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Article

Influencing Factors of Usage Intention of Metaverse Education Application Platform: Empirical Evidence Based on PPM and TAM Models

by
Gaofeng Wang
1 and
Changhoon Shin
2,*
1
School of Management, Henan University of Technology, Zhengzhou 450001, China
2
College of Ocean Science and Engineering, Korea Maritime & Ocean University, Busan 49112, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(24), 17037; https://doi.org/10.3390/su142417037
Submission received: 25 October 2022 / Revised: 12 December 2022 / Accepted: 13 December 2022 / Published: 19 December 2022
(This article belongs to the Special Issue Virtual Reality/Metaverse Applications in STEAM Education and Creativity: Towards Student Learning Sustainability)
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Abstract

:
We explored the influencing factors of the usage intention of a metaverse education application platform that directly influence the optimization of its service function, improve the usage intention, and realize the promotion and application of metaverse technology in the education domain. Based on the characteristics of the metaverse education application platform, we integrated the PPM (push–pull–mooring) model and the TAM (technology acceptance model) to construct the model of influencing factors of usage intention. Ultimately, 275 valid questionnaires were collected through expert demonstration, pre-investigation, formal investigation, and other processes. In addition, our paper used the SEM (structural equation model) and fsQCA (fuzzy-set qualitative comparative analysis) to analyze the influencing factors of user willingness and their configuration paths. The study found that personalized learning, contextualized teaching, perceived usefulness, perceived ease of use, social needs, and social impact play significant positive roles in the willingness to use the metaverse education platform. Meanwhile, the obtained findings show that the experience-led community-driven mode, personality-led community-driven mode, and social-led utility-driven mode serve as potential guidelines for usage intention enhancement.

1. Introduction

The outbreak of COVID-19 has promoted the rapid development of virtual education content, and the digitalization of offline education scenes has become a necessary development trend [1]. During this period, online classes, online graduation ceremonies, and virtual conferences appeared in large numbers. However, due to the limitations of technical conditions, infrastructure, software applications, and other factors, the effect of online education could have been better. Many problems emerged, such as the lack of context, low interactivity, and a weak sense of participation, which urgently need improvement in online teaching [2,3]. Nevertheless, in recent years, the rapidly developing metaverse technology has had outstanding advantages in online education [4,5].
Edu-Metaverse is the application of metaverse technology in the field of education. It is the twin of the actual educational world, giving teachers, students, managers, and other personnel a virtual identity. They achieve seamless connection and virtual integration between the virtual world and the actual teaching environment in the Edu-Metaverse and obtain a new experience in the social communication space, creating and sharing freedoms, high immersion, and virtualization [4,5,6]. The metaverse education application platform is a specific application of the educational metaverse [7]. Meta, Roblox, Zepeto, and other enterprises have started to develop the metaverse education platform. They integrate the metaverse into the teaching project practice and bring a better experience to users. However, the application of metaverse technology in the education industry has a “false name” phenomenon. For example, in the development of the education application platform, there is a significant deviation between the product software and the needs of user groups [8]. According to the policy statement issued by the Brookings Institution of the United States, only 7 of the 80,000 educational application platforms that users can download through the mobile app store get approved by users, and the rating of free platforms is even worse. Willingness to use is the premise of user behavior and satisfaction [9,10]. Therefore, exploring the influencing factors of user intent on the application platform will help solve the problem of mismatch between the supply of educational technology and users’ demand, and truly bring users into the metaverse education ecosystem.
Firstly, research on the willingness to use educational technology application platforms has been discussed previously. Relevant research mainly involves e-learning systems [11], mobile learning [12], learning management systems [13], AR/VR technologies [14,15], and social media services [16,17]. However, due to the differences in technical equipment, educational content, educational purpose, technical support, application fields, and user groups, the research results based on the influencing factors of other educational technology users’ willingness to use technology do not apply to the field of meta cosmic educational technology [4,18]. Therefore, the influencing factors of users’ willingness to use the metaverse education application platform have specific research significance.
Secondly, the models used in previous educational technology research are the TAM model or the TAM model combined with TTF, IDT, ISSM, ECT, and other models [16,19,20,21]. The PPM model (push–pull–mooring) was first used in population migration research and later used to explain user behavior transfer [22]. Its applicability in educational technology has also been verified [11,23]. The TAM model and PPM model highly correlate with the research on willingness to use educational technology [9,11].
Finally, looking at the empirical research on the user intent of educational technology, we can find that most of the analysis methods use the SEM (structural equation model). No research has been found in which SEM and fsQCA (fuzzy-set qualitative comparative analysis) methods are combined. Many factors influence the willingness to use [9]. FsQCA is mainly adopted to explain possible configurations in complex causal relationships [24]. In this context, the following questions are brought out in this study:
  • What are the key factors influencing the use intention of the users of the metaverse education application platform?
  • What antecedent configurations trigger the user’s willingness to use the metaverse education application platform?
  • Is the composite model integrating the TAM and PPM models valuable?
This research integrates the PPM model and TAM model and takes the usefulness and ease of use of the TAM model as an essential sub-dimension to play the pull effect in the PPM model. From an integrated perspective, it constructs a model of factors affecting the use intention of users of the metaverse education application platform. The questionnaire survey obtained the data needed for the study. The SEM implemented data analysis and model validation. The study used fsQCA to conduct configuration analysis on the antecedents that trigger the user’s willingness to use the metaverse education application platform. The research results can provide more guiding suggestions for the scene design and practical application of the metaverse education application platform, and further promotes the metaverse education application platform to provide users with satisfactory services and optimize the platform.
The remainder of the article is structured as follows. Section 2 is the literature review and theoretical foundation. Section 3 introduces the research hypothesis, fuses the PPM and TAM models, and constructs the research model of factors influencing usage intention. The structural equation modeling is analyzed in Section 4. Section 5 is the quantitative comparative analysis of fuzzy sets. Finally, Section 6 is the conclusion, discussion, and outlook on future research.

2. Literature Review and Theoretical Foundation

2.1. Literature Review

2.1.1. Research on the Education of the Universe

The unique advantages of the metaverse in the educational application have triggered a research upsurge of scholars at home and abroad. Relevant research mainly includes: (1) The definition of a metaverse in the field of education: metaverse education can be defined as a new educational environment formed under the promotion of metaverse technology [25], which contains four types: augmented reality, life logging, mirror world, and virtual reality [5]. (2) Characteristics of the metaverse in the field of education: Ko et al. [26] proposed that the metaverse has 5C characteristics (Canon, Creator, Currency, Continuity, Connectivity). Zhang et al. [25] found that metaverse education has a high degree of freedom and low restriction in time and space through comparison of in-person learning, screen-based remote learning, and metaverse-based learning; with the characters of solid sociability, strong virtual identity, immersive experience, open and accessible creation, strong interaction, and comprehensive teaching evaluation. (3) The key technologies and tools of the universe in the field of education: Tlili et al. [6] divided the technologies and tools into seven categories: immersive, artificial intelligence (AI), game applications, education, modeling and simulation, mobile, sensors and wearable devices. Gastón et al. [27] emphasized the supporting role of graphics processing units (GPUs), photographic 3D engines, artistic intelligence, cloud computing, 5G, blockchain, extended reality (XR), virtual reality (VR), augmented reality (AR), and brain–computer interfaces (BCI) in the development of metaverse education. (4) The role and influence of the metaverse in the field of education: Lin et al. [4] expound on the positive influence of the metaverse in the field of education from seven aspects, such as helping to enhance the visualization of learning content, improve learning efficiency, break the restriction of educational resources, and reduce educational costs and risks. Kye et al. [5] believed that applying the metaverse in education has brought students greater autonomy and creative freedom. (5) Application scenario and effect evaluation of the universe in the field of education: Hu et al. [28] summarized five potential educational application scenarios, including a learning platform, immersive curriculum, virtual school, twin campus, and open university, based on cases. Lee et al. [29] verified the advantages of the metaverse in terms of ease of use, interactivity, immersion, and interest in educational applications through the user’s analysis of the use effect of the spatial system learning platform. Aleksandar et al. [30] investigated the effect of collaborative learning activities in the virtual environment. (5) Risks and challenges of the metaverse in the field of education: Bermejo et al. [31] found that there would be privacy infringement and moral hazards in the application of metaverse technology in education. Challenges such as the feasibility of applying technology, threats to learners’ physical and mental health, and insufficient computing power must be considered seriously [28].

2.1.2. Intention to Use the Metaverse Education Application Platform

Before the rise of the metaverse, research on the willingness to use educational application platforms can be categorized according to the following three aspects: (1) From the perspective of research objects, it mainly includes the research on e-learning systems such as e-learning platforms and e-learning environments [32,33]; mobile learning research based on tablet computer and mobile device technology [34,35]; research on learning management systems such as Blackboard and Moodle [36,37]; research based on social media service platforms such as WeChat and Facebook [17,38]; and based on AR&VR technology research [14,39]. (2) From the research model, the TAM model [9], IDT model [10], ISSM [19], UTAUT model [40,41], and the UTAUT2 model [42]. In addition, there are also studies combining the TAM model with TTF, IDT, ISSM, ECT, and other models [19]. (3) From the perspective of analysis methods, the structural equation model analysis method is mainly used in existing studies [14,19].
With the further promotion of the depth and breadth of the metaverse application, the research on the influencing factors of the use intention has received attention. For example, Teng et al. [43], based on the extended UTAUT model, used SEM analysis to study the influencing factors of learners’ adoption of the educational metaverse platform. Based on the TAM model and multiple regression analysis, Kim et al. [44] studied the influence mechanism of the perceived value of undergraduate students in higher education on the intention of the metaverse learning environment. Akour et al. [45], using the extended TAM model and the SEM-ANN method, studied college students’ views on metaverse education. With the TAM model and SEM-ML method, Almarzouqiet et al. [46] predicted college students’ views of using the metaverse in medical education. Maryam et al. [47] studied the influencing factors of medical students’ willingness to use the metaverse in medical training by combining the TAM model and the SEM method.
In summary, the research on metaverse education focuses more on the applicability, conception, and design of application scenarios. Alternatively, it takes a metaverse education application platform as an example to evaluate its implementation effect. The research results provide ideas for implementing a metaverse education application platform. However, on the whole, the research results are not universal and lag, and cannot provide a reliable basis for platform design promptly, which may lead to a waste of resources and loss of customers. Furthermore, most of the analysis methods used in the existing research are qualitative, which are difficult to effectively provide practical suggestions for the scene design and application of the metaverse education application platform. User willingness is a critical factor that affects the implementation of the platform. However, the research on the user’s willingness to use the metaverse education application platform mainly focuses on student groups using SEM analysis methods. At the same time, they ignore the complexity of the user’s willingness. The influence of the mechanism between the influencing factors on user intent is yet to be studied. Therefore, it is necessary to use a variety of models and research methods to conduct empirical research on the influencing factors of users’ willingness to use from the perspective of the demand side.

2.2. Theoretical Foundation

2.2.1. PPM Model

The push–pull–mooring (PPM) model was first used in the study of population migration. Later, Bansal [22] used the PPM model to explain the transfer of consumer behavior. In the PPM model, the influencing factors were divided into three aspects: the thrust factor, the pull factor, and the mooring factor. Among them, the thrust factor refers to the factor that pushes the user away from the original product, and the pull factor refers to the factor that pulls the user to accept the new product. Finally, the anchor factor refers to the personal/social factor that can hinder or promote the transfer behavior of the user [48]. Scholars have conducted a wealth of research using the PPM model and achieved fruitful results. Lisana [12] used the PPM model to study the influencing factors of college students turning to mobile learning. Nayak et al. [11] explored the influencing factors of students turning to online learning during the epidemic based on the PPM model. Jin et al. [49] used the PPM model to study the influencing factors of students’ transfer from traditional learning to e-learning.
In this paper, transfer behavior refers to the transfer process of users from the traditional education application platform to the metaverse education application platform. The push factor refers to the factors that push users away from the traditional education application platform, the pull factor refers to the factors that pull users to accept the metaverse education application platform, and the anchor factor refers to the personal/social factors that affect the transfer of users from the traditional education application platform to the metaverse education application platform.

2.2.2. TAM Model

The technology acceptance model (TAM) is proposed by Davis [50], and is based on rational behavior theory. It was initially used to explore the decisive factors that led to the wide acceptance of computers. This model explores the relationship between user attitude, behavior intention, and system use through perceived usefulness, perceived ease of use, and external variables. The TAM model proposes two main decisive factors: perceived usefulness and perceived ease of use. Perceived usefulness mainly reflects the degree to which a person thinks that using a certain system will improve his or her work performance, and perceived ease of use mainly reflects the degree to which a person thinks it is easy to use a certain system [51]. With the continuous deepening of scholars’ research on the TAM model, the TAM model has matured and has been applied to many fields, and has been explored in combination with other models to solve complex system problems. In the field of education, Callum et al. [52] used the TAM model to study the influencing factors of teachers’ adoption of mobile learning. Furthermore, Al-Rahmi et al. [38], based on the integrated model of TAM and IDT, constructed the influencing factors of college students’ adoption of e-learning technology. Finally, Prasetyo et al. [19], based on the integrated model of TAM and ISSM, analyzed the influencing mechanism of high school students’ acceptance of e-learning platforms in the context of the COVID-19 epidemic.
In this paper, perceived usefulness refers to the degree to which users use the metaverse education application platform to improve their learning effect, and perceived ease of use refers to the ease with which users use the metaverse education application platform.

3. Research Hypotheses and Conceptual Model

3.1. Hypothesis of Influencing Factors of Users’ Willingness to Use the Metaverse Education Application Platform

3.1.1. Influence of Push Factors on Users’ Willingness to Use the Metaverse Education Application Platform

According to the PPM model, the push factor refers to the factor that pushes users away from the traditional education application platform [53]. In combination with the purpose and demand of this study, personalized learning and situational teaching are taken as the thrust factors of users’ willingness to use the metaverse education application platform. Personalized learning means that users can formulate their own learning plans through the metaverse education application platform. Ma et al. [54] studied the key factors affecting personalized learning and resource recommendation by taking the intelligent learning environment as the research object. Lv et al. [55] believed that in the era of artificial intelligence, personalized learning is the mainstream of future development, and the platform should provide users with a stronger independent learning environment. Xiao et al. [56] verified the impact of personalized demand on the application of library virtual reality technology. Situational teaching means that the metaverse education application platform can provide users with an immersive, vivid, and interactive teaching mode. Liu et al. [57] found through teaching experiment research that an immersive virtual environment can promote users’ understanding and application. Yuan et al. [58] took the academic virtual community as the research object and analyzed the positive impact of interaction intensity on users’ perceived learning. Based on the above analysis, the following research hypotheses are proposed:
H1a. 
Personalized learning has a positive impact on users’ use intention of the metaverse education application platform.
H1b. 
Situational teaching has a positive impact on users’ use intention of the metaverse education application platform.

3.1.2. Influence of Pulling Force on Users’ Willingness to Use the Metaverse Education Application Platform

According to the PPM model, the pull factor refers to the factor that pulls users to accept the metaverse education application platform [59]. Integrating the TAM model, the three factors of perceived usefulness, perceived ease of use, and social needs are taken as the pull factors of users’ willingness to use the metaverse education application platform. Perceived usefulness refers to the user’s expectation of obtaining benefits from using the metaverse education application platform. Eksail et al. [60] pointed out that perceived usefulness positively impacts the willingness to use online learning. Chen and Keng [61] analyzed the conversion intention of the English teaching platform and believed that if the user’s teaching platform were applicable, it would strengthen the user’s conversion intention. Perceived ease of use refers to how easy it is for users to master the metaverse education application platform. Balakrishnan et al. [62] focused on the use of social media and believed that ease of use positively impacts social media learning transfer behavior. Huang et al. [63] emphasized the positive impact of ease of use on the willingness to use online learning. Social demand refers to the social expectation of users on the metaverse education application platform. Xie [64] took the tourism virtual community as the research object and verified that social demand is the core element that affects the interaction of members of the tourism virtual community. Hou et al. [65] believe that in the virtual community, social demand will have a positive impact on competitive behavior. Based on the above analysis, the following research hypotheses are proposed:
H2a. 
Perceived usefulness has a positive impact on users’ willingness to use the metaverse education application platform.
H2b. 
Perceived ease of use has a positive impact on users’ willingness to use the metaverse education application platform.
H2c. 
Social needs have a positive impact on users’ willingness to use the metaverse education application platform.

3.1.3. Influence of Mooring Factors on Usage Intention of the Metaverse Education Application Platform

According to the PPM model, the mooring factors refer to the personal/social factors that affect the transfer of users from the traditional education application platform to the metaverse education application platform [66]. At the same time, considering the needs of this study, the technology maturity, perceived privacy risk, and social impact are taken as the mooring factors of the users’ willingness to use the metaverse education application platform. Technology maturity refers to the service quality and system quality of the metaverse education application platform. Tan et al. [67] believe that the service quality of WeChat official accounts has a positive impact on the confirmation of users’ expectations. Yuan et al. [66] found through empirical research that system quality has a significant impact on mobile government app users’ behavior. Perceived privacy risk refers to the risk loss of personal privacy information caused by uncertain factors when users use the metaverse education application platform. Yuan et al. [68] found that perceived privacy is a key factor affecting the non-sustainable use behavior of users in online health communities. Deng et al. [69] believe that both privacy social risk and privacy information risk have an impact on the personal information disclosure behavior of social platforms, and the impact of privacy social risk is greater. Social impact refers to users being affected by friends, classmates, public opinion, news, or other factors when using the metaverse education application platform. Zhou et al. [59] found through empirical analysis that social impact has a significant positive impact on the transfer behavior of social media users. Wang et al. [70] carried out research on live streaming apps and believed that social impact factors have a positive effect on the user’s use intention, thus affecting the user’s use behavior. Based on the above analysis, the following research hypotheses are proposed:
H3a. 
Technology maturity has a positive impact on users’ use intention of the metaverse education application platform.
H3b. 
Perceived privacy risk has a positive impact on users’ willingness to use the metaverse education application platform.
H3c. 
Social impact has a positive impact on users’ use intention of the metaverse education application platform.

3.2. Influence Factor Model of Use Intention on Metaverse Education Application Platform

On the basis of the above analysis, inspired by Lin [23] and Jin [71], the study considers the simultaneous use of PPM models and TAM models to construct a research model from an integrated perspective. The factors affecting the willingness of metaverse education application platform users are analyzed in three dimensions: push factors, pull factors, and mooring factors, in which the push factors are divided into personalized learning and contextualized teaching; perceived usefulness and perceived ease of use are derived from the TAM model, which has been applied in various studies regarding the willingness to use educational technologies [9]. They are taken as important sub-dimensions to exert the pull effect in the PPM model, combining with social needs. Mooring factors take technical maturity, perceived privacy risk, and social influence as the main aspects. Based on this, this study constructs a conceptual model of factors influencing usage intention of the metaverse education application platform, as shown in Figure 1.

3.3. Questionnaire Design

Based on the above research hypotheses and conceptual models, and with reference to the research scales of domestic and foreign scholars, a measurement scale of factors affecting the use intention of users of the metaverse education application platform is designed. The contents of this research questionnaire can be divided into two parts. First, the basic information of the respondents mainly included gender, age, education level, occupation, monthly income, and the way to learn about the metaverse education application platform. Second, as shown in Table 1, the factors that affect the user’s willingness to use the metaverse education application platform include 9 measurement variables and 34 measurement items. The items are in a declarative sentence structure, and each item includes five response options, namely, “strongly disagree”, “disagree”, “general”, “agree” and “strongly agree”. Before the large-scale distribution of the questionnaire, the research group first invited six industry experts (three from the academic community and three from the industry) to review the content of the questionnaire and the setting of the questions. After confirming that there were no problems in the questionnaire, a presurvey was conducted with college students. A total of 56 questionnaires were collected. The contents of the questionnaire were corrected by analyzing the results of the presurvey, such as unclear items, difficult-to-understand items, and repetitive and overlapping items. Finally, formal questionnaire collection was started.

4. Structural Equation Model Analysis

4.1. Sample and Data Collection

Affected by the “COVID-19 epidemic”, the research group decided to adopt online and offline research methods and collect data through online questionnaires, on-site interviews and symposiums on the premise of complying with epidemic prevention and control policies. To ensure the validity of the data, this study is based on the actual situation in the region, through virtual experience (before implementing the survey, the research team sent the interviewees “Metaverse of Education and Learning Scenarios”(https://www.bilibili.com/video/BV1hL411375k?spm_id_from=333.337.search-card.all.click) (accessed on 1 March 2022), which constructs an intuitive visualization and interactive virtual scene through interactive technology, artificial intelligence, 3D vision, etc., to let respondents know the overall shape of the metaverse education platform better) to obtain information about usage intention and provide pavement and guidance for the subsequent real platform design and scene construction. Then, by sharing the QR code and website of the questionnaire, the respondents were encouraged to scan the code or click the website to enter the questionnaire response page in the form of a red packet lottery. The questionnaire was conducted anonymously. After one month (21 April 2022–20 May 2022) of investigation, 339 questionnaires were received in Henan province. According to the principles of short response time, incomplete answers, and the same answers completed in all questionnaires, 275 valid questionnaires were obtained after excluding invalid questionnaires. Table 2 shows the basic characteristics of the interviewees. Among them, 72.36% of the interviewees were under 35 years old, which basically conforms to the age portrait of the audience on the metaverse education platform. Therefore, the sample of this survey is fairly representative.

4.2. Reliability and Validity Tests

There are many analysis software programs with a structural equation model (SEM), and the common ones include Amos 26, Mplus, and Smart PLS 3.0. SmartPLS 3.0 is selected for this study. This software is based on the logic of variance-based partial least squares structural equation modeling and has no more restrictions on data distribution and sample size. In addition, there is a bootstrapping function, which can create a new sample representative of the distribution characteristics of the mother by repeatedly extracting samples, which is easy to operate and has rich functions.
Because this study collected data through a questionnaire survey, first, a Harman single-factor test was conducted to check whether there was a common method deviation in the scale. The analysis found that the interpretation percentage of the first common factor was less than 40%, indicating that there was no serious common method variance. Second, the reliability and validity of the constructed index system were tested by indicators such as factor loadings, Cronbach’s alpha, composite reliability (CR), average variance extracted (AVE), and variance inflation factor (VIF). It was found that the load of all factors was greater than 0.7, the CR was greater than 0.7, the AVE was greater than 0.5, the Cronbach’s alpha was greater than 0.7, and the VIF was less than 5, indicating that all the indicators of this study met the relevant requirements [77]. Table 3 presents the result of the reliability analysis.
Table 4 shows that the correlation coefficient between each latent variable and other latent variables is less than the square root of the latent variable AVE, which indicates that the discrimination validity of this study model is good [77].

4.3. Model Fitting Tests

The use of SmartPLS in this study involves SRMR, d ULS, d_G, Chi-Square, NFI, and RMS_theta. They are generally used to evaluate the fitting of the model [78]. SRMR is the absolute goodness of fit index [79], which requires it to be lower than 0.08 [80], and the standard adaptation index NFI is above 0.9 [77]. However, NFI changes when the parameters change [81], which does not apply to the evaluation model fitting [79]. D_ULS, d_G is the complete adaptation standard, which helps find any difference between the empirical covariance matrix and the covariance matrix understood by the composite factor model [79,82]. RMS_theta is used to evaluate the correlation of external model residuals, mainly used in reflective models [83]. The closer the value is to 0, the better the model is, and less than 0.12, the better the goodness of fit [84]. Hair et al. [79] believed that the estimation model mainly considered the overall impact and model structure, while the saturation model evaluated all structures’ relationships.
As seen from Table 5, the RMS_theta is 0.108, indicating the effectiveness of the global PLS model.

4.4. Hypothesis Tests

Based on the above reliability, effectiveness, and suitability measurement models, the next step is the structural model [85]. The hypothesis was tested by the bootstrap test (5000 samples). The model has been proved to have high predictive power [86], and the variance percentage of use intention being 69.8%. The results are shown in Figure 2. Personalized learning ( β = 0.132 ,   p < 0.05 ), situational teaching ( β = 0.154 ,   p < 0.05 ), perceived usefulness ( β = 0.109 ,   p < 0.10 ), perceived ease of use ( β = 0.296 ,   p < 0.001 ), social needs ( β = 0.226 ,   p < 0.05 ), and social impact ( β = 0.219 ,   p < 0.001 ) have a significant positive impact on the use intention of users of the metaverse education platform, supporting H1a, H1b, H2a, H2b, H2c, and H3c. Technology maturity ( β = 0.013, p > 0.05 ) and perceived privacy risk ( β = −0.018, p > 0.05 ) have no significant impact on the use intention of users of the metaverse education platform and do not support H3a and H3b. This indicates that users like to try new things, and trust in emerging technologies has gradually become a habit. Although the metaverse education platform, as an emerging technology, may have problems in terms of technology maturity and user perceived privacy risk, it will not affect the users’ willingness to use the metaverse education platform.

5. Qualitative Comparative Analysis of Fuzzy Sets

5.1. Variable Selection and Calibration

There are complex relationships among the factors that affect the user’s willingness to use the metaverse education application platform. Although SEM analysis has verified which factors affect the user’s willingness to use the metaverse education application platform, it remains to be confirmed whether these influencing factors are the result of a single contribution to the user’s willingness to use the metaverse education application platform or the interaction of multiple influencing factors. Fuzzy-set qualitative comparative analysis (fsQCA) is mainly used to explain the possible configuration in complex causality, which can make up for the defect that SEM mainly focuses on net benefit [24,87]. This study combines the SEM and fsQCA methods, first verifies the model hypothesis, and then takes the personalized learning, situational teaching, perceived usefulness, perceived ease of use, social needs, and social impact potential variables as antecedent variables and the use intention of the metaverse education platform as the outcome variable. The fsQCA method is used to explore the configuration effect among the factors that affect the user’s willingness to use the metaverse education application platform to improve the descriptive power, predictive power, and explanatory power of the theoretical model of this study [88]. Figure 3 shows the fsQCA research framework.
Before the configuration analysis, the dependent variable before calibration was calibrated according to the standard (95%, 50%, 5%) [24]. When an integer is encountered, the case classification problem is solved by adding a tiny number (0.001).

5.2. Necessity Analysis of Conditions

After data calibration, this study conducted a necessity analysis of the antecedent variables and found that the consistency of all antecedent variables was less than 0.90 [12]. To judge the necessity of antecedent variables more accurately, the NCA package of R software was used for in-depth analysis, and it was found that the effect size of all antecedent variables was less than 0.3, indicating that a single antecedent variable cannot form the user’s intention to use the metaverse education application platform, and it needs to be formed by the mutual dependence and interaction of multiple antecedent variables [89]. Table 6 presents the result of necessity analysis of precedent variables.

5.3. Sufficiency Analysis of the Conditional Configuration

When discussing the configuration sufficiency of the factors influencing the user’s willingness to use the metaverse education application platform, the case frequency threshold is set to 1, more than 80% of the total number of cases are kept, the minimum standard value of original consistency is set to 0.8, and the minimum threshold value of PRI consistency is set to 0.75. The configuration analysis results of the influencing factors and conditions are shown in Table 7. Generally, three solutions (complex solution, intermediate solution and simple solution) are obtained by fsQCA. Considering that the intermediate solution contains both observation samples and logical residuals, its explanatory power is relatively scientific. Here, the intermediate solution is selected to explain the configuration results. Table 7 shows that the coverage of the solution is 0.733 and the consistency of the solution is 0.917, which indicates that the model has good explanatory power.
FsQCA results show that there are four condition configurations that form the user’s intention to use the metaverse education application platform. In this paper, the configurations with the same core conditions are summarized into one mode. According to the utility requirements reflected by the core conditions, they can be divided into the experience-driven community-driven type (S1), personality-driven community-driven type (S2 and S3), and social-driven utility-driven type (S4).

5.3.1. Experience-Driven Community-Driven Type

The antecedent configuration of configuration S1 is “perceived ease of use ✱ social influence ✱ perceived usefulness ● situational teaching”. The core conditions that trigger users’ willingness to use the metaverse education platform are perceived ease of use and social impact. When the user perceives that the metaverse education platform is easy to operate and driven by friends and classmates with common interests or public opinion news related to the metaverse education platform, and feels that the metaverse education platform is useful and integrated into the situational teaching mode, the user will be triggered to use it. In this kind of mode, users pay more attention to their experience utility; that is, users are more inclined to perceive ease of use and social impact, and the simultaneous existence of perceived usefulness and social impact will trigger users’ use intention.

5.3.2. Community-Driven Type under Personality Guidance

The core conditions of this trigger type are personalized learning and social impact, and there are two sub-modes. Among them, the edge conditions of configuration S2 are situational teaching, perceived usefulness and social needs, and the edge conditions of configuration S3 are situational teaching, perceived ease of use, and social needs. That is, when users feel high personalized learning and social impact, or when situational teaching, perceived usefulness, and social demand are high, or when situational teaching, perceived ease of use and social demand are high, users’ willingness to use the metaverse education platform will be triggered. This kind of mode essentially represents the concern of the users of the metaverse education platform for personalized-oriented situational teaching, perceived usefulness and social needs, as well as situational teaching, perceived ease of use, and social needs.

5.3.3. Utility-Driven Type under Social Guidance

The antecedent configuration of configuration S4 is “social needs, perceived ease of use, personalized learning, perceived usefulness, perceived ease of use”, The core conditions that trigger the user’s willingness to use the metaverse education platform are social needs and perceived ease of use. When users have high social needs and perceived ease of use and perceive that the metaverse education platform has high personalized learning and perceived usefulness, the will to use will be triggered. This kind of mode essentially represents the driving force of users’ equal use of personalized learning and perceived usefulness under the guidance of social interaction.

5.4. Robustness Test

There are many methods to check the robustness of QCA. The commonly used method is to adjust the calibration basis, the consistency threshold, and other parameters to compare whether the analysis results have substantial changes before and after the parameter adjustments. If there is no change, the analysis results are robust [90]. By adjusting the consistency threshold and the minimum case frequency, this study found that components such as the configuration path, core condition, and edge condition, as well as the consistency, coverage, and other indicators, did not change, and the analysis results are relatively reliable.

6. Conclusions and Discussion

6.1. Research Conclusion

Combining the metaverse technology with education, with a view toward exploring the user’s willingness to use the metaverse education platform, this research integrates the PPM and TAM models and builds an index system of influencing factors on the user’s willingness to use the metaverse education platform. Through expert demonstration, pre-investigation, formal investigation, and other methods to collect survey data, and based on the structural equation model and the fuzzy-set qualitative comparative analysis method, the scientificity of the index system construction and the feasibility of the hypotheses are verified, and the configuration analysis between antecedent variables that trigger users’ willingness to use the metaverse education platform is discussed. The specific analysis results are as follows:
First, the empirical results of the structural equation model show that personalized learning, situational teaching, perceived usefulness, perceived ease of use, social needs, and social impact have a significant positive impact on the use intentions of users of the metaverse education platform. However, technology maturity and perceived privacy risk have no significant impact on the usage intention of the metaverse education platform. Among the pushing factors, situational teaching has a more significant impact. Among the pull factors, perceived ease of use and social needs are more influential than perceived usefulness. Finally, among mooring factors, only social influence significantly influences the intention to use.
In the research model, perceived usefulness and perceived ease of use have both been verified to have a significant positive impact on the user’s willingness to use the metaverse education platform, which is consistent with previous research [44,45,46,47]. At the same time, the influence coefficient of usability is higher. The technology maturity and perceived privacy risk have yet to be reverse-verified in the model. Only social impact significantly impacts the use intention, which is different from the conclusion that perceived risk has a reverse impact on the use of education intention proposed by Teng et al. [43]. The metaverse education platform is a new technology product. Although it has hidden dangers regarding technology maturity and user-perceived privacy risks, users have a higher tolerance and reasonable psychological expectations for its technology maturity. Therefore, technology maturity and perceived privacy risk have no marked impact on the intention to use.
The results of qualitative comparative analysis of fuzzy sets show that the three modes of experience-driven community-driven, personality-driven community-driven, and social-driven utility-driven can trigger users’ willingness to use the metaverse education platform. Among them, the coverage rate of the community-driven model under the guidance of personality is higher than that of the other models, which indicates that this model has greater explanatory power for the user’s use intention.
Finally, a more specific research framework and a more hierarchical integration model have been constructed through the integration of PPM and TAM models. It is more conducive to exploring the key factors that affect the use intentions of the metaverse education application platform and can deduce the main types of antecedent configurations that trigger the use intentions of users. In addition, the model provides direction and reference for governments, enterprises, and other institutions in specific policies and practices.

6.2. Theoretical Contribution and Practical Enlightenment

Theoretical Contribution: First, in the context of a metaverse+ education environment, a model of factors influencing usage willingness is constructed from the demand side, which enriches the empirical research in the field of metaverse education in academia. At the same time, the applicability and theoretical value of the integrated model get validated. Second, this paper integrates the PPM and TAM models from an integrated perspective. It introduces two variables in the TAM model into the pull factors in the PPM model to provide a more specific and hierarchical research framework, which gives the research results better theoretical guidance. Third, we introduce social needs, personalized learning, situational teaching, and other variables to better reflect the user’s use intention of the metaverse education platform. Meanwhile, it also has a particular reference significance in exploring the influencing factors of usage intention in the metaverse library and the metaverse museum. Fourth, combining SEM and fsQCA methods, the key influencing factors of users’ willingness to use the metaverse education platform are revealed. Fourth, the configuration paths that trigger users’ willingness to use the metaverse education platform are explored, and the characteristics and core leading factors of different dominant modes are summarized. Fifth, in the context of the normalization of COVID-19, this study provides more possibilities for crisis management in the education industry.
In terms of practical enlightenment: (1) At the top design level, (a) based on the significant positive effect of social impact on the user intention, the government departments should create a loose and stable business environment for the healthy and orderly development of the metaverse education industry. Furthermore, considering the security of the metaverse education application platform, the government needs to give full play to the public opinion guidance and render the characteristics and advantages of the metaverse technology. Thus, it can enhance users’ willingness to use the metaverse education platform. (b) Although the impact of technology maturity and perceived privacy risk on the willingness to use the platform has shown insignificant results, it still needs to be addressed in the development process. Technology and security play a fundamental role in the digital transformation process of the education industry. Therefore, the government should provide financial and policy support for upgrading industry technology and legal protection for the network security of the metaverse platform. (2) Middle-level planning: community-driven mode dominated by personality and utility-driven mode dominated by social interaction have a more decisive influence on users’ use intention. Therefore, platform operators should consider subdividing user groups and adopt appropriate marketing strategies for dominant types of users. (a) Create content IP, establish connections with audience groups, trigger users’ spontaneous UGC, drive communication with content, and trigger community marketing fission. (b) The platform operator should strengthen cooperation with the government and social platforms and carry out a personalized push for the audience so that users can accurately obtain the information and services they are interested in. (3) At the bottom operational level: (a) Perceived ease of use has a higher impact on user use intention than perceived usefulness. Therefore, platform designers should pay more attention to the ease of operation in the development process, enhance the availability of high-quality learning resources, and consider the cost performance of platform services. (b) Personalized learning and social needs are the core factors that affect users’ use of the metaverse education platform. Therefore, in terms of setting the main functions of the platform, more attention should be paid to diversification and personalization. Moreover, it requires efforts to create a new social communication space that spans time and space to meet users’ broader and deeper social needs.

6.3. Future Outlook

Due to factors such as the workforce and COVID-19, the questionnaire collection in this study had time and regional limitations. As a new technology, the user intent will change with the development and promotion of the metaverse. In the future, systematic research can be carried out according to a certain time interval to explore the evolution trend of the influencing factors of users’ willingness to use the metaverse education application platform at different stages. Due to regional limitations, this region’s physical construction of the metaverse education technology platform is in the exploration stage. There is a shortage of platforms formally placed in service, and the actual testing of real scenarios could not be conducted during the questionnaire collection process. In addition, with the global application and promotion of the metaverse education platform, users in different countries may be affected by income differences, cultural differences, and other factors. Their willingness to use the metaverse education platform may also be different. In the future, users in multiple countries may be selected for research to compare users’ cognitive differences in using the metaverse education platform among countries.

Author Contributions

Conceptualization, methodology, software, formal analysis, investigation, writing—original draft preparation, visualization, G.W.; writing—review and editing, supervision, C.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Social Science Foundation of China (grant number: 20CGL017), the Logistics Research Center of the Key Research Base of Humanities and Social Sciences of Henan Province fund Project (grant number: 2022-JD-01), the Henan University of Technology High-level Talents Scientific Research Start-up fund Project (grant number: 31401371), and the Henan University of Technology Young Key Teacher Training Program (grant number: 21420174).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Acknowledgments

The authors are grateful for the funding of the National Social Science Foundation of China, the Logistics Research Center of the Key Research Base of Humanities and Social Sciences of Henan Province, and the Henan University of Technology.

Conflicts of Interest

Authors have no conflict of interest.

References

  1. Dhawan, S. Online learning: A panacea in the time of COVID-19 crisis. J. Educ. Technol. Syst. 2020, 49, 5–22. [Google Scholar] [CrossRef]
  2. Li, H.; Wang, W. Metaverse + education: A new pattern of education development in the future. Mod. Distance Educ. 2022, 1, 47–56. [Google Scholar]
  3. Nie, Z.; Zhang, Z. From intelligent education to future learning: The new normal of educational technology innovation and application in the new era—A summary of the 19th International Forum on educational technology. Open Educ. Res. 2021, 27, 18–25. [Google Scholar]
  4. Lin, H.; Wan, S.; Gan, W.; Chen, J.; Chao, H.C. Metaverse in Education: Vision, Opportunities, and Challenges. arXiv 2022, arXiv:2211.14951. [Google Scholar]
  5. Kye, B.; Han, N.; Kim, E.; Park, Y.; Jo, S. Educational applications of metaverse: Possibilities and limitations. J. Educ. Eval. Health Prof. 2021, 18, 1149230. [Google Scholar] [CrossRef]
  6. Tlili, A.; Huang, R.; Shehata, B.; Liu, D.; Zhao, J.; Metwally, A.H.S.; Wang, H.; Denden, M.; Bozkurt, A.; Lee, L.H.; et al. Is Metaverse in education a blessing or a curse: A combined content and bibliometric analysis. Smart Learn. Environ. 2022, 9, 1–31. [Google Scholar] [CrossRef]
  7. Kim, H.C.; Kim, M.; Park, I. How to Develop a Metaverse Platform for Educational Purpose: A Focus Group Study. Korean Soc. Comput. Inf. 2022, 27, 253–265. [Google Scholar]
  8. Teräs, M.; Suoranta, J.; Teräs, H.; Curcher, M. Post-Covid-19 education and education technology ‘solutionism’: A seller’ s market. Postdigital Sci. Educ. 2020, 2, 863–878. [Google Scholar] [CrossRef]
  9. Granić, A. Educational Technology Adoption: A systematic review. Educ. Inf. Technol. 2022, 27, 9725–9744. [Google Scholar] [CrossRef]
  10. Lee, Y.H.; Hsieh, Y.C.; Hsu, C.N. Adding innovation diffusion theory to the technology acceptance model: Supporting employees’ intentions to use e-learning systems. J. Educ. Technol. Soc. 2011, 14, 124–137. [Google Scholar]
  11. Nayak, B.; Bhattacharyya, S.S.; Goswami, S.; Thakre, S. Adoption of online education channel during the COVID-19 pandemic and associated economic lockdown: An empirical study from push–pull-mooring framework. J. Comput. Educ. 2022, 9, 1–23. [Google Scholar] [CrossRef]
  12. Lisana, L. Factors affecting university students switching intention to mobile learning: A push-pull-mooring theory perspective. Educ. Inf. Technol. 2022, 1–21. [Google Scholar] [CrossRef] [PubMed]
  13. Al-Nuaimi, M.N.; Al-Emran, M. Learning management systems and technology acceptance models: A systematic review. Educ. Inf. Technol. 2021, 26, 5499–5533. [Google Scholar] [CrossRef]
  14. Papakostas, C.; Troussas, C.; Krouska, A.; Sgouropoulou, C. Exploring Users’ Behavioral Intention to Adopt Mobile Augmented Reality in Education through an Extended Technology Acceptance Model. Int. J. Hum. Comput. Interact. 2022, 1–9. [Google Scholar] [CrossRef]
  15. Jaehong, J.; Yujung, K.; Won, S.S.; Insook, H. Augmented Reality and Virtual Reality for Learning: An Examination Using an Extended Technology Acceptance Model. IEEE Access 2021, 9, 6798–6809. [Google Scholar] [CrossRef]
  16. Al-Rahmi, A.M.; Shamsuddin, A.; Alturki, U.; Aldraiweesh, A.; Yusof, F.M.; Al-Rahmi, W.M.; Aljeraiwi, A.A. The influence of Information System Success and Technology Acceptance Model on social media factors in education. Sustainability 2021, 13, 7770. [Google Scholar] [CrossRef]
  17. Yu, Z.G. Extending the learning Technology Acceptance Model of WeChat by adding new psychological constructs. J. Educ. Comput. Res. 2020, 58, 1121–1143. [Google Scholar] [CrossRef]
  18. Park, S.; Kang, Y.J. A Study on the intentions of early users of metaverse platforms using the Technology Acceptance Model. J. Digit. Converg. 2021, 19, 275–285. [Google Scholar]
  19. Prasetyo, Y.T.; Ong, A.K.S.; Concepcion, G.K.F.; Navata, F.M.B.; Robles, R.A.V.; Tomagos, I.J.T.; Young, M.N.; Diaz, J.F.T.; Nadlifatin, R.; Redi, A.A.N.P. Determining Factors Affecting Acceptance of E-Learning Platforms during the COVID-19 Pandemic: Integrating Extended Technology Acceptance Model and DeLone & McLean IS Success Model. Sustainability 2021, 13, 8365. [Google Scholar] [CrossRef]
  20. Huang, C.-Y.; Wang, H.-Y.; Yang, C.-L.; Shiau, S.J.H. A Derivation of Factors Influencing the Diffusion and Adoption of an Open Source Learning Platform. Sustainability 2020, 12, 7532. [Google Scholar] [CrossRef]
  21. Roca, J.C.; Chiu, C.M.; Martinez, F.J. Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. Int. J. Hum.-Comput. Stud. 2006, 64, 683–696. [Google Scholar] [CrossRef] [Green Version]
  22. Bansal, H.S.; Taylor, S.F.; James, Y.S. “Migrating” to New Service Providers: Toward a Unifying Framework of Consumers’ Switching Behaviors. J. Acad. Mark. Sci. 2005, 33, 96–115. [Google Scholar] [CrossRef]
  23. Lin, C.L.; Jin, Y.Q.; Zhao, Q.; Yu, S.W.; Su, Y.S. Factors influence students’ switching behavior to online learning under COVID-19 pandemic: A push–pull–mooring model perspective. Asia-Pac. Educ. Res. 2021, 30, 1–17. [Google Scholar] [CrossRef]
  24. Nistor, N.; Stanciu, D.; Lerche, T.; Kiel, E. “I am fine with any technology, as long as it doesn’t make trouble, so that I can concentrate on my study”: A case study of university students’ attitude strength related to educational technology acceptance. Br. J. Educ. Technol. 2019, 50, 2557–2571. [Google Scholar] [CrossRef]
  25. Zhang, X.; Chen, Y.; Hu, L.; Wang, Y. The metaverse in education: Definition, framework, features, potential applications, challenges, and future research topics. Front. Psychol. 2022, 13, 1–18. [Google Scholar] [CrossRef]
  26. Ko, S.Y.; Jeong, H.K.; Kim, J.I.; Shin, Y.T. The Concept and development directions of metaverse. Korea Inf. Proc. Soc. Rev. 2021, 28, 7–16. [Google Scholar]
  27. Contreras, G.S.; González, A.H.; Fernández, M.I.S.; Martinez, C.B. The Importance of the Application of the Metaverse in Education. Mod. Appl. Sci. 2022, 16, 1–34. [Google Scholar] [CrossRef]
  28. Hu, Y.; Ni, Q.; Yang, X. International Research Progress, Trends and Challenges of Metaverse in Education. Mod. Distance Learn. Res. 2022, 34, 37–46. [Google Scholar]
  29. Lee, Y.; Jung, K.-T. Analysis of User Experience for the Class Using Metaverse—Focus on ‘Spatial’. J. Pract. Eng. Educ. 2022, 14, 367. [Google Scholar]
  30. Jovanović, A.; Milosavljević, A. VoRtex Metaverse platform for gamified collaborative learning. Electronics 2022, 11, 317. [Google Scholar] [CrossRef]
  31. Bermejo Fernandez, C.; Lee, L.H.; Nurmi, P.; Hui, P. Para: Privacy management and control in emerging iot ecosystems using augmented reality. In Proceedings of the 2021 International Conference on Multimodal Interaction, Montreal, Canada, 18–20 October 2021; pp. 478–486. [Google Scholar]
  32. Bessadok, A. Analyzing student aspirations factors affecting e-learning system success using a structural equation model. Educ. Inf. Technol. 2022, 27, 9205–9230. [Google Scholar] [CrossRef] [PubMed]
  33. Song, Y.J.; Kong, S.C. Investigating students’ acceptance of a statistics learning platform using Technology Acceptance Model. J. Educ. Comput. Res. 2017, 55, 865–897. [Google Scholar] [CrossRef]
  34. Lai, H.J. Investigating older adults’ decisions to use mobile devices for learning, based on the unified theory of acceptance and use of technology. Interact. Learn. Environ. 2020, 28, 890–901. [Google Scholar] [CrossRef]
  35. Gómez-Ramirez, I.; Valencia-Arias, A.; Duque, L. Approach to M-learning acceptance among university students: An integrated model of TPB and TAM. Int. Rev. Res. Open Distrib. Learn. 2019, 20, 1–25. [Google Scholar]
  36. Nagy, J.T. Evaluation of Online Video Usage and Learning Satisfaction: An Extension of the Technology Acceptance Model. Int. Rev. Res. Open Distrib. Learn. 2018, 19, 160–185. [Google Scholar] [CrossRef] [Green Version]
  37. Vanduhe, V.Z.; Nat, M.; Hasan, H.F. Continuance intentions to use gamification for training in higher education: Integrating the Technology Acceptance Model (TAM), social motivation, and Task Technology Fit (TTF). IEEE Access 2020, 8, 21473–21484. [Google Scholar] [CrossRef]
  38. Al-Rahmi, W.M.; Yahaya, N.; Aldraiweesh, A.A.; Alamri, M.M.; Aljarboa, N.A.; Alturki, U.; Aljeraiwi, A.A. Integrating technology acceptance model with innovation diffusion theory: An empirical investigation on students’ intention to use E-learning systems. IEEE Access 2019, 7, 26797–26809. [Google Scholar] [CrossRef]
  39. Ibáñez, M.B.; Di Serio, A.; Villarán, D.; Delgado-Kloos, C. The acceptance of learning augmented reality environments: A case study. In Proceedings of the 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT), Austin, TX, USA, 25–28 July 2016; IEEE: Piscataway, NJ, USA; pp. 307–311. [Google Scholar]
  40. Tiwari, P. Measuring the impact of students’ attitude towards adoption of online classes during COVID 19: Integrating UTAUT Model with perceived cost. Test Eng. Manag. 2020, 83, 8374–8382. [Google Scholar]
  41. Abbad, M.M.M. Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Educ. Inf. Technol. 2021, 26, 7205–7224. [Google Scholar] [CrossRef]
  42. El-Masri, M.; Tarhini, A. Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educ. Technol. Res. Dev. 2017, 65, 743–763. [Google Scholar] [CrossRef]
  43. Teng, Z.; Cai, Y.; Gao, Y.; Zhang, X.; Li, X. Factors Affecting Learners’ Adoption of an Educational Metaverse Platform: An Empirical Study Based on an Extended UTAUT Model. Mob. Inf. Syst. 2022, 2020, 5479215. [Google Scholar] [CrossRef]
  44. Kim, K.; Yang, E.; Ryu, J. Work-in-progress—The Effect of Students’ Perceptions on Intention to use Metaverse Learning Environment in Higher Education. In Proceedings of the 2022 8th International Conference of the Immersive Learning Research Network (iLRN), Vienna, Austria, 30 May–4 June 2022; IEEE: Piscataway, NJ, USA, 2022. [Google Scholar]
  45. Akour, I.A.; Al-Maroof, R.S.; Alfaisal, R.; Salloum, S.A. A conceptual framework for determining metaverse adoption in higher institutions of gulf area: An empirical study using hybrid SEM-ANN approach. Comput. Educ. Artif. Intell. 2022, 3, 100052. [Google Scholar] [CrossRef]
  46. 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]
  47. 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, 5759. [Google Scholar]
  48. Moon, B. Paradigms in Migration Research: Exploring “Moorings” as a Schema. Prog. Hum. Geogr. 1995, 19, 504–524. [Google Scholar] [CrossRef] [PubMed]
  49. Jin, Y.Q.; Lin, C.L.; Zhao, Q.; Yu, S.-W.; Su, Y.-S. A study on traditional teaching method transferring to E-learning under the COVID-19 pandemic: From Chinese students’ perspectives. Front. Psychol. 2021, 12, 632787. [Google Scholar] [CrossRef]
  50. Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Manag. Sci. 1989, 35, 982–1003. [Google Scholar] [CrossRef] [Green Version]
  51. Moon, J.W.; Kim, Y.G. Extending the TAM for a World-Wide-Web Context. Inf. Manag. 2001, 38, 217–230. [Google Scholar] [CrossRef]
  52. Mac Callum, K.; Jeffrey, L. Factors impacting teachers’ adoption of mobile learning. J. Inf. Technol. Educ. 2014, 13, 1–22. [Google Scholar] [CrossRef] [Green Version]
  53. Liang, S.; Li, J. Research on user transfer behavior of mobile digital reading platform. Libr. Inf. Work. 2022, 66, 99–108. [Google Scholar] [CrossRef]
  54. Ma, X.; Liang, J.; Li, X. Exploration of influencing factors and weights of Resource Recommendation in intelligent learning environment. China Audio Vis. Educ. 2019, 3, 110–119. [Google Scholar]
  55. Wei, L.V.; Yang, Y.; Zhang, Y. The impact of consumer perceived personalization on their click willingness under AI personalized recommendation. Manag. Sci. 2020, 33, 44–57. [Google Scholar]
  56. Xiao, Q.; Zhu, X. Investigation and analysis of user needs of library virtual reality technology and implementation suggestions. Libr. Sci. Res. 2021, 11, 40–49. [Google Scholar]
  57. Liu, R.; Ren, Y. Research on heart flow experience and empathy effect in immersive virtual environment. Audiov. Educ. Res. 2019, 40, 99–105. [Google Scholar]
  58. Yuan, Q.; Mao, C. Research on the influence of the characteristics of academic virtual community on the effect of knowledge exchange. Mod. Inf. 2021, 41, 3–12. [Google Scholar]
  59. Zhou, T.; Lin, X.; Deng, S. Research on transfer behavior of social media users based on PPM model. Inf. Sci. 2021, 39, 114–119. [Google Scholar]
  60. Eksail, F.A.A.; Afari, E. Factors affecting trainee teachers’ intention to use technology: A structural equation modeling approach. Educ. Inf. Technol. 2020, 25, 2681–2697. [Google Scholar] [CrossRef]
  61. Chen, Y.-H.; Keng, C.-J. Utilizing the Push-Pull-Mooring-Habit framework to explore users’ intention to switch from offline to online real-person English learning platform. Int. Res. 2019, 29, 167–193. [Google Scholar] [CrossRef]
  62. Balakrishnan, V.; Teoh, K.K.; Pourshafie, T.; Liew, T.K. Social media and their use in learning: A comparative analysis between Australia and Malaysia from the learners’ perspectives. Australas. J. Educ. Technol. 2017, 33. [Google Scholar] [CrossRef] [Green Version]
  63. Huang, F.; Teo, T. Influence of teacher-perceived organizational culture and school policy on Chinese teachers’ intention to use technology: An extension of technology acceptance model. Educ. Technol. Res. Dev. 2019, 68, 1547–1567. [Google Scholar] [CrossRef]
  64. Xie, L.; Zhao, Q.; Ma, K. Interaction, perceived benefits and citizen behavior of members of tourism virtual community—From the perspective of value co creation. J. Tour. 2019, 34, 28–40. [Google Scholar]
  65. Hou, N.; Zhao, X.; Yang, J. Personal needs, sharing motivation and competitive behavior in virtual communities. Manag. Rev. 2018, 30, 112–122. [Google Scholar]
  66. Yuan, S.; Zhang, H.; Duan, H. Research on Influencing Factors of mobile government app user churn behavior from the perspective of PPM. Inf. J. 2021, 40, 182–188. [Google Scholar]
  67. Tan, C.; Yi, Y.; Li, L. Research on factors influencing users’ willingness to continue using academic wechat official account. Mod. Intell. 2021, 41, 50–59 + 77. [Google Scholar]
  68. Yuan, J.; Guo, L. Analysis of influencing factors of non sustainable use behavior of online health community users. Mod. Intell. 2022, 42, 81–93. [Google Scholar]
  69. Deng, S.; Hu, S.; Zhao, H. Research on dynamic personal information disclosure behavior of social platforms from the perspective of configuration. Inf. Work. 2020, 41, 88–98. [Google Scholar]
  70. Wang, X.; Liu, W.; Jia, F. Model and Empirical Study on Influencing Factors of online live app usage. Libr. Inf. Work. 2020, 64, 22–31. [Google Scholar]
  71. Jin, Z.; Jin, M.; Qian, S.; Yang, B. Study on consumer’s willingness to migrate from offline to online channel concerning on fresh agricultural products: Moderating role of food safety crisis impact. Financ. Econ. Ser. 2019, 9, 92–102. [Google Scholar]
  72. Li, Z. Who created experience—Research on three modes of experience creation and their operating mechanism. Nankai Manag. Rev. 2019, 22, 178–191. [Google Scholar]
  73. Zhu, K. Research on the influence of interactivity and immersion of online learning space on students’ willingness to continue using. China Audio Vis. Educ. 2017, 2, 89–95. [Google Scholar]
  74. Huang, Y.; Mao, L.; Zhang, X. Analysis and process design of online immersive teaching system based on digital twin platform. J. Distance Educ. 2021, 39, 51–62. [Google Scholar]
  75. Chen, Y.; Zhu, Y. Research on Influencing Factors of users’ transfer behavior from paper reading to mobile reading from the perspective of PPM theory. Libr. Sci. Res. 2020, 2, 70–80. [Google Scholar]
  76. Zainab, A.M.; Kiran, K.; Karim, N. UTAUT’S Performance Consistency: Empirical Evidence from a Library Management System. Malays. J. Libr. Inf. Sci. 2018, 23, 17–32. [Google Scholar] [CrossRef] [Green Version]
  77. Fornell, C. Structural Equation Models with Unobservable Variables and Measurement Error:Algebra and Statistics. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  78. Dash, G.; Paul, J. CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technol. Forecast. Soc. Change 2021, 173, 121092. [Google Scholar] [CrossRef]
  79. 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]
  80. Hu, L.; Bentler, P.M. Fit Indices in Covariance Structure Modeling: Sensitivity to Underparameterized Model Misspecifi-Cation. Psychol. Methods 1998, 3, 424. [Google Scholar] [CrossRef]
  81. 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]
  82. 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]
  83. Lohmöller, J.B. Latent Variable Path Modeling with Partial Least Squares; Physica-Verlag: Heidelberg, Germany, 1989. [Google Scholar]
  84. 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. Calantone Common beliefs and reality about PLS: Comments on rönkkö and evermann (2013). Organ. Res. Methods 2014, 17, 182–209. [Google Scholar] [CrossRef] [Green Version]
  85. Saeed Al-Maroof, R.; Alhumaid, K.; Salloum, S. The Continuous Intention to Use E-Learning, from Two Different Per-Spectives. Educ. Sci. 2021, 11, 6. [Google Scholar] [CrossRef]
  86. Chin, W.W. The partial least squares approach to structural equation modeling. Mod. Methods Bus. Res. 1998, 295, 295–336. [Google Scholar]
  87. Duarte, P.; Pinho, J.C. A mixed methods UTAUT2-based approach to assess mobile health adoption. J. Bus. Res. 2019, 102, 140–150. [Google Scholar] [CrossRef]
  88. Zhang, M.; Du, Y. Application of QCA method in organization and management research: Orientation, strategy and direction. J. Manag. 2019, 16, 1312–1323. [Google Scholar]
  89. Dul, J. Necessary Condition Analysis (NCA): Logic and Methodology of “Necessary but not Sufficient” Causality. Organ. Res. Methods 2016, 19, 10–52. [Google Scholar] [CrossRef]
  90. White, L.; Lockett, A.; Currie, G. Hybrid Context, Management Practices and Organizational Performance: A Configurational Approach. J. Manag. Stud. 2021, 58, 718–748. [Google Scholar] [CrossRef]
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Figure 1. Conceptual Model of Influencing Factors of Usage Intention of Metaverse Education Application Platform.
Figure 1. Conceptual Model of Influencing Factors of Usage Intention of Metaverse Education Application Platform.
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Figure 2. Structural Equation Model Path Analysis.
Figure 2. Structural Equation Model Path Analysis.
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Figure 3. FsQCA Research Framework.
Figure 3. FsQCA Research Framework.
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Table
Table 1. Measurement Scale of Influencing Factors of Usage Intention of Metaverse Education Application Platform.
Table 1. Measurement Scale of Influencing Factors of Usage Intention of Metaverse Education Application Platform.
ConstructItemSources
Personalized Learning (PL)PL1I think I can get the learning resources I need on the metaverse education application platformMa et al. [54]
Xiao et al. [56]
PL2I think on the metaverse education application platform, I can make plans according to my own needs
PL3I think the metaverse education application platform can effectively strengthen its own weak points
PL4I think I can find a learning method that suits me on the metaverse education application platform
Situational Teaching (ST)ST1I think the classroom content of the metaverse education application platform is easier to understandLi [72]
Zhu [73]
Liu et al. [57]
ST2I think the classroom forms of the metaverse education application platform are more diverse
ST3I think the classroom context of the metaverse education application platform is more vivid
ST4I think metaverse education application platforms can create different roles
ST5I think the metaverse education application platform can be entertaining and educational
Perceived Usefulness (PU)PU1I think the metaverse education application platform can learn more high-quality coursesEksail et al. [60]
Chen et al. [61]
PU2I think the metaverse education application platform can make up for the lack of knowledge in the classroom
PU3I think the metaverse education application platform can provide me with more learning resources
PU4I think the metaverse education application platform can expand the scope of my knowledge
Perceived Ease of Use (PE)PE1I think it is not difficult to use the metaverse education application platformCallum et al. [52]
Huang et al. [63]
PE2I think the metaverse education application platform is cheap and affordable
PE3I think the metaverse education application platform will benefit students
Social Needs
(SN)		SN1I will experience the metaverse education application platform with friends/classmatesXie et al. [64]
Hou et al. [65]
Huang et al. [74]
SN2I will communicate with friends/classmates on the metaverse education application platform
SN3I will make more friends on the metaverse education application platform
SN4I will share the information obtained on the metaverse education application platform with friends/classmates
Technical Maturity (TM)TM1I don’t think the current metaverse technology is mature enoughTan et al. [67]
Kye et al. [5]
TM2I think the current metaverse education application platform is biased towards entertainment
TM3I think the current metaverse education application platform is not yet universal
Perceived Privacy Risk (PR)PR1I’m concerned that metaverse education application platforms collect too much personal informationYuan et al. [68]
Chen et al. [75]
Zhu et al. [69]
Teng et al. [43]
PR2I am concerned about leaking personal privacy when using the metaverse education application platform
PR3I am concerned that personal information is being used illegally by the metaverse education application platform
PR4I am concerned that third parties collect user information through the metaverse education application platform
Social Influence
(SI)		SI1I will be influenced by my friends/classmates to use the metaverse education application platformBansal [22]
Zhou et al. [59]
Wang et al. [70]
SI2I will be influenced by public opinion/news and use the metaverse education application platform
SI3I will show off my virtual identity on the metaverse education application platform to my friends/classmates
Intention to Use
(IU)		IU1I am willing to use the metaverse education application platformWang et al. [70]
Zainab et al. [76]
IU2I will continue to pay attention to the products related to the metaverse education application platform
IU3I will spend more time on the metaverse education application platform
IU4I will share the metaverse education application platform with friends/classmates
Table
Table 2. Basic Characteristics of Samples.
Table 2. Basic Characteristics of Samples.
DescriptionFrequencyPercentage (%)
GenderMan13047.20
Woman14552.80
AgeLess than 22 years9434.18
Between 22–35 years old10538.18
Between 36–50 years old5520.00
More than 50 years217.64
Level of educationLower than High School259.09
College5018.18
University13749.82
Postgraduate or Above6322.91
OccupationStudent10538.18
Civil Servants or Public Institutions8631.27
Enterprise5218.91
Self Employed or Others3211.64
Monthly income (Monthly living expenses)Less than 2000 yuan9233.45
2001~5000 yuan6523.64
5001~7000 yuan8631.27
More than 7000 yuan3211.64
Ways to learn about the application platform of metaverse educationNews5720.73
WeChat, Weibo,
Wechat Moments, etc.		13549.10
Advertising248.72
Friends and Family3312.00
Others269.45
Table
Table 3. Reliability Analysis.
Table 3. Reliability Analysis.
ConstructItemFactor LoadingCronbach’s AlphaCRAVE
Personalized Learning (PL)PL10.8760.8980.9290.765
PL20.864
PL30.859
PL40.899
Situational Teaching (ST)ST10.8740.9150.9360.746
ST20.856
ST30.861
ST40.885
ST50.842
Perceived Usefulness (PU)PU10.8910.9160.9410.799
PU20.899
PU30.913
PU40.871
Perceived Ease of Use (PE)PE10.8460.8180.8920.733
PE20.872
PE30.850
Social Needs (SN)SN10.8990.9190.9420.804
SN20.912
SN30.899
SN40.876
Technical Maturity (TM)TM10.8950.7070.8180.607
TM20.578
TM30.829
Perceived Privacy Risk (PR)PR10.8640.9280.9480.819
PR20.918
PR30.912
PR40.926
Social Influence
(SI)		SI10.8400.7790.8720.694
SI20.880
SI30.777
Intention to Use
(IU)		WU10.7900.8540.9020.697
WU20.875
WU30.820
WU40.851
Table
Table 4. Differentiate Validity Results of Model.
Table 4. Differentiate Validity Results of Model.
PLSTPUPESNTMPRSIIU
PL0.875
ST0.6840.864
PU0.5380.6380.894
PE0.4630.3700.4870.856
SN0.5230.5860.4840.3810.897
TM0.2270.2270.2390.0430.2370.779
PR0.2200.3660.2580.0770.2130.5190.905
SI0.2490.5560.4190.3750.4390.0820.0700.833
IU0.6040.6230.6220.6350.6460.2020.1890.5560.835
Note: Numbers in diagonal are the square root values of the latent AVE.
Table
Table 5. Model Fit Indicators.
Table 5. Model Fit Indicators.
CriteriaComplete Model
Saturated ModelEstimated Model
SRMR0.0680.068
d_ULS2.7862.893
d_G1.1661.261
Chi-Square1160.2081160.208
NFI0.7570.757
RMS Theta0.108
Table
Table 6. Necessity Analysis of Precedent Variables.
Table 6. Necessity Analysis of Precedent Variables.
Conditional VariableConsistencyCoverage
Personalized Learning0.7850.782
~Personalized Learning0.7980.801
Situational Teaching0.8830.734
~Situational Teaching0.7060.868
Perceived Usefulness0.8340.772
~Perceived Usefulness0.7730.835
Perceived Ease of Use0.8010.809
~Perceived Ease of Use0.8260.819
Social Needs0.8220.805
~Social Needs0.8160.833
Social Influence0.8290.750
~Social Influence0.7450.826
Note: “~” represents the absence of conditions.
Table
Table 7. Analysis Results of the Condition Configuration of the Factors Affecting Usage Intention of Metaverse Education Application Platform.
Table 7. Analysis Results of the Condition Configuration of the Factors Affecting Usage Intention of Metaverse Education Application Platform.
ConditionUsage Intention
S1S2S3S4
Personalized learning ✱✱●
Situational Teaching●●●●
Perceived Usefulness●● ●
Perceived Ease of Use✱ ●✱
Social Needs ●●✱
Social Influence✱✱✱
Original Coverage0.5780.5550.5080.534
Unique Coverage0.0970.0740.0270.053
Consistency0.9490.9450.9620.950
Consistency of the solution0.917
Coverage of the solution0.733
Note: “✱” represents the core condition that appears, “●” represents the presence of an edge condition, “blank” represents the condition may or may not occur.
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Wang, G.; Shin, C. Influencing Factors of Usage Intention of Metaverse Education Application Platform: Empirical Evidence Based on PPM and TAM Models. Sustainability 2022, 14, 17037. https://doi.org/10.3390/su142417037

AMA Style

Wang G, Shin C. Influencing Factors of Usage Intention of Metaverse Education Application Platform: Empirical Evidence Based on PPM and TAM Models. Sustainability. 2022; 14(24):17037. https://doi.org/10.3390/su142417037

Chicago/Turabian Style

Wang, Gaofeng, and Changhoon Shin. 2022. "Influencing Factors of Usage Intention of Metaverse Education Application Platform: Empirical Evidence Based on PPM and TAM Models" Sustainability 14, no. 24: 17037. https://doi.org/10.3390/su142417037

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