• The Journal of Information Systems
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• v.26 no.3
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• pp.313-337
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• 2017

Purpose Financial technology, also known as FinTech, is conceptually defined as a new type of financial service which is combined with information technology and other traditional financial services like payments, investments, financing, insurance, asset management and so on. Most of the studies on FinTech services have been conducted from the viewpoint of technical issues or legal and institutional studies, and few studies are conducted from the health belief perspectives and security behavior approaches. In this regard, this study suggest an extended information protection behavior model. Design/Methodology/Approach The Health Belief Model (HBM), the Protection Motivation Theory (PMT), and the Technology Threat Avoidance Theory (TTAT) were employed to identify constructs relevant to information protection behavior of FinTech services. A new extended information protection behavior model in which the influence factors of information protection behavior (i.e., perceived susceptibility, perceived severity, perceived benefits, perceived barriers, perceived self-efficacy, subjective norms) affect perceived threats and perceived responsiveness positively, leading to information protection behavior of FinTech users eventually. This study developed an extended information protection behavior model to explain the protection behavior intention in FinTech users and collected 272 survey responses from the mobile users who had experiences with such mobile payments and FinTech services. Findings The finding of this study suggests that the influence factors of information protection behavior affect perceived threats and perceived responsiveness positively, and information protection behavior of FinTech users as well.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.421-437
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• 2015

This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on information fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the performance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.

• Journal of Distribution Science
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• v.20 no.5
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• pp.75-84
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• 2022

Purpose: The paper employs the theory of planned behaviour, social identity theory, social presence theory, and social exchange theory to propose a model for using the social media platform Facebook in distributing information in universities. Research design, data and methodology: Data were obtained from 618 students at Hanoi University of Industry (HaUI) in Vietnam. The study used structural equation modeling in SPSS and the statistical analysis software AMOS version 24 to explore the information distribution role of Facebook. Results: Findings reveal that subjective norms and social presence play a key role in driving students to use Facebook for studying. In addition, university administrators can boost student satisfaction and academic achievement through Facebook. Interestingly, students who strongly identify with the brand name of the university have a strong connection with the university are more likely to engage in behaviors supportive toward the university. Conclusions: These findings show that Facebook had played a key information distribution role in universities. Thus, the study provides the executive board of the university and teachers can gain new understanding of why students employ Facebook for studying as well as capitalize on the platform to improve several operational metrics such as brand identification, student satisfaction, and academic achievement.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.369-373
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• 2005

Virtual Communities are many and varied have been around for a number of years now. Cyworld has all the makings of a successful website, with its unique and profitable business model. Our purpose in studying Cyworld is two fold ; we want to know the motivations people have for using the service and why they are willing to pay to use it. This study is based on motivation theory and flow theory to enhance our understanding of Cyworld user's behavior.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3630-3656
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• 2018

Degree distribution can provide basic information for structural characteristics and internal relationship in social network. It is a critical procedure for social network topology analysis. In this paper, based on the mean-field theory, we study a special type of social network with exponential distribution of time intervals. First of all, in order to improve the accuracy of analysis, we propose a spreading coefficient algorithm based on intimate relationship, which determines the number of the joined members through the intimacy among members. Then, simulation show that the degree distribution of follows the power-law distribution and has small-world characteristics. Finally, we compare the performance of our algorithm with the existing algorithms, and find that our algorithm improves the accuracy of degree distribution as well as reducing the time complexity significantly, which can complete 29.04% higher precision and 40.94% lower implementation time.

• Journal of information and communication convergence engineering
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• v.1 no.1
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• pp.53-57
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• 2003

Chaos theory is a study researching the irregular, unpredictable behavior of deterministic and non-linear dynamical system. The interpretation using Chaos makes us evaluate characteristic existing in status space of system by tine series, so that the extraction of Chaos characteristic understanding and those characteristics enables us to do high precision interpretation. Therefore, This paper propose the new method which is adopted in extracting character features and recognizing characters using the Chaos Theory. Firstly, it gets features of mesh feature, projection feature and cross distance feature from input character images. And their feature is converted into time series data. Then using the modified Henon system suggested in this paper, it gets last features of character image after calculating Box-counting dimension, Natural Measure, information bit and information dimension which are meant fractal dimension. Finally, character recognition is performed by statistically finding out the each information bit showing the minimum difference against the normalized pattern database. An experimental result shows 99% character classification rates for 2,350 Korean characters (Hangul) using proposed method in this paper.

• Journal of Information Technology and Architecture
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• v.11 no.2
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• pp.125-130
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• 2014

Today in different higher educational institutions there is the active on a structure of expert models of monitoring the quality of education in the higher educational institutions, assuming continuous tracking an education status as a whole and its separate components. In most cases creation of a monitoring system of quality of education relies on the intermediate results of activities.

• Journal of Korean Library and Information Science Society
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• v.55 no.1
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• pp.173-192
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• 2024

Through a survey of college students, this study explained the process and aspects of how Generation Z interacted with information while collaborating on a daily basis. Applying the Radical Change Theory, the study investigated the platforms through which university students engaged in challenges and their information behaviors. University students primarily participated in challenges using platforms such as Instagram and Naver Blog. In terms of changes in information seeking, which is the first type of information seeking in the theory, a wide and diverse range of information behaviors were observed, with the way of searching for information being quite different from when the digital age arrived 10 years ago. Information sources included not only digitalized ones but also traditional sources such as printed materials and personal contacts. The utilization of various media types was prominent, and collaborative efforts were voluntarily undertaken for challenges. In terms of changing perspectives, type 2 of information behavior, the main motivation for acquiring information and securing knowledge led to participation in the challenge. Participants exhibited a sense of community consciousness, including mutual influence awareness, social participation consciousness, and emotional connection.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.183-191
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• 2013

The purpose of this current study is to explore use of pod casting. First, the study investigated types of motivation for using pod casting. Secondly, based on theory of planned behavior, we analyzed how social norm, attitude, and self-efficacy regarding using of pod casting influence people's using behavior of pod casting. We found the four different types of motivation for using pod casting: Everyday life information, entertainment, flee from reality, and professional information. Regarding theory of planned behavior, the theory was significant model to explain use of pod casting, and self-efficacy was the most significant variable to predict use of pod casting.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.1
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• pp.157-167
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• 2014

Increasing productivity of knowledge workers is a significant issue in the 21st century referred as knowledge-based society. The core key word is behavior of knowledge transfer among members of an organization. The objective of this study is to investigate a model based on Triandis theory and Social Capital theory. This explored the antecedent factors of knowledge Transfer in ITO(Information Technology Outsourcing) Organization. Data were derived from 42 respondents working IT Cooperation in Seoul, Korea. In this paper, we introduce the research model for the knowledge transfer. In order to validate the proposed research model, social network analysis tool, UCINET, a structural equation modeling tool, SmartPLS, was utilized. The empirical result showed that, all antecedent factors (intention of knowledge sharing, anticipated reciprocal relationships, subjective norm, closeness network centrality) of knowledge transfer behavior were significant. In conclusion, findings and implications were discussed and limitations of the study and future research directions were suggested.