• Information Systems Review
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• v.5 no.2
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• pp.53-69
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• 2003

This paper tried to find how the characteristics of purchasing items would have influence on the relational e-procurement. The characteristics of purchasing items include the importance and the complexity of purchasing items. In order to define these characteristics, this paper reviewed various literatures about purchasing strategies. The empirical analysis through the multi-nominal logistic regression shows that the importance of items has influence on relational e-procurement, while the complexity of purchasing process has little influence. And also, the relational e-procurement through EDI as a long term buying relationship is being converted toward the exchange through purchasing sites.

• Asia pacific journal of information systems
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• v.14 no.2
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• pp.87-114
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• 2004

This paper empirically examined the relationships between the characteristics of purchasing items and the effective e-procurement strategies. We found that the importance of items has influence on the effective e-procurement, while the complexity of purchasing process has little influence. This fact exhibits the automation of transactional process has little influence on carrying out e-procurement. We also found, companies prefer the horizontal e-marketplace to the vertical e-marketplace. In addition, companies want to purchase through the e-bidding or reverse auction for lower prices.

• The Research Journal of the Costume Culture
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• v.15 no.2 s.67
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• pp.193-202
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• 2007

The purpose of this study was to find out effects of textile motifs and the application methods on wearer's image perception. A survey was conducted to total of 255 male and female university students who are residing in Daejeon and Chungnam province. The stimuli were composed of 2 level tones(dark and light), 3 level complexity(simple, medial and complex), 3 patterns(polka dot, stripe and check) and the 2 way of stimuli application methods(fabric and garment). The instrument for measuring preference of stimuli consisted of 4 items, encouraging, preference, purchasing and popularity. The instrument for measuring image of stimuli consisted 24 pair items. Factor analysis for the adjective pair images(24 inquiries) about the textile patterns which were used in this study was performed. It resulted as three factors which are attraction, salience, and potential. Attraction, salience, and potency dimensions showed the most significant interaction effects of application methods and patterns. And tone and application method effected attraction and salience, tones and patterns effected attraction, tones and complex effected salience. Application methods and patterns effected potential and patterns and complex effected salience. The preferences toward stimuli, it resulted only interaction of tones and patterns affected the preferences('total preference' and 'purchasing'). Pearson's product-moment correlation analysis carried out to find out the relation of images of clothing and preferences. As a result, salience was significant relation with attraction and potency. In correlation between image of textile pattern and preference, attraction is most significant relation with the preference. The results of the study could be used for the marketing strategies of the motif in fashion product.

• The Journal of Industrial Distribution & Business
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• v.10 no.7
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• pp.39-48
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• 2019

Purpose - The purpose of this study was to explore the impact of chatbots' innovation attributes on the innovation acceptance for consumers who have used chatbots to purchase fashion products that account for a large share of transactions in mobile shopping. Research design, data, and methodology - Data were collected from Korean consumers aged 20 to 49 who had experience using chatbots when purchasing fashion-related products via mobile circumstances. After a pilot survey of 31 customers, pre-questionnaire was revised for the final test, and the final questionnaire was distributed to 1,500 subjects. Out of these, 244 were retrieved. After excluding 48 inappropriate responses, 196 were used for statistical analysis. Frequency analysis, exploratory factor analysis, one-way ANOVA, regression analysis and independent t-test using SPSS 23.0 were employed for data analyses. Results - First, four factors of chatbots' attributes were extracted: relative advantages and compatibility, complexity, sensibility, and diversity. Second, two factors were extracted for fashion leadership: fashion opinion leadership and fashion innovativeness. Two groups based on the fashion leadership were identified: active innovation adopters and passive innovation adopters. Third, relative advantages and compatibility, diversity, sensibility of innovation attributes were found to have effects on the innovation acceptance in order. Fourth, significant differences were found in sensibility of innovation attributes and innovation acceptance in groups by marital status and age. The married in their 30s and 40s perceived sensibility as a more important attribute of chatbots than the unmarried in their twenties. Among the groups of different income levels, meaningful differences were found in diversity of innovation attributes and innovation acceptance. Fifth, there were significant differences found in relative advantages and compatibility, sensibility of innovation attributes, and acceptance of Innovation among the groups by fashion leadership. Active innovation adopters were found to be more aware of the importance of relative advantages and compatibility, and sensibility of innovation attributes, and innovation acceptance. Conclusions - The present study provides chatbots' marketing strategies for fashion items need to be modified by demographic characteristics and fashion leadership. Particularly, fashion leadership was found to be an important factor in determining the perception of innovation attribute as well as innovation acceptance.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.