• The Research Journal of the Costume Culture
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• v.23 no.3
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• pp.468-483
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• 2015

Fast fashion brand marketers should develop marketing strategies that effectively satisfy the values consumers seek when purchasing fast fashion brands. This study aimed to identify the consumption value factors of fast fashion brands and to reveal the value factors that influence attitudes toward purchasing fast fashion brands. Data were gathered by surveying university students in the Seoul metropolitan area using convenience sampling. Three hundred and five questionnaires were used in the statistical analysis, which consisted of exploratory factor analysis using SPSS and confirmatory factor analysis and path analysis using AMOS. The factor analysis revealed the following six value factors: Emotional value, social value, price/value for money, durability value, eco-value, and consistency value. The fit statistic for the six-factor model was quite acceptable. Two of the six value factors, emotional value and price/value for money, positively influenced attitudes toward purchasing fast fashion brands. The overall fits of the revealed model suggested that the model fit the data well. The results suggested that fast fashion marketers need to understand the value factors that motivate consumers to purchase fast fashion brands. In addition, marketers should focus their efforts on satisfying emotional value and price/value for money in order to establish their brands in the increasingly competitive fast fashion industry.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.17-22
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• 2022

With the spread of web services, data types are becoming more diversified. In addition to the form of storing data such as images, videos, and texts, the number and form of properties and metadata expressing the data are different for each data. In order to efficiently process such unstructured data, a key-value store is widely used for state-of-the-art applications. LSM-Tree (Log Structured Merge Tree) is the core data structure of various commercial key-value stores. LSM-Tree is optimized to provide high performance for small writes by recording all write and delete operations in a log manner. However, there is a problem in that the delay time and processing speed of user requests are lowered as batches of deletion operations for expired data are inserted into the LSM-Tree as special key-value data. This paper presents a Filtered LSM-Tree (FLSM-Tree) that solves the above problem by separating the deleted key from the main tree structure while maintaining all the advantages of the existing LSM-Tree. The proposed method is implemented in LevelDB, a commercial key-value store and it shows that the read performance is improved by up to 47% in performance evaluation.

• Journal of Information Technology Applications and Management
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• v.28 no.6
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• pp.45-67
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• 2021

Snack culture, which can be easily enjoyed and consumed in a short period of time just like eating sweets, is spreading rapidly. Among them, TikTok is gaining popularity mainly among the MZ generation, and TikTok is rapidly emerging as a means of advertising or marketing. In this study, we studied the effect of TikTok Advertising's informativeness, interactivity, and impediment on brand attitudes. Previous studies have suggested that brand attitudes are formed through usefulness. However, this study divided usefulness into utilitarian value and hedonic value, and proved that brand attitudes are formed through these mediation variables. This is because of the TikTok advertising includes entertainment as well as usefulness. Therefore, this study established and verified the model in which informativeness, interactivity, and impediment of TikTok advertising form brand attitude through utilitarian value and hedonic value. In order to verify this research model, survey questionnaires were distributed to TikTok users and a total of 220 data samples were collected and analyzed. As a result of data analysis, informativeness and interactivity have a positive effect on utilitarian value and hedonic value, but impediment has a negative effect. In addition, informativeness, interactivity, impediment influenced brand attitudes through practical value and hedonic value, not directly influencing brand attitudes. This study is meaningful in that it suggests a marketing strategy that can further enhance brand attitude by considering utilitarian value and hedonic value simultaneously, rather than focusing on either utilitarian value or hedonic value.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.495-498
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• 2004

In this note, we consider several types of convergence theorems for the expected value of fuzzy variables defined by Liu and Liu [IEEE Trans. Fuzzy Systems, 10(2002), 445-450].

• Journal of Wetlands Research
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• v.18 no.1
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• pp.100-112
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• 2016

In recent, the direction of water resources policy is changing from the typical plan for water use and flood control to the sustainable water resources management to improve the quality of life. This change makes the information related to water resources such as data collection, management, and supply is becoming an important concern for decision making of water resources policy. We had analyzed the structured data according to the purpose of providing information on water resources. However, the recent trend is big data and cloud computing which can create new values by linking unstructured data with structured data. Therefore, the trend for the management of water resources information is also changing. According to the paradigm change of information management, this study tried to suggest an application of big data and cloud computing in water resources field for efficient management and use of water. We examined the current state and direction of policy related to water resources information in Korea and an other country. Then we connected volume, velocity and variety which are the three basic components of big data with veracity and value which are additionally mentioned recently. And we discussed the rapid and flexible countermeasures about changes of consumer and increasing big data related to water resources via cloud computing. In the future, the management of water resources information should go to the direction which can enhance the value(Value) of water resources information by big data and cloud computing based on the amount of data(Volume), the speed of data processing(Velocity), the number of types of data(Variety). Also it should enhance the value(Value) of water resources information by the fusion of water and other areas and by the production of accurate information(Veracity) required for water management and prevention of disaster and for protection of life and property.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.47-48
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• 2005

Multidimensional scaling is a multivariate technique for constructing a configuration of n points in Euclidean space using information about the distances between the objects. This can be done by the singular value decomposition of the data matrix. But it is known that the singular value decomposition is not resistant. In this study, we provide a resistant version of the multidimensional scaling.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.277-278
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• 2015

The volume sub-challenge requires novel approaches, often referred to as Big Data technologies and methodologies. Data is generated constantly in an ever growing number of places and by an ever growing number of actors while a large proportion of potentially re-usable data resides within silos within institutions or companies. These are needed when conventional database technologies cannot be applied to storage and computing issues. The issue of big data has been referred to as the next frontier in computing. In this paper, we research on factors to design an organizational value chain for Big Data.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.315-321
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• 2011

Gene expression microarray data often include multiple missing values. Most gene expression analysis (including gene clustering analysis); however, require a complete data matric as an input. In ordinary clustering methods, just a single missing value makes one abandon the whole data of a gene even if the rest of data for that gene was intact. The quality of analysis may decrease seriously as the missing rate is increased. In the opposite aspect, the imputation of missing value may result in an artifact that reduces the reliability of the analysis. To clarify this contradiction in microarray clustering analysis, this paper compared the accuracy of clustering with and without imputation over several microarray data having different missing rates. This paper also tested the clustering efficiency of several imputation methods including our propose algorithm. The results showed it is worthwhile to check the clustering result in this alternative way without any imputed data for the imperfect microarray data.

• The Journal of Bigdata
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• v.6 no.1
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• pp.153-160
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• 2021

This study examines previous studies on the income (profit) model, which is most used for valuation of data held by companies or institutions, and discusses key factors of the model and considerations in the data asset valuation process. Through this, it was confirmed that the shareability and utilization period of data assets are different from those of other companies. In addition, the value of data should be reviewed from various perspectives such as timeliness and accuracy. And for data asset value evaluation, it was derived that the user's use, ability to use, and value chain should be reviewed as a whole. As a future research direction, continuous research and development of models to be applied to actual business and revision of accounting law were proposed.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.1241-1248
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• 2021

This study employs data from CRSP/Compustat files for the period from 2003 to 2017 and applies a panel data analysis. The results of this study show a positive relationship between trade credit and the firm's market value, however, the results show a negative relationship if we test the impact of financial credit on the firm's market value. The results have direct policy implications for investors, the firm's management, and financial strategy. An implication of our study is that using trade credit as a source of financing may give a positive signal of the firm's creditworthiness and increase the firm's market value. Also, the results of our study indicate that the benefits of using trade credit may outperform the cost of using it as a source of finance. Prior studies examine the impact of financial leverage on the firm's value, however, this study contributes to the existing studies that examine the factors that affect the firm's market value by examining the impact of using trade credit finance on the firm's market value. The main limitation of this study is that the results are based on listed firms, using data from unlisted firms is not available.