• Fashion & Textile Research Journal
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• v.22 no.3
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• pp.323-334
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• 2020

This study analyzed the effects of consumers' characteristic variables on information search and purchase decisions in a decision-making process that validated the path model in purchasing apparel products. In constructing a structural equation model using AMOS 19.0., the variables including enjoyment pursuit, price pursuit, product involvement and product risk were selected as consumers' characteristic variables affecting the stage of information search. A questionnaire was distributed to consumers over 20 years old who purchased apparel products using offline and online channels within one year; consequently, we were able to analyze 468 effective data. The results were as follows. First, the path model of this research proved to be the appropriate model explaining the effects of consumers' characteristic variables on the stage of purchase decision-making. Second, enjoyment pursuit had a significant positive influence on offline information search; in addition, price pursuit and product risk affected the online information search significantly. Product involvement affected online information search as well as offline information search. Third, the offline information search affected offline purchase and online information search affected online purchase. However, consumer's channel switching behavior between the stage of information search and the stage of purchase decision was not proven. The findings suggest that companies need to develop distribution strategies according to consumers' characteristic factors that effect consumer's purchase decision-making.

• International Journal of Contents
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• v.8 no.1
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• pp.100-107
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• 2012

While considerable research suggests that users' uncertainty gradually decreases, as they proceed through the information seeking process, others argue that it can arise at any stage of their information seeking process. Reflecting the latter view, this study examined user perceptions of uncertainty in the final stage of users' information seeking process, the stage of search results evaluation. Considering the significance of Web search engines for academic study, this study investigated the relevance decision stage of scholarly researchers in the field of science, who use Web search engines for their academic study. Based on the analysis of the users' uncertainty, this study provided implications to improve information systems and Web contents design.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1392-1400
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• 1999

In this paper, we propose a two-stage fast full search algorithm for block motion estimation that produces the same performance to that of full search algorithm (FSA) but with remarkable computation reduction. The proposed algorithm uses the search region subsampling and the difference of adjacent pixels in the current block. In the first stage, we subsample the search region by a factor of 9, and then calculate mean absolute error (MAE) at the subsampled search points. And in the second stage, we reduce the search points that need block matching process by using the lower bound of MAE value at each search Point. We Set the lower bound of MAE value for each search point from the MAE values which are calculated at the first stage and the difference of adjacent pixels in the current block. The experimental results show that we can reduce the computational complexity considerably without any degradation of picture quality.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.1B
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• pp.24-33
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• 2002

The cell search scheme for W-CDMA consists of the following three stages: slot synchronization(1st stage), group identification and frame boundary detection(2nd stage), and long code identification(3rd stage). The performance of the cell search when a mobile station is switched on, which is referred to as initial cell search, is decreased by the initial frequency and timing error. In this paper, we propose the pipeline structured initial cell search scheme using time trackers to compensate for the impact of the initial timing error in the stage 2 and stage 3. The simulation results show that the performance of the proposed scheme is maximal 1.5dB better than that of the conventional one when the initial timing error is near ${\pm}T_c$/2.

• Journal of Distribution Research
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• v.12 no.3
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• pp.69-90
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• 2007

This study is set out to investigate the factors that influence customers' behavior of choice and switching between online and offline channels, separating the purchase decision into two stages, i.e., information search and purchase. Factors influencing channel choice are found to differ from stage to stage. The main results of this study are as follows. At the information search stage, customers' channel knowledge had impacts on the choice of the channel. Customers are more likely to visit offline bookstores when they have hedonic shopping orientation and higher involvement level with books. On the contrary, customers are more apt to search online when they have a lot of online shopping experiences. At the purchase stage, the results varied according to the search channel. When customers search for information online, the following variables lead to online purchases: online shopping experiences with books, price-focused shopping orientation, and time availability for shopping. Perceived risk made customers purchase offline even though they searched online. In case of offline searching, customers with more convenience-focused, hedonic-focused shopping orientation and less tim availability purchased offline.

• The Journal of Information Systems
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• v.26 no.3
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• pp.171-185
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• 2017

Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.

• KIEE International Transactions on Power Engineering
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• v.5A no.2
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• pp.177-185
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• 2005

This paper proposes an expert system that can enhance the accuracy of real-time bus reconfiguration strategy by adopting the local minimum tree search method and that can minimize the spreading effect of the fault by considering the operating condition when a main transformer fault occurs in an automated substation. The local minimum tree search method is used to expand the best-first search method. This method has the advantage that it can improve the solution performance within the limits of the real-time condition. The inference strategy proposed expert system consists of two stages. The first stage determines the switching candidate set by searching possible switching candidates starting from the main transformer or busbar related to the event. The second stage determines the rational real-time bus reconfiguration strategy based on heuristic rules from the obtained switching candidate set. Also, this paper proposes generalized distribution substation modeling using graph theory, and a substation database based on the study results is designed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.128-140
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• 2000

In this paper, we propose a fast block matching algorithm using the mean absolute error (MAE) of neighbor search point and search region reduction. The proposed algorithm is composed of two stages. At the first stage,the search region is divided into nonoverlapped 3$\times$3 areas and MAE of the center point of each area iscalculated. The minimum MAE value of all the calculated MAE's is determined as reference MAE. At thesecond stage, because the possibility that final motion vector exist near the position of reference MAE is veryhigh, we use smaller search region than first stage, And, using the MAE of center point of each area, the lowerbound of rest search point of each area is calculated and block matching process is performed only at the searchpoints that the lower bound is smaller than reference MAE. By doing so, we can significantly reduce thecomputational complexity while keep the increasement of motion estimation error small.

• Fashion & Textile Research Journal
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• v.11 no.4
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• pp.537-548
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• 2009

Consumers' price related response in the clothing purchase decision-making process includes their expectation of price, price perception, attitude toward price and consequent behaviors. The purposes of this research are to systematically organize consumers' price related responses in the clothing purchase decision-making process, and to explain the effect of price on their purchasing. The qualitative research including shopping observation and in-depth interview was conducted. The result identified stages that showed different price related responses in clothing purchase decision-making process, and clarified each stage's characteristics. In the internal search stage, consumers recalled price information from memory and had a specific expectation about the price. This set a direction for the external search. In the external search stage, consumers selected brands or stores by a non-compensatory evaluating with an expectation of the price, and narrowed these down to several determinant alternatives by actively evaluating the products. In case a sufficient amount of price information was not recalled, the consumer established reference price through the external search. Finally, in the purchasing stage, consumers evaluated the determinant alternatives based on their compensatory evaluation. When perception of price was negative, consumers evaluate price combined with the higher criteria of clothing benefits, such as symbolic value and usability. The research is expected to contribute to predicting consumers' responses to price, and to establishing an effective pricing strategy.