• The Journal of Society for e-Business Studies
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• v.26 no.4
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• pp.81-93
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• 2021

Through processors, wholesale markets, intermediate sellers, and retailers, agricultural products have been distributed in a multi-level customary manner for a long time as they are easy to deteriorate and no not have a standardized system of size and quality. However, with the advancement of Internet networks and logistic services during the 2000s that facilitated the development of offline markets, and the rise of the non-contact purchase preference in direct response to COVID-19, previous offline consumers flowed into the online market to purchase agricultural goods. In other words, the volume of online agricultural transactions exploded since the pandemic. Against this social backdrop, this study focused on the difference in distribution costs as a result of converting from conventional offline distribution channels to online channels, and analyzed the reduced distribution costs through a case study of garlic sales on the online platform "H" shopping mall. The analysis found that considerable economic effects occurred, some of the effects being an approximate 39% decrease in distribution cost when comparing direct online transactions of the online shopping mall with other more traditional means, a reduced distribution cost rate of approximately 28%p, and increased profit for farmers.

• The Journal of Bigdata
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• v.5 no.1
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• pp.41-54
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• 2020

With the recent expansion of Internet channels and the development of financial technology and information and communication technology, direct overseas purchases have expanded. Although direct overseas purchases dominate consumers in terms of price and scarcity by providing relatively low-priced products and products that are difficult to obtain in Korea, there is a higher chance of consumer dissatisfaction in terms of delivery, product, A/S and refund than domestic purchases. Therefore, this study analyzed consumer dissatisfaction caused by active overseas direct purchase and studied ways to improve problems with overseas direct purchase. As a research method, Several statistical data were collected from the Korea Consumer Agency(KCA), the Korea Customs Service(KCS) and the Korea International Trade Association(KITA) and analyzed using the Exploratory Data Analysis Technique (EDA). The analysis confirmed that consumers were not well aware of information about direct overseas purchases and that the type or degree of consumer complaints varied depending on the type of purchase. Therefore, this study suggests a direction for the revitalization of overseas direct purchases by using EDA to identify the overall status of overseas direct purchases and consumer dissatisfaction and to improve them.

• Proceedings of the Korean Society for Information Management Conference
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• 2012.08a
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• pp.29-32
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• 2012

정보통신기술의 발달로 학술정보가 디지털화되고 온라인으로 유통이 이뤄지면서 영리 출판사를 통한 신속하고 광범위한 배포가 활발해졌다. 그러나 이 과정에서 불명확한 저작권 귀속 규정으로 인한 저작권 침해 문제가 부각되었다. 이 연구에서는 국내 학술지의 저작권 귀속 규정과 저작권 양도 동의서의 현황에 대해 살펴보고, 학술정보 유통 채널의 유형 간 원문 제공 빈도를 비교하고 분석하였다. 본 고는 분석 결과를 통해 현행 저작권 귀속 규정의 문제점을 도출하고, 향후 진행될 저작권 동의서 표준안 개발의 연구 방향을 설정하는 것으로 제한한다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.241-248
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• 2021

Firms have many ways to expand their businesses including M&A. Big companies in online and offline businesses show different ways of expansion with different objectives to expand their digital businesses quickly. Expansions for technical reasons are to acquire technologies they do not have while those for business reasons are M&A for offline companies to have competence in markets by acquiring online companies. Other ways of expansions include spin-off and group participation after investments for startups. Various ways of expansions are chosen because they are optimal choices depending on situations the companies face, and they have different strengths and weaknesses. To analyze the strengths and weaknesses of those options for expansion at this stage would be academically valuable, and also practically meaningful in terms of providing insights for companies' decision making in choosing opitions for expansions. M&A of online companies to make multi-channels by offline companies have risks of failing to internalize online companies and have enough synergy effects. Also, spin-off is a relatively less risky way of expansion while the speed of expansion is slower than establishing external startups with some shares of equity and making them as affiliated companies. External startups are good for speed of expansion while there are risks of legal regulations and negative awareness by the public.

• Knowledge Management Research
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• v.21 no.3
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• pp.249-269
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• 2020

Recently, as the online market has matured, it is facing many problems to prevent the growth. The most common problem is the homogenization of online products, which fails to increase the number of customers any more. Moreover, although the portion of the online market has increased significantly, it now becomes essential to expand offline for further development. In response, many online firms have recently sought to expand their businesses and marketing channels by securing offline spaces that can complement the limitations of online platforms, on top of their existing advantages of online channels. Based on their competitive advantage in terms of analyzing large volumes of customer data utilizing information technologies (e.g., big data and artificial intelligence), they are reinforcing their offline influence as well through this online for offline (O4O) business model. On the other hand, most of the existing research has primarily focused on online to offline (O2O) business model, and there is still a lack of research on O4O business models, which have been actively attempted in various industrial fields in recent years. Since a few of O4O-related studies have been conducted only in an experience marketing setting following a case study method, it is critical to conduct an empirical study on O4O selection attributes and their impact on customer satisfaction and loyalty. Therefore, focusing on China's representative O4O business model, 'Fresh Hema,' this study attempts to identify some key selection attributes specialized for O4O services from the customers' viewpoint and examine the impact of these attributes on customer satisfaction and loyalty. The results of the structural equation modeling (SEM) with 300 O4O (Fresh Hema) experienced customers, reveal that, out of seven O4O selection attributes, four (mobile app quality, mobile payment, product quality, and store facilities) have an impact on customer satisfaction, which also leads to customer loyalty (reuse intention, recommendation intention, and brand attachment). This study would help managers in an O4O area well adapt to rapidly changing customer needs and provide them with some guidelines for enhancing both customer satisfaction and loyalty by allocating more resources to more significant selection attributes, rather than less significant ones.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.103-110
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• 2022

Botnets have been exploited for a variety of purposes, ranging from monetary demands to national threats, and are one of the most threatening types of attacks in the field of cybersecurity. Botnets emerged as a centralized structure in the early days and then evolved to a P2P structure. Bitcoin is the first online cryptocurrency based on blockchain technology announced by Satoshi Nakamoto in 2008 and is the most widely used cryptocurrency in the world. As the number of Bitcoin users increases, the size of Bitcoin network is also expanding. As a result, a botnet using the Bitcoin network as a C&C channel has emerged, and related research has been recently reported. In this study, we propose an encrypted botnet C&C communication mechanism and technique in the Bitcoin network and validate the proposed method by conducting performance evaluation through various experiments after building it on the Bitcoin testnet. By this research, we want to inform the possibility of botnet threats in the Bitcoin network to researchers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.8
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• pp.5485-5491
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• 2015

The purpose of this study is to investigate PR-VEP characteristics on the perception function(S/N) and the judgement function(T/F) of MBTI. The 136 study participants, over 20 years old adults, were examined by PR-VEP and MBTI test for two months in July and August in 2013. PR-VEP was conducted in O1 and O2 by 32 channels EEG system and MBTI test was measured by Form-M online. We found that the time interval(Duration) between N75 and P100 of PR-VEP was 5.49 ms significantly shorter in the group preferring S indicator. And the latency until N75 was 4.83 ms significantly shorter in O1 and 4.27 ms shorter in O2 in the group preferring F indicator. According to these, the characteristics of groups preferring S and F indicator have influence on visual cognitive function, which is meaningful that the interpretation of brain-science can be used with recognition/judgement function of MBTI.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.197-218
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• 2019

In addition to economic growth and national income increase, China is also experiencing rapid growth in consumption of cosmetics. About 67% of the total trade volume of Chinese cosmetics is made by e-commerce and especially K-Beauty products, which are Korean cosmetics are very popular. According to previous studies, 80% of consumer goods such as cosmetics are affected by the word of mouth information, searching the product information before purchase. Mostly, consumers acquire information related to cosmetics through comments made by other consumers on SNS such as SINA Weibo and Wechat, and recently they also use information about beauty related video channels. Most of the previous online word-of-mouth researches were mainly focused on media itself such as Facebook, Twitter, and blogs. However, the informational characteristics and the expression forms are also diverse. Typical types are text, picture, and video. This study focused on these types. We analyze the unstructured data of SINA Weibo, the SNS representative platform of China, and Meipai, the video platform, and analyze the impact of K-Beauty brand sales by dividing online word-of-mouth information with quantity and direction information. We analyzed about 330,000 data from Meipai, and 110,000 data from SINA Weibo and analyzed the basic properties of cosmetics. As a result of analysis, the amount of online word-of-mouth information has a positive effect on the sales of cosmetics irrespective of the type of media. However, the online videos showed higher impacts than the pictures and texts. Therefore, it is more effective for companies to carry out advertising and promotional activities in parallel with the existing SNS as well as video related information. It is understood that it is important to generate the frequency of exposure irrespective of media type. The positiveness of the video media was significant but the positiveness of the picture and text media was not significant. Due to the nature of information types, the amount of information in video media is more than that in text-oriented media, and video-related channels are emerging all over the world. In particular, China has made a number of video platforms in recent years and has enjoyed popularity among teenagers and thirties. As a result, existing SNS users are being dispersed to video media. We also analyzed the effect of online type of information on the online cosmetics sales by dividing the product type of cosmetics into basic cosmetics and color cosmetics. As a result, basic cosmetics had a positive effect on the sales according to the number of online videos and it was affected by the negative information of the videos. In the case of basic cosmetics, effects or characteristics do not appear immediately like color cosmetics, so information such as changes after use is often transmitted over a period of time. Therefore, it is important for companies to move more quickly to issues generated from video media. Color cosmetics are largely influenced by negative oral statements and sensitive to picture and text-oriented media. Information such as picture and text has the advantage and disadvantage that the process of making it can be made easier than video. Therefore, complaints and opinions are generally expressed in SNS quickly and immediately. Finally, we analyzed how product diversity affects sales according to online word of mouth information type. As a result of the analysis, it can be confirmed that when a variety of products are introduced in a video channel, they have a positive effect on online cosmetics sales. The significance of this study in the theoretical aspect is that, as in the previous studies, online sales have basically proved that K-Beauty cosmetics are also influenced by word-of-mouth. However this study focused on media types and both media have a positive impact on sales, as in previous studies, but it has been proven that video is more informative and influencing than text, depending on media abundance. In addition, according to the existing research on information direction, it is said that the negative influence has more influence, but in the basic study, the correlation is not significant, but the effect of negation in the case of color cosmetics is large. In the case of temporal fashion products such as color cosmetics, fast oral effect is influenced. In practical terms, it is expected that it will be helpful to use advertising strategies on the sales and advertising strategy of K-Beauty cosmetics in China by distinguishing basic and color cosmetics. In addition, it can be said that it recognized the importance of a video advertising strategy such as YouTube and one-person media. The results of this study can be used as basic data for analyzing the big data in understanding the Chinese cosmetics market and establishing appropriate strategies and marketing utilization of related companies.

• Journal of Distribution Research
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• v.17 no.3
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• pp.25-57
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• 2012

This study identified motives for writing apparel product reviews in online stores, and determined what motives increase the behavior of writing reviews. It also classified store customers based on the type of writing motives, and clarified the characteristics of internet purchase behavior and of a demographic profile. Data were collected from 252 females aged 20s' and 30s' who have experience of reading and writing reviews on online shopping. The five types of writing motives were altruistic information sharing, remedying of a grievance and vengeance, economic incentives, helping new product development, and the expression of satisfaction feelings. Among five motives, altruistic information sharing, economic incentives, and helping new product development stimulate writing reviews. Store customers who write reviews were classified into three groups based on their writing motive types: Other consumer advocates(29.8%), self-interested shoppers(40.5%) and shoppers with moderate motives(29.8%). There were significant differences among three groups in writing behavior (the frequency of writing reviews, writing intent of reviews, duration of writing reviews, and frequency of online shopping) and age. Based on results, managerial implications were suggested. Long Abstract : The purpose of present study is to identify the types of writing motives on online shopping, and to clarify the motives affecting the behavior of writing reviews. This study also classifies online shoppers based on the motive types, and identifies the characteristics of the classified groups in terms of writing behavior, frequency of online shopping, and demographics. Use and Gratification Theory was adopted in this study. Qualitative research (focus group interview) and quantitative research were used. Korean women(20 to 39 years old) who reported experience with purchasing clothing online, and reading and writing reviews were selected as samples(n=252). Most of the respondents were relatively young (20-34yrs., 86.1%,), single (61.1%), employed(61.1%) and residents living in big cities(50.9%). About 69.8% of respondents read and 40.5% write apparel reviews frequently or very frequently. 24.6% of the respondents indicated an "average" in their writing frequency. Based on the qualitative result of focus group interviews and previous studies on motives for online community activities, measurement items of motives for writing after-purchase reviews were developed. All items were used a five-point Likert scale with endpoints 1 (strongly disagree) and 5 (strongly agree). The degree of writing behavior was measured by items concerning experience of writing reviews, frequency of writing reviews, amount of writing reviews, and intention of writing reviews. A five-point scale(strongly disagree-strongly agree) was employed. SPSS 18.0 was used for exploratory factor analysis, K-means cluster analysis, one-way ANOVA(Scheffe test) and ${\chi}^2$-test. Confirmatory factor analysis and path model analysis were conducted by AMOS 18.0. By conducting principal components factor analysis (varimax rotation, extracting factors with eigenvalues above 1.0) on the measurement items, five factors were identified: Altruistic information sharing, remedying of a grievance and vengeance, economic incentives, helping new product development, and expression of satisfaction feelings(see Table 1). The measurement model including these final items was analyzed by confirmatory factor analysis. The measurement model had good fit indices(GFI=.918, AGFI=.884, RMR=.070, RMSEA=.054, TLI=.941) except for the probability value associated with the ${\chi}^2$ test(${\chi}^2$=189.078, df=109, p=.00). Convergent validities of all variables were confirmed using composite reliability. All SMC values were found to be lower than AVEs confirming discriminant validity. The path model's goodness-of-fit was greater than the recommended limits based on several indices(GFI=.905, AGFI=.872, RMR=.070, RMSEA=.052, TLI=.935; ${\chi}^2$=260.433, df=155, p=.00). Table 2 shows that motives of altruistic information sharing, economic incentives and helping new product development significantly increased the degree of writing product reviews of online shopping. In particular, the effect of altruistic information sharing and pursuit of economic incentives on the behavior of writing reviews were larger than the effect of helping new product development. As shown in table 3, online store shoppers were classified into three groups: Other consumer advocates (29.8%), self-interested shoppers (40.5%), and moderate shoppers (29.8%). There were significant differences among the three groups in the degree of writing reviews (experience of writing reviews, frequency of writing reviews, amount of writing reviews, intention of writing reviews, and duration of writing reviews, frequency of online shopping) and age. For five aspects of writing behavior, the group of other consumer advocates who is mainly comprised of 20s had higher scores than the other two groups. There were not any significant differences between self-interested group and moderate group regarding writing behavior and demographics.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.