The online shopping market is expanding, with online shopping malls now subdivided into personal computer(PC) and mobile versions. Meanwhile, various efforts to promote online sales are being carried out in a bid to improve performance, and detailed research is required to inform such strategies. The purpose of this study was to classify online shopping mall types into PC fashion malls and mobile fashion malls with the aim of assessing sales promotion satisfaction and investigating the relationship between sales promotion satisfaction and consumers’ behavioral intentions. Data were collected by a survey firm in June 2023, and 248 copies of the data were used for analysis. SPSS 28.0 was used to process the data, and frequency analysis, factor analysis, reliability analysis, and regression analysis were performed. The satisfaction factors for various sales promotions used by PC and mobile fashion shopping malls were empirically subdivided in consideration of consumer perspectives, and potentially effective marketing strategies were presented. Differences were observed in the type of satisfaction with sales promotion between PC fashion shopping malls and mobile fashion shopping malls and in the effect of sales promotion satisfaction on behavioral intention. Based on the study’s findings, effective sales promotion strategies that can increase satisfaction and enhance behavioral intention may be developed and implemented through the use of various and different sales promotion strategies in PC and mobile fashion shopping malls.

In this study, text mining was conducted on the product names of skirts, pants, shirts/ blouses, and dresses to analyze the characteristics of keywords appearing in online shopping product names. As a result of frequency analysis, the number of keywords that appeared 0.5% or more for each item was around 30, and the number of keywords that appeared 0.1% or more was around 150. The cumulative distribution rate of 150 terms was around 80%. Accordingly, information on 150 key terms was analyzed, from which item, clothing composition, and material information were the found to be the most important types of information (ranking in the top five of all items). In addition, fit and style information for skirts and pants and length information for skirts and dresses were also considered important information. Keywords representing clothing composition information were: banding, high waist, and split for skirts and pants; and V-neck, tie, long sleeves, and puff for shirts/blouses and dresses. It was possible to identify the current design characteristics preferred by consumers from this information. However, there were also problems with terminology that hindered the connection between sellers and consumers. The most common problems were the use of various terms with the same meaning and irregular use of Korean and English terms. However, as a result of using co-appearance frequency analysis, it can be interpreted that there is little intention for product exposure, so it is recommended to avoid it.

This paper presents a way of classifying qualitative online consumer reviews (OCRs) in terms of functional and emotional dimensions and measures the direct and indirect impact of both volume and valence of OCRs on product sales. Utilizing four million online postings across 342 mobile games for thirty months, the authors use text analysis and word classification and identify 74 representative words to describe the various levels of functional OCRs consisting of product quality, product innovativeness, price acceptability, and product simplicity, and emotional OCRs including anger, fear, shame, love, contentment, and happiness. They combine the resulting OCR volumes with weekly sales, resulting in 1,835 observations for analysis with hierarchical Bayesian methods. Results suggest that the volume and valence of aggregated functional OCRs and the valence of aggregated emotional OCRs have the positive effects on sales. The volume and valence of functional OCR subcategories have mixed effects on sales and the link is moderated by the share of emotional OCR subcategories. Further, a sales forecasting model which includes 13 variables of OCR subcategories shows the best predictive validity.

Online social interactions are known to be useful to improve business performances. As l ocal business retailers have limited resources in marketing, they can benefit by using onli ne social interactions for their business performances. In the same line of purpose, the ret ailers also exploit an online platform, such as discount coupon sites: they sell online coup ons for their offline products and services in the platform. Notably, the online platform ca n play an important role in generating online social interactions as well as final sales arou nd the retailing brands. It also provides a distinctive setting for consumers in that they pur chase products and services online only to consume their use offline. Given that consume rs are motivated by different purposes, their online social interactions may differ in the di sparity of purchasing online and consuming offline. Previous studies have witnessed the r elationship between social interactions and sales, but the relationship between environme ntal influences and social interactions remains unexplored. In this paper, we focus on the influences of online and offline environments where consumers are situated with the online platform on generating online social interactions as well as final sales. To this end, we look into two types of social interactions, i.e., product discussion and social referral, and two distinctive environmental influences, i.e., the influences from the same product page and from the local retail revenue where the focal business is located. Using data on online social interactions and offline retail revenues around a major coupon site in South Korea, our empirical analysis demonstrates interesting findings. The two types of social interactions and final sales respond in different way to the environmental influences. To be specific, in the online purchase context, the absolute influence lowers the generation of product discussion and sales while promoting social referrals. In the offline consumption context, however, the proportionate influence plays a role in driving these three outcomes. Our findings suggest that local business retailers should deploy their online platform strategies by concerning online and offline environments, in accordance with the specific marketing objectives regarding social interactions and sales.

Introduction Online shopping has become an important part of people’s daily lives. The very nature of online shopping makes it unlikely for consumers to examine products with their senses (e.g., touch, smell) as they can do in offline stores. The consumer obtains information from a variety of online sources (sellers, other buyers, and third parties) to assess a product and make a purchase decision. This variety of online information (e.g., product description, reviews and ratings) informs and persuades consumers. While sellers’ decisions comprise most information displayed on their product’s website, other information is shown because consumers have a moral, ethical, and legal “right” to know (e.g., ingredients, weight, size) (Jacoby, Speller, & Berning, 1974). Regarding the latter information, some countries (e.g., the U.S., China, Canada, the EU and India) have regulations that require pre-packaged food manufacturers to provide a nutrition-fact label and claims displaying standardized information on product packaging (Health Canada, 2010). We ask the following question to public policy makers and marketers: Should online pre-packaged food shops also need to present nutrition facts? There are two perspectives one might adopt regarding the array of information confronting online shoppers. The first perspective deals with human information processing. This position maintains that humans’ ability to assimilate and process information has finite limits during any given unit of time, and that once these limits are surpassed, behavior tends to become confused and dysfunctional (Miller, 1956; Driver & Streufert, 1969). Conceivably, such information overload might also occur in online shopping. Online shoppers often make their selections from a range of products, each with an array of information. Moreover, they make such purchase decisions within a relatively short time period. An alternative perspective is that nutrition-fact information provides key cues for consumers to assess product quality in the online marketplace. Cues can be categorized as extrinsic or intrinsic to the product (Maheswaran & Chaiken, 1991; Anderson, 1981). Extrinsic cues are product-related attributes that can be altered whereas intrinsic cues are inherent to the product itself (e.g., ingredients) and cannot be easily altered (Rao & Monroe, 1988; Purohit & Srivastava, 2001). An online shopper's evaluation of a product is based upon both intrinsic and extrinsic cues. In the online shopping environment, few intrinsic cues are available to consumers and the disclosure of nutrition facts (an intrinsic product feature) can help to fill this gap. Theoretical Development The understanding of how nutrition information presentation influences online food sales is a substantial topic for both industry and academia. With the convenience of online shopping, the potential for food producers and retail stores to take their products online is enormous. eMarketer (2014) reports that online food and beverage purchases increased 15.2% in U.S. retail ecommerce sales, and that this trend will remain consistent. Online food shopping is extremely popular in China, with 92% of consumers purchasing food or beverages at least once a month (Weber Shandwick, 2014). Moreover, eMarketer (2016) reports that by 2020, one-fourth of China's online purchases will be made directly from foreign websites or from third-party platforms. Thus, it is important for other countries to learn about the Chinese market. Among these potential issues, whether nutrition-fact information affects consumer purchase decisions in the online shopping context remains unexplored. Nutrition-fact labels have proven to be useful cues for consumer purchasing decision in offline conditions (Shah, Bettman, Ubel, Keller, & Edell, 2014). However, researchers have been unable to determine the effects of nutrition information in online conditions with network virtualization (Mavlanova, Benbunan-Fich, & Koufaris, 2012) and information multiplicity. In addition, the nutrition information disclosed by online sellers may cue consumers to acquire healthy food. Previous research has found that when information pertaining to a food’s nutritional content is provided, less-healthy food tastes better (Raghunathan, Naylor, & Hoyer, 2006). This literature raises the issue of whether nutrition information is more effective for healthy or unhealthy products. In summary, we investigate the effect of nutrition-fact information on online food shopping. The research questions address: (1) whether and how nutrition-fact information influences food sales in online conditions; (2) how nutrition-fact information interacts with other online extrinsic cues (i.e., word of mouth and historical sales); and (3) whether nutrition-fact information is more effective for healthy or unhealthy products. Research Design We then address these issues using panel data collected from Taobao.com (the largest online shopping platform in China). We selected 45 days as our study period, and the sample comprised 273 sellers. In addition, we conduct an experiment using an eye-tracking system to test the necessity and helpfulness of nutrition-fact information. Results and Conclusion The results show that the nutrition-fact information has a significant impact on sales. More specifically, consumers are more likely to choose sellers with the nutrition-fact information, and the healthy (unhealthy) food with nutrition-fact information tends to attract more (fewer) purchase. In addition, our results reveal some interesting interactions between nutrition-fact information and other cues. Specifically, WOM and historical sales strengthen the sales impact of nutrition-fact information. Our eye-tracking experiment leads to several interesting results. First, consumers pay attention to nutrition-fact information and spend considerable time reading it. Second, a long fixation length on nutrition-fact information would reasonably increase sales. This study makes several academic contributions. First, we extend the topic of nutrition information to an e-commerce context. Second, this is one of the first studies to examine the role of nutrition-fact information from an experimental perspective. Third, we supplement the findings of previous studies on the role of food type. This study also provides several practical implications. First, governments could require online sellers to reveal nutrition information in a truthful and detailed manner at the point of sale. In addition, labeling policies not only increase nutrition awareness and protect consumers, but they can also offer a profitable path for marketers. Second, sellers should design nutrition information and other cues strategies jointly. Third, compared with unhealthy food, nutrition-fact information is more effective for the purchase of healthy food. Sellers might be encouraged by this trend and consider more strategies to display nutrition-fact information on healthy food.

The goal of this study is to get a better understanding of the relationship between online customer reviews (OCRs), product returns and sales after returns in online fashion. Furthermore, we generate deeper insights about the moderating role of mobile shopping usage, product involvement and brand equity in this context. We answer our research questions by empirically analyzing a unique data set from a European fashion e-commerce company. This study links a wide range of transaction data (2.5 billion page clicks, 46 thousand different products, 700 brands, 40 product categories, 72 million sold and 33 million returned items) with a large set of OCRs (0.9 million). Our results show that positive OCRs can lead to higher sales, lower returns, and better conversion rates. Considering higher search costs on mobile devices, we reveal a weaker impact of OCRs in the mobile than in the desktop sales channel. Furthermore, in line with involvement theory, we see a significant impact of product involvement in this context such as the influence of positive OCRs is stronger for high-involvement products than vice versa. Moreover, we find strong support for statements from brand signaling literature, that OCRs matter more for weak than for strong brands.

Social interactions have been established as a means to help promotions and sales for manufacturers and retailers. Word-of-mouth (WOM), in particular, is proven to increase awareness and drive purchases. Given that small offline retailers have limited resources in marketing, online WOM can play a key role for their offline business performance. In this paper, we focus on two types of online WOM, public discussion and social referral, and study their generation processes by taking into account the multichannel context of both online purchases and offline consumption. To this end, we combine data from three sources: product (or deal)-level sales from a major deal site in South Korea, social interaction records collected by web crawling, and retail revenues at a district level from Korea National Statistical Office. We use a multivariate poison lognormal model to estimate three equations in the same structure with correlated errors, which only differs by the following dependent variables: number of product discussion, number of social referral, and the number of social coupons sold. Our empirical analyses suggest the following. First, the two types of WOM respond in opposite directions for the influencers in the multichannel sales context: the greater number of co-located online deals decreases public discussion but increases social referral. Next, the larger offline retail size increases public discussion, but has no significant effect on social referral. Finally, the results provide practical insights that small offline retailers can improve sales in the multichannel context by effectively managing the generation of different types of online WOM.

Customers’ final purchase decisions for electronic products are understandably influenced by previous experiences, marketing messages such as price and promotion, and opinions from other consumers (Simonson and Rosen 2014). In particular, millions of product reviews are posted daily on online review boards or social media represent aggregate consumer preference data (Decker and Trusov 2010). Past studies analyzing online reviews or word-of-mouth (WOM) have focused more on the quantitative dimension of volume of WOM (or “how much people say”), but less on qualitative dimension of valence of WOM (or “what people say”) (Gopinath, Thomas, and Krishnamurthi 2014). However, recent studies have analyzed disaggregate-level UGC by performing text mining in addition to a general analysis of volume and valence of OUGC. Onishi and Manchanda (2012) investigate the relationship between movie sales and both TV advertising and blogs. Although the authors find that the volume and the valence of OUGC (i.e., blogs) are predictive of market outcomes, they retain only certain words (i.e., advertising, award, interesting, and viewed) that consumers would find useful, therefore having general predictive power for market outcomes. Gopinath, Thomas, and Krishnamurthi (2014) address the relationship between the content of online WOM, advertising, and brand performance of cell phones and find that the volume of OUGC does not have significant impact on sales, but only the valence of recommendation UGC has a direct impact. Liu, Singh, and Srinivasan (2015) find that both the volume and sentiments of Tweets do not outperform the information content of Tweets in predicting TV series ratings. Although these three papers have investigated the importance of qualitative UGC through text mining techniques, such studies have not accounted for the detailed dimensions of specific contents. For example, Onishi and Manchanda (2012) use only 4 words out of top 30 frequently cited words for their analysis, and Gopinath, Thomas, and Krishnamurthi (2014) classify the OUGC into three disaggregated dimensions (i.e., attribute, emotion, and recommendation) without further classifications of subcategories and valence of positivity and negativity. Liu, Singh, and Srinivasan (2015) mainly focus on positive and negative Tweet contents about TV shows, lacking further classification of functional and emotional dimensions. In contrast to these studies, this study aims to examine in-depth multidimensional aspects of the content of online reviews, i.e., qualitative UGC, and their impacts on product sales. In this process, we develop defensible measurements of UGC by executing a comprehensive empirical text analysis and evaluate the impact of measures of qualitative UGC relative to volume measure of quantitative UGC. Specifically, we analyze a large data set of UGC on the 350 most talked-about smartphone games from seven different genres (e.g., action, arcade, shooting, puzzle, role playing, simulation, and sports) over a 30 month period, August 2010 to February 2013. We utilize a theoretical framework that classifies qualitative UGC into two major perceptions of functional and emotional dimensions. Prior studies show that perceptions of both functional (cognitive) and emotional (affective) dimensions should be considered to investigate their effects on perceived user satisfaction (Coursaris and van Osch 2015) and online shopping behavior (Van der Heijden 2004). It is evident that both functional and emotional UGC influence consumers to purchase a focal product (Lovett, Peres, and Shachar 2013). The functional UGC relates to the positive and negative attributes and beliefs about a product, and the emotional UGC pertains to the feelings and emotions in response to product experience. As an example, consider one innovative car-racing mobile game which, although expensive, has 3D graphics and high level of complexity. After playing this game, consumers may express their feedback on this game online by describing it as well-made, unique, but sometimes fearful (because a high bill charge is expected from excessive playing time), and addictive (because they like the game too much to stop playing it). This type of online reviews contains different types of UGC: functional (e.g., quality, innovativeness) and emotional (e.g., fear). Another layer of our analysis involves the heterogeneity of impact on product sales across different qualitative UGCs. Specifically, we consider the effects of functional UGC on product sales across emotional contexts such as anger and happiness, in other words, a simultaneous association between functional UGC and emotional UGC. For example, although a consumer may be attracted by some reviews on the high quality graphics of a mobile game (functional UGC), she may hesitate to purchase this product because other reviews express their fear about high cost of purchasing virtual goods (emotional UGC). Accordingly, we expect the functional UGC’s effects on sales to be moderated (amplified or reduced) by emotional UGC. We accommodate such interaction effects in both aggregate and disaggregate models. To the best of our knowledge, this study is the first to empirically identify two dimensions of qualitative UGC (functional and emotional), and shed light on the effects of multidimensional UGC categories on sales. Our findings on the influence of qualitative UGC on product sales are quite different from the prevailing view that firms should pay attention more to the volume of UGC (Chevalier and Mayzlin 2006; Liu 2006) but little to the valence of UGC (Duan, Gu, and Whinston 2008; Godes and Mayzlin 2004; Liu 2006). Rather, our research is in line with recent three papers (Gopinath, Thomas, and Krishnamurthi 2014; Liu, Singh, and Srinivasan 2015; Onishi and Manchanda 2012) in terms of the importance of considering specific contents from a vast amount of text data. However, our paper provides two key contributions. First, we show that specific categories of qualitative online UGC such as functional and emotional variables can be used to predict product sales; this result will be of a high managerial relevance. Especially, traditional methods that use simple metrics such as volume and valence of UGC are less accurate than our method that employs a sophisticated, multidimensional content analysis. Second, the results offer guidance to firms in determining which specific UGC (quantitative or qualitative; functional or emotional; under what contexts) they should focus on for increasing the efficiency of their online marketing activities. Utilizing a large dataset of online reviews on 350 mobile games consisting of four million postings generated for thirty months, the authors identified 76 representative words to describe the functional and emotional UGC using text analysis and word classification. We combined the resulting UGC volumes with weekly sales, resulting in 1,835 observations for analysis with hierarchical Bayesian methods. We find that functional UGC includes 54 representative words to describe various levels of product quality, product innovativeness, price acceptability, and product simplicity, and emotional UGC includes 22 words to express anger, fear, shame, love, contentment, and happiness. The results show that the volume and valence of aggregated functional UGC and the share of aggregated emotional UGC have the positive effects on sales. The volume and valence of functional UGC subcategories have mixed effects on sales and the link is moderated by the share of emotional UGC subcategories. These results are in contrast to those in the literature. Further, a sales forecasting model which includes 13 variables of UGC subcategories shows the best predictive validity. The authors discuss the implications of these results for online marketers.

본 연구에서는 온라인 쇼핑몰 상황에서 이전사업경험, 제품속성과 온라인 고객 의견이 신제품 매출성과에 미친 영향을 살펴보았다. 인터넷 쇼핑몰에서 제품 성과에 대한 연 구들은 선진국 온라인 쇼핑몰을 중심으로 고객들의 구전효과에 초점을 두고 연구를 진행해 왔으며 상대적으로 기업특성이나 제품속성에 대한 연구는 미흡하였다. 본 연구에서는 중국 인터넷 쇼핑몰에서 판매중인 총 407개 TV모델들을 대상으로 기업특성, 제품속성 및 온라인 고객의견이 제품 매출성과에 미친 영향을 살펴보았다. 기업특성에서는 이전TV제조업체들의 제품이 신규 진입기업들의 제품들보다 매출성과가 높았다. 제품속성에서는 경쟁제품 대비 초기 가격수준이 낮을수록 성과가 높으며 가격할인율이 높은 경우에는 오히려 매출성과가 낮았다. 전반적인 제품의 기술경쟁력 수준이 높을수록 판매성과가 높으며 신기능의 특성에 따라 매출성과에 미친 효과는 다르게 나타났다. 제품별 온라인 고객평가 의견수가 많을수록 해당 제품의 매출성과는 높은 것으로 나타난 반면, 온라인 고객평가 점수는 매출성과에 유의 한 영향관계나 나타나지 않았다. 본 연구에서는 온라인 쇼핑몰 상황에서 신제품 매출성과 향 상을 위한 이론적 실무적 의의를 제시하고 향후 연구과제들을 제시하였다.

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.

게임머니 판매 모델은 기존의 유료 아이템 판매 모델에 이어 온라인 게임의 대표적인 소액 결재 기반 과금 방식으로 자리 잡고 있다. 본 논문에서는 게임머니 판매 모델을 사용하고 있는 게임과 유료 아이템 판매 모델의 비교 분석을 통해 게임머니 판매 모델의 특징을 도출하고 게임머니 판매 모델에서 효과적으로 경제시스템을 관리하고 안정적인 수익을 창출하기 위해 고려해야 할 요소들을 제시한다. 이 연구는 게임머니 판매 모델이 유료 아이템 판매 모델과 함께 대표적인 유료 콘텐츠 판매 모델로 대표되는 현재의 온라인 과금 시스템의 체계를 확립하는데 기여할 것으로 판단된다.

최근 무역환경 변화에 따라 경쟁력이 열악한 국내 농산물 시장은 혼란이 가중되고 있으며, 시장이 세분화되고 고객의 욕구가 다양해지며 농산물 제품도 품질, 기능, 가격 등의 경쟁력만으로는 더 이상 시장에서 경쟁력을 확보할 수 없다. 급속도로 변화하는 사회 환경과 시장 변화에서 소비자의 구매 욕구를 충족시키고 기존 농산물과 차별화된 독창성을 부각시키기 위해 농산물 마케팅이 중요 과제로 연구되고 있다. 또한 농업 마케팅 분야에서 포장디자인이 소비자의 구매 욕구를 유발시키는 중요한 요소로 부각되며 제품 경쟁력을 확보하는데 필수적이라는 인식이 광범위하게 확산되고 있다. 본 연구에서는 농업 생산물 중계란 포장디자인 개발을 중심으로 소비자가 기억할 수 있는 브랜드네이밍, 구매 욕구를 유발시킬 수 있는 포장디자인 콘셉트를 제시하고자 한다. 특히 온라인 판매용으로 개발되는 계란 포장디자인은 브랜드네이밍이 매우 중요하며 유통과정에서 제품의 운반, 보호를 위한 기능까지도 고려하여 하기 때문에 조형성, 기능성, 심미성, 합목적성으로 제작, 설계된 디자인 방안을 제언하고자 한다.