• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.490-504
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• 2021

The online market is gradually increasing due to the increase in single-person households, the development of information and communication technologies, the emergence of various new products, and price comparison competition. Companies need differentiation strategies to adapt to changes in the online environment and secure a competitive edge. In this environment, the objective is to consider the importance of consumer perception of websites in order to generate continuous growth and revenue in the online market as well as to differentiate them from competitors using an online service environment that can affect consumers' internal responses. In this study, we present aesthetic, functional, privacy, and interaction factors as components of e-servicescape to study the impact of e-servicescape on website trust, brand attitude, and repurchase intention. In the data analysis, 485 ordinary people with online shopping experience were surveyed. The questionnaire was based on a 7-point Likert scale for each question and statistical analysis was conducted using SPSS 24.0 and AMOS 25.0. The analysis shows that in e-servicescapes aesthetic and privacy factors influence website trust and brand attitudes and consequently affect repurchase intention.

• The Research Journal of the Costume Culture
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• v.27 no.1
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• pp.20-32
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• 2019

This study examined whether and how consumers who seek a bargain in their shopping for luxury fashion brands differ from traditional luxury consumers or non-luxury consumers on their market-related attitudes. To do so, this study compared multi-dimensional perceived values, fairness price perceptions, satisfaction with purchase, brand loyalty, and future purchase intention among luxury consumers, luxury-bargain seekers, and non-luxury consumers. Data was obtained from online surveys and the market-related attitudes were compared using an ANOVA test. The comparion of three types of consumers revealed that luxury-bargain seekers and regular luxury consumers are distinct consumer markets. Overall, luxury consumers displayed high perceived values and brand loyalty and were fairly satisfied with the purchase at full-prices. On the other hand, luxury-bargain seekers showed significantly low perceived social value, perceived fairness toward the original price of the brands, and brand loyalty. They were satisfied with the bargain purchase but not likely to purchase the luxury at full-prices in the future. Understanding these distinct types of consumers and targeting them with different product and pricing strategies are important for luxury brands and retailers to expand luxury consumer base without diluting their brands' prestige image. Potential marketing strategies based on the findings of this study were suggested.

• Journal of the Korea Fashion and Costume Design Association
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• v.15 no.4
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• pp.29-45
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• 2013

The purpose of this study is to provide base line data required to establish a viable knitwear marketing strategy targeting young people by comparing and analyzing preference and buying behavior of customers in their twenties. A survey was conducted in 2009 and 2013 on people in their 20's male and female living in Busan. The total of 362 people participated in the survey and the results are as follows: Firstly, the seeking factors for knitwear were utilization factor, functionality factor, care for clothing factor, economics factor and vogue factor. Secondly, a differentiated marketing strategy targeting 20-something customers needs to be established as there was a steep rise in the number of customers purchasing clothes on online shopping malls using smart phone devices according to the survey.'Low-price, broad-line strategy'is also required as those surveyed preferred stores offering a wide choice of designs with reasonable price. Considering the survey results that a growing number of people tended to buy a variety of knitwear items regardless of the seasons, knitwear production needs to be diversified in terms of designs and materials. Thirdly, the survey revealed that pastel-colored knitwear was preferred for spring/summer season whereas knitwear with achromatic colors was voted the most-preferred one during the autumn and winter season. In terms of knitwear shapes, the gap between genders continues to narrow and tendency sensitive to fashion trend became more apparent reflecting the change of the times.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.9-15
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• 2012

With the rise of internet and e-commerce, this is more applicable now than ever. People rely on computer networks to provide them with news, stock prices, e-mail and online shopping. People's credit card details, medical records and other personal information are stored on computer systems. Many companies have a web presence as an essential part of their business. The research community uses computer systems to undertake research and to disseminate findings. The integrity and availability of all these systems have to be protected against a number of threats. Amateur hackers, rival corporations, terrorists and even foreign governments have the motive and capability to carry out sophisticated attacks against computer systems. Therefore, the field of information and communication security has become vitally important to the safety and economic well being of society as a whole. This paper provides an overview of IDS and IPS, their functions, detection and analysis techniques. It also presents comparison of security capability and characteristics of IDPS techniques. This will make basis of IDPS(Intrusion Detection and Protection System) technology integration for a broad-based IDPS solutions

• Journal of Fashion Business
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• v.21 no.5
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• pp.91-108
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• 2017

Interests in 3D virtual clothing technology and its application in online shopping malls are increasing with the advent of the Fourth Industrial Revolution. Most studies on 3D virtual clothing, however, are focused on observing drapes or ease of virtual clothing depending on fabric properties of representative clothing items. Therefore, the purpose of this study is: first, to determine if current input of typical material characteristics in 3D CLO are sufficient to formulate virtual skirts with different front and back silhouettes; second, to determine if subjective appearance evaluation matched physical shape characteristics of those skirts. In this study, appearances of typical cotton, wool, silk, rayon, and polyester skirts with different front and back pattern were compared between actual and virtual clothing depending on fabric materials. Subjective appearance evaluation was conducted by 7 experts regarding similarity between actual and virtual clothing with a 5-point scale. For objective evaluation of the both types of skirt shape, degree of roundness at the cross section, displacement of side seam, position of back waistline, and the number of folds at the skirt back were observed. In the case of cotton and wool, not the subjective appearance evaluation as well as shape characteristics of virtual skirts were well matched to the actual shape of skirts with a few material inputs. However, current material inputs for silk, rayon and polyester were insufficient to cover material differences in formation of virtual skirts with different front and back silhouettes.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.495-520
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• 2012

A secure Electronic Payment System (EPS) is essential for the booming online shopping market. A successful EPS supports the transfer of electronic money and sensitive information with security, accuracy, and integrity between the seller and buyer over the Internet. SET, CyberCash, Paypal, and iKP are the most popular Credit Card-Based EPSs (CCBEPSs). Some CCBEPSs only use SSL to provide a secure communication channel. Hence, they only prevent "Man in the Middle" fraud but do not protect the sensitive cardholder information such as the credit card number from being passed onto the merchant, who may be unscrupulous. Other CCBEPSs use complex mechanisms such as cryptography, certificate authorities, etc. to fulfill the security schemes. However, factors such as ease of use for the cardholder and the implementation costs for each party are frequently overlooked. In this paper, we propose a Web service based new payment system, based on ANSI X9.59-2006 with extra features added on top of this standard. X9.59 is an Account Based Digital Signature (ABDS) and consumer-oriented payment system. It utilizes the existing financial network and financial messages to complete the payment process. However, there are a number of limitations in this standard. This research provides a solution to solve the limitations of X9.59 by adding a merchant authentication feature during the payment cycle without any addenda records to be added in the existing financial messages. We have conducted performance testing on the proposed system via a comparison with SET and X9.59 using simulation to analyze their levels of performance and security.

• Journal of Families and Better Life
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• v.32 no.2
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• pp.99-116
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• 2014

Domestic PB products have emerged and been distributed by hypermarkets, department stores, convenience stores, as well as TV home shopping channels and Internet shopping malls. However, the fierce competition among the distributors due to the emergence of the PB products have caused the diversion of consumers' recognition to be reduced weight and volume as well as had the effect of misleading consumers about the prices. The width of the PB product price's up and down is larger than the width of the NB product. Thus, following consumers' purchases of PB products, there has been an increasing number of consumer complaints. In order to research consumers' recognition of PB products and to examine how consumers' recognition and information search comparative to PB products affect consumers' dissatisfaction, an online survey targeted consumers with experience purchasing PB products. This study was conducted and analyzed using SPSS 19 Statistics. The findings can be summarized as follows. Even though more consumers who frequently purchased and used the PB products, the more they compared with information search comparative to the NB product and then purchased the PB product. We investigated the result that the relevant variables of consumer complaints have some relative influence in the purchasing of PB products. There will be a higher probability o the group having high recognition about price and safety not making consumer complaints in comparison with the probability of other consumers making complaints after the purchase of a PB product. Therefore, based on the results of this study, companies need to build a system so that they can figure out consumers' needs in order to prevent the occurrence of consumer complaints related to the products of distribution companies' brands. By means of the system, it is also necessary for companies to collect consumer complaints and analyze them by category. Then they eventually should develop a consumer-centered management system which may contribute to quality improvement, product development and the reduction of consumer complaints.

• Science of Emotion and Sensibility
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• v.24 no.3
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• pp.41-60
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• 2021

The purpose of this research was to determine whether multimodal cues in an online shopping environment could enhance tactile consumer mental imagery, purchase intentions, and attitudes towards an apparel product. One limitation of online retail is that consumers are unable to physically touch the items. However, as tactile information plays an important role in consumer decisions especially for apparel products, this study investigated the effects of multimodal cues on overcoming the lack of tactile stimuli. In experiment 1, to explore the product, the participants were randomly assigned to four conditions; picture only, video without sound, video with corresponding sound, and video with discordant sound; after which tactile mental imagery vividness, ease of imagination, attitude, and purchase intentions were measured. It was found that the video with discordant sound had the lowest average scores of all dependent variables. A within-participants design was used in experiment 2, in which all participants explored the same product in the four conditions in a random order. They were told that they were visiting four different brands on a price comparison web site. After the same variables as in experiment 1, including the need for touch, were measured, the repeated measures ANCOVA results revealed that compared to the other conditions, the video with the corresponding sound significantly enhanced tactile mental imagery vividness, attitude, and purchase intentions. However, the discordant condition had significantly lower attitudes and purchase intentions. The dual mediation analysis also revealed that the multimodal cue conditions significantly predicted attitudes and purchase intentions by sequentially mediating the imagery vividness and ease of imagination. In sum, vivid tactile mental imagery triggered using audio-visual stimuli could have a positive effect on consumer decision making by making it easier to imagine a situation where consumers could touch and use the product.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.