• Journal of the Korean Society of Clothing and Textiles
• /
• v.29 no.1 s.139
• /
• pp.136-148
• /
• 2005

The purpose of this study identifies how attributes of the website influences on consumer attitude toward the website. For this purpose, the study tested covariance structural model which set relationships among independent variables(interactivity, search and visual information of website), mediated variables(utilitarian shopping value and hedonic shopping value) and dependent variables(attitude toward website). The data were collected from a sample of 271 internet shopper of university students(male: 82, female: 189). They visited the website for apparel shopping and, after searching a casual clothing which they wanted to buy, requested to answer the questionnaire. The covariance structural model and research hypothesis analyzed by using AMOS 4.0 program. The results are as follows: First, the structural model is accepted significantly. Second, interactivity of the website has a positive impact on perceived utilitarian and hedonic shopping values of the website and visual information of the website also influence hedonic shopping value of the website positively. Third, utilitarian and hedonic shopping values have a positive influence on attitude toward the website for apparel shopping.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.28 no.11
• /
• pp.1482-1494
• /
• 2004

This study identified convergent validity and discriminant validity of measurement variables by factor analysis using Spss program and tested covariance measurement model including latent variables such as the website attributes (interactivity, search and visual information of website), shopping values(utilitarian and hedonic value) and attitude toward website by AMOS program. The data were collected from a sample of 271 internet shopper of university students(male: 82, female: 189). They visited the website for apparel shopping and, after searching a casual clothing which they wanted to buy, requested to answer the questionnaire. The results were as follows: Variables that reduce validity were deleted in the several steps of factor analysis and initial measurement model testing. Final measurement model was constructed by valid variables was accepted. This measurement model will be input for testing causal research model that can explain how attributes of the website influences on consumer attitude toward the website.

• Journal of Korea Society of Industrial Information Systems
• /
• v.19 no.4
• /
• pp.133-146
• /
• 2014

This paper mainly focuses on the design factors of a website targeted at shopping mall websites in China and Korea. We formulated 15 different hypotheses regarding how those design factors are different. The targeted websites are "Gmarket" in Korea and "Taobao" in China. The selected design factors of a shopping mall website are (1) color and design, (2) photo, graphic, and font size, (3) the clarity of information delivery, (4) website structure, (5) screen balance, (6) product display, (7) product harmony, (8) a form of font and icon, (9) visual atmosphere, (10) differentiated color, (11) interesting words, (12) technical skills, (13) entire visual design and atmosphere, (14) navigation, (15) search function for products or information. As a result of analyzing the result using the SAS 9.2 package tool, we figured out that there was a difference between design factors. Additionally, we analyzed this difference and suggested a strategy to design the effective shopping mall web sites.

• Science of Emotion and Sensibility
• /
• v.21 no.1
• /
• pp.45-58
• /
• 2018

As information and communication technologies (ICTs) become more advanced, consumers are able to experience retailing activities such as searching for products and services in online retail shops and for Internet-exclusive branded contents. Specifically, fashion retailers are facing the need to develop more novel experiential design than one another to maximize customers' experience in Internet websites and secure sustainable competency. Confirming methods of organic integration of experiential and visual features of both online and mobile channels is an important aspect of the study of extended consumers' interfaces of retail channels. Mehrabian and Russell's stimulus-organism-response (S-O-R) paradigm and Sugiyama and Andree's attention, interest, search, action, and share (AISAS) model were used for this research. Specifically, the present study considered the effect of e-commerce website features on consumers' emotional reactions (pleasure and arousal) and the consequent impact on online consumer behaviors (search, action, and share). Hence, plus the self-reported survey methods, each subject's psychophysiological indicators (i.e., pleasure and arousal) were measured to obtain more objective and reliable data and to redeem the results of the self-reported survey. Findings revealed the implications of the e-commerce website feature by comprehending the S-O-R paradigm and AISAS model and extending the understanding of the role of variables associated with comprehended frameworks based on psychophysiological data.

• Journal of the Korea Convergence Society
• /
• v.13 no.2
• /
• pp.177-183
• /
• 2022

This study investigated through eye tracking what gaze path the audience searches through portal sites (Naver, Daum, Zoom, and Nate). As a result of the layout analysis according to the gaze path of the search engine, the four main pages, which can be called to be the gateway to information search, appeared in the form of a Z-shaped layout. The news and search pages of each site use an F-shape, which means that when people's eyes move from top to right in an F-shape, they read while moving their eyes from left to right(LTR), which sequentially moves to the bottom. As a result of analyzing through the heat map, gaze plot, and cluster, which are the visual analysis indicators of eye tracking, the concentration of eyes on the photo and head copy was found the most in the heat map, and it can be said to be of high interest in the information. The flow of gaze flows downward from the top left to the right, and it can be seen that the cluster is most concentrated at the top of the portal site. The website designer should focus on improving the accessibility and readability of the information desired by the user in the layout design, and periodic interface changes are required by investigating and analyzing the tendencies and behavioral patterns of the main users.

• Journal of Intelligence and Information Systems
• /
• v.22 no.3
• /
• pp.113-127
• /
• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.