Generally speaking, “cool” is a positive evaluation of a person, thing (such as a product or technology), or brand that deviates from everyday perceptions and provides a unique or trendy aspect desirable in society (Sundar et al., 2014). “Cool” is typically perceived as a favorable and attractive characteristic (Nan et al., 2022; Li et al., 2024). The term has been applied to describe numerous subjective entities. Whereas some researchers perceive it as a personal trait that people possess (Dar-Nimrod et al., 2012). Prior research indicates that consumers may exhibit favorable behavioral intentions if they perceive a product or technology as cool (Niu and Mvondo, 2024). Since VR technology is a relatively new IoT product at present, its “cool” features, such as compatibility with other devices (e.g., smartphones, computers), Bluetooth/WiFi connectivity, user-friendly interface, fashion, uniqueness, excellent quality, good performance (Park et al., 2018) may increase its functional value and overall perceived value. This study focuses on student’s “cool” factor, specifically virtual reality in learning, rather than on individuals such as teachers. This study examines the relationship between students’ use of communication technologies in the classroom and students’ perceptions of their coolness.

According to Ford (1992), learning motivation is defined as a psychological state aimed at achieving goals, beliefs, and emotions. Motivation refers to a stimulus that encourages, instructs, and sustains behavior, endorsing students’ commitment to a particular subject. Given direction and persistence concerning learning (Fredricks et al., 2004; Reeves, 2006) is crucial for achieving successful academic performance. Learning motivation plays a significant role in this pursuit. Different predictions have been found regarding the predictions of VR on learning attitudes and motivation. The VR method based on spherical video did not significantly correlate with learners’ attitudes toward it (Chang et al., 2019). However, students in the VR-based group showed significantly more favorable attitudes than control groups (Van Ginkel et al., 2019). Students are very interested in VR-based learning methods (Lin et al., 2003; Ogbuanya and Onele, 2018; Winkelmann et al., 2020; Lee, 2023).

Satisfaction, according to Bedggood and Donovan (2012), is commonly defined as the level to which students enjoy their studies and is influenced by factors such as engagement and skill enhancement. Assessing and evaluating learning predictions, including in blended learning environments (Arbaugh, 2014; Rahman et al., 2015), satisfaction with learning is crucial. Students who are content with their academic achievements tend to equate it with satisfaction in their academic experience. Key satisfiers for university students entail experiencing a sense of accomplishment with their learning (Douglass et al., 2012). Performance in subjects is assessed based on scores obtained on assessment items that measure achievement of learning outcomes, so student satisfaction depends on their perceptions of achievement of learning outcomes.

We propose “perceived coolness” as an antecedent factor in students’ learning motivation and satisfaction. Studies have demonstrated that consumers, when they feel they are at the forefront of a trend, tend to develop positive attitudes toward the related device (Kim, 2016). positive academic emotions part of the intermediary role between college students’ online learning motivation and online learning performance (Zhu et al., 2022). Past studies showed that it is also an essential perceptual factor technology can motivate students to engage in learning and stimulate intense learning motivation (Bracq et al., 2019; Chang et al., 2019, 2020a; Shi et al., 2019; Taranilla et al., 2019; Bourgeois-Bougrine et al., 2020; Singh et al., 2020). VR applications can improve students’ quality of learning (McGovern et al., 2020) and perceived satisfaction (Chang et al., 2020a) in presentation skills. VR technology can improve learning satisfaction (Jung et al., 2012; Chang et al., 2019; Makransky et al., 2019; Meyer et al., 2019; Winkelmann et al., 2020). VR technology did not significantly change learners’ self-efficacy (Chang et al., 2020a), but a positive prediction of self-efficacy was found in VR-based conditions (Meyer et al., 2019; Shu et al., 2019; Strzelecki, 2023).

We explored the concept of perceived cool technology using iPads in educational research. Teachers’ and children’s perceptions heavily predicted our project, particularly regarding the iPad as a cool technology (Culén and Gasparini, 2012). Our study considers the application of VR technology in the classroom as a scenario of perceived cool application. Whether it is an iPad, iPhone, or VR technology, they represent the emergence of new technologies and practical application scenarios. These technologies can be used by anyone as application scenarios, enabling individuals to experience the coolness factor and conduct follow-up behavioral research. Therefore, we formulated these hypotheses.

Blended learning utilizes the Internet’s convenience and valuable resources in conjunction with the benefits of traditional learning, enhancing teaching by incorporating online computer and network activities into conventional courses. This new educational mode has been proposed to serve as a supplement to traditional face-to-face learning (Benbunan-Fich, 2008). The implementation of blended learning represents a restructuring of curriculum design with the aim of encouraging student engagement in online learning. Research has demonstrated that the incorporation of information technology into the teaching and learning process fosters the development of enhanced educational outcomes. Acquisition of course resources and improving the learning experience have been identified as key factors in enhancing learning outcomes (Bai et al., 2016; Darling-Aduana and Heinrich, 2018; Turvey and Pachler, 2020; Deng et al., 2023).

Blended learning performance can be defined in various ways. For example, it may relate to students’ examination predicts (Law and Geng, 2019), or satisfaction with learning. Yang conducted research on online learning and measured students’ performance using an online learning system. The study relied on the definition of learning performance from the Association for Educational Communications and Technology. In 2004, the definition of learning performance was established, stating that it measures a learner’s ability to apply newly acquired knowledge or skills. This definition emphasizes not only the acquisition of basic knowledge and skills but also the capacity to use them effectively.

The VR virtual technology stimulates students’ interest in learning, increases their enthusiasm for learning, and improves their learning performance (Geng et al., 2018). Chang et al. (2020b) used VR technology for interactive learning in primary school classrooms, and compared with students using traditional learning, students who studied using VR achieved positive predicts in learning satisfaction and motivation. Research results show that this teaching method can improve students’ learning performance and motivation, enable them to solve problems, and actively improve their academic performance.

Blended learning methods have proven to significantly enhance learning. The learning method has a higher course satisfaction rate than traditional classroom teaching (Darling-Aduana and Heinrich, 2018). The learning method has a higher course satisfaction rate than traditional classroom teaching. As stated by Yildirim (2005), it enhances student devotion to the learning process. Greater flexibility and convenience are appreciated by students, making it their preferred option (Hogarth, 2010). So, we formulated these hypotheses.

As shown in the literature review above, the application of VR technology in education has been proven to stimulate students’ learning motivation (Bracq et al., 2019; Chang et al., 2019, 2020a; Shi et al., 2019; Taranilla et al., 2019; Bourgeois-Bougrine et al., 2020; Singh et al., 2020) and improve students’ learning satisfaction (Jung et al., 2012; Chang et al., 2019; Makransky et al., 2019; Meyer et al., 2019; Winkelmann et al., 2020). At the same time, students’ strong motivation (Law et al., 2010; Law and Breznik, 2017; Law and Geng, 2019) and high learning satisfaction can improve students’ learning performance (Meyer et al., 2019; Shu et al., 2019). In this study, we investigated how VR technology associates with students’ blended learning performance in a class by mixing online and offline learning, so we formulated these hypotheses.

Based on the above literature review of the relationship between the perceived coolness of using VR technology, learning motivation, learning satisfaction and blended learning performance, (Figure 1) illustrates the hypothetical relationship between the theoretical model and various structures (perceived coolness of using VR technology, learning motivation, learning satisfaction, and blended learning performance) of all indicators tested in our research.

This research is grounded in a sample size of 82 second-year college students studying international tourism and management in 2021 at City Polytechnic of Shenzhen, encompassing varying genders, and ages. The participants were selected through a convenient sampling method and data was collected through a detailed survey, graded on a 5-point Likert-type scale ranging from “strongly disagree” to “strongly agree,” and fully implemented in the questionnaire. We conducted data analysis using SPSS 24.0 and PLS 3. The questionnaire survey is a form of offline data collection and analysis undertaken after the mid-term examination of students between the dates of November 10th, 2023 and November 15th, 2023.

The measurement scale employed to gauge multiple aspects of the suggested model (i.e., Perceived coolness, learning satisfaction, learning motivation, and Blended learning performance) is derived from existing literature. Four items from (Yin and Yuan, 2021) were used to measure learning motivation with good reliability, its internal consistency coefficient Cronbach α = 0.87; while five items from (Sun et al., 2008) were adopted to evaluate learning satisfaction with good reliability, its internal consistency coefficient Cronbach α = 0.91. Blended learning performance was assessed via seven items taken from Yin and Yuan (2021) with good reliability, its internal consistency coefficient Cronbach α = 0.88. The paper utilized a four-item Perceived Coolness Scale adapted from Sundar et al. (2014) with good reliability, its internal consistency coefficient Cronbach α = 0.89. All items are measured using a 5-point Likert scale.

Partial Least Squares-Structural Equation Modeling (PLS-SEM) analysis was preferred over traditional covariance-based structural equation models (CB-SEM) due to the complexity of the model, which includes many constructs and indicators, set against the sample size (N = 200). Such complexity, when using CB-SEM, would cause our model to be empirically under-identified (Kline, 2004). Furthermore, using PLS-SEM is also useful in overcoming any deviations from normality in data sets (Chin, 1998). It is contended that PLS-SEM produces reliable and sturdy outcomes with limited sample sizes of roughly 100 observations and non-normal data, making it well-suited for the current investigation (Valle and Assaker, 2016). SEM-PLS processing using smart pls 3 is conducted in two stages: (1) The first step is to test the outside model. (2) The second stage is testing the inner model. At this stage the aim is to find out whether there is an influence between variables. Tests are carried out using the t-test.

Firstly, we distributed and recycled the scales, followed by the employment of SPSS23.0 to perform demographic analysis and measure reliability and validity on the collected data from the scales. The data was then imported into smartpls3 to execute a confirmatory factor analysis to establish the research hypothesis.

In September 2023, a questionnaire was prepared, and a pilot study was carried out to evaluate the validity and reliability of the scales employed. In the pilot study, 30 students were surveyed and requested to provide feedback on the comprehensibility of the scales. The findings indicated that all variables met the pre-determined criteria for validity and reliability, affirming the consistent reliability of the developed scale in measuring students’ levels of acceptance toward VR technology in higher education. This conclusion was supported by composite reliability, Cronbach’s alpha, reliability coefficients exceeding 0.7, and average variance extracted (AVE) surpassing 0.5. It was proved that the experimental scale has good reliability and validity.

The first part of the questionnaire represents the demographic characteristics of respondents. A total of 82 copies were disbursed, and all were recovered, yielding a 100% recovery rate. Female group with 67.1% are 55 students and males with 32.9% are 27 students. Of the total, 76.8% are 63 graduate students aged between 20 and 25 years old. The remaining 23.2% are 19 graduate students above 26 years old.