• Journal of Information Technology Applications and Management
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• v.19 no.4
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• pp.41-59
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• 2012

This study examines the roles of user resistance and social influences on the acceptance and continuous usage of smartphones at different stages of adoption. The respondents were classified into three groups according to their innovation adoption stage : non-user group, the potential user group and the trial user group. Theories relevant to user resistance, social influences including normative social influences and informational social influences, as well as user adoption and continuance behavior were reviewed and integrated into our research model. In order to verify the proposed structured equation model, we conducted an online survey by targeting mobile phone users and collected data to be analyzed through a partial least squares (PLS) test. This study tested whether there exists differences in the effects of user resistance and different types of social influence on user's adoption or continuance intetion among these three groups. The results showed that user resistance exists in all adopter groups and that it has significant negative influences on intention to use a smartphone. The findings also revealed that user resistance can be enhanced or resolved by two types of social influence; informational social influence resolves user resistance regardless of the adopter category, while normative social influence enhances the user resistance of potential users. Furthermore, the findings show that social influence regardless of the type positively affects user intention. Several theoretic and practical implications pertaining to the results are discussed.

• Journal of Families and Better Life
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• v.20 no.6
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• pp.129-140
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• 2002

The purpose of this study was to collect baseline data for women＇s needs for social education, in order to eventually contribute to improving the quality of women's social education. In the needs survey, information on the motive to participate in social education programs, obstacles to participation, and program preferences was collected. The data were then analyzed by women＇s socio-demographic characteristics. Survey participants were married women between the ages of twenty to fifty, who were sampled from Seoul, six metropolitan areas, and nine provinces. For the final analysis, 1,026 survey forms were used. The motive for participating in women＇s social education programs was examined by each category. Overall, the participants showed the highest level in educational achievement motif. The motivations for lifestyle change, self-realization, and social accomplishment were also high and at a similar level. As for obstacles to participation, social obstacle received the highest rate, followed by family obstacle and personal reasons. As for the type of social education programs, home management programs were the most preferred, followed by psychological education, family education, leisure activity programs, physical education, and social education programs.

• Journal of the Korean Society of School Health
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• v.28 no.3
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• pp.248-255
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• 2015

Purpose: This study was conducted to explore the relationship between high school students' mental social health and their tendency toward social networking addiction. Methods: The subjects were 543 high school boys and girls living in D city. The data were collected from the 3rd to 21st of March in 2014. Data were analyzed using t-test, ANOVA, Duncan's post-hoc test, Pearson's correlation analysis, and hierarchical regression with SPSS/ Win 21.0. Results: Social networking addiction showed significant differences depending on gender (t=-7.03, p<.001), academic achievement (t=4.571, p=.011), and the level of maternal education (t=3.344, p=.019). Social health was correlated with the tendency toward social networking addiction. Multiple regression analysis found that gender, academic achievement and social health were associated with the level of social networking addiction (F=8.750, p<.001, Adj. $R^2=.201$). Conclusion: The results suggest that it is necessary to take into account gender characteristics, academic achievement and social health in order to develop effective management programs for social networking addiction among high school students.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.142-151
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• 2020

The reason why today's public actively uses social communication is that the necessary information is collected and classified under the name of social big data through the web space to create the big data era, an ecosystem of information. In order for big data information to be used by the public, it is necessary to visualize it easily. This study categorized the types of visualization according to the information of social big data, and targeted the experienced students including the related majors and the general public who need to directly utilize and study the actual big data visualization as an experience evaluation target. As a result of analyzing the experiences of the experienced people, important implications for the visualization method for managing, analyzing, and utilizing the data were derived. The big data visualization strategy is to be expressed in a way that fits the data environment and user's eye level on SNS. In the future, if big data visualization is applied to product service or social trend, it will be an important data in terms of broadening its role, scope of application, and application.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.514-522
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• 2017

Traffic offloading is a promising solution to solve the explosive growth of mobile traffic. One of offloading schemes, in LIPA/SIPTO(Local IP Access and Selected IP Traffic Offload) offloading, we can offload mobile traffic that can satisfy QoS requirement for application. In addition, it is necessary for traffic offloading using social context due to large traffic from SNS. Thus, we propose the LIPA/SIPTO offloading algorithm using social context. We define the application selection probability using social context, the application popularity. Then, we find the optimal offloading weighting factor to maximize the QoS(Quality of Service) of small cell users in term of effective data rate. Finally, we determine the offloading ratio by this application selection probability and optimal offloading weighting factor. By performance analysis, the effective data rate achievement ratio of the proposed algorithm is similar with the conventional one although the total offloading ratio of the proposed algorithm is about 46 percent of the conventional one.

• Journal of Digital Convergence
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• v.15 no.8
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• pp.247-256
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• 2017

The purpose of this study was to identify the convergent influence of social awareness and health status on social support in Korean echo generation by using Korea adult psycho-social anxiety survey data. Korea adult psycho-social anxiety survey data were collected from August to September 2015 and included 1,653, who responded to the question regarding social support. The data were analyzed by t-test, chi-square and hierarchial multiple regression using SPSS WIN 23.0 program. The mean score of social support was 19.60. The influencing factors on social support were neighborhood relations, perceived class, euphoria, equality in society, stability in society, self Esteem, communication, and stress, respectively. Therefore, it is necessary to establish strategies to strengthen social support of echo generation. A more careful examination may be warranted.

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.29-34
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• 2008

This study examines the effects of information and communication technologies on individual performance. The literature has paid a considerable amount of attention to social influence as a determinant of individual behavior. We combine task-technology fit with concepts from adaptive structuration theory to specify social influence. In our model, we suggest that individuals should receive support from proper social construction to have additional performance improvement. Empirical data from 317 individuals across 43 teams in 10 companies is used to assess the theoretical model. Our theoretical model received support from the data.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.83-88
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• 2024

In this paper we used classification algorithms on social networking. We are proposing, a new classification algorithm called the improved Decision Tree (IDT). Our model provides better classification accuracy than the existing systems for classifying the social network data. Here we examined the performance of some familiar classification algorithms regarding their accuracy with our proposed algorithm. We used Support Vector Machines, Naïve Bayes, k-Nearest Neighbors, decision tree in our research and performed analyses on social media dataset. Matlab is used for performing experiments. The result shows that the proposed algorithm achieves the best results with an accuracy of 84.66%.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.140-149
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• 2018

Social data such as users' comments are unstructured in nature and up-to-date technologies for analyzing such data are constrained by the available storage space and processing time when fast storing and processing is required. On the other hand, it is even difficult in using a huge amount of dynamically generated social data to analyze the user features in a high speed. To solve this problem, we design and implement a topic association analysis system based on the latent Dirichlet allocation (LDA) model. The LDA does not require the training process and thus can analyze the social users' hourly interests on different topics in an easy way. The proposed system is constructed based on the Spark framework that is located on top of Hadoop cluster. It is advantageous of high-speed processing owing to that minimized access to hard disk is required and all the intermediately generated data are processed in the main memory. In the performance evaluation, it requires about 5 hours to analyze the topics for about 1 TB test social data (SNS comments). Moreover, through analyzing the association among topics, we can track the hourly change of social users' interests on different topics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2571-2586
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• 2022

The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to analyze the big data of the Chinese provincial government Weibo. First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022. Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships. Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using LDA algorithm. The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly. Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo. Third, the contents of government Weibo account for more than 30% of negative sentiments. The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo.