The effect of interactive fitting rooms on the in-store
experience of customers
MSc in Business Administration – Marketing
Paola Vuijk 11130970 Final version 26-01-2018
Statement of originality
This document is written by Paola Vuijk who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Table of contents
Abstract ... 4
1. Introduction ... 5
2. Literature review ... 8
Consumer behavior in the choice of shopping mode ... 9
Self-service technology (SST) ... 12
Interactive Fitting Rooms (IFR) ... 14
Technology readiness ... 16
Personality characteristics ... 18
3. Method ... 20
Design and stimuli ... 20
Sample and procedure ... 21
Variable operationalization ... 22 4. Results ... 24 Reliability ... 24 Hypothesis testing ... 28 Technology readiness ... 28 Personality characteristics ... 31 5. Dicussion ... 35 General Discussion ... 35
Teoretical and managerial implications ... 36
Limitations and future research ... 37
6. Conclusion ... 38
With a fast growing e-commerce and increasingly demanding customers, brick-and-mortar retailers are starting to adopt new in-store technologies to create a competitive advantage for themselves. This study examines a new in-store technology: interactive fitting rooms (IFRs), by comparing two different interactive fitting rooms in an online experiment. A comparison between a service-focused interactive fitting room and social-focused interactive fitting room was made. An extensive analysis showed that participants in this experiment evaluated the in-store experience significantly higher when present with an interactive fitting room compared to a normal fitting room. Technology readiness and personality characteristics where added to this comparison as moderating variables. The two moderating variables both showed significant influences on in-store evaluation for IFRs. Technology readiness significantly moderated the IFR service condition and personality characteristics significantly moderated the social IFR evaluation.
Key words: interactive fitting rooms, self-serving technology, in-store evaluation, technology readiness, personality characteristics
‘’Shoppers who use dressing rooms are seven times more likely to make a purchase than those who just browse the sales floor’’ (Townsend, 2017)
The quotation above points out the importance of fitting rooms in the purchase process of customers. Baumstarck & Park (2010) emphasize that fitting rooms are a key part of the purchase decision process for physical stores as poor atmosphere can negatively affect the shopping experience and result in lost sales. A considerable part of literature focused on the role of fitting rooms in the store experience of customers and purchase decision (Netemeyer, Maxham III & Lichtenstein, 2010, Ainsworth & Foster, 2017).
Nowadays brick-and-mortar retailers are adopting new technologies to create a competitive advantage (Lee, 2015). Berry, Bolton, Bridges, Meyer, Parasuraman & Seiders (2010) emphasize in their research that a shift in power occurred from brick-and-mortar retailers to consumers. This shift asks for demand-driven innovations that fulfil customer’s needs. Interactive fitting rooms (IFRs) can be such an innovation that fulfils customers’ needs, which brick-and-mortar retailers can use to their benefit. As the technology of IFRs is still in its early stages, it is important to conduct research to see which type of IFRs brick-and-mortar retailers should implement in their stores to best fulfil customers’ needs.
In 2014, fashion designer Rebecca Minkoff opened a digitally ‘’connected store’’ in New York teaming up with eBay. eBay has been focusing on implementing technology that helps brick-and-mortar retailers. eBay’s Head of Innovation and New Ventures, Steve Yankovich, is looking for new ways for customers to have a more engaging shopping experience by transforming the in-store experience using digital initiatives ("Digital changing room and interactive mirrors at Rebbecca Minkoff", 2014). Brick-and-mortar retailers are
doing this by merging physical and online shopping experiences ("Digital changing room and interactive mirrors at Rebbecca Minkoff", 2014). Fashion giant Zara is momentarily testing IFRs in a couple of their stores after announcing wanting to introduce IFRs to their stores originally foreseen in December 2015. The real introduction of IFRs to physical stores is still on its way but brick-and-mortar retailers are systematically implementing modern technology to enhance the in-store customer experience (Zagel, 2015).
The current study deals with the question how brick-and-mortar retailers can provide customers a fascinating and exiting shopping experience by using innovative technology in the form of IFRs. For this reason, the following research question was formulated:
Which type of interactive fitting room has a more positive effect on in-store experience, and how is this relationship moderated by technology readiness and personality type?
Using innovative technology sets brick-and-mortar retailers apart from traditional brick-and-mortar retailers and the growing e-commerce. Online technologies are used to improve the offline in-store experience. Brick-and-mortar retailers strive to combine online strengths with offline strengths in the form of a holistic omni-channel approach (Zagel, 2015). The company Oak Lab sees online and offline no longer as separate spaces but tries to bridge the world of retail and technology by providing intuitive customer experiences. Oak Lab produces the Oak Mirror, which is: ‘’an interactive, touch-screen mirror that empowers shoppers to customize their fitting room’s ambiance, explore product recommendations and digitally seek assistance from store associates’’ (http://www.oaklabs.is/). This Oak mirror is already being used in the Rebecca Minkoff stores and at retailer Ralph Lauren. Early results of the Oak mirror show that people spend less time in the dressing room but buy more
products (Townsend, 2017). Investing in IFRs can be beneficial for brick-and-mortar to cope with today’s volatile economy (Berry, et al., 2010).
Innovative technologies used in stores are often self-service technologies (SSTs). SSTs are changing the way customers interact with firms (Meuter, Ostrom, Roundtree & Bitner, 2000). As e-commerce grows, brick-and-mortar retailers focus on retail technologies that can offer high levels of service to consumers and which offer an entertaining and fun retail environment (Arnold & Reynolds, 2003) to compete with online stores. ‘’Retailers are continuously encouraging consumers to adopt emergent technologies to improve the consumer experience and ultimately, elevate the consumers image of the store’’ (Moorhouse, Dieck & Jung, 2017, p. 3).
IFRs can be seen as an innovative retail technology and this research will approach IFRs as SST. Technologies as IFR are new SSTs. SSTs are established in physical stores for multiple reasons. A stream of literature provides important insights on the extant to which SST are beneficial to implement for brick-and-mortar retailers. Cost-efficiency, customer retention (Scherer, Wünderlich & Von Wangenheim, 2015), improving services to customers, improving management operations and costs (Renko & Druzijanic, 2014) are important factors in this decision. SST is not only beneficial for retails but also customers benefit from SST implementation. Customer benefits are mostly reduction of time needed in the store and higher speed in providing in-store service (Renko & Druzijanic, 2014). This research should bridge the gap between the insights on SSTs and how these apply to IFRs. As not much research has already been done on IFRs, this thesis will fill a literature gap and have managerial implications for brick-and-mortar retailers. The existing knowledge on SST and their applicability to IFR will be broadened by the moderation variables in this research: technology readiness and personality characteristics. We know that these two factors have an influence on the use of SSTs. As IFRs are SSTs with this research we want to study if
technology readiness and personality characteristics also have an influence on the use of IFRs. This research will focus on the difference in in-store experience between two types of IFRs: service and social.
Two different kinds of IFRs will be researched to see what/which effect these IFRs have on the in-store experience of consumers. A distinction is made between the ‘service’ IFR and the ‘social’ IFR. The service IFR is an IFR with a focus on enhancing the shopping experience and improving the overall in-store service evaluation. The social IFR is an IFR that is focused on social engagement with friends from inside the IFR and on enhancing the overall shopping experience as well. The new technology of IFRs has been put in place by a couple of brands but it is not clear how many IFR are actually used today. Stores as Zara and Ralph Lauren have implemented IFRs in several physical stores to test how the customer perceives these technological innovations. Current technological solutions are merely concentrated on delivering functional value by providing additional product information (Zagel, 2015). IFRs are aimed at improving the shopper’s overall in-store experience. Improving the in-store experience can positively influence customer behavior and attitude. A positive customer attitude can increase sales, which in the end is the ultimate goal of retailers. Before brick-and-mortar retailers will invest in IFRs, it is necessary to research which type of IFR will generate the highest return on investment. This study should answer the question which type of interactive fitting rooms brick-and-mortar retailers should introduce in their stores for a more positive evaluation of in-store experiences. With the managerial contributions of this thesis brick-and-mortar retailers can hopefully establish an interactive shopping experience best suited for their customers.
2. Literature review
This literature review provides an overview of the literature on shopping mode, SST and IFRs. First of all, the concept of offline vs. online shopping is discussed. Secondly, the SST is discussed followed by a definition and explanation of IFRs and the different types of IFRs. Thirdly, the concept of technology readiness will be presented. Finally, the link between personality characteristics and their influence on the use of IFRs is discussed.
Consumer behavior in the choice of shopping mode
The last ten years a lot of discussion revolved around consumer offline and online consumer shopping behavior and attitudes. Consumer behavior and attitudes influence the way consumers perceive and use a brand. Using consumer data and insights to create a competitive advantage for oneself as brick-and-mortar retailer is very important in the changing shopping landscape. Brick-and-mortar retailers are increasingly challenged by (new) online retailers and channels. The numbers of consumers shopping online has increased enormously the last decade. In 2014 $1.471 billion dollars was spent on online shopping by 191 million online shoppers in the United States (Kooti, et al., 2016). Global e-retail sales amounted to 1.9 trillion U.S. dollars in 2016 and projections show that this number will grow to 4.06 trillion U.S. dollars in 2020 ("Online-Shopping and E-Commerce worldwide: Statistics & Facts", n.d.). Looking at the numbers above, it can be concluded that online shopping has grown and is growing rapidly. Therefore, in-store experience is crucial for brick-and-mortar retailers to provide added value for their customers in order to attract them to the physical store instead of online shopping.
reduction and increased efficiency (Renko & Druzijanic, 2014, Scherer, et al., 2015). A positive in-store experience for customers can lead to a stronger customer-retailer relationship and higher customer satisfaction (Ainswort & Foster, 2017). These are two important factors in the cultivation of customer loyalty, very important but also challenging for retailers. Retailers do not only want to attract and satisfy customers, but also develop a long-term relationship with them. The most important reason to establish a long-term relation with customers is the direct value which these relations add to the company or brand. Revenues can increase due to loyal customers and loyal customers are more likely to buy additional goods, which in turn leads to more predictable sales and profit (Gremler & Brown, 1999). As loyal customers have advantages over regular customers, brick-and-mortar stores need a more online approach in their offline stores.
When purchasing goods and services different ways of shopping are available to consumers. Consumers nowadays prefer multiple channels when shopping (Blázquez, 2012). The number if channels through which consumers can freely, compare, choose and purchase items is rapidly increasing (Pantano & Viassone, 2015). Nowadays consumers may use one shopping channel for collection information about a certain product and use a different channel for the purchase of that product (Pantano & Viassone, 2015). The current study focuses on offline shopping, but as offline and online shopping are becoming increasingly integrated, the latter will also be discussed. Moreover, the innovative technologies established by brick-and-mortar retailers in physical stores are increasingly combining offline and online shopping experiences which makes boundaries between the two less clear.
Offline shopping is known as in-shop shopping, shopping at a physical store. Traditionally when shopping at a physical store, the shop provides: social interaction, entertainment and movement (Mokhtarian, 2004). Nowadays consumers are also able to shop via the Internet. This new way of shopping via the Internet is also known as e-shopping,
online shopping, network shopping, Internet shopping or Web-based shopping. Online shopping gives consumers the opportunity to shop from home, freeing them from personally having to go to physical stores (Hsiao, 2009). Online shopping is defined as searching or buying goods online (Farag, Schwanen, Dijst & Faber, 2007).
Offline and online shopping both have different advantages and disadvantages and consumers have different preferences for offline and online shopping. It is important to understand why customers select a particular shopping mode and under which circumstances (Nicholson, Clarke & Blakemore, 2002). Nicholson, et al. (2002) found that female shoppers favor in-store shopping when in company of friends and family (Nicholson, et al., 2002). In the research of Burke (2002) participants saw shopping as a social leisure experience (leisure-oriented), while friends and family provide advice and social interaction during the shopping experience. Physical shops are preferred when shopping for items that need a sense of feel and fit, also know as experience goods (Wang & Goldfarb, 2017). Important when shopping offline is reliability, responsiveness, access, assurance and customization/personalization (Burke, 2002).
Internet shopping is used for more functional purchases; ordering standard everyday items quickly, repeated purchases (Farag, et al., 2007). In the research of Burk (2002), flexibility, efficiency, site aesthetics and price knowledge were critical to the online environment. Online shopping is also seen as a way of avoiding social contact with salespersons. Farag, et al. (2007) found that online shopping is frequently used if consumers feel time-pressured. In sum, consumer’s select different channels in different situations, shopping at a physical store is a social experience and used for the purchase of personal products. Online shopping is focused on repeated everyday purchases and easiness of shopping. Problems with online shopping can be due to the lack of experiential information and physical interaction with the product (Balazques, 2012). This barrier is especially
applicable for buying fashion, as clothing requires a multisensory input (Balazques, 2012). Past studies have researched the influence of different retails settings to better understand the effect of some setting on customer behavior (Kim et al., 2009, Hsieh et al., 2012, White et al., 2013). The retail setting this study will focus on is the use of SST in physical stores and how this theory can be applied to the IFR.
Self-service technology (SST)
Another stream of literature provides important insights in the extant research of SSTs. ‘’Self-service technologies are a classic example of market space transactions in which no interpersonal contact is required between buyer and seller’’ (Meuter et al., 2000, p. 51). SSTs are systems that allow customers to provide their own service. SSTs are technological interfaces that enable customers to carry out tasks themselves without the assistance of an employee (Jackson, Parboteeah & Metcalfe-Poulton, 2014). Meuter, et al. (2000) define three types of SST’s: 1) telephone-based technologies and various interactive voice response systems, 2) direct transactions, which enables customers to order, buy and exchange resources with companies without any direct interaction with their employees, 3) SST self help, which refers to technologies that enable customers to learn, receive information, train themselves and provide their own services.
Brick-and-mortar retailers introduce SSTs to reduce costs, reach new customers segments and increase customer satisfaction and loyalty (Bitner, Ostrom, & Meuter, 2002). Interactive shopping technologies can provide powerful screening and searching tools, product selection and information. If the digital information people receive from the technology is poor, consumers rely on social or physical interaction for product quality (Burke, 2002). Research on SSTs has shown that SST is most satisfactory when it is easy to use, works reliably, saves time, addresses a salient need, offers greater control and 24/7 access
(Bitner, et al., 2002). Encounters with SSTs are not satisfactory if the processes and the technology fails and if the technology is poorly designed (Burke, 2002, Bitner, et al., 2002, Moorhouse, et al., 2017).
Dean (2008) found that older people have fewer experiences with fewer types of SSTs and are less confident in using SSTs compared to younger people. Older people miss the human interaction and make less use of self-checkouts when the option is available. Older people are willing to pay a higher price for a checkout with human interaction. Elliott & Hall (2005) found a gender difference in the embrace of SSTs between men and women. They show that men have a stronger desire to experiment with new technologies. Females are less confident in using new technology and need a greater assurance that the technology is reliable and accrued. The impact of gender and age are already well established in the SST literature. Differences in technology readiness and personality are important but less well research factors that influence the use of SSTs. These are also factors that need to be taken into account when establishing SSTs in the form of IFRs in a physical store by brick-and-mortar retailers These items influence the use of SSTs and therefore probably the use of IFRs. These two factors will be discussed in more detail after an in depth explanation of IFRs.
Interactive Fitting Rooms (IFR)
IFRs can be seen as SST. Fitting rooms are one of the most important aspects of physical stores, more specific fashion stores. However, the last century fitting rooms didn’t change much. Zagel (2015) views this as a missed opportunity as fitting rooms are at the core of service provided by physical stores. Kaluschka (2006) emphasizes that retailers have to realize that fitting rooms can have a great potential and are an important commercial area for brick-and-mortar retailers.
IFRs can be seen as a machine-delivered service or as a virtual channel. Virtual channels employ telecommunications, information and multimedia technologies to communicate with consumers. Physical channel environments are delivering service to customers through face-to-face communication (Sousa & Voss, 2006). SSTs are self–services platforms that are established in physical stores and online for multiple reasons. SSTs emotionally connect customers to products, provide a better in-shop experience and are a competitive advantage and cost reduction for retailers (Scherer, et al., 2015). The success of the IFR concept depends on the implementation of interaction concepts that addresses the customer’s needs. The main goal of IFRs is connecting customers emotionally to a product (Zagel, 2015).
There are different kinds of IFRs, this study will focus on two types of IFRS: 1) the service-enhanced interactive fitting room and 2) the social connection interactive fitting room. At the core of all IFRs is the additional product information which customers receive when using the room. This additional information supports consumers in their fitting and buying process.
Service-enhanced IFRs focus on providing additional product information to customers and provide them a higher efficiency during shopping. Customers entering the fitting room will be able to see detailed product information about the items they took into the fitting room, on a build-in touch-screen mirror. Among others, the screen will show customers if any other sizes, colors or materials of the products are available. The mirror is also able to show product recommendations and ratings retrieved from the Internet. A customer can request the help of a salesperson using the screen, for example when a different size of the product is needed.
Social connection IFRs also posses the aforementioned options but are also expanded with an extra function: the social connection with friends or family. Some IFR systems can
connect in-shop customers and their social networks. Customers have a chance to invite their friend in their shopping experience (Zagel, 2015).
Although there clearly there is an advantage to SSTs, there is very little research on on IFR technology (Zagel, 2015). The research of Zagel (2015) shows that IFRs are seen as entertaining, are perceived as highly interesting and lead to desirability. As SSTs have shown to improve customer satisfaction it is expected that both types of IFRs will increase evaluation of in-store experience compared to a normal fitting room. Based on this expectation, I hypothesize that there will be a difference in the evaluation of in-store experience for the IFR service and the IFR social. In the following section we will discuss how this general positive effect of IFRs on in-store evolution differs when interacting with technology readiness and personality characteristics.
H1: There will be a difference in the evaluation of in-store experience for service and social interactive fitting room.
‘Technology-readiness refers to the natural tendency a customer may have in embracing or using technologies for accomplishing goals’ (Jackson, Parboteeah & Metcalfe-Poulton, 2014, p. 17). The level of innovativeness that one possesses influences the adoption of technology. Innovativeness is a desire by the consumer to seek out new things. The desire for innovativeness influences the desire to use technology such as SSTs (Jackson, et al., 2014). When using SSTs the technology readiness of consumers is important. Technology readiness influences a customer attitude towards SSTs, SST adoption behavior and SST evaluation (Liljander, Gillberg, Gummerus, & Van Riel, 2006).
Lin & Hsieh (2007) found that a higher technology readiness has a positive effect on the intentions to use a SST. Lin & Hsieh (2007) also found that if customers experience a high level of satisfaction when using SSTs, the more likely they are to use a SST again and recommend it to others. That is why I predict that a customer with a high technology readiness will have a higher in-store evaluation when using an IFR compared to a customer with a low technology readiness. The scale that will be used to test in-store evaluation measures three different behavioral intentions: purchasing likelihood, recommendations and return intentions. These three items are comparable to the factors Lin & Hsieh (2007) found to have a positive effect on SST adoption. Jackson, et al., (2014) explain that the desire for innovativeness influences the desire to use technology. The innovativeness and technology of the social IFR is even more complex and advantaged then the service IFR technology. The social IFR provides contacting personal contacts from the inside of the IFR. A person with a high desire for innovativeness and high technology readiness will be based on technology readiness literature evaluate the social IFR higher than the service IFR as innovativeness is higher in the social IFR condition. This is why I also hypothesize that people with a high technology readiness will have a higher in-store evaluation for a social IFR compared to a service IFR. These hypotheses will test whether the effect of technology readiness as found by Jackson, et al. (2014) has the same influences on IFRs as on SSTs.
H2a: A high technology readiness will have a positive effect on the evaluation of in-store experience for both IFRs.
H2b: The difference in in-store evaluation between both IFRs will be higher for the social IFR when technology readiness is high.
Personality is defined by Mathai & Haridas: ‘’as that the unique dynamic organization of characteristics of a particular person, physical and psychological, which influence behaviour and responses to the social and physical environment’’ (2014, p. 49). A popular tool to measure personality is the Big Five model. The ten-item personality inventory scale (TIPI) of Gosling, Rentfrow & Swann (2003) is a modification of the Big Five model and will be used in this study to measure personality type. By understanding an individual’s personality type, there is a possibility to connect these traits to the use of SST (Jackson, et al., 2014). The research shows that when having the chance, 43%, of extroverts would use a SST compared to only 14% of introverts (Jackson, et al., 2014). Extrovert persons have an energetic approach to the social and material world and mostly possess personality traits such as assertiveness, social and positively emotions (John & Srivastava, 1999). Personality plays a role in the attitude towards use of SSTs. For that reason I hypothesize that people that have an extrovert personality will have a higher IFR in-store evaluation. The IFR social gives customers the option to introduce friends and family to their shopping experience. Extrovert persons are characterized as social human beings, that is why hypothesis 3b will study the difference in in-store evaluation between the social and service IFR.
H3a: Persons with extrovert personality characteristics will have a positive effect on the evaluation of in-store experience for both IFRs.
H3b: The difference in in-store evaluation between both IFRs will be higher for the social IFR when a person has an extrovert personality.
An illustration of the hypothesized relationship between type of interactive fitting room and evaluation of in-store experience is presented in the conceptual model in figure 1. The conceptual model below graphically shows the model used in this research.
The aim of this thesis is to examine the difference between the two types of interactive fitting rooms and their relation to in-store evaluation. The effects of technology readiness and personality characteristics are also tested to see if they moderate the relationship described above. The study provides direct empirical evidence that IFRs have a positive effect
on in-store evaluation.
Design and stimuli
An online survey-based experiment with a between-subject condition design is used to test the hypothesized relationships. The experimental design provides a way to investigate causal relationships between the different variables. In condition 1, participants were presented with the neutral stimulus: a normal fitting room and an experimental condition: the service interactive fitting room. In condition 2, participants were presented the neutral stimulus: a normal fitting room and the other experimental condition: the social interactive fitting room.
The stimuli selected for the study were based on information available about interactive fitting rooms. Three self-written vignettes (see APPENDIX A.) were used as stimuli. A descriptive text about a normal fitting room situation as neutral stimulus was used in both conditions. In the first condition a descriptive texts and picture were used for the service IFR condition and in the second condition almost the same descriptive texts was used for the social IFR condition. The descriptive text for all three fitting rooms were kept as equal in words and content as possible, little tweaks were added to differentiate for the experimental conditions. The vignettes were all based on previous literature (Zagel, 2015, Townsend, 2017). The picture was added to give participants an understanding of the look and feel of an interactive fitting room, as many participants probably never used an IFR in the past. The
different IFRs is possible at a later stage. No brand or store name was used in the descriptive texts. All three descriptive texts took the participants on a shopping trip to create a realistic shopping experience; the experience was similar for all the conditions. A pre-test was done to assure the conditions represented three different fitting rooms. During the pre-test six persons were asked to read one of the three texts and afterwards explained what they just read. All test persons were able to replicate the special features of the text they had read.
Participants starting the study were first presented with an informed consent form, where they were briefed on the nature of the research and asked for permission to begin the survey. After accepting the consent form, participants were asked about their demographics. The following questions were aimed to assess the participant’s technology readiness and personality characteristics. Participants were then randomly allocated to one of the two conditions. When participants participated in the experiment, they faced an equal probability of seeing one of the two treatment conditions. After viewing the descriptive text on a normal fitting room, participants were asked to rate their in-store evaluation. Next a descriptive text of either the service or social IFR was shown which was followed by the same questions as in the neutral stimulus: to rate their in-store evaluation. In the last question, participants were asked if they would like to use an IFR in the future. Participants were then debriefed on the purpose of the study and thanked for their time.
Sample and procedure
As stated above, data was gathered using an online survey, for which the survey software Qualtrics was used. A non-probability sampling technique was used to reach participants, personal contacts were asked to participate and Facebook and LinkedIn were used to further spread around the survey. Snowball sampling was used to find more respondents. The survey was written in English, this made the survey available to a large group of people. A total of
161 respondents have participated in the survey from which 132 respondents fully completed the survey. Participants were on average 30 years old and 40.2% of the participants were male. Each respondent had to answer 11 questions in total.
All constructs were measured using a seven point Likert-scale. Scales form previous studies are used to ensure construct validity.
In-store evaluation was measured using a validated 3-item scale used by Ladhari, Souiden & Dufour (2017). This scale measures three different behavioral intentions: purchasing likelihood, recommendations and return intentions. The following statements were presented to all participants: (1) I would buy or shop at the presented store, (2) I will certainly recommend this store to friends and acquaintances and (3) I will return to this store. Participants rated these statements on a seven-point Likert scale (1=strongly disagree, 7=strongly agree) with a Cronbach’s alpha of 0.86 from Ladhari, et al. (2017). This was used for both the neutral stimulus and the experimental stimulus in both conditions. To use this scale in the data analysis, the store evaluation score for the IFR was constructed and the in-store evaluation score for the normal fitting room. The difference in in-in-store evaluation score (IFR_EVALUATION) between the IFR and the NF is used as dependent variable in the statistical testing of the hypothesis. As stated previously this ensured that differences in general attitude towards shopping did not confound the analysis.
Technology readiness was measured using the ‘The Technology Readiness Index (TRI). The TRI, a 36-items counting scale from Parasuraman & Colby (2015), measures people’s propensity to embrace and use cutting-edge technologies. From this scale four items were chosen to measure technology readiness. These four items represented the items with the
were included in this research: new technologies contribute to a better quality of life (0.69), other people come to me for advice on new technologies (0.76), when I get technical support from a provider of a high-tech product or service, I sometimes feel as if I am being taken advantage of by someone who knows more than I do (0.63) and people are too dependent on technology to do things for them (0.60). The Cronbachs’s alpha scores from Parasuraman & Colby (2015) are directly stated after the statements, which are included in this research. The elimination from 36-items to 4-items was made to shorten the time needed to complete the survey.
Personality characteristic was measured using a ten-item personality inventory scale (TIPI) in line with the work of Gosling, et al. (2003). Gosling, et al. (2003) composed the TIPI scale from other already existing personality measuring scales. The authors were aiming to shorten the already existing personality measuring scales and researched the TIPI scale as replacement for the longer scales with more than 40 items. The TIPI scale measures the extent to which people are: extraverted, agreeable, dependable, emotionally stable, and open to experience. Participants were presented with 10 personality traits, which may or not apply to them. Participants were asked to indicate on a seven-point Likert scale (1=strongly disagree to 7=strongly agree) to which extend the ten traits applied to their personality. For the hypothesis testing only two items from the TIPI will are used. These two items (I see myself as: 1) extraverted, enthusiastic and 2) reserved, quiet) measure to which extend a person has an extrovert or introvert personality. The Cronbachs’s alpha score for the extroverted personality scale in Gosling, et al. was 0.77.
In this chapter the results of the data analysis will be presented. First, a short summary of the data will be provided. Second, a description of the data preparation will be given, followed by the hypothesis testing which is done using a hierarchical regression.
An amount of 161 participants started the survey and 132 participants fully completed the survey. Therefore, 132 participants will be included in the study. 40.2% of the participants were male and 59.8% were female. The average age of the participants was 30 years old with a minimum age of 19 and a maximum age of 62. Participants were highly educated as 75% of the participants had a college degree.
Of the total 161 respondent 29 participants were excluded from the survey because of an incomplete response. Missing values were excluded from the analysis using the option excluding cases listwise. After deleting the uncompleted responses, one item (I see myself as: reserved, quite) of the personality characteristic scale - measuring to which extend a person has an extroverted personality - was recoded and two items of the technology readiness scale (item 3 and item 4) were recoded.
After recoding three items and excluding missing cases listwise, the reliability of the in-store evaluation for the normal fitting room and interactive fitting rooms, technology readiness and personality characteristics were analysed. To measure the consistency of the measurements a reliability check was run for the three above variables. The Cronbach’s Alphas, were valid and above 0.7 for the three fitting rooms: normal fitting room (0.77), normal fitting room – service (0.82), normal fitting room –social (0.69), service IFR (0.95)
which is below the 0.7 thresholds. Scale item reduction by eliminating scale items did not increase the Cronbach’s Alpha for the TR scale. To compute the TR scale all four items were included. The personality characteristics scale had a Cronbach’s Alpha of 0.72 for the extraverted items. The Cronbach’s Alpha for the technology readiness was lower than the 0.7 Cronbach’s Alpha thresholds indicating a non-reliable scale with a low level of internal consistency. Although the scale did not reached a Cronbach’s Alpha of > 0.7 or higher, the scale of technology readiness was included in the analysis.
Before computing scales for the in-store evaluation (normal fitting room and IFRs), technology readiness and personality characteristics (only using the extraversion scale) a factor analysis was run to see the component loading for the different scale items. A principal axis factoring analysis (PAF) as explained in Pallant (2010) was conducted for all the above-mentioned variables. A Bartlett’s test of sphericity was used and a Kaiser’s criterion to check if the scales loaded on component with eigenvalues over of 1. From the IFR service and IFR social evaluation scale 1 variable is made which is the evaluation of IFRs in general. Also a dummy variable is computed for the different IFRs (Dummy_IFR_SO), zero being service and 1 being social IFR. All scales load on 1 factor so the final step was composing new variables; this was done by making 4 new scales from the items in each measurement: the variable assessing the difference between the IFR evaluation and the NF evaluation (IFR
Table 1: Cronbachs Alpha
Variable Cronbachs Alpha
In-store experience – NF In-store experience - IFR
Technology readiness - TR 0.46
Personality characteristics (extraversion) – EXTRO 0.72
minus the NF evaluation, (IFR_EVALUATION), technology readiness (TR) and the personality scale (EXTRO). After composing the scales the two IFR scales were combined in one scale, a new scale was created to measure the difference between the normal fitting room evolution score and the IFR evaluation score. The new scale IFR_EVALUATION, was made to only test for the effect of the IFR in-store evaluation and to control for different levels in attitude towards shopping. The means and standard deviations after computing the scales are shown in table 2.
Table 2: Mean, Standard deviation and correlations of variable
M SD 1 2 3 4 5 6 1. TR 4.41 0.88 - 2. EXTRO 5.07 1.26 0.159 - 3. IFR_EVALUATION 0.28 1.34 0.285** 0.196* - 4. Dummy_IFR_SO 0.48 0.50 0.10 -0.025 -0.092 - 5. Interaction_TR 2.14 2.31 0.213* -0.003 -0.063 0.961** - 6. Interaction_EXTRO 2.44 2.64 0.036 0.156 0.001 0.956** 0.928** -
**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
Table 2 also shows the correlation coefficients for all the combination of variables that will be used during the hypothesis testing. These relations were investigated using the Pearson product-moment correlation coefficient. Preliminary analyses were performed to ensure no violation of the assumptions of normality, linearity and homoscedasticity. The correlation outcomes indicate a strong positive correlation between TR and the IFR_EVALUATION, (r = .285, n = 132, p < .01). In other words high technology readiness is associated with higher levels of IFR evaluation .A positive correlation was found between
Interaction_TR (r = .961, n = 132, p < .01) and Interaction_EXTRO (r = .956, n = 132, p < .01) both have a strong positive effect on the social IFR. Both interaction variables are associated with higher levels of the IFR evaluation.
A paired-sample t-test was conducted before the hypothesis testing to compare the evaluation scores of the normal fitting room against the IFR evaluation scores. The IFR evaluation score (M = 5.51, SD = 1.28), t (131) = -2.42, p < .05, two-tailed) was significantly higher than the evaluation score for the normal fitting room (M = 5.23, SD = 0.75). The mean increase in evaluation score was .28 with a 95% confidence interval ranging from -.51 to -.05. The eta-squared statistic (0.04) indicated a small effect size.
A paired-sample t-test was also conducted to compare the evaluation scores for both experimental conditions separately. In condition 1: normal fitting room service evaluation (M = 5.33, SD = 0.79) compared to IFR service evaluation (M = 5.74, SD = 1.17; t (67) = -2.57, p < .05, two-tailed) there was a significant difference in the evaluation scores. The magnitude of the difference in the means (mean difference = -.40, 95% CI: -.71 to -.09) had a moderate effect (eta squared = 0.09). In experimental condition 1 the IFR evaluation score was significantly higher than the normal fitting room evaluation score.
For experimental condition 2 the paired-sample t-test showed no statistically significant. Evaluation score IFR social (M = 5.27, SD = 1.35), t (63) = -.90 p > .05, two-tailed), did not differ significant from normal fitting room social evaluation (M = 5.11, SD = 0.69). These findings show that the mean of the normal fitting evaluation and the mean of the IFR evaluation significantly defers. This finding also holds for the service evaluation but no significant difference in evaluation mean scores was found for the social IFR condition.
To test all hypotheses hierarchical multiple regression linear regression was used. In the first step of the regression for each hypothesis, three predictors were entered as control variables: gender, age and education. This model was not statistically significant F (3,126) = 2.469; p > .05 and explained only 5.6% of variance in IFR evaluation.
To test the whether there was a difference in the evaluation of in-store experience for the IFR service and IFR social (H1). A dummy variable (Dummy_IFR_SO) was created (service = 0 and social =1) for this analysis. This dummy variable is used as independent variable in this regression analysis and IFR_EVALUATION as the dependent variable. The total variance explained by the model as a whole was 6.6% F (1,125) = 2.197; p >.05. The introduction of IFR_EVALUATION explained an additional 1.4% variance in IFR evaluation, after controlling for gender, age and education (R2 Change = 0.01; F (1, 125) = 1.36; p >.05). In the final model only one predictor was statistically significant, age (B = -.027, p = .013 < 0.01). In other words, if a person’s age increases for one, their evaluation of the IFR will decrease forthwith 0.27. The regression analysis shows that there is no significant difference in the evaluation scores for both IFR. Hypothesis 1 is not accepted.
To hypothesis 2a and 2b another hierarchical regression is used again with the Dummy_IFR_SO variable as independent variable and the technology readiness scale. H2a will test whether a higher TR will have a positive effect on the IFR evaluation, which is the dependent variable in this model. The total variance explained by the model as a whole was 14.2% F (2,124) = 4.12; p < .01. The introduction of TR explained additional 8.7% variance in IFR evaluation, after controlling for gender, age and education (R2 Change = .09; F (2, 124) = 6.27; p < .01). In the final model only two predictors were statistically significant,
technology readiness (B = .43, p < .01) and age as showed above. A higher TR readiness leads to an increase in-store evaluation for the IFR. Hypothesis 2a is accepted, as the influence of technology readiness on IFR evaluation is significant.
To test the difference in in-store evaluation between both IFRs another hierarchical regression is used. To test whether the relationship between the type of IFR and IFR in-store evaluation is moderated by technology readiness an interaction technology readiness variable (Dumm_IFR_SO multiplied by the TR scale) was created. The significant interaction shows that when a person’s technology readiness will increase the evaluation of the IFR social will decrease. Hypothesis 2b was expecting this relationship to be the opposite that is why hypothesis 2b is rejected. The influence of the interaction variable technology readiness on IFR social evaluation is significant but negative. This means that the relationship between type of IFR is moderated by technology readiness but that this effect is only positive for the service IFR evaluation. A higher technology readiness leads to a positive effect on the in-store evaluation of a service fitting room. The regression results are shown in table 3 and figure 4.
The introduction of the technology readiness interaction explained an additional 11.3% variance in IFR evaluation, after controlling for gender, age and education (R2 Change = .11; F (3, 123) = 5.58; p < .01). In the final model three predictors were statistically significant; age, TR (B = .70, p < .01) and the Interaction_TR (B = -.50, p = .05).
The hierarchical regression shows that technology readiness has an interaction effect on the main effect. With moderation model 1 of PROCESS (Hayes, 2012) the interaction effect was tested again for technology readiness. The moderation regression equation used to study the conditional effect of X on Y was Y= b1+ b3M. The conditional effect of type of IFR
(X) on IFR evaluation (Y) is b1. The effect of technology readiness (M) on IFR evaluation (Y)
is shown by B2. B3, the interaction term shows how much the effect of type of IFR on IFR
The regression coefficient for XM is b3 = -.53 , t (128) = -1.97, p = 0.052. Thus the
effect of type of IFR on IFR evaluation is marginally significantly influenced by the technology readiness of a person. A closer inspection of the conditional effects indicates that the relationship between type of IFR and the evaluation of the IFR is only significant for persons with a high technology readiness (CI: -1.22 to -2.20), compared to low a person with low technology readiness (CI: -.557 to .983). The confidence interval of high technology readiness does not contain zero which means that there is an effect. However, as can be seen from probing the interactions (figure 2.), the slope linking type of fitting rooms and IFR evolution is negative for all different technology readiness levels for the social IFR. In other words, although type of fitting room significantly affects the IFR evaluation among high technology readiness persons, such trend has the same direction for other levels of technology readiness as well. From both analyses we can concluded that technology readiness has a moderating effect on the main effect.
Hypothesis 3a will answer if a person with an extrovert personality will have a positive effect on the IFR evaluation. Dummy_IFR_SO and EXTRO the personality scale are the independent variables in this model. The the total variance explained by the model as a whole was 9.7% F (3,123) = 2.67; p < .05. The introduction of personality characteristics explained an additional 4.2% variance in IFR evaluation, after controlling for gender, age and education (R2 Change = .11; F (2, 124) = 2.86; p > .05). In the final model two predictors were
statistically significant, EXTRO (B = .20, p < .05) and age (B = -.026, p < .05). The evaluation of an extrovert person for the IFR will increase by 0.20 for one unit of the extraversion scale. Hypothesis 3a is accepted, as customers with a more extrovert personality will have a higher in-store evaluation when an IFR is presented in the store.
Hypothesis 3b will compare the moderating effect of personality characteristics on the relation between the type of IFR and the evaluation difference between the IFR evaluation service and social. We expect that the moderating effect of an extrovert personality will have more influence on the IFR social evaluation than on the service IFR evaluation. Dummy_IFR_SO, EXTRO scale and the Interaction_EXTRO variable are the independent variables in the model. This model was statistically significant and the total variance explained by the model as a whole was 11.4% F (3,123) = 3.77; p < .01 (R2 Change = .10; F (3, 123) = 4.85; p < .01). In the final model three predictors were statistically significant, age, Dummy_SO (B = -2.94, p < .01) and Interaction_EXTRO (B = .532, p < .01). A person with an extrovert personality will more positively evaluate a social IFR. Hypothesis 3b is accepted based on this regression analysis, as the influence of the personality characteristics interaction on IFR social evaluation is significant. A higher extrovert personality leads to a more positive effect on the in-store evaluation for a social IFR. The regression results are shown in table 3
and figure 4 shows the conceptual model with path coefficients from the hierarchical multiple regressions.
Moderation model 1 of the Hayes PROCESS macro (Hayes, 2012) is also used to test the findings of the hierarchical multiple regressions for H3b. The moderation regression equation used to study the conditional effect of X on Y was Y= b1+ b3M was the same as
used above. The overall model with personality characteristic as moderation variable (M), type of IFR (X) as independent variable and IFR evaluation (Y) as dependent variable is significant (p < 0.01). The interaction term shows how much the effect of type of IFR on IFR evaluation is different for different levels of extroversion. The regression coefficient for XM is b3 = .51 and is statistically different from zero, t (128) = 2.62, p < 0.01. Thus the effect of
type of IFR on IFR evaluation is significantly influenced by a person’s personality characteristic. This would mean acceptance of H3b, but a closer inspection of the conditional effects indicates that the relationship between type of IFR and IFR evaluation is only significant for persons with a less extrovert personality (CI: -1.56 to -.18), compared to a person with a more extrovert personality (CI: -.21 to 1.05). The confidence interval of low extroverts does not contain zero which means that there is an interaction effect. The probing of the interactions (figure 3.), shows that the slope linking type of fitting room and IFR evolution is negative for low extrovert people (B = -.87, p = .013 < 0.05) and positive for high extrovert persons (B = .42, p = .19). Hypothesis 3b stated that the difference in in-store evaluation between both IFRs would be higher for the social IFR when a person has an extrovert personality. This PROCESS moderation analysis shows that type of IFR significantly affects the IFR evaluation among low extrovert people for the service IFR. A reverse trend for a high extrovert person is noticeable but this trend is not significant. This is why H3b is partially excepted as the overall moderation model is significant but the
interaction effect is only significant for low extrovert persons instead of high extrovert persons.
Figure 3. Interaction plot EXTRO
Table 3: Regression Results from hierarchical multiple regressions
Testing of independent variables against IFR evaluation
Variable B Std. Error b*1 t Dummy_SO -.272 .233 -.101 -1.166 TR .432** .130 .281 3.328 Interaction_TR -.503* .255 -.862 -1.976 EXTRO .196* .094 .184 2.080 Interaction EXTRO .532** .183 1.045 2.910
**Significant at the 0.01 level (2-tailed) * Significant at the 0.05 level (2-tailed)
Figure 4. Model with path coefficients from the hierarchical multiple regressions
**Significant at the 0.01 level (2-tailed) * Significant at the 0.05 level (2-tailed)
To conclude the survey participants were asked: if they would use an IFR in the future when one was available to them. More than of the participants, 58% indicated that they would use an IFR when available in a store.
In this chapter, the results will be evaluated considering their theoretical and managerial implications. The limitations and suggestions for future research will be explained.
This study aimed to investigate the differences in two types of IFRs. Theories from SSTs were borrowed and used to explain the possible effects of implementing IFRs to stores. The results provide support for three of the five hypothesis. The main effect measured by H1 did not provide support for a difference in IFR evaluation for the service and social condition. These findings indicate that both IFR could have a positive effect on in-store evaluation of customers as the IFR condition significantly scored better on in-store evolution compared to the normal fitting room. This is in line with previous research on SST. IFR would have the potential to benefit brick-and-mortar retailers, saving cost and retaining customers (Scherer, et al, 2015) and building customer relations to increase customer loyalty (Gremler & Brown, 1999).
H2b and H3b provided more insight in the moderating effect of technology readiness and personality characteristics on the in-store IFR evaluation. Both conditions expected a more positive IFR in-store evaluation for the social IFR. For personality characteristic was this partially true, the overall model showed a significant effect on social IFR evaluation. A closer look showed that this effect was reversed for persons with a less extrovert personality. More introvert persons had a higher in-store evaluation for the service IFR. This is in line with personality traits often seen by introvert persons. Extrovert persons are highly social and introverts are less social human beings (John & Srivastava, 1999). The fact that introvert prefer a more individual shopping experience without social interaction in the fitting room could be a possible explanation fort his reversed effect.
High, low and medium technology ready persons also preferred the service IFR compared to the social IFR. The innovativeness from the social IFR is a bit higher which would generate a more positive evaluation of the high technology ready people (Jackson, et al. (2014), but this wasn’t the case. Another explanation that would also hold for more introvert persons is that people like to shop alone and are not interested in inviting friend and family into the fitting room. Shopping could be a more personal and private activity for these persons.
Theoretical and managerial implications
The findings of this study have a number of theoretical and managerial implementations. Starting with the theoretical implementations, this research has focused on applying already existing SST literature on IFRs. Two concepts applicable to SST are tested on their application to IFRs. These two factors: technology readiness and personality characteristics have shown to have similar effects on IFR evaluation. Technology readiness an important factor for explaining the SST usage by customers showed to have the same kind of effect on IFRs. Persons familiar with technology, who have a high technology readiness, will evaluate IFRs more positively. This also holds for the personality characteristics. More over this study in one of the first researching the overall effect of IFR on customer evaluation.
Moreover this research found some important practical implications where brick-and-mortar retailers can benefit from. The first managerial implication is that implementing an IFR would be beneficial for the in-store evaluation. Combined with the insights from Baumstarck & Park (2010) and Townsend (2017) this fact is a key part for brick-and-mortar retailers as purchase decision can negatively affect shopping experience and result in lost sales. When other retailers start implementing innovative technologies client retention can
become a problem. When wanting to improve the shopping experience using an IFR, brick-and-mortar retailers can benefit from higher and more repeated sales by loyal customers who are returning for the differentiating customer experience.
Up until this point no research was focused on comparing different IFRs. This research, tried to add knowledge to this theoretical and managerial gap by focusing on two different IFR. Although there is after this research still not a totally clear answer on which IFR will be more beneficial for brick-and-mortar retailers, both IFR will have a positive effect on in-store evaluation both serving specific customer groups.
Limitations and future research
Empirical studies in general face certain limitations, this study is no exception on this rule and has also some limitations. Fortunately, these limitations create new opportunities for further research.
Firstly, this research conducted used a hypothetical situation. Using hypothetical stimuli can be difficult to grasp for participants. As IFRs are not yet a standard in every day life, participants are not familiar with the concept. Using a descriptive text and picture can be not specific enough to fully understand all the features and technology advantages the IFR holds. Inline with this argument is always remains an open question whether the participants will behave the same way when faced with a real IFR during their store visits (Shampanier, Mazar and Ariely, 2007)
Secondly, the sample size of this study was fairly small for the amount of conditions. As the experiment consisted of 4 conditions, a sample size around the 160 participants would be considered good. The sample size rule of thumb according to Green (1991), considers a sample size of 200 respondents as ‘’good’’. A sample size of 100 is adequate, therefore this study with a sample size of 132 is closer to adequate than good.
Thirdly, the survey used to collect data for this research didn’t contain a manipulation check for the experimental conditions. The pre-test used to test the difference in experimental conditions revealed that the conditions were clear and were different for all there conditions. A manipulation check in the survey itself would have been an even better way to check whether participants carefully read the experimental conditions and understood the differences.
Fourthly and lastly, the personality scale used in this research from Gosling, et al. (2003) received a lot of criticism, from multiple authors. As the scale reduces other personality scales with over 40 items to a 10-point personality scale. Claims are made by others that this reduction of the personality scales is reducing the reliability of the measurement. As the Chronbach’s alpha for the extroverted items in the scale was above 0.7 in Gosling, et al. (2003) and because of the reduction in the amount of time needed to filling the personality scale this measurement was still included in the research.
Future research should build upon this research findings by expanding knowledge on customer characteristics. If brick-and-mortar want to start introducing IFR to their stores in-depth knowledge on their customer base would be needed. More research is also needed on the combination of the two interaction variables. Future research should combine the technology readiness of a person with their personality characteristics to see what will happen to the in-store evaluation for both interactive fitting rooms.
The purpose of this research was to contribute to the existing IFR literature, by examining how customer behavior changes when using an IFR. This study has contributed to the existing IFR literature through three main findings.
First, the results show that IFRs significantly increases customer’s in-store experience. When comparing the change in in-store evaluation for the service IFR and the social IFR, changes in in-store evaluation are smaller and no significant differences are visible for the different IFR types.
Secondly, TR has a direct and moderating effect on the in-store evaluation. A person with a high technology readiness will in general evaluate the in-store experience higher when an IFR is available in the store. Comparing for both IFRs the moderating effect of technology readiness shows to be positive for the service IFR. A high technology readiness will increase the in-store evaluation for the service IFR. This is not as expected as the social IFR in this research had more technological capabilities than the service interactive fitting room.
Thirdly, as expected personality characteristics did have an influence on the in-store evaluation of IFRs. In other words, extrovert persons will have a higher in-store evaluation after using an IFR as more introvert persons. Extroverted persons as expected have a more favourable evaluation on in-store experience using a social IFR than a service IFR, this effect was as expected. As the total personality interaction model was significant H3b was only partially accepted. Moderation analysis showed that for extrovert persons the relation with the social IFR evaluation was not significant and that there was only a significant interaction effect between introvert persons and the evolution of the service IFR.
To conclude this research we can answer the research question by saying that establishing an IFR is a store will have a positive influence on the in-store evaluation of
customers. To make a choice between investing in a service or social IFR, brick-and-mortar retailers need a good understanding of who their customers are.
85% Of Consumers Prefer To Shop In Physical Stores. (2015, 15 juni). Retrieved from http://www.retailtouchpoints.com/topics/shopper-experience/85-of-consumers-prefer to-shop-in-physical-stores
Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361-391.
Ainsworth, J., & Foster, J. (2017). Comfort in brick and mortar shopping experiences: Examining antecedents and consequences of comfortable retail experiences. Journal of Retailing and Consumer Services, 35, 27-35.
Arnold, M. J., & Reynolds, K. E. (2003). Hedonic shopping motivations. Journal of Retailing, 79(2), 77-95.
Barkhi, R., & Wallace, L. (2007). The impact of personality type on purchasing decisions in virtual stores. Information Technology and Management, 8(4), 313-330.
Baumstarck, A., & Park, N. (2010). The effects of dressing room lighting on consumers' perceptions of self and environment. Journal of Interior Design, 35(2), 37-49.
Belk, R.W. (1974) ‘Application and analysis of the behaviour differential inventory for assessing situational effects in consumer behaviour’, in Ward, S. and Wright, P. (eds) Advances in Consumer Research, 1, Urbana: Association for Consumer Research.
Berry, L. L., Bolton, R. N., Bridges, C. H., Meyer, J., Parasuraman, A., & Seiders, K. (2010). Opportunities for innovation in the delivery of interactive retail services. Journal of Interactive Marketing, 24(2), 155-167.
Bitner, M. J., Ostrom, A. L., & Meuter, M. L. (2002). Implementing successful self-service technologies. The Academy of Management Executive, 16(4), 96-108.
Blázquez, M. (2012). Fashion shopping in multichannel retail: The role of technology in enhancing the customer experience. International Journal of Electronic Commerce, 18(4), 97-116.
Burke, R. R. (2002). Technology and the customer interface: What consumers want in the physical and virtual store. Journal of the Academy of Marketing Science, 30(4), 411-432.
Dean, D. H. (2008). Shopper age and the use of self-service technologies. Managing Service Quality: An International Journal, 18(3), 225-238.
Digital changing room and interactive mirrors at Rebbecca Minkoff. (2014, 8 december). Retrieved on 6 december 2017, van http://retail-innovation.com/digital-changing room-and-interactive-mirrors-at-rebbecca-minkoff
Elliott, K. M., & Hall, M. C. (2005). Assessing consumers'propensity to embrace self-service technologies: Are there gender differences? Marketing Management Journal, 15(2)
Farag, S., Schwanen, T., Dijst, M., & Faber, J. (2007). Shopping online and/or in-store? A structural equation model of the relationships between e-shopping and in-store shopping. Transportation Research Part A: Policy and Practice, 41(2), 125-141.
Hayes (2012). Introduction to Mediation, Moderation, and Conditional Process Analysis. A Regression-Based Approach. The Guilford Press.
Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003). A very brief measure of the big-five personality domains. Journal of Research in Personality, 37(6), 504-528.
Gremler, D. D., & Brown, S. W. (1999). The loyalty ripple effect: Appreciating the full value of customers. International Journal of Service Industry Management, 10(3), 271-293.
Hsiao, M. (2009). Shopping mode choice: Physical store shopping versus e-shopping. Transportation Research Part E: Logistics and Transportation Review, 45(1), 86-95. Hsieh, Y., Roan, J., Pant, A., Hsieh, J., Chen, W., Lee, M., et al. (2012). All for one but does
one strategy work for all? building consumer loyalty in multi-channel distribution. Managing Service Quality: An International Journal, 22(3), 310-335.
Jackson, T. W., Parboteeah, P., & Metcalfe-Poulton, S. (2014). The effects of consumer personality types on the attitudes and usage of self-checkout technology in the retail sector among 18–22 years old. International Journal of Marketing Studies, 6(2), 15.
John, O. P., & Srivastava, S. (1999). The big five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of Personality: Theory and Research, 2(1999), 102-138.
Kaluschka, I. (2006), Shopping Habits Under Spotlight. In: Womens Wear Buyer (2006), 22 24.
Kim, J., Ju, H. W., & Johnson, K. K. (2009). Sales associate's appearance: Links to consumers’ emotions, store image, and purchases. Journal of Retailing and Consumer Services, 16(5), 407-413.
Kooti, F., Lerman, K., Aiello, L. M., Grbovic, M., Djuric, N., & Radosavljevic, V. (2016). Portrait of an online shopper: Understanding and predicting consumer behavior. Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 205-214.