• The Journal of Economics, Marketing and Management
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• v.2 no.2
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• pp.1-9
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• 2014

This study investigated solutions of not only weak organization but also poor communication with customers from point of view of SNS such as Naver blog, Naver band, Kakao Talk and Facebook to promote local business market. The study gave strategies: First, SNS marketing strategy shall be used to do public relations and communicate in accordance with features of each SNS. Naver Blog that is opened SNS shall be used to do public relations and to invite new customers, and Kakao Talk that is closed SNS shall be used to increase customers having high loyalty, and Facebook that has both properties of SNS, that is to say, openness and closure, shall be used to raise effects of word-of-mouth to make use of new sales window. The communication can find out customers' needs to provide customers with customized services. Second, Naver Band and/or cafe shall be opened to increase link and friendliness and to have community consciousness realizing common goal and to destroy merchants' helplessness and individualism and to make change of the market for the place of community. Changes among the merchants may get better ideas to do events continuously and to get more SNS marketing effects and synergy. Third, the merchants shall make change not momentarily but continuously by making efforts steadily. Good communication in and out of the market may create inherent brand value to differ from super chain and/or SSM and to increase sales as well as traditional market image and to attain customers' loyalty.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.236-243
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• 2018

The purpose of this study was to investigate the mediating effects of parental attachment and ego resiliency in the tendency toward social networking site (SNS) addiction in college students. For this purpose, 382 college students in the Jeju area were surveyed and their responses analyzed. To analyze the collected data, structural equations were implemented using SPSS Statistics version 18.0. According to the results of the study, there is a significant negative correlation between attachment formation and SNS intoxication in college students, and a negative correlation between ego resiliency and SNS addiction. Also, there is a significant positive correlation between formation of parental attachment and ego resiliency in college students, indicating that parental attachment and ego resiliency are significant factors in lowering SNS addiction. Second, in order to examine the mediating effects of ego resiliency in the relationship between the formation of parental attachment and SNS addiction in college students, a research model and a competition model were established and verified. As a result, the relationship between parent attachment and SNS addiction was mediated completely, and the research model is more appropriate. These findings suggest that ego resiliency is of practical significance in seeking ways to improve the problem of SNS addiction in the future. In other words, overuse of SNSs by university students can lead to addiction, but strengthening ego resiliency enables sound SNS use.

• Journal of Family Resource Management and Policy Review
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• v.27 no.4
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• pp.19-33
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• 2023

The purpose of this study was to examine the mediating effect of ego resilience on the relationship between family strengths of Chinese international students and Social Networking Services(SNS) addiction tendency. The data were collected through a survey of 349 Chinese international students studying in Korea and analyzed using the SPSS Statistics version 28 program and SPSS Process Macro 4.3. The main results are as follows. First, Chinese international students exhibited higher-than-average levels of SNS addiction tendency, family strengths, and ego resilience. Second, the SNS addiction tendency of Chinese international students had a significant positive correlation with the average time spend on SNS per day. while demonstrating a negative correlation with family strengths and ego resilience. Third, the hierarchical regression analysis revelated that gender, frequency of contact with family, average time spend on SNS per day, qualitative bond as a sub variable of family strengths, and control as a sub variables of ego resilience were significant factors influencing SNS addiction tendency of Chinese international students. Fourth, the study confirmed the complete mediation of ego resilience in the effect of family strengths on SNS addiction tendency. Based on the research results, this study suggested specific directions for counseling interventions aimed at mitigating SNS addiction tendencies among Chinese international students.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.107-116
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• 2022

In the Chinese workplace, the use of SNS such as Wechat and QQ for work delivery out of duty hours has become a very common phenomenon, which is also known as the "new night shift". This study is to empirically analyze the effect of work delivery using SNS out of duty hours on job performance. A questionnaire survey was conducted with 310 Chinese workplace employees. In order to verify the hypothesis, structural equation model analysis will be implemented. Analyzing the questionnaire data and receiving frequent work delivery using SNS out of duty hours will increase job stress, aggravate the conflicts between work and family, and lead to the decrease of job engagement and job performance. The study provides implications for the establishment of related theories by providing a different view form previous research results that work delivery using SNS out of duty hours will definitely improve job performance. In addition, in future studies, it will be necessary to analyze the effect of the moderating effect in converging these conflicting views.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• The Journal of Society for e-Business Studies
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• v.17 no.4
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• pp.197-208
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• 2012

Recently, the development of smart devices such as smart phones and tablet PCs, with mobility, convenience and real-time computing, promotes proliferation and activation of social media. It also causes innovative changes in communication methods. Since 2010, researches in SNS (Social Networking Services) have focused on developing marketing strategies using SNS. On the other hand, the main purpose of this study is to provide a requirement analysis method of smart phone users by using content analysis of SNS. An information systems developed in this study in order to analyze content of SNS automatically because it is very difficult and time consuming to analyze it manually. In addition, this study compares the result of content analysis with that of Kano survey in order to examine consistency between the two results.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.268-272
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• 2023

With the development of SNS, companies and individuals are actively marketing through social media to develop their own products. It is also important to post posts promoting on simple SNS or to show a lot of exposure using algorithms, but customers upload reviews or proof shots of the product on their own, naturally increasing the exposure of the product and increasing the purchasing power of potential customers. As the number of products that users want to purchase through SNS is increasing, they want to access and purchase not only tangible products such as goods and food, but also intangible services through SNS. In this paper, we would like to study exhibitions that have both tangible and intangible characteristics. SNS accounts that mainly introduce these products by searching for reviews have been created while spending leisure time such as exhibitions and fairs, reducing the hassle of searching for personal interests on search engines, and providing prices and reviews from the exhibition's schedule, lowering entry barriers and increasing purchasing power. Using this point, many exhibitions not only display works, but also open various experience centers, and create a photo zone or a unique exhibition hall atmosphere to attract many customers. In this study, we study the impact of SNS on the leisure culture of exhibition. The marketing direction in the situation where SNS marketing is becoming the mainstream is presented, and the change in the form of exhibition is described and presented as an academic approach.

• Management & Information Systems Review
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• v.35 no.4
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• pp.99-122
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• 2016

The social networking services (SNSs) have become popular among smartphone users, and one of the most popular services. In order to explain users' motivations toward SNS, this study considers uses and gratification theory which can explain individuals' motivations to select certain media channels. The purposes of this study is to investigate the relationships between motivations and addiction of SNS, and between addiction of SNS and decline in the subjective wellbeing. We examine moderating effects of social tie based on the social capital theory in the relationships between SNS addiction and decline in the subjective wellbeing. The motivations of SNS are subdivided into emotional motive (entertainment and fantasy) and cognitive motive (information share burden and challenge burden) based on the use and gratifications theory. The addiction of SNS is subdivided into time tolerance, withdrawal symptoms, interruption, and barrier of living. The data used in this study were collected from 286 SNS users through surveys. The data analysis in this study was performed using AMOS 17.0, and we used SEM(Structural Equation Modeling) methods in order to test the research model. The result shows that the emotional motive(entertainment and fantasy) and cognitive motive(information share burden and challenge burden) have an effect on the addiction of SNS. Especially emotional motive such as entertainment and users' fantasy toward SNS is an important factor that can cause SNS addiction. The addiction of SNS such as time tolerance, withdrawal symptoms, interruption, and barrier of living has an effect on the decline in the subjective wellbeing. Our result show that social tie partially moderates the relationship SNS addiction and decline in the subjective wellbeing. In addition, social tie between interruption of SNS and decline in the subjective wellbeing is an important moderating factor. The results focuses on the understanding toward relationship between SNS addiction based on the online and decline in the subjective wellbeing in the real world. The findings of this study also provides theoretical as well as practical implications which reflect the major features of SNS, and moderating effects of social tie based on the social capital.

• Journal of Internet Computing and Services
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• v.19 no.3
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• pp.7-14
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• 2018

As SNS(Social Network Service) becomes a part of our life, new information can be derived through various information provided by SNS. Through the public timeline analysis of SNS, we can extract the latest tour trends for the public and the intimacy through the social relationship analysis in the SNS. The extracted intimacy can also be used to make the personalized recommendation by adding the weights to friends with high intimacy. We apply SNS elements such as analyzed latest trends and intimacy to item-based collaborative filtering techniques to achieve better accuracy and satisfaction than existing travel recommendation services in a new way. In this paper, we propose a social travel recommendation system using item - based collaborative filtering.

• Journal of information and communication convergence engineering
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• v.17 no.4
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• pp.239-245
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• 2019

Recently, effort to obtain various information from the vast amount of social network services (SNS) big data generated in daily life has expanded. SNS big data comprise sentences classified as unstructured data, which complicates data processing. As the amount of processing increases, a rapid processing technique is required to extract valuable information from SNS big data. We herein propose a system that can extract human sentiment information from vast amounts of SNS unstructured big data using the naïve Bayes algorithm and natural language processing (NLP). Furthermore, we analyze the effectiveness of the proposed method through various experiments. Based on sentiment accuracy analysis, experimental results showed that the machine learning method using the naïve Bayes algorithm afforded a 63.5% accuracy, which was lower than that yielded by the NLP method. However, based on data processing speed analysis, the machine learning method by the naïve Bayes algorithm demonstrated a processing performance that was approximately 5.4 times higher than that by the NLP method.