• 한국정보시스템학회지:정보시스템연구
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• 제31권4호
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• pp.209-232
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• 2022

Purpose In the current environment of online shopping, the cost for consumers to obtain the information they need is decreasing, and the price of products is becoming more transparent, leading to increased price competition among enterprises for similar products. Given the widespread usage of limited-time promotion as a marketing method for enterprises in the context of e-commerce, it is great meaning to study and reveal the internal influence mechanism of limited-time promotion on consumers' impulsive consumption. Design/methodology/approach Based on the S-O-R theory, this study constructs a model of consumers' impulsive consumption in the context of e-commerce from the perspective of perceived price discount, with evoking sense and pleasure as mediating variables and perceived time pressure as moderating variables. Findings The results show that perceived price discount has a significant positive impact on evoking sense and pleasure. Evoking sense has a significant positive impact on pleasure. Both evoking sense and pleasure have a significant positive impact on consumers' impulsive consumption. Meanwhile, perceived time pressure plays a significant moderating role between perceived price discount and evoking sense, between perceived price discount and pleasure, and between evoking sense and consumers' impulsive consumption. Finally, based on the above findings, this study provides effective suggestions for e-commerce participants in the formulation of limited-time promotion strategies.

• 대한산업공학회지
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• 제36권3호
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• pp.193-202
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• 2010

The recommender system is a typical software solution for personalized services which are now popular in e-commerce sites. Most of the existing recommender systems are based on customers' explicit rating data on items (e.g., ratings on movies), and it is only recently that recommender systems based on implicit ratings have been proposed as a better alternative. Implicit ratings of a customer on those items that are clicked but not purchased can be inferred from the customer's navigational and behavioral patterns. In this article, a dimensionality reduction (DR) technique is newly applied to the implicit rating-based recommender system, and its effectiveness is assessed using an experimental e-commerce site. The experimental results indicate that the performance of the proposed approach is superior or at least similar to the conventional collaborative filtering (CF)-based approach unless the number of recommended products is 'large.' In addition, the proposed approach requires less memory space and is computationally more efficient.

• 한국융합학회논문지
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• 제11권6호
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• pp.237-248
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• 2020

본 연구의 목적은 중국 3대 전자상거래 플랫폼의 농산물 시장 효율성을 소비자 가격 공정성 인식의 관점에서 측정하고, 비효율적 브랜드의 품질과 가격 차이를 분석하여 농산물 판매 기업이 합리적인 가격 전략을 수립할 수 있도록 하기 위함이다. 본 연구에서는 전자상거래 플랫폼에서 판매되는 농산물의 특성(품질, 원산지, 맛, 안전성 수준)을 산출 지표로 하고, 제품 가격을 투입 지표로 선정하여 DEA 분석을 통해 시장의 효율성을 평가하였다. 분석 결과 효율적 브랜드의 비중은 JD몰이 가장 높고, YHD.com은 평균 시장 효율이 가장 높았으며, 동북 쌀은 3개 플랫폼에서 평균 효율성 차이가 가장 큰 것으로 나타났다. 이러한 결과는 가격 비효율성이 여전히 전자 시장에 존재한다는 것을 보여준다. 농산물 온라인 시장의 발전을 위해서는 소비자 가격공정성에 주의하고 가격과 품질의 조화를 중시해야 한다. 이 논문의 한계점은 인터넷 시장에서 소비자 경험에 의한 입소문 마케팅의 영향력에 초점을 맞추지 않았다는 점이며, 이는 향후 연구 과제이다.

• Asia pacific journal of information systems
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• 제27권1호
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• pp.18-37
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• 2017

Many retailers have extended their extant online channels (i.e., websites) to mobile channels for communicating with and delivering their products or services to customers. However, retailers have trouble delivering a cohesive and seamless customer experience across the Web and mobile channels. To address this challenge, we propose a way for retailers to enrich customers' seamless experiences across channels by configuring mobile channels (functionality- and interactivity-oriented configurations) along with traditional Web channels. This study theoretically contributes a research framework that posits the role of mobile channels as an extension of existing websites. It also provides practical insight for effectively articulating an e-commerce strategy in cross-channel electronic commerce.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.47-51
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• 2004

In this paper we propose a fair and secure e-commerce model for P2P network, in which communication entities can buy and sell products by P2P contract. In particular, we focus on a fair transaction protocol that is based on a collaboration with distributed communication entities. This feature makes our model very attractive in P2P networking environment which does not depend on any central trusted authority for managing communication entities.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2003년도 춘계학술대회
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• pp.416-425
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• 2003

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering (CF) has been known to be the most successful recommendation technology. However its widespread use in e-commerce has exposed two research issues, sparsity and scalability. In this paper, we propose several hybrid recommender procedures based on web usage mining, clustering techniques and collaborative filtering to address these issues. Experimental evaluation of suggested procedures on real e-commerce data shows interesting relation between characteristics of procedures and diverse situations.

• ETRI Journal
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• 제46권4호
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• pp.633-647
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• 2024

In e-commerce platforms, sentiment analysis on an enormous number of user reviews efficiently enhances user satisfaction. In this article, an automated product recommendation system is developed based on machine and deep-learning models. In the initial step, the text data are acquired from the Amazon Product Reviews dataset, which includes 60 000 customer reviews with 14 806 neutral reviews, 19 567 negative reviews, and 25 627 positive reviews. Further, the text data denoising is carried out using techniques such as stop word removal, stemming, segregation, lemmatization, and tokenization. Removing stop-words (duplicate and inconsistent text) and other denoising techniques improves the classification performance and decreases the training time of the model. Next, vectorization is accomplished utilizing the term frequency-inverse document frequency technique, which converts denoised text to numerical vectors for faster code execution. The obtained feature vectors are given to the modified convolutional neural network model for sentiment analysis on e-commerce platforms. The empirical result shows that the proposed model obtained a mean accuracy of 97.40% on the APR dataset.

• 한국지능시스템학회논문지
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• 제12권2호
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• pp.109-114
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• 2002

요즘 전자상거래 고객들은 쇼핑몰에 있어 물품들의 가격 정보를 수집하는 비교쇼핑 에이전트들의 도움을 받아서 구매 비용을 절감할 수 있다. 그러나 사용자는 가격이외의 다양한 구매 조건을 만족하는 제품 정보들을 추천하는 에이전트의 개발을 요구하고 있다. 본 논문에서는 에이전트 기반의 전자상거래를 실현하기 위해 다양한 사용자 요구에 적합한 도서 정보를 검색하고 추천하는 멀티에이전트 시스템을 제안한다. 본 멀치에이전트 시스템은 온라인 서점 고객들을 돕기 위해 구현되고 테스트되었다. 실험 결과 전자상거래를 이용하는 구매자에게 여러 온라인 서점의 다양한 도서판매 조건에 대한 정보를 실시간으로 추천할 수 있게 되었다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.11-15
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• 2001

The explosion of the Internet, and most recently e-commerce, has caused great interest in agent technologies. The development of virtual environments has also increased in the last few years. A growing number of real-time applications use graphics with photorealistic quality, especially in the field of training, but also in the areas of design and ergonomic research. We describe an attempt to develop a framework that provides customers with multimedia information and interactive experiences with a virtual shopping environment. The application presented consists on a virtual visit to a music -store where the user is guided by an intelligent agent named Lizeth which responds in real-time to user's requests with precise information about music, artist's biographies, prices and related products to help the user to make decisions. The potential of UML and the Java programming language is discussed to show their application in the field of intelligent agents as mediators on shopping processes. We conclude that the proposed framework leads to the creation of application with a potentially significant impact in the development of e-commerce systems embedded in virtual environments.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제14권9호
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• pp.835-840
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• 2008

본 논문에서는 고객의 다양한 행동 분석을 통해 e-commerce 포탈에서 향상된 개인화 기법을 제안한다. 고객의 행동은 "상품 구매" '장바구니에 상품 추가", "상품 정보 확인" 세가지로 구분하였다. 추천된 상품에 대한 평점을 측정하기 위해 사용자의 행동을 암묵적으로 추적한다. 제안하는 추천 기법은 Cross Correlation Coefficient를 변형하여 비슷한 선호도를 가진 고객들을 분류한 후 대상 고객이 선호하는 상품과 비슷한 선호도를 가진 고객들의 상품 유사도를 측정한다. 본 시스템의 가장 주목할만한 특징은 고객의 행동을 바탕으로 상품에 대한 평점을 암묵적으로 계산하는 것이다. 상품의 선호도에 대하여 고객의 직접적인 대답을 요구하면 고객들이 불편함을 느낄 수 있기 때문에 고객의 행동을 통하여 상품에 대한 선호도를 반영한다. 실험결과 부분에서 우리의 시스템과 협업 필터링을 기반으로 한 다른 기법의 비교를 통하여 각 기법들의 장단점을 보일 것이다.