• Korean journal of communication and information
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• v.68
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• pp.87-129
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• 2014

This paper addresses questions on the marginalized position of youths of 'Gangbuk' and elucidates how they construct their own identities in the individual trajectories of everyday life. Three years of research, including participatory observation and in-depth interviews, was conducted on nine students from Northeastern district of Seoul. The research also adopted reflexive photography interview method in order to encourage the informants to actively participate in the research. The result illustrates the diversity of the everyday life experiences. More 'marginalized' youths from middle to lower class background had to endure the burdens of their daily lives without programs. Still, they were elaborating their own cultural taste and positive self-narratives at the periphery of the mainstream culture, by practicing music, online community activities and bodily performances. They had to negotiate the crucial turn of life after their graduation, when they entered into the harsh social competition with limited resources. We observed how they gradually assimilate the identity of the 'working youth', some of them developing a positive valorization of their experiences labor. Findings underline the active role of the cultural practices in the making of class identity of the youth and the necessity of researches situating the making of class identity and the reproduction of the class for the youth in the larger geography of class culture in the contemporary Korean society. Lastly, it is argued that these youths should not be considered as determined subjects, who reproduce already established class identities, but as active agents of their lives who deserve more respects and attentions from the society.

• Asia pacific journal of information systems
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• v.20 no.2
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• pp.157-180
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• 2010

Rapid development of information technology has generated a new industry and market. In particular, network technology such as the Internet and other computer networks made transaction activities switched from traditional offline commerce to e-commerce. Among them, digital content is bit-based object which is created and distributed through electronic environment. In particular, many entertainment contents such as the music, the movies, and the computer game softwares are main products. Although digital content commerce has high potential demand, it lacks the consideration about the factor related to maintaining existing customer such as customer loyalty and switching costs. There has been a number of research on customer loyalty and other factors affecting it in the traditional electronic commerce environment, but there is a lack of research which examines the characteristics of digital content. The study about the effect of switching costs on customer loyalty in digital content commerce is necessary because the customers of digital content commerce market are from those of other e-commerce market or traditional offline commerce market. In addition, customer loyalty and switching costs are important factors because they may build up greater customer retention. For that reason, this study focused on examining the relationships among switching costs, antecedents for switching costs, and customer loyalty in online digital music service industry. The study has three major purposes: (1) to find antecedents of switching costs on digital content commerce and examine effect of antecedents for switching costs; (2) to identify effect of switching costs on customer loyalty in digital content commerce and examine moderating effects of alternative attractiveness; (3) to identify the differences of antecedents for switching costs by contents transmission type(streaming service and downloading service). And, the online digital music service industry is selected in this study since there are many users and transactions incurring. To accomplish these purposes, a survey questionnaire was developed and distributed to 256 informants. Survey instrument was developed based on previous research and pre-established survey items. Total of 206 surveys are collected and used in the data analysis. Among the respondents, 56.8% is male and 43.2% is female. Also, 86 responses were streaming service user group and 120 responses were download service user group. These data was analyzed using regression analysis. Major findings of empirical analysis can be summarized as follows. First, switching costs have positive effect on customer loyalty in digital content commerce environment. Second, the influence of switching costs on customer loyalty increases under conditions of high alternative attractiveness. Third, DRM convenience and breadth of use have positive effect on switching costs. The findings imply that the digital content provider should pay more attention to switching costs in addition to customer satisfaction in order to attract customers. Also, increasing the convenience of DRM use by securing the convenience of user interface and expanding the support device and increasing the service use scope by providing diverse value-added service helps to create a switching barrier. The result of the study can become a practical use in marketing strategy for maintaining existing customer. In particular, switching barrier is very important under conditions of high competition in the online music service market. This study can be used as a basis for further studies about customer retention in digital content commerce.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.156-163
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• 2020

This study was conducted to provide basic data for realistic applicable changes of sirens by identifying the stress due to the currently used sirens and the need for improving siren. The data was collected from an online survey of 267 subjects who voluntarily agreed to join this study from August 2019 to September, 2019. A structured questionnaire was used as a research tool. The results were analyzed by means and standard deviations, percentages and frequency analysis, and independent t-tests using SPSS. For the current workplace emergency siren type, it is believed that the number of hybrid formats was 143, the most negative opinion of the siren is high at 132, so periodic replacement is needed. For contextual stresses, the highest was 4.35 ± 0.94, when the siren was heard during bedtime. In the stress during sleep, which was based on the daily mean number of movements,, a statistically significant difference was shown between groups of 9 or more movements and below 9 movements (p <0.05). The type of siren paramedics wanted was 'soft music' and 'sound of nature'. More than half of 168 people (62.9%) wanted to replace the current siren, and 163 (91.6%) wanted that periodically within 24 months. Therefore, it is necessary to improve the siren.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.560-569
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• 2015

Crowd funding is the method of raising money for a project, companies from a large group of people via the Internet, in return for future products or equity. Kickstarter is the largest and most successful crowdfunding site where creative projects raise reward based funding. Drawing on dataset of 80,267 projects with combined funding over $1.3b from 8.1m people, this paper suggest that backer select project based on their preference on the project, instead profitability of the project. It suggests that well-established platform and big size of network increases the chance of success of the project due to a ripple effect and blockbuster effects. Clear communication about the project's idea and goal is highly correlated with success. Regular communication on the project site, such as by constant progress updates, helps the success of the project. Equity-based crowdfunding is emerging as an innovative means of raising capital for businesses, so it has been receiving a lot of attention and expectation from the government and the market. The findings of this paper and others will help to get some understanding and insight into equity-based crowdfunding. However, Kickstarter differs from equity-based crowdfunding in the goals of the backers. Kickstarter's backers are not investors, they are contributors. To understand equity-based crowdfunding, the subject will need further study.

• Journal of Korea Entertainment Industry Association
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• v.15 no.1
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• pp.27-38
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• 2021

Non-Contact society has arrived due to social distinctions by COVID 19 pandemic. The arrival of the era of non-contact is having a profound impact on educational activities as well as on our social and economic lives. In response to the pandemic situation universities and all other educational institutions have implemented non-contact online classes. In particular arts physical educations and other practical classes are experiencing many difficulties due to the limited environment caused by social distancing from COVID 19 pandemic. Vocal classes are undergoing a transition mainly from 1:1 individual face-to-face lessons or group teaching methods to the non-contact era of online teaching or lesson methods. It is necessary to look at the direction of non-face-to-face practical classes in preparation for accelerated educational innovation. Edu-tech, which innovates technology in the wake of the age of non-contact after COVID 19 pandemic is expected to begin in earnest at school sites in Korea which have remained in the traditional way of education. The purpose of this study is to effectively non-contact vocal instructional methods by cogitating the current state of higher practical education and vocal classes in Korea. In addition, This study conducted two components of satisfied instructions such as 'Priorlearning of monitoring of recorded singing', and 'Immediate analyzing of various vocal contents and supplementary lessons of music theory' with a research on the peos and cons of non-face-to-face vocal class. Over a period of time, The effective non-contact of vocal instructional methods is in need to supplement non-face-to-face vocal class problems and further research and system construction with non-face-to-face vocal class's pros and cons to construct high-quality lecture contents is warranted.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.