• 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 Distribution Science
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• v.21 no.3
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• pp.113-121
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• 2023

Purpose: Online distribution channel alludes to the many different digital channels utilized in marketing and distributing goods and services to end users. The present research aims to explore and provide various retail strategy for the success of the art market within online distribution channel. Research design, data and methodology: The current author has conducted and investigate the qualitative textual methodology to take a look at carefully the current and prior literature dataset to achieve the purpose of the present research so that the present author could obtain total 27 relevant prior studies. Results: According to the comprehensive literature investigation, this research has found that there are six kinds of retail strategy for the prosperity of the art market within online distribution channel as follows: (1) Blockchain Technology, (2) Artificial Intelligence (AI), (3) Virtual Reality (VR), (4) Online Market Places, (5) Social Media, and (6) Regulations. Conclusions: The results of this analysis of the relevant literature show that the art market industry needs to adjust to keep up with the quickly shifting landscape of the digital world. In addition, although these technologies can be helpful in addressing difficulties linked to authenticity and transparency, they cannot eliminate the hazards of fraud and misrepresentation.

• The Research Journal of the Costume Culture
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• v.26 no.2
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• pp.172-187
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• 2018

For the past decade, the convenience of sharing information online has improved drastically with the development of smart devices and social media. Such changes have contributed to regarding online word-of-mouth (WOM) as one of the most important consumer information sources. Therefore, the purpose of this study is to examine online WOM effects (acceptance/redelivery intention) with the two-way interaction effects of fashion involvement and the market maven. The empirical study consisted of an offline survey that collected data from 341 respondents and analyzed the data by factor analysis, independent t-test, and two-way ANOVA with SPSS 20.0, producing the following results. First, the market maven effect was found to differ significantly based on the level of fashion involvement, and is also higher when fashion involvement is high. Second, fashion involvement primarily affected online WOM acceptance, while the market maven significantly affected redelivery intention. Moreover, fashion involvement and market maven had relevant two-way interaction with both of the online WOM effects. Third, market maven had measurable effects on WOM redelivery types (objective/subjective) and directions (positive/negative/ neutral), whereas fashion involvement did not have any primary effects on them. However, fashion involvement and market maven had two-way interaction effects on the positive and negative direction of WOM redelivery. Based on these findings, the study suggests the importance of investigating and understanding the complicated online WOM behaviors of consumers, specifically from both managerial and theoretical perspectives.

• 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.

• 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.

• Journal of Distribution Science
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• v.20 no.5
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• pp.49-64
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• 2022

Purpose: This research aims to determine the quality of online store designs that make consumers who use online market board applications have the urge to make impulse buying. This research was conducted because impulse buying is the most common buying behavior. Research design, data and methodology: This research used non-probability sampling. The sample size was 195 respondents from the distribution and service industries by applying a purposive sampling technique. The data collection technique employed a questionnaire distributed online according to predetermined criteria: mobile device users who accessed the online market board application and made at least one purchase in the last six months. The data analysis method utilized was structural equation modeling (SEM). Results: The findings revealed that usability, functionality, and sociability factors affected hedonic and utilitarian search. Furthermore, these findings proved that hedonic search affected impulse buying drives. In contrast, the utilitarian search did not affect impulse buying drives. Conclusions: The usability, functionality, and sociability factors supported hedonic and utilitarian searches. Consumer information security increased consumer confidence in an online store because it was considered to protect matters related to their privacy. The hedonic search also increased impulse buying drives. Consumers prefer to use their spare time to search through online market board applications, which provide many attractive promos.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.343-346
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• 2004

The purpose of our research is to identify the critical variables and to develop a new methodology for market segmentation of online game market. Our research tested the model with Korean online game users because Korean online game industry is the frontier of global online game industries. Conclusively, the critical variables are the suitability of feedback, the reality of design, the precision of information and the involvement of virtual community. The analysis of segmentation shows that the primary target audiences are positively influenced by the reality of design and the involvement of virtual community. To attract the primary target audiences, online game companies should develop strategies depending on the effectiveness of the variables and the demographic and behavioral characteristics of target audiences.

• 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 Digital Convergence
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• v.15 no.8
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• pp.477-487
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• 2017

This paper releases strategies for firms that wish to enter into online circulation business in China. SWOT based on a Korean manufacturing company teaches us better way to approach Chinese online market. Chinese online market is under oligopoly, where Alibaba and Jingdong account for about 80% of the total market. Game theory is used as a measure of threat and opportunity between Korean manufacturer and Chinese online market retailer. Game shows that they are easy to accept opportunity and sales uplift rather than low risk. Analysis shows that Korean companies should improve its products and brand competitiveness in the offline market before entering into the Chinese online retail market. They need to prepare a localization model.

• 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.