• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.376-383
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• 2019

In recent decades, China's economy has grown rapidly and two-dimensional bar code-based payments services have been activated. This is a model that will be introduced by developing countries that lack mobile payment infrastructure, and countries that are looking to leap into cashless societies are also drawing attention. Along with this, the existing CRM market is expanding around the mobile device called m-CRM, and research is active on the importance of collective use for online advertising and two-dimensional bar code-based payments services. Therefore, through this study, we have explored the concepts and status of online advertising and customer relationship management which using the two-dimensional bar code-based system, and designed and implemented the method and mobile application system to effectively disseminate transactional advertising information at the point of sale or at the point of share. We also presented an application approach to look at the direction of two-dimensional bar code-based ads contents sharing system and its development.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.179-187
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• 2009

Most real world systems contain a series of dynamic and complex phenomena. One of common methods to understand these systems is to build a model and analyze the behavior of them. A two-step methodology comprised of clustering and then model creation is proposed for the analysis on time series data. An interface is designed for CRM(Customer Relationship Management) that provides user with 1:1 customized information using system modeling. It was confirmed from experiments that better clustering would be derived from model based approach than similarity based one. Clustering is followed by model creation over the clustered groups, by which future direction of time series data movement could be predicted. The effectiveness of the method was validated by checking how similarly predicted values from the models move together with real data such as stock prices.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.211-219
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• 2022

The purpose of this study is to improve the operation of shopping mall by examining the effect of service characteristics of Greenanum on customer satisfaction, repurchase and recommendation intention. The hypothesis was verified with SPSS22.0 and PROCESS macro 3.5. As a result, some hypotheses were supported between satisfaction, repurchase, and recommendation for service characteristics. Second, positive effects were found between satisfaction and repurchase, and recommendation intention. Third, a mediating effect appeared. Implications include improvement of low site awareness, benchmarking, and product quality improvement. In the future, it will be necessary to study the differences in the various characteristics of the products sold rather than the differentiation of the shopping mall itself.

• Asia Marketing Journal
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• v.8 no.3
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• pp.101-121
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• 2006

This case study explores various marketing strategies of Grundfos Pumps Korea Inc. that has become a front-runner in the industrial pump market of Korea as the enterprise that got into the business later. Grundfos Group, a multinational company with its head office in Denmark, has acknowledged diversity of each country and has granted its subsidiary companies with discretionary power so that they can establish marketing strategies suitable for the market they were competing in. Grundfos Pumps Korea has been very successful in its localization and differentiation of marketing strategies. First, Grundfos Pumps Korea achieved the trust of their customers by its high quality and consistent pricing strategies and has expanded its market very successfully with continuous product innovation and new product ideas. Second, it increased its brand awareness and recognition by brand differentiation along with the product differentiation strategy, which is exceptional in industrial market. It has conducted creative marketing activities and strategies to build brand equity and successful customer relationship management.

• Asia Marketing Journal
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• v.13 no.3
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• pp.79-102
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• 2011

Researchers in channel dyads have devoted much attention to relationship between interdependence (i.e. interdependence enymmetry and total interdependence) and conflict that promote channel performance. In social science, in spite of the inconsistent results in marketing practice, there are two contradictory theories explain the relationship between interdependence and conflict - bilateral deterrence theory and conflict spiral theory. The authors apply these theories to co-marketing alliance situation in terms that this relationship is also incorporated both company's dependence, either from one company's perspective or each partner about its respective dependence. Using survey data and archival data from 181 companies enlisted in a telecommunication membership program, the authors find out the relationship between interdependence and conflict as well as investigate the antecedents of interdependence - transaction age, transaction frequency, the numbers of alliance partner, and co-marketing alliance specific assets according to previous researches. Using PLS analysis, the authors demonstrate that, with increasing total interdependence in a telecommunication membership program, two co-marketing partners' conflict level is increased in accord with the author's conflict spiral theory predictions. As expected, higher interdependence asymmetry has negative value to level of conflict even though this result is not statistically significant. Other findings can be summarized as follows. In the perspective of telecommunication company, transaction age, transaction frequency, and co-marketing alliance specific assets have influence on its dependence on a partner as independent variables. To the contrary, in a partner's perspective, transaction frequency, co-marketing alliance specific assets and the numbers of alliance partner have significantly impact on its dependence on a telecommunication company. In direct effect analysis, it is shown that transaction age, frequency and co-marketing alliance specific assets have direct influence on conflict. This results suggest that it is more useful for a telecommunication company to select a co-marketing partner which is frequently used by customers and earned high rates of mileage. In addition, the results show that dependence of a telecommunication company on a co-marketing partner is more significantly effected to co-marketing alliance conflict than partner's one. It provide an effective conflict management strategy to a telecommunication company for controling customer's usage rate or having the co-marketing partner deposit high level of alliance specific investment (i.e. mileage). To a co-marketing partner of telecommunication company, it is required control the percentage of co-marketing sales in total sales revenue or seek various co-marketing partners in order for co-marketing conflict management. The research implications, limitation and future research of these results are discussed.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.4-14
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• 2010

Collaboration strategies entail information sharing and other varied forms of cooperation that are mutually beneficial to the company and stakeholder groups. This study addresses the specific types of collaboration used in the fashion industry while also examining strategies that have been most successful for fashion companies and perceived benefits of collaboration from the customer perspective. In the present study we define fashion companies and brands as collaborators and their partners or stakeholders as collaboratees. We define collaboration as a cooperative relationship where more than two companies, brands or individuals provide customers with beneficial outcomes utilizing their own competitive advantages on an equal basis. Collaboration strategies entail information sharing and other varied forms of cooperation that are mutually beneficial to the company and stakeholder groups. Through collaboration, fashion companies have pursued both tangible differentiation, such as design and technology applications, and intangible differentiation such as emotional and psychological benefits to customers. As a result, collaboration within the fashion industry has become an important, value creating concept. This qualitative study utilized case studies and in-depth interview methodologies to examine customers' attitudes concerning collaboration in the fashion industry. A total of 173 collaboration cases were identified in Korean and international markets from 1998 through December 2008, focusing on fashion companies. Cases were collected from documented data including websites and industry data bases and top ranked portal search sites such as: Rankey.com; Naver, Daum, and Nate; and representative fashion information websites, Samsungdesignnet and Firstviewkorea. Cases were collected between November 2008 and February 2009. Cases were selected for the analysis where one or more partners were associated with the production of fashion products (excluding textile production), retail fashion products, or designer services. Additional collaboration case information was obtained from news articles, periodicals, internet portal sites and fashion information sites as conducted in prior studies (Jeong and Kim 2008; Park and Park 2004; Yoon 2005). In total, 173 cases were selected for analysis that clearly exhibited the benefits and outcomes of collaboration efforts and strategies between fashion companies and stakeholders. Findings show that the overall results show that for both partners (collaborator and collaboratee) participating in collaboration, that the major benefits are reduction of costs and risks by sharing resource such as design power, image, costs, technology and targets, and creation of synergy. Regarding types of collaboration outcomes, product/design was most important (55%), followed by promotion (21%), price (20%), and place (4%). This result shows that collaboration plays an important role in giving life to products and designs, particularly in the fashion industry which seeks for creative and newness. To be successful in collaboration efforts, results of the depth interviews in this study confirm that fashion companies should have a clear objective on why they are doing the collaboration. After setting the objective, they should select collaboratees that match their brand image and target market, make quality co-products that have definite concepts and differentiating factors, and also pay attention to increasing brand awareness. Based on depth interviews with customers, customer benefits were categorized into six factors: pursuit for individual character; pursuit for brand; pursuit for scarcity; pursuit for fashion; pursuit for economic efficiency; and pursuit for sociality. Customers also placed more importance on image, reputation, and trust of brands regarding the cases shown in the interviews. They also commented that strong branding should come first before other marketing strategies. However, success factors recognized by experts and customers in this study showed different results by subcategories. Thus, target customers and target market should be studied from various dimensions to develop appropriate strategies for successful collaboration.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.23-38
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• 2012

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems. Our experiments use a real dataset acquired from one of the largest internet shopping malls in Korea. We use 66,278 transactions of 3,847 customers conducted during the last two years. Overall results show that the accuracy of association rules of frequent shoppers (whose average duration between orders is relatively short) is higher than that of causal shoppers. In addition we discover that with frequent shoppers, the accuracy of association rules appears very high when the co-occurrence criteria of the training set corresponds to the validation set (i.e., target set). It implies that the co-occurrence criteria of frequent shoppers should be set according to the application purpose period. For example, an analyzer should use a day as a co-occurrence criterion if he/she wants to offer a coupon valid only for a day to potential customers who will use the coupon. On the contrary, an analyzer should use a month as a co-occurrence criterion if he/she wants to publish a coupon book that can be used for a month. In the case of causal shoppers, the accuracy of association rules appears to not be affected by the period of the application purposes. The accuracy of the causal shoppers' association rules becomes higher when the longer co-occurrence criterion has been adopted. It implies that an analyzer has to set the co-occurrence criterion for as long as possible, regardless of the application purpose period.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• Management & Information Systems Review
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• v.29 no.4
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• pp.123-142
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• 2010

This study suggest that firms can use online brand communities as an IMC tool to achieve high brand loyalty through marketer-controlled or loyal customer-controlled brand contacts. In this perspective, the online brand community as a marketing communication tool can help the firm in eliciting favorable responses from customers. This study finds that an online brand community, as a critical marketing promotion tool, helps a firm elicit favorable relationship with customers and build strong brand loyalty. In particular, this study suggests several important theoretical and managerial implications. First, this study confirm that "advertising usefulness" is the most powerful and important factor that affects cgerial 's positive emotionomehile "sales promotion usefulness" impacmehin "interactivity" but dies not impacmhin "cgerial iexperience"ltyevent usefulness" impacmehin "cgerial iexperience"but dies not impacmhin "interactivity." In addition, "cgerial iexperience" signifn "itly impacmehin "cgerial -to-cgerial iinteractivity." This indicates that online environment provides participapacmwith a fun and exciting environment. In that sense, enhancing the online brand community experiencemwould be a critical factor for building strong brand. Thi", mword of mouth can play a riclly important role in making many cgerial s to trust brand and to enhance online brand community loyalty. Web users are becoming web authoore owning and creating content limited only by their imaginations.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.165-178
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• 2017

O2O platforms are developing in a business area of daily life with evolution of IT technology. The aim of this research is to identify any quality factor of online and offline which affects the satisfaction of O2O service by business characteristic and to varify the relationship between service satisfaction and reuse intention. In this study, information quality, system quality and service quality are defined as factors for online quality and perceived quality and perceived price defined as offline quality. This study was conducted on 199 users who use O2O service. The results showed that online quality system and perceived price of offline quality had a positive effect on user satisfaction and that user satisfaction had a positive effect on Intention to Reuse. These results will provide guidelines for O2O service providers to improve their online services and to select an offline vendor for the O2O platform. It would provide customized O2O service according to personal property of a customer, in case that further research by types of business or service, which are not covered this paper.