• Journal of the Korean Society of Costume
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• v.55 no.3 s.93
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• pp.136-149
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• 2005

The purpose of this study was to analyze how the purchase of BURBERRY prestigious fashion brand, accomplishing the most successful luxury brand in Korea, correlated to the demographic and psychological characteristics of the BURBERRY purchasers, especially, conformity and individuality. The responses of 304 female living in Seoul and the metropolitan area between 20sover 50s who had ever bought BURBERRY were used in the study. The following results were found: 1) BURBERRY consumer's psychological characteristics were classified into 2 types of conformity (normative conformity and identificational conformity) and 2 types of individuality (anticonformity and independence) 2) 3 factors of the attributes of BURBERRY products were identified: functionality (color, multipurpose, easy-care, pattern), symbolic(brand renown & trust, history & heritage, scarcity) and aesthetic(quality, design) 3) Women conforming to higher clothing conformity attached great importance to the symbolic attributes of BURBERRY 4) The importance factors of decision on purchasing of BURBERRY were design, brand renown & trust and product quality among which the design was the highest in degree and order.

• KDI Journal of Economic Policy
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• v.39 no.1
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• pp.1-18
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• 2017

This paper empirically studies the effect of public procurement on small and medium-sized enterprises (SMEs) in the Republic of Korea using firm-level data. Public procurement, the purchase of goods and services from private firms by the public sector, is regarded as an important policy measure for providing support to firms, particularly SMEs. This study uses establishment-level panel data of the mining and manufacturing sectors from the Korean National Bureau of Statistics (Statistics Korea) and procurement history from the Korean Public Procurement Service to empirically estimate the effects of public procurement on firms' productivity (total factor productivity) and survivability. Using a propensity score matching estimation method, we find that participating firms showed higher productivity than non-participating ones in the control group only for the year of participation, that is, 2009. After two years, in 2011, they exhibited significantly lower productivity. In contrast, establishments that participated in public procurement for SMEs in 2009 were more likely to survive than those that did not do so in 2011. These results can be interpreted as the negative consequences of government intervention. The market's efficiency enhancement is hindered if underserving companies survive owing to government intervention but fail to improve efficiency.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.75
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• pp.117-136
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• 2017

Trade finance services have been played an important role in the Korea trade development history since 1960's. These days the trade environment is confronted by the 4th Industrial revolution and new trade protectionism. So we need to improve the Korea Trade Finance Services in order to improve Korea trade volume. Bank of Korea(BOK) also revised the Rule of Korea Trade Finance in 2014 and enlarged the trade fund for commercial banks where they handle the trade finance to small and medium enterprises(SME) in 2016. This article handle the current state and problems of Korea trade finance services and suggest the improvement measures as follows; First, the commercial banks, which handle trade finance fund, should improve the customs and practice of judge loan for SMEs. Second, the export volume counting rule for trade loan should harmonize between BOK's Rule and Foreign Trade Management Regulation under the Foreign Trade Act. Third, the processing trade and intermediate trade also can use the trade finance like other trade. Fourth, Trade finance should be in balance between export and import finance to defend the new protectionism. It means that the trade finance should expand to import in the certain conditions. Lastly, the related trade promotion agencies and their employees should improve their skills and abilities for handling trade finance.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.309-321
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• 2014

Recently, due to the advent of ubiquitous computing and the spread of intelligent portable device such as smart phone, iPad and PDA has been amplified, a variety of services and the amount of information has also increased fastly. It is becoming a part of our common life style that the demands for enjoying the wireless internet are increasing anytime or anyplace without any restriction of time and place. And also, the demands for e-commerce and many different items on e-commerce and interesting of associated items are increasing. Existing collaborative filtering (CF), explicit method, can not only reflect exact attributes of item, but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, using a implicit method without onerous question and answer to the users, not used user's profile for rating to reduce customers' searching effort to find out the items with high purchasability, it is necessary for us to analyse the segmentation of customer and item based on customer data and purchase history data, which is able to reflect the attributes of the item in order to improve the accuracy of recommendation. We propose the method of recommendation system using association rule and weighted preference so as to consider many different items on e-commerce and to refect the profit/weight/importance of attributed of a item. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• Journal of Convergence Society for SMB
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• v.6 no.3
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• pp.79-83
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• 2016

The purpose of this study is to provide customized information to student which study and live in a university campus. In this study, we collect internal data of campus and external data on the Internet such as the blog or SNS and, then store and process them. After that, we propose a system for providing individual students one-to-one marketing by analyzing these data in detail. This system analyzes purchase history information and the attendance of the building, and then transmits the coupon and information individually according to the pattern to the student's mobile phone.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.193-200
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• 2022

In this study, the intellectual property rights of digital contents (creations) are protected by using block chain technology that cannot be damaged or forged. So, we build a blockchain-based content sales revenue tracking system and platform that activates the transaction and distribution of digital content (creation). We developed an API server that can be used for content registration and revision history management smart contract, license management smart contract according to content purchase, content inquiry function through files and hashes, and web and APP services. Through this, it is possible to prove the relationship between the rights of the creators of digital content creations and protect the rights of the creators.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.347-351
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• 2022

Farmers E-Market is a website that allows agricultural workers to direct market their products to buyers without the use of a middleman. That theory is blockchain system will be used by authors to accomplish this. The system, which is built on a public blockchain system, supports sustainability, shippers, and consumers. Farmers can keep track of their farming activities. Customers can review the product's history and track its journey through carriers to delivery after making a purchase. Farmers are encouraged to get information about their interests promptly in a blockchain-enabled system like this. This functionality is being used by small-scale farmers to form groups based on their location to attract large-scale customers, renegotiate farming techniques or volumes, and enter into contracts with buyers. The analysis shows the use of blockchain technology with a farmer's portal that keeps the video of trading data of crops, taking into account the qualities of blockchain such as values and create or transaction data. The proposal merges python as a programming language with a blockchain system to benefit farmers, vendors, and individuals by preserving transactions.

• The Korean Journal of Food & Health Convergence
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• v.9 no.4
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• pp.19-23
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• 2023

The integration of ChatGPT, an AI-powered language model, is causing a profound transformation within the food industry, impacting various domains. It offers novel capabilities in recipe creation, personalized dining, menu development, food safety, customer service, and culinary education. ChatGPT's vast culinary dataset analysis aids chefs in pushing flavor boundaries through innovative ingredient combinations. Its personalization potential caters to dietary preferences and cultural nuances, democratizing culinary knowledge. It functions as a virtual mentor, empowering enthusiasts to experiment creatively. For personalized dining, ChatGPT's language understanding enables customer interaction, dish recommendations based on preferences. In menu development, data-driven insights identify culinary trends, guiding chefs in crafting menus aligned with evolving tastes. It suggests inventive ingredient pairings, fostering innovation and inclusivity. AI-driven data analysis contributes to quality control, ensuring consistent taste and texture. Food writing and marketing benefit from ChatGPT's content generation, adapting to diverse strategies and consumer preferences. AI-powered chatbots revolutionize customer service, improving ordering experiences, and post-purchase engagement. In culinary education, ChatGPT acts as a virtual mentor, guiding learners through techniques and history. In food safety, data analysis prevents contamination and ensures compliance. Overall, ChatGPT reshapes the industry by uniting AI's analytics with culinary expertise, enhancing innovation, inclusivity, and efficiency in gastronomy.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.257-266
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• 2014

In this paper, we propose the development of an eBook Service System based on VOD Broadcasting Contents(eBSS-VOD) which protects the illegal copy through the effective DRM strategy. Here, eBSS-VOD is composed of three sub-systems - eBook server management system, ePub Builder, and eBook App - according to the functional points of view. The eBook server management system operates to manage the eBooks, the encription keys, the VOD contents, the membership and the history of users' viewing log. The ePub Builder assists to produce eBooks by DRM strategy. The eBook App provides users with the utility services of VOD viewing and eBook purchase processing. The proposed eBSS-VOD, differently from the current eBook systems, helps to increase the users' interests in reading books and to motivate the users' purchasing needs and it also makes new distribution channel for eBooks based on VOD broadcasting contents through smart applications. We provides the differentiated eBook builder which are based on ePub3.0 and DRM strategy. Through empirical survey results on users' interests and purchase intention for eBooks, we show that the proposed eBSS-VOD has the comparative effects compared to the current systems.

• Information Systems Review
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• v.17 no.3
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• pp.95-111
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• 2015

The recommendation system automatically provides the predicted items which are expected to be purchased by analyzing the previous customer behaviors. This recommendation system has been applied to many e-commerce businesses, and it is generating positive effects on user convenience as well as the company's revenue. However, there are several limitations of the existing recommendation systems. They do not reflect specific criteria for evaluating products or the factors that affect customer buying decisions. Thus, our research proposes a collaborative recommendation model algorithm that utilizes each customer's online product reviews. This study deploys topic modeling method for customer opinion mining. Also, it adopts a kernel-based machine learning concept by selecting kernels explaining individual similarities in accordance with customers' purchase history and online reviews. Our study further applies a multiple kernel learning algorithm to integrate the kernelsinto a combined model for predicting the product ratings, and it verifies its validity with a data set (including purchased item, product rating, and online review) of BestBuy, an online consumer electronics store. This study theoretically implicates by suggesting a new method for the online recommendation system, i.e., a collaborative recommendation method using topic modeling and kernel-based learning.