1 Introduction

The Metaverse can be defined as a futuristic realm that goes beyond reality, seamlessly blending physical and digital elements. It encompasses a perpetual and immersive environment where multiple users can interact [1]. Thus, the metaverse platform is an influential tool in recent learning developments. The perception of the metaverse from an educational point of view may entail different interpretations. Teachers usually focus on the importance of training and support that may enhance the effective use of the metaverse platform whereas the students may focus on other influential factors.

By creating a virtual version of metaverse in the educational institutions, educational centers can establish a digital campus that replicates their physical facilities, including classrooms, dining areas, and faculty rooms. This virtual environment enables seamless communication and interaction among students, teachers, and staff, closely resembling an in-person experience through features like video calls and conferences [2, 3]. This is especially advantageous for e-learning institutions that provide fully online training, as it brings a human touch to the educational journey. Furthermore, metaverses offer educational centers the ability to provide a comprehensive 360-degree experience for students and staff, enhancing flexibility and adaptability during unforeseen circumstances [4]. By integrating augmented and virtual reality, immersive learning takes center stage, enabling gamified lessons, virtual travel experiences that bring history and culture to life, and captivating scientific explorations that ignite students' passion for the subject matter [5]. The educational landscape stands to benefit significantly from the symbiotic relationship between metaverses and learning.

Previous research has predominantly examined the impact of the metaverse on education from various perspectives. Existing studies have provided extensive backgrounds on the metaverse, discussed its strengths and weaknesses in educational settings, and some have developed conceptual models incorporating variables such as perceived use, perceived usefulness, personal innovativeness, and perceived enjoyment [6,7,8]. However, these studies have primarily focused on technological aspects, overlooking core educational variables encapsulated in the PACK framework, which prioritizes educational considerations, including Pedagogy, Assessment, Content, and Knowledge (PACK). Therefore, this study diverges from prior literature by integrating the PACK framework as a fundamental variable in its conceptual model, thus emphasizing essential educational dimensions.

The aim of this research is to develop a conceptual model that predicts the sustained intention to use Metaverse platforms among students in public and private universities in Oman. To achieve this, an extensive review of literature related to technology acceptance and continuous usage intentions was conducted, identifying the most influential factors impacting the ongoing use of e-learning platforms [6,7,8]. The focus was on theories with strong predictive capabilities in understanding user perceptions and those elucidating the significance of continuous usage from multiple perspectives. Consequently, the study incorporated the Technological Pedagogical Content Knowledge (TPACK) framework as proposed [9, 10], along with factors such as Immersion, Interaction, and Imagination [11], Perceived Enjoyment [12], Perceived Complexity [13], and Perceived Value [14].

This study stands out due to its unique approach of correlating technology-based and educational-based features. It employs hybrid analyses, including Machine Learning (ML) algorithms and Structural Equation Modelling (SEM). Additionally, the study utilizes Importance Performance Map Analysis (IPMA) to evaluate both the importance and performance of various factors.

2 The previous studies and the gap in knowledge

2.1 The Metaverse and its usage

According to recent studies [15], educators and specialists have extensively developed the educational environment to create an optimal setting that promotes economic and social development, as well as fundamental structures and practices. As a result, the concept of the metaverse has emerged as an innovative teaching method, aiming to revolutionize online education. Referred to as the "Eduverse," this virtual online environment enables users to interact without constraints of time and space, fostering collaboration and the construction of user identities through the use of avatars [16, 17]. The Eduverse offers several advantages, such as flexible log-in and log-out options, allowing individuals to access and retrieve previous information whenever they reconnect [16, 17]. By incorporating problem-based learning (PBL) and learner-centered teaching (LCT), this new learning platform empowers teachers and students to achieve learning objectives effectively. The implementation of these approaches not only addresses various challenges but also strengthens teamwork and fosters a keen interest in learning. This, in turn, promotes collaborative and autonomous learning—the key factors contributing to its success [16,17,18]. However, it is crucial to acknowledge that despite the metaverse's popularity in diverse contexts, there are certain drawbacks that have been identified in studies. Many educators and specialists express concerns regarding potential risks and privacy issues associated with its use [19, 20].

2.2 Tracing approaches to Metaverse from 1997 till the present

Approaches to the metaverse have expanded significantly, with researchers from diverse fields exploring its applications from various angles. The literature is replete with examples that highlight both past and current interest in the metaverse. Notably, studies dating back to 1997 demonstrate the sustained attention given to the concept. Table 1 presents different definitions proposed by scholars, showcasing the diverse perspectives surrounding the metaverse. In the realm of art and humanities [21], emphasize the distinctive aspect of user-created content in the metaverse. Unlike pre-designed worlds, users have the freedom to construct their identities from scratch, whether in educational or professional settings. The pursuit of social interaction is also crucial in the utilization of the metaverse.

Table 1 Old approaches to Metaverse
Full size table

From a business, management, and accounting standpoint [22], liken the metaverse to a digital domain mirroring our physical lives. It serves as a reflection of the real world, enabling lifelogging and the representation of individuals and objects in both digital and physical realms. Similarly, Jayens et al. [23], approach the metaverse from a computer science perspective, defining it as an open, immersive environment with a visual sense that eliminates traditional barriers of time and space. Users can access this world through self-configuring, interaction, and collaboration. In the field of environmental science, Li and Huang [24] describe the metaverse as a convergence of virtualization, 3-D web tools, and objects, seamlessly integrating into our surroundings and becoming an integral part of our daily lives. Considering the social science domain, Taylor [25] views the metaverse as a world that fosters interaction among individuals through artistically and virtually created avatars, representing people from the real world.

Recent approaches to the metaverse, in contrast, have demonstrated greater practicality and a broader embrace of other crucial fields. These new approaches diverge from the old ones both in terms of scope and in their methods of investigating the impact of the metaverse. The expanded scope now encompasses the significance of the metaverse in hospitality, sports, and investment. For instance, Buhalis et al. [26], delve into the opportunities and challenges associated with utilizing the metaverse within the context of hospitality and tourism management. Similarly, recent studies have examined the role of the metaverse in sports. Efendioğlu [27] investigates how information quality, information reliability, and perceived risk in the metaverse influence consumers' purchase intentions. Education is another field that has been significantly impacted by the metaverse, with recent studies focusing on its various applications. Mereu [28] explores the effect of the perceived value derived by sports fans from utilizing the metaverse, presenting exciting prospects for spectator sports brands to engage fans and provide them with a unique metaverse experience. Additionally, [29] investigate students' perception and acceptance of the metaverse in the context of medical education. Table 2 illustrates the purposes of recent studies with their significance.

Table 2 Recent approaches to Metaverse
Full size table

Recent studies on Metavese have contributed significantly to the current literature by adding innovative objectives.A study by Alfaisal et al. [31], addresses the gap in the current literature on the application of the metaverse in education. While previous research has extensively explored the metaverse technological aspects and its role in enhancing motivation, immersion, and real-life problem-solving experiences for students, it has largely overlooked core educational variables. Another study by Al-Adwan [32] approached the issue of metaverse adoption in higher education from a different perspective. This study aimed to identify factors influencing these intentions using an extended Decomposed Theory of Planned Behavior (DTPB) model. The vonceptual model incorporated variables such as perceived enjoyment, herd behavior, student autonomy, and student innovativeness were effective in explaining variance in these antecedents. Furthermore, Al-Adwan [33] identified a gap in research on students' intentions to adopt meta-education despite its transformative potential in higher education. The study aimed to identify factors influencing these adoption intentions using an extended Decomposed Theory of Planned Behavior (DTPB) model. The key factors include attitude, social influence, perceived behavioral control, perceived enjoyment, herd behavior, student autonomy, and student innovativeness. The findings aimed to provide insights and practical implications for encouraging the adoption of meta-education.

Finally, Chahal and Rani [34] conducted a comprehensive examination of the impact of the metaverse on education, exploring it from multiple perspectives. Their study specifically focused on the adoption of e-learning among students, analyzing various factors and viewpoints to provide a well-rounded understanding of this emerging technology's influence on educational practices.

The study aimed to identify factors influencing these intentions by extending the Decomposed Theory of Planned Behavior (DTPB) model. Additionally, variables like perceived enjoyment, herd behavior, student autonomy, and student innovativeness were effective in explaining variance in these antecedents. This research extends the DTPB model in the context of emerging educational technologies and offers practical insights for policymakers and educators to encourage meta-education adoption.

3 The determinants of the Metaverse adoption model and the hypotheses

This study addresses a gap by investigating a conceptual model that includes critical factors such as Technological Pedagogical Content Knowledge (TPACK), Technology Self-Efficacy (TSE), Perceived Enjoyment (PE), Perceived Complexity (PC), Perceived Value (PV), and Controlled Immersion, Interaction, and Imagination (III) to understand the influence of Metaverse adoption in Oman. To enhance the analysis, some variables may act as mediators to assess their effectiveness in predicting Metaverse adoption. The model aims to explore both direct and indirect relationships among various factors affecting Metaverse adoption by Omani students.

3.1 Interaction and imagination (III)

The widespread adoption of AI technology has created a new environment that requires a comprehensive understanding of the nature and sources of artificial intelligence anxiety. While recent studies have addressed this issue to some extent, their limited scope fails to reflect the extensive use of AI in various settings. Consequently, previous research has focused on identifying the sources of anxiety and categorizing them into different levels. These studies have proposed various dimensions of AI anxiety based on theoretical models and aimed to develop standardized tools for measuring this phenomenon by defining the construct of Artificial Intelligence Anxiety (AIA) [11].

In order to examine learners' attitudes and intentions to use technology, three features are integrated with AIA. It is important to note that immersion, which is a subjective psychological response, cannot be objectively measured as a property of a system. Another multi-dimensional construct, interaction, encompasses aspects of human–computer interaction and computer-mediated communication among humans. Additionally, the content of virtual environment applications stimulates imagination by leveraging the user's ability to perceive non-existent objects, influenced by their prior and newly acquired knowledge [11, 33, 35]. Therefore, it is hypothesized that:

H1: There is a positive relation between ‘Immersion, Interaction and Imagination’ and the behavior intention to use Metaverse system.

3.2 Technological pedagogical content knowledge (PACK)

Teachers' knowledge is a complex concept with various interconnected elements. One of the key elements is pedagogical content knowledge (PCK), which has been extensively studied by researchers and practitioners. Its significance lies in its ability to combine content knowledge and pedagogy to effectively explain how a particular topic is organized and presented to learners [36].

The emergence of Technological Pedagogical Content Knowledge (TPACK) is crucial, as it enables teachers to develop a comprehensive understanding of the relationship between pedagogy and technology. TPACK refers to the specific type of knowledge that teachers require to proficiently organize and present teaching materials. TPACK encompasses Technological Content Knowledge (TCK), Technological Pedagogical Knowledge (TPK), and Pedagogical Content Knowledge (PCK). Evaluating TPACK can be achieved through self-assessment surveys and performance-based assessments, which serve as fundamental instruments for assess [9, 37]. Drawing from current research, the hypotheses is formulated as follows:

H2a: There is a positive relation between PACK and the perceived enjoyment (PE).

H2b: There is a positive relation between PACK and the perceived complexity (PC).

H2c: There is a positive relation between PACK and the perceived value (PV).

3.3 Perceived enjoyment (PE)

According to research, users' intention to use technology can vary significantly, and one aspect that influences their acceptance of technology is their perceived enjoyment. Perceived enjoyment refers to the level of engagement and happiness users experience while using technology, fostering a sense of involvement. The pleasure users derive from their real experience with technology plays a crucial role in shaping their enjoyment [38, 39].

Different studies have approached the assessment of perceived enjoyment in distinct ways. In a study conducted in Saudi Arabia, it was found that perceived enjoyment did not have a significant impact on a group of undergraduate students. This finding contrasts with previous studies conducted in Malaysia. For instance, Moghavvemi et al. [40], examined the effect of perceived enjoyment among a group of undergraduate students and acknowledged its significant and influential role in supporting students' willingness and readiness to continue using technology. Therefore, perceived enjoyment has a positive and significant impact on technology acceptance [41, 42]. Based on the existing literature, it is hypothesized that:

H3: There is a positive relation between perceived enjoyment and the behavior intention to use Metaverse system.

3.4 The perceived complexity (PC)

The perceived complexity plays a significant role in shaping individuals' attitudes towards technology adoption. Understanding user perspectives and the factors influencing this perception can help designers, developers, and decision-makers create technology that is more user-friendly, relevant, and appealing. By addressing the cognitive load, focusing on user-centered design, mitigating perceived risks, and acknowledging sociocultural influences, we can foster a smoother transition towards embracing new technologies in various domains. Previous studies have shown that the system complexity has a direct impact on the perceived usefulness and the perceived ease of use which in turn affect the users’ attitude towards using or adopting a technology [43]. However, other the close relation between the perceived complexity and the performance expectancy, effort expectancy, and social influence which can affect the adoption differently. The higher perceived complexity can reduce the intention to adopt technology unless its benefits are significantly compelling [44, 45]. Building on the foundation of existing scholarly works, this study posits the hypothesis that:

H4: There is a positive relation between perceived complexity and the behavior intention to use Metaverse system.

3.5 Perceived value (PV)

Perceived value is an intriguing concept that is closely linked to the utility of effort and time, and it significantly influences users' intentions to continue using technologies. Notably, perceived value holds immense importance for businesses as it serves as a potent source of competitive advantage [46]. In different contexts, such as human value and entertainment value, perceived value exhibits a positive impact on users' attention. Additionally, perceived value is closely associated with satisfaction, which is a significant variable in predicting the effectiveness of perceived value. Consequently, three key characteristics, namely time, cost, satisfaction, and benefits, can collectively shape the perceived value experienced by users [46,47,48]. Guided by the existing body of literature, the following hypothesis is proposed:

H5: There is a positive relation between perceived value and the behavior intention to use Metaverse system.

The research model proposed in this study is predicated on the hypotheses outlined in Fig. 1. The theoretical framework has been operationalized as a structural equation model.

Fig. 1
figure 1

Research model

Full size image

4 Research methodology

4.1 Data collection

The data collection for this study was conducted through online surveys et al. Buraimi University College, Oman, spanning from September 25, 2023 to January 05, 2024. The research team randomly distributed 500 questionnaires, out of which 477 were completed and returned, indicating a high response rate of 95.4%. However, 23 questionnaires were discarded due to incomplete information, leaving 477 usable responses. This sample size aligns with Krejcie and Morgan's sample size estimates, applicable for a population of 500 [49]. Notably, there was a significant deviation between the actual sample size of 477 and the minimum required number for robust analysis. In evaluating the sample, the research employed Structural Equation Modeling (SEM), referenced as methodology [50], which played a crucial role in validating the study's hypotheses. The theoretical framework of the study was grounded in existing theories related to the Technology Adoption Rate. Additionally, the assessment of the measurement model was carried out using SEM, specifically employing SmartPLS Version 3.2.7. The research team utilized this advanced analytical technique to conduct a comprehensive analysis of the final path model, ensuring a thorough and precise interpretation of the data.

4.2 Students’ personal information/demographic data

Figure 2 presents the demographic and personal characteristics of the participants involved in the study. The data reveals a gender distribution among the respondents, with 36% being male students and 64% female. Age-wise, 46% of the participants fell within the 18–29 year age bracket, while the remaining were older than 29 years. The majority of these participants are university students, noted for their significant expertise and qualifications. The educational background of the respondents varied, with different university degrees represented. Specifically, 5% held a diploma, 6% had an advanced diploma, 63% were bachelor's degree holders, 21% had a master's degree, and 6% possessed a doctoral degree. This diversity in educational attainment offers a comprehensive perspective on the study's findings. In reference to the study by Salloum et al. [51], the concept of respondents' willingness to volunteer is highlighted. This inclination towards volunteering is indicative of a "purposive sampling approach", suggesting a deliberate selection of participants. The sample thus comprises university students of different ages and from various academic majors. For the analysis and measurement of this demographic data, IBM SPSS Statistics version 23 was utilized. This statistical software facilitated the detailed and accurate analysis of the participants' demographic information, providing a solid foundation for the study's overall data analysis.

Fig. 2
figure 2

Demographic data of the respondents (n = 477)

Full size image

4.3 Study instrument

In the current study, the survey instrument was employed to validate the hypothesis. To ensure a meticulous and effective measurement of the questionnaire's six constructs, a total of 18 items were thoughtfully incorporated into the survey. These items were selected for their relevance and efficacy in addressing the specific constructs under study. The origins and details of these constructs are outlined in an accompanying table. This Table 3 is an essential component of the research, as it not only delineates the practical application of the research constructs but also fortifies the current model with empirical support drawn from existing literature. This integration of previous research findings into the current study's framework helps in establishing a more robust and comprehensive analytical approach. Furthermore, the researchers made deliberate modifications to the questions used in previous studies. These amendments were likely aimed at refining the survey to better suit the specific context and objectives of the current research, thereby enhancing the validity and reliability of the data collected. Through these adjustments, the survey instrument was tailored to more accurately test the hypotheses and contribute valuable insights to the field.

Table 3 Measurement items
Full size table

4.4 Pilot study of the questionnaire

To ascertain the reliability of the questionnaire items, a pilot study was conducted. This preliminary study involved a random selection of data from 50 students, a subset of the overall target population designated for the pilot phase. The choice of 50 students was strategic, representing 10% of the total projected sample size of 500 students for the main study. This proportion was determined in accordance with the research guidelines, ensuring a representative and manageable pilot group. For the analysis of the pilot study data, the Cronbach's alpha test was employed to assess the internal reliability of the questionnaire. This test, conducted using IBM SPSS Statistics version 23, is a widely recognized method for evaluating the consistency of a measurement instrument. The use of Cronbach's alpha in this context aimed to ensure that the questionnaire items reliably measure the intended constructs. In social science research, a Cronbach's alpha coefficient of 0.70 is generally regarded as an acceptable threshold for reliability, as noted in research literature [57]. Table 4 in the study details the Cronbach’s alpha values for each of the five measurement scales used in the questionnaire. These values provide a quantitative assessment of the internal consistency of the questionnaire items, thus underpinning the validity of the pilot study's findings and informing any necessary refinements before the main study.

Table 4 Cronbach’s Alpha values for the pilot study (Cronbach’s Alpha ≥ 0.70)
Full size table

5 Findings and discussion

5.1 Data analysis

The data analysis in the current study was executed using Partial Least Squares-Structural Equation Modeling (PLS-SEM), specifically employing SmartPLS version 3.2.7 [58, 59]. The methodology involved a two-step assessment approach, encompassing both the measurement model and the structural model [60, 61]. The selection of PLS-SEM as the analytical tool was influenced by several key factors highlighted in the research paper. The first consideration was the analysis of the conceptual theory proposed in this study. PLS-SEM was preferred for its effectiveness in handling complex theoretical frameworks [60]. This method facilitates a comprehensive and nuanced analysis of the proposed models, ensuring that all aspects of the theory are adequately examined. The second step involved using PLS-SEM to address the exploratory research collected based on the conceptual models [62]. This step is crucial for exploring and validating new theories or models, where traditional methods might not be as effective. The third aspect of the analysis involved applying PLS-SEM to the entire model as a single unit, rather than dividing it into smaller segments [63, 64]. This holistic approach ensures that the interactions and relationships between different components of the model are fully captured and understood. Finally, the study utilized PLS-SEM to concurrently analyze the structural and measurement models [65]. This concurrent analysis is vital for understanding the relationships between the variables and for validating the measurement scales used in the study [66]. The strength of PLS-SEM lies in its ability to provide accurate and reliable measurements, making it a powerful tool for complex data analysis in social sciences and other research fields.

5.1.1 Convergent validity

The assessment of the measurement model, as advised in [60], involves evaluating both construct reliability and validity. Construct reliability includes measures such as Cronbach’s alpha (CA), Dijkstra-Henseler's rho (ρA), and composite reliability (CR). In the study, CA values ranged between 0.799 and 0.881, as shown in Table 5, surpassing the standard threshold of 0.7 [67]. The CR values, also reported in Table 5, varied from 0.825 to 0.893, exceeding the established threshold. Additionally, Dijkstra-Henseler's ρA is recommended for assessing construct reliability, with a minimum acceptable value of 0.70 for exploratory research and higher values (0.80 or 0.90) for advanced research stages [67,68,69]. Table 5 indicates that all measurement constructs met the minimum ρA criterion of 0.70, thereby confirming their reliability and accuracy.

Table 5 Convergent validity results which assures acceptable values (Factor loading, Cronbach’s Alpha, composite reliability, Dijkstra-Henseler's rho ≥ 0.70 & AVE > 0.5)
Full size table

In terms of convergent validity, it is critical to examine the mean variance extracted (AVE) and factor loading [60]. Data presented in Table 5 reveal that each factor loading surpassed the minimum threshold of 0.7, except for a few instances. Moreover, Table 5 displays AVE values ranging from 0.532 to 0.715, all exceeding the threshold of 0.5, with certain exceptions. These results indicate the achievement of convergent validity, as the measurement constructs effectively capture the variance they are intended to measure. This adherence to the established thresholds for both reliability and validity ensures the robustness and precision of the measurement model used in the study.

5.1.2 Discriminant validity

The study aimed to assess discriminant validity, an essential aspect of ensuring the distinctiveness of constructs within a model. To achieve this, two widely recognized criteria were revisited: the Heterotrait-Monotrait ratio (HTMT) and the Fornell-Larker criterion [60]. According to the findings presented in Table 6, the Fornell-Larker criterion was met. This criterion checks whether the square root of the Average Variance Extracted (AVE) for each construct is greater than its highest correlation with any other construct. The fulfillment of the Fornell-Larker condition in the study indicates that each construct is indeed distinct and shares more variance with its own indicators than with those of other constructs [70]. Table 7 details the HTMT ratio findings. The HTMT is another method for assessing discriminant validity, where values below a certain threshold (commonly 0.85) indicate that constructs are sufficiently distinct [71]. The study's results showed that the HTMT values for each construct were below this threshold, affirming the presence of discriminant validity. The successful calculation of discriminant validity, as evidenced by these findings, confirms the robustness of the measurement model in terms of both reliability and validity. This validation is crucial as it underpins the confidence in the model's constructs and their measurements. Consequently, with the reliability and validity of the measurement model established, the collected data is deemed suitable for further analysis, specifically for evaluating the structural model. This progression ensures that the structural model assessment is grounded in a sound and rigorously tested measurement foundation.

Table 6 Fornell-Larcker Scale
Full size table
Table 7 Heterotrait-monotrait ratio (HTMT)
Full size table

5.1.3 Hypotheses testing using PLS-SEM

The structural equation model in this study was constructed using Smart PLS, which employs maximum likelihood estimation for analyzing the interrelations among various theoretical constructs within the structural model [72,73,74]. This method enabled the examination and analysis of the proposed hypotheses, results of which are detailed in Tables 8 and Fig. 3. These tables demonstrate that the model possesses substantial predictive capabilities, as evidenced by nearly 80% variance in the "Behavior Intention to use Metaverse" [75]. Furthermore, Table 9 provides a comprehensive overview of the beta (β) values, t-values, and p-values for all the developed hypotheses, derived using the PLS-SEM technique. This detailed breakdown confirms that each hypothesis received robust support from the research findings. The empirical data, when analyzed with respect to the study's hypotheses, reveals strong support for hypotheses H1 through H5, underscoring the validity and reliability of the research findings in capturing the dynamics and implications of the behavior intention towards Metaverse usage. The relationships between ‘Immersion, Interaction and Imagination’ (III) has significant effects on Perceived Enjoyment (PE) (β = 0.635, P < 0.001); hence H1 is supported. Perceived Enjoyment (PE), Perceived Complexity (PC), and Perceived Value (PV) has significant effects on Ease of Doing Business (PACK) (β = 0.819, P < 0.001), (β = 0. 0.549, P < 0.001), and (β = 0.612, P < 0.01), respectively; hence H2a, H2b, and H2c are supported. The results also showed that Behavior Intention to use Metaverse (BIU) significantly influenced Perceived Enjoyment (PE) (β = 0.446, P < 0.01), Perceived Complexity (PC) (β = 0.592, P < 0.001), and Perceived Value (PV) (β = 0.418, P < 0.001) supporting hypothesis H3, H4, and H5 respectively.

Table 8 R2 of the endogenous latent variables
Full size table
Fig. 3
figure 3

Path coefficient of the model (significant at p** <  = 0.01, p* < 0.05)

Full size image
Table 9 Hypotheses-testing of the research model (significant at p** <  = 0.01, p* < 0.05)
Full size table

5.1.4 Model fit

SmartPLS provides several fit measures to evaluate the adequacy of PLS-SEM models. Key indicators include the Standard Root Mean Square Residual (SRMR), d_ULS, d_G, Chi-Square, Normal Fit Index (NFI), and RMS_theta [76]. SRMR values below 0.8 are generally indicative of a good fit [62], suggesting minimal discrepancy between the experienced correlations and the model-implied correlation matrix [77]. The NFI, a comparative measure of fit relative to a null model, is considered satisfactory when it exceeds 0.90 [78]. It is calculated as the ratio of the Chi-Square value of the proposed model to that of the null model. Higher NFI values reflect better fit, although NFI alone is not recommended as the sole fit indicator due to its limitations [62]. The discrepancy measures, squared Euclidean distance (d_ULS) and geodesic distance (d_G), quantify the difference between the empirical covariance matrix and the model-implied covariance matrix derived from the composite factor model [62, 79]. RMS_theta is applicable only to reflective models and measures the correlation of outer model residuals; values closer to zero indicate a better fit, with those below 0.12 deemed satisfactory [80]. This measure assesses the global validity of the PLS-SEM model by comparing the saturated model—which considers all possible links between constructs—and the estimated model, which accounts for total effects and structural relationships.

According to the data presented in Table 10, the RMS_theta value is 0.073, suggesting that the model's goodness-of-fit is robust enough to validate the global validity of the PLS-SEM model.

Table 10 Model fit indicators
Full size table

6 Discussion of results

The main objective of this study was to evaluate the integration of the metaverse platform by including Technological Pedagogical Content Knowledge (TPACK) as a key variable. Tobe able to carry on the objectives, two primary variables were identified, namely perceived enjoyment, perceived value, and perceived complexity. Practically speaking the findings indicate a positive influence of TPACK on perceived enjoyment, perceived complexity, and perceived value. These results are consistent with prior studies highlighting the significant impact of TPACK on self-efficacy and motivation [34, 81]. This suggests that a higher level of Technological Pedagogical Content Knowledge (TPACK) corresponds to an elevated level of self-efficacy. High self-efficacy plays a pivotal role in enabling educators to overcome challenges and navigate obstacles in the development of TPACK. Teachers with heightened self-efficacy approach difficulties with a positive mindset, actively seeking solutions, and drawing lessons from their experiences. Consequently, they are more likely to persist in utilizing the metaverse, enhancing their competence in leveraging technology in educational contexts [82, 83]. Similarly, heightened motivation fosters active user involvement in the development of TPACK. Users with strong motivation exhibit a robust seek to achieve c proficiently use technology in learning contexts. This motivation stimulates awareness and active participation in the pursuit of new knowledge, skills updates, and increased capability in technology utilization.

The influence of perceived enjoyment on the adoption of the metaverse is evident, serving as a mediator in the relationship between PACK and the intention to use the metaverse. The hypothesis put forth in this study supported, affirming a positive impact of perceived enjoyment on metaverse adoption. This aligns with findings from prior research, emphasizing the positive effects of the virtual environment's atmosphere on satisfaction and perceived enjoyment, subsequently contributing to a favorable purchase intention [84]. It is noteworthy, however, that some studies present conflicting results, suggesting that perceived enjoyment may not necessarily impact the adoption of the metaverse, in contrast to the outcomes observed in the current study [85]. Similarly, the influence of perceived value has been affirmed in the present analysis, aligning with findings from prior studies that establish the impact of perceived value on the adoption of the metaverse [86]. Consequently, the current results are in agreement with the existing literature, indicating that perceived value plays a pivotal role in shaping the relationship between user adoption and their perceptions. Additionally, the effect of perceived complexity stands out as a significant factor in the adoption of the metaverse, wherein the reduction of teachers' workload becomes feasible through the utilization of these technologies. The current study's hypothesis is supported, consistent with previous research wherein perceived complexity contrasts with ease of use and perceived usefulness, serving as a noteworthy factor that motivates users to engage with technology and predict their intention to do so [87].

The impact of immersive learning significantly influences the adoption of the metaverse, particularly through its positive effect on perceived enjoyment. Immersive learning, by definition, involves engaging students in an imaginary world through a combination of physical and digital means. According to the current findings, the influence of immersion, imagination, and interaction on perceived enjoyment is crucial and substantial, aligning with outcomes from prior studies. Renoult et al. [88], underscore the significance of immersive learning using the metaverse, drawing on neuroscientific insights into human memory. Their study affirms that immersive learning can activate semantic memory, encompassing general, encyclopedic, and concept-related knowledge. Another contribution by [89] introduces a unique perspective, highlighting that conceptual representations alone may not fully capture the complexity of knowledge. Building on these insights, Mystakidis and Lympouridis [1] conclude that immersive educational interventions may specifically activate episodic memory, thereby enhancing the creation of a meaningful learning environment. Numerous reviews have documented the advantages of immersive learning across cognitive, affective-emotional, and psychomotor domains, signaling a paradigm shift in communication compared to the era of social media.

6.1 Implications at theoretical and practical levels

Taking into consideration the theoretical level of implications, the current study contributes to the literature by signifying the influential effect of the PACK on the adoption of the metaverse in relation with other variables which are the perceived enjoyment, perceived complexity and the perceived value. The implication of this finding encourages users in colleges and universities to develop a positive attitude and willingness to adopt the use of the metaverse. The current study is a n attempt to clarify the significant future effect of metavers by reinforcing the conclusions that are approved by previous studies regarding the effect of the immersive learning in the educational environment where both physical and digital means are activated. The final theoretical implication suggests that government institutions exhibit a high level of trust in the role of the metaverse and are technologically ready to embrace its potential.

The practical implications are related to the success that can be achieved in adopting the metaverse at the government level. The perceived enjoyment that has a close effect on the immersive learning is one of the strong points in this conceptual model which increases significantly the users’ willingness and trust. These two variables enhance the use of metaverse The fact that immersion imagination and interaction have proven to have a positive correlation with the intention to adopt the metaverse; suggesting that the adoption rate for metaverse can be increased if the developers of the educational platforms can work more effectively and practically to facilitate the use of this platform in the near future in educational institutions, thus, the platform developers and programmers should consider adding imaginary tools that enhance the memory and sensation to engage the teachers and the students. The system can be developed by providing detailed information about the procedure of implementation through official websites and advertisements. Thus, these platforms will see the light in the near future paving the way for a more innovative system in the future.

6.2 Managerial implications

Based on the study findings, policymakers stand to benefit significantly from the managerial implications of metaverse integration in education. By promoting the use of the metaverse in colleges and universities, policymakers can foster environments known for their innovative educational practices. This approach not only enhances the learning experience for students and educators but also positions educational institutions as pioneers in leveraging emerging technologies. Policymakers should prioritize initiatives that support the development and adoption of immersive learning tools within the metaverse framework. Additionally, they can utilize these findings to formulate policies that address potential challenges and promote widespread understanding of the metaverse's educational benefits at the governmental level.

6.3 The study limitations and future perspectives

While this study contributes valuable insights, it is essential to acknowledge its inherent limitations. Firstly, the research model is confined to a specific set of factors designed to elucidate the impact of the metaverse. Future investigations could enhance the comprehensiveness of the model by incorporating additional variables that align with users' goals and objectives, thereby enriching the exploration of factors influencing metaverse adoption intention. Secondly, the study predominantly focuses on Omani students, thereby limiting the generalizability of findings. Future research endeavors should broaden their scope to encompass diverse nationalities, facilitating a more nuanced understanding of variant views and attitudes towards metaverse adoption. Given that our empirical evidence derives from a singular country, attention must be exercised in generalizing our findings. To establish the robustness and universality of our insights, further research in varied settings is needed. Lastly, while this study sheds light on the relevance of PACK in the context of metaverse adoption in developing countries, future studies may explore alternative theoretical frameworks to corroborate and complement the current findings, thereby contributing to a more holistic understanding of this timely and crucial subject.

7 Conclusion

The adoption of the metaverse will offer valuable insights into the future role of technology in colleges and universities. This study intend to evaluate the integration of the metaverse platform by incorporating Technological Pedagogical Content Knowledge (TPACK) as a critical variable, identifying perceived enjoyment, perceived value, and perceived complexity as primary variables. The findings revealed a positive influence of TPACK on perceived enjoyment, perceived complexity, and perceived value, aligning with prior studies emphasizing TPACK's significant impact on self-efficacy and motivation. Higher TPACK levels correlated with elevated self-efficacy, enabling educators to navigate challenges positively and enhance their competence in leveraging technology in education. Additionally, perceived enjoyment served as a mediator between PACK and metaverse adoption, supporting the hypothesis of a positive impact. The study also affirmed the influence of perceived value on metaverse adoption, consistent with previous research, while perceived complexity emerged as a significant factor, reducing teachers' workload through technology utilization. The impact of immersive learning on metaverse adoption, particularly through its positive effect on perceived enjoyment, was highlighted, emphasizing the importance of immersion, imagination, and interaction. Neuroscientific insights supported the significance of immersive learning in activating semantic and episodic memory, contributing to a meaningful learning environment. Numerous reviews documented the advantages of immersive learning, marking a paradigm shift in communication compared to the era of social media.