• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제4권11호
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• pp.499-508
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• 2015

높은 유틸리티 항목집합 마이닝은 트랜잭션 데이터베이스에서 사용자가 지정한 최솟값 이상의 유틸리티를 갖는 항목집합들을 항목의 수량과 가중치값을 동시에 고려하여 찾아내는 것이다. 최근에 연구된 유틸리티-리스트 기반의 높은 유틸리티 항목집합 마이닝 알고리즘은 많은 후보 항목집합들을 피하기 위해 제안되었으며 비용이 높은 조인 연산을 수행한다. 본 논문은 유틸리티-리스트 구조에 항목집합의 트랜잭션 유틸리티와 공통 유틸리티 속성을 추가한 새로운 알고리즘을 제안한다. 이 새로운 알고리즘은 조인 연산의 수를 줄이고 탐색 공간을 효과적으로 가지치기한다. 생성 데이터와 실 환경 데이터상의 실험 결과를 통해 제안된 알고리즘이 다른 최근 알고리즘들에 비해 실행 시간 면에서 아주 우수하고, 특히 데이터가 조밀하거나 항목집합의 길이가 긴 경우에 더 효율적이라는 것을 보여준다.

• 마케팅과학연구
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• 제17권4호
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• pp.51-89
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• 2007

본 연구는 의류 브랜드 온라인 커뮤니티의 이용욕구충족과 커뮤니티 몰입간의 관계 및 이들 관계에 대한 브랜드 이미지의 조절효과를 검증하는 것이다. 9개 캐주얼 의류 브랜드 커뮤니티 회원 317명을 대상으로 온라인 서베이를 실시하여 자료를 수집하였다. 다중회귀분석 결과, 의류 브랜드 커뮤니티에서의 이용욕구 충족은 커뮤니티에 대한 몰입과 유의한 관계가 있었다. 그리고 조절회귀분석 결과, 의류 브랜드 커뮤니티에서의 관계욕구 충족이 커뮤니티 몰입(감정적 몰입, 지속적 몰입, 규범적 몰입)에 영향을 미칠때 의류 브랜드 이미지 수준에 따른 조절효과가 작용함이 발견되었다. 또한 의류 브랜드 이미지의 조절효과는 의류 브랜드 커뮤니티에서의 거래욕구 충족과 커뮤니티에 대한 감정적 몰입의 관계에서도 나타났다. 특히 의류 브랜드 커뮤니티인 경우, 커뮤니티에서의 관계욕구 충족수준에 따른 커뮤니티 몰입의 정도는 브랜드 이미지가 낮을 때 보다 높을 때 더 크게 나타났다. 이것은 의류 브랜드 커뮤니티에서의 이용욕구 충족을 통해 회원들의 커뮤니티 몰입을 증대시키는 전략은 의류 브랜드 이미지 수준이 다른 의류 브랜드 유형에 따라 그 효과에 차이가 있음을 의미한다. 따라서 의류기업의 마케터들은 자사 브랜드의 이미지 수준을 평가하고, 이에 맞춰 커뮤니티 몰입을 증대시키는 전략을 모색할 필요가 있다. 브랜드 커뮤니티에 대한 몰입은 브랜드에 대한 구전이나 재구매 행동과 연결되므로, 명품 의류 브랜드들인 경우 온라인 브랜드 커뮤니티를 구축하고 회원들의 커뮤니티에 대한 몰입을 증대시킴으로써 브랜드 자산을 극대화시킬 수 있을 것이다.

• Asia pacific journal of information systems
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• 제23권2호
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• pp.1-20
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• 2013

Social commerce has been gaining explosive popularity, with typical examples of the model such as Groupon and Level Up. Both local business owners and consumers can benefit from this new e-commerce model. Local business owners have a chance to access potential customers and promote their products in a way that could not have otherwise been easily possible, and consumers can enjoy discounted offerings. However, questions have been increasingly raised about the value and future of the social commerce model. A recent survey shows that about a third of 324 business owners who ran a daily-deal promotion in Groupon went behind. Furthermore, more than half of the surveyed merchants did not express enthusiasm about running the promotion again. The same goes for the case in Korea, where more than half of the surveyed clients reported no significant change or even decrease in profits compared to before the use of social commerce model. Why do local business owners fail to exploit the benefits from the promotions and advertisements through the social commerce model and to make profits? Without answering this question, the model would fall under suspicion and even its sustainability might be challenged. This study aims to look into problems in the current social commerce transactions and provide implications for the social commerce model, so that the model would get a foothold for next growth. Drawing on justice theory, this study develops theoretical arguments for the effects of transaction characteristics on consumers' distributive justice and purchase intention in the social commerce. Specifically, this study focuses on two characteristics of social commerce transactions-the discount rate and the purchase rate of products-and investigates their effects on consumers' perception of distributive justice for discounted transactions in the social commerce and their perception of distributive justice for regular-priced transactions. This study also examines the relationship between distributive justice and purchase intention. We conducted an online experiment and gathered data from 115 participants to test the hypotheses. Each participant was randomly assigned to one of nine manipulated scenarios of social commerce transactions, which were generated based on the combination of three levels of purchase rate (high, medium, and low) and three levels of discount rate (high, medium, and low). We conducted MANOVA and post-hoc ANOVA to test hypotheses about the relationships between the transaction characteristics (purchase rate and discount rate) and distributive justice for each of the discounted transaction and the regular-priced transaction. We also employed a PLS analysis to test relations between distributive justice and purchase intentions. Analysis results show that a higher discount rate increases distributive justice for the discounted transaction but decreases distributive justice for the regular-priced transaction. This, coupled with the result that distributive justice for each type of transaction has a positive effect on the corresponding purchase intention, implies that a large discount in the social commerce may be helpful for attracting consumers, but harmful to the business after the promotion. However, further examination reveals curvilinear effects of the discount rate on both types of distributive justice. Specifically, we find distributive justice for the discounted transaction increases concavely as the discount rate increases while distributive justice for the regular-priced transaction decreases concavely with the dscount rate. This implies that there exists an appropriate discount rate which could promote the discounted transaction while not hurting future business of regular-priced transactions. Next, the purchase rate is found to be a critical factor that facilitates the regular-priced transaction. It has a convexly positive influence on distributive justice for the transaction. Therefore, an increase of the rate beyond some threshold would lead to a substantial level of distributive justice for the regular-priced transaction, threrby boosting future transactions. This implies that social commerce firms and sellers should employ various non-price stimuli to promote the purchase rate. Finally, we find no significant relationship between the purchase rate and distributive justice for the discounted transaction. Based on the above results, we provide several implications with future research directions.

• 지식경영연구
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• 제21권1호
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• pp.117-136
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• 2020

In the studies for the recommender systems which solve the information overload problem of users, the use of transactional data has been continuously tried. Especially, because the firms can easily obtain transactional data along with the development of IoT technologies, transaction-based recommender systems are recently used in various areas. However, the use of transactional data has limitations that it is hard to reflect domain knowledge and they do not directly show user preferences for individual items. Therefore, in this study, we propose a method applying the word embedding in the transaction-based recommender system to reflect preference differences among users and domain knowledge. Our approach is based on SAR, which shows high performance in the recommender systems, and we improved its components by using FastText, one of the word embedding techniques. Experimental results show that the reflection of domain knowledge and preference difference has a significant effect on the performance of recommender systems. Therefore, we expect our study to contribute to the improvement of the transaction-based recommender systems and to suggest the expansion of data used in the recommender system.

• 경영과학
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• 제21권2호
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• pp.273-289
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• 2004

Although the Internet is useful for transferring information, Internet auction environments make fraud more attractive to offenders, because the chance of detection and punishment is decreased. One of these frauds is the phantom transaction, which is a colluding transaction by the buyer and seller to commit the illegal discounting of a credit card. They pretend to fulfill the transaction paid by credit card, without actually selling products, and the seller receives cash from the credit card corporations. Then the seller lends it out with quite a high interest rate to the buyer, whose credit rating is so poor that he cannot borrow money from anywhere else. The purpose of this study is to empirically investigate the factors necessary to detect phantom transactions in an online auction. Based upon studies that have explored the behaviors of buyers and sellers in online auctions, the following have been suggested as independent variables: bidding numbers, bid increments, sellers' credit, auction lengths, and starting bids. In this study. we developed Internet-based data collection software and collected data on transactions of notebook computers, each of which had a winning bid of over W one million. Data analysis with a logistic regression model revealed that starting bids, sellers' credit, and auction length were significant in detecting the phantom transactions.

• 한국정보과학회논문지:데이타베이스
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• 제30권6호
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• pp.629-640
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• 2003

공유 디스크(Shared Disks: SD) 클러스터는 온라인 트랜잭션 처리를 위해 다수 개의 컴퓨터를 연동하는 방식으로, 각 노드들은 디스크 계층에서 데이타베이스를 공유한다. SD 클러스터에서 트랜잭션 라우팅은 사용자가 요청한 트랜잭션을 실행할 노드를 결정하는 것을 의미한다. 이때, 동일한 클래스에 속하는 트랜잭션들을 가급적 동일한 노드에서 실행시킴으로써 지역 참조성을 극대화할 수 있으며, 이를 친화도 기반 라우팅이라 한다. 그러나 친화도 기반 라우팅은 트랜잭션 클래스의 부하 변화에 적절히 대처할 수 없으며, 특정 트랜잭션 클래스가 폭주할 경우 해당 노드는 과부하 상태에 빠질 수 있다는 단점을 갖는다. 본 논문에서는 친화도 기반 라우팅을 지원하면서 SD 클러스터를 구성하는 노드들의 부하를 동적으로 분산할 수 있는 동적 트랜잭션 라우팅 기법을 제안한다. 제안한 기법은 지역 버퍼에 대한 참조 지역성을 높이고 버퍼 무효화 오버헤드를 줄임으로써 시스템의 성능을 향상시킬 수 있다.

• Journal of Korea Trade
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• 제27권2호
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• pp.1-21
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• 2023

Purpose - The bank payment obligation is a transaction method that combines the certainty of L/C transactions with the speed of remittance payments, so the main purpose of this study is to highlight the superiority of bank payment obligation, noting the difference between bank payment obligation and L/C transactions. In addition, we would like to examine how bank payment obligations can actually be applied to support various valuable proposals such as post-shipment and post-shipment finance according to the payment process.. Design/methodology - This study focused on literature based on data from ICC and SWIFT along with previous domestic and international studies. In terms of a research method, a literature review was adopted with electronic trade-related books and journals and policy-related reports from international trade-related agencies. Findings - Unlike L/C transaction, BPO transaction verify the data inquiry process based only on the combination result of the established baseline and dataset. Accordingly, it is superior to L/C transaction in that there is no confrontation between the parties over the results of the inquiry, and clear transactions are possible according to the principle of proof after prepayment. In addition, unlike credit transactions, data inconsistency acceptance procedures confirm payment obligations in consideration of importers' intentions. As a result, as long as trade documents are in the hands of exporting countries, flexible document disposition is possible in response to the situation after payment, which is more advantageous than L/C transaction. Originality/value - Specifically, from the importer's point of view, BPO transactions have the advantage of reducing the manpower required to prepare and review trade documents and processing transaction negotiations with exporters advantageously due to the strength of payment obligations. From the perspective of the exporter, it has the advantage of enabling rapid recovery of trade payments and reducing the risk of importer's cancellation of transactions or content change. From the perspective of participating banks, it is possible to strengthen relations with importer and obtain high commission income by increasing the role of bank reduced by reducing L/C transaction.

• 한국IT서비스학회지
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• 제18권2호
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• pp.143-159
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• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

• 한국생활과학회지
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• 제20권4호
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• pp.801-817
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• 2011

The purpose of this research was to explore the levels of ethical consumption of the college students and classify their types on ethical consumption behaviors. This research was conducted with university students living in Gwangju. Statistical analysis was achieved by using t-test, one-way ANOVA, Duncan's multiple range test, $X^2$, and Ward' hierarchical cluster analysis with a total of 761 questionnaires. The research results are summarized as follows: First, the overall ethical consumption average mark of college students was 3.14. Second, all surveyed college students were classified into five types based on the means scores of three dimension ethical consumption behaviors. A total 16.7% of students belonged to Type 1 (named as entire region active group) where students scored high points on three dimension ethical consumption behaviors. Type 2 (named as entire region average group) had about 41.6% of students whose scores were the average mark level in three dimension ethical consumption behaviors. Type 3 (named as future-oriented group) occupied 13.9% and this group scored low on the ethical consumption in commercial transaction but high on the ethical consumption for the future generation. Type 4 (named as commercial transaction oriented group) occupied 9.1% and this group scored low on the ethical consumption for contemporary humankind and the ethical consumption for the future generation but high on the ethical consumption in commercial transaction. Type 5 (named as entire region passive group) had 18.7% of students whose scores of three dimension ethical consumption behaviors were low.

• 유통과학연구
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• 제14권12호
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• pp.71-78
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• 2016

Purpose - This paper is for the Analysis on the Hindrance Factors and Activation Scheme by the Type of Distribution Channel in Direct transaction of Agricultural Products. As the distribution structure of agricultural products has become changable, farmers seem to use the type of direct distribution in order to enhance the receiving price. This study aims to explore the hindrance factors and income variation rate in direct transaction of agricultural produces, specifically focusing on the 167 farmers. Research design, data, and methodology - To ascertain the hindrance factors exactly by the type of distribution channel, the managements were classified by four subcategories, that is high sales percentage with shopping malls, SNS, shopping malls and SNS, and off-line direct transaction. Results - As a result of the hypothesis test, hinderance factors in online direct deal activation were found to be in the order of the difficulty in continuous content production, the difficulty in shopping mall operation and maintenance, and the difficulty in card commission problems, and in the order of the difficulties in continuous content production, the difficulty in continuous content production, the difficulty in shopping mall operation and maintenance, and the difficulty in branding for the SNS group. Thus, it can be seen that the difficulty in continuous content production, shopping mall operation and maintenance were found to be the biggest obstacles. In addition, hindering factors in online direct deal activation were found to be in the order of the difficulty in credit card settlement, the difficulty in publicity, and the difficulty in dealing with unsold goods. The group with high sales rate in shopping mall was found to be increased by 23.9% in the gross income compared to the previous year, the group with high SNS sales ratio increased by 56.5%, the group with direct offline transaction increased by 37.1%, among which the group with the highest increase rate of SNS sales ratio was found to be the highest from the rate of increase/decrease of the income, which was statistically significant. Conclusions - It can be suggested that government and local government may provide agricultural management with supporting plan which in turn can activate direct transaction in any possible ways.