The global luxury goods sector is particularly buoyant and showing steady growth, it was worth an estimated €122.2 billion in 2012 (Mintel, 2013), and generated more than 230 billion Euros in worldwide sales in 2014 (Bain 2014). These factors signify that the luxury sector is experiencing a boom with strong annual growth in terms of both value and volume. Practitioners and researchers are increasingly interested in the complexities associated with the consumption of luxury products, a focus that has led to the need of contribution to the understanding in the field and as a result, new concepts and research frameworks are emerging. Strong competition from an over-subscribed luxury market is further challenged by the practice of counterfeiting, which can damage brand image and profitability of luxury brands, but serves to further reinforce the importance and aspirational attributes of luxury goods. Similarly, e-commerce is growing rapidly and the fashion industry has become the fastest growing product category in the UK (Mintel, 2012). As Internet penetration is increasing rapidly, e-commerce becoming an indispensable complimentary distribution channel for offline retailers. In particular, the demand for luxury online sales is growing as the luxury consumer is reported to be more tech savvy and willing to purchase online in comparison to the offline purchases preferred by the average consumer (Google, 2013, Brandchannel, 2014) Luxury fashion brands have been slow to adopt digital and online channels, as the internet challenges a number of characteristics that have been intrinsic to luxury fashion brands (Okonkwo, 2010). Kapferer and Bastien (2012) proclaim that digital is in strong contradiction with luxury in most aspects. The main question this poses for luxury brands is how to be exclusive in an inherent democratic medium that can be accessed by anyone at anytime (Okonkwo, 2017, Kontu and Vecchi, 2014, Jin 2012). Whereas the digital world is about being instant, available and accessible, luxury fashion brands are very careful to exude timeless style, heritage, rarity and service (Kapferer and Bastien 2012, Pucci-Sisti Maisonrouge, 2013). Luxury brands have been keen to maintain full control over their distribution channels and marketing messages whereas the internet and in particular social media empowers the consumer, allowing them to co-create the brand message (Christodoulides et al., 2012). Further distribution is essential in luxury management (Kapferer and Bastien, 2012) and direct operated points of sale of luxury fashion brands are often based on their flagship stores and act as a marketing communication tool as much as a sales channel (Manlow and Knobbs, 2013; Moore and Doherty, 2007). As such, luxury retail brands have been careful to create exclusive and sensory rich experiences with particular attention to the materials of the product and the environment they are being displayed and sold in (Okonkwo, 2007, Fionda and Moore, 2009, Kapferer, 1998, Kapferer and Bastien, 2012). This points to another difficulty for luxury fashion brands who seek to communicate and distribute their goods online; the sensory appeal of websites is limited to visual and audio stimulation which does not satisfy the requirement for multisensory experiences deemed necessary for promoting luxury products. Due to these challenges scholars are in disagreement whether online fashion brands should distribute their products online or purely use the online channels to engage their consumers, but keep sales to their physical stores. Kapferer and Bastien (2012) consider selling a luxury product online as “extremely dangerous” as it reduces the “dream value”, Okonkwo (2007, p179) argues that the “adoption of the internet as a sales channel is now essential for luxury brands that aim to maintain a competitive edge.” This is supported by the prediction that by 2018 the online channel will account for 14% of worldwide luxury expenditure (Verdict, 2013), and that the luxury consumers in all markets are more tech-savvy than the general population with over 97% of luxury buyer using the internet (Google, 2013). This study adopts Okonkwo’s (2007) point of view that most luxury fashion brands will have to adapt and distribute their products across multiple channels to satisfy the consumer’s expectations. It is theorised that the digital revolution has empowered consumers, raising expectations for different experiences and changing behaviour (Deloitte, 2011; Pine and Gilmore, 2011; Rosenblum and Rowen, 2012): The consumer experiences their shopping experience as a whole (Interbrand, 2012) and looks for an integrated and consistent experience between channels (Roy et al., 2005; Zhang et al. 2010). These new shopping behaviours have exposed retailers’ vulnerabilities in brand and process and the challenge for fashion retailers is to provide a consistent experience and tone of voice across each of these channels. This difficulty is even more prominent for luxury fashion brands. Despite the difficulties and risks for luxury fashion brands to adapt to the multichannel retail environment several advantages in serving the multichannel consumer are identified: the multichannel consumer is considered to spend on average more money (Schoenbachler and Gordon, 2002; Lu and Rucker, 2006, Weinberg et al., 2007), buy more frequently (Kumar and Venkatesan, 2005) and has a longer customer lifetime value (Neslin et al., 2006, Schramm-Klein et al., 2011). An alterative interpretation of the luxury brand paradox could be viewed as the contradiction between the need for luxury brands to increase sales and explore new consumer segments, whilst also retaining their aura of mystery and exclusivity.. Consequently luxury fashion brands have to develop strategies to sustain their luxurious image in the online channels and even though there are an increasing number of luxury fashion houses to do so successfully like Burberry (Kapferer and Bastien, 2012) there remains a need to gain insight into how luxury fashion brands can utilise the online channel efficiently to communicate and engage with its customers. (Tungate, 2009; Okonkwo, 2010; Hanna, Rohm and Crittenden, 2011; Geerts, 2013; Heine and Berghaus, 2014; Kontu and Vecchi, 2014). This working paper seeks to address questions relating to the online consumption experiences and explores online atmospherics and their role in providing a luxury experience. This robust, inclusive approach aspires to contribute to current understanding of online luxury fashion consumption with the aim of identifying meaningful, workable recommendations for both future research and current practice within these sectors. The study will integrate the research findings with current literature in the experiential marketing and atmospherics debate positioning the research in an area where experience and atmospherics are found to be of crucial importance to the brand: luxury fashion retail. We propose a new phrase called ‘e-luxury’ to denote our conceptual framework, which has been developed to address the current gap in knowledge surrounding online experiences in the luxury retail sector. Research that employs experiential e-luxury as the conceptual framework has not been used in the context of understanding online luxury brand consumption, as it is a recent area of growth. As such, more research is required that interrogates the complexities associated with this sector, so it is hoped that by doing so, we may better understand what is driving luxury brands to co-exist online alongside fast fashion and how they can retain their brand equity and position whilst doing so. This notable rise in interest in online behaviour in the luxury good sector is receiving increasing attention from both practitioners and researchers as an area of growth, as such, there are many gaps in our understanding of e-luxury and the experiences of consumers online, that a study of this kind hopes to address. Thus, the aim of this working paper is to explore how to translate the crucial experiential value from the physical luxury retail environment to an omni-channel brand experience to engage a luxury consumers senses and emotions across all channels.

New communication challenges for companies that use social media are: 1) the knowledge and control of the degree of alignment between communicated and perceived brand personality in order to measure the effectiveness of competitive positioning, and 2) the measurement of engagement among consumers who share comments about brands in online communities. Our research proposes research tools that can help fashion companies meet these challenges. In particular, we present an innovative methodological approach that combines netnography and text-mining to extract and analyze data from online communities of fashion brands.

Consumers struggle to find clothes that are fit. This is a problem that has been accentuated with the increasing online sales. It is the largest contribute to sales returns and make customer return about every third item they buy (Ratcliff, 2014). If consumers select to buy online, they can no longer try them out in a fitting room. Most of fast fashion brands have opened their online shops, and many designers’ brands have also started to sell their ready-to-wear lines online. While online shopping is seen as a significant new way to reach more consumers in fashion industry, the size problem becomes more visible and is waiting to be solved. It may decrease the fashion industry’s ambition to piggyback on recent advances in e-commerce. Previous studies on clothing consumption have already touched upon the topic of size and fitting. Consumer surveys in the clothing industry indicate that in general between 35% and 50% of female consumers in the USA were not satisfied with the fitting in their clothing already ahead of the surge in online stores (Desmarteau 2000; Goldsberry et al. 1996). Alexander et al. studied the shopping behaviour of young women in south-eastern USA and found that almost 64% of the respondents frequently changed ready-to-wear clothes to achieve the desired fit (Alexander et al. 2005). Ashdown and Loker pointed out the size problem in current clothing shopping experiences and proposed a conceptual framework called “mass-customized target market sizing”, which is a size system “based on and derived from measurement data exclusively from the people who represent an apparel firm’s target market” (Ashdown and Loker 2010, p147). In this paper we study how innovative mobile technology, social media and crowd sourcing can contribute to solving the size problem. We summarize the current digital approaches that deal with the fitting issues in online clothing shopping and present a new concept, called “Figuracy”, which attempts to find consumers new fitted clothing items through matching their own clothes with anonymous persons’ virtual wardrobe. We have built a mobile application to implement the idea and have done two initial feedback studies to see the consumers’ attitudes. This concept provides new perspectives and opportunities of tackling the fitting problem in online shopping. Ready-to-wear and fast fashion brands are selling their clothes all over the world, not just to one local community. This business model of constantly new collections, draws on standardizing size series that come only in a selected number of models. This adds to the continuous size problem where the interpretation of a particular size varies in-between brands, and even within different models from a single manufacturer. Firms in the ready-to-wear apparel industry in the world use different sizing systems, like general sizes as XS, S, M, L, or more specific standards as numbered sizes from UK, US, FR systems, which can make consumers confused. These sizing systems are usually based on the ideal body types of consumers from certain geographic regions (Nordic Council of Ministers, 2009), thus, a size standard from one region may not fit all types of body within this region. What’s worse, there has been vanity sizing in clothing industry in recent several decades, which actually makes the garments of the normal sizes bigger in physical size (Dooley, 2013). This adds to the difficulty of finding well-fitting apparels. Recently, a few companies have provided a number of solutions to the fitting problem. The solutions are based on fine-grained automatic body measurements; self-generated extended body or garment measurement and body matching. First, the approach to use technology for more automatic and detailed measurements has attracted many interests. For example, it has been to generate a visual presentation of the body by using three-dimensional scan technology. Then consumers do not need to type information manually. A UK-based company, called Bodymetrics, provides the services that use three-dimensional body scanner and “On-line Virtual Try-On” technology to help find the perfect pair of jeans. Second, there are a number of services that require users to manually type the measurements of their bodies or garments that they own and fit. It can be done by users input information of their body types by answering simple questions, such as weight, height, body type, or measurements of bust, waist and hip. Some systems like Mipso, SmartFit can add more personal preferences, such as colours, cuts and budgets. Then the system runs some algorithms to determine the users’ full set of body measurements. It can also be done such as Virtusize, Truefit and Clothes Horse by measuring a garment that the user knows it fits in detail (Perez, 2012). When the personal data is added to the service, it can then match individual’s body measurements with specific brands and sizes of garments. The service recommends fitted ready-to-wear clothing from various brands in online stores, like Fashion Metric and Virtusize. The data can be used to see how fitted the clothing item in an online website is on a virtual avatar built upon the input measurements of the users, like in Virtusize and Mipso. Third, it has been suggested to use self-described body descriptions and then match consumers’ new purchases as a way to recommend clothes that are fit. For instance, Fitbay attempts to personalize clothes suggestions based on the selection of other users with similar descriptions of size and body shape (Lomas, 2014). Through the help of anonymous persons in the community, one could find fitted clothing items. We investigate a fourth approach, called “Figuracy”, where people get suggestions on the garments that are fit by crowd sourcing their existing clothing items and then matching wardrobes among members in the community. It draws on utilizing the matching of consumer-generated data, i.e. crowd sourcing as Fitbay also does. But it uses a description of existing fitting garments in people’s wardrobes in terms of brands, models and size, rather than self-description of bodies. The idea depends on that a community of people are willing to share information of their favourite and fitting clothes from their own wardrobes. The system matches the clothes from one wardrobe with that of other members in order to recommend new fitting clothes to the user. In specific, if the system finds that a user shares a single clothing item with another person in the community, it will predict that the two of them have similar body types. Then it recommends other items from the second person’s wardrobe to the first user. Therefore, the system gains data from the fitting clothes in users’ wardrobe and matches the information with the items from someone else’ wardrobe. If the approach is successful, consumers would not need to input any personal body-data but only data on existing fitting clothes. The concept depends on the existence of a massive database of clothing items from individual wardrobes, i.e. a critical mass, which is also a big challenge to build. The Figuracy concept and implementation is at an early state and it has not yet the amount of content to start generating matching suggestions. Still, the concept is intriguing and the size problem is highly relevant. Thus we were interested in acquiring early users’ feedback. We have done two user studies of two variations of the concept, with a focus on one type of clothing item- the bra. Bras have high requirements on fitting (Chena et al. 2010) and it is considered specifically difficult to find garment of this type that is fit. Since the number of test persons was limited to around ten people each, the system did not generate any matching suggestions. The first test was done in November in 2013 on the app that was available in iPhone’s App-store. This version of the system reads the barcodes, which are usually attached to the price tag, to acquire a unique identifier for brand, model and size. We invited the participants to download the app and scan the barcodes of fitting bras when they try new bras in stores. We conducted phone interviews with six participants. The early feedback identified that users hesitated to add garments based on the barcodes. They did not feel comfortable with adding it in a fitting room in a shop, and they did not have any saved prize tags for the bras at home. Thus, although the barcode is theoretically and technically an easy and effective way of uploading garments, it proved to be inadequate in practice. Based on this feedback, we built a new version, which includes a manual and text-based tag function allowing users to add information about a garment, which do not require barcode reading. The second user feedback study was carried out in December in 2014. We recruited female students on the campuses of Stockholm University and Royal Institute of Technology in Stockholm, Sweden. In the end, nine participants tested the application and input data of their bras at home. After the try-out, we contacted the participants for either a phone interview or a face-to-face interview, each of which lasted between twenty and forty minutes. Each interview was recorded and transcribed. We used a method of qualitative content analysis to study the transcripts. “Qualitative”, as opposed to quantitative, indicates that the analysis goes beyond systematic data coding to identify interesting topics and allow us to contextualize the interpretation of the materials, given theoretical preconceptions (Mayring 2004). Through studying the transcripts, we find the following preliminary results. First, our participants all provided concrete examples of size problems they met in real life. Seven participants considered bras as one of the clothing items that are most difficult in this respect. Three participants stated that size should not be independent from other qualities, such as comfort and style. This implies that future design of similar systems may take into account not only sizes, but also other aspects of clothing. Second, in terms of the input function, all of them followed the instructions and managed to input information of their fitted bras from their wardrobes. Seven participants considered it easy to use while two of them thought it was a bit confusing when to add information. They got stuck at whether they should scan the barcode or just type, since the two functions were presented in one place. All of them used “type” to add items and thought the information on what they should input was acceptable. Four participants felt it complicated to take photos by themselves, so they downloaded pictures from the Internet. In addition, the participants tended to add the latest and favourite items from their wardrobes into the system. Thus these items could represent their personal tastes and styles, which will be useful to link fitting and styles in the future improvement of the application. This also points to a general problem. From an individual perspective, a single item would in the long run be enough to get matches from other people’s wardrobes, if they have added more than one item. If they also only add only a single item, the first person would not get any matches. The users must crowd source their wardrobe, and not for example their latest purchase. The user study shows that this concept might be restricted by users’ unwillingness to present data in such a way. Third, as to social interaction, eight participants at least “liked” one item from others’ wardrobe. Although none of the participants commented on other’s items, they all thought it was important to have some kind of social interactions. Four participants expected more interactions, such as reviews and discussions of certain clothing items, or following fashionable people. Most of the existing fitting systems, except Fitbay, lack the opportunities for social interaction, but Figuracy provides such a platform for users to share their interests and exchange their ideas. Two participants asked for increased opportunities to add the information on their own profile pages even though they were sharing images of their underwear. They asked for means similar to those available when building a blog space so that people in the community would get to know each other better. According to the participants, more social interactions could make them more engaged with the application. Social communities are good for those looking to learn, help and/or support like-minded individuals with common interests. Last but not the least, most of the participants were very interested in the “buy” function, which link the item to online shopping sites. But still three participants revealed their concerns and discussed the problems of it, such as how the system can guarantee the fitting of the item if they conduct a purchase through the system. In sum, the use of crowd sourcing and social interaction to fixing the size problem in online shopping gained great interests among participants. However, they also expressed their uncertainties towards how the system would work. They needed the system to make sure that it could provide fitting clothing items. They thought the biggest problem was that it was unclear what real help they could get from the application at this stage. Although the application failed to provide new recommendations based on matching, the user study allows us to see the feedback of the potential users so that we could improve the system, such as clearer interaction flows in adding items and more interesting interactions. Future research will continue the improvement of the system, start collaborations with online clothing stores and a third recruitment of users to test. On a general level, the study provides an example on how to investigate critical topics in online fashion through the so-called design research. Such research needs to be interdisciplinary and span technology and social science.

The purpose of this study was to compare the effects of internet fashion advertisement (Ad) formats according to university students' online lifestyle. Static banner, rich media, floating, shopping, and target advertisement were selected as stimuli and a self-administered questionnaire was used for data collection. SPSS PC (Ver. 16.0) was used for factor analysis, ANOVA, and Chi-square test. Factors of online lifestyle were economy, early adaption, cyberspace activity, sociability, innovation, and entertainment, and subjects were segmented into online activity (OA) retard group, OA mania group, hedonic early adapter group, and OA intermediate group. OA retard group was positive to a static banner Ad with intimacy, and OA mania group and OA intermediate group were positive to a static banner Ad with confidence, attention, and intimacy and rich media Ad and floating Ad with confidence and attention. Hedonic early adapter group was positive to a target Ad with attention and intimacy. Internet shopping mall managers should select internet Ad format after segmenting their customers according to OA lifestyle.

Nowadays, personal media is a new tool for communication as digital cameras and mobile phones are developing rapidly. We are concerned over Cyworld users who could have different personal characteristics which will influence on buying patterns in on line shopping behaviors. The purpose of this research was to observe fashion attitudes and purchase behavior of Cyworld mini homepage users, for establishing marketing strategies by understanding consumers. For this study, one line survey was used for 500 male and female subjects who are 20 to 40 years old. Only reliable 441 questionnaires were used for analysis. The SPSS program was used for frequency, K-means cluster, t test, and chi-square test. A total of 441 respondents were clustered on the basis of 8 item self-disclosure scale, using the K-means procedures. The results indicated that respondents were clustered into two segments; 267 respondents(active attitude towards self-disclosure) and 164 ones(not active). We examined fashion attitudes in mini home page and buying behavior by self-disclosed variable. Those who are involved actively in self expression and self-disclosure considered more fashion style and trend. The major motivates of web surfing was finding a good design, and good price. High self-disclosure group tends to search many shopping mall for right design and low self disclosure group tends to search them for the right price. High self-disclosure group tend to shop the fashion products more, while low self disclosure group tend to purchase books more through the internet. We realized that active group in self-disclosure purchased their clothing accidently when they visit Cyworld.

The purposes of this study is to investigate the attitudes and value of fashion product description and consumer replies used in online shopping malls, and to examine the differences in the perceived reliability(objectivity, expertise, trustworthiness) preference and purchase intention toward the product as determined by the appeal type (evaluation-sentimental vs. factual-information) of the product description, the direction(negative vs. positive) and type(factual vs. evaluative) of consumer replies for the product in online shopping malls. Data was collected from female college students with fashion products purchase experience at online shopping malls by questionnaire survey (N=424) and analyzed by using frequency analysis, t-test and ANOVA. Results showed that consumers respondents tended to read product description and other consumer replies before purchasing, when shopping for fashion products through an on-line shopping mall. They thought that sellers' product description and the consumers' replies were helpful in making their decision; but, they were also skeptical about product description. Respondents showed higher perceived reliability, preference and purchase intention to the factual-information type product description than the evaluation-sentimental type. Positive consumer replies were more effective in yielding higher preferences and purchase intentions. Factual replies tend to yield higher reliability than evaluative replies.

This study aims to examine the influence of attitudes on customers’ intention to participate in online fashion sharing. A framework was proposed to investigate the relationships between consumer motivation, consumer attitude, and purchase intention in the manner of adopting a fashion-sharing platform. Consumer motivations are divided into three categories: utilitarian, hedonic, and ecological. The moderating effects of product replacement cycle (PRC) on consumer attitude and purchase intention are also investigated. Data collection was developed using a web-based survey where 180 consumer respondents from South Korea participated. The results of our analysis indicate that consumers’ hedonic and ecological motivations are positively related to favorable consumer attitudes, even when consumers’ utilitarian motivation is denied. Consumer attitude is also positively related to purchase intention in the fashion-sharing platform. A moderating effect of PRC is recorded between consumer attitude and purchase intention based on high and low PRC, as well as the effect of ecological motivation and consumer attitude on high PRC. This study enhances knowledge of consumer motivational factors in a fashion-sharing platform and provides insights for service providers to help them improve their target marketing.