtual world, is widely recog-
nized for its considerable potential across a range of industries. However, the long-term viability
and success of the Metaverse are contingent upon the extent to which users accept and adopt it.
Despite this critical aspect, there is a scarcity of research that investigates the factors influencing
user acceptance of the Metaverse. To address this research gap, the present study expands upon
the technology acceptance model by integrating social and psychological constructs such as social
interaction, social presence, conformity, emotional attachment, flow, and perceived enjoyment.
The data for this study were obtained through an online survey. A total of 418 responses were
collected from Metaverse users, with a response rate of 84%. Partial least squares structural equa-
tion modeling was used to analyze the survey data. Results showed that: (1) perceived ease of use
(b¼0.214, p<0,001), emotional attachment (b¼0.375 p<0,001), and enjoyment (b¼0.194,
p<0,001) could have a positive effect on perceived usefulness; (2) emotional attachment
(b¼0.142, p¼0.018), enjoyment (b¼0.179, p¼0.008), and social presence (b¼0.183, p¼0.008)
could have a positive effect on perceived ease of use; (3) perceived usefulness (b¼0.432,
p<0,001), perceived ease of use (b¼0.106, p¼0.011), emotional attachment (b¼0.209,
p<0,001), and social interaction (b¼0.209, p<0,001) have a positive effect on attitudes toward
using the Metaverse; (4) perceived usefulness (b¼0.174, p¼0.001), attitudes (b¼0.35, p<0,001),
flow (b¼0.128, p¼0.011), and social interaction (b¼0.106, p¼0.015) could positively influence
users’intention to use the Metaverse. However, social presence could not significantly influence
perceived usefulness (b¼0.078, p¼0.141). These results offer important implications for develop-
ers and practitioners looking to design and promote the utilization of the Metaverse.
KEYWORDS
The Metaverse; technology
acceptance; social
interaction; social presence;
flow; perceived enjoyment;
emotional attachment;
conformity
1. Introduction
The Metaverse, an immersive and interactive 3D virtual
world that serves as a digital twin of the physical world, has
garnered significant scholarly and popular interest (Ning
et al., 2023). It has been developed using emerging technolo-
gies such as 5 G, artificial intelligence, digital twin, aug-
mented reality, virtual reality, mixed and extended reality,
and blockchain (Wang et al., 2022). The Metaverse is distin-
guished by its interactive, decentralized, and persistent
nature, effectively representing a virtual society (Hwang &
Chien, 2022). Specifically, it provides a range of interactive
spaces where individuals, represented as digital avatars, can
interact and communicate with other entities (Cheng et al.,
2022). This perpetual and persistent multi-user environment
allows users to engage in various activities such as work,
shopping, tourism, conferences, concerts, and museum visits
(Skalidis et al., 2022). Moreover, the Metaverse is built on
decentralized technologies, thereby enabling users to have
ownership and autonomy (Ng, 2022). Notable examples of
popular Metaverse applications currently include The
Sandbox, Zepeto, Roblox, Horizon Worlds, Gather Town,
and Decentraland (see Figure 1).
The value, effectiveness, and sustainability of the Metaverse
may hinge on user acceptance (Wang & Shin, 2022). A grow-
ing number of people have now accepted the Metaverse and
benefited from using it (Ni & Cheung, 2023). Moreover, the
acceptance of a specific technology by its end users serves as
an important precursor to investment, development, and
implementation (Al-Emran et al., 2018). In the context of the
Metaverse, designers and practitioners must also comprehend
the drivers behind user acceptance before fully investing,
developing, and implementing this promising technology.
However, existing research on the Metaverse has predomin-
antly concentrated on technical aspects (Du et al., 2023;H.J.
Lee & Gu, 2022). Insufficient attention has been given to the
factors that influence user acceptance, with minimal published
research available (Afkar et al., 2022). Therefore, understand-
ing the determinants of user acceptance of the Metaverse is
critical at this time.
Various factors may contribute to the acceptance of a
specific technology by users. When it comes to immersive
technologies, users have shown a strong preference for social
interaction and social presence (Oh et al., 2023). Hence, the
presence of these factors could exert a significant influence
CONTACT Zhonggen Yu 401373742@qq.com Faculty of Foreign Studies, Beijing Language and Culture University, Beijing 100083, China
ß2023 Taylor & Francis Group, LLC
INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION
https://doi.org/10.1080/10447318.2023.2241295

on users’acceptance of immersive technologies (J. Lee et al.,
2019). Immersive technologies, in general, are designed to
provide enjoyable and flow-inducing experiences for users
(Holdack et al., 2022). Perceived enjoyment and flow, in
turn, can serve as motivational drivers for users to accept
and adopt these technologies (C. C. Chen & Lin, 2018;
Sriworapong et al., 2022). Moreover, psychological experien-
ces such as conformity and emotional attachment are also
likely to play a crucial role in shaping user acceptance
behaviors (Hsiao & Tang, 2021).
The adoption and acceptance of the Metaverse can be
attributed to several factors. First, the Metaverse offers
increased opportunities for interpersonal interaction and
recreational activities (Arpaci et al., 2022). This, in turn,
contributes to a heightened sense of fun, social presence,
and immersion (Y. Lee et al., 2023). As a result, a growing
number of individuals have begun using the Metaverse and
actively promoting its use to others, potentially developing
an emotional attachment to this immersive technology.
Notably, the acceptance of the Metaverse is likely to be
influenced by both social and psychological factors. Social
factors, such as social presence and social interaction, are
expected to have a significant impact on users’acceptance of
the Metaverse. Moreover, psychological factors, including
conformity, emotional attachment, flow, and perceived
enjoyment, are likely to play a crucial role in shaping indi-
viduals’acceptance of this technology.
Nevertheless, little is known of the joint effects of social
factors (i.e., social presence, social interaction) and psycho-
logical factors (i.e., conformity, emotional attachment, flow,
and perceived enjoyment) on users’acceptance of the
Metaverse. In response to research gaps, the current study
set out to understand whether social factors (i.e., social
presence and social interaction) could significantly influence
the acceptance of the Metaverse. This study also aimed to
analyze whether users’acceptance of the Metaverse could be
affected by psychological factors, including emotional attach-
ment, conformity, flow, and perceived enjoyment. The
importance of this study is thus that it integrates social con-
structs and psychological constructs into the original TAM
to explore individuals’acceptance of the Metaverse.
This study contributes to this existing research on
Metaverse acceptance in a variety of ways. First, this work
contributes to the existing knowledge by reaffirming the the-
oretical validity and empirical applicability of the original
TAM in the context of the Metaverse. Second, the novelty
of the present study lies in its conceptual model that inte-
grates social constructs and psychological constructs into the
original TAM. This study used this conceptual model to pre-
dict users’acceptance of the Metaverse. In this respect, this
study makes an important contribution to the existing litera-
ture by unlocking social and psychological determinants of
Metaverse acceptance. Third, the findings of this study
might be of value to designers and practitioners wishing to
optimize the Metaverse and increase individuals’acceptance
of the Metaverse.
2. Literature review
2.1. The Metaverse
The Metaverse has been adopted in various areas, bringing
great benefits to humans (Z. Chen, 2022). For example, inte-
gratingtheMetaverseintoeducationcouldprovidefullysimu-
lated environments of immersive and collaborative learning,
greatly improving users’learning motivation (Akour et al.,
2022), effectiveness (Al-Adwan et al., 2023), and achievement
Figure 1. Current examples of popular metaverse applications.
2 R. WU AND Z. YU

(Suh & Ahn, 2022). In the healthcare domain, patients and
practitioners have used Metaverse technology to perform med-
ical interventions, such as exercise rehabilitation (Wang et al.,
2022;J.O.Yang&Lee,2021). Additionally, the Metaverse is
available in the tourism and hospitality industry, allowing
travel customers to fully immerse themselves in virtual tour-
ism destinations (Tsai, 2022).
On the other hand, there has been growing recognition of
challenges associated with the use of the Metaverse (Z. Chen,
2022). The Metaverse has been found to increase physical
and mental health risks, such as eye problems (Tlili et al.,
2023), stress (Bibri & Allam, 2022), loneliness (Oh et al.,
2023), cognitive load (Inceoglu & Ciloglugil, 2022), and
addiction to virtual worlds (Bojic, 2022). Additionally, users’
private information stored in the Metaverse might be hacked
when users are immersed in the Metaverse (Njoku et al.,
2023). Moreover, using the Metaverse could cause many
potential ethical issues, such as discrimination, inequalities,
and bullying (Dwivedi et al., 2022).
Given this situation, the implementation of Metaverse is
still in its infancy in various industries, such as education
(Al-Adwan et al., 2023) and retailing (Yoo et al., 2023). It is
particularly relevant to explore contributing factors to the
successful implementation of the Metaverse. Among these,
the acceptance of the Metaverse (IS) by its end users could
be essential for its successful implementation (Al-Qaysi
et al., 2020). Therefore, there is an urgent need to under-
stand the contributing factors to users’acceptance of the
Metaverse. The technology acceptance model (TAM) is a
robust theoretical framework for exploring contributing fac-
tors to users’acceptance of the Metaverse (Ren et al., 2022).
2.2. The technology acceptance model and its robustness
The technology acceptance model (TAM) proposed by Davis
et al. (1989) was developed based on the Theory of Reasoned
Action (Fishbein & Ajzen, 1975). TAM could be regarded as
one of the most extensively-used Information System (IS)
models to investigate factors that affect user acceptance of a
specific technology, either before or/and after the use (Davis,
1989; Mascret et al., 2022). Several important extensions of
TAM are TAM2 (Venkatesh & Davis, 2000), TAM3
(Venkatesh & Bala, 2008), and the Unified Theory of
Acceptance and Use of Technology (Venkatesh et al., 2012).
According to Davis et al. (1989), TAM consists of four
key determinants, including perceived usefulness, perceived
ease of use, attitude, and intentions to use. In this study,
perceived usefulness is operationally defined as the extent to
which users believe that the Metaverse is helpful for their
effectiveness and productivity. Perceived ease of use is oper-
ationally defined as the extent to which users believe that
using Metaverse requires less effort and time. Attitude refers
to the extent to which users were interested in the use of
the Metaverse. Intentions to use refers to users’willingness
to continuously use the Metaverse. In TAM, perceived use-
fulness and perceived usefulness are key determinants of
attitude. Additionally, perceived usefulness and attitude are
key determinants of intentions to use. External constructs
influence four determinants of TAM directly or indirectly
(Al-Adwan et al., 2023).
A growing body of research has provided evidence for
the robustness and validity of TAM in different contexts
(Al-Fraihat et al., 2020). For example, recent research dem-
onstrated that TAM could be adopted to investigate users’
acceptance of emerging technologies, such as Rain
Classroom (Yu & Yu, 2019), AI-powered intelligent systems
(Ni & Cheung, 2023), chatbots (de S
a Siqueira et al., 2023),
IoT-based healthcare technologies (Rodi
c et al., 2023), digital
assessment systems (Chueh & Huang, 2023), virtual reality
(M. J. Kim, Lee, & Preis, 2020), online meeting platforms
(R. Wu & Yu, 2022), contact tracing apps (Dzandu, 2023),
and recycling apps (de Wildt & Meijers, 2023). More
recently, there has been growing interest in testing the
robustness and validity of TAM in the Metaverse context.
2.3. TAM in the acceptance of the Metaverse
To understand factors influencing user acceptance of the
Metaverse, some studies have attempted to integrate external
constructs into the original TAM. Under the models of
push-pull-mooring and technology acceptance, Wang &
Shin (2022) advanced an extended TAM for the Metaverse
educational application. This model included several external
constructs, including personalized learning, situational teach-
ing, social needs, perceived privacy risk, and technical
maturity. Afkar et al. (2022) predicted users’intention to
use Metaverse technology using an extended TAM with
external constructs of perceived consumer experience, per-
ceived brand engagement, and gamification. Almarzouqi
et al. (2022) adopted the TAM and found that user accept-
ance of the Metaverse was strongly related to users’satisfac-
tion, personal innovativeness, perceived observability, users’
compatibility, and perceived triability. Al-Adwan et al.
(2023) modified TAM with external constructs of personal
innovativeness, perceived enjoyment, perceived cyber risk,
and self-efficacy. Akour et al. (2022) investigated university
students’intentions to use the Metaverse using the TAM
with external constructs of perceived triability, perceived
observability, perceived compatibility, perceived complexity,
and personal innovativeness.
However, relatively little research has committed to
incorporating both social constructs and psychological con-
structs into the original TAM to investigate users’accept-
ance of the Metaverse. Specifically, scant research has
incorporated social constructs (i.e., social presence and social
interaction) and psychological constructs (i.e., emotional
attachment, conformity, flow, and perceived enjoyment) into
the original TAM to investigate the acceptance of the
Metaverse. To provide a deeper understanding of the accept-
ance of the Metaverse, the current study proposed and
empirically examined an extended TAM with several social
constructs and psychological constructs, including social
presence, social interaction, emotional attachment, conform-
ity, flow, and perceived enjoyment.
INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION 3

3. Hypotheses development
3.1. Key determinants of TAM in the Metaverse context
Recently, several studies have validated that TAM could be a
robust model for understanding the acceptance of the
Metaverse. However, empirical research on Metaverse accept-
ance provided conflicting evidence concerning the relation-
ships between key determinants of TAM. Almarzouqi et al.
(2022) identified that the perceived usefulness of the
Metaverse was significantly associated with the perceived ease
of use of the Metaverse. Additionally, it was found that users’
perception of the useful and easy-to-use Metaverse positively
influenced their attitudes towards Metaverse (Wang & Shin,
2022). The perceived usefulness of the Metaverse (Akour
et al., 2022) and attitude towards the Metaverse (Aburbeian
et al., 2022) positively influenced intentions to use the
Metaverse. However, Ren et al. (2022) argued that there was
no significant correlation between students’perceived useful-
ness of the Metaverse and intentions to use the Metaverse.
They also argued that students’positive attitudes towards
Metaverse did not significantly and positively affect their
intentions to use Metaverse. Therefore, we proposed the fol-
lowing hypotheses:
H1: The perceived ease of use of the Metaverse could have a
significant positive effect on the perceived usefulness of the
Metaverse.
H2: The perceived ease of use of the Metaverse could have a
significant positive effect on attitudes towards the Metaverse.
H3: The perceived usefulness of the Metaverse could have a
significant positive effect on attitudes towards the Metaverse.
H4: The perceived usefulness of the Metaverse could have a
significant positive effect on intentions to use Metaverse.
H5: Attitudes towards the Metaverse could have a significant
positive effect on intentions to use the Metaverse.
3.2. Social interaction
Based on social interaction theory, social interaction in this
study was operationally defined as the process of behaving
and communicating with each other in the Metaverse. The
majority of users put a high value on social interaction while
using a specific technology (Van-Tien Dao et al., 2014). As
emerging technologies could provide more opportunities for
social interaction, people might have a positive attitude and
feel motivated to use a specific technology (Bassiouni et al.,
2019). Certainly, existing research has demonstrated the
positive effect of social interaction on users’attitudes as well
as their intentions to use technologies, such as mobile 2.0
(Dalvi-Esfahani et al., 2020) and health and fitness apps
(Zhu et al., 2023). The interactive features of the Metaverse
enable people to appear as avatars and engage in social
interaction (Akour et al., 2022). These useful features may
positively influence their attitudes and intentions to use the
Metaverse. Therefore, we proposed the following hypotheses:
H6: Social interaction could have a significant positive effect
on attitudes towards the Metaverse.
H7: Social interaction could have a significantly positive
effect on intentions to use the Metaverse.
3.3. Social presence
According to social presence theory, social presence could
be deemed as the extent to which users have the feelings of
being a “real”person and socially connected to other beings
in the Metaverse. Extensive research has been carried out on
the effect of social presence on technology acceptance. Social
presence was found to be positively associated with the per-
ceived usefulness of emerging technologies, for example, vir-
tual world shopping (White Baker et al., 2019) and voice
assistants (S. Lee et al., 2023). Social presence also was a
determining factor of the perceived ease of use of Microsoft
Teams (C. J. Chang et al., 2021) and e-learning technology
(Smith & Sivo, 2012).
The Metaverse could offer significant potential for
enhancing users’sense of social presence. By creating their
digital avatars and moving them around freely to cooperate
and work, users could experience a great sense of social
presence in the Metaverse (S. Lee et al., 2023). The sense of
social presence in the Metaverse has been found to be
strongly related to positive outcomes, including the
improvement in social skills (Oh et al., 2023), healthy behav-
ior (Plechat
a et al., 2022), and learning engagement
(Sriworapong et al., 2022). These positive outcomes may
lead users to find the usefulness and ease of use of the
Metaverse. Therefore, we proposed the following hypotheses:
H8: Social presence could have a significant positive effect
on the perceived ease of use of the Metaverse.
H9: Social presence could have a significant positive effect
on the perceived usefulness of the Metaverse.
3.4. Conformity
Conformity could be defined as the tendency to use technol-
ogy in order to follow others’expectations, avoid exclusion,
or reduce uncertainty and risk based on the theory of con-
formity. According to Deutsch & Gerard (1955), there are
two main categories of conformity, i.e., information con-
formity and normative conformity. Normative conformity
arises when one is encouraged to meet others’expectations,
avoid exclusion, and get approval from others. Informative
conformity refers to the tendency to adopt others’ideas and
views due to their expertise and authority (Deutsch &
Gerard, 1955).
There has been disagreement on the effect of conformity
on intentions to use. Many studies have indicated that con-
formity could be a reliable predictor of users’intentions to
use innovative technologies, such as unmanned driving
4 R. WU AND Z. YU

technology (Meng & Dong, 2022), WeChat (Yu, 2020), and
augmented reality games (Alha et al., 2019). However, some
studies found no significant correlation between social con-
formity and intentions to use Facebook (C. C. Chang et al.,
2015) and automated vehicles (Y. Liu et al., 2022). The
Metaverse has emerged as a new tool for information aggre-
gation and mass communication. While a growing number
of people have engaged in using the Metaverse, they are
more likely to be influenced by the majority and continue to
use the Metaverse. We thus proposed the following
hypothesis:
H10: Conformity could have a significant positive effect on
intentions to use the Metaverse.
3.5. Emotional attachment
According to emotional attachment theory, humans have
developed an affective bond with other beings, things, and
even places (VanMeter et al., 2018). In the information sys-
tem usage context, emotional attachment could be defined
as the affective connection between users and technologies
(Hsiao & Tang, 2021). In general, people have developed a
strong emotional attachment to a specific technology after
they have interacted with it for a long period (Zhang et al.,
2021). This kind of emotional attachment to the technology
may give rise to a positive appraisal of the technology (Lin
et al., 2022).
However, the existing literature has offered contradictory
findings on the effect of emotional attachment within the
technology acceptance model. According to Teo (2016), stu-
dents’emotional attachment to Facebook was found to be
significantly associated with perceived usefulness, perceived
ease of use, and attitudes. By contrast, S
anchez-Prieto et al.
(2019) showed that pre-service teachers’emotional attach-
ment to mobile technologies could not significantly influ-
ence perceived usefulness, perceived ease of use, and
attitudes. In our research, we postulated that an emotional
connection to the Metaverse would bring a positive evalu-
ation of the Metaverse. Therefore, we proposed the follow-
ing hypotheses:
H11: Emotional attachment could have a significant positive
effect on the perceived ease of use of the Metaverse.
H12: Emotional attachment could have a significant positive
effect on the perceived usefulness of the Metaverse.
H13: Emotional attachment could have a significant positive
effect on attitudes towards the Metaverse.
3.6. Perceived enjoyment
Perceived enjoyment is operationally defined as the extent to
which users believe that utilizing the Metaverse is joyful,
pleasant, and interesting. Perceived enjoyment could be one
of the psychological factors that could positively influence
users’perception of a specific technology (Oyman et al.,
2022). Specifically, individuals might have a positive
perception of a specific technology while they felt that using
the technology was fun and enjoyable (Faqih, 2022). The
existing body of research provided strong empirical confirm-
ation that perceived enjoyment could be a key driver of per-
ceived usefulness and ease of use across a wide range of
technologies, such as virtual clinical simulation training
(Choi & Tak, 2022), short video platforms (Xu et al., 2022),
and social media (Sakshi et al., 2020).
The Metaverse has predominatly been used for recreation
and entertainment (Kshetri & Dwivedi, 2023). Notably, a
growing body of evidence has established that the Metaverse
could be more fun and enjoyable than other immersive tech-
nologies (Dwivedi et al., 2022). Salloum et al. (2023) pro-
vided empirical evidence for the argument that perceived
enjoyment had a significant effect on students’acceptance of
the Metaverse system. Consistent with Salloum et al.’s
(2023) findings, Al-Adwan et al. (2023) revealed the positive
influence of perceived enjoyment on Metaverse adoption
intentions in education. Therefore, we proposed the follow-
ing hypotheses:
H14: Perceived enjoyment could have a significant positive
effect on the perceived ease of use of the Metaverse.
H15: Perceived enjoyment could have a significant positive
effect on the perceived usefulness of the Metaverse.
3.7. Flow
Flow theory proposed by Csikszentmihalyi (2000) has been
widely used to explore users’behaviors (Y. Li & Peng,
2021). Flow is a specific psychological state in which an
individual is totally absorbed in a particular activity to the
point of filtering out other irrelevant perceptions and
thoughts (S. H. Liu et al., 2009). Research on live-stream (C.
C. Chen & Lin, 2018) and e-store (I. L. Wu et al., 2020)
empirically supported that flow had a positive influence on
users’intentions to use technologies. Additionally, Franque
et al. (2020) performed a meta-analysis of 115 empirical
studies and demonstrated that flow could be an important
determinant of intentions to use the information system.
Flow in this study refers to a psychological state that
individuals experience when they are deeply absorbed in the
Metaverse. However, the literature on flow observed incon-
sistent results on the effect of flow on intentions to use the
Metaverse. On the one hand, the Metaverse using innovative
AR storytelling had the potential to provide a fully immer-
sive environment, which could put users in a state of flow
(S. Yang, 2023). Users who experienced a state of flow in
the Metaverse had higher intentions to use the Metaverse
(Wongkitrungrueng & Suprawan, 2023). However, a recent
study conducted by Ren et al. (2022) showed that flow
experience did not have a direct effect on university stu-
dents’intentions to use the Metaverse technology.
Therefore, we proposed the following hypothesis:
H16: Flow could have a significant positive effect on inten-
tions to use the Metaverse.
INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION 5

Based on the comprehensive review of the relevant litera-
ture, this study proposed a structural model as shown in
Figure 2.
4. Research methodology
4.1. Sampling and data collection
The current investigation utilized a cross-sectional research
design to fulfill its objectives. In order to gather relevant
data, a survey research strategy was implemented, targeting
individuals who had prior experience with the Metaverse.
However, as the precise count of Metaverse users remained
elusive, the absence of a sampling frame prevented the
establishment of a definitive population. Consequently, a
purposive sampling technique was employed within this
study. This approach was selected based on previous studies
which had demonstrated its efficacy in targeting participants
with Metaverse experience (e.g., Al-Adwan et al., 2023).
Cochran’s formula, shown below, was used to determine
the sample size in this study (Cochran & Cochran, 1977).
Z2 is the abscissa of the normal curve that cuts off an area a
at the tails; eis the desired level of precision; n0 is the
appropriate sample size; pis an estimated proportion of the
population who have used the Metaverse; qis 1-p. It is well
established from a variety of studies that this formula could
be suitable for the PLS-SEM analysis when the exact size of
the target population is unknown (e.g., Iqbal et al., 2022;
Poudel et al., 2022). According to Cochran’s formula, the
adequacy of the sample size was 385 at a confidence level of
95%, Z¼1.96, and a margin error of 5%.
n0¼Z2pq
e2
To mitigate the issue of potential invalid responses, a sam-
ple of 500 participants was recruited from Beijing, China. The
study utilized both phone calls and emails to establish contact
with the participants during the period of February 2023 to
March 2023. Prior to their involvement, participants were
assured that their personal information would be treated as
confidential and that their responses would remain anonym-
ous. To ensure voluntary participation, each participant
provided informed consent prior to completing the online
questionnaire. Out of the 500 participants recruited, 418 suc-
cessfully completed the questionnaire, resulting in a response
rate of 84%. Among the respondents, 289 identified as female,
whereas 129 were male. Furthermore, more than half of the
participants fell within the age range of 18 to 22 years
(N¼231, 55.3%). Concerning educational attainment, the
majority held bachelor’s degrees (256, 61.2%), while those
with postgraduate degrees accounted for 117 participants
(28%). A comprehensive breakdown of the respondents’
demographic characteristics can be found in Table 1.
4.2. Measurement
This study employed a self-administered questionnaire as
the data collection method. The questionnaire consisted of
two sections, with the first section aimed at gathering demo-
graphic information about the participants, including varia-
bles of gender, age, and educational level. The second
section of the questionnaire comprised 42 items designed to
assess 11 constructs within the proposed model (see
Appendix A). Each item was rated on a 5-point Likert scale,
ranging from 1 (strongly disagree) to 5 (strongly agree). To
measure conformity, valid scales adapted from Yu (2020)
were utilized. Scales developed by Oh et al. (2023) were
employed for measuring social presence, while Verkijika
Figure 2. Proposed research model. Note. PU: perceived usefulness; PEOU: perceived ease of use; ATT: attitudes; INT: intentions to use; SI: social interaction;
SP: social presence; EA: emotional attachment, FL: flow; EN: perceived enjoyment; CON: conformity.
Table 1. Demographic characteristics of respondents.
Variables Frequency Percent (%)
Gender
Male 129 30.9
Female 289 69.1
Total 418 100
Age
17 years old and younger 12 2.9
18–22 years old 231 55.3
23–25 years old 128 30.6
26 years old and older 47 11.2
Total 418 100
The educational level
High school or below 32 7.7
Bachelor 256 61.2
Postgraduate 117 28
Doctoral students or above 13 3.1
Total 418 100
6 R. WU AND Z. YU

(2019) provided the scales for assessing technostress. Social
interaction constructs were measured using scales from M. J.
Kim, Lee, and Preis (2020), and scales from Hung et al.
(2021) were adapted for evaluating enjoyment. Emotional
attachment constructs were based on scales from Read et al.
(2011), while flow constructs were derived from M. J. Kim,
Lee, and Jung (2020). Technology acceptance measures were
obtained from scales developed by Davis (1989), Moon and
Kim (2001), and Venkatesh et al. (2012). It is worth men-
tioning that slight modifications were made to the wording
of these scales to appropriately align them with the context
of Metaverse use.
4.3. Data analysis
The data analysis in this study was performed using the par-
tial least squares structural equation modeling (PLS-SEM)
approach. PLS-SEM is a robust method for evaluating the
proposed model and validating research hypotheses (Hair
et al., 2012). Unlike covariance-based structural equation
modeling, PLS-SEM is less restrictive in terms of data distri-
bution and can effectively handle both small and large sam-
ple sizes (Hair et al., 2011). Moreover, PLS-SEM is
particularly well-suited for analyzing complex models with
multiple constructs, making it a preferred choice for extend-
ing existing theories (Hair et al., 2011). Considering the
complexity of the technology acceptance model (TAM) with
numerous external constructs, the application of PLS-SEM
was deemed more appropriate for this study. This study
conducted analyses using the software SmartPLS 4 because
of its robustness in predicting endogenous variables and its
straightforward, user-friendly interface (AL-Dosari et al.,
2023). Following the recommendations of Hair et al. (2014),
a two-stage approach was employed for data analysis. The
first stage involved analyzing the measurement model,
assessing the reliability and validity of the selected con-
structs. Subsequently, the hypothesized relationships between
constructs were tested using the PLS-SEM approach.
5. Results
5.1. Bias examinations
Prior to analyzing the measurement model and structural
model, this study addressed common method bias (CMB)
and non-response bias. CMB can pose a potential threat to
the validity of research findings (X. Li et al., 2023). As rec-
ommended by Podsakoff et al. (2003), this study employed
Harman’s one-factor test to evaluate CMB. The confirma-
tory principal factor analysis results indicated that the pri-
mary factor accounted for 40.69% of the total explained
variance, which was below the threshold of 50%. This sug-
gests a low likelihood of CMB in this study. In addition,
latent construct correlations were assessed to further exam-
ine CMB, following the approach suggested by Hayat et al.
(2022). According to Bagozzi et al. (1991), the highest cor-
relation between constructs should be less than 0.9 to miti-
gate concerns related to CMB. In Table 3, the highest
correlation was found between attitude and intention
(r ¼0.71), indicating that CMB was not a significant con-
cern in the present study (Podsakoff et al., 2012).
Non-response bias represents another potential threat to
the validity of research findings. To address this concern, a
widely accepted method for assessing non-response bias is to
compare early responses with later responses using t-tests
(Armstrong & Overton, 1977; Lahaut, 2003). In line with pre-
vious studies (e.g., Alyahya et al., 2023), the present study clas-
sified respondents into two groups: early respondents and
later respondents based on their completion time. The later
respondents were surveyed twenty days after the early
respondents. The t-test comparisons revealed no significant
differences between the two groups across various constructs,
including perceived ease of use (p¼0.38), perceived useful-
ness (p¼0.34), attitude (p¼0.20), intention (p¼0.78), social
interaction (p¼0.15), social presence (p¼0.19), perceived
enjoyment (p¼0.07), flow (p¼0.41), emotional attachment
(p¼0.34), and conformity (p¼0.14). These findings indicate
that non-response bias is not a significant issue in this study.
5.2. Multicollinearity test
Following the recommendation of Hair et al. (2011), the
potential issue of multicollinearity among constructs was
assessed in this study using the variance inflation factor (VIF).
To ensure that multicollinearity was not present, the VIF val-
ues were examined, with a generally accepted threshold of 3.3
(Kock & Lynn, 2012). The findings indicate that there was no
evidence of multicollinearity among the constructs under
investigation, as the VIF values ranged from 1.228 to 2.761.
This suggests that the variables in the study are not highly cor-
related, supporting the validity of the regression analysis.
5.3. Assessment of the measurement model
The assessment of the measurement model could draw on
three criteria, including internal consistency reliability, con-
vergent validity, and discriminant validity (Hair et al., 2014).
The internal consistency reliability could be evaluated
through Cronbach’s alpha and composite reliability (CR)
values. It is generally recommended that Cronbach’s alpha
and CR values should be higher than the threshold value of
0.7 (Chin, 1998). As can be seen from Table 2, Cronbach’s
alpha values range from 0.78 to 0.87, and the CR values
range from 0.86 to 0.93. All Cronbach’s alpha and CR values
are higher than the threshold value of 0.7. The internal con-
sistency reliability could thus be deemed adequate.
This study calculated average variance extracted (AVE)
and factor loading values to further evaluate convergent val-
idity (Hair et al., 2014). Ideally, the factor loading values
should be greater than 0.7, and the AVE values should be
above 0.5 (Hair et al., 2014). A confirmatory principal factor
analysis with varimax rotation was conducted to group
manifest constructs into latent constructs (Esposito De Falco
et al., 2021). In this new model, 38 manifest constructs were
partitioned into ten latent constructs. An inspection of
Table 2 reveals that factor loading values range from 0.73 to
INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION 7

0.93, which are higher than the threshold value of 0.5. All
items had a sufficiently strong relationship with correspond-
ing latent constructs. Moreover, AVE values range from 0.6
to 0.76. Therefore, the convergent validity could be consid-
ered adequate in this study.
According to Lisha et al. (2017), the criteria for discrimin-
ant validity assessment are the Heterotrait-Monotrait
(HTMT) ratio of correlation (Henseler et al., 2015) and the
Fornell–Larcker criterion (Fornell & Larcker, 1981). A num-
ber of empirical studies have demonstrated the suitability of
HTMT and Fornell–Larcker criterion for validating discrimin-
ant validity (e.g., Al Shamsi et al., 2022; Hahm et al., 2022;
Sagnier et al., 2020). According to Henseler et al. (2015), dis-
criminant validity could be confirmed when the HTMT values
are less than 0.85. The results in Table 3 show that HTMT val-
ues range from 0.04 to 0.85. Regarding the Fornell–Larcker
criterion, the square root of AVE values should be greater
than the inter-construct correlation coefficients (Fornell &
Larcker, 1981). Table 4 reveals that the square root of AVE
(i.e., bold values on the diagonal) exceeds the correlation coef-
ficients among the paired constructs (i.e., values on the off-
diagonal). It is apparent that the discriminant validity in this
study is satisfactory.
5.4. Assessment of structural model
We tested the hypothesized model using the bootstrapping
procedure with 5000 subsamples. The criteria for assessing
the structural model are the coefficient of determination
(R2), Stone–Geisser’sQ2, effect size (f2), and path coefficient
(b) (Geisser, 1975; Hair et al., 2014; Stone, 1974). Table 5
and Figure 3 display the results obtained from the structural
model testing.
As Table 5 and Figure 3 show, the majority of proposed
hypotheses are statistically significant except for H9. In par-
ticular, the perceived ease of use could exert a significant
positive effect on the perceived usefulness (b¼0.214,
p<0.001). Perceived ease of use and perceived usefulness
could have a significant positive effect on attitudes
(b¼0.106, p¼0.011; b¼0.432, p<0.001). Perceived useful-
ness and attitudes could positively influence intentions
(b¼0.174, p¼0.001; b¼0.35, p<0.001). We thus accepted
H1, H2, H3, H4, and H5.
The results also show that emotional attachment, social
presence, and perceived enjoyment could have a significant
positive effect on perceived ease of use (b¼0.142, p¼0.018;
b¼0.183, p¼0.008; b¼0.179, p¼0.008). Besides, emotional
attachment and perceived enjoyment could have a significant
positive effect on perceived usefulness (b¼0.375, p<0.001;
b¼0.194, p¼0.008). Social interaction and emotional attach-
ment could have a significant positive effect on attitudes
(b¼0.209, p<0.001; b¼0.209, p<0.001). Additionally, flow,
conformity, and social interaction could have a significant
positive effect on intentions (b¼0.128, p¼0.011; b¼0.188,
p<0.001; b¼0.106, p¼0.015). Surprisingly, social presence
Table 2. Results of the measurement model.
Constructs Items Factor loading Cronbach’s alpha CR AVE
PEOU PEOU1 0.82 0.82 0.88 0.65
PEOU2 0.85
PEOU3 0.78
PEOU4 0.78
PU PU1 0.81 0.85 0.90 0.68
PU2 0.87
PU3 0.80
PU4 0.83
ATT ATT1 0.81 0.85 0.90 0.69
ATT2 0.84
ATT3 0.85
ATT4 0.82
INT INT1 0.87 0.81 0.89 0.72
INT2 0.86
INT3 0.82
SI SI1 0.84 0.84 0.90 0.68
SI2 0.83
SI3 0.82
SI4 0.82
SP SP1 0.78 0.80 0.87 0.63
SP2 0.81
SP3 0.77
SP4 0.80
CB CB1 0.76 0.771 0.87 0.69
CB2 0.86
CB3 0.87
AM AM1 0.82 0.87 0.91 0.72
AM2 0.86
AM3 0.89
AM4 0.83
EN EN1 0.88 0.89 0.93 0.76
EN2 0.87
EN3 0.85
EN4 0.87
FL FL1 0.73 0.78 0.86 0.60
FL2 0.80
FL3 0.76
FL4 0.81
Note. PU: perceived usefulness; PEOU: perceived ease of use; ATT: attitude
towards technology use; INT: intention to use; SI: social interaction; SP: social
presence; EA: emotional attachment, FL: flow; EN: perceived enjoyment;
CON: conformity; CR: composite reliability; AVE: average variance extracted.
Table 4. Fornell–Larcker criterion.
EM ATT INT CB EN FL PEOU PU SI SP
EM 0.85
ATT 0.60 0.83
INT 0.67 0.71 0.85
CB 0.57 0.53 0.57 0.83
EN 0.53 0.67 0.55 0.54 0.87
FL 0.66 0.67 0.61 0.51 0.63 0.78
PEOU 0.35 0.45 0.40 0.31 0.37 0.41 0.81
PU 0.60 0.70 0.62 0.43 0.52 0.54 0.45 0.83
SI 0.48 0.55 0.54 0.50 0.65 0.55 0.36 0.46 0.83
SP 0.61 0.61 0.54 0.54 0.65 0.67 0.38 0.51 0.60 0.79
Note. Bold values represent the square root values of AVE. PU: perceived use-
fulness; PEOU: perceived ease of use; ATT: attitude towards technology use;
INT: intention to use; SI: social interaction; SP: social presence; EA: emotional
attachment, FL: flow; EN: perceived enjoyment; CON: conformity.
Table 3. HTMT ratios.
EM ATT INT CB EN FL PEOU PU SI SP
EM
ATT 0.69
INT 0.80 0.85
CB 0.69 0.66 0.72
EN 0.59 0.77 0.64 0.65
FL 0.80 0.83 0.76 0.66 0.77
PEOU 0.41 0.53 0.49 0.39 0.42 0.50
PU 0.70 0.83 0.75 0.54 0.60 0.67 0.53
SI 0.55 0.65 0.65 0.62 0.75 0.68 0.42 0.54
SP 0.72 0.73 0.67 0.68 0.76 0.84 0.46 0.62 0.73
Note. PU: perceived usefulness; PEOU: perceived ease of use; ATT: attitude
towards technology use; INT: intention to use; SI: social interaction; SP: social
presence; EA: emotional attachment, FL: flow; EN: perceived enjoyment;
CON: conformity.
8 R. WU AND Z. YU

failed to significantly affect perceived usefulness (b¼0.078,
p¼0.141). Therefore, we accepted H6, H7, H8, H9, H10,
H11, H12, H13, H14, H15, and H16 but rejected H9.
The amount of variance in the endogenous latent con-
struct and the predictive accuracy of the model could be
evaluated using the coefficient of determination R2 values
(Chin, 1998; Shami et al., 2022). R2 values greater than 0.19,
0.33, and 0.67 is considered as having weak, moderate, and
substantial levels of predictive accuracy, respectively (Chin,
1998). Our model could explain 19%, 46%, 60%, and 60% of
the variance in perceived ease of use, perceived usefulness,
attitude, and intentions to use, respectively (Table 6). It
clearly indicates the predictive accuracy of the hypothesized
model. We evaluated the predictive relevance of the
hypothesized model by using the cross-validated redundancy
measure Stone-Geisser’sQ2 values. The model could have a
good predictive relevance when the Q2 values are greater
than zero (Geisser, 1975; Stone, 1974).
In Table 6, it is found that the Q2 values range from 0.11
to 0.42, thereby demonstrating a good predictive relevance
of the hypothesized model.
The effect size (f2) values of 0.02, 0.15, and 0.35 corres-
pond to the small, medium and large effects of independent
latent constructs on dependent latent constructs, respectively
(Chin, 1998). As Table 5 reports, perceived usefulness had a
medium effect size on attitude (f2¼0.254), and emotional
attachment had a medium effect size on perceived usefulness
(f2¼0.154). All other independent latent constructs had a
relatively small effect on dependent latent constructs.
Overall, the hypothesized model could be considered valid
so that it could offer an adequate explanation for the accept-
ance of the Metaverse.
6. Discussion
The findings showed that there were significant and positive
relationships between four key determinants of TAM in the
Metaverse context. Users who are skillful in the use of
Metaverse tend to find the Metaverse useful in increasing their
effectiveness, performance, and productivity. When they find
the Metaverse useful and easy to use, users are more likely to
have a positive attitude towards the Metaverse. Eventually, they
are more willing to continue to use the Metaverse. Although
these results are contrary to those of Ren et al. (2022), they are
Table 5. Hypotheses testing results.
Hypothesis bt-Value p-Value f
2
Results
H1 PEOU ->PU 0.214 4.788 0.000 0.069 Supported
H2 PEOU ->ATT 0.106 2.548 0.011 0.021 Supported
H3 PU ->ATT 0.432 9.583 0.000 0.254 Supported
H4 PU ->INT 0.174 3.375 0.001 0.037 Supported
H5 ATT ->INT 0.35 5.693 0.000 0.110 Supported
H6 SI ->ATT 0.209 4.410 0.000 0.075 Supported
H7 SI ->INT 0.106 2.442 0.015 0.017 Supported
H8 SP ->PEOU 0.183 2.658 0.008 0.020 Supported
H9 SP ->PU 0.078 1.471 0.141 0.005 Rejected
H10 CON ->INT 0.188 4.200 0.000 0.056 Supported
H11 EA ->PEOU 0.142 2.374 0.018 0.015 Supported
H12 EA ->PU 0.375 7.539 0.000 0.154 Supported
H13 EA ->ATT 0.209 3.818 0.000 0.062 Supported
H14 EN ->PEOU 0.179 2.640 0.008 0.022 Supported
H15 EN ->PU 0.194 3.814 0.000 0.022 Supported
H16 FL ->INT 0.128 2.529 0.011 0.038 Supported
Note. H: hypothesis; PU: perceived usefulness; PEOU: perceived ease of use;
ATT: attitude towards technology use; INT: intentions to use; SI: social inter-
action; SP: social presence; EA: emotional attachment, FL: flow; EN: perceived
enjoyment; CON: conformity.
Figure 3. The results of PLS-SEM. Note. PU: perceived usefulness; PEOU: perceived ease of use; ATT: attitude towards technology use; INT: intentions to use; SI: social
interaction; SP: social presence; EA: emotional attachment, FL: flow; EN: perceived enjoyment; CON: conformity.
Table 6. The values of R
2
and Q2.
Q2R
2
ATT 0.42 0.60
BI 0.42 0.60
PEOU 0.11 0.19
PU 0.31 0.46
INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION 9

in agreement with those of Almarzouqi et al. (2022)and
Aburbeian et al. (2022).
The findings also revealed that social interaction could
have a significant positive influence on attitudes and inten-
tions to use the Metaverse. The result is in accord with a
recent study indicating strong, positive relationships between
social interaction and attitude as well as social interaction
and intentions to use (Dalvi-Esfahani et al., 2020). The
majority of participants aged between 18 and 25 belong to
Generation Z, also known as digital natives (Al-Adwan
et al., 2023). As digital natives, they often have an inherent
need to use online communication technologies for social
interaction and relationship maintenance (N. Chen et al.,
2023). The Metaverse acts as a communication platform that
could fulfill their needs for social interaction (Dwivedi et al.,
2022). Beyond other online communication technologies,
the Metaverse provides a highly realistic, interactive, and
simulated environment where users could share experiences,
establish new relationships, and collaborate with others in a
highly flexible way (Cheng et al., 2022). Having more fre-
quent social interaction, users might be more likely to have
positive attitudes and high intentions to use the Metaverse.
Additionally, the results showed that social presence
could have a significant positive influence on the perceived
ease of use of the Metaverse. The finding was also reported
by C. J. Chang et al. (2021). In the Metaverse, there are
many social situations similar to those in the physical world
(Shin, 2022). Users presented by avatars could move seam-
lessly through these social situations and conduct a variety
of social activities (Dwivedi et al., 2022). These new social
experiences could lead to an enhanced sense of social pres-
ence among Metaverse users. Having an enhanced sense of
social presence, Metaverse users have voluntarily developed
and maintained their virtual communities (Oh et al., 2023).
It could further give rise to a better sense of belonging and
community among users so that they are willing to support
one another in using the Metaverse (S. Lee et al., 2023;
Smith & Sivo, 2012). With the assistance of others, individu-
als were more likely to feel easy to use the Metaverse.
Unexpectedly, the influence of social presence on per-
ceived usefulness was found to be insignificant. Even while
users have an enhanced sense of social presence in the
Metaverse, they might not have a perception of the useful-
ness of the Metaverse. This finding is contrary to that of S.
Lee et al. (2023), who found the positive effect of social
presence on the perceived usefulness of voice assistants. This
may be explained by the argument that users have not used
the Metaverse for a long time so they do not seem to benefit
from their sense of social presence in the Metaverse.
Conformity could have a positive influence on users’
intentions to use the Metaverse. Users who need approval
from others or meet others’expectations are more inclined
to use the Metaverse. This result is in line with that of
Meng & Dong (2022), who found that conformity predicted
users’continuance intentions. The Metaverse has social sys-
tems in which a growing body of people have now joined
virtual communities, held social status, and complied with
certain norms similar to those within the physical world
(Shi et al., 2023). In virtual communities, individuals need
to comply with norms, gain recognition, and follow others’
expectations (Chung & Han, 2017). Therefore, in the
Metaverse, individuals could display the same behaviors as
others, such as following the adoption behaviors and con-
tinuing to use the Metaverse. Additionally, if an important
or influential person continues to use the Metaverse, indi-
viduals are also more inclined to use the Metaverse in the
future (Whelan & Clohessy, 2021).
Emotional attachment positively influenced users’percep-
tion of the usefulness and ease of use of the Metaverse as well
as their attitudes towards the Metaverse. These results are con-
sistent with those of Teo (2016), who found strong, positive
relationships between emotional attachment and perceived
usefulness, perceived ease of use, and attitudes. The Metaverse
allows users to buy and sell digital properties, services, and
goods, leading users to establish close relationships with digital
objects and feel a strong emotional attachment to the
Metaverse. The strong emotional attachment to the Metaverse
could help keep their interest and drive them to devote more
time and effort to using the Metaverse (Hsiao & Tang, 2021).
Therefore, they could have the perception of the usefulness
and ease of use of the Metaverse. Additionally, users’emo-
tional attachment to the Metaverse could enhance their loyalty
to the Metaverse, leading them to develop positive attitudes.
This study provides empirical evidence that perceived
enjoyment positively influences perceived usefulness and
perceived ease of use. This finding is consistent with prior
research (e.g., J. Lee et al., 2019), indicating that the enjoy-
able and memorable experiences offered by the Metaverse,
attributed to user-friendly features, engaging content, enter-
taining elements, and interesting scenes (Sriworapong et al.,
2022), contribute to users perceiving the Metaverse as useful
and easy to use. Additionally, incorporating innovative ele-
ments such as augmented reality (AR) storytelling and vir-
tual reality (VR) storytelling in the Metaverse enhances
interactivity and further heightens enjoyment (Ryan, 2015;
S. Yang, 2023). Consequently, users who derive significant
enjoyment from the Metaverse are more likely to concen-
trate on it for longer periods (Wongkitrungrueng &
Suprawan, 2023), leading to increased perceptions of its use-
fulness and ease of use.
Furthermore, as hypothesized, the flow experienced
within the Metaverse positively influences users’intentions
to use it. This finding aligns with similar studies conducted
by Akbari et al. (2020) and I. L. Wu et al. (2020). The
Metaverse, through its incorporation of virtual reality (VR),
interactive narratives, immersive storytelling, and mixed
reality, offers users multisensory and immersive experiences
(Gu et al., 2022; Monaco & Sacchi, 2023; S. Yang, 2023).
These features facilitate the attainment of a state of flow
(Dwivedi et al., 2023), characterized by heightened satisfac-
tion with the Metaverse (N. R. Kim, 2022), increased goal
achievement, and enhanced performance. Consequently,
users immersed in a state of flow within the Metaverse are
more motivated to utilize it, thereby strengthening their
intentions to use the platform. In conclusion, this study
demonstrates the positive influence of perceived enjoyment
10 R. WU AND Z. YU

and flow on users’perceptions of usefulness, ease of use,
and intentions to use the Metaverse. These findings are sup-
ported by previous literature and attest to the immersive
and interactive nature of the Metaverse as a platform that
offers engaging and enjoyable experiences to users.
7. Conclusion
7.1. Major findings
This study aimed to investigate the factors that influence
users’acceptance of the Metaverse. To achieve this, the tech-
nology acceptance model was extended with six external con-
structs, namely social interaction, social presence, perceived
enjoyment, emotional attachment, conformity, and flow.
Applying a partial least squares structural equation modeling
(PLS-SEM) analysis, the study identified these six constructs
as essential determinants of Metaverse acceptance. Specifically,
emotional attachment, perceived enjoyment, and social pres-
ence were found to have a positive impact on perceived ease
of use. Additionally, emotional attachment, perceived enjoy-
ment, and perceived ease of use were found to positively influ-
ence perceived usefulness. Moreover, emotional attachment,
perceived usefulness, perceived ease of use, and social inter-
action were found to positively impact attitudes towards the
Metaverse. Furthermore, flow, attitudes, perceived usefulness,
conformity, and social interaction were found to positively
influence intentions to use the Metaverse. Notably, contrary to
previous research, the results of this study indicate that social
presence does not significantly impact perceived usefulness.
7.2. Theoretical contributions
This current study seeks to address the limited research avail-
able on users’acceptance of the Metaverse, thus making
important contributions to the existing literature. Firstly, it
enhances understanding of whether acceptance of the
Metaverse is influenced by six significant factors, namely social
interaction, social presence, conformity, emotional attachment,
flow, and perceived enjoyment. While these factors have been
explored extensively in recent studies, their effects on
Metaverse acceptance have not been adequately examined.
Secondly, this study contributes to the broader body of
research on technology acceptance by proposing a robust, the-
ory-driven model that integrates the well-established
Technology Acceptance Model (TAM) with theories of flow,
emotional attachment, social presence, social interaction, and
conformity. Lastly, the findings of this research offer statistical
evidence supporting the validity and reliability of the TAM in
the context of the Metaverse.
7.3. Practical implications
The findings of this study have important implications for
Metaverse designers and practitioners. Specifically, the
results suggest that incorporating enhanced social interaction
and social presence features into the Metaverse could posi-
tively influence users’acceptance of the platform. Designers
and practitioners are encouraged to integrate more vivid
content, multisensory experiences, and comprehensive inter-
action mechanisms to improve interpersonal relationships
and foster a stronger sense of social presence among users,
thereby promoting continuous usage.
Furthermore, the study highlights the benefits of making
the Metaverse more entertaining and immersive. It is recom-
mended that designers and practitioners incorporate gami-
fied elements into the Metaverse, such as weekly prizes,
badges, leaderboards, and competitions (Kunkel et al., 2021).
These gamified elements can enhance user enjoyment and
promote a sense of flow. Additionally, interactive narratives
and storytelling have been shown to increase users’sense of
flow and satisfaction (Gu et al., 2022). Therefore, designers
and practitioners should consider integrating interactive nar-
ratives and storytelling features into the Metaverse to further
engage users and facilitate a state of flow.
The findings of the study indicate that emotional attach-
ment and conformity significantly influence the acceptance
of the Metaverse. To increase users’willingness to continue
using the Metaverse, one effective approach is to attract
more users to collectively engage with applications, thus
promoting emotional attachment and conformity (Hsiao &
Tang, 2021). This can be achieved by enhancing the accessi-
bility and flexibility of interactive and cooperative activities.
Moreover, introducing the multi-user virtual world through
popular social media platforms such as Twitter, TikTok, and
YouTube can expand the reach and user base of the
Metaverse. In order to further encourage the adoption and
usage of the Metaverse, designers and practitioners should
provide ongoing technical support, counseling, and training
to users. This consistent support can serve as an effective
means to motivate more individuals to engage with the
Metaverse on a regular basis.
7.4. Limitations
Several limitations should be acknowledged in relation to the
present study. Firstly, the survey sample was limited to
Metaverse users in Beijing, thereby excluding users from other
cities. Consequently, caution should be exercised when
attempting to generalize the findings to other regions.
Secondly, a broad definition of the Metaverse was employed,
encompassing the entire industry, which precluded the identi-
fication of distinctions among specific Metaverse applications.
Thirdly, while the technology acceptance model was expanded
to include additional constructs, the potential moderating and
mediating effects of variables such as personality traits, age,
educational level, income, and gender were not investigated.
Finally, due to resource constraints, this study relied solely on
the Partial Least Squares–Structural Equation Modeling (PLS-
SEM) technique, thereby forgoing the use of a hybrid PLS-
artificial neural network modeling approach.
7.5. Suggestions for future studies
Given the exponential growth of the Metaverse, it is impera-
tive to conduct further research to understand the factors
INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION 11

influencing user acceptance of this technology. Additionally,
it is worth exploring users’acceptance of the Metaverse
using qualitative methodologies to gain a deeper under-
standing of their perspectives. It is important to note that
users’acceptance of the Metaverse may depend on various
moderators and mediators, including but not limited to per-
sonality traits, age, gender, income, occupations, marital sta-
tus, educational attainment, type of Metaverse platform,
familiarity with the Metaverse, and frequency of use of the
Metaverse. Future research should aim to confirm the influ-
ence of these variables on Metaverse acceptance.
Furthermore, there is a need for investigations on the
potential impact of other social, technical, and psychological
factors on user acceptance of the Metaverse. These factors
could include innovativeness, cyber risks, digital self-efficacy,
digital literacy, social norms, trust, and habitual tendencies.
Understanding the role of these factors would contribute to a
more comprehensive understanding of Metaverse acceptance.
Lastly, future research should consider employing a
hybrid approach, utilizing both Partial Least Squares-
Structural Equation Modeling (PLS-SEM) and neural
network modeling, to capture both linear and non-linear
associations among the theoretical model constructs. This
approach would enable a more nuanced exploration of the
relationships within the research model. To advance the
field and enhance our understanding of user acceptance of
the Metaverse, it is crucial to address these research gaps
and undertake further investigations using a combination of
methodologies.
Acknowledgments
We would like to extend our gratitude to anonymous reviewers and
funding.
Informed consent
All participants voluntarily signed the consent form before answering
the questionnaire.
Disclosure statement
We have no conflicts of interest to declare that are relevant to the con-
tent of this article.
Funding
This work is supported by Key Research and Application Project of the
Key Laboratory of Key Technologies for Localization Language Services
of the State Administration of Press and Publication, “Research on
Localization and Intelligent Language Education Technology for the
‘Belt and Road Initiative”(Project Number: CSLS 20230012), Special
fund of Beijing Co-construction Project-Research and reform of the
“Undergraduate Teaching Reform and Innovation Project”of Beijing
higher education in 2020-innovative “multilingual þ”excellent talent
training system (202010032003), Beijing Language and Culture
University Excellent Doctoral Dissertation Cultivation Program
Funding Project, and The Fundamental Research Funds for the Central
Universities, and the Research Funds of Beijing Language and Culture
University [23YCX004].
ORCID
Rong Wu http://orcid.org/0000-0002-2985-1197
Zhonggen Yu http://orcid.org/0000-0002-3873-980X
Data availability statement
The data that support the findings of this study are available on
request from the corresponding author. The data are not publicly avail-
able due to privacy or ethical restrictions.
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About the authors
Rong Wu is a doctoral student at Department of English Studies,
Faculty of Foreign Studies, Beijing Language and Culture University.
She has already participated in six scientific research projects and pub-
lished over ten academic papers. Her research interest includes educa-
tional technologies, language acquisition, and translation.
Zhonggen Yu, Professor in Faculty of Foreign Studies, Beijing
Language and Culture University, Research Fellow of Academy of
International Language Services, Director of Center for Intelligent
Language Education Research, National Base for Language Service
Export, has published over 150 academic papers. He focuses on educa-
tional technologies.
Appendix A. Research instruments
Perceived ease of use (adapted from Davis, 1989)
1. Learning to use the Metaverse is easy for me.
2. I find the Metaverse easy to use.
3. It is easy for me to become skillful at using the Metaverse.
4. My interaction with the Metaverse is clear and understandable.
Perceived usefulness (adapted from Davis, 1989)
1. I find the Metaverse useful in my daily life.
2. Using the Metaverse increases my productivity.
3. Using the Metaverse improves my performance.
4. Using the Metaverse enables me to accomplish tasks more
quickly.
Attitude (adapted from Davis, 1989)
1. Using the Metaverse is a good idea.
2. I like the Metaverse.
3. I am satisfied with the Metaverse.
4. Overall, I have a positive attitude towards the Metaverse.
Intention to use (adapted from Moon & Kim, 2001; Venkatesh et al.,
2012)
1. I intend to continue using the Metaverse in the near future.
2. I will frequently use the Metaverse.
3. I will recommend the Metaverse to others.
Conformity (adapted from Sun et al., 2017;Yu(2020)
1. Since many people use the Metaverse, I also use it.
2. Since many people think the Metaverse is useful, I also consider it
useful.
3. Since many people like the Metaverse, I also like it.
Social presence (adapted from Oh et al., 2023)
1. I felt like I was in the presence of another people in the
Metaverse.
2. I felt that people in the Metaverse were aware of my presence.
3. The people in the Metaverse appeared to be conscious and alive
to me.
4. I have a sense that I was interacting with other people in the
Metaverse rather than in a computer simulation.
Social interaction (adapted from Kim et al., 2020)
1. The Metaverse enables me to create social relationships with
others.
2. The Metaverse helps me maintain social relationships with
others.
3. The Metaverse helps me make new friends.
4. The Metaverse enhances my social relationships with others.
16 R. WU AND Z. YU

Perceived enjoyment (adapted from Hung et al., 2021)
1. Using the Metaverse is enjoyable.
2. Using the Metaverse is interesting.
3. Using the Metaverse is pleasurable.
4. Using the Metaverse is exciting.
Emotional attachment (adapted from Kim et al., 2020; Read et al.,
2011)
1. Using the Metaverse is part of my everyday life.
2. I am attached to using the Metaverse.
3. Using the Metaverse is important to me.
4. I feel connected to the Metaverse.
Flow (adapted from Kim et al., 2020)
1. When using the Metaverse, I concentrated on the Metaverse.
2. When I was using the Metaverse, time seemed to pass very
quickly.
3. When using the Metaverse, I forget all concerns.
4. Using the Metaverse often makes me forget who I am.
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