# Current Perceptions of Virtual Reality Technology
## 1 Introduction
### Analysing Attitudinal Data  
Behaviour modelling can be achieved through the analysis of attitudinal data, which can be obtained through primary or secondary sources. In this section, a number of ways such data has been analysed will be discussed. A content review of 321,746 tweets related to the keyword \"metaverse\", posted in a 1.5 year period, was analysed [92] with the goal of developing an understanding of the opinions towards the security and privacy of users within the Metaverse. After formatting the data, quantitative and qualitative analyses were conducted. It was revealed that 538 tweets included the keyword 'privacy'. Although the current research study is not surrounding the Metaverse, the theme of privacy is likely to appear given the potential use of VR in the metaverse. Sentiment analysis showed that 59% of the users were positive and 33% were neutral about the security of the Metaverse. In the current study, a similar procedure will be undertaken to assess attitudes towards VR; however, the data will be primary rather than secondary. These authors conducted a similar analysis on tweets pertaining to the opinions held by users of the Metaverse Marketplace [93]. Here, tweets were analysed using a lexicon-based technique that assigned sentiment scores to individual words. This technique allows for quick analysis of large datasets and provides scores that can be quantitatively analysed in terms of both frequency and sentiment. Sentiment analysis will therefore be used in the presented study. A related study, using a more quantitative approach, was conducted on the perceptions towards marketing in the Metaverse [94]. Partial least squares structural equation modelling and an artificial neural network were used to analyse survey responses, specifically looking at whether perceived social benefits and trust lead to brand loyalty. It was revealed that perceived social benefit and trust had significant mediating effects on attitude toward the product, loyalty to the seller, and purchase intention. These are important factors for manufacturers to take into consideration; however, the goal of the current research is more exploratory in nature, to determine the experiences and attitudes toward the technology more generally. An experiment that gathered participants' views on the use of 3D video tours in travel destination websites used a different method of analysing the collected data [95]. Here, participants browsed an allocated travel website, and navigated through the 3D video tour. Following this, a series of Likert questions were administered, where ratings from \(1\) to 7 were given on topics such as aesthetics, informativeness, and novelty. This method of collecting levels of agreement is useful, especially when the prompts are simple. For this reason, the presented study will include such methods; however, instead of analysing using the mean response and skew direction, the mode responses and Wilcoxon signed-rank tests will be used to determine the direction and strength. Other relevant studies have attempted to further our understanding of why VR has or has not been adopted in different domains. An important application of VR is the realm of mental health [96]; one such study gathered attitudinal data on participants after they had witnessed VR therapy [97]. Much like in the previous studies, data were formatted before analysis, positive and negative sentiments were calculated, and Likert questions were ranked. The findings suggested there is a willingness to try VR therapy, and with the rise in uptake of VR equipment, it is predicted that positive attitudes towards virtual therapy will also increase. A proof-of-concept study explored the attitudes that a selection of university students and teachers had about VR in education [98]. Although small, the quantitative design was able to gather insights through a series of closed questions.