Determinants of students’ adoption of virtual reality-based learning systems: An individual difference perspective

ABSTRACT

This study investigates the individual difference antecedents of students’ behavioural intention to use VR-based learning systems, especially from an integrated perspective of Big Five personality traits and perceived physiological vulnerability to IT use (PPVITU). Data collected from 149 respondents are tested against the research model using the partial least squares structural equation method (PLS-SEM). The results indicate that extraversion and neuroticism positively affect perceived usefulness, conscientiousness negatively influences usefulness, and agreeableness and neuroticism have positive effects on perceived enjoyment. Furthermore, this study finds that musculoskeletal discomfort: neck and shoulder back pain (one PPVITU component) positively influences perceived ease of use. The findings of this study provide several important theoretical and practical implications for promoting VR-based learning system use behaviour.

KEYWORDS:

• Virtual reality(VR)-based learning systems
• individual difference
• personality traits
• perceived physiological vulnerability to IT use (PPVITU)

Introduction

Information Technology (IT) is currently being applied to educational contexts to enhance student learning motivation. Compared to traditional teaching settings, in which students are passive and knowledge-receiving learners, the E-learning approach is more engaging and efficient in supporting students’ learning, but lacks an interactive process (González, 2018). Chang et al. (2020) indicated that virtual reality (VR) is a potentially helpful education tool, and VR provides an interactive and simulated environment that can transform students into active learners. Furthermore, some studies also advocated that VR can increase learners’ engagement and active learning because VR creates more immersive and interactive learning contexts (Chen, 2016; Perez-Ramirez et al., 2021).

E-learning systems optimise students’ and teachers’ learning and teaching processes, which helps to overcome drawbacks associated with traditional educational methods. The use of e-learning is vast and continues to grow. VR-based learning systems unite two main areas: e-learning, which is a timely and convenient learning method, and VR-based learning systems, which feature immersion, interaction, and imagination. Though previous studies have explored e-learning and digital technology in teaching and learning contexts, few educators pay attention to the application of VR-based learning systems in higher education, and little literature explores the influence of VR-based learning systems on students. Hence, this study investigates the factors that influence individuals’ behaviour in a visionary, interactive, and engaging 3D environment combined with an e-learning approach.

The Technology Acceptance Model (TAM) is a robust model for analysing users’ technology adoption and e-learning acceptance. TAM focuses on the effects of utilitarian motivation. However, hedonic motivation is also an important factor in individuals’ adoption. Van der Heijden (2004) proposed an extended TAM incorporating three important antecedents, i.e. utilitarian motivation – perceived usefulness (PU) and perceived ease of use (PEOU), hedonic motivation – perceived enjoyment (PE). VR-based learning systems belong to a kind of technological applications and have interesting and entertaining properties. Therefore, the extended TAM is suitable as the theoretical basis.

Individual differences are user characteristics that include personality traits, demographic variables, and experience. Some studies show that individual differences are important antecedents affecting behavioural beliefs and can be integrated into TAM for increasing explanatory power (Şahin et al., 2021; Zhang et al., 2022).

Personality traits are one of the important individual differences. They are manifested in behaviour and are found to have influences on students’ behavioural intention (Bazkiaei et al., 2020; Smiderle et al., 2020). IT use can cause repetitive strain injury, musculoskeletal disorders, discomfort, and vision syndromes. The discomfort and healthy trauma should be concerned in VR adoption and use (Dehghani et al., 2021; Herz & Rauschnabel, 2019; Lin et al., 2020).

Virtual reality-based learning systems

The concept of VR was originally proposed as a triangle displayed by a computer that showed mathematical phenomena as part of our own natural world. VR is defined as a computer-generated simulation of a three-dimensional image or artificial environment experienced by the user through sensory stimuli, with which it is possible to interact in a natural manner using a helmet with a screen inside and electronic tools (Chang et al., 2020; Perez-Ramirez et al., 2021; Radianti et al., 2020).

E-learning with immersive VR-based learning systems involves students experiencing a virtual environment in which they can act directly; this system makes learning more interactive and realistic (Radianti et al., 2020). VR-based learning systems offer a rich sensory experience that presents an innovative and fresh approach to satisfy technologists and students. VR systems can effectively stimulate students’ interest in different subject areas, such as medical surgery and industrial design (Perez-Ramirez et al., 2021). VR-based learning systems are engaging and exciting developments in the education contexts.

Theoretical background and hypotheses

Extended TAM

The purpose of TAM is to elucidate individual behaviour towards the adoption of technology. TAM posits PU and PEOU are fundamental determinants of IT acceptance. (Davis, 1989). The former was the degree to which a person believes using a particular system would enhance his/her job performance, while the latter was as the degree to which a person believes that using a particular system would be free of effort. However, TAM only focuses on the utilitarian motivation. Therefore, Van der Heijden (2004) proposed an extended TAM including another important antecedent – perceived enjoyment (PE) from a hedonic perspective. PE refers to the extent to which the activity of using IT is perceived to be enjoyable. According to the assertions of extended TAM, this study hypothesises:

H1. PU, PEOU, and PE positively affect BI.

H2. PEOU positively affects PU and PE.

Big five personality traits

Extraversion refers to warmth, gregariousness, assertiveness, excitement seeking, and positive emotions (Costa & McCrae, 1995). Extroverted students tend to be skilled in play, gesturally expressive, enthusiastic (McCrae & John, 1992), and have a goal learning orientation. Wang et al. (2012) found that extroverted students tend to like interacting via IT techniques with others. Therefore, extroverted students are likely to find VR-based learning systems more useful and interesting in their learning. Hence, this study hypothesises:

H3. Extraversion positively affects PU and PE.

Conscientiousness refers to competence, order, dutifulness, achievement striving, self-discipline, and deliberating (Costa & McCrae, 1995). Conscientious students tend to avoid engaging in Internet usage. Accordingly, this study assumes that conscientious students may be interested in VR-based learning system use because they see it as a useful tool to increase learning efficiency. On this basis, this study hypothesises:

H4 Conscientiousness positively affects PU and PE.

Openness to experience is related to fantasy, aesthetics, feelings, actions, ideas, and values. Therefore, individuals with openness to experience usually have broad interests and are imaginative and curious (Costa & McCrae, 1995). In view of the preceding literature, students who are open to experience are likely to be more curious about VR-based learning systems, which are new, fancy, and exciting. Thus, this study posits:

H5. Openness to experience positively affects PU and PE.

Agreeableness refers to people who have the tendency to be kind, considerate, flexible, optimistic, and good-natured (Costa & McCrae, 1995). Agreeable personalities are more likely to be accommodating and cooperative when asked to adopt a new technology (Devaraj et al., 2008). Therefore, agreeable individuals are inclined to perceive a new technology as useful. Agreeableness was observed to have a positive effect on PE (Wang et al., 2012). Thus, this study hypothesises:

H6. Agreeableness positively affects PU and PE.

Neuroticism refers to an individual’s tendency to be emotional unstable, worried, prone to stress, angry and hostile. Neurotic individuals are less willing to try a change or an innovation (Gupta, 2021) and are more likely to suffer from computer anxiety and feel frustrated in their computer interactions. When faced with new changes or situations, neurotic individuals usually have negative reactions and have difficulty relaxing and enjoying their advantages (Devaraj et al., 2008). This study therefore posits:

H7. Neuroticism negatively affects PU and PE.

PPVITU

Excessive computer usage is likely to lead to IT-related health risks, such as eye strain, tension headache, lower backache, and psychosocial stress. Computer users who spend more time working at the screen or being exposed daily to long hours of computer work are associated with neck, wrist, shoulder, and elbow symptoms, and musculoskeletal disorders (Stanam et al., 2019). LaViola (2000) specified that VR users can develop symptoms of motion sickness and cybersickness including eye strain, headaches, sweating, dryness of mouth, nausea, and vomiting due to visual stimulation. Lin et al. (2020) described these common IT use symptoms as personal susceptibility to pain/discomfort and physical symptoms. In addition, they defined the relationship of VR-based learning system use and physical health as PPVITU. There are four components of PPVITU: visual discomfort, musculoskeletal discomfort: limb pain, head discomfort, and musculoskeletal discomfort: neck, shoulder, and back pain. PPVITU will hinder users’ perceptions on ease of IT use. Thus, this study posits:

H8. The four PPVITU components negatively affect PEOU.

Method

Measures

The measures were adapted from the pertinent literature and refined for the VR-based learning system. The study employed 7-point Likert-type scales ranging from “strongly disagree’ to ‘strongly agree’ to measure all constructs. The items measuring five personality traits were derived from the works of Wang et al. (2012) and Zhou and Lu (2011). The 17 items of PPVITU were based on Lin et al. (2020). The measures for PU, PEOU, and BI were based on Davis (1989). The four-item construct of PE was modified from Van der Heijden (2004).

Sample and data collection

The data was collected using online survey procedure. As the focus of this study was to explore university students’ perceptions and BI regarding VR-based learning systems, respondents needed to be university students and have experience with VR systems. A total of 154 questionnaires were received, of which five were eliminated because of incomplete answers. This left 149 useful responses for analysis. 39.6% of the respondents were female, 60.4% male, and most of the respondents were 18 to 26 years old.

Data analysis method

The partial least squares structural equation method (PLS-SEM) was used to analyse the empirical data. To reduce potential confounding effects from the sample demographics and distribution, gender and age were treated as control variables. They were hypothesised to have direct influences on behavioural intention.

Data analysis and results

Measurement model

Measurement model was assessed based on reliability and validity. As shown in Table 1, the values of composite reliability for all constructs exceeded the recommended value of 0.7, supporting internal consistency. Convergent validity exists when the outer loading is greater than 0.7 and average variance extracted (AVE) exceeds 0.5 (Hair et al., 2019). As Table 1 shows, all outer loadings except one item (VD5. I felt eye pain during or after IT product use) are above 0.70. The loading of VD5 is not too low (0.66) and the item is an important indicator of visual discomfort. Therefore, VD 5 is retained.

Table 1. Reliability and validities.

Constructs	Item Numbers	Outer Loading	Composite Reliability	AVE
Extraversion	3	0.78 ~ 0.96	0.92	0.80
Conscientiousness	3	0.88 ~ 0.95	0.93	0.82
Openness	3	0.86 ~ 0.91	0.92	0.79
Agreeableness	3	0.76 ~ 0.88	0.88	0.70
Neuroticism	3	0.86 ~ 0.93	0.91	0.78
Visual discomfort	6	0.66 ~ 0.94	0.92	0.65
Limb pain	5	0.78 ~ 0.91	0.92	0.70
Head discomfort	3	0.74 ~ 0.97	0.90	0.76
Neck, shoulder, and back pain	3	0.88 ~ 0.93	0.93	0.81
PU	4	0.83 ~ 0.92	0.93	0.78
PE	4	0.80 ~ 0.90	0.91	0.72
PEOU	4	0.81 ~ 0.89	0.92	0.74
BI	3	0.79 ~ 0.87	0.88	0.71

The Heterotrait-Monotrait (HTMT) ratio was used to evaluate the discriminant validity. All HTMT ratios were below the threshold value of 0.85, justifying discriminant validity.

Structural model and hypotheses testing

The hypotheses were tested by assessing the structural model. Figure 1 shows results of the structural model. Because of the exploratory nature, the significance level was extended to 0.1 suggested by Hair et al. (2019).

Figure 1. Results of structural model.

The model explained 62% of BI variance. Concerning Hypothesis 1, PU and PE were found to have significantly positive influences on BI. However, PEOU was found to have no significant effect on BI.

Fifty-three percent of PU variance and 43% of PE variance were explained in the model. As expected, the effects of PEOU on PU and PE were positive and significant (i.e. Hypothesis 2 was supported).

Concerning Hypotheses 3–7, three personality traits (extraversion, conscientiousness, and neuroticism) significantly affect PU, and two personality traits (agreeableness and neuroticism) significantly affect PE. As expected, the results show that extraversion positively affects PU, and agreeableness positively affects PE. However, the impacts of conscientiousness and neuroticism are contrary to hypothetical directions. The results show that: (1) Conscientiousness negatively influences PU, (2) Neuroticism positively influences PU and PE.

The model explained 12% of PEOU variance. Only one of the four PPVITU dimensions, musculoskeletal discomfort: neck, shoulder, and back pain, was found to have a significant effect on PEOU. Surprisingly, the effect was positive.

Discussions

As expected, PU and PE have significantly positive effects on BI. The findings are consistent with previous IT-acceptance studies. The determinants of IT behavioural intention are mainly divided into two categories, utilitarian motivation and hedonic motivation. PU focuses on utilitarian motivation and PE focuses on hedonic motivation. Furthermore, unlike most previous studies, this study found that the effects of PU and PE on behavioural intention are similar (β = 0.43 and 0.41). One reason for this result is that VR-based learning systems have both learning and entertainment effects. Therefore, students may attach equal importance to utilitarian and hedonic values. When students think the VR-based learning system is interesting and helpful to their learning, they will have a higher willingness to use it.

This study found that PEOU has no significantly direct influence on BI. This is likely because this study used a sample of university students. Most of them have rich experiences and skills in operating IT and VR systems. Therefore, operating the VR-based learning system is not an issue for them.

However, this study found that PEOU has significant influences on PU and PE. If the VR-based learning systems are too difficult to use and learn, and the user interface is unfriendly, this will hinder PU and PE. VR-based learning systems developers should pay attention to these influences. To enhance the level of students’ PU and PE, educators may design easier panels, interactive scenarios and systems, and more interesting simulations to facilitate learning in their courses.

The results show that extraversion positively affects PU. Extraverted individuals are more ambitious, competitive, and energetic. They have a more positive attitude and are more willing to try new technology for enhancing performance and competitiveness. Therefore, they are more inclined to regard VR-based learning systems as a useful way to learn.

Surprisingly, contrary to the hypothesised direction, this study found that conscientiousness has a significant negative impact on PU. This finding is inconsistent with the findings of previous studies. It could be that the special characteristics of VR-based learning systems and the personality traits of conscientious individuals conflict. VR-based learning systems are characterised as fun, lively, interactive, flexible, and nonlinear. However, conscientious individuals are cautious, deliberate, concerned with details, follow an order, and tend to make plans first and then commit to them (Zhou & Lu, 2011). Furthermore, conscientious students prefer structured activities and the reading learning style to gain knowledge. Therefore, conscientious students are less likely to think VR-based learning systems are useful for increasing learning efficiency.

This study found that agreeableness has a significantly positive effect on PE. Agreeable individuals are optimistic, flexible, accommodating, and altruistic. Agreeableness is negatively related to computer anxiety and focuses more on positive dimensions when adopting a new technology (Devaraj et al., 2008). Therefore, students that are agreeable are more likely to feel playful in VR-based learning systems. As Wang et al. (2012) suggest, educators can take advantage of the positive relationship between agreeableness and PE, and the altruistic characteristic of agreeableness in promoting the use of VR-based learning systems. They can first evaluate and understand each student’s personality traits, and then encourage students with agreeableness personality to use VR-based learning systems. These agreeable students will enjoy sharing their fun experiences with others.

Surprisingly, contrary to the hypothesised direction, this study found that neuroticism has significant positive impacts on PU and PE. The findings are different from previous studies (Devaraj et al., 2008). Neurotic individuals are usually emotionally unstable, self-conscious, and uncomfortable. Because VR-based learning systems are more interface-friendly, learners have less learning and social pressure. Neurotic students may find VR-based learning systems more useful than traditional learning methods. Furthermore, the easy, fun, and stress-free learning situations created by VR-based learning systems also make neurotic students feel that learning is enjoyable.

The three dimensions (visual discomfort, musculoskeletal discomfort: limb pain, and head discomfort) among four PPVITU components were found to have no significant effects on PEOU. The participants of this study were all university students with a low mean age (μ = 20.52). Younger individuals may be too young to sense their own physiological vulnerability. More frequent health complaints from adult computer users are expected at higher ages (Palm et al., 2007). Therefore, most PPVITU components have no significant effects on PEOU. Palm et al. also indicated that most students are unaware of potential physiological disorders associated with IT use. Educators who use IT techniques such as computers or VR systems should draw students’ attention to these possibilities and remind them of suggested limits regarding the frequency of use and time.

Only the ‘musculoskeletal discomfort: neck, shoulder, and back pain’ dimension has a significant impact on PEOU. Surprisingly, the impact was positive. When students have neck, shoulder, and back pain due to excessive use of IT, they will be more uncomfortable when they move on to reading traditional books or using e-learning via personal computers, laptops, and mobile phones. If they use VR-based learning systems to learn at this time, it will be a more relaxed and soothing way for them. Therefore, ‘musculoskeletal discomfort: neck, shoulder, and back pain’ has a positive influence on PEOU.

Conclusions and limitations

This study confirms that the extended TAM factors of PU and PE are significant determinants of BI to accept VR-based learning systems, and PEOU significantly influences both determinants.

Further, the results indicate that personality traits can influence PU and PE. Agreeableness and neuroticism were found to have significant impacts on PE. Extraversion, conscientiousness, and neuroticism were found to have significant effects on PU. These findings suggest that both educators and practitioners can consider designing VR-based learning systems with easy, useful, and enjoyable functions. In turn, students may exhibit increased learning motivation and improved learning efficiency with VR systems. In addition, educators and practitioners may improve VR systems by adding novel and attractive properties to raise users’ learning motivation.

This study also has several limitations that future researchers should consider. First, this study had a small sample size that was predominantly male. The non-significant direct effect of PEOU on BI and the non-significant effects of three PPVITU components on PEOU may be the result of the gender variable. PEOU may be more important to women than men in IT adoption decision. Further research could consider using a larger and more balanced sample. Second, this study included the hedonic factor of PE in extended TAM. However, other potential determinants of BI may also be important, such as perceived importance, goal expectancy, and social influence. Third, this study did not include all of the individual differences. We encourage future researchers to examine and include other individual differences such as computer self-efficacy, domain knowledge, intelligence, education background, and personal innovation in IT. Furthermore, future research can compare the influences of individual differences and belief factors by age group.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research was financially supported by the Ministry of Science and Technology, Taiwan under grant number MOST 106-2813-C-018-031-U.

Notes on contributors

Yu-Min Wang

Yu-Min Wang is a Professor in the Department of Information Management at National Chi Nan University, Taiwan. He has over 6 years of IT experiences in various organisations and positions. He received his Ph.D. degree in information management from National Sun Yat-Sen University of Taiwan in 2004. His main research interests include e-learning, business and technology education, IT-supported teaching and learning, and user acceptance and outcome assessment of information technologies. His research has been published in several refereed journals, including Computers & Education, Computers in Human Behavior, Interactive Learning Environments, Information & Management, Electronic Commerce Research and Applications, Technological Forecasting & Social Change, Journal of Information Science, International Journal of Mobile Communications, International Journal of Service Industry Management, Psychological Reports, International Journal of Human-Computer Interaction, Journal of Computer Information Systems, Computers in Industry, CyberPsychology & Behavior, International Journal of Human Resource Management, International Journal of Technology Management, The Service Industries Journal, and Information Systems and e-Business Management. He is currently serving as a Chairman for the Research Discipline of Applied Science Education in the Ministry of Science and Technology of Taiwan.

Wan-Ching Chiu

Wan-Ching Chiu is a post-doctoral fellow at National Taichung Unversity of Science and Technology, Taiwan. She received her Ph.D. in Business Education from National Changhua University of Education, Taiwan. Her current research interests include information and educational technology adoption, online consumer behaviour, and business education. Her work has been published in academic journals such as Asia-Pacific Education Researcher, Journal of Management Research, and Management Research.

Hsin-Hui Lin

Hsin-Hui Lin is a Professor in the Department of Distribution Management at National Taichung University of Science and Technology, Taiwan. She received her Ph.D. in Business Administration from National Taiwan University of Science and Technology. Her current research interests include electronic commerce, service marketing, online user behavior, and customer relationship management. Her work has been published in academic journals such as Academy of Management Learning & Education, Computers & Education, British Journal of Educational Technology, Interactive Learning Environments, Information & Management, Information Systems Journal, International Journal of Information Management, Internet Research, Managing Service Quality, Service Industries Journal, Computers in Human Behavior, Journal of Global Information Management, Information Systems and e-Business Management, and Journal of Educational Computing Research.”

Yi-Shun Wang

Yi-Shun Wang is a Distinguished Professor in the Department of Information Management at the National Changhua University of Education, Taiwan. He received his Ph.D. in MIS from National Chengchi University, Taiwan. His current research interests include information and educational technology adoption strategies, IS success models, online user behaviour, knowledge management, Internet entrepreneurship education, and e-learning. He has published papers in journals such as Academy of Management Learning and Education, Computers & Education, British Journal of Educational Technology, Interactive Learning Environments, Internet Research, Journal of Educational Computing Research, Information Systems Journal, Information & Management, International Journal of Information Management, Government Information Quarterly, Internet Research, Journal of Business Research, Journal of Retailing and Consumer Services, International Journal of Human-Computer Interaction, Information Technology and People, Information Technology and Management, Computers in Human Behavior, Thinking Skills and Creativity, among others. He is the former Chairman for the Research Discipline of Applied Science Education in the Ministry of Science and Technology of Taiwan.

Yu-Yin Wang

Yu-Yin Wang is an assistant professor in the Department of Computer Science and Information Management at Providence University, Taiwan. She received her PhD in information management from National Sun Yat-sen University. Her current research interests include mobile learning, technology upgrade model, and educational technology success. She has published articles in Interactive Learning Environment, Information Technology & People, Internet Research, Behaviour & Information Technology, and International Journal of Information Management.

I-Fan Chen

I-Fan Chen received her master’s degree in Information Management from National Cheng Kung University, Taiwan and her bachelor’s degree in Information Management from National Changhua University of Education, Taiwan. Her current research interests include information and educational technology acceptance and online user behaviour. She has ever served as a principal investigator of the university student project in the Ministry of Science and Technology of Taiwan.