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Article

Sustainability Model for the Continuous Intention to Use Metaverse Technology in Higher Education: A Case Study from Oman

by
Said Salloum
1,
Amina Al Marzouqi
2,
Khaled Younis Alderbashi
3,
Fanar Shwedeh
4,
Ahmad Aburayya
4,*,
Mohammed Rasol Al Saidat
5 and
Rana Saeed Al-Maroof
6
1
School of Science, Engineering, and Environment, University of Salford, Salford M5 4WT, UK
2
Department of Health Service Administration, College of Health Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
3
Department Chair-Professional Postgraduate Diploma in Teaching, City University Ajman, Ajman P.O. Box 18484, United Arab Emirates
4
MBA Department, Business Administration College, City University Ajman, Ajman P.O. Box 18484, United Arab Emirates
5
Computer Centre, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
6
English Language & Linguistics Department, Al Buraimi University College, Al Buraimi 512, Oman
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(6), 5257; https://doi.org/10.3390/su15065257
Submission received: 20 February 2023 / Revised: 7 March 2023 / Accepted: 14 March 2023 / Published: 16 March 2023
Browse Figures
Review Reports Versions Notes

Abstract

:
This paper investigates adopting metaverse in academic institutions based on a conceptual model. The proposed conceptual model was created to empower future perspectives of the metaverse system. Even though the metaverses system was just recently launched, few attempts have been made to evaluate its impact on the educational sector. This paper attempted to determine the impact of metaverse system by providing a conceptual model that encompasses innovativeness, context awareness, perceived enjoyment, ubiquity, complexity, and value. The information was gathered using an online questionnaire completed by 953 people. The findings indicate that an innovative academic environment can influence teachers’ and students’ attitudes toward new technology. Understanding how the metaverse system functions as an innovative educational tool can influence students’ views regarding the use of new technology, assisting higher education institutions in developing regulations that enhance the learning process. As a result, the study discovered that the moderating impact of’ innovativeness is critical since it contributes to the pervasiveness of users’ perceptions of adopting new technology. The findings show that inventiveness is important in determining the metaverse system’s effectiveness. However, apparent ubiquity appears to have a less effective function in promoting the use of the metaverse system. The factors of context-awareness, perceived complexity and perceived enjoyment possess a substantial impact on the metaverse system’s adoption. Researchers have gained some insight into how university leaders can use current findings to spread awareness about the metaverse system in the educational environment by organizing seminars and workshops, offering incentives for teachers to use it, and allowing experts to assist teachers in better understanding the system’s benefits.

1. Introduction

Metaverse is a term that involves “meta and verse”. Meta refers to something that is transcendent and virtual [1]. Verse means world or cosmos. The term was originally used in a novel titled (Snow Crash), which was released in 1992. Metaverse was initially used to refer to the online virtual world, but it has since grown into a mean which connects the online world with the offline one in the post-pandemic era. Metaverse was developed based on the virtual world in the years leading up to the COVID-19 pandemic. Still, it garnered additional attention following to overcome the constraints, such as the constraints related to individual demands and external activities. It’s a concept that is rapidly gaining traction. Members of Generation Z are the primary users of Metaverse. Individuals born after 1995 belong to Generation Z, which has distinct traits from preceding generations [2,3,4,5].
Because the metaverse system was only recently developed to address issues in online learning environments, few studies have focused on its adoption and acceptance by users in developed countries. In fact, according to recent metaverse studies, users appear to value Metaverse as a tool for more easily managing the learning environment [6,7]. Therefore, this study represents an important potential for metaverse technology use and acceptance, attempting to investigate the impact of the innovative three-dimensional technology.
According to a previous study, it is critical to have a clear grasp of how users perceive the value of technology [8]. Some research has focused on the technology’s innovativeness as an external factor rather than a moderator factor [9,10]. More crucially, research has emphasized immersion, engagement, and imagination as three key components of the flow theory [11,12]. On the other hand, previous research has never attempted to link immersion, engagement, and imagination to perceived enjoyment and worth. On the other side, context awareness with apparent ubiquity and perceived value. Exploring the metaverse system’s creative elements by focusing on users’ inventiveness is thus a good idea.
Even though some studies have focused on the significance of metaverse, none of them have produced satisfactory results based on a comprehensive model which incorporates all critical variables. The fact that the Metaverse is a newly constructed system is one of the key reasons for the absence of a full understanding of its effectiveness. This study aims to prevent effective techniques and a solid conceptual model from being used to research the Metaverse system’s effects in depth. To that end, the current study will look into a set of predictors of perceived metaverse system value, such as reported delight, perceived complexity, perceived ubiquity, and users’ innovativeness, and see if and how this value influences real usage in relatively unexplored circumstances.

2. Review of Literature

2.1. The Development of the Metaverse System

The Metaverse, a three-dimensional virtual world, is being examined as a solution to contemporary issues and obstacles in online learning. In the Fourth Industrial Revolution era, the Metaverse evolved into a condition similar to virtual reality. Metaverse has unique elements, such as human interactions with social and economical software in a virtual space of three dimensions. In this world, real characters are capable of engaging in activities of economic, cultural, and social natures. These activities are quite comparable to those found in the real world. The Metaverse system is the playground of ‘Generation MZ’, which is the millennial generation born during the period (early 2000s–early 1980s). One of the distinctive elements of the Metaverse system is the endless options for activating virtual space [6,7].
The Metaverse system was distinct from earlier technologies such as virtual environments and reality. The advancement of Metaverse systems is closely linked to virtual reality technologies, which have accelerated due to increased processing efficiency. Virtual reality users, for example, interact with a virtual environment as if it were the actual world. Users can also detect the presence of items in virtual worlds [13,14]. Virtual reality software and technology are used to complete the exercise without regard to time or space constraints, resulting in the development of new applications. Virtual Reality (VR) applications allow users to engage with virtual targets in VR settings using various devices [12].

2.2. The Importance of Metaverse in Education

Metaverses have the potential to change the way universities and academic work are commercialized [15]. Previously, university lectures were delivered live to a small audience by a genuine lecturer—a single commodity. The Metaverse will provide students with a more “cyber-physical” academic experience, in which the virtual and physical worlds collide. Students can navigate smoothly between online shops and lecture halls using a single avatar, thanks to metaverses. There’s a chance that the Metaverse will emerge due to some form of standard university teaching. Many students may choose cyber-physical colleges over traditional brick-and-mortar universities. They could learn from virtual experiences from various worldwide universities in the Metaverse.
Academics and developers may collaborate in the future to construct trainers that can assist teachers in their metaverse reality [16,17]. Researchers are investigating the Metaverse system’s importance in the educational area due to its recent development [18]. Han & Noh [16] conducted a study to examine the attitudes and needs of higher education instructors regarding metaverse-based education, focusing on its pedagogical relevance. The study’s main goal is to determine how teachers feel about using the Metaverse system in higher education. They conclude that the Metaverse can be employed as a supplement to traditional delivery methods.
Additionally, teachers felt systems and assistance connected to the classroom environment, such as curricular material and teaching practices, where required. Another study looked into the impact of the Metaverse system as an innovative technology in local universities. The Metaverse system has been proposed as a creative solution to the dilemma that professors and students encounter in online learning environments. One of the notable issues that can be resolved is the difficulty that teachers have in transmitting specific classes, and the level of pleasure students have with online learning [6].

2.3. Smart PLS Technique

In this study, the Smart PLS technique was applied to validate the proposed research model, which included eight latent variables: immersion, interaction, imagination, context awareness, user innovativeness, perceived enjoyment, complexity, ubiquity, perceived value, and behavioral intention to use. These variables were operationalized through observed variables that were measured using a questionnaire administered to higher education students. To ensure the validity and reliability of the data collected, the study followed rigorous data collection and analysis procedures. Prior to data collection, the questionnaire was pilot-tested with a small sample of participants to identify any potential issues or errors. Then, data were collected from a larger sample of participants using a convenience sampling method. After data collection, the Smart PLS model was validated using a series of statistical tests, including the assessment of the model’s measurement properties (reliability, convergent validity, and discriminant validity) and the evaluation of the structural model’s goodness of fit. The results of the Smart PLS model validation revealed that the proposed research model had acceptable measurement properties and demonstrated a significant effect on the continuous intention to use Metaverse technology in higher education.
The Smart PLS model validation technique provided a rigorous and reliable scientific approach to investigating the factors that influence the continuous intention to use Metaverse technology in higher education. The study’s findings have important implications for the design and implementation of Metaverse technology in higher education and can inform future research in this area.

3. The Theoretical Framework

This work identified the acceptance of metaverse. The users’ innovativeness functions as a moderator that can enhance the use of the innovation. This paper concentrates on three theoretical constructs: perceived enjoyment, complexity, and ubiquity. These are considered determinants of perceptions toward the intention to utilize the system of the Metaverse.

3.1. Immersion, Interaction, and Imagination and Perceived Enjoyment

Immersion, interaction, and imagination are the three significant features of technology. Immersion encourages users to live in the virtual world, stimulating their imagination through interaction with the virtual world. These three features positively impact learning motivation and users’ attitudes in the environment deemed virtual [19,20]. Immersion is the users’ consciousness to enter other temporal and spatial dimensions. As for interaction, it has a relation with the physical or sensory actions of users. It is detected as a result of using technology. Imagination is connected with the thinking ability of users which is triggered to have the innovation perceived [21]. The immersion in activity implies that technology users have shifted their full attention to the concerned activity and ignored any information that is unnecessary and sensory. The sense of presence is deemed as the characteristic that is most noticeable in a virtual environment, which enables users to artificially create an environment that is much similar to the real world [11,12].
Perceived enjoyment is the level to which the concerned users feel enjoyment when performing the task. It may also refer to the extent to which the concerned users consider themselves satisfied with the virtual environment. Perceived enjoyment is added because it has been evaluated as a factor of qualitative nature that conveys the users’ feeling of depression, joy, hate, or disgust, which appear as a consequence of using technology, enabling users to act in a particular way [22,23,24]. The perceived enjoyment of technology users can influence the intention and intensity of using the technology because convenience and enjoyment of using technology will enable users to develop a positive perception of the application, resulting in initial comfort [24,25].

3.2. The Perceived Complexity

This complexity is the level to which the concerned users consider the innovation as relatively hard to use and understand. The relevant articles suggest that the degree of complexity is connected with TAM’s two factors of perceived easiness of use along with usefulness. They possess a positive effect on users’ attitudes. The degree of complexity stood in opposition to ease of use along with the perceived usefulness that remarkably represents an advantage that encourages users to use technology and predict the users’ intention [26,27]. Accordingly, the perceived complexity is added to investigate the perceived consequences that appear as a result of using technology.

3.3. Context Awareness and the Perceived Ubiquity

Research on context awareness and ubiquity has shown that they positively impact the acceptance of technology and are crucial aspects that can measure the acceptance of it. Both are deemed instrumental in terms of driving the use of those systems. As indicated by [28], ubiquity and context awareness are deemed as new research areas which have rarely been addressed. That may have been accounted for due to its unexplored nature in terms of technology adoption [29].
Context-awareness refers to the status of a specific entity. This entity may be a place, individual, computational, or physical item that enhances the communication among participants and technology. Context has been defined as having reference to the location that identifies accessible devices & nearby hosts which are changing over time. Context enhances applications programs/software with those abilities to examine the concerned environment and show reactions based on the concerned environment in terms of one’s location, identity, and the resources that are reachable by one. Those three things are significant elements of context [30,31,32,33].
On the other hand, technological developments led to remarkable growth in ubiquitous use. This growth is affected directly by context awareness. Being engaged regardless of the spatial and temporal limitations shall lead to a better technology use. Spatial flexibility along with time convenience are deemed as key elements of using innovational technology.
Refs. [8,34,35] added that ubiquity is a major factor affecting decision-making behavior. They added that ubiquity must be incorporated with perceived easiness of use along with the technology usefulness Refs. [34,35,36] asserted that ubiquity strengthens the attitudes of users toward the technology being used and the trust of users in such use. Similarly, Ref. [37] has shown that the perceived positively influences behavioral intentions to use technology. More importantly, ubiquity has significant benefits, including continuity, speed, immediacy, portability, search ability, mobility, and reachability. Finally, through the mobile factor of ubiquity—that is not limited to a certain time and space—the capability of users and the public to obtain the ability to access the innovative services affect the individuals’ perceptions for performance, intention to use technology, and effort expectations [29].

3.4. Perceived Value

This value is the users’ views about the cost–benefit trade-off. The users usually perceive the perceived value as the maximum utility, assuming that the benefits are greater than the cost. The perceived value is dependent on the theory of utility which is used as an indicator of usage intention [8]. Based on previous studies, the perceived value positively impacts the users’ satisfaction and loyalty. It possesses an important impact on the intention to use the concerned technology along with the intention to use [8,38,39].

3.5. Users’ Innovativeness

The theory of diffusion of innovations suggests that individuals respond in a different manner to new items and ideas [40]. It suggests that individuals are driven by their natural innovativeness. The innovativeness of users is a personal characteristic that distinguishes some users who are more open inherently to the use of modern innovations, in addition to technologies. Some users tend to be more resistant to any change. As such, personal innovativeness is the level to which a certain person possesses willingness to use a new technology. Being early technology adopters can be leaders and pioneers in using innovations. Perceiving it as being [41,42]. The feature of innovativeness is highly dependent on the users’ natural predisposition toward attempting new technologies, which may influence their innovativeness toward recent technologies. Hence, identifying the characteristics of users is something deemed significant in digital information and the virtual environment because it helps measure innovative technologies’ degree of success [9,10]. Even though innovativeness was addressed as an external factor in the previous article, the current study considers users’ innovativeness as a moderator that can enhance the use of the Metaverse. This attempt is consistent with previous studies which examine innovativeness as having a positive impact on the relationship existing between new involvement or new product and users’ intention to use and buy technology [43,44].
As mentioned in the text above, the current article investigated the acceptance and adoption of the Metaverse proposing the hypotheses below
H1. 
Immersion, imagination, and interaction possesses a positive impact on the perceived enjoyment.
H2. 
Context awareness possesses a positive impact on the perceived ubiquity.
H3. 
User innovativeness possesses a positive impact on affects the MS.
H4. 
Perceived enjoyment possesses a positive impact on the perceived MS value.
H5. 
Perceived complexity possesses a negative impact on the perceived MS value.
H6. 
Perceived ubiquity possesses a positive impact on the perceived MS value.
H7. 
Perceived value positively affects the intention to use MS.
M1. 
User innovativeness possesses moderating impacts on the relationship between the perceived enjoyment and the perceived MS value.
M2. 
User innovativeness possesses moderating impacts on the relationship between the perceived complexity and the perceived MS value.
M3. 
User innovativeness possesses moderating impacts on the relationship between perceived ubiquity and the perceived MS value.
The model in this paper was created based on the hypothesis and is shown in the Figure 1 that is shown below.

4. Methodology

4.1. Data Collection

Data was obtained during the period (10 January 2022 till 29 April 2022)/i.e., during the academic year 2021/2022, specifically the winter semester. That was carried out through the use of surveys that were passed online to the students who were enrolled at Al Buraimi University College. This college is abbreviated in (BUC). It’s located in Oman. The study has received ethical clearance from the university targeted. The students who were interested in participating in this paper received an e-mail message. This message includes data about the goal of this paper and a link to access the concerned survey. The link of the survey was passed to pupils through the Fb and WhatsApp groups of BUC in order to raise the response rates. The participation of the respondents was voluntary. The study department randomly distributed 1000 questionnaires, with respondents answering 953 of them, resulting in a 95% response rate. Forty-seven of the submitted survey forms, though, were rejected owing to incompleteness. As a result, the survey team looked at 953 questionnaires that were correctly filled and deemed useful. The inclusion of sole students could be attributed to the reason that students are the individuals who can significantly impact the research’s usefulness. When technology fails to meet the needs of students, colleges, and universities can substitute it with a more useful instrument. Teachers, on the other side, may find it more convenient to accept new technologies than students due to their extensive expertise.
Students may have acquired information about Metaverse through social media or colleagues. A total of 953 students filled in the survey. Thus, the response rate is suitable for meeting the study’s goals as added by the researcher in Ref. [45]. That is because when the population consists of 1500 individuals, the sample must be 306 ones. The sample chosen in this work (953) is much greater than the minimum requirement. As a result, the assessment with Structural Equation Simulation (SEM) is appropriate for the sample of the present paper [46]. SEM aims at testing the developed hypotheses. In terms of the developed hypotheses, they were mostly founded on well-known theories but frequently modified to metaverse definitions. The researcher’s team used SEM, the SmartPLS Version (3.2.7), to evaluate the model of measurement. In terms of the resulting path model, it was used for additional analysis.

4.2. Demographic (Personal) Data

Such data is shown in Table 1. Male students represent 58% of the whole sample. The females represent 42% of the whole sample. The ones whose ages are between 18–29 years represent 76% of the respondents. The ones who are older than 29 years represent 24% of the sample. Most of the responders are holders of a university degree, 77% of the respondents possess a BA degree, and 23% of the students possess an MA degree. The purposive technique of sampling was adopted due to the easiness in contacting the respondents. The respondents participated voluntarily in this work as per [47]. By meeting the criterion of voluntariness, the study can enhance the reliability of the student responses regarding the use of Metaverse technology. When students participate voluntarily, they are more likely to provide honest and accurate responses to the survey questions, which can improve the quality of the data collected and the validity of the study’s findings [47].
The sample consists of several students chosen from several faculties. Those students vary in terms of age. They were enrolled in various programs. They include BA and MA students. IBM SPSS/Ver. 23 was utilized for analyzing the obtained data of demographic nature. The latter data is shown in the table below.

4.3. Instrument

This work offered a survey for collecting data to have the developed hypothesis tested. To ensure that this survey has high validity, modifications were made to the items in this survey by faculty members. The survey has twenty items and targets eight variables. The Table 2 identifies those variables.

4.4. The Pilot Study

The instrument’s reliability was measured through carrying out a (pilot study). About one hundred students were chosen in a random manner from the population for letting them participate in this pilot study. Then, the Cronbach alpha coefficient values were calculated. They represent the internal reliability of the survey. They must be equal to 0.70 or greater than this value to consider them accepted [55]. They are all accepted. They indicate that the survey has a high reliability. They were calculated by using IBM SPSS Statistics version No. 23. The Table 3 displays those values for the seven variables.

4.5. The Structure of the Survey

The researcher passed the questionnaire forms to the sample chosen from (BUC) in Oman.
The forms of the questionnaire were passed to the students. This questionnaire has three main parts:
  • Section one: It collects personal data from the participants.
  • Section two: It highlighted the two items showing the general question of metaverse technology in education.
  • Section three: It included eighteen items demonstrating immersion, interaction, imagination, context awareness, user innovativeness, and perceived enjoyment, complexity, ubiquity, and value.
The answers to the (twenty items) were examined based on using a Likert scale that includes five categories which represent five scores. This scale involves five categories for rating the answers. Those categories are:
Disagree, strongly disagree, agree, neutral and strongly agreed.

5. Discussion and Findings

5.1. Analysis of the Obtained Data

The gathered data was processed through carrying out an assessment that includes two steps. This assessment involves structural models and measurement models [56]. In terms of the partial least squares-structural equation modeling (PLS-SEM), it was employed for having the collected data analyzed [57,58]. It was utilized along with using the SmartPLS V.3.2.7 software. PLS-SEM was employed for meeting several goals. It is considered the perfect model [59]. The PLS-SEM [60] can be utilized for conducting exploratory investigations. PLS-SEM can be used for analyzing the whole model as one entity [60,61]. PLS-SEM allows one to make accurate computations in a sequential manner. It allows doing a concurrent analysis for the concerned structural models and measurement ones [60].

5.2. Convergent Validity

Researcher in Ref. [62] adds that construct reliability along with validity should be addressed when assessing the model used for measurement. The Cronbach’s alpha coefficient values in this work fall under the following range (0.787–0.901). They represent the construct reliability of the survey. They are displayed in the Table 4. They are greater than 0.7 [63]. Based on the Table 4, composite reliability (CR) has numbers that range between 0.782 and 0.893. Those numbers are greater than 0.7 [64]. Alternatively, academics should calculate the Dijkstra-Henseler’s rho (pA) coefficient for having the construct reliability measured [62]. The reliability coefficient values ρA-, e.g., CA and CR—must be 0.70 or more in exploratory research. They must be more than 0.80 or 0.90 in more established phases [63,65,66]. ρA value of every variable exceeds 0.70, as shown in the Table 4. Those results affirmed construct reliability. In terms of all constructs, they are error-free.
To have the convergent validity identified, AVE (average variance extracted) must be measured. To have the latter validity measures, the factor loading must be measured too [62]. The recommended value (0.7) was maintained less than the values of the factor loadings based on the Table 4. AVE yielded values that fall under the range (0.584–0.786). Those values are more than 0.5 as displayed in the Table 4. The constructs show a convergent validity that is high.

5.3. Discriminant Validity

In terms of the discriminant validity, two parameters should be measured. The first one is the Fornell-Larker criterion. The second one is the Heterotrait-Monotrait ratio that is abbreviated as (HTMT) [62]. The first parameter validates the criteria, as all AVEs and their associated square roots are deemed higher than their correlation with the other constructs, as shown in the Table 5.
Table 6 presents the ratio of HTMT. Based on the latter table, the minimum value (0.85) is higher than the value of every variable [67]. Hence, the HTMT ratio is identified. These results were utilized for measuring the discriminant validity. Based on the findings, there is not any difficulty in terms of the evaluation of the model’s validity along with reliability. Hence, one can evaluate the structural model through the utilization of the gathered data.

5.4. Model Fit

In terms of the fit measures mentioned in this work, they are deemed available through SmartPLS. In terms of the model fit in PLS-SEM, it’s displayed through the standard root mean square residual that is abbreviated as (SRMR). It’s also displayed through exact fit criteria, d G, d ULS, and Chi-Square. It’s also displayed through NFI, and RMS theta. In terms of SRMR, it stands for the variation between the correlations being observed and the model representing the correlation matrix, with values that are lower than 0.08 regarded as good measures of model fit [68]. The model fit deemed good is shown by having NFI values that are more than 0.90 [69]. The NFI is a ratio that represents the Chi2 value of the designed model’s to the benchmark or the model deemed null [70]. It is not considered as a model fit measure. That is because the greater the value of the parameters, the more the NFI shall be [71]. The d ULS, squared Euclidean distance and the geodesic distance in addition to d G are deemed as metrics that represent the discrepancy across composite and empirical factor model covariance matrices [68,72]. RMS theta is used for analyzing the level of the outer model residual correlation. It’s deemed relevant to the models that are reflective [73]. The finer the model of PLS-SEM, the closer the value of RMS theta shall be to 0, and the values that are less than 0.12 are deemed as being a good fit. As for everything else shown, it is not deemed a good fit [73]. Based on Ref. [73], the saturated model assesses the relationship between the whole constructs, while the model being estimated considers the overall impact and the structure of the model.
Table 7 indicates that the RMS theta value was 0.066, suggesting that the PLS-SEM model’s association with goodness-of-fit was important for having the validity of the global PLS model confirmed.

5.5. Testing the Hypotheses through the Use of PLS-SEM

The model of the structural equation was employed along with employing the Smart PLS and the highest probability estimation [74,75,76]. That was done to identify the interdependence of several structural model theoretical constructs [77]. In terms of the developed hypotheses, they were tested in this technique. The model shows a high predictive ability [78], as displayed by the Table 8 and the Figure 2. It accounts for about 70% of the variability percentage within the intention to employ metaverse.
Table 9 displays the p, t, and beta (β) values of every hypothesis tested. They are calculated through the utilization of the method of PLS-SEM. The analysis of data offered support to the validity and acceptance of hypotheses No. 2, 1, 4, 5, 3, and 7. As for H6, the researchers rejected it.
In terms of the perceived enjoyment (PE), it significantly affects immersion, Interaction (IMM), and imagination (β = 0.241, p < 0.05). That supports the acceptance of hypothesis H1. The relationships between Context Awareness (CON) and Perceived Ubiquity (PUB) (β = 0.699, p < 0.001) were deemed significant. Thus, H2 is accepted. Perceived enjoyment (PE) and perceived complexity (PC) significantly affect perceived value (PV) (β = 0.746, p < 0.001) and (β = 0.202, p < 0.05) in a respective manner. Thus, H4 and H5 are accepted. The relationships between perceived ubiquity (PUB) and value (PV) (β = −0.047, p = 0.533) do not have any effect that is classified as significant. Hence, H6 got rejected. The user innovativeness (UI), and perceived value (PV) have effects that are significant on the intention to utilize Metaverse (β = 0.776, p < 0.001) and (β = 0.814, p < 0.001) in a respective manner. Therefore, H3, in addition to H7 got accepted.
User innovativeness (UI) possesses a moderating impact on the relationship existing between the perceived enjoyment (PE), complexity (PC), and ubiquity (PUB), and from one hand and perceived metaverse value from another hand, the results were obtained from a group of constructs that were unfolded through a special type of further testing. The influence of these variables on the strength or nature of the relationship among both types of dependent variables and independent ones can be illustrated depending on the moderating effect. Table 10 indicates that the analysis of the results of the present study has excluded M2. Hence, other hypotheses were approved, meaning that the Perceived Complexity (PC) and Perceived Value (PV) of constructs’ relationship are not influenced by User innovativeness (UI). However, Perceived Enjoyment (PE), Perceived Ubiquity (PUB), and Perceived Value (PV) have witnessed a positive effect that is significant because it creates the relationship existing between PE, PUB, and PV and the aspect of User innovativeness (UI). As for UI, it was employed as a moderator.

5.6. Importance-Performance Map Analysis (IPMA)

We used the IPMA as a sophisticated approach in PLS-SEM with the behavioral intention as the objective variable in this paper. As added by [79], IPMA helps in comprehending PLS-SEM analysis results. It involves the average value of the underlying variables and their associated indicators (i.e., measure of performance) in addition to assessing the path coefficients (i.e., importance measure) [79]. Based on IPMA, the total impact stands for the significance of the previous factors in having the target factor (i.e., the behavioral intention to utilize MS) defined. In contrast, the average of the latent construct values represents their level of performance. The IPMA findings are presented in Figure 3. The performance & significance of the nine variables (Immersion, Interaction, Imagination, Context Awareness, User Innovativeness, and perceived enjoyment, complexity, ubiquity, and value) were determined in this study. Based on the results, the perceived value has the maximum standard for significance and performance measures. It is also worth noting that Perceived Enjoyment hold the second rank in terms of importance along with performance measures. Furthermore, while Immersion, Interaction, and Imagination have the third-highest score on the important measure, it possesses the lowest value on the performance measure. Although Perceived Ubiquity has the lowest significance measure, it is significant to remember that on the measure of performance, it has the maximum equivalent score to the Context Awareness.

6. Discussion of Results

In terms of essential factors involving a new characteristic of the metaverse system, a model was suggested to clarify the acceptance of the Metaverse in educational environments. Interaction, imagination, and perceived enjoyment were all deemed significant factors of the metaverse system’s acceptance in the model. Furthermore, in the context of the metaverse system, technology context awareness, perceived ubiquity, and perceived complexity have been explored, making the current analysis unique. The employment of innovativeness as a moderator, among other relationships, increases the possibilities for a deeper comprehension of the metaverse system’s outstanding factors. Using structural equations and the PLS method, all of the suggested models and hypotheses were tested at the same time.
The findings reveal that interaction, imagination, and context awareness substantially impact the acceptance of the metaverse system, owing to the metaverse system’s new features that can significantly enhance the educational environment. The current findings appear consistent with prior research that showed that these three aspects had a beneficial impact on users’ perceptions of the virtual environment [19,20]. Due to the presence of these critical elements, virtual environments allow users to construct a world comparable to the real world by employing artificially manufactured environments [11,80]. The findings revealed that innovativeness has a large moderator effect, consistent with earlier research. In earlier studies, the characteristics of innovativeness have a significant impact on technological adoption and acceptability. In reality, innovativeness is linked to individuals’ desire to explore new technologies, which may influence the acceptance of innovative technology. Innovativeness may play a key part in its adoption in some countries where it significantly impacts the educational landscape [40,41,51].
The impact of perceived value has been supported in the current paper’s analysis due to the cost–benefit and trade-off analysis. The current findings are deemed consistent with the literature study, which shows that perceived value favors the relationship between user adoption and user perceptions. Cost benefits and maximal utility significantly influence perceived value, which has a significant impact on technology adoption [8,38,39]. Even though all of the prior hypotheses were validated and approved, the hypothesis relating to apparent ubiquity was denied. The current conclusion contradicts prior research that claimed that the absence of time and space constraints could lead to higher technology adoption due to time convenience and spatial flexibility. According to studies, the ubiquity factor is improved by users’ views and trust in technology. Aside from its advantages in terms of time and space, ubiquity has had a substantial impact on earlier research in economics. Furthermore, studies claim that users’ and the general public’s perceptions of performance, effort expectations, and propensity to use technology are influenced by their capacity to access innovative services. [8,29,34,35].

7. Research Implications

7.1. Theoretical Implications and Practical Ones

Based on the findings, the proposed contracts in the conceptual model support the usage of metaverse in the educational setting. Among all of these structures, the innovativeness factor has been discovered to significantly influence the suggested technology’s adoption. The consequences of this can be linked to a variety of factors. One is its normative influence on educational institutions, which may inspire them to adopt this type of innovation. Teachers may be more concerned with beneficial educational results and rewards while using the metaverse system because it can provide various tools not found in traditional classrooms, avoiding a monotonous and less intuitive classroom setting. As a result, the normative influence may have a greater impact on teachers’ teaching styles. From the standpoint of students, using the metaverse system may lead to more effortless academic activities that demand less time and provide a more delightful environment.

7.2. Managerial Implications

The current study’s findings have substantial practical consequences because they provide important practical instructions for university administrators to encourage instructors’ adoption and use of the metaverse system. Teachers’ willingness to employ the latest technologies is affected by perceptions of enjoyment and complexity, both of which are important determinants. The current study has demonstrated that the new system’s ease of use and delightful surroundings are undeniable, providing future opportunities to use it. Similarly, by demonstrating the creative aspects of such technology, practitioners can help teachers enhance their perspectives of metaverse system adoption. The important characteristics could be related to usefulness, which is directly tied to academic success. Teachers can achieve their objectives in this regard by supporting a convincing teaching technique.
As a result, university officials should hold workshops to inform teachers about the benefits of using the metaverse system. The main purpose of these workshops is to display favorable attitudes toward building the educational system and evaluating faculty performance assessment criteria by providing a positive incentive for those who begin to use it. Faculty who have had success in the classroom can be asked to share their experiences with other teachers in order to stimulate them. Universities should designate people to address teachers’ inquiries about the metaverse system since they require support.

8. Limitations and Suggestions for Articles in the Future

The research is limited to a theoretical model with a set of constructs that can be developed for future research. The data collection is limited to a group of Middle Eastern students. As a result, information from literature studies can be used to develop comparison studies. Because the study only looked at one e-learning system, the conclusions cannot be applied to other educational systems. Future studies will use the existing model to assess the efficacy of different systems. In terms of moderating factors, the current study used innovativeness as a decisive element in moderating the relationship, among other things. As a result, future studies can include other notable criteria as moderators depending on the investigated technology.

9. Conclusions and Articles in the Future

The use of Metaverse technology in higher education has been gaining significant attention in recent years. Metaverse technology refers to virtual reality platforms that allow users to interact with digital environments and other users through avatars. Oman is one country that has been exploring the potential of Metaverse technology in higher education. The results of the study showed that the use of the Metaverse-based educational platform had a positive impact on students’ learning outcomes. Students reported that the platform was engaging, immersive, and helped them to better understand complex concepts. The platform also allowed for more personalized learning experiences, as students could interact with instructors and peers in real-time. Overall, the study suggests that Metaverse technology has the potential to revolutionize the way that higher education is delivered in Oman and beyond. By providing students with immersive, interactive, and personalized learning experiences, Metaverse-based educational platforms could help to bridge the gap between traditional classroom-based education and the digital age. To summarize, the use of the metaverse can improve the educational system all over the world because it is easily accessible to individuals all over the world. Creating new options for students from other countries to attend courses creatively is critical to developing prominent universities using the power of digital technology. Even though the quality and quantity of innovative technologies have improved, the metaverse system has distinguishing traits that will lessen faculty opposition to adopting technology. Most educators, teachers, professors, and stakeholders might be unaware of the metaverse system’s distinctive features, not to mention the prospective and innovative applications that emerge as a consequence of using this technology. It is well-known that researchers specialized in CS and education would have a comprehensive image about the essence and reality of metaverse and the way in which it can be used for meeting educational goals. It should be noted that it’s highly appreciated to have studies in the future about metaverse. Such studies must address the effectiveness of the Metaverse in other scientific fields such as technology, health, and economy. Accordingly, future studies can significantly report on the use of the Metaverse in health improving the use of certain medical issues. Furthermore, studies can add remarkable information on how the Metaverse will support effectively the entrepreneurs, enterprises and the world economy shortly.

Author Contributions

Conceptualization, S.S., F.S. and A.A.; Methodology, A.A.M., A.A. and M.R.A.S.; Software, S.S. and M.R.A.S.; Validation, A.A.M., K.Y.A. and A.A.; Formal analysis, K.Y.A.; Investigation, S.S.; Resources, F.S.; Data curation, K.Y.A., F.S. and M.R.A.S.; Writing—original draft, S.S., F.S., A.A. and R.S.A.-M.; Writing—review & editing, A.A.M.; Supervision, A.A.; Project administration, M.R.A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data available on request from the authors.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Almarzouqi, A.; Aburayya, A.; Salloum, S.A. Prediction of User’s Intention to use Metaverse System in Medical Education: A Hybrid SEM-ML Learning Approach. IEEE Access 2022, 10, 43421–43434. [Google Scholar] [CrossRef]
  2. Duan, H.; Li, J.; Fan, S.; Lin, Z.; Wu, X.; Cai, W. Metaverse for social good: A university campus prototype. In Proceedings of the 29th ACM International Conference on Multimedia, Virtual, 20–24 October 2021; pp. 153–161. [Google Scholar]
  3. van der Merwe, D. The Metaverse as Virtual Heterotopia. In Proceedings of the 3rd World Conference on Research in Social Sciences, London, UK, 21 May 2021. [Google Scholar]
  4. Park, S.; Min, K.; Kim, S. Differences in learning motivation among Bartle’s player types and measures for the delivery of sustainable gameful experiences. Sustainability 2021, 13, 9121. [Google Scholar] [CrossRef]
  5. Park, S.; Kim, S. Identifying World Types to Deliver Gameful Experiences for Sustainable Learning in the Metaverse. Sustainability 2022, 14, 1361. [Google Scholar] [CrossRef]
  6. Jeon, J.H. A Study on Education Utilizing Metaverse for Effective Communication in a Convergence Subject. Int. J. Internet Broadcast. Commun. 2021, 13, 129–134. [Google Scholar]
  7. Sang-kyun, K. Metaverse-Digital Earth. In World Floating, Plan; By Design Publishing: Hayden, ID, USA, 2020. [Google Scholar]
  8. Kleijnen, M.; De Ruyter, K.; Wetzels, M. An assessment of value creation in mobile service delivery and the moderating role of time consciousness. J. Retail. 2007, 83, 33–46. [Google Scholar] [CrossRef]
  9. Aroean, L.; Michaelidou, N. Are innovative consumers emotional and prestigiously sensitive to price? J. Mark. Manag. 2014, 30, 245–267. [Google Scholar] [CrossRef]
  10. Gao, Y.; Luo, H.; Zhang, Z.; Kang, L.; Chen, Z.; Du, J.; Kanehira, M.; Cao, C. Nanoceramic VO2 thermochromic smart glass: A review on progress in solution processing. Nano Energy 2012, 1, 221–246. [Google Scholar] [CrossRef]
  11. Bowman, D.A.; McMahan, R.P. Virtual reality: How much immersion is enough? Computer 2007, 40, 36–43. [Google Scholar] [CrossRef]
  12. Fang, Y.-M.; Huang, Y.-J. Comparison of the usability and flow experience of an exercise promotion virtual reality programme for different age groups. Behav. Inf. Technol. 2021, 40, 1250–1264. [Google Scholar] [CrossRef]
  13. Diemer, J.; Alpers, G.W.; Peperkorn, H.M.; Shiban, Y.; Mühlberger, A. The impact of perception and presence on emotional reactions: A review of research in virtual reality. Front. Psychol. 2015, 6, 26. [Google Scholar] [CrossRef] [Green Version]
  14. Schuemie, M.J.; Van Der Straaten, P.; Krijn, M.; Van Der Mast, C.A.P.G. Research on presence in virtual reality: A survey. Cyberpsychol. Behav. 2001, 4, 183–201. [Google Scholar] [CrossRef]
  15. Alawadhi, M.; Alhumaid, K.; Almarzooqi, S.; Aljasmi, S.; Aburayya, A.; Salloum, S.A.; Almesmari, W. Factors Affecting Medical Students’ Acceptance of the Metaverse System in Medical Training in the United Arab Emirates. South East. Eur. J. Public Health 2022, 5, 1–4. [Google Scholar] [CrossRef]
  16. Han, S.; Noh, Y. Analyzing Higher Education Instructors’ perception on Metaverse-based Education. J. Digit. Contents Soc. 2021, 22, 1793–1806. [Google Scholar] [CrossRef]
  17. Preston, J. Facebook, the metaverse and the monetisation of higher education. In Proceedings of the International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Mauritius, 7–8 October 2021. [Google Scholar]
  18. AlHamad, A.Q.; Alomari, K.M.; Alshurideh, M.; Al Kurdi, B.; Salloum, S.; Al-Hamad, A.Q. The Adoption of Metaverse Systems: A hybrid SEM-ML Method. In Proceedings of the 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Maldives, Maldives, 16–18 November 2022; pp. 1–5. [Google Scholar]
  19. Huang, H.-M.; Rauch, U.; Liaw, S.-S. Investigating learners’ attitudes toward virtual reality learning environments: Based on a constructivist approach. Comput. Educ. 2010, 55, 1171–1182. [Google Scholar] [CrossRef]
  20. Barrett, A.J.; Pack, A.; Quaid, E.D. Understanding learners’ acceptance of high-immersion virtual reality systems: Insights from confirmatory and exploratory PLS-SEM analyses. Comput. Educ. 2021, 169, 104214. [Google Scholar] [CrossRef]
  21. Jong, M.S.; Tsai, C.; Xie, H.; Kwan-Kit Wong, F. Integrating interactive learner-immersed video-based virtual reality into learning and teaching of physical geography. Br. J. Educ. Technol. 2020, 51, 2064–2079. [Google Scholar] [CrossRef]
  22. So, K.K.F.; Kim, H.; Oh, H. What makes Airbnb experiences enjoyable? The effects of environmental stimuli on perceived enjoyment and repurchase intention. J. Travel Res. 2021, 60, 1018–1038. [Google Scholar] [CrossRef]
  23. Liu, Z.; Park, S. What makes a useful online review? Implication for travel product websites. Tour. Manag. 2015, 47, 140–151. [Google Scholar] [CrossRef] [Green Version]
  24. Mohamad, M.A.; Hanafiah, M.H.; Radzi, S.M. Understanding tourist mobile hotel booking behaviour: Incorporating perceived enjoyment and perceived price value in the modified Technology Acceptance Model. Tour. Manag. Stud. 2021, 17, 19–30. [Google Scholar] [CrossRef]
  25. Venkatesh, V.; Bala, H. Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 2008, 39, 273–315. [Google Scholar] [CrossRef] [Green Version]
  26. Momani, A.M.; Jamous, M. The evolution of technology acceptance theories. Int. J. Contemp. Comput. Res. 2017, 1, 51–58. [Google Scholar]
  27. Taylor, S.; Todd, P.A. Understanding information technology usage: A test of competing models. Inf. Syst. Res. 1995, 6, 144–176. [Google Scholar] [CrossRef]
  28. Al-Nuaim, H. Context-aware mobile interface design for M-government. In User-Centric Technology Design for Nonprofit and Civic Engagements; Springer: Cham, Switzerland, 2014; pp. 171–186. [Google Scholar]
  29. Mensah, I.K.; Mwakapesa, D.S. The Impact of Context Awareness and Ubiquity on Mobile Government Service Adoption. Mob. Inf. Syst. 2022, 2022, 5918826. [Google Scholar] [CrossRef]
  30. Cao, Q.; Niu, X. Integrating context-awareness and UTAUT to explain Alipay user adoption. Int. J. Ind. Ergon. 2019, 69, 9–13. [Google Scholar] [CrossRef]
  31. Rocha, N.P.; Dias, A.; Santinha, G.; Rodrigues, M.; Rodrigues, C.; Queirós, A. Smart cities: Drivers to increase context-awareness based on a systematic review of the literature. In Trends and Applications in Information Systems and Technologies, Proceedings of the 9th World Conference on Information Systems and Technologies (WorldCIST’21), Azores, Portugal, 30 March–2 April 2021; Springer: Cham, Switzerland, 2021; pp. 609–618. [Google Scholar]
  32. Dey, A.K. Understanding and using context. Pers. Ubiquitous Comput. 2001, 5, 4–7. [Google Scholar] [CrossRef]
  33. Schilit, B.; Adams, N.; Want, R. Context-aware computing applications. In Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications, Santa Cruz, CA, USA, 8–9 December 1994; pp. 85–90. [Google Scholar]
  34. Okazaki, S.; Li, H.; Hirose, M. A Study of Mobile Advertising in Japan. J. Advert. 2009, 38, 63–77. [Google Scholar] [CrossRef] [Green Version]
  35. Okazaki, S.; Mendez, F. Perceived ubiquity in mobile services. J. Interact. Mark. 2013, 27, 98–111. [Google Scholar] [CrossRef] [Green Version]
  36. Ashraf, A.R.; Thongpapanl, N.; Menguc, B.; Northey, G. The role of m-commerce readiness in emerging and developed markets. J. Int. Mark. 2017, 25, 25–51. [Google Scholar] [CrossRef]
  37. Kim, S.; Garrison, G. Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Inf. Syst. Front. 2009, 11, 323–333. [Google Scholar] [CrossRef]
  38. Kuo, Y.-F.; Wu, C.-M.; Deng, W.-J. The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Comput. Human Behav. 2009, 25, 887–896. [Google Scholar] [CrossRef]
  39. Wang, Y.; Lin, H.; Luarn, P. Predicting consumer intention to use mobile service. Inf. Syst. J. 2006, 16, 157–179. [Google Scholar] [CrossRef]
  40. Rogers, E.M. Diffusion of Innovations; Free Press: New York, NY, USA, 2003; p. 551. [Google Scholar]
  41. Agarwal, R.; Prasad, J. A conceptual and operational definition of personal innovativeness in the domain of information technology. Inf. Syst. Res. 1998, 9, 204–215. [Google Scholar] [CrossRef]
  42. Yi, M.Y.; Fiedler, K.D.; Park, J.S. Understanding the role of individual innovativeness in the acceptance of IT-based innovations: Comparative analyses of models and measures. Decis. Sci. 2006, 37, 393–426. [Google Scholar] [CrossRef]
  43. Eastman, J.K.; Iyer, R.; Liao-Troth, S.; Williams, D.F.; Griffin, M. The role of involvement on millennials’ mobile technology behaviors: The moderating impact of status consumption, innovation, and opinion leadership. J. Mark. Theory Pract. 2014, 22, 455–470. [Google Scholar] [CrossRef]
  44. Li, G.; Zhang, R.; Wang, C. The role of product originality, usefulness and motivated consumer innovativeness in new product adoption intentions. J. Prod. Innov. Manag. 2015, 32, 214–223. [Google Scholar] [CrossRef]
  45. Krejcie, R.V.; Morgan, D.W. Determining sample size for research activities. Educ. Psychol. Meas. 1970, 30, 607–610. [Google Scholar] [CrossRef]
  46. Chuan, C.L.; Penyelidikan, J. Sample size estimation using Krejcie and Morgan and Cohen statistical power analysis: A comparison. J. Penyelid. IPBL 2006, 7, 78–86. [Google Scholar]
  47. Al-Maroof, R.S.; Salloum, S.A. An Integrated Model of Continuous Intention to Use of Google Classroom. In Recent Advances in Intelligent Systems and Smart Applications; Al-Emran, M., Shaalan, K., Hassanien, A., Eds.; Springer International Publishing: Cham, Switzerland, 2021; Volume 295. [Google Scholar]
  48. Burdea, G.C.; Coiffet, P. Virtual Reality Technology, 2nd ed.; John Wiley & Sons: New York, NY, USA, 2003. [Google Scholar]
  49. Venkatesh, V.; Davis, F.D. Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Manag. Sci. 2000, 46, 186–204. [Google Scholar] [CrossRef] [Green Version]
  50. Okazaki, S.; Molina, F.J.; Hirose, M. Mobile advertising avoidance: Exploring the role of ubiquity. Electron. Mark. 2012, 22, 169–183. [Google Scholar] [CrossRef]
  51. Lai, H.-M.; Chen, C.-P. Factors influencing secondary school teachers’ adoption of teaching blogs. Comput. Educ. 2011, 56, 948–960. [Google Scholar] [CrossRef]
  52. Anwar, A.; Thongpapanl, N.; Ashraf, A.R. Strategic imperatives of mobile commerce in developing countries: The influence of consumer innovativeness, ubiquity, perceived value, risk, and cost on usage. J. Strateg. Mark. 2021, 29, 722–742. [Google Scholar] [CrossRef]
  53. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef] [Green Version]
  54. Davis, F.D. User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. Int. J. Man-Machine Stud. 1993, 38, 475–487. [Google Scholar] [CrossRef] [Green Version]
  55. Nunnally, J.C.; Bernstein, I.H. Psychometric Theory; McGraw-Hill: New York, NY, USA, 1978; ISBN 0070474656. [Google Scholar]
  56. Urbach, N.; Ahlemann, F. Structural equation modeling in information systems research using partial least squares. J. Inf. Technol. Theory Appl. 2010, 11, 5–40. [Google Scholar] [CrossRef] [Green Version]
  57. Hair, J.F., Jr.; Sarstedt, M.; Ringle, C.M.; Gudergan, S.P. Advanced Issues in Partial Least Squares Structural Equation Modeling; SAGE Publications: Thousand Oaks, CA, USA, 2017; ISBN 1483377385. [Google Scholar]
  58. Acharjya, D.P. Behavioural intention of customers towards smartwatches in an ambient environment using soft computing: An integrated SEM-PLS and fuzzy rough set approach. Int. J. Ambient Comput. Intell. 2020, 11, 80–111. [Google Scholar]
  59. Goodhue, D.L.; Lewis, W.; Thompson, R. Does PLS have adavantages for small sample size or non-normal data? MIS Q. 2012, 36, 981–1001. [Google Scholar] [CrossRef] [Green Version]
  60. Barclay, D.; Higgins, C.; Thompson, R. The Partial Least Squares (PLS) Approach to Casual Modeling: Personal Computer Adoption Ans Use as an Illustration. Technol. Stud. 1995, 2, 285–309. [Google Scholar]
  61. Acharjya, D.P.; Acharjya, B. An integrated partial least square and rough set approach for studying pilgrimage attitude towards cultural heritage of Odisha. J. Ambient Intell. Humaniz. Comput. 2020, 1–17. [Google Scholar] [CrossRef]
  62. Hair, J.; Hollingsworth, C.L.; Randolph, A.B.; Chong, A.Y.L. An updated and expanded assessment of PLS-SEM in information systems research. Ind. Manag. Data Syst. 2017, 117, 442–458. [Google Scholar] [CrossRef]
  63. Nunnally, J.C.; Bernstein, I.H. Psychometric Theory, 3rd ed.; McGraw-Hill: New York, NY, USA, 1994. [Google Scholar]
  64. Kline, R.B. Principles and Practice of Structural Equation Modeling; Guilford Publications: New York, NY, USA, 2015; ISBN 1462523358. [Google Scholar]
  65. Hair, J.F.; Ringle, C.M.; Sarstedt, M. PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract. 2011, 19, 139–152. [Google Scholar] [CrossRef]
  66. Henseler, J.; Ringle, C.M.; Sinkovics, R.R. The use of partial least squares path modeling in international marketing. In NEW Challenges to International Marketing; Emerald Group Publishing Limited: Bradford, UK, 2009; ISBN 1848554680. [Google Scholar]
  67. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef] [Green Version]
  68. Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); SAGE Publications: Thousand Oaks, CA, USA, 2016. [Google Scholar]
  69. Hu, L.; Bentler, P.M. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychol. Methods 1998, 3, 424. [Google Scholar] [CrossRef]
  70. Lohmöller, J.B. Latent Variable Path Modeling with Partial Least Squares; Physica-Verlag: Heidelberg, Germany, 1989. [Google Scholar]
  71. Bentler, P.M.; Bonett, D.G. Significance tests and goodness of fit in the analysis of covariance structures. Psychol. Bull. 1980, 88, 588. [Google Scholar] [CrossRef]
  72. Dijkstra, T.K.; Henseler, J. Consistent and asymptotically normal PLS estimators for linear structural equations. Comput. Stat. Data Anal. 2015, 81, 10–23. [Google Scholar] [CrossRef] [Green Version]
  73. Henseler, J.; Dijkstra, T.K.; Sarstedt, M.; Ringle, C.M.; Diamantopoulos, A.; Straub, D.W.; Ketchen, D.J., Jr.; Hair, J.F.; Hult, G.T.M.; Calantone, R.J. Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organ. Res. Methods 2014, 17, 182–209. [Google Scholar] [CrossRef] [Green Version]
  74. Mouzaek, E.; Alaali, N.; Salloum, S.A.; Aburayya, A. An Empirical Investigation of the Impact of Service Quality Dimensions on Guests Satisfaction: A Case Study of Dubai Hotels. J. Contemp. Issues Bus. Gov. 2021, 27, 1186–1199. [Google Scholar]
  75. Al-Maroof, R.S.; Alnazzawi, N.; Akour, I.A.; Ayoubi, K.; Alhumaid, K.; AlAhbabi, N.M.; Alnnaimi, M.; Thabit, S.; Alfaisal, R.; Aburayya, A. The effectiveness of online platforms after the pandemic: Will face-to-face classes affect students’ perception of their Behavioural Intention (BIU) to use online platforms? Informatics 2021, 8, 83. [Google Scholar] [CrossRef]
  76. Almarzouqi, A.; Aburayya, A.; Salloum, S.A. Determinants of intention to use medical smartwatch-based dual-stage SEM-ANN analysis. Inform. Med. Unlocked 2022, 28, 100859. [Google Scholar] [CrossRef]
  77. Al-Skaf, S.; Youssef, E.; Habes, M.; Alhumaid, K.; Salloum, S.A. The Acceptance of Social Media Sites: An Empirical Study Using PLS-SEM and ML Approaches. In Proceedings of the Advanced Machine Learning Technologies and Applications, Cairo, Egypt, 20–22 March 2021; Springer International Publishing: Cham, Switzerland, 2021; pp. 548–558. [Google Scholar]
  78. Chin, W.W. The Partial Least Squares Approach to Structural Equation Modeling. In Modern Methods for Business Research; Psychology Press: London, UK, 1998; Volume 295, pp. 295–336. ISBN 1135684138. [Google Scholar]
  79. Ringle, C.M.; Sarstedt, M. Gain more insight from your PLS-SEM results. Ind. Manag. Data Syst. 2016, 116, 1865–1886. [Google Scholar] [CrossRef]
  80. Khlaisang, J.; Teo, T.; Huang, F. Acceptance of a flipped smart application for learning: A study among Thai university students. Interact. Learn. Environ. 2021, 29, 772–789. [Google Scholar] [CrossRef]
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Figure 1. The model proposed in this work.
Figure 1. The model proposed in this work.
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Figure 2. The values of the path coefficient of the created model (significant at ** p ≤ 0.01, * p < 0.05). The value of R2 is highlighted in red from the rest of the values that represent the path.
Figure 2. The values of the path coefficient of the created model (significant at ** p ≤ 0.01, * p < 0.05). The value of R2 is highlighted in red from the rest of the values that represent the path.
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Figure 3. IPMA results.
Figure 3. IPMA results.
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Table
Table 1. Data of demographic nature about the respondents.
Table 1. Data of demographic nature about the respondents.
Variable CategoriesFrequencyPercent %
GenderFemales40042%
Males55358%
Age18 to 2972576%
30 to 3914015%
40 to 49748%
50 to 59141%
Academic qualificationBA73477%
MA21923%
Table
Table 2. Measurement Items.
Table 2. Measurement Items.
Variable Definitions ItemsInstrumentSources
Immersion, Interaction, and ImaginationThe three IIIs are used to evaluate a virtual world that is not static virtual worlds are not static and associated with real-time interaction. The stimulating effects of user interaction with the metaverse world reflect an immersion in the visual and audio world where objects and scenarios are imaginary. In terms of the imaginary factor, it influences perceptual knowledge. It allows for carrying out a constructivist learning process (Burdea and Coiffet, 2003; Huang etIMM1MS enables me to live in a completely real-world experience.[19,48]
IMM2MS enables me to interact in a free manner regardless of the limitations of temporal or spatial nature.
IMM3MS enables me to use my imagination freely.
Context awarenessContext awareness refers to the location of the user and the host. It has a close relationship with the user’s place, environment, or state.CON1MS provides information based on users’ environment.[30]
CON2MS provides information based on users’ status.
Perceived EnjoymentIt’s the level to which the targeted users feel that technology is pleasurable as a system apart from any performance consequences. Enjoyment causes people to feel comfortable.PE1MS offers a fun environment.[49]
PE2MS offers an entertaining educational setting.
PE3I am ready to use MS because it provides me with a comfortable atmosphere.
Perceived complexityThe perceived complexity is the level to which MS is viewed as relatively hard to use and understand.PC1MS has complex features to be used by uses.[26]
PC2MS is difficult to be used annually.
Perceived ubiquityPerceived ubiquity indicates an individual’s perception toward flexibility in terms of space and time [35], p. 98). It indicates that there is a kind of interrelated dimension of time-saving and spatial flexibility [50].PUB1MS has no time and space limitation.[35,50]
PUB2MS has a high flexibility level which allows me to go from one place to another in a free manner.
PUB3I am ready to start using MS. That is because its interrelated dimensions do not have limits.
User innovativenessInnovativeness (INNO) is strongly connected to the willingness of the concerned user to utilize a technology that is new.UI1MS possesses innovative features. Thus, I would like to start using it for studying.[41,51]
UI2MS provides one with a unique experience that is considered one of its kind.
UI3I wish to utilize MS due to its amazing features that are innovative.
Perceived valueThis value is the users’ views about the value of the technology by comparing it with attained against the cost and the benefits. It is used as indicator to the behavior intention to utilize the technology.PV1MS offers great benefits in comparison with its cost.[52]
PV2MS helps in doing different task with cheap price.
Behavioral Intention to Use MSBehavioral intention is meant to refer to the intention of the targeted users to utilize the technology deemed as new. It is part of Davis’s TAM theory.IU1MS offers a good opportunity to try.[53,54]
IU2I plan to use MS.
Table
Table 3. The Cronbach alpha coefficient (α) values. (α ≥ 0.70).
Table 3. The Cronbach alpha coefficient (α) values. (α ≥ 0.70).
Variable(α)
IMM0.721
CON0.838
PE0.817
PC0.801
PUB0.812
UI0.752
PV0.740
IU0.765
Table
Table 4. Convergent validity (α, Factor loading, and composite reliability ≥ 0.70 & AVE > 0.5).
Table 4. Convergent validity (α, Factor loading, and composite reliability ≥ 0.70 & AVE > 0.5).
Variable ItemsFactor LoadingαCRPAAVE
Immersion, Interaction, and ImaginationIMM10.8490.8390.8570.9570.899
IMM20.828
IMM30.823
Context awarenessCON10.8280.7150.8390.8350.638
CON20.755
Perceived EnjoymentPE10.8670.7200.8060.8470.659
PE20.818
PE30.862
Perceived complexityPC10.9120.8400.8560.9040.758
PC20.886
Perceived ubiquityPUB10.8650.9070.9140.8410.842
PUB20.833
PUB30.729
User innovativenessUI10.7860.8420.8350.9050.760
UI20.803
UI30.794
Perceived valuePV10.7340.7910.8100.8800.711
PV20.748
Behavioral Intention to Use MSIU10.8180.9100.9100.9570.918
IU20.839
Table
Table 5. Fornell-Larker values.
Table 5. Fornell-Larker values.
IMMCONPEPCPUBUIPVIU
IMM0.958
CON0.5720.870
PE0.6460.4660.948
PC0.5660.2880.3300.871
PUB0.7770.2120.6660.5440.812
UI0.6130.5840.6480.3710.3710.918
PV0.6740.5680.6550.5900.3190.3030.843
IU0.6190.6540.5420.4620.6260.3690.3090.872
Table
Table 6. HTMT.
Table 6. HTMT.
IMMCONPEPCPUBUIPVIU
IMM
CON0.645
PE0.6970.364
PC0.6390.3310.366
PUB0.5480.2620.1820.776
UI0.6710.6650.6990.4190.210
PV0.4580.7200.7620.7110.6020.725
IU0.1170.7670.3290.5440.5260.3060.383
Table
Table 7. Model fit indicators.
Table 7. Model fit indicators.
The Whole Model
The Model Being SaturatedThe Model Being Estimated
SRMR0.0640.064
d_ULS0.7421.265
d_G0.5300.530
Chi-Square416.170416.650
NFI0.8230.823
Rms Theta0.066
Table
Table 8. R2 of the endogenous latent variables.
Table 8. R2 of the endogenous latent variables.
VariableR2Result
PE0.401Moderate
PUB0.404High
PV0.483Moderate
IU0.697High
Table
Table 9. The results reached through having the hypotheses tested (significant at ** p ≤ 0.01, * p < 0.05).
Table 9. The results reached through having the hypotheses tested (significant at ** p ≤ 0.01, * p < 0.05).
Hypothesis No.RelationshipPatht-Valuep-ValueNATUREResult
1IMM -> PE0.2413.0660.046PAccepted *
2CON -> PUB0.69911.9060.000PAccepted **
3UI -> IU0.77614.9870.000PAccepted **
4PE -> PV0.7468.0430.000PAccepted **
5PC -> PV0.2022.2510.025PAccepted *
6PUB -> PV−0.0470.6240.533N.Not Accepted
7PV -> IU0.81410.7440.000P.Accepted **
P means positive; N means negative.
Table
Table 10. The results reached through the moderator analysis.
Table 10. The results reached through the moderator analysis.
HRelationshipPath a
IV- -> Mediator		Path b
Mediator --> DV		Indirect EffectSE
Standard Deviation		t-ValueBootstrapped Confidence IntervalResult
95% LL95% UL
M1PE * UI -> PV0.4550.6090.2770.0556.0150.1690.385Accepted
M2PC * UI -> PV0.1010.3030.0310.0305.413−0.0280.089Not Accepted
M3PUB * UI -> PV0.4370.5160.2250.0596.1640.1100.341Accepted
* means “significant or accepted”.
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Salloum, S.; Al Marzouqi, A.; Alderbashi, K.Y.; Shwedeh, F.; Aburayya, A.; Al Saidat, M.R.; Al-Maroof, R.S. Sustainability Model for the Continuous Intention to Use Metaverse Technology in Higher Education: A Case Study from Oman. Sustainability 2023, 15, 5257. https://doi.org/10.3390/su15065257

AMA Style

Salloum S, Al Marzouqi A, Alderbashi KY, Shwedeh F, Aburayya A, Al Saidat MR, Al-Maroof RS. Sustainability Model for the Continuous Intention to Use Metaverse Technology in Higher Education: A Case Study from Oman. Sustainability. 2023; 15(6):5257. https://doi.org/10.3390/su15065257

Chicago/Turabian Style

Salloum, Said, Amina Al Marzouqi, Khaled Younis Alderbashi, Fanar Shwedeh, Ahmad Aburayya, Mohammed Rasol Al Saidat, and Rana Saeed Al-Maroof. 2023. "Sustainability Model for the Continuous Intention to Use Metaverse Technology in Higher Education: A Case Study from Oman" Sustainability 15, no. 6: 5257. https://doi.org/10.3390/su15065257

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