• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1856-1862
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• 2010

Last rapidly changing mobile handset market in many industries, as well as market conditions affect the prevalence of various content types of consumer spending in general is affecting. Changes in mobile handset market by separating the various consumer-tier analysis, or differentiated in consumers' purchasing behavior are being reflected in the property analysis and overview of customer satisfaction can be assessed by measuring. These mobile devices and the purchase of the devices to investigate the tendency to analyze and weigh the various content industries, you can see how important one can be considered. The development of mobile communication systems and wireless internet and multimedia applications can lead the industry accelerated. In this study, tried to investigate that the mobile communication systems in consideration of the special form of mobile handsets, and customer satisfaction of mobile phone handsets, and the relationship between customer loyalty. Outcome of this study, the mobile properties and the mobile handset brand, price, design and related details to be statistically significant variables were analyzed. In this study, the characteristics of future mobile handset market with a wide range of content-based guidelines for the development of the industry is expected to be able to provide.

• Journal of Distribution Science
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• v.9 no.3
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• pp.47-54
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• 2011

This paper presents exploratory research on consumer awareness and attitudesabout organic food, for which consumer demand continues to increase the paper also assesses consumers' organic food distribution channel preferences. By conducting a literature review, a case study has been carried out in order to glean customer behavior, market condition and typesof distribution channels, and development of specialty stores for organic foods. The early research indicates that consumer awareness and customer attitudes toward organic food are mostly positive however, organic food's high price, as well as a lack of organic food stores, cause a negative effect on consumers' purchase intention. Secondly, the U.S. organic food retail channel consists of such mainstream supermarket/grocery stores and leading natural and organic food supermarket chains as Whole Foods, Trader Joe's, and Sunflower Farmers Market. For the current retail distribution of organic food in Korea, off-line stores are composed of direct management stores and franchise chains. Most of the organic food retail distribution operates through the Internet shopping mall, and are commonly located at retail distribution centers as multi-channel, shop-in-shop stores. Moreover, unlike in the U.S., association and consumers' cooperatives (Co-Ops), and such other member-direct retail stores as Hansallim, iCOOP, Nature Dream,and online shopping malls, are all active in Korea. Thirdly, as a result of an analysis of the present state of the organic food retail channel, as well as building a case for organic food specialty stores, the distinctive featuresand rapid growth of such unique organic food stores as Whole Foods Market, or Trader Joe's successful downsizing strategies, as well as Sunflower Farmers Market low-price approach, show steady industry growth. Moreover, as a result of a case studyof such domestic representative organic food specialty stores as "Olga" and "Chorokmaeul," a similar management style to the United States' "Whole Foods Market" and "Trader Joe's," respectively, can be seen. Similar to the U.S. market, Korean organic food markets should also implement active retail distribution opportunities, allowing consumers to select from various diverse and differentiated choices. In order to accomplish this goal, it is necessary to prepare such measures as sustaining reasonable prices, securing various suppliers for unique products,and improving consumer trust through advertisement strategies that are suitable for each company's branding processes.

• Archives of design research
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• v.19 no.5 s.67
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• pp.203-212
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• 2006

With interests and participation in the costumeplay that mimics characters appeared on carton or animation in recent days, the costumeplay becomes one of cultural phenomena. Using a qualitative research method, this study identified costumeplayers' costume consumption pattern and explored its meanings from the perspective of consumption culture. Indeed, this study intended to help for understanding costumeplayer group as a consumer, and to provide basic knowledge about new market analysis related to fashion design and marketing. The results from the analyzing participant observation and in-depth interviews data are as follows: first, costumeplayers usually begin costumeplay by friends' invitations or by themselves and then continue on participating. Through the costumeplay, participants have benefits such as fun, departure from the daily life, and social interaction. Second, participants acquire costumes through purchase, rent, producing or combination of daily wear, but both purchase and rent account high. Third, the meanings of consumption culture in costumeplay include consumption behavior repeating possession and disposal. Also, costumeplayers concerns efficiency when purchasing or renting the costumes, and internet is a place where information search, comparison, and actual purchasing are occurred. Based on the results, fashion design and marketing implication, limitation of this study and further research ideas were suggested.

• Journal of Global Scholars of Marketing Science
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• v.19 no.4
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• pp.1-10
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• 2009

The purpose of this study was to categorize the gap between coffee shop 'importance' (as perceived by customers before patronizing the coffee shop) and 'satisfaction' (perception of customers after patronizing the coffee shop) as positive or negative and to analyze the effect of these gaps on purchasing behavior. To do this, I used the gap between importance and satisfaction regarding the choice of a coffee shop as the explanatory variable and performed an empirical analysis of the direction and size of the effect of the gap on purchasing behavior (overall satisfaction, willingness-to-revisit) by applying the Ordered Probit Model (OPM). A previous study that used IPA to evaluate the effects of gaps estimated the direction and size of a quadrant but failed to analyze the effect of gaps on customers. In this study, I evaluated the effects of positive and negative gaps on customer satisfaction and willingness-to-revisit. Using OPM, I quantified the effect of positive and negative gaps on overall customer satisfaction and willingness-to-revisit. Per-head expenditure, frequency of visits, and coffee-purchasing place had the most positive effects on overall customer satisfaction. Frequency of visits, followed by per-head expenditure and then coffee-purchasing place, had the most positive impact on willingness-to-visit. Thus per-head expenditure and frequency of visits had the greatest positive effects on overall satisfaction and willingness-to-revisit. This finding implies that the higher the actual satisfaction (gap) of customers who spend KRW5,000 or more once or more per week at coffee shops is, the higher their overall satisfaction and willingness-to-revisit are. Despite the fact that economical efficiency had a significant effect on overall satisfaction and willingness-to-revisit, college and university students still use coffee shops and are willing to spend KRW5,000 because they do not only purchase coffee as a product itself, but use the coffee shop for other activities, such as working, meeting friends, or relaxing. College and university students also access the Internet in coffee shops via personal laptops, watch movies, and study; thus, coffee shops should provide their customers with the appropriate facilities and services. The fact that a positive gap for coffee shop brand had a positive effect on willingness-to-revisit implies that the higher the level of customer satisfaction, the greater the willingness-to-revisit. A negative gap for this factor, on the other hand, implies that the lower the level of customer satisfaction, the lower the willingness-to-revisit. Thus, the brand factor has a comparatively greater effect on satisfaction than the other factors evaluated in this study. Given that the domestic coffee culture is becoming more upscale and college/university students are sensitive to this trend, students are attentive to brands. In most upscale coffee shops in Korea, the outer wall is built out of glass that can be opened, the interiors are exotic with an open kitchen. These upscale coffee shops function as landmarks and match the taste of college/university students. Coffee shops in Korea have become a cultural brand. To make customers feel that coffee shops are upscale, good quality establishments and measures to provide better services in terms of brand factor should be instituted. The intensified competition among coffee shop brands in Korea as a result of the booming industry indicates that provision of additional services is needed to differentiate competitors. These customers can also use a scanner free of charge. Another strategy that can be used to boost brands could be to provide and operate a seminar room for seminars and group study. If coffee shops adopt these types of strategies, college/university students would be more likely to consider the expenses they incur worthwhile and, subsequently, they would be more likely to be satisfied with the brands of these coffee shops, with an associated increase in their willingness-to-revisit. Gender and study year had the most negative effects on overall satisfaction and willingness-to-revisit. Female students were more likely to be satisfied and be willing to return than male students, and third and fourth-year students were more likely to be satisfied and willing-to-return than first or second-year students. Students who drink coffee, read books, and use laptops alone at coffee shops are easily noticeable. High-grade students tend to visit coffee shops alone in order to use their time efficiently for self-development and to find jobs. The economical efficiency factor had the greatest effect on overall satisfaction and willingness-to-revisit in terms of a positive gap. The higher the actual satisfaction (gap) of students with the price of the coffee, the greater their overall satisfaction and willingness-to-revisit. Economical efficiency with a negative gap had a negative effect on willingness-to-revisit, which implies that a less negative gap will result in a greater willingness-to-revisit. Amid worsening market conditions, coffee shops located around colleges/universities are using strategies, such as a point or membership card, strategic alliances with credit-card companies, development of a set menu or seasonal menu, and free coffee-shot services to increase their competitive edge. Product power also had a negative effect in terms of a negative gap, which indicates that a higher negative gap will result in a lower willingness-to-revisit. Because there are many more customers that enjoy coffee in this decade, as compared to previous decades, the new generation of customers, namely college/university students, want various menu items in addition to coffee, and coffee shops should, therefore, add side menu items, such as waffles, rice cakes, cakes, sandwiches, and salads. For example, Starbucks Korea is making efforts to enhance product power by selling rice cakes flavored in strawberry, wormwood, and pumpkin, and providing coffee or cream free of charge. In summary, coffee shops should focus on increasing their economical efficiency, brand, and product power to enhance the satisfaction of college/university students. Because shops adjacent to colleges or universities enjoy a locational advantage, providing differentiated services in terms of economical efficiency, brand, and product power, is likely to increase customer satisfaction and return visits. Coffee shop brands should, therefore, be innovative and embrace change to meet their customers' desires. Because this study only targeted college/university students in Seoul, comparative studies targeting diverse regions and age groups are required to generalize the findings and recommendations of this study.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Journal of Distribution Research
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• v.15 no.3
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• pp.71-110
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• 2010

1. Introduction Today Internet is recognized as an important way for the transaction of products and services. According to the data surveyed by the National Statistical Office, the on-line transaction in 2007 for a year, 15.7656 trillion, shows a 17.1%(2.3060 trillion won) increase over last year, of these, the amount of B2C has been increased 12.0%(10.2258 trillion won). Like this, because the entry barrier of on-line market of Korea is low, many retailers could easily enter into the market. So the bigger its scale is, but on the other hand, the tougher its competition is. Particularly due to the Internet and innovation of IT, the existing market has been changed into the perfect competitive market(Srinivasan, Rolph & Kishore, 2002). In the early years of on-line business, they think that the main reason for success is a moderate price, they are awakened to its importance of on-line service quality with tough competition. If it's not sure whether customers can be provided with what they want, they can use the Web sites, perhaps they can trust their products that had been already bought or not, they have a doubt its viability(Parasuraman, Zeithaml & Malhotra, 2005). Customers can directly reserve and issue their air tickets irrespective of place and time at the Web sites of travel agencies or airlines, but its empirical studies about these Web sites for reserving and issuing air tickets are insufficient. Therefore this study goes on for following specific objects. First object is to measure service quality and service recovery of Web sites for reserving and issuing air tickets. Second is to look into whether above on-line service quality and on-line service recovery have an impact on overall service quality. Third is to seek for the relation with overall service quality and customer satisfaction, then this customer satisfaction and loyalty intention. 2. Theoretical Background 2.1 On-line Service Quality Barnes & Vidgen(2000; 2001a; 2001b; 2002) had invented the tool to measure Web sites' quality four times(called WebQual). The WebQual 1.0, Step one invented a measuring item for information quality based on QFD, and this had been verified by students of UK business school. The Web Qual 2.0, Step two invented for interaction quality, and had been judged by customers of on-line bookshop. The WebQual 3.0, Step three invented by consolidating the WebQual 1.0 for information quality and the WebQual2.0 for interactionquality. It includes 3-quality-dimension, information quality, interaction quality, site design, and had been assessed and confirmed by auction sites(e-bay, Amazon, QXL). Furtheron, through the former empirical studies, the authors changed sites quality into usability by judging that usability is a concept how customers interact with or perceive Web sites and It is used widely for accessing Web sites. By this process, WebQual 4.0 was invented, and is consist of 3-quality-dimension; information quality, interaction quality, usability, 22 items. However, because WebQual 4.0 is focusing on technical part, it's usable at the Website's design part, on the other hand, it's not usable at the Web site's pleasant experience part. Parasuraman, Zeithaml & Malhorta(2002; 2005) had invented the measure for measuring on-line service quality in 2002 and 2005. The study in 2002 divided on-line service quality into 5 dimensions. But these were not well-organized, so there needed to be studied again totally. So Parasuraman, Zeithaml & Malhorta(2005) re-worked out the study about on-line service quality measure base on 2002's study and invented E-S-QUAL. After they invented preliminary measure for on-line service quality, they made up a question for customers who had purchased at amazon.com and walmart.com and reassessed this measure. And they perfected an invention of E-S-QUAL consists of 4 dimensions, 22 items of efficiency, system availability, fulfillment, privacy. Efficiency measures assess to sites and usability and others, system availability measures accurate technical function of sites and others, fulfillment measures promptness of delivering products and sufficient goods and others and privacy measures the degree of protection of data about their customers and so on. 2.2 Service Recovery Service industries tend to minimize the losses by coping with service failure promptly. This responses of service providers to service failure mean service recovery(Kelly & Davis, 1994). Bitner(1990) went on his study from customers' view about service providers' behavior for customers to recognize their satisfaction/dissatisfaction at service point. According to them, to manage service failure successfully, exact recognition of service problem, an apology, sufficient description about service failure and some tangible compensation are important. Parasuraman, Zeithaml & Malhorta(2005) approached the service recovery from how to measure, rather than how to manage, and moved to on-line market not to off-line, then invented E-RecS-QUAL which is a measuring tool about on-line service recovery. 2.3 Customer Satisfaction The definition of customer satisfaction can be divided into two points of view. First, they approached customer satisfaction from outcome of comsumer. Howard & Sheth(1969) defined satisfaction as 'a cognitive condition feeling being rewarded properly or improperly for their sacrifice.' and Westbrook & Reilly(1983) also defined customer satisfaction/dissatisfaction as 'a psychological reaction to the behavior pattern of shopping and purchasing, the display condition of retail store, outcome of purchased goods and service as well as whole market.' Second, they approached customer satisfaction from process. Engel & Blackwell(1982) defined satisfaction as 'an assessment of a consistency in chosen alternative proposal and their belief they had with them.' Tse & Wilton(1988) defined customer satisfaction as 'a customers' reaction to discordance between advance expectation and ex post facto outcome.' That is, this point of view that customer satisfaction is process is the important factor that comparing and assessing process what they expect and outcome of consumer. Unlike outcome-oriented approach, process-oriented approach has many advantages. As process-oriented approach deals with customers' whole expenditure experience, it checks up main process by measuring one by one each factor which is essential role at each step. And this approach enables us to check perceptual/psychological process formed customer satisfaction. Because of these advantages, now many studies are adopting this process-oriented approach(Yi, 1995). 2.4 Loyalty Intention Loyalty has been studied by dividing into behavioral approaches, attitudinal approaches and complex approaches(Dekimpe et al., 1997). In the early years of study, they defined loyalty focusing on behavioral concept, behavioral approaches regard customer loyalty as "a tendency to purchase periodically within a certain period of time at specific retail store." But the loyalty of behavioral approaches focuses on only outcome of customer behavior, so there are someone to point the limits that customers' decision-making situation or process were neglected(Enis & Paul, 1970; Raj, 1982; Lee, 2002). So the attitudinal approaches were suggested. The attitudinal approaches consider loyalty contains all the cognitive, emotional, voluntary factors(Oliver, 1997), define the customer loyalty as "friendly behaviors for specific retail stores." However these attitudinal approaches can explain that how the customer loyalty form and change, but cannot say positively whether it is moved to real purchasing in the future or not. This is a kind of shortcoming(Oh, 1995). 3. Research Design 3.1 Research Model Based on the objects of this study, the research model derived is