Factors Influencing Vietnamese Generation MZ’s Adoption of Metaverse Platforms

Abstract

The Metaverse is a blend of our tangible and digital worlds, marking the upcoming direction of the internet sector. This study strives to identify the elements that affect the uptake of Metaverse platforms in Vietnam. To that end, we pinpointed the factors influencing usage intentions using the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model and added a new “switching cost” variable. We gathered and examined data from 520 participants, with 230 from Generation Y (Gen Y) and 290 from Generation Z (Gen Z) using the multi-group analysis in structural equation modeling. The findings reveal that elements such as effort expectancy, performance expectancy, facilitating conditions, hedonic motivation, and value price positively affect the intent to embrace Metaverse platforms. While social influence does not impact the entire dataset, it positively influences Gen Y’s intention. On the other hand, the switching cost acts as a hindrance to adopting Metaverse platforms. It is also noteworthy that significant variations exist between Gen Y and Gen Z concerning these factors. These observations are crucial for industry leaders looking to capitalize on the immense opportunities of Metaverse platforms for sustainable growth in the internet sector.

1. Introduction

The pervasive influence of computer science in our everyday experiences cannot be overstated, with particularly notable advancements enhancing human interactions and communication paradigms. The Metaverse, an emergent sector catalyzed in prominence by the COVID-19 pandemic, represents a prime example of such technological progression. Derived from “meta” (beyond) and “universe”, the term “Metaverse” portrays a sophisticated virtual world that promises an interaction far surpassing conventional video conferencing [1,2]. The ambit of the Metaverse stretches beyond just gaming; it is envisioned to redefine multiple facets of our quotidian activities, potentially emerging as a global agora for myriad events and interactions.

Despite its current ubiquity, technological innovation has had humble beginnings, marked by the proliferation of personal computers, the internet, and mobile devices. AR and VR signify the fourth wave of this evolution, setting the stage for the inception of the Metaverse—a paradigm shift in our online engagements spanning education, business, remote work, and leisure [3,4,5].

Recent studies reveal a mounting interest in the Metaverse. Over half the global internet users express eagerness to collaborate in this virtual domain, with live entertainment and potential cryptocurrency engagements being significant lures [6,7]. Analyzing from a Vietnamese lens, the National IT Industry Promotion Agency (NIPA) survey conducted in Ho Chi Minh and Hanoi suggests a growing awareness and optimism toward the Metaverse, despite a significant portion being yet unfamiliar with its intricacies.

The “Generation MZ”, an amalgamation of individuals born between the 1980s and 2000s, notably stands out in this context. These digital natives, blending elements from Millennials to Generation X (Gen X), have inherent proclivities for platforms like the Metaverse, influenced by their unique experiences, values, and backgrounds [8,9,10]. The technological divide between Generation Z (Gen Z) and Generation Y (Gen Y) significantly sways the adoption rates of such innovations. The variances in technology perceptions across these generations have been well documented [11,12,13,14,15,16,17,18].

This research ventures into a deeper exploration of the Metaverse, specifically focusing on the nuances of its adoption across generational divides in the Vietnamese context. There exists a lacuna in understanding the determinants influencing the adoption of the Metaverse by Generation MZ, especially in a market as dynamic as Vietnam, further amplified by the rise of blockchain technologies and non-fungible tokens (NFT) [19]. As businesses increasingly venture into Metaverse platforms, Gen Z’s adoption rate starkly contrasts with older cohorts. This disparity, accentuated by the restricted outdoor activities during the COVID-19 pandemic, has necessitated an exploration into the factors influencing the Metaverse’s growing traction.

Thus, our study aims to elucidate this phenomenon, structured around two central research questions:

Q1: Which variables influence Generation MZ’s affinity for the Metaverse, and which among them stand out prominently?

Q2: What inherent factors differentiate Gen Y from Gen Z in this context?

The ensuing sections of this article provide a comprehensive review of literature relevant to our topic, an outline of our research methodology, a presentation of the findings, and a concluding discussion encompassing implications and future research avenues.

2. Literature Review and Theoretical Framework

2.1. Metarverse

The concept of the “Metaverse” has seen various definitions by scholars and was notably introduced in Neal Stephenson’s 1992 book, “Snow Crash”. In this work, Stephenson depicted a universe parallel to the Metaverse, highlighting one of its earliest mentions [20]. Fundamentally, the Metaverse is considered a transformative phase of the internet—a 3D virtual realm allowing users to interact with individuals, locations, and a vast range of digital activities. While the internet offers website browsing, the Metaverse evolves this concept, providing a more immersive experience driven by the fusion of augmented reality (hereafter AR) and virtual reality (hereafter VR). These technologies enable users to interact with digital entities, surroundings, and other participants within a mutual environment [2,3]. Initial versions of the Metaverse were imagined as virtual domains where user teleportation was feasible. Essentially, the Metaverse is a communal 3D space crafted by its inhabitants [8,21]. In this domain, individuals can craft alternative universes, diving deep into numerous digital experiences and interactions with other members [22,23].

While the precise definition of the Metaverse remains in flux, it represents an innovative melding of spatial and digital experiences in both virtual and physical realms. At its core, the Metaverse is an expansive, collective virtual environment, integrating elements from online gaming, social media, and VR [24]. The rapid growth of extended reality (hereafter XR) has caught the eye of several major corporations, with companies like Google, Samsung, and Meta taking the lead in exploring its potential [25]. In 2007, the Acceleration Studies Foundation (ASF) undertook a project to draft a roadmap for Metaverse development, delineating four key types: virtual worlds, mirror worlds, lifelogging, and augmented reality [20]. A more recent evolution termed “Digital X” encompasses three manifestations of virtual reality: XR (covering both VR and AR), digital twin, and digital ME [26]. Essentially, Digital X is a multi-platform gaming service that empowers users to connect and immerse themselves in diverse experiences using head-mounted displays and virtual avatars.

The Metaverse, with its multifaceted nature, appeals to a broad spectrum of users, notably the younger demographic. Its rise in popularity has been marked by an array of diverse services spanning education, entertainment, and social interaction [9,27]. This surge in interest has catalyzed the inclusion of well-known gaming platforms like Fortnite, Minecraft, and ZEPETO into the Metaverse ecosystem. Within these virtual realms, users adopt avatars, offering them the liberty to engage in a multitude of social, cultural, and political endeavors, mirroring the freedoms experienced in the tangible world [1,28]. As user numbers swell and the intricacies of the platform’s business model evolve, novel Metaverse business models have begun to materialize [8,29]. Central to these models is the conception of the Metaverse as a multi-platform gaming domain, fostering interpersonal interactions and immersive experiences through a blend of VR and AR technologies [1,8,21].

Ref. [30] explored factors in the Metaverse that influence word-of-mouth intention through the variables of flow and satisfaction. The study showed that enjoyment, challenge, and telepresence have positive effects on flow, which in turn positively impacts both satisfaction and word-of-mouth intention. Moreover, satisfaction was found to bolster word-of-mouth intention directly. Ref. [31] determined that attitudes toward behavioral intention and actual use of the Metaverse are influenced by its perceived ease of use, perceived pleasure, self-efficacy, and prevailing social norms. Their findings also highlighted a more favorable view of the technology among individuals below 20 years of age. In a separate investigation, ref. [32] analyzed how virtual world experiences impact consumer behavior. The study found that educational, relational, and deviant experiences enhance perceptions related to Metaverse experience factors. However, it was also noted that aesthetic and entertainment experiences did not significantly alter these perceptions. Lastly, ref. [33] pinpointed three primary factors driving the propensity to engage with the virtual world: perceived usefulness, perceived ease of use, and perceived enjoyment. The study concluded that both the perceived ease of use and content enjoyment significantly dictate intentions to use the platform. Additionally, there was a correlation between the perceived ease of use, the perceived usefulness of Metaverse technology, and the social influences affecting the intention to adopt it.

Ref. [34] discovered that within a learning immersion using the Metaverse, factors such as course design, content support, and evaluation played pivotal roles in shaping participants’ performance expectations. Further, elements like social influence, effort expectancy, and hedonic motivation were found to significantly bolster their behavioral intentions. Ref. [35] investigated factors determining user behavior on Metaverse game platforms. The study emphasized the extent to which user attitudes and intentions were molded by various immersion-related elements of the game. Ref. [36] undertook a comprehensive analysis of the Metaverse platform’s use intention among college students in Oman, UAE, and Saudi Arabia. Employing both qualitative and quantitative methods, they discerned that the perceived ease of use and usefulness were the pivotal influencing factors. Ref. [37] established that aspects like social influence, interactivity, and self-efficacy positively affected Metaverse use intentions. Notably, even though there was no substantial evidence indicating significant differences in the perceived ease of use and usefulness of the platform, participants’ overall favorable attitudes toward the Metaverse ensured continued engagement. Lastly, Ref. [38] delved into factors influencing the intention to engage with a virtual world. Their findings highlighted the perceived enjoyment and usefulness of the platform as significant drivers of user behavior. However, perceived ease of use did not exhibit a similar impact. The study also identified computer playfulness and self-efficacy as crucial influencers.

2.2. Unified Theory of Acceptance and Use of Technology

The Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) offers a comprehensive model for understanding how individuals engage with and perceive technology (see Figure 1). This model elucidates their behavioral intentions in a manner that is more accessible to others [39].

Hedonic motivation pertains to the pleasure and rewards derived from using technology. Price value reflects the gap between what consumers pay for a service and the perceived benefits they gain from using the technology. Habitual use, or the unconscious reliance on a specific technology or service, stems from user experience. As per UTAUT2, there was an 18% increase in the intention to use, while technology acceptance also rose by 12%. These data underscore the advantages technology offers to consumers [40].

Ref. [41] probed into how various factors impact the acceptance of media technologies, particularly in the realm of mobile AR. They discerned that these elements had a moderating effect on usage intentions. In a related study, ref. [42] delved into the factors driving AR adoption in educational contexts. Their findings reveal that such factors positively influence the adoption process, with participants’ performance expectancy, social influence, and hedonic motivation playing significant roles in the adoption of the technology.

The current study seeks to identify the determinants of Metaverse platform adoption intentions in Vietnam, utilizing the UTAUT2 model as its framework. Numerous past research vouch for the UTAUT2 model’s aptness as a foundational model for such investigations [41,42,43,44,45,46,47].

3. Hypotheses Development and Research Model

3.1. Performance Expectancy

The term “performance expectation” in UTAUT2 refers to an individual’s belief in how technology will aid them in achieving their objectives. This concept resonates with the principles presented in the innovation resistance model and the technology acceptance model. Research utilizing the UTAUT2 model has indicated that performance expectations correlate with the perceived benefits of technology to consumers [39]. Ref. [48] found that the performance expectations of older Portuguese adults could sway their inclination toward using information and communications technology (ICT). Similarly, ref. [49] highlighted that the determinants of VR usage in national parks are intricately linked to both the environment and performance expectancy. Given these insights, we hypothesize:

H1. 

Performance expectancy will have a positive influence on the intention to use the Metaverse platform.

3.2. Effort Expectancy

Effort expectancy assesses the ease with which individuals can use new technologies or systems [39,50]. Past research indicates that this expectancy can shape people’s intentions to adopt new technologies [51,52]. Effort expectancy pertains to the perceived complexity of a system’s design and implementation. This can influence consumer behavior, especially when evaluating the perceived effort required. When consumers consider adopting an innovative product or technology, they often need to understand its functionality or learn how to use it. Ref. [53] established that effort expectancy influences users’ intentions to engage with online games on mobile devices. With these findings in mind, we hypothesize:

H2. 

Effort expectancy will have a positive influence on the intention to use the Metaverse platform.

3.3. Social Influence

The concept of social influence is a foundational element of UTAUT2, drawing parallels with the theory of reasoned behavior [54]. People often perceive their environment as having a significant influence on their capabilities. Critical mass theory posits that individuals’ actions and behaviors are swayed by group decisions, emphasizing that social influence is a pivotal factor in personal choices [55]. When it comes to technological advancements and system upgrades, individuals often lean toward the consensus of their peers [41,56]. Thus, social influence is recognized as a potent driver in shaping decisions related to new technologies, guiding individuals toward well-informed judgments in product and service adoption [57,58]. Consequently, we hypothesize:

H3. 

Social influence will have a positive influence on the intention to use the Metaverse platform.

3.4. Facilitating Conditions

Facilitating conditions refer to an individual’s belief that both the organization and the necessary technological infrastructure are in place to support the adoption of new technology. Essentially, it gauges their confidence in receiving the needed support for the technology in question. Such a belief can empower users by providing them with the essential skills and knowledge required for the technology’s usage. Moreover, from the UTAUT2 perspective, facilitating conditions influence not only the actual use of technology but also the intention to adopt it [39]. Refs. [39,50] posited that a firm belief in the readiness of organizational and technical infrastructure enhances a user’s intention to adopt technology. [44] found that favorable facilitating conditions can bolster a consumer’s inclination toward AR. Similarly, studies by [53,59] indicated that such conditions positively influence the intention to use VR in education and online games, respectively. Based on these insights, we hypothesize:

H4. 

Facilitating conditions will have a positive influence on the intention to use the Metaverse platform.

3.5. Hedonic Motivation

Hedonic motivation pertains to the pleasure individuals derive from utilizing new technologies and services [39]. This concept posits that intrinsic motivation plays a more crucial role than extrinsic motivation in the adoption of new technology. It encompasses elements of amusement, fantasy, and enjoyment. Furthermore, this motivation is not only pivotal in predicting users’ intention to embrace new technologies but has also been linked with heightened loyalty and satisfaction levels [60], and ref. [61] demonstrated its positive impact on the acceptance of VR headsets. Based on these findings, we posit:

H5. 

Hedonic motivation will have a positive influence on the intention to use the Metaverse platform.

3.6. Price Value

The price value of a product or service reflects the extent to which it enhances a user’s experience and bolsters their economic benefits, relative to the financial costs incurred [39]. When individuals possess the requisite resources to access a service, they are more inclined to form an intention to use it. Nevertheless, the cost associated with adopting new technology remains a pivotal determinant in user decision-making [62]. Ref. [63] underscores the significance of price value in shaping consumer intentions, particularly in the adoption of VR and AR within the tourism sector. Building on these insights, we propose:

H6. 

Price value will have a favorable bearing on the intent to engage with the Metaverse platform.

3.7. Habit

Habits, as described by [64], are behaviors autonomously executed based on past learning experiences. These behaviors, having been ingrained through repetition, can guide future actions. Prior research indicates that motivations rooted in habit are intertwined with media usage motives and can profoundly influence the probability of individuals acquiring performance-centric skills. Empirical studies suggest that such habitual behaviors play a significant role in shaping user intentions, influencing actions such as online gaming or the use of AR and VR glasses [49,53,65]. Based on this understanding, we propose:

H7. 

Habit will have a positive influence on the intention to use the Metaverse platform.

3.8. Switching Costs

Switching costs encompass the various experiences customers encounter when transitioning between products or providers. These costs are not solely monetary; they factor in the risks, uncertainties, and potential contractual penalties associated with severing an established relationship [66]. Ref. [67] emphasized the role of switching costs in customer retention, positing that heightened costs increase the likelihood of customers sticking with their current services. Ref. [68] shed light on determinants influencing user switching intentions, establishing that service quality, the inherent costs of switching, and the allure of new offerings are decisive elements. By bolstering system capabilities and preserving service quality, platform providers can diminish these switching costs. Similarly, educators can enrich the social interactions of their students by deploying effective social and learning strategies. Based on these insights, we propose:

H8. 

Switching costs will have a negative influence on the intention to use the Metaverse platform.

The UTAUT2 framework stands as one of the most robust models for elucidating user intention, making it apt for our research. In this study, we identify performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit, and switching cost as independent variables. These are anticipated to significantly influence use intention. Drawing from the UTAUT2 model, we introduce our proposed research design (refer to Figure 2). Subsequent to our analysis, we juxtapose the influences of these factors between Gen Z and Gen Y, aiming to discern any distinct impacts of the independent variables on the dependent variable across these demographic cohorts.

4. Research Methodology

4.1. Measurement Development

We conducted a survey among Vietnamese users to explore the factors influencing their intention to use the Metaverse platform. The questionnaire comprised 34 questions in total. The dependent variable, ‘intention to use’, is represented by 3 questions. The independent variables include: social influence, effort expectancy, hedonic motivation, price value, habit, facilitating conditions, and performance expectancy. All variables are represented by 3 questions. Detailed breakdowns can be found in Appendix A.

4.2. Data Collection

For this study, we collected data from Vietnamese individuals aged between 15 and 41. The questionnaire items were constructed based on those from prior studies, where their reliability and validity had been confirmed. Between March and April 2022, we gathered 600 responses through both online and offline channels. To ensure the validity of the constructs, we excluded responses that were invalid, unfinished, or incomplete. This exclusion led to the removal of 80 samples, leaving us with a valid data set of 520 respondents (290 samples from Gen Z and 230 samples from Gen Y).

We utilized SPSS 25 and AMOS 20 to analyze the data. SPSS facilitated the demographic analysis of the questionnaire, and Cronbach’s alpha coefficient was employed to gauge the reliability of the measurement items for each variable. Furthermore, we conducted confirmatory factor analysis (CFA) to ascertain the validity of the measurement items. To test our research hypothesis, a structural equation model and multi-group analysis were applied.

Table 1. Demographics of respondents.

Demographic		Frequency	Percentage (%)
Gender	Male	263	50.6
	Female	257	49.4
Age	15~25	290	55.8
	25~40	230	44.2
Occupation	Student	205	39.4
	Officer	183	35.2
	Doctor	15	2.9
	Police	18	3.5
	Businessman	39	7.5
	Teacher	42	8.1
	Others	18	3.5
Education	High School	121	23.3
	Bachelor’s Degree	307	59
	Master’s Degree	53	10.2
	Doctoral Degree/Higher	21	4
	Other	18	3.5
Place	Ha Noi	139	26.7
	HaiPhong	93	17.9
	Da Nang	89	17.1
	Ho Chi Minh City	150	288
	Other	49	9.4
Income	<VND 500	262	50.4
	VND 500~1000	204	39.2
	VND 1000~1500	25	4.8
	Other	29	5.6
Platform	Game platform	256	49.2
	Business platform	214	41.2
	Video platform	50	9.6

presents the demographic statistics of the sample. The survey was conducted in four major cities in Vietnam. Of the 520 respondents, 50.6% were female and 49.4% were male. Respondents aged between 15 and 25 made up 55.8% of the sample, while the remainder ranged from 28 to 41 years old. Notably, a significant portion of the respondents, 59%, held a bachelor’s degree.

5. Data Analysis and Results

5.1. Reliability and Validity Test

As depicted in

Table 2. Reliability, CR, and AVE.

Construct	Items	Item Loadings	CR	Cronbach’s Alpha	AVE
Performance Expectancy (PE)	PE1	0.855	0.907	0.846	0.764
	PE2	0.818
	PE3	0.818
Effort Expectancy (EE)	EE1	0.841	0.908	0.847	0.766
	EE2	0.816
	EE3	0.805
Social Influence (SI)	SI1	0.805	0.904	0.840	0.757
	SI2	0.800
	SI3	0.780
Facilitating Conditions (FC)	FC1	0.826	0.902	0.837	0.753
	FC2	0.819
	FC3	0.810
Hedonic Motivation (HM)	HM1	0.816	0.901	0.835	0.752
	HM2	0.806
	HM3	0.755
Price Value (VP)	VP1	0.774	0.904	0.841	0.759
	VP2	0.835
	VP3	0.803
Switching Cost (SC)	SC1	0.860	0.892	0.820	0.734
	SC2	0.852
	SC3	0.700
Habit (HB)	HB1	0.786	0.899	0.831	0.748
	HB2	0.772
	HB3	0.821
Intention to Use (IU)	IU1	0.877	0.914	0.859	0.764
	IU2	0.868
	IU3	0.876

, all the scales exhibit a Cronbach’s alpha greater than 0.7, indicating good reliability. Moreover, the composite reliability (CR) for all the scales exceeds 0.7, and the average variance extracted (AVE) surpasses 0.5, affirming the scales’ convergent validity [69].

The square root of the AVE exceeds the correlations between the latent variables, confirming discriminant validity [70] (refer to

Table 3. Discriminant validity.

	EE	FC	HB	HM	IU	PE	PV	SC	SI
EE	0.875
FC	0.310 **	0.868
HB	0.333 **	0.342 **	0.865
HM	0.330 **	0.348 **	0.431 **	0.867
IU	0.527 **	0.478 **	0.562 **	0.593 **	0.882
PE	0.452 **	0.302 **	0.297 **	0.269 **	0.543 **	0.874
PV	0.240 **	0.276 **	0.442 **	0.507 **	0.568 **	0.207 **	0.871
SC	−0.294 **	−0.305 **	−0.458 **	−0.349 **	−0.503 **	−0.183 **	−0.377 **	0.864
SI	0.363 **	0.447 **	0.357 **	0.405 **	0.542 **	0.362 **	0.405 **	−0.349 **	0.870

Note: ** p < 0.01. Values in bold represent the square root of the AVE.

).

5.2. Structural Model

Table 4. Model fit.

Fit Index	Recommended Value	Structural Model
χ2/DF	<3.00	1.283
GFI (goodness of fit index)	≥0.90	0.950
CFI (comparative fit index)	≥0.90	0.989
NFI (normed fit index)	≥0.90	0.952
AGFI (adjusted goodness of fit index)	≥0.90	0.934
RMSEA (root mean square error of approximation)	≤0.050	0.023
RMR (root mean square residual)	≤0.050	0.025

presents the model fit for the entire dataset. The χ²/DF value is 1.283, which is below the recommended threshold of 3.0. The GFI, CFI, NFI, and AGFI all exceed the suggested value of 0.9. Additionally, the RMR stands at 0.025, falling below the recommended 0.05, and the RMSEA is 0.023, also below the standard threshold of 0.05. These metrics indicate a strong model fit.

Table 5. Test results of hypotheses.

Hypothesis		Estimate	SE	CR	p-Value	Result
H1	PE→IU	0.294	0.032	7.332	***	Accepted
H2	EE→IU	0.157	0.033	4.007	***	Accepted
H3	SI→IU	0.063	0.033	1.460	0.144	Rejected
H4	FC→IU	0.104	0.032	2.717	0.007	Accepted
H5	HM→IU	0.206	0.032	4.669	***	Accepted
H6	VP→IU	0.232	0.034	5.252	***	Accepted
H7	HB→IU	0.121	0.033	2.804	0.005	Accepted
H8	SC→IU	-0.160	0.030	−4.081	***	Accepted

Note: *** p < 0.001.

displays the results of the hypothesis tests. With the exception of H3, all hypotheses are supported. Referring to Table 5, performance expectancy (PE) exhibits the highest path coefficient among all variables, while facilitating conditions (FC) show the lowest. Additionally, switching cost (SC) has a negative impact on the intention to use the Metaverse platform.

Table 6 reveals the factors influencing intention to use, ranked in descending order of influence. For Gen Y, the order is: switching cost (−0.314), performance expectancy (0.287), social influence (0.210), hedonic motivation (0.186), price value (0.164), effort expectancy (0.121), and facilitating conditions (0.095). For Gen Z, the sequence is: price value (0.281), performance expectancy (0.219), habit (0.208), effort expectancy (0.158), hedonic motivation (0.177), switching cost (−0.122), and facilitating conditions (0.121). To discern differences between Gen Z and Gen Y, we employed the method proposed by [71]:

$$t_{ij}=\frac{p_1-p_2}{\sqrt{\frac{\left(n_1-1\right)\times {SE}_1^2+\left(n_2-1\right)\times {SE}_2^2}{n_1+n_2-2}\times \sqrt{\frac{1}{n_1}+\frac{1}{n_2}}}}$$

(1)

p_i: path coefficient in the structural model;

n_i: sample size of the dataset;

SE_i: standard error of path in the structural model;

t_ij: t-statistic with n₁ + n₂ − 2 degrees of freedom

Table 6. Multi-group analysis.

	Gen Y (230)			Gen Z (290)			t-Value for the Coefficient Difference (t > 1.96)
	χ2/df = 1.055; GFI = 0.917 AGFI = 0.901; NFI = 0.923 CFI = 0.996; RMSEA = 0.016			χ2/df = 1.269; GFI = 0.917 AGFI = 0.906; NFI = 0.905 CFI = 0.978; RMSEA = 0.031
Path	Estimate	SE	p-Value	Estimate	SE	p-Value	tij	Result
PE→IU	0.287	0.035	***	0.219	0.037	***	21.388	exist
EE→IU	0.121	0.033	0.007	0.158	0.045	0.011	34.538	exist
SI→IU	0.210	0.043	***	0.088	0.033	0.152	36.726	exist
FC→IU	0.095	0.038	0.042	0.121	0.037	0.040	−8.194	exist
HM→IU	0.186	0.043	0.001	0.177	0.032	0.004	2.744	exist
PV→IU	0.164	0.039	0.002	0.281	0.036	***	−35.591	exist
HB→IU	0.054	0.038	0.287	0.208	0.038	0.002	−46.053	exist
SC→IU	−0.314	0.041	***	−0.122	0.031	0.036	−61.000	exist

Note: *** p < 0.001.

Table 6. Multi-group analysis.

	Gen Y (230)			Gen Z (290)			t-Value for the Coefficient Difference (t > 1.96)
	χ2/df = 1.055; GFI = 0.917 AGFI = 0.901; NFI = 0.923 CFI = 0.996; RMSEA = 0.016			χ2/df = 1.269; GFI = 0.917 AGFI = 0.906; NFI = 0.905 CFI = 0.978; RMSEA = 0.031
Path	Estimate	SE	p-Value	Estimate	SE	p-Value	tij	Result
PE→IU	0.287	0.035	***	0.219	0.037	***	21.388	exist
EE→IU	0.121	0.033	0.007	0.158	0.045	0.011	34.538	exist
SI→IU	0.210	0.043	***	0.088	0.033	0.152	36.726	exist
FC→IU	0.095	0.038	0.042	0.121	0.037	0.040	−8.194	exist
HM→IU	0.186	0.043	0.001	0.177	0.032	0.004	2.744	exist
PV→IU	0.164	0.039	0.002	0.281	0.036	***	−35.591	exist
HB→IU	0.054	0.038	0.287	0.208	0.038	0.002	−46.053	exist
SC→IU	−0.314	0.041	***	−0.122	0.031	0.036	−61.000	exist

Note: *** p < 0.001.

The group analysis highlights notable differences between Gen Y and Gen Z. The most prominent disparity arises from switching costs, which exhibit a negative influence across the entire dataset. For Gen Y, switching costs dominate as the primary factor affecting intention to use, whereas in Gen Z, their impact is minimal. In contrast, while the habit factor has limited influence on Gen Y’s intention to use and a moderate overall effect, it stands out as a critical determinant for Gen Z. Social influence significantly affects Gen Y’s intention to use but does not hold the same sway across the entire dataset or for Gen Z. Price values, effort expectancy, performance expectancy, facilitating conditions, and hedonic motivation influence intentions across all datasets. The multi-group analysis further underscores variances in these factors and their effects on the intention to use the Metaverse.

6. Conclusions

6.1. Discussion of Key Findings

This study aimed to understand the factors affecting Vietnamese Gen Y and Gen Z users’ intention to engage with the Metaverse platform. Results revealed that both generations consider performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, price value, switching cost, and habit when deciding to use the Metaverse. Our multi-group analysis further showed that only the habit factor in Gen Y was rejected. Notably, Gen Z prioritizes social influence more than Gen Y, although this factor was not significant in the broader dataset.

Gen Y, often called “digital natives”, grew up during the dawn of the Internet, witnessing the rise of digital technologies and social media. In contrast, Gen Z, born into a digital age, never experienced life without technology. They are more likely to hold liberal views and are less traditional in their activities. Consequently, Gen Y may possess a more profound grasp of emerging technologies like the Metaverse, relying heavily on them for communication and valuing peers’ opinions. Both generations emphasize habit and price value when considering the Metaverse, but Gen Z’s inclination is stronger, possibly due to their intrinsic tech-savviness and practicality [11,12,13,14,15,16,17,18].

Switching cost emerges as a key determinant for Metaverse engagement, attributed to Gen Y’s preference for immersive experiences, loyalty programs, and consistent online support. In contrast, Gen Z shows a discerning attitude toward marketing, while Gen Y leans toward enhancing product and service quality. E-commerce businesses can tailor their strategies by considering the unique preferences and behaviors of both generations [66,67,68].

6.2. Academic Implications

This study, drawing upon the UTAUT2 model, delves into the nuances influencing Vietnamese Gen Y and Gen Z’s adoption of the Metaverse platform, unveiling several critical insights:

Firstly, our research stands at the forefront of studies leveraging the UTAUT2 model to elucidate the intent to adopt the Metaverse platform in Vietnam. The results indicate that, on the whole, every factor except for social influence (H3) significantly shaped the intent to utilize the platform. Gen Y’s intention was not governed by habit, while both generational cohorts found performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation pivotal. Intriguingly, Gen Y was notably more swayed by social influence compared to Gen Z. Conversely, Gen Z ascribed more weight to price value than Gen Y—a variation rooted in Gen Z’s ingrained digital acumen.

Secondly, we enriched the UTAUT2 model by incorporating “switching cost” as a determinant of the Metaverse platform’s adoption intent. Both generations assessed the cost implications of transitioning to the Metaverse platform, with Gen Z demonstrating a pronounced propensity to migrate.

In essence, our revelations largely resonate with the tenets of the UTAUT2 framework, reinforcing its applicability, while concurrently expanding our comprehension of the factors underpinning Metaverse adoption.

6.3. Practical Implications

The Metaverse is poised to redefine the contours of e-commerce, enabling enterprises to discern and court their desired audience effectively. Our findings are instrumental for commercial ventures, offering granular perspectives about their potential users. Derived from our study, the following practical implications are paramount:

First, performance expectancy, potential impediments, and the economic considerations associated with transitioning underscore users’ adoption decisions. Consequently, enterprises ought to fortify platform functionalities and employ cutting-edge technology to ensure a seamless user experience.

Second, captivating a diverse and expansive user base necessitates the curation of eclectic content, emblematic of unparalleled service quality. Thus, businesses should prioritize content innovation to continually pique user interest.

Third, the economic, temporal, and psychological facets of adopting a new platform reign supreme in user decisions. As such, enterprises should be circumspect about not only the monetary cost but also the time and mental investments users allocate to familiarize themselves with new platforms.

Fourth, the generational divergence in expectations underscores the importance of tailored strategies. To optimize engagement, businesses should harness technology, amplifying the caliber of their Metaverse offerings. Initiatives like electronic word-of-mouth (e-WOM) can be instrumental for Gen Y, given their susceptibility to social influence—a factor that is palpably less decisive for Gen Z.

Lastly, understanding the myriad determinants influencing user adoption choices in the Metaverse remains crucial across generations. The emphasis on cost-efficiency during transitions is salient and can be tackled by formulating robust strategies that foster a profound user affiliation with a brand’s digital realm.

7. Limitations and Future Directions

This study, while providing valuable insights, carries with it certain limitations that need acknowledgment and contemplation.

First, one of the primary constraints of this study is the omission of moderating factors intrinsic to the UTAUT2 model. Specifically, age can greatly influence technology adoption behaviors, with distinct age groups having unique perspectives and experiences. Gender can often influence technological interaction, with men and women sometimes demonstrating differing inclinations toward technology usage. Experience or familiarity with a platform can dramatically affect adoption rates and perceptions. Given that the UTAUT2 framework acknowledges the significance of these variables, their absence in the current study leaves room for further exploration. Future research endeavors should consider integrating these moderating factors, facilitating a more nuanced and layered understanding of Metaverse platform adoption.

Second, another limitation pertains to the lack of investigation into the myriad factors that govern service providers’ behavioral intentions. Specifically, the reputation or standing of a company in the market can significantly influence its adoption strategy and user trust. And the stability, security, and efficiency of the platform can dramatically affect user adoption rates. Moreover, the degree to which users trust a platform often hinges on the credibility and reliability of the service provider. User trust is a linchpin for any technology’s successful adoption.

Addressing these determinants in subsequent studies can provide a holistic understanding of both user and provider perspectives, contributing to a comprehensive analysis of Metaverse platform adoption dynamics.

Given the identified limitations, forthcoming studies should seek to encompass the aforementioned variables and perspectives. Such a broadened scope can not only validate the findings of this study but also enrich the extant literature on the adoption and implementation of the Metaverse platform.