• Journal of KIISE:Computing Practices and Letters
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• v.12 no.6
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• pp.367-378
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• 2006

A mobile phone has became as a payment tool in e-commerce and on-line banking areas. This trend of a payment system using various types of mobile devices is rapidly growing, especially in the Internet transaction and small-money payment. Hence, there will be a need to define its standard for secure and safe payment technology. In this thesis, we consider the service types of the current mobile payments and the authentication method, investigate the disadvantages, problems and their solutions for smart and secure payment. Also, we propose a novel authentication method which is easily adopted without modification and addition of the existed mobile hardware platform. Also, we present a simple implementation as a demonstration version. Based on virtual machine (VM) approach, the proposed model is to use a pseudo-random number which is confirmed by the VM in a user's mobile phone and then is sent to the authentication site. This is more secure and safe rather than use of a random number received by the previous SMS. For this payment operation, a user should register the serial number at the first step after downloading the VM software, by which can prevent the illegal payment use by a mobile copy-phone. Compared with the previous SMS approach, the proposed method can reduce the amount of packet size to 30% as well as the time. Therefore, the VM-based method is superior to the previous approaches in the viewpoint of security, packet size and transaction time.

• Informatization Policy
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• v.22 no.4
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• pp.22-44
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• 2015

With the growing interest being reflected in the FinTech industry, much attention has been paid to mobile easy payment services as well. In the era of mobile commerce, the core advantage of using mobile easy payment services(simplifying complex payment procedures and thus facilitating user convenience and reducing the chance of giving up payment) are being more emphasized. Mobile easy payment service market not only includes mobile easy payment service providers but also users, non-users, affiliates, banks, and credit card companies as main stakeholders. Exploring those stakeholders is thus important to thoroughly understand such market. However, extant literature on mobile easy payment services mostly focuses on examining adoption intention of users or non-users. This study, an exploratory research based on interviews, thus aims to extract driving as well as inhibiting factors of mobile easy payment service use from six different perspectives (i.e., social platform, bank, credit card company, affiliate, user, and non-user) and analyze a sequence of cause and effect for each factor. For this, the causal loop diagram was developed to deduce key issues and propose an alternative. Theoretical and practical implications of this study will also be discussed.

• Journal of Distribution Science
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• v.12 no.6
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• pp.5-13
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• 2014

Purpose - The purposes of this editorial review are twofold: firstly, to introduce the four flagship international journals of the Korea Distribution Science Association(KODISA): the Journal of Distribution Science(JDS), the Journal of Industrial Distribution & Business(JIDB), the East Asian Journal of Business Management(EAJBM), and the Journal of Asian Finance, Economics and Business(JAFEB), and secondly, to identify the direction of the KODISA journals and the roles and responsibilities of the editors of the KODISA journals. Research design, data, and methodology - To achieve the goals, firstly, this review paper addresses the current progress of the four KODISA journals: JDS, JIDB, EAJBM, and JAFEB. Secondly, this paper defines the aims and missions of the four KODISA journals. JDS publishes the articles of examining past, current, and emerging trends and concerns in the area of distribution science and economics, logistics and SCM, transportation, distribution channel management, distribution innovation and information technology, merchandising and procurement, distribution and marketing, consumer behavior, and manufacturing, wholesaling, and retailing. JDS publishes both quantitative and qualitative research as well as scholarly commentaries, case studies, book reviews and other types of reports relating to all aspects of distribution. JIDB publishes the articles of examining past, current, and emerging trends and concerns in the areas of industry and corporate behavior, industry policy making, industrial distribution and business, e-commerce, and service industry. EAJBM publishes empirical and theoretical research papers as well as scholarly commentaries, case studies, book reviews, and other types of reports relating to all aspects of East Asian business and economy. JAFEB publishes original research analysis and inquiry into the contemporary issues of finance, economics and business management in Asia, including Central Asia, East Asia, South Asia, Southeast Asia, and Middle East. The mission of JAFEB is to bring together the latest theoretical and empirical finance, economics and business management research in Asian markets. The audiences of the KODISA journals include higher education institutions, scholars, industry researchers and practitioners, scientists, economists, and policy makers throughout the world. The main mission of the KODISA journals is to provide an intellectual platform for international scholars, promote interdisciplinary studies in social sciences and economics, and become leading journals in the social science and economics category in the world. Thirdly, this paper addresses the current status of indexing in major databases of the KODISA journals, namely: Cabell's Directories, EBSCO, SCOPUS (Elsevier), and Social Sciences Citation Index^® (SSCI, Thomson Reuters). Fourthly, this paper identifies the roles and responsibilities of the editors of the KODISA journals as the following: (1) Make sure that the journal be published in a timely manner and in international standards both in print and online versions. (2) Maintain the online homepage of the journal is always accessible to, and (3) Make sure that every article should go through a peer review process that meets international standards. Findings and conclusion - To accomplish the goals and missions of the KODISA journals, the editors of the KODISA journals must work together to publish high scholarly journals that meet international standards of journal publications.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.127-137
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• 2017

The role of smartphones is changing from a communication system for exchanging calls and information into a universal platform for cultural services. Also, satisfaction for mobile application services on smartphones is a very important factor in the smart business. In This paper, we analyze the effects of the outcome, service scape, costs, and especially the impact of whether costumers having to pay or free for the app on customer satisfaction. For this purpose, we analyzed survey data on service quality of mobile app service from Chinese mobile app service users. We also analyzed the moderating effects of paid and free mobile app services. As a result, it was confirmed that the quality, servicescape quality and cost of mobile app service that customers perceive have a positive effect on customer satisfaction. In addition, the effect of the cost of mobile app service perceived by the customer on customer satisfaction showed that free mobile app service was more significant than paid mobile app service. This paper can be used as an alternative to monetization for providing a mobile app service provider or a mobile app service provider who wants to switch mobile app service from free to paid service.

• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• 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 Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.