Survey Population, Sample Size and Data Collection Process

Quota sampling is a type of non-probability, stratified sampling that is entirely non-random (Saunders et al., 2016; Barnett, 2002). It is based on the premise that the selected sample will represent the target population because the variability in the selected sample for various quota variables is the same as that of the target population (Saunders et al., 2016;

Barnett, 2002). This method involves collecting data until the pre-determined number of people (i.e. the quota) within each of a number of different categories has been achieved (Greenfield and Greener, 2016). The categories can be defined by attributes such as age, gender and working status (Greenfield and Greener, 2016). The selection of quota variables is important in order to increase the likelihood of the sample being reasonably to highly representative and to reduce the risk of bias (Saunders et al., 2016). Therefore, quota sampling was used for the survey data collection in order to gather data from a specific population of VR users combined with the typical demographic of individuals who travel for shopping based on prior marketing research outlined below.

According to a report by Nielsen (2017a), 44% of people interested in purchasing a VR device where between the ages of 18 and 34. Similarly, Greenlight VR (2015) gathered 2,282 responses from internet users aged 10 years and over in the US and found that 73% of respondents aged between 18 and 34 years (millennials), 70% of respondents aged between 35 and 50 years (Generation X), and 64% of respondents aged between 51 and 69 years (Baby Boomers) were interested in VR. The millennial generation in particular is heavily motivated by innovative devices and will play a major role in defining what remains popular (Nielsen, 2017a). Another report by Nielsen (2017b) found that millennials account for as much as 50% of the shoppers in the total travel retail market.

147 | P a g e This specific market wants to be in control of their experiences and are strongly driven by search including searching for the right experience that fits their mood, interest and personality (Nielsen, 2017b). Figure 5.5 shows the share of travel retail by region and demonstrated that Asia Pacific is the largest travel retail market, with a 36.7% share, followed by Europe (32.3%) (Nielsen, 2017b). Additionally, the North West of England attracted higher proportions of short stay holidays from younger people and young adult holidays including those aged between 16 and 34-years old accounting for 43% of visits in 2013 (Visit Britain, 2013).

Figure 5.5 Share of Travel Retail by Region

(Source: Nielsen, 2017b)

Drawing on the statistics for both the typical VR user demographic and the demographic for global shoppers, it could be argued that the sample population for this study is mainly millennials aged between 18 and 34 years old. This is because this specific market accounts for 50% of the total travel market (Nielsen, 2017a) and demonstrated most interest in VR (Nielsen, 2017a; Greenlight VR, 2015). Additionally, 69% of men stated intent to purchase a VR device, compared with 31% of women (Nielsen, 2017a).

Therefore, it could be argued that the typical VR user demographic is largely males aged

38.60%

148 | P a g e between 18 and 34-years old, and a large portion of the travel retail market is also within this age category while not specified by gender. More specifically, in order to calculate the quota sample, the target population of VR users and retail travellers was divided into mutually exclusive subgroups of age and gender. The quota for each group was based on the relevant and available data discussed above (Saunders et al., 2016). The two age groups included millennials aged between 18 and 34-years accounting for 75% of respondents, and Generation X and Baby Boomers including individuals aged between 35 and 69-years and accounting for 25% of respondents. The gender subgroups were weighted as 60% male and 40% female. A slightly larger proportion of males were targeted based on the VR statistics.

Sample sizes are often much larger in quantitative studies compared with qualitative research so that statistical methods ensuring representative samples can be used (Carey, 1993). The larger the sample size, the lower the likely of error in generalising to the population (Saunders et al., 2012). Previous VR studies employing quantitative survey method have used varying sample size of between 100 and 150 (e.g. Disztinger et al., 2017; Gibson and O’Rawe, 2017; Domina et al., 2012), 151 and 200 (e.g. Huang et al., 2016) and up to 274 participants (Shin, 2009). This study tested a VR retail application with a total of 158 participants. However, eight surveys had only been partially answered (more than 80% of questions unanswered) and were therefore not included in the analysis. This resulted in a total of 150 survey responses analysed, which seems an appropriate number based on prior studies with similar focus outlined above.

Prior to completing the survey, respondents experienced one VR retail application set in the context of an urban shopping destination (hereafter, Application C). Figure 5.6 shows where the user could browse through the selection of virtual products, access product information including price, add the items to the virtual shopping cart and virtually checkout. Figure 5.7 and Figure 5.8 show where the user could tailor the virtual retail environment to suit his/her preferences. The user could also change the aesthetic design of the store between a blue theme and a brown theme (Figure 5.7) and the user also had a choice of three urban places to be shopping in (Figure 5.8).

149 | P a g e Figure 5.6 Application C1

Figure 5.7 Application C2

Figure 5.8 Application C3

150 | P a g e 5.5.5 Data Analysis

PLS-SEM was employed for the survey data analysis. SEM is one of the most popular statistical methodologies available to quantitative social scientists (Kaplan, 2009: Cheng, 2001) and is widely used in behavioural sciences (Hox and Bechger, 1998). The interest in SEM is the theoretical constructs represented by the latent factors and the relationships between these theoretical constructs, which are represented by regression or path coefficients between the factors (Hox and Bechger, 1998). This method has been found useful in various studies such as impulse buying behaviour in retailing (e.g. Bellini et al., 2017), consumer adoption of smart in-store technology (e.g. Kim et al., 2017), soundscape and tourist satisfaction (e.g. Liu et al., 2018), m-commerce acceptance (e.g.

Liébena-Cabanillas et al., 2017) and social CRM adoption (e.g. Ahani et al., 2017). It is widely used in marketing research because it can test theoretically supported linear and additive causal models (Statsoft, 2013; Haenlein and Kaplan, 2004; Chin et al., 1996).

With SEM, marketers can visually examine the relationships that exist among variables of interest in order to prioritize resources to better serve their customers (Wong, 2013).

SEM is useful for tackling business research problems because unobservable, hard-to-measure latent variables can be used (Wong, 2013).

Specifically, this study takes the approach of PLS to SEM and was carried out using Smart PLS software. This particular method was employed because it is considered a good alternative to covariance-based SEM using software packages such as SPSS AMOS or Lisrel (Wong, 2013). This is particularly the case when certain situations are encountered including small sample size (Wong, 2010; Hwang et al., 2010; Bacon, 1999), and it has been applied in many research projects when there are limited participants (Wong, 2011).

Many researchers have employed PLS-SEM from marketing (e.g. Henseler et al., 2009) and behavioural sciences (e.g. Bass et al., 2003), and more specifically, when investigating the role of atmospherics in museum settings (Loureiro, 2019), place attachment in tourism (e.g. Loureiro, 2014) and VR and consumer behaviour in tourism (e.g. Kim et al., 2018).

Hair et al., (2013) suggested that sample size can be driven by the following factors in SEM design: the significance level, the statistical power, the minimum coefficient of

151 | P a g e determination (R² values) used in the model, and the maximum number of arrows pointing at a latent variable. According to Wong (2013), a typical marketing research study would have a significance level of 5%, a statistical power of 80%, and R2 values of at least 0.25.

Using these parameters and drawing on the guidelines suggested by Marcoulides and Saunders (2006), the minimum sample size was calculated depending on the maximum number of arrows pointing at a latent variable as specified in the structural model. Based on the maximum number of arrows pointed at a latent variable in the model being nine, the minimum sample size required is 88 (Wong, 2013). However, Wong (2013) argued that the goal should not be to merely fulfil the minimum sample size requirement and prior research (Hoyle, 1995) has suggested that a sample size of between 100 and 200 is usually a good starting point for carrying out path modelling. Therefore, it was concluded that a sample of 150 was suitable for conducting PLS-SEM analysis in this study.