• Journal of Families and Better Life
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• v.22 no.4
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• pp.15-27
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• 2004

This study investigated the factors in evaluating internet shopping mall sites, and developed a self-report evaluation scale of such sites, This article identifies factors to determine criteria for evaluating the sites from the consumers' viewpoint. Eighty three items were extracted initially, which were then categorized as follows: \circled1 Type of business, \circled2 Navigation ease, \circled3 Technological level, \circled4 Design of sites, \circled5 Communication ease, \circled6 Information available, \circled7 Ease of access, and \circled8 Site security, Overall reliability coefficients were high in the individual domains ($\alpha$=.86 to $\alpha$=.95). Face validity and contents validity were demonstrated to be good, There were significant differences in the means of these categories. The results also show that the technological level is the most important in shopping mall sites evaluation. These results may be used not only as a tool to evaluate shopping mall sites but also as a guideline to improve the quality of a shopping mall site that is under development.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.3
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• pp.102-113
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• 2010

In this research, we proposed a quality evaluation standard model which is suitable for the internet shopping mall based on the Malcolm Baldrige National Quality Award model. A 7-Point Likert Scale was used based on the seven categories within the 2008 Malcolm Baldrige Criteria : Leadership, Strategic Planning, Customer and Market Focus, Measurement Analysis and Knowledge Management, Workforce Focus, Process Management, and Result. Furthermore, we analysed the validity and causal relationship among the factors within the model. The goal of this research is to find a rational standard to evaluate internet shopping malls nationwide and help the structuring and the operation of these malls. The results may be used not only as a tool to evaluate internet shopping mall sites but also as a guideline to improve the quality of a internet shopping mall site that is under development.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.