• The Research Journal of the Costume Culture
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• v.27 no.6
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• pp.666-680
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• 2019

Prevalent usage of mobile devices among consumers has been well recognized and this is especially imperative among young adult consumers. The mobile phone became the gateway of their communication, media consumption, retail transaction, education, and (virtual) social life. However, there is little empirical research explaining the dynamics behind the psychological underpinning of young adult consumers, specifically Generation Y, to understand their usages and dependency on mobile phones. This study, therefore, aims to unveil antecedents and consequences of Gen Y consumers' mobile phone dependency from a media psychological perspective. We developed a conceptual model based on theory of self-monitoring (Snyder 1974, 1987), extended self-concept (Belk, 1988), and media dependency theory (Ball-Rokeach & Defluer, 1976). Four hundred ninety-eight students in the U.S. provided usable responses to our pencil-and-paper survey. Causal modeling analysis results demonstrated that both ability to modify one's behavior and sensitivity to cues for social appropriate behavior dimensions of the self-monitoring tendency positively predicted one's level of fashion involvement, which in turn positively predicted his/her mobile phone dependency. Individual's mobile phone dependency, fashion involvement and self-monitoring's ability dimension exhibited positive and direct impact on one's perception of the salience of mobile phone case product attributes. Based on the findings, we provided pragmatic and theoretical implications for the industry and academia.

• Cartoon and Animation Studies
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• s.25
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• pp.203-225
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• 2011

This study investigated hypothesized relationships between three personality traits, as defined by PEN model (Psychoticism, Extraversion and Neuroticism), and individual media dependency. The basic idea of the study was that individuals' goals are related to active media use, and the goals will be different based on differences in individuals' personality types. In addition, this study attempted to find whether there are gender difference in constructing media dependency relations with the media. The study was conducted online and the total number of participants was 337 (158 male and 179 female). Correlation analyses indicated no relationship between the extraversion and any of the IMD media use categories. Lower levels of psychoticism were related to a greater likelihood of utilizing the media in an effort to obtain self understanding and having fun more than higher levels of psychoticism. Individuals with higher levels of neuroticism were significantly more likely to depend on the media for achieving self understanding than those with lower levels of neuroticism. When the variable of participant gender was controlled for, there were different patterns of the relationships between personality types and IMD goals. This study showed that to varying degrees certain personality types are related to the goals individuals seek to fulfill with the media use. The implications of the study were discussed.

• Journal of Information Technology Applications and Management
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• v.26 no.3
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• pp.135-157
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• 2019

Information technology has economic, social and cultural impacts is closely linked to our lives. This information technology is becoming a key to the change of human civilization through connecting people and objects on the earth. In addition, future information technology is becoming more intelligent and personalized with the development of computing technology, and due to the rapid development of alcohol, environment without time and space constraint is realized, Is spreading. Since existing portable storage media are made of physical form, there is a limit to usage due to the risk of loss and limitation of capacity. Cloud services can overcome these limitations. Due to the problems of existing storage media, it is possible to overcome the limitations of storing, managing and reusing information through cloud services. Despite the large number of cloud service users, the existing research has focused mainly on the concept of cloud service and the effect of introduction on the companies. This study aims to conduct a study on individual characteristics that affect the degree of cloud use. We will conduct research on the causes of IT knowledge, personal perception of security, convenience, innovation, economical trust, and platform dependency affecting the intention to use the cloud. These results show that the variables affecting individual 's use of cloud service are influenced by individuals, and this study can be used as a basic data for individuals to use cloud service.

• Journal of the Korean Society of Hazard Mitigation
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• v.5 no.3 s.18
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• pp.1-9
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• 2005

The primary source of disaster information for victims as well as ordinary people is mass media. In spite of their importance, the media often inaccurately portrays reality, which has stimulated academic debates. In Korea, however, media reporting patters of disaster have been hardly addressed. Therefore, the paper analyzes how newspaper and television news have reported typhoon Rusa between August 29 and October 1 in 2002 by using KINDS(Korean Integrated News Database System). The results show that television news tend to present more soft news stories emphasizing human interest stories than newspaper articles, relying on victims as primary interviewees. It is also pointed out that the Korean media do not play a significant role in providing disaster information to public regarding how to lessen the effects of impact through preparation. Disaster mythology representing wrong beliefs about human behavior in disaster is found in Korean media reporting, too. Unlike their western counterparts, however, Korean media seem to use the dependency image of helpless victims in order to stimulate donations. Analyses of disaster reporting patterns suggest that, in make disaster warning messages associated with behavioral responses, credible and official sources should provide clear and precise warning messages to the media, and the media also need to stress individual responsibilities in protecting his or her own properties not to make victims heavily dependent on public supports, while inducing donations.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.283-294
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• 2021

This study was a descriptive research study conducted to understand the relationship between the risk perception and preventive behavior of university students for COVID-19 infection, and to identify the factors that influence the preventive behavior. The subjects collected data from 228 university students of S University in Seoul. The collected data were analyzed using pearson's correlation and multiple regression. The results was the risk perception was 2.5 points out of 5, and the preventive behavior was confirmed as 3.1 points out of 4, and the preventive behavior was found to be affected by resilience, risk perception, self-isolation, and media dependence. In order to prevent new infectious diseases and to adapt to and recover from the post-COVID, it was discussed that there is a need to explore ways to strengthen individual resilience by utilizing the pure functions of the media along with disaster education.

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.39-49
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• 2019

Advances in IoT data analysis technology have made it easier to analyze situation and provide interactive services based on the context. Most of digital signage application have been used to provide information uni-directionally, but in the future it will evolve to provide personalized content according to the individual user situation and responses. However, it is not easy to modify or apply the existing interactive digital signage platforms due to their hardware dependency. The proposed platform is modularized by dividing main functions into two, the cloud and the edge, so that advertisement resources can be easily generated and registered. Thus, interactive advertisement can be rendered in a timely manner based on sensor analysis results. At the edge, personal data can be processed to minimize privacy issues, and real-time IoT sensor data can be analyzed for quick response to the signage player. The cloud is easier to access and manage by multiple users than edge. Therefore, the signage content generation module improves accessibility and flexibility by handling advertisement contents in the cloud so that multiple users can work together on the cloud platform. The proposed platform was developed and simulated in two aspects. First is the provider who provides the signage service, and second is the viewer who uses the content of the signage. Simulation results show that the proposed platform enables providers to quickly construct interactive signage contents and responses appropriately to the context changes in real-time.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.