• Journal of Preventive Medicine and Public Health
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• v.26 no.4 s.44
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• pp.508-533
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• 1993

A wide range of health professionals have interest in changing the health behavior of individuals. To intervene effectively and to make informed judgements about how to measure the success of such interventions, health professionals must have an deep understanding of health behavior. This paper provides an overview of the thories of health-related behaviors and the strength and weakness of each, how the theories relate to others, and how they can be used in practice. The theories reviewed include Suchmann's stages of illness experience, Health belief model, Attribution theory, Fishbein's theory of reasoned action, Multiattribute utility models, Consumer information processing, and Anderson's models. Finally, this paper introduces the reader to interesting research findings in our country.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.6
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• pp.135-139
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• 2012

The wide diffusion of internet and the overall increased reliance on technology for information communication, dissemination and gathering have created an unparalleled mass of data. Sifting through this data is defining and will define in the foreseeable future a big part of contemporary computer science. Within this data, a growing proportion is given by personal information, which represents a unique opportunity to study human activities extensively and live. One important recurring challenge in many disciplines is the problem of people re-identification. In its broadest definition, re-identification is the problem of newly recognizing previously identified people, such as following an unknown person while he walks through many different surveillance cameras in different locations. Our goals is to review how several diverse disciplines define and meet this challenge, from person re-identification in video-surveillance to authorship attribution in text samples to distinguishing users based on their preferences of pictures. We further envision a situation where multidisciplinary solutions might be beneficial.

• Management & Information Systems Review
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• v.33 no.4
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• pp.99-118
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• 2014

Using the theory of attribution, this study investigates the determinants if controllability and explores underlying dimensions of online information credibility, and then investigates if the credibility of online information influences the users' intention to use the online information for evaluating or judging the involved products. Moreover, as a research attempt to investigate the impacts of online information credibility, this study examines whether the main effect of perceived online information credibility on the intention of using online information to make a decision of purchase is mediated by both perceived risk and perceived usefulness. A total of 287 survey forms were collected from online consumers. We examined reliability by exploring internal consistency of the multiple item scales in the overall sample. Convergent and discriminant validity were also examined for evidence of construct validity. Then, PLS technique was employed to test the research model. As a result of analyzing data from a dataset of 287 responses via PLS technique, it is found that (1) both sources (controllability and stability) of perceived credibility are significantly associated with both perceived risk and perceived usefulness, and (2) perceived risk as well as perceived usefulness partially mediate the link between the two sources of credibility and intention to use. The findings of this study also suggest that the two dimensions of online information credibility influence information recipient's intention to use. Moreover, the online information including descriptions about controllability and stability can trigger potential consumers to perceive risk about consumption of the informed products and services. Therefore, providing online information with highly described controllability and stability can increase not only the credibility of the online information itself, but also the intention to use the online information through perceived risk and usefulness.

• Journal of Korean Library and Information Science Society
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• v.34 no.1
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• pp.351-378
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• 2003

This study has been examined in information service in related aspect of its user. Also this subject is researching of information itself attribution and relationship with end user And, in information facilities of library and information center it has been studied how to develope satisfaction service for user, along with fur causing both theory and practical application. For this object it has been also reviewed theoretically through out 5 fields as below; 1) information need and using 2) information storage and retrieval 3) information property and value 4) bibliometrics and citation analysis 5) administration and management.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.155-162
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• 2023

This study analyzed the impact of negative information on co-branding, targeting co-branding consisting of a symbolic brand and a functional brand with similar brand assets. Through attribution theory, when consumers are exposed to negative information, they will infer the cause of the negative information. As a result, it was confirmed that when co-branding with a symmetrical symbolic brand and a functional brand, negative information has different effects on co-branding depending on the type. Negative information about the quality of co-branded products had a more negative impact on consumer attitudes than negative information about the iconic brand. It was confirmed that negative information about functional brands has a smaller impact than negative information about co-branding, but has a greater impact than negative information about symbolic brands. In addition, it was confirmed that negative information about the symbolic brand had a smaller negative impact on co-branding than negative information about the quality or functionality of the co-branded product.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.399-408
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• 2017

This study was conducted to investigate the effects of a career preparation program (CPP) on self-leadership (SL) and career locus of control (CLOC) among University Students in South Korea. Self-leadership is the ability to control and lead oneself to achieve goals. CLOC is that attribute own's success of failure to own's effort and ability internally or luck or environment externally. Both have been known to strongly affect variables associated with career planning. We also explored SL and CLOC based on the variable related subjects' characteristics. Method: After receiving informed consent, 58 subjects were collected from University in D metropolitan area in Korea from March to June 2016. CPP was provided to college students who agreed to join this study, which consisted of a 15 week career development program. During the program, recruitment information was provided as well as encouragement and motivation. The program also focused on self-exploring and self-initiating experiences by themselves. Data were collected before and post CPP and were analyzed using SPSS 21.0. Result: self-leadership increased from $3.02{\pm}0.36$ points to $3.23{\pm}0.35$ points after CPP (t=-4.45, p=0.00). There were significant differences in self-leadership and in internal attribution (t=-3.26, p=0.00). The CPP based on the understanding of oneself was effective to cultivate university students' self-leadership. Moreover, the CPP increased internal attribution of the college students significantly from $2.96{\pm}0.35$ points to $3.15{\pm}0.32$ points (t=-3.26, p=0.00). CPP was also found to effectively influence the internal locus of control. Although external attribution was reduced from $2.19{\pm}0.31$ to $2.13{\pm}0.35$, this different was not significant (t=1.68, p=0.09). Conclusion: The results of this study suggest that the CPP based on understanding of oneself should be actively applied in career education.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.13-21
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• 2020

The Android author identification study can be interpreted as a method for revealing the source in a narrow range, but if viewed in a wide range, it can be interpreted as a study to gain insight to identify similar works through known works. The problem found in the Android author identification study is that it is an important code on the Android system, but it is difficult to find the important feature of the author due to the meaningless codes. Due to this, legitimate codes or behaviors were also incorrectly defined as malicious codes. To solve this, we introduced the concept of survival network to solve the problem by removing the features found in various Android apps and surviving unique features defined by authors. We conducted an experiment comparing the proposed framework with a previous study. From the results of experiments on 440 authors' identified apps, we obtained a classification accuracy of up to 92.10%, and showed a difference of up to 3.47% from the previous study. It used a small amount of learning data, but because it used unique features without duplicate features for each author, it was considered that there was a difference from previous studies. In addition, even in comparative experiments with previous studies according to the feature definition method, the same accuracy can be shown with a small number of features, and this can be seen that continuously overlapping meaningless features can be managed through the concept of a survival network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.71-80
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• 2011

This paper presents a technique of contstructing and processing the metadata information, which is for UML class authorization tool. Construction of the information authorization tool defines Classes and Relationship based on the elements of the consisting of UML class diagram. Definition of the information class is a visibility of Class, a name of Class, an attribution of Class and an operation of Class. Definition of the relationship with information is a name of Relationship, a type of Relationship, a FromClass and a ToClass. In this paper, for a class authorization tool, it suggests a technique to save and read information from information Storage based on the constructed information.

• The Journal of the Korea Contents Association
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• v.8 no.10
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• pp.351-360
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• 2008

The purpose of this study is to segment visitors for beer franchise restaurants. Total of 308 questionnaires were distributed to the beer franchise restaurant customers of Kangnam area, Seoul. To accomplish the aim of this study, author took the following three steps. Firstly, literature review was done to find out selective attributes for restaurant and bar and market segmentation. secondly, Selective attributes of beer franchise were analyzed and five factors, -'additional service', 'side dish quality', 'service class', 'accessibility' and 'outlet image'- were extracted. Lastly, customers of beer franchise shop were divided into two groups by the selective attributes, -'simple visiting group' and 'active lover group'. Those variables-loyalty, intent to recommend and overall satisfaction- have significant influence on 'active lover group'. Other than that, other variables -demographic characteristics and customer's behavior characteristics- didn't play important role to distinct two groups. As a result, only basic information for setting up marketing strategy of each market was provided.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.