LIMITATIONS 18

2.2. UTILITARIAN VALUE Utilitarian attributes are imperative to the consumer decision-making process for online shoppers, as the tactile experience of brick-and-mortar is missing. Merle, Senecal, and St-Onge (2012) argues that personalized VTO technology will increase consumers’ hedonic value, utilitarian value, confidence in apparel, and thus greater purchase intentions. However, during the lab study of only a female population, this hypothesis was rejected. Personalized VTO, using one’s own body measurements to create a virtual model, was used in another study, where the interviewees thought the VTO technology was not functional because the clothing on the virtual model didn’t help them visualize how it would in real life (Kim & Forsythe, 2008). Other research, using AR VTO, suggests that the hedonic and utilitarian attributes of the product information from the technology will positively affect consumer experience (Kim and Jung, 2021). This study found that participants who had the utilitarian product information scenario experienced an enhanced sensory experience and thus a positive product evaluation, demonstrating the importance of product information in the acceptance of VTO technology and purchase intention. The difference in these studies is the type of VTO used, personalized versus AR, which can explain the contrast in data, as personalized VTO did not help consumers visualize how the product with actually look on their own body (Merle, Senecal, and St-Onge, 2012). Kim and Forsythe (2008) explain, “many of the characteristics of apparel that are important in consumer making decision, such as appearance on the body and fit, are difficult to present onscreen and standard descriptors of a product often are insufficient for product evaluation…” As AR merges sensory information and virtual content with real environments, consumers can gain a greater understanding of the product (Suh and Prophet, 2018, cited in Kim and Jung, 2021). If the consumer views the technology as practical, functional, and beneficial through the utilitarian attributes, they are more likely to purchase that item, because it is seen as solving problems within online shopping.

H2: The information provided by the Virtual Try-On technology positively influences the consumer’s intention to purchase an item

2.3. PRODUCT RISK While VTO technology can bridge the gap between online and offline shopping, there is still risk associated with purchasing clothing online. Kim and Forsythe (2008) argue that “insufficient information on product attributes and shoppers’ inability to accurately evaluate the quality of the product in the online environment results in increased product risk.” Other studies have supported this idea and found that the richer the imagery of the item is, the lesser the perceived product risk is, which increases consumers’ confidence in fit (Merle, Senecal, and St-Onge, 2012). In that study, non-personalized and personalized VTO were used for research, so the participants in the study did not feel a higher confidence in fit. However, using AR technology uses enhanced imagery and presence to create an immersive shopping experience (Kim and Jung, 2021). In relation to confidence in fit, Zhang et al. (2019) hypothesizes product risk as a negative influence on consumers’ attitudes because “consumers are not confident that the clothes they purchase can meet their expectations. ” However, in this literature the study’s hypothesis was rejected. Thus, following previous arguments of perceived product risk positively influencing consumer attitudes. Virtual Try-On technology can be used for better product evaluation in online shopping, resulting in the intention to purchase an item.

H3: Perceived product risk while using augmented reality VTO technology positively impacts purchase intention

Figure 2.2. Conceptual Model (Freund, 2022)

3.1. RESEARCH PHILOSOPHY According to the Research Onion (See Figure 3.1), we must follow a research paradigm, starting with the outer ‘Philosophy’ ring. After reviewing previous studies on the topic of VTO, the lack of research on augmented reality use in VTO dictates the use of mixed methods for research, showing both Positivist and Interpretivist approach. Positivism adopts the ideals of the less-biased, testable hypotheses where research is replicable from previous literature and can be tested using statistical analysis. Interpretivism conducts research amongst people rather than objects, creating more biased research when you enter the mindset of the research subjects (Saunders et. al., 2019). These ideals align with quantitative (quant) and qualitative (qual) research, respectively. The philosophical stance that this report follows is Pragmatism, as this philosophy uses mixed methods, combining the testing of theories and hypotheses.

Figure 3.1. Research Onion (Saunders et. al., 2019, p. 130)

3.2. APPROACH TO THEORY DEVELOPMENT With the pragmatist ideology determining the use of mixed methods to derive the knowledge from this technology, the research approach will be a deductive quantitative focus, with the support of inductive qualitative data. A deductive approach follows the analysis of findings and theories of previous literature, so this report conducts a literature review prior to primary research. Deductive research produces a large, representative sample from target consumers. This focus will emphasize consumer experience with AR VTO and its influence on purchase intention using statistical data to describe the consensus of the adoption of this technology (Creswell, 2009). Inductive reasoning will support the quantitative data with the exploration of such facts, seeking to explain consumer perceptions of digital try-ons for online purchasing.

3.3. RESEARCH METHOD As this research will undergo a dominantly deductive with inductive theory development, the methodical choice to execute the collection of data will be a mixed method simple. Mixed method means that analysis of the quant and qual data will be connected as some point in the research phase. With a combined research purpose, the mixed method simple strategy will facilitate a “combination of exploratory, descriptive,

explanatory, and evaluative research” (Saunders et. al., 2019). With a restraint of time on research collection, the Concurrent Triangulation design seeks to conduct quant and qual at the same time (See Figure 3.2). After the data has been collected concurrently, analysis of data will be conducted separately, and then triangulation of the databases happens to “determine if there is convergence, difference, or some combination” (Creswell, 2009) (See Figure 3.3). Triangulation is used to confirm the consistency and validity of the research data, which will “help reveal the reality in the data” and add depth to the research, based on pragmatist assumptions (Saunders et. al., 2019 & Jick, 1979). While the use of one qual and one quant method will be used, the deductive focus prioritizes the data collected through the quantitative method and uses qual data to explain the statistical data. This report exemplifies the concurrent collection of a survey and semi-structured interviews, analysis of the findings, and a synthesis of all research to exhibit an in-depth study surrounding the topic.

Figure 3.2. Concurrent Triangulation Design (Creswell et. al., 2003)

Figure 3.3. Triangulation Model (adapted from Creswell, 2009 and Jick, 1979)

3.4. RESEARCH STRATEGY Within this paper’s mixed method simple design, one quantitative and one qualitative strategy will be utilized. The survey strategy will be designed using Qualtrics for quant data to be easily analysed in a statistical sense (See Table 3.1). A questionnaire allows for a generalization of attitudes and behaviour from the sample to a general population (See Appendix 1.2). The collection of data will then be used in SPSS Statistics through a Multiple Linear Regression Analysis to assess the significance of each independent variable on the dependent variable, either proving or disproving the hypotheses. For qual data collection, the narrative enquiry strategy will be used in two semi-structured interviews of luxury consumers to understand the opinions surrounding VTO technology and clarify the problems of online purchasing (See Appendix 1.4). The deductive approach will prioritize the questionnaire data and use the interviews as supporting evidence for the outcome. For both types of research, there will be a control of the technology used; the application DressX will be studied for both quant

Table 3.1: Qualtrics Survey Hypotheses

Due to time constraints of this report, the cross-sectional time horizon is best for this study. Cross-sectional studies usually apply a survey strategy, which seeks to explain the relationship between variables (Saunders et al, 2019). However, in this study of a mixed methods research strategy, a combination of a survey and interviews based on the same application, DressX, will be conducted. After observing the course of research design through the Research Onion, data collection and analysis can now take place (Saunders et. al., 2019).

4.1. QUANTITATIVE DATA A survey was conducted using a 7-point Likert scale, and for demographic questions using a Likert scale between 1 and 6 (See Appendix 1.2). A sample size of 37 respondents- removing participants who did not fulfil the test questions (See Appendix 1.3)- contained a majority of females between 18-24 years old, with adequate luxury shopping experience; 1-2 times a year or less often (See Figure 4.1). With more than 30 respondents, we can assume the normality based on the Central Limit Theorem (Pallant, 2020). Based on the normality tests, entertainment value (M= 5.87, SD=1.03) and information richness (M= 5.20, SD= 1.00) had positive responses and perceived product risk (M= 4.41, SD= 1.23) received a neutral response (See Appendix 1.8). The level of significance to examine the null hypotheses (H0) will be a= .05, ensuring 95% confidence in the data.

Figure 4.1. Frequency tables of demographics

Statistical analysis of a Multiple Linear Regression was conducted to see the significance of each of the independent variables on the dependent variable, purchase intention. This type of analysis will test each hypothesis through the null hypothesis (H0) of what is assumed to be true and the alternative (HA) of which to prove the null as untrue (See Table 4.1).

Table 4.1. Null & Alternative Hypotheses

In this first round of significance testing, the adjusted r-square of .678 indicates that 67.8% of the variation in willingness to purchase can be explained by the independent variables. However, the outcome of the test proved that only the utilitarian attribute of information richness failed to reject the null hypothesis, therefore an insignificant impact on purchase intention (r=.998>0.05). This signifies that it’s neither positive nor negative, and thus rejecting H2 (See Figure 4.2). According to some previous literature findings (See Appendix 1.1), this conclusion is not supported in which respondents who received a greater utilitarian value (or perceived

Figure 4.2. Initial testing of Multiple Linear Regression Analysis

After removing the insignificant variable, the adjusted r-square increased to 68.7%, showing that entertainment and product risk have a significant impact on purchase intention. Entertainment value (r=.001) and perceived product risk (r=.001) are statistically significant (r<0.05) and show a positive relationship between the dependent variable and independent variables (See Figure 4.3). Based on evidence from a multiple linear regression analysis, there is evidence to support H1 and H3. The adjusted conceptual model demonstrates only significant and positive influences on the dependent variable of purchase intention (See Figure 4.4).

Figure 4.3. Second round of Multiple Linear Regression Analysis

4.2. QUALITATIVE DATA With a deductive approach, the qualitative data is used to understand the data from the survey outcome. The qualitative interviews focused on discussing open-ended questions about how interviewees felt about the VTO technology when asked to use the DressX app (See Appendix 1.4). After coding these interviews into themes, we can compare this data to the survey data, and use it to support and explain what the quantitative data does not discuss.

Entertainment (H1) was proven to have a significant influence on purchase intention. This can be illustrated by consumer perceptions from the interviews, as one participant exclaimed, “It was a fun experience. You know, good to do to pass time with friends, try on different things…” (See Appendix 1.5). Interviewees introduced ideas that enhanced entertainment can be explained by the emerging theme of consumer benefits from VTO technology (See Figure 4.5). Findings from prior literature support this claim through proven hypotheses and supporting quotes from an interview focus group (See Table 4.2).

Figure 4.5. Qualitative Data Structure from Coded Interviews

Table 4.2. Comparison with Previous Literature (H1)

As the utilitarian value hypothesis (H2) of information richness was rejected from the quantitative research, we can look to the interview themes that emerged to assess the reasoning for this outcome. According to respondents, when shopping for luxury clothing, size & fit, material & product attributes, and detailing of the garments is important for making online purchasing decisions (See Appendix 1.7). Consumers gain benefits from online shopping when there is an in-depth product description with added features. One participant rationalized, “…the description part of the website was good. Where they do the composition, which tells me if the textiles are going to be good. Some websites that I shop from often would give me matching items that will help me style it or even put up a runway video so I can see a 360 [view], see how an item would look in its movement. ” If there is extensive product information given in addition to the VTO technology, the utilitarian value would be enhanced. Benefits of in-store shopping become barriers of VTO technology, as the tactile experience and enhanced customer service is not fulfilled with this technology that is theorized to blend online and offline shopping (See Figure 4.6), however product attribute information can reduce these barriers if done correctly.

Figure 4.6. Qualitative Data Structure Analysis 1

Evidence from the questionnaire supports that perceived product risk (H3) had a significant impact on purchase intention. Product risk is the amount of confidence you have in the garment fit and sizing and the brand’s accuracy in depicting that item, as explained by Merle, Senecal, and St-Onge (2012). The two interviewees had differing opinions in which one explained, “I like the color on my skin, I like the details and the features because you can get the fur on this, you can see the fur quite well” (See Appendix 1.6). The other respondent illustrated what the technology is missing, “designers need to work on is the texture of the fabric and the way it fits. If they improve upon how it's flowing and it looks a little more realistic with its movement, maybe it could reach out to a wider audience” and “If I could try on a coat and know that it crops right above my hip, I would get that sense of security in online purchasing” (See Appendix 1.5). These varying opinions could clarify the neutral response exhibited from the normality test data (M=4.41, SD=1.23), which represented the “neither agree nor disagree” option. Opinions presented in this report’s qualitative research aligns with previous literature, as other interviewees had mixed reviews on the confidence in apparel accuracy from online shopping and the emerging VTO technology (See Table 4.3).

Through quantitative research of a survey, independent variables of entertainment and product risk were proven to have a significant influence on the dependent variable, purchase intention. This is substantiated through the qualitative research conducted and prior academic literature that was evaluated to formulate the hypotheses. Drawing from previous studies, H1, which proved that enhanced entertainment leads to purchase intention, was underpinned by Kim and Forsythe’s (2008) research findings extracting theories from the TAM and Hedonic & Utilitarian models. Recommendations to strengthen the entertainment value (M= 5.87, SD=1.03) was explained through the themes that emerged from the two interviews conducted (See Appendix 1.7). Two categories coded from the interviews- opportunities for a better sensory experience and role of app design on consumer experience- can be deduced to the role of application design for a more immersive experience, leading to overall consumer experience. (See Figure 5.1). This idea is clarified by the literature with the discussion of virtual presence and how it has a direct positive effect on enjoyment and usefulness and indirect positive effect on purchase intention (Leonnard, Paramita, and Maulidiani, 2019).

Figure 5.1: Qualitative Data Structure Analysis 2

Considering the confirmed product risk impact on purchase intention, this variable is shown to be less impactful on purchase intention than entertainment value (See Figure 5.2). In addition to the descriptive statistics conducted (M= 4.41, SD= 1.23; 4 representing a neutral response), these varied conclusions are sustained through previous studies on this variable and the qualitative research examined in this study (See Table 5.1). On the other hand, the information richness variable received somewhat positive responses through normality testing (M= 5.20, SD= 1.00), and this was refuted with a Multiple Linear Regression analysis. The outcome of this statistical analysis proved that the independent variable associated with H2 did not have a significant influence on purchase intention. Utilitarian attributes in general received positive effects on purchase intention and the mediating variables associated (See Table 5.1). However, the outcome of Kim and Jung’s (2021) study revealed that specifically product attribute information did not have a significant influence on product evaluation (a mediating variable proved to have a positive effect on purchase intention). This is interesting to note, as interviewees in this report justified that product attribute information was an important factor in purchasing online and lack thereof was considered a barrier for buying luxury through VTO technology (See Appendix 1.7). As this report is one of the only studies conducted on luxury shoppers and AR VTO, interpreting the rejected H2 hypothesis through the qualitative research draws conclusions that product attribute information, including the sizing, fit, material, and brand information would likely produce a positive influence on consumer experience, attitudes, and thus purchase intention.

Table 5.1. Synthesis of Quantitative, Qualitative, and Secondary Literature Findings

Hypotheses H1- Accepted

Consumer shopping entertainment enhanced by Virtual Try-On technology has a positive impact on consumer purchase intention

Interview

“It was a fun experience. You know, good to do to pass time with friends, try on different things. The dresses that they have on is something you in person would see very rarely. So again, you are exposed to more trendy designs and high fashion designs that often you would not come in contact with…”

“I like the aspect of having fun and trying on, even if you're not going to buy. That's one thing that you do struggle with in person, especially if it's a really expensive brand. And you don't feel comfortable trying stuff on because of any stigma, you can try on without any guilt, shame, without being fearful of judgment, or even to break it.”

Literature Kim & Forsythe (2008)

- VTO was more entertaining than functional, useful for hedonic attributes - Positive effects & strong influence of perceived usefulness & entertainment value on attitudes towards VTO

“It was interesting to create my models and try clothing on it… more amusing than anything else… ”

“For fun, I will do it…play around with outfits, colors, and combinations of clothes. ”

H2- Rejected

The information provided by the Virtual Try-On technology positively influences the consumer’s intention to purchase an item “…it may be misleading in terms of sizing; the sizing may be off, and you buy something or the coloring you buy and then it turns out to be completely wrong. And that can be quite upsetting, especially if you've spent hundreds of pounds on it.”

“If I could try on a coat and know that it crops right above my hip, I would get that sense of security in online purchasing.”

Kim & Jung (2021):

- The effect of product attribute information on product evaluation was not significant - Utilitarian condition participants felt enhanced imagery which led to high information fulfilment & positive product evaluation

Leonnard, Paramita, & Maulidiani (2019):

- Usefulness has a positive effect on purchase intention

Merle, Senecal, & St-Onge (2012):

- Women who experience personalized VTO perceived more utilitarian value & had greater purchase intention

H3- Accepted

Perceived product risk while using VTO technology positively impacts purchase intention “…Maybe not the best for seeing fitting again, because it's not going to fit the way it's fitting on camera…”

“…everything looks better online. So, I do not trust the product as much and I do not know how it's going to fit on my body personally…”

“Often online, you do not know how long or short or broad an item will look. And I think with coats and jackets, that's a good virtual experience; to know how long a long coat is actually going to be on you”

“ …if you're serious about the product, you will look and try it on and maybe get like 50-60% of a sense of what this product is going to be.”

“ …I like the color on my skin, I like the details and the features because you can get the fur on this, you can see the fur quite well… ”

Zhang, et. al. (2019):

- Attitude is not affected by perceived product risk

Merle, Senecal, & St-Onge (2012):

- Using mix-and-match VTO, participants did not have greater confidence in fit or purchase intentions

Kim & Forsythe (2008):

“Virtual Try-on would be useful for online apparel shopping because it shows how the clothing would look on the body… seeing how colors look with my skin and hair colors and seeing if the clothes fit properly on my upper and lower body lengths help”

This study has a few limitations that would make it difficult to replicate for future research. To ensure that survey respondents downloaded the DressX application and were answering honestly, two test questions were developed and an introduction video of how to use DressX was embedded. However, after publishing the survey, the application was updated, making the how-to video obsolete. This altered the way respondents viewed product and brand information when trying on a product, thus limiting the utilitarian information fulfilment. In addition, the survey did not receive an extensive number of participants (37 after removing invalid responses), and 70% of the respondents were between 18-24 years old, which is a population most willing to engage in and understand advanced technology. Lastly, because DressX has many product categories and respondents could try on any item, the outcome of the survey data is not specified to one product category and didn’t assess the influence of trying on certain items (i.e., accessories) on purchase intention.

Clark, T. (2020). A Return to Lockdown: Where Next for Consumers? [online] Mintel. Mintel. Available at: https://clients-mintel-com.arts.idm.oclc.org/insight/a-return-to-lockdown-where-next-forconsumers?fromSearch=%3Ffilters.region%3D10%26freetext%3Decommerce%252C%2520fashion%2520industry%26last_filter%3Dregion [Accessed 2 Feb. 2022].

Creswell, J.W. (2009). Research Design: Qualitative, Quantitative and Mixed Methods Approaches. 3rd ed. [online] Sage Publications, Inc. Available at: http://www.drbrambedkarcollege.ac.in/sites/default/files/researchdesign-ceil.pdf [Accessed 25 Jan. 2022].

Creswell, J.W., Plano Clark, V.L., Gutmann, M.L. and Hanson, W.E. (2003). Advanced Mixed Methods Research Designs. [online] California: Sage Publications, Inc. Available at: https://us.corwin.com/sites/default/files/upm-binaries/19291_Chapter_7.pdf [Accessed 25 Jan. 2022].

Dover, S. (2018). The Online Fashion Myth. [online] Mintel. Mintel. Available at: https://reports-mintelcom.arts.idm.oclc.org/display/904490/?fromSearch=%3Ffilters.region%3D10%26freetext%3Decommerce%252C%2520fashion%2520industry%26last_filter%3Dregion [Accessed 2 Feb. 2022].

Dover, S. (2019). Retailers face up to the fashion industry’s returns problem. [online] Mintel. Mintel. Available at: https://reports-mintelcom.arts.idm.oclc.org/display/954552/?fromSearch=%3Ffilters.region%3D10%26freetext%3Decommerce%252C%2520fashion%2520industry%26last_filter%3Dregion [Accessed 2 Feb. 2022].

Houghton, L. and Taylor, R. (2018). State of Luxury: UK. [online] LS:N Global. Available at: https://wwwlsnglobal-com.arts.idm.oclc.org/markets/article/22537/the-state-of-luxury-uk [Accessed 2 Nov. 2021].

Jick, T.D. (1979). Mixing Qualitative and Quantitative Methods: Triangulation in Action. Administrative Science Quarterly, [online] 24(4), pp.602–611. Available at: https://www.jstor.org/stable/2392366?seq=2#metadata_info_tab_contents [Accessed 25 Jan. 2022].

Kim, J. and Forsythe, S. (2008). Adoption of Virtual Try-on technology for online apparel shopping. Journal of Interactive Marketing, [online] 22(2), pp.45–59. Available at: https://www.sciencedirect.com/science/article/abs/pii/S1094996808700100 [Accessed 27 Oct. 2021].

Kim, T.H. and Jung, C.H. (2021). Augmented reality as a product presentation tool: focusing on the role of product information and presence in AR. Fashion and Textiles, [online] 8(1). Available at: https://www.proquest.com/docview/2554647024/abstract?accountid=10342&source=fedsrch [Accessed 27 Oct. 2021].

Lee, A. (2020). Gucci Reveals Snapchat AR Shoe Try-ons. [online] WWD. Available at: https://wwd.com/business-news/technology/gucci-reveals-snapchat-ar-shoe-try-ons-1203661812/ [Accessed 2 Feb. 2022].

Leonnard, L., Paramita, A.S. and Maulidiani, J.J. (2019). The Effect of Augmented Reality Shopping Applications on Purchase Intention. Esensi: Jurnal Bisnis dan Manajemen, [online] 9(2), pp.131–142. Available at: http://journal.uinjkt.ac.id/index.php/esensi/article/view/9724 [Accessed 7 Nov. 2021].