• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.4
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• pp.638-656
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• 2022

This study aimed to identify characteristics of Generation MZ's consumer purchase decision journey to develop the new fashion CDJ model. The initial stage was affected by habit, online community, social media, aesthetics, circumstantial need, and proxy. In the search and consideration stage, mobile channels were used actively. In the active search and evaluation stage, online media, experiential data, and personal information were employed. In the purchase stage, zoomers took plenty of time in search and evaluation before spending, contrary to millennials who made their purchases more quickly. In the post-purchase experience stage, zoomers actively displayed follow-up behaviors depending on their satisfaction, such as retaining or deleting the app. While, millennials did not turn away from the store or brand, but followed up on their purchases even when they had an unsatisfactory experience. Based on the characteristics of CDJ, iterative cycle CDJ models were developed. Zoomers CDJ model was presented as a search loop that consists of the search and evaluation process, in which information accumulates, and a purchase loop in which the actual purchase occurs. The iterative cycle CDJ model was presented connected to the loyalty loop as the main section, which is accelerated in millennials' CDJ model.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.217-227
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• 2023

This study aims to stratify consumer market evaluation items from the Consumer Decision Journey(CDJ) perspective and understand the relationship between laws/systems and consumer orientation through the Korea Consumer Agency's '19 Korea Consumer Markets Evaluation Indicators. This study divided consumer market evaluation items into the selection comparison stage, selection decision stage, and post-purchase experience stage. And present a model that stratified the relationship with consumer orientation of laws/systems and verified using the CDJ model's experience as a control variable. Studies have shown that the relationship between the consumer market evaluation index that evaluates consumer orientation can be stratified according to the consumer decision-making stage and positively affects the relationship with consumer orientation of laws/systems. In addition, the impact of consumer market evaluation variables (reliability, and price) on the consumer orientation of laws/systems was different depending on the presence or absence of consumer damage experience.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.5
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• pp.805-826
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• 2022

This study explored the process by which consumers purchase products from small fashion business owners via online and mobile channels. In addition, group types were classified given that the purchasing process depends on the consumers' life cycle. The consumer focus group interview (FGI) was conducted on 18 participants that were divided into six groups by age, work, and children. Results revealed that first, consumer journey comprised four stages. Factors influencing need recognition were "attention to information of social media influencer," "attention to information of affiliated groups," and "repeated advertising of SME products/brands." For information searching, "exploring purchase reviews," "environment for mobile shopping information exploration," and "continuous product tracking" were important factors. Purchasing and shopping stages were affected by "price-free, improvised purchase decision" and "convenient mobile payment system and point benefits." After the purchase, "active sharing and repeated purchase when satisfied" and "blocking relationships when dissatisfied" occurred. Second, six consumer groups based on the fashion life cycle are the "Platform lover," "Influencer follower," "Trust builder," "Novelty seeker," "Convenience seeker," and "New designer supporter." Ultimately, small business owners can develop the process of planning and selling fashion products more efficiently.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.