• Journal of the Korean Society of Costume
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• v.65 no.7
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• pp.101-117
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• 2015

Along with the development of overall internet service, online fashion communities appeared and have started to thrive. The growth of mobile service has led to an even bigger expansion of these communities, and will wield a strong influence for the foreseeable future. The purpose of our study is to investigate the relationship between the behavior of these fashion communities and personality characteristics of the community features. For our data analysis, our study used 302 men and women, aged from their 20's and their 40's. The study results are as follows: First, some features, such as interaction, entertainment, promotion, information, and system technique, were drawn as the factors of the online fashion communities. Second, we could divide the community behavioral types into three different groups: buyer, maven, and onlooker groups. Third, all three groups showed different community characteristics. The primary concern for the buyer group was promotion while the mavens and the on-lookers were most interested in information. Fourth, they also showed different demographic characteristics in terms of gender, age, duration of membership, on-line time per visit and number of visits per week. These results show that a new strategy is necessary for online fashion communities to differentiate themselves and their marketing to consumers and their community behavioral types.

• Journal of Distribution Science
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• v.17 no.5
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• pp.85-94
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• 2019

Purpose - The purpose of this study is to analyze the impact of customer's communication on sales performance in the online market. Research design, data, and methodology - This study uses linear regression analysis to examine the effects of product review characteristics which are the result of customer's communication, on sales performance by using product reviews of online marketplace Amazon. Result - The increase in the number of product reviews positively affected sales performance. An increase in extreme opinions in the product review has a positive effect on sales performance. The product review length has a negative effect on sales performance. Conclusions - This study has shown the online marketplace customers' communication can influence sales performance using product review big data. This study contributed to the theoretical completeness by analyzing all the products of the book category in Amazon online market. This research will complement the theories regard to the customer behavior affecting sales performance. We expect the empirical analysis result will provide empirical help to sellers, online marketplace operators, and customers. In particular, the number of letters in the product may negatively affect sales performance, so sellers need to consider this effect carefully when exposing product reviews.

• Journal of Distribution Science
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• v.12 no.10
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• pp.67-74
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• 2014

Purpose - This study analyzed how image affects customer trust and satisfaction in the online shopping mall market, which is becoming more competitive; future implications for customer management in online shopping malls were presented. Consumers visit and prefer a few shopping mall sites instead of many sites. Consumers do not visit sites that cannot provide trust and satisfaction. Therefore, establishing trust and satisfaction with differentiated image is essential for survival and growth. Specifically analyzing company image, shop image, and brand image, I studied how symbolic image, functional image, and empirical image affect satisfaction mediated by trust in the online shopping malls of hypermarket retailers. Research design, data, and methodology - To investigate the relationship between image and satisfaction of big box retailers' shopping malls in the online market, the study is based on analyzed data from questionnaires involving advanced research. From May 1st to 20th in the year 2014, a questionnaire survey targeting university students using big box retailers' shopping malls in Seoul was conducted. A total of 282 questionnaires were conducted, and 276 questionnaires were used for empirical analysis, excluding invalid data. Using the SPSS 21.0 statistics package, factor analysis and regression analysis were implemented, and effects of image on trust and satisfaction were presented. Results - First, symbolic image can affect satisfaction with only trust. Among 3 image factors, symbolic image exerts the most influence on trust; trust is important in coupling the medium to satisfaction. Second, functional image and empirical image affect satisfaction directly and indirectly with trust. Conclusions - As I classified the image of hyper market retailers' online shopping malls into symbolic, functional, and empirical image, I analyzed the effects of image on trust and satisfaction empirically. The results of the study and strategic implications are as follows. First, symbolic image can affect satisfaction with only trust. Among 3 image factors, symbolic image exerts the most influence on trust; trust is important in coupling the medium to satisfaction. The establishment of a distinctive symbolic image, such as the online shopping mall's loyalty, level of awareness, and special service, is needed. With the establishment of symbolic image, trust and satisfaction could be improved. Second, functional image and empirical image affect satisfaction directly and indirectly with trust. Especially, as functional image affects trust more than empirical image, setting and implementing a strategy for empirical image based on the right price, service, and convenience could raise trust and satisfaction. Empirical image affects trust and satisfaction substantially. Even though empirical image's influence on trust is lower than that of other three image factors, empirical image's influence on satisfaction is higher than symbolic image. Therefore, it requires a strategy for providing joyful use, and information research functions and distinctive use experience are important to improve satisfaction. This study analyzed image characteristics of hyper-market retailers' online shopping malls in the fast-growing online market; future strategic implications were presented.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.426-435
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• 2017

Since online betting occurs on online where anybody can access through internet, thus, the online gambling market has been expanding. Domestic illegal gambling market size is 4 times bigger than that of legal gambling market, and illegal online betting market is rapidly growing as well. Currently online gambling does exist domestically in form of Sports promotion voting rights, also known as, Toto and Proto and internet lottery. However, the need for other types of online gambling to be permitted, expanded and managed has been suggested. This research has analyzed online gambling users, online gambling addicts and general population's awareness on expansion and operation of online gambling, contraction measures of illegal online gambling and operation measures in case of permitted expansion of online gambling. The result of this research's analysis showed that each group has significantly different awareness level on positive and negative effects of permitting online gambling's expansion. Also, it showed that each group has significantly different awareness on permitting expansion of racing betting as a legal gambling item. Since it is the proven fact that illegal online gambling users could turn into legal users and that users could use the service more positively if online gambling is legalized through allowance and expansion process; one could expect the positive effects from permitting expansion of online gambling.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.155-166
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• 2018

These days, online media, such as blogospheres, online communities, and social networking sites, provides the uncountable user-generated content (UGC) to discover market intelligence and business insight with. The business has been interested in consumers, and constantly requires the approach to identify consumers' opinions and competitive advantage in the competing market. Analyzing consumers' opinion about oneself and rivals can help decision makers to gain in-depth and fine-grained understanding on the human and social behavioral dynamics underlying the competition. In order to accomplish the comparison study for rival products and companies, we attempted to do competitive analysis using text mining with online UGC for two popular and competing ramens, a market leader and a market follower, in the Korean instant noodle market. Furthermore, to overcome the lack of the Korean sentiment lexicon, we developed the domain specific sentiment dictionary of Korean texts. We gathered 19,386 pieces of blogs and forum messages, developed the Korean sentiment dictionary, and defined the taxonomy for categorization. In the context of our study, we employed sentiment analysis to present consumers' opinion and statistical analysis to demonstrate the differences between the competitors. Our results show that the sentiment portrayed by the text mining clearly differentiate the two rival noodles and convincingly confirm that one is a market leader and the other is a follower. In this regard, we expect this comparison can help business decision makers to understand rich in-depth competitive intelligence hidden in the social media.

• CRM연구
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• v.4 no.2
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• pp.19-29
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• 2011

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems.

• Fashion & Textile Research Journal
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• v.15 no.1
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• pp.46-56
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• 2013

This study categorizes online fashion shopping malls according to consumer store patronage behavior as well as classifies consumer groups by online shopping mall patronage to understand the unique characteristics in each phases of purchase. A quantitative survey was conducted using 487 questionnaires from women in their 20s and 30s. The data were analyzed using frequency analysis, cross-tabulations, factor analysis, T-test, ANOVA, cluster analysis, and ${\chi}^2$-test. The findings of this study are as follows. First, online shopping malls were classified into three types of 'integrated mall', 'open market' and 'specialized fashion mall'. Second, based on one of the three types of categorization consumer groups patronizing each type turned out as follows: integrated mall patrons (141, 28.95%), open market patrons (226, 46.41%) and the specialized mall patrons (119, 24.64%). Third, the characteristics of each group had significant differences according to clothing shopping orientation, information search, shopping mall behavior, spending on online shopping, and e-loyalty.

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.272-284
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• 2009

In this paper, we proposed the methodology for analyzing online customer reviews by using text mining technologies. We introduced marketing segmentation into the methodology because it would be efficient and effective to analyze the online customers by grouping them into similar online customers that might include similar opinions and experiences of the customers. That is, the methodology uses categorization and information extraction functions among text mining technologies, matched up with the concept of market segmentation. In particular, the methodology also uses cross-tabulations analysis function which is a kind of traditional statistics analysis functions to derive rigorous results of the analysis. In order to confirm the validity of the methodology, we actually analyzed online customer reviews related with tourism by using the methodology.

• Journal of Korea Game Society
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• v.11 no.5
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• pp.31-41
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• 2011

To sustain the competitive edges in the online game industry, many publishers invest online game developers at early stages. However, the fair value of online game developers at early stages cannot be calculated with traditional valuation methods due to the high level of uncertainties in technology and market. Based on the literature reviews, we recommended real options analysis for the proper valuation methods of online game developers at early stages, and compared it with other methods using "Bluehole Studio" investment case. The case study result showed that real options analysis is better in explaining the market value as expected.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.69-78
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• 2021

With technological development including digitization, movie demand and supply in digital online movie market are increasing. This study aims to explore demand concentration of the digital online movie market, which is characterized by product variety compared to cinemas. Major findings of the empirical analysis on the TV VOD data during the recent seven years(2013 ~ 2019) are as follows. First, the analysis on 1,137 titles reveals that movie demand of theatrical market is more concentrated than that of TV VOD. Second, absolute long tail index of TV VOD, measured by the download number of indie & artistic movies(niche product), is increasing as more such movies are released in the market. However, both relative long tail index, measured by the share of indie & artistic movie demand, and top-ranked movies' share do not show consistent increase or decrease trend. Third, regression analysis exhibits that the relationship between demand concentration and market size is insignificant for TV VOD market. This study might have usefulness in that it provides empirical evidence for the nature of the Korean digital online movie market.