The Classification Attempts In Online Environments

Published Date: 02 Nov 2017

Clifford (1998) also reviewed the methods used for classification of retail stores. He adopted a deterministic approach (i.e., perceptions of property developers and town planners) in the classification studies that he presented. He did not include the classification studies that take into consideration shoppers’ opinions and perceptions of shopping. Thus, he presented the UK government classification of retail businesses (i.e., food retailers drink, confectionery and tobacco retailers, clothing, footwear and leather goods retailers, household goods retailers, other non-food retailers, mixed retail businesses, hire and repair businesses) and goods (i.e., convenience goods, comparison goods, recreational goods, other goods). Then, he continued by providing retail classifications based on "shopping trip purpose", "size and type of stores", and "store ownership". He also provided classification of shopping centers based on "the central place hierarchy", the "size", the "physical form", "trip purpose" and lastly, he presented a classification based on retail "location".

Levy and Weitz (2001) presented the three established types of layout of traditional retailing. The "grid" layout type is mainly used by grocery stores, as it facilitates planned shopping. The design of retail stores that adopt this layout type is based on repetitive long aisles and rectangular arrangement and display of products. The long aisles offer the ability to display a larger amount of products. This type of layout helps customers to easily find what they are looking for, and to not spend much time in these stores, as they visit these stores quite frequently and know where they can find the products they are interested in. A shopping list prepared by the customers before the store visit, is a common practice in stores of this layout type. The visualization of this store layout is depicted in Figure 2.2.

Other retail stores, such as department stores or smaller specialty stores, adopt the free-form layout which facilitates a superior view of the products. Usually, there is a main aisle in a ring form that connects all the entrances of the store (Levy and Weitz 2001). Retailers adopt this store design when they want to encourage customers to view an existing or a new product that they had not intended to buy (i.e., unplanned purchases); that is why this layout serves impulse buying. The allocation and display of products increases the time customers spend within the store.

Finally, the "racetrack-boutique" is mainly used by large department stores. In this layout type, the aisles and display of the products are arranged irregularly within the store. This design does not guide the customers through the store and sacrifices enough space of the store to create a pleasant and tempting atmosphere. The pleasant atmosphere combined with the asymmetric design reveal personal selling as an important managerial dimension. In this regard, this layout is also adopted by boutique stores that wish to create a unique atmosphere in terms of the quality of the products and the shopping experience.

2.6.2. Classification attempts in online environments

In online environments (Table 4), Spiller and Lohse (1997-98) adopted an empirical method to classify internet retail stores. Their classification was based on 35 observable site attributes. Descriptive statistics of the respondents provided 44 site features. Five categories of online stores resulted from factor and cluster analysis. According to their research, this categorization is important for marketers to develop their strategy. Also, the categorization is important for designers, in order to design their graphical interface so as to meet their customers’ needs. Vrechopoulos, Papamichail, and Doukidis (2002) transformed the established layout types of traditional retailing (i.e., "grid", "freeform" and "racetrack") in corresponding online retailing ones ("tree", "pipeline", and "guiding pathway" respectively). They run a preliminary survey in order to develop an online store design attribute selection framework and empirically tested 551 retail sites. Based on the scores of the observable attributes they concluded that almost half of the retail sites (51.3%) use the pipeline layout, 21.2% use the tree hub, and a mere 1.5% adopt the guiding pathway.

Similarly, Vrechopoulos et al. (2004) based on the "Object-Oriented Hypermedia Design Methodology" developed virtual store layouts that simulate traditional states. The three different stores in terms of layout, that were developed, were tested through lab experiment. Based on t-Tests and ANOVA, the researchers confirmed that the layout of online stores affects consumer behavior. Indicatively, the hierarchical structure of the transformed Grid layout has been proves to positively influence navigation within the online store. The Free-form layout better facilitates ease of use perceptions and entertainment, while a mixed grid/freeform layout seems to be promising in the context of online retailing. Finally, the Racetrack layout along with the Free-form, increase the time that consumers spend with the online stores.

Similarly, Griffith (2005) based on information processing theory and the Technology Acceptance Model (TAM), investigated how two different types of layout (i.e., tree and tunnel) affect consumers in terms of elaboration and response. He designed two interfaces based on the layout types and employed a two-treatment, between-subjects design. Among others, Griffith (2005) thinks of layout as a viable designing factor in decision making process and considers TAM as an adequate research model for such issues.

More recently, Vrechopoulos and Atherinos (2009) elaborating on the work of Vrechopoulos (2004) designed and developed a web banking site employing three different layouts (traditional, modern, future) as treatments of a laboratory experimental setting. They employed the TAM and found that layout affects users’ behaviour confirming the findings of previous studies. Similarly, Vrechopoulos, et al (2009) replicating the work of Vrechopoulos et al. (2004) in a 3D virtual world context, employed a fourth store layout format labeled "boxes" in their classification scheme, which served as one of their treatments in their experimental design.

2.6.3. Current Classification Perspectives in 3D Online Environments

Porter (2004) was one of the first to propose a typology of virtual communities. Virtual communities included the "member initiated" (subsequent level: "social", and "professional") and "organization sponsored" (subsequent level: "commercial", "nonprofit" and "government") categories. He also contended that the five attributes of virtual communities are, "purpose", "place", "platform", "population interaction structure", and "profit model".

Messinger et al., (2009) adopted and extended Porter’s typology in order to classify VWs. They proposed five categories of VWs: "education-focused", "theme-based", "community-specific", "children-focused", and "self-determined". According to their classification scheme, Second Life is a "self determined" virtual world as there is no specific objective but urging users in business and social activities.

Porter (2004) and Messinger et al. (2009) argue that researchers from different disciplines (Marketing, Information Systems) study virtual communities. Yet, Marketing related phenomena regarding the layout dimension of store image have not been adequately studied in VWs. To that end, Messinger et al. (2009) consider that an open research question is whether store layout in virtual 3D stores, should be customizable or not.

Vrechopoulos, Apostolou, and Koutsiouris (2009) in an initial research attempt on that topic, studied the influence of store layout in virtual 3D stores and found that various layout types (i.e., grid, freeform, racetrack and boxes), do not affect "ease of use, perceived usefulness, entertainment, time spent within the store, and promotional and impulse purchases". However, they call for future research towards further investigating 3D store layout effects on consumer behaviour by employing experimental designs in the context of causal conclusive research initiatives that will exploit all the specific attributes that characterize such environments (e.g., teleporting capability, flying within the store, etc.).

2.7. STORE LAYOUT AND USER-CONSUMER BEHAVIOUR

As aforementioned earlier in this chapter, the layout of store is considered a main component of store atmosphere. Academic research presented in the previous sections recognized the influential role of store layout on consumer behaviour (e.g., Dailey 2004; Griffith 2005; Manganari et al. 2009) and described the classification schemes of retail stores based on the store layout (e.g., Griffith 2005; Vrechopoulos et al. 2009). The following table (i.e., Table 2.7) summarizes indicative research studies investigating store layout effects on various consumers’ cognitive and experiential states.

Baker et al. (2002) considered store layout as a design factor of the store environment and investigated among others, its influence on merchandise quality perceptions, and in turn, on store image. Their study followed a between-subjects factorial experimental design. While they didn’t found any significant effects of design factors on quality perceptions, they strongly encourage further research on that topic, as their results are based upon their experimental design decisions.

Novak et al. (2000) emphasizing on the definition of flow and its influence on critical consumer behaviour variables they developed a conceptual model and a structural equation modeling was used to test these variables. They suggest that the web site design should follow specific guidelines regarding navigation, in order to arouse customers, but it should not be too sophisticated, as it is likely that will confuse its visitors. Navigation has been widely studied both in traditional (e.g., Weisman 1981; Levy and Weitz 2004) and 2D online retail settings (e.g., Childers et al. 2001). While the traditional retail store layout in some cases is considered more easy to understand and to navigate than the 2D online layout, the 3D online environments share more common characteristics with traditional retail stores regarding navigation, than with the 2D online stores. For example, the avatar, which is the consumer’s representative within the 3D online store has to walk around and explore the store mimicking real world patterns.

3D online retail environments provide the technological availability of various superior ways of communication with their customers. The store layout could be confronted is such ways in order to provide a calm, friendly, or a forceful store layout. In the 2D online context, Verhagen and Dollen (2011) investigated through a consumer survey the influence of web site communication style and confirmed its influence on impulse buying behaviour.

The direct influence of store layout on perceived ease of use, and perceived usefulness has been widely studied in the traditional and 2D online retail context (e.g., Lightner et al. 1996; Wei and Ozok 2005; Vrechopoulos et al. 2004). Harris and Goode (2010) adopted a cross-sectional online survey approach to investigate the influence of e-servicescape on trust in the web sites, and in turn, on online purchase intentions. The layout of the web site and its functionally was considered as one of the three e-servicescape determinants of their conceptual model, and their results were strongly supported their conceptual framework. Similarly, Vrechopoulos et al. (2004), confirmed the influence of store layout on perceived ease of use and usefulness in the 2D online retail context.

The effects of store layout on providing specific entertaining emotions is clarified both in Levy and Weitz’s (2004) and Mason’s (1991) retailing textbooks as far as the traditional retail environments are concerned. A hedonic layout is more likely to provide a rather entertaining experience than a utilitarian store layout (e.g., specialty or department stores and supermarkets). The influence of store layout on entertainment has also been acknowledged in the 2D online context Vrechopoulos et al. (2004).

Based on the S-O-R (Stimulus-Organism-Response) model and related literature review in traditional and 2D online retail settings, Oh et al (2008) investigated the effects of two stimulus store atmosphere determinants (i.e., store front design and information display) on three organism characteristics of store image (i.e., safety, convenience, and entertainment). Following a factorial experimental design, suggest that retailers of 2D online stores should develop a store that would create positive emotions in light of safety, convenience, and entertainment. Similarly, Geissler’s (2001) considers that a simple layout will be considered more convenient than a sophisticated one. As far as the safety is concerned, Jarvenpaa and Todd (1997) also recognize the importance of safety in store image.

Titus and Everett (1995) developed a conceptual model in order to discuss the retail search process in traditional environments. They consider the store layout as a component of the environmental stimulation and propose that the use of navigational signs in proper locations within the store will facilitate consumers locating the products they are looking for, and understand the layout/structure of the store. Similarly, Puccineli et al. (2009) support that the use of structures within the store that do not allow customers to have an overall view of the products will negatively influence efficiency.

Chang and Chen (2009) studying the interface quality, security, and loyalty in web based stores, they consider interactivity as the degree of communication between the customers and the store. They suggest to retailers to invest on identifying their customers’ needs in order to provide better interface designs so as to positively influence customers’ convenience and interactivity.

Kim et al. (2007) incorporated the principles of the consciousness-emotion-value (C-E-V) model and cognition-affect-behavior (C-A-B) model in the stimuli-organism-response (S-O-R) model from environmental psychology and investigated among others, the influence of store layout as a stimulus design factor on cognitive states (e.g., beliefs, perceptions, etc.). They consider that the direct interaction between the customers and stores affects their preferences and perceptions towards the store (e.g., store image, store perceptions). They also hypothesized that the online shopping enjoyment is influenced by the store environment in traditional and 2D online settings, and their experiment’s results confirmed the hypothesis.

Verhoef et al. (2009) reviewed past literature in order to develop a holistic model regarding all the features and characteristics that create the customer experience. Along with customer experience in other retailing channels, past customer experience, assortment, brand, etc., the store layout is considered a retail store atmosphere determinant which influences customer experience. Similarly, Kaltcheva and Weitz (2006) studying the effects of environmental characteristics on arousal, and in turn, on pleasantness, and shopping retailers, they advice retailers of grocery stores to create a simple layout in order to positively affect customer experience, while they advice retailers of the sporting/athletic sector to create more complex layout as their customers is likely to be less task oriented.

In conclusion, this section provides an overview of the existing literature on store layout. Based on these studies, the store atmosphere is considered an important store atmosphere determinant which influences various aspects of consumer behaviour both in traditional and 2D online retailing.

2.8. SUMMARY AND RESEARCH QUESTIONS

This chapter provides a detailed presentation of current business practice in virtual environments. A review of the various stages and forms of retailing activity in this context is presented, placing particular emphasis on its distinctive technological and commercial characteristics. The engagement of users and some indicative aspects of the behaviour of the consumers are also discussed. Then, store atmosphere and store layout as major determinants of this research were thoroughly investigated, along with the identification of the criteria that are most important for users-consumers, when they select to visit a 3D online store. Since research in this context is insufficient, store selection criteria of traditional and online stores were also discussed. Furthermore, several studies classifying retail stores in terms of store layout, store atmosphere and other dimensions were also presented, placing emphasis on indicative studies regarding the store layout effects on users’/consumers’ characteristics/ features. The analysis of literature review clarified the development of specific research questions.

Based on the proceeding literature review on store selection criteria, store atmosphere, and store layout in alternative retailing channels, the following research objectives and questions were formulated. In summary, store atmosphere was shown to be a major store selection criterion in store selection process, while the layout of the store is considered as one of the most important store atmosphere’s determinants. Thus, the research questions formulated are:

Which are the characteristics of users-consumers in 3D online environments?

Which are the potential criteria users-consumers use for selecting and visiting a 3D online store?

How do these (store selection) criteria influence the choice of users-consumers for visiting a 3D online store?

Which are the factors that seem to influence sales of 3D online stores?

Which are the components that are influenced by the layout of 3D online stores?

Which is the classification scheme of 3D online stores in terms of layout?

Are there causal relationships between the store layout of 3D online stores and customer experience?

How do the various types of store layout affect the shopping process in 3D online stores?

This chapter indicated the importance of the store criteria in the store selection process in traditional and 2D online retail stores. However, literature in terms of the 3D environments is limited, underlying the importance of conducting an exploratory study to identify the consumers’ behavioural characteristics in 3D retail stores. The following chapter (i.e., Chapter 3) presents the necessary initial research study towards investigating store atmosphere factors, and store selection criteria in 3D online environments.

CHAPTER 3. INITIAL RESEARCH: STORE ATMOSPHERE DETERMINANTS AND STORE SELECTION CRITERIA IN VIRTUAL WORLDS

The major objective of this chapter is to provide insights and an understanding of the purpose of internet and virtual worlds’ use in the context of 3D online environments and to analyze users’ profile and behaviour in VWs. Exiting research in the 3D context presented in the previous chapter clarified the need for such type of research. To this end, an exploratory (Phase#A) and a conclusive research design (Phase#B) have been adopted to portray the behaviour of consumers and users in VWs. The results of a first exploratory study and review of past literature led to specific research hypotheses formulation addressed in a conclusive research effort.

The chapter provides insights about how store atmosphere determinants are perceived by users-consumers and the importance of store selection criteria in this context. Indicatively, the way the users learn about virtual worlds, the activities they are engaged in when visiting a virtual environment and the intention of visiting, the determinants of store atmosphere that are most important, are some of the issues that were considered important, when trying to form their profile.

3.1. INITIAL STUDY MOTIVATION AND RESEARCH OBJECTIVES

This chapter aims to explore consumer behavioral preferences and patterns in the context of VWs, adopting an interdisciplinary research approach that draws theoretical insights from the Information Systems and Marketing disciplines. While business activity in VWs takes many different forms, as discussed earlier, the present study focuses on virtual worlds where retailing activity is taking place, while other VWs (e.g., pure gaming 3D environments) remain beyond the scope of this chapter.

This initial study (Krasonikolakis et al. 2010b) employs as main research vehicle the criteria that users, as both shoppers and non-shoppers, adopt when they opt to visit a virtual reality retail store and measures the importance consumers attach to each of these criteria. Similarly, the chapter investigates the joint variability and correlation of store atmosphere characteristics/determinants in response to store atmosphere.

It’s worthwhile noting that the term non-shoppers in this initial study refers to users that are not engaged in commercial activities within VWs, but visit VWs for other purposes (e.g., socialization, information search, education or entertainment). Non-shoppers may also visit VWs retail stores to search and evaluate product and service information in order to "decide online but buy offline" (as is the case in the 2D Internet retailing environment for a large proportion of Internet non-shoppers). Thus, studying the behavior towards VWs retail stores of non-shoppers that use VWs (along with that of VWs shoppers) is highly relevant and important for understanding store selection criteria and store atmosphere characteristics in this environment. The study also explores potential differences between shoppers and non-shoppers in terms of the importance each group attaches to specific store selection criteria.

Finally, as an initial research effort to predict sales in VWs, a list of relevant independent variables shown in the literature to affect sales in alternative electronic retailing channels is tested in this new VW context.

The research gaps that this initial study (Krasonikolakis et al. 2010b) aims to address are therefore summarized in the following research questions:

In what ways do users learn about Virtual Worlds and reach them?

Which are the activities users are engaged in?

Which are the purposes of VWs’ use and in what ways can these be affected by internet use?

Which is the attitude of shoppers compared to non-shoppers towards store atmosphere determinants?

How can store atmosphere determinants be grouped in factors and how are these factors perceived by users-consumers?

Which are the potential criteria users-consumers use for selecting and visiting a virtual retail store?

How can these criteria be grouped to a set of underlying factors?

How do these criteria influence the choice of users-consumers for visiting a virtual retail store?

Which are the differences per type of user-consumer (i.e., shopper vs. non-shopper) in terms of these criteria?

How are the specific capabilities provided by VWs’ platforms perceived by users?

Which factors seem to influence sales of virtual retail stores?

The following section presents the specific methodological approach that these questions are investigated.

3.2. RESEARCH FRAMEWORK AND METHODOLOGY

The following table (Table 3.1) depicts the research framework of this initial study. The research questions are presented alongside the respective phase in which they were investigated and the relevant methodological approach employed in each phase. Both research phases refer to the initial research discussed in this chapter.

Building on earlier work on store selection criteria in the traditional and online environments, as outlined in the previous section, the present research employs an exploratory design (Phase #A of the study) in order to gather and group the criteria that seem to affect users’ selection process of a virtual store in the context of VWs and store atmosphere’s components. This phase of the study is presented in detail in a next section and employs factor analysis.

The results of the exploratory phase are then used in the context of a conclusive research design (Phase #B) aimed at investigating the importance consumers attach to each of the set of criteria and examining (using ANOVA) potential corresponding differences for shoppers and non-shoppers (separately) for each of the resulting factors (Phase #B(a)). Potential statistical significant differences between these two groups of users in terms of the importance they attach to specific VWs’ characteristics were also measured using t-Tests (Phase #b(b)). Finally, multiple regression analysis served towards measuring the predicting power of a series of factors that, according to theoretical evidence, seem to affect the amount of money shoppers spend in a virtual environment (Phase #B(c)). The conclusive research design is presented in section 3.7. SPSS (v.16.0) was used to analyze the results in both phases of the research.

3.3. DATA COLLECTION AND SAMPLE DEMOGRAPHICS

Given the distinctiveness of the VWs’ environment, before embarking on the main data collection phase, a qualitative exploratory study was conducted with eight shop owners operating exclusively in VWs. These shop owners sell virtual reality goods, such as those associated with avatars’ appearance needs (e.g., clothes, skins, hair, shoes, make-up, movements and dancing scripts). They served as experts, providing input on any special and unique characteristic of virtual stores they consider important and common in VWs’ store selection process. Also, they provided input about distinct characteristics that comprise virtual store atmosphere in a 3D context. Their responses complemented the store atmosphere determinants and store selection criteria derived from the extant literature, forming a comprehensive list.

Using this list, an electronic questionnaire was developed and served as the data collection instrument for this research. The use of this type of instrument was considered appropriate for studying the opinions and beliefs of a large group of people at low cost. The function of the questionnaire was to collect data about the respondents’ use of the internet (i.e., frequency, consuming activity, etc.), the respondents’ use of VWs (i.e., frequency, habits, consuming activity, etc.) as well as demographic data of the respondents. Finally, all VW users are expected to have used the internet at least once, due to the fact that the use of the internet is necessary in order to become member of a virtual world.

The questionnaire consists of three parts. In the first part, there are nine questions aiming to segment subjects according to how often they use the internet (heavy-medium-light) users whether they buy products over the internet and the type of products they buy. Part two, addresses issues concerning the activity of internet users in a VW (frequency of visiting, usage rates of Web 2.0 applications such as Facebook and Twitter, types of places-business that they visit, hours and money spent, what they usually do, etc). The questions of the questionnaire were derived by reviewing similar research studies in the context of the internet as well as on conducting eight in-depth interviews with the experts of the field (six of whom manage a virtual business). As noted earlier the interviews with experts aimed to depict current practice, and to find new characteristics of virtual environments that do not exist in Web 1.0 or 2.0. Finally, part 3 of the questionnaire is related to demographics.

Pre-tests were conducted in order to test the questionnaire’s reliability and to modify any unclear questions. An electronic text message, explaining the purpose of the research and containing the web link that the questionnaire was hosted, was sent to 400 users that are fans of Second life on Facebook (sampling frame #1). The questionnaire that was developed with Google Docs, is presented in electronic form (i.e., as appeared in a URL) in Appendix A.1.

Also, questionnaire kiosks were developed and placed in two regions in Second Life (sampling frame #2), where avatars crossing by were invited to take part in the research by filling in the questionnaire. The kiosk was bought for the purpose of the study for an amount of 350 Linden dollars, from a Second Life virtual store which was developing kiosks. For the upload of the questionnaire in the kiosk, it was necessary to develop specific software (see Appendix A.2).

In order to increase the response rate, an award of 30 Linden Dollars (currency of the specific VW that corresponds approximately to 12 cents of US Dollars) was given as a participation motive to each avatar that had filled in a usable questionnaire. Since a large part of questionnaire referred to perceptions and habits in VWs, only users who had experienced at least one visit in VWs were considered appropriate respondents (i.e., these users constitute the population from which the sample was drawn).

After distributing the questionnaires, a total of 104 usable responses were collected, 61 through the invitation through the Facebook group and 43 through the Second Life questionnaire kiosks. The gender dimension of the participants was split roughly evenly (53.8% being male). The majority (81.8%) of the sample was below 36 years old; approximately 40% were aged between 18 and 25 years old and 38.5% between 26 and 35 years old. Also, about 46,1% of the respondents were students and 21.2% were researchers. Finally, at least 65.5% of the population have an average income up to 1500 Euros whereas the majority of the respondents were Greek (87.4%). In terms of nationality, the great amount of Greek participants can be possibly explained by the fact that the virtual questionnaire kiosks were sited in two regions of Second Life owned by Greeks, which increased the probability of Greek users passing by and filling in the questionnaires. This is a limitation in the present study that is also discussed further on in the chapter. All demographic characteristics of the respondents are presented in Table 2 and show the heterogeneity of profiles of Second Life visitors.

3.4. DESCRIPTIVE CHARACTERISTICS

The vast majority of the respondents (84%) responded that besides other things, they "visit virtual worlds in order to meet their friends or meet new friends", an answer that supports the social aspect of these environments. Certainly, 16.3% of the respondents reported that the only reason that they participate in VEs is to meet existent or new friends. Only 7.7% of the respondents reported that they "don’t visit other social web sites and applications" such as Facebook, Myspace, MSN, YouTube, etc. While 77.9% answered that they visit VEs once a week or more, only 0.8% of them don’t visit other social web sites. The researchers’ thought that users who are not consumers as far as web retailing is concerned (i.e., they don’t buy products over the internet), will behave similarly in VEs, was mainly confirmed by this research. Specifically, 27.9% of our respondents have never conducted any purchases over the internet. Only 6.9% of the users that have never made any purchases over the internet take part in economic transactions in VEs. It is probable that this proportion would be much smaller if VEs didn’t give the opportunity to its residents to make money with no need to invest money or pay for the VRE first. To that end, one can build things such as furniture, cars, clothes, homes, etc. and can sell them without the need to use his/her credit card which is an inhibitory factor (48.3% of those who don’t buy products over the internet reported that they are worried about trust and security conditions). In sum, users can merely have an empty bank account where money from their sales will be transferred.

A considerable topic is whether activities in a virtual world are similar to physical activities. Only the 11.5% of the respondents retain a negative attitude to this topic. At the same time, 58.7% of the respondents reported that they would prefer an avatar representing a store's employee (e.g., salesperson) helping them in real time when shopping. This is a remarkable point as it presents a similarity between the process of shopping in a brick-and-mortar store and a virtual store. Along these lines, Burke (1996), stated that 3D effectiveness in e-commerce lies in 3D online environments’ ability to generate a virtual environment for the end-user in which his/her experiences will affect patronizing the physical environment. If we take also into account the social aspect of these environments, probably many aspects of the shopping experience in a physical store, could be simulated and generate a positive attitude towards users-consumers.

It was attempted to segment respondents in two groups: (a) those that buy and sell products in VREs and (b) those that don’t. The responses of these groups in terms of their attached importance on store atmosphere relevant attributes when selecting a VRE to buy products/services are depicted in Figure 1 (1: Not at all important – 5: Very Important). The cues (i.e., attributes) that comprise the atmosphere of a VR store were derived from Lewison’s (1994) model and Vrechopoulos (2001) study as well as through in-depth interviews with experts of this field, as noted earlier in this chapter.

The findings imply that shoppers are more interested in innovative services and crowding of a store while non-shoppers are more interested in the music and store layout. Figure 3.2 presents the importance that the whole sample of respondents attaches to each criterion of a store’s atmosphere. In general, respondents are more concerned about crowding (mean: 4.16) and innovative store atmosphere services (e.g., electronic tongue, electronic touch, electronic smell, flying through the store, teleporting) (mean: 4.16). It is remarkable to note that most people are interested in innovative store atmosphere services as this is a main characteristic of a VRE that makes it discrete, opposite to other retailing channels. People’s emotions can be affected proportionately by the manipulation of these cues. On the other hand, respondents are less considered about storefront (external impressions) (mean: 2.47) and music (mean: 2.83).

It is also clear that crowding has a negative impact on consumers as 16.4% of the respondents replied that crowding is a factor that they particularly dislike in VREs.

3.5. GROUP SEGMENTATION ANALYSIS

Employing the "Purpose of Internet Use" construct discussed in Chapter 2 (O’ Keefe et al. 2000), it was first attempted to segment the sample according to the construct that each subject classified to. In other words, groups of users that use VW either for social communication or e-commerce or information search or hobbies were formed. (i.e., mutually exclusive groups – total 4 groups). However, it was observed that there was not sufficient number of users that visit VWs only for information search purposes, or hobbies or e-commerce. Due to this fact, the sample was segmented into three groups. The first group, labeled "Social Communication", involves users that visit VWs to satisfy their social needs only (i.e., they do not use VW for any other of the remaining 3 purposes). The second group, labeled "E-Commerce", involves users that visit VWs for e-commerce purposes (i.e., to buy/sell products, retain their own virtual business, etc) regardless of any other activities they are engaged in (i.e., games, information search, dance, etc). Finally, the third group involves the remaining users that visit VWs for all purposes except e-commerce. The relevant answers and corresponding segmentation of the sample are summarized in Table 3.3. Specifically, 30 respondents of the sample belong to the first group, 40 in the second and 34 in the third (total sample: 104).

The activities "meet friends", "meet new people", "play games", "shopping", "sell goods", "education", "build things" and "information search" were adopted from other studies concerning VWs (Huang 2003; Messinger et al. 2009). Finally, the activity "dancing" came up through the interviews with experts; specifically, it was reported that is one of the most popular activities that users are engaged in.

3.5.1. Online User (2D) Behaviour

The first descriptive statistic considered concerning online user behaviour is the frequency with which each group visited the internet (Table 3.4). About 46.7% of the respondents that belong in the "Social Communication" group use the internet many times or at least once per day. The corresponding percentage is much greater (82.5%) for "E-Commerce" users (i.e., group #2) and about the same (41.2%) for the third group respectively.

As the sample was segmented taking into consideration the activities that users engage in virtual environments, the "consuming aspect" of these users (i.e., group #2) was considered important in order to form their profile. Therefore, it is probable that someone buys or sells products over the internet but not in a VW and reversely. The results (Table 3.5) indicate, however, that most all (97.5%) "E-Commerce" users buy products over the internet. A significant amount (53.3%) of group #1 users buy products over the internet while they are not engaged in shopping activities in VWs. Finally, the same stands for the subjects of the third group (58.9% of them buy products through the Internet).

The frequency of buying products over the internet is summarized in Table 3.6. Approximately half of the users (50.1%) that visit VWs only for communication purposes buy products over the internet at least once or twice a month.

The corresponding proportion for "E-Commerce" users is greater (61.5%) and for the third group is 40%.

Finally (Table 3.7), regarding the nature of products that users buy, the proportions are lower in intangible products in all groups. Indicatively, only 6.3% of "Social Communication" users buy only intangible products and the percentages for the other groups are 10.3% and 15%, respectively.

3.5.2. User Behaviour in Virtual Worlds

As VWs constitute a promising new environment for e-business, it is important to better understand the profile of users that visit these worlds.

According to the results presented in Table 3.8, 83.3% of the "Social Communication" users (group 1) visit VWs at least once a week. The corresponding percentage is greater (92.5%) for "E-Commerce" users and for the third group (76.5%).

The following Table (Table 3.9) highlights the social aspect of VWs. It is noteworthy that the percentage of users of group #1 (13.3%) and #3 (5.9%) that do not visit other social web sites is greater than that of "E-Commerce" users (2.5%).

As part of our study of VW user profiles, we also investigated how the users first learned about the existence of VWs (Table 3.10). For the first group, most of the users (86.8%) learned about VWs from friends (offline and online) and through e-mails. The same applies to 75% of the respondents of the second group and 70.6% of the third group. It is notable that only 5.9% of the respondents of the third group were informed through scientific articles and journals, while 20% of "E-Commerce" users, randomly.

The majority of users, especially those of the first two groups, seem to embrace the idea that VWs are becoming an emerging alternative retail channel. Nevertheless, approximately one in four (26.5%) of the users of the third group do not (Table 3.11).

Looking further into the perception of VWs as an e-business outlet, it was considered important to investigate what types of stores or business users visit in VWs. As users had the ability of choosing more than one option, Table 3.12 depicts the percentages of users that chose only one option and the percentage of users that chose more than one option (combination of answers). The findings show that 33.3%, 15% and 20.1% of the users within each group respectively (i.e., for groups 1,2 and 3), visit apparel stores only. However, the frequency that the second "E-Commerce" users group visit a combination of the stores, is greater (77.5% ) to that of the first "Social Communication" users group. It should be noted that no respondent (in any of the groups) visits grocery stores in VWs, either as a sole option, or in combination with other stores.

The amount of money that people spend in transactions in VWs is depicted in the following Table (Table 3.13). The first and the third group have been excluded as users that belong to these groups do not make any economic transactions in VW.

Approximately, half of the users/shoppers (47.5%) spend up to thirty Euros per month, while (17.5%) spend more than a hundred.

Having reviewed the characteristics of the research sample which provides a better understanding of VWs’ use and preferences, the next section proceeds to discuss the factors characterizing store atmosphere and driving store selection in VWs.

3.6. EXPLORATORY STUDY: FACTOR ANALYSIS OF STORE ATMOSPHERE DETERMINANTS & STORE SELECTION CRITERIA

An exploratory factor analysis research was considered as the most appropriate approach to address the aforementioned goals outlined in the research framework. (i.e., section 3.2.). The electronic questionnaire that was developed served as the data collection instrument of this study.

3.6.1. Store Atmosphere Determinants: Layout as a Distinct Dimension

Table 3.14 displays the results of factor analysis for store atmosphere determinants. A minimum of five subjects per variable is required for factor analysis (Malhotra and Birks 2000). This requirement is fully met in the case of this research that involves 9 variables and 104 subjects. Tests of normality (Kolmogorov-Smirnov and Shapiro-Wilk) and linearity support the appropriateness of the factor analytical model. Furthermore, the several sizable correlations resulted from the correlation matrix, imply that the matrix is appropriate for factor analysis (Hair et al. 2006). Also, multicollinearity and singularity were conducted to check if any of the squared multiple correlations are near or equal to one. Finally, Bartlett’s test of sphericity (Approx. Chi- Square 138.716, df 36.000, Sig 0.000) and Kaiser-Mayer-Olkin measure (0.643) were conducted in order to prove the appropriateness of the model (Coakes et al. 2009).

Table 3.14 displays the three factors that were extracted. Storefront, store theatrics, colors, music and graphics were grouped in one factor (Factor #1). This factor is labeled Store Appeal because all these attributes are related to the "artistic" part of a store (e.g., the store as a theater), the way the aesthetics of the store are perceived by customers. Crowding, product display techniques and innovative store atmosphere services were grouped in a second factor (Factor #2). This factor is labeled Innovative Atmosphere; these elements are directly related to the innovative aspect offered by VREs in the sense that 3D technology provides such capabilities for displaying products, providing services and manipulating crowding that are new to the world of retailing. Also, innovative product display techniques (this is actually a core retail service) guide avatars’ navigational behavior within the store and, therefore, affect the crowding dimension. Finally, Store Layout constitutes the only attribute included in Factor #3. This finding highlights the importance of this graphical user interface dimension as a major consumer influencing factor in V-Commerce, in the sense that consumers perceive it as a selection criterion that is not related to others. Therefore, this factor should be investigated on its own, similarly to the relevant research practice. This finding confirms the available knowledge on the topic of online store layout effects on consumer behaviour in the context of multichannel retailing (Baker et al. 1994; Burke 2002; Grewal and Baker 1994; Griffith 2005; Lohse and Spiller 1999; Merrilees and Miller 2001; Simonson 1999).

Furthermore, it should be noted that all factor scores indicate that consumers attach significant importance to them when they select a V-Commerce store to conduct purchases (Average scores: Factor 1: 3.42, Factor 2: 3.88, Factor 3: 3.84). This finding is consistent with an earlier study in Web retailing by Vrechopoulos et al. (2001). Specifically, that study found that consumers attach high importance to store atmosphere variables when they select a Web based retail store to conduct their purchases. It also reported that the score consumers attached to the importance of store selection criteria is higher for potential shoppers compared to the current ones. This finding was attributed to the various concerns (e.g., security, effectiveness, etc.) that a shopper has when he/she uses a new retail channel to conduct purchases. Similarly, since the percentage of current V-Commerce shoppers is lower that the potential ones it is expected to obtain such high average scores for the store selection criteria. In other words, consumers that plan to adopt a new shopping channel, compared to the current ones, usually attach higher importance to the majority of the potential criteria in order to select a particular store (Vrechopoulos et al. 2001).

Finally, it should be underlined that the resulted factors’ content (i.e., variables) differ from earlier research on both conventional and traditional web retailing, implying that VWs’ visitors perceive them differently. Thus, factor analysis results do not confirm established knowledge; this finding, along with the implications of all findings of this empirical research are discussed extensively later on in this chapter.

3.6.2. Store Selection Criteria

The store selection criteria (i.e., store attributes) identified through the literature and discussed in chapter 2, were complemented by the responses of experts in the preliminary qualitative study, leading to the list of variables presented in the first column of Table 3.15. This constitutes a concise, rather than an exhaustive, list of store selection criteria in VWs. The selection of a concise list of criteria is a deliberate choice in the research design, because shopping through VWs is an emerging phenomenon.

Therefore, current or potential consumers may not be experienced enough to provide reliable answers when evaluating complicated and advanced VW store features. For example, the ability of flying with the avatar instead of walking in a 3D online environment is likely to be a feature that users are not familiar with. Factor analysis was used to examine the structure of interrelationships among variables, leading to a smaller set of underlying factors (Hair et al. 1995). The variables of Table 3.15 were grouped into four underlying factors for store selection in VWs.

The appropriateness of the model for factor analysis was thoroughly tested. First, the sample of 104 respondents exceeds the requirement of a minimum of five subjects per variable for factor analysis (Malhotra 2000). Furthermore, several variables were sufficiently correlated with each other. Also, multicollinearity and singularity were conducted to check if any of the squared multiple correlations are near or equal to one. Additionally, Bartlett’s test of sphericity (Approx. Chi- Square 215.389, df 66.000, Sig 0.000) and Kaiser-Mayer-Olkin measure (0.636) suggest that the data structure was adequate for factor analysis (Coakes et al. 2009). Principal components analysis and principal axis factoring are among the most commonly used methods for factor analysis, leading in most cases to the same results (Coakes et al. 2009). Principal axis factoring was adopted in the present study and the factors that extracted are based on the eigenvalue criterion (eigenvalues greater than 1 should be included in the model). After retrieving the number of factors, the varimax rotation procedure was adopted, that is an orthogonal procedure enabling the enhanced interpretability of the factors (Malhotra 2000). The results of the factor analysis are presented in Table 3.15.

Factor #1 had positive loadings on Variety of the Products, Quick Access and Easy Walking through the Virtual Store, Prices of the Products and Store Atmosphere. This factor is labeled "Core Store Features". Specifically, Variety of the Products enables "one-stop-shopping" and is preferred by consumers both offline and online, mainly due to time constraints. Quick Access and Easy Walking through the Virtual Store also constitutes a core store feature since it is related to ease of use and convenience. Prices of the Products constitutes a critical success factor for e-tailing due to the tremendous information search and evaluation of alternatives capabilities offered to the online users today. The importance of price is also strengthened by the current global economic climate. In sum, these three variables constitute core store features of a retail store in VWs, as they do in offline and online retail stores (Vrechopoulos 2005).

Conversely, Store Atmosphere, which constitutes a distinct factor in offline and 2D online retail stores (Vrechopoulos 2005) (i.e., usually, it is perceived differently by consumers), is identified in this research as one of the core store features for VW stores. This finding could be explained by the advanced graphic capabilities (i.e., 3D) that may be available in a VW retail store. This implies that VWs consumers hold high expectations for Store Atmosphere (perhaps due to their familiarity with online gaming 3D interfaces), similar to their expectations for reasonable prices, convenience and "one-stop-shop" capabilities. Therefore, consumers that select the virtual retailing shopping channel to conduct their purchases perceive Store Atmosphere in a similar fashion with the other three variables (Variety of the Products, Quick Access and Easy Walking through the virtual store and Prices of the Products). This finding is also supported by the fact that the average score of this factor was the highest one observed (the average score of responses was 4, in the five-point Likert scale used) compared to the remaining three factor scores.

Factor #2 has positive loadings on Quality of the Products, Store Reputation and Value Added Services and Customer Support. This factor is labeled "Peripheral Store Features". Specifically, while Quality of the Products is usually (in studies similar to the present one) grouped with product variety and price attributes, this was not the case in the present study. Also the average score of responses for this factor (3.44 in the five-point Likert scale) is lower than the corresponding scores of factors #1 and #3 in the total ranking. Probably, consumers believe that nowadays most of the products have reasonable quality and, therefore, price and variety are more important than quality. Also, the experience of shopping through Web 1.0 (i.e., the early stage of web stores with static pages where interactive or social features were lacking) contributed towards confronting any concerns regarding quality of products and services bought "from distance" (i.e., not through the physical store where consumers have more options than online for testing product quality). The same may stand for Store Reputation. Finally, Value Added Services and Customer Support could be also characterized as "peripheral" services because consumers are aware that such type of services may be offered online due to the combination of technological capabilities with low cost.

Factor #3 has positive loadings on Security and Privacy Protection, thus it has been labeled "Security and Privacy". This label highlights users’ concerns about issues such as security in transactions and privacy, as these first arose with the advent of the internet. Thus, this grouping was expected. However, while these attributes usually obtain the highest scores (see Vrechopoulos et al. 2009), in the case of the present study they were ranked as the second most important store dimension (i.e., average score of responses was 3.9 in the five-point Likert scale). This finding could be probably explained by the fact that VWs users are usually experienced Internet users and, therefore, are not as concerned about security issues or privacy protection as early shoppers in the Web 1.0 environment were.

Factor #4 has positive loadings on My Friends Visit the particular store, Quality of Advertising and Exhibitions and Entertaining Activities within the store. This factor, labeled "Social and Promotional Impulsion", characterizes people (or avatars!) that are extrovert and motivated by their friends or are looking for amusement and entertainment. Specifically, advertising, exhibitions and entertaining activities within the store constitute elements of the promotional mix. Also, the effects of friends constitute a promotional tool in the sense that these friends may operate as reference groups (e.g., opinion leaders) and, thus, companies invest in formulating their opinions and use them as promoters of their VW stores. This grouping implies that consumers perceive their friends’ influence (e.g., through online "word-of-mouth/mouse") as comparable to promotional effects. In other words, it appears that consumers perceive any type of promotional effect similarly. However, this factor obtained the lowest score compared to the other factors (the average score of responses was 3.37 in the five-point Likert scale). This is consistent with the findings of earlier studies exploring the influence of advertising and promotion on online or offline store selection criteria (see Vrechopoulos 2005).

3.7. CONCLUSIVE RESEARCH APPROACH

The second phase (Phase B) of the initial study follows a conclusive research design to investigate:

How do these store selection criteria influence the choice of users-consumers for visiting a virtual retail store?

Which are the differences per type of user-consumer (i.e., shoppers vs. non-shoppers) in terms of these store selection criteria?

How are the specific capabilities provided by VWs’ platforms perceived by users?

Which factors seem to influence sales of virtual retail stores?

3.7.1. Research Hypotheses Formulation of the Initial Study

There are several studies in the context of brick-and-mortar and web retailing addressing the different characteristics and behavioral patterns of shoppers (multichannel or not) and non-shoppers. They all seek to investigate various motivations for brick-and-mortar as well as web activity. Indicatively, in the context of web retailing, Vijayasarathy (2004) reported that users’ general acceptance of the internet affected their shopping behavior accordingly. Similarly, Farag et al. (2006) and Sorce et al. (2005) showed that demographics play an important role in the shopping adoption process, while Vrechopoulos et al. (2009) found significant differences between VWs’ retail store selection criteria in terms of the importance consumers attach to them.

Thus, it is assumed on the one hand that the store selection factors derived through the exploratory study in the previous section significantly differ in terms of the importance both shoppers and non-shoppers (i.e., the total sample of the study) attach to them. On the other hand, it is assumed that VW shoppers perceive the importance of store selection factors differently to VW users who are non-shoppers (hereafter called VW non-shoppers). In other words, it is important to investigate whether the differences observed between the four factors’ average scores have any statistical significant difference, as well as to investigate whether such potential differences (and/or ranking of importance) apply to both shoppers and non-shoppers. Thus:

Hypothesis 1(a): There are statistically significant differences in the importance that all VW users (shoppers and non-shoppers) attach to store selection factors (i.e., Factors 1, 2, 3 and 4).

Hypothesis 1(b): There are statistically significant differences in the importance that VW shoppers attach to store selection factors (i.e., Factors 1, 2, 3 and 4).

Hypothesis 1(c): There are statistically significant differences in the importance that VW non-shoppers attach to store selection factors (i.e., Factors 1, 2, 3 and 4).

In the same vein, it is important to compare shoppers and non-shoppers in terms of the importance they attach to each of the four factors separately, in order to investigate whether and why these groups exhibit different behavioral patterns and attitudes towards each of these factors. The results of such comparison can contribute to the design of targeted promotional and communication campaigns in the sense that a company could approach shoppers and non-shoppers differently, according to the importance they attach to different store selection criteria. Melancon (2011), based on the study of Yee (2006) who investigated the typology of users’ motivations in virtual environments, argues that information on the motivations of different groups of users is valuable for marketers wishing to enhance users’ experiences through branded policies. Jin (2009) argues that the majority of consumers are "inexperienced" shoppers in the context of VWs. Thus, investigating their attitudes towards VWs store selection criteria is important, especially because non-shoppers may visit VWs stores, search for and evaluate information, decide online and buy offline (or even buy online but through Web 1.0 online retail stores). Based on this discussion, the following research hypotheses are formulated in order to investigate the perceptions of different types of users (shoppers vs. non-shoppers):

Hypothesis 2: There are statistically significant differences in each store selection factor between VW shoppers and non-shoppers:

Hypothesis 2.1: There are statistical significant differences in Core Store Features between VW shoppers and non-shoppers

Hypothesis 2.2: There are statistical significant differences in Peripheral Store Features between VW shoppers and non-shoppers

Hypothesis 2.3: There are statistical significant differences in Security and Privacy between VW shoppers and non-shoppers

Hypothesis 2.4: There are statistical significant differences in Social and Promotional Impulsion between VW shoppers and non-shoppers

Focusing on the novel features of VWs, it is interesting to investigate whether these account for differences in perception between shoppers and non-shoppers. According to Hackbarth et al. (2003), shoppers are more likely to adopt and use a system than non-shoppers, as they spend more time exploring its capabilities. Also, computer anxiety is likely to create negative feelings in the direction of use (Venkatesh 2000). Computer anxiety is the notion or even the worry of an individual as far as the use of computers is concerned (Shen and Eder 2009). Webster et al. (1993) claim that if a computer task is too endeavoring, it will probably cause a negative effect on anxiety. In the same vein, Hoffman and Novak (1996) state that in a very demanding environment (e.g., with many options and buttons) users will consider that their capabilities are not enough to face environment requirements. Based on this claim, Shen and Eder (2009) investigated the factors that influence users to visit VWs for business purposes and concluded that computer anxiety does not influence the users’ perceived ease of use (PEOU) of the Second Life VW. However the results of their study imply that the difficulty or ease an individual faces with technology use, influence the use of Second Life respectively (Shen and Eder 2009). Specifically, in Second Life, the process of creating an avatar may not be a one-step process. Inexperienced users have to face issues such as creating (or buying) skin, clothes, body, face and shoes. Also, the directional buttons that can be used to direct an avatar in a virtual place can be time consuming, for users not familiar with teleporting and flying capabilities, when visiting a virtual mall. In sum, creating an avatar and navigating through VWs are considered as difficult in-world activities (Kaplan and Haenlein 2009). Therefore, it is anticipated that non-shoppers consider the processes of creating an avatar and walking around and visiting places in a virtual reality world more difficult than shoppers do. Thus:

Hypothesis 3: There are statistically significant differences between VW shoppers and non-shoppers in terms of their perceived difficulty in:

Hypothesis 3.1: Creating an avatar

Hypothesis 3.2: Walking around and visiting places in a virtual world

Users that visit VWs frequently are expected to be more experienced than those who are not frequent visitors and, in accordance with the findings of computer anxiety studies aforementioned, more likely to be engaged in shopping activities. At the same time, there are some economic, political, virtual experiences and regulatory issues in VWs that are similar to the physical world ones (Messinger et al. 2009). Indicatively, naturalness of virtual in-world activities may generate a familiar environment for visitors, creating or strengthening consumption of virtual or real products (Vrechopoulos et al. 2009). Herrington and Capella (1995) state that store design decisions relate to the time that customers spend shopping. Indeed, Eroglu et al. (2001) found that virtual store design influences the time that customers spend within a Web site. Similarly, van der Heijden (2000) and Li et al. 1999) state that Web site characteristics determine the duration of a Website visit. Moreover, the time spent shopping in a virtual store has proved to be an important factor that positively affects the amount of money spent in virtual environments (Shih 1998). In sum, several studies in the past (both offline and online) attempted to predict shopping behavior employing "sales" (or money spent) as the dependent variable in any given research design.

Bellman et al. (1999) note that "the most important information for predicting online shopping habits are measures of past behavior." Furthermore, they state that "looking for product information on the Internet is the most important predictor of online buying behavior" (p.35-38). As far as the context of VWs is concerned, Vrechopoulos et al. (2009) attempted to measure the predicting power of online activity related determinants (e.g., perceived usefulness, perceived ease of use and entertainment, time spent within the store, promotional sales and impulse purchases) on the overall evaluation of a virtual reality store layout, but did not find any significant relationships. However, according to Huang (2008) perceived ease of use is the strongest predictor of e-consumer attitudes followed by perceived usefulness, irritation and entertainment. In light of this earlier work and in order to investigate the determinants of shopping behavior in the Virtual Reality Retailing (VRR) environment further, the following hypothesis is posited:

Hypothesis 4: The amount of money spent in to the VRR environment is predicted by the:

Hypothesis 4.1: Frequency of visiting Virtual Worlds

Hypothesis 4.2: Perceived difficulty (vs. ease of use) of creating an avatar

Hypothesis 4.3: Perceived difficulty (vs. ease of use) of walking around and visiting places in a virtual world

Hypothesis 4.4: Perceived similarity between virtual and physical worlds

Hypothesis 4.5: The time sp