• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.105-113
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• 2017

Network externality can be defined as the effect that one user of a good or service has on the value of that product to other people. When a network externality is present, the value of a product or service is dependent on the number of others using it. There exist asymmetries in network externalities between the online and traditional offline marketing channels. Technological capabilities such as interactivity and real-time communications enable the creation of virtual communities. These user communities generate significant direct as well as indirect network externalities by creating added value through user ratings, reviews and feedback, which contributes to eliminate consumers' concern for buying products without the experience of 'touch and feel'. The offline channel offers much less scope for such community building, and consequently, almost no possibility for the creation of network externality. In this study, we analyze the effect of network externality on the competition between online and conventional offline marketing channels using game theory. To do this, we first set up a two-period game model to represent the competition between online and offline marketing channels under network externalities. Numerical analysis of the Nash equilibrium solutions of the game showed that the pricing strategies of online and offline channels heavily depend not only on the strength of network externality but on the relative efficiency of online channel. When the relative efficiency of online channel is high, the online channel can greatly benefit by the network externality. On the other hand, if the relative efficiency of online channel is low, the online channel may not benefit at all by the network externality.

• English Language & Literature Teaching
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• v.11 no.3
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• pp.113-137
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• 2005

The purpose of this research is to introduce a new pedagogy for developing English education in the university. It is based on the favorable results of the application of Network English, which is a method that has been used in three classes at Y University for a period of one semester. Network English is a kind of integrated teaching method of offline and online education, and can be a learner-oriented educational method. The online aspect gives learners easier access to the text, regardless of time and place. And in addition to the characteristic of online itself, the various contents in conformity with the learning ability of learners are provided in the text, in order that the learners can get and maintain a desire for study without losing their interest in English. Although the offline part is not much different from traditional classes, the offline education can complement the deficiency of the online and also offer the learners the opportunity for questions and answers. From the results of application of Network English in three classes, the students were satisfied with the method and have more interest in English, which resulted in better grades.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.377-393
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• 2020

Freight management systems require a new business model for rapid decision making to improve their business processes by dynamically analyzing the previous experience data. Moreover, the amount of data generated by daily business activities to be analyzed for making better decisions is enormous. Online-to-offline or offline-to-online (O2O) is an electronic commerce (e-commerce) model used to combine the online and physical services. Data analysis is usually performed offline. In the present paper, to extend its benefits to online and to efficiently apply the big data analysis to the freight management system, we suggested a system architecture based on O2O services. We analyzed and extracted the useful knowledge from the real-time freight data for the period 2014-2017 aiming at further business development. The proposed system was deemed useful for truck management companies as it allowed dynamically obtaining the big data analysis results based on O2O services, which were used to optimize logistic freight, improve customer services, predict customer expectation, reduce costs and overhead by improving profit margins, and perform load balancing.

• Journal of Communications and Networks
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• v.7 no.1
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• pp.65-75
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• 2005

In this paper, we consider a scenario in which the source has been offered QoS guarantees subject to token-bucket regulation. The rate of the source should be controlled such that it conforms to the token-bucket regulation, and also the distortion obtained is the minimum. We have developed an optimal scheduling algorithm for offline (like pre-recorded video) sources with convex distortion function and which can not tolerate any delay. This optimal offline algorithm has been extended for the real-time online source by predicting the number of packets that the source may send in future. The performance of the online scheduler is not substantially degraded as compared to that of the optimal offline scheduler. A sub-optimal offline algorithm has also been developed to reduce the computational complexity and it is shown to perform very well. We later consider the case where the source can tolerate a fixed amount of delay and derive optimal offline algorithm for such traffic source.

• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.1-12
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• 2017

Item-based collaborative filtering (IBCF) is an important technology that is widely used in recommender system of online shopping malls. It uses historical information to compute item-item similarity and make predictions. However, in offline shopping each customer's purchasing pattern can be occurred continuously and repeatedly due to time and space constraints contrast to online shopping. Those facts can make IBCF to have limitations from being applied to offline shopping malls directly. In order to improve the quality of recommendations made by IBCF in offline shopping mall, we propose an ensemble approach that considers both item-item similarity of IBCF and each customer's purchasing patterns which are modeled by item networks. Our experimental results show that this approach produces recommendation results superior to those of existing works such as pure IBCF or bestseller approaches.

• The Research Journal of the Costume Culture
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• v.30 no.5
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• pp.656-674
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• 2022

The purpose of this study is to reveal and compare the differences in the types and characteristics of purchase channel journeys of MZ generation consumers. In this study a survey was conducted on the purchase channel journey of 20 women in the MZ generation using the ethnographic method of in-depth interviews and observations. As a result, three purchase channel journeys were identified: mobile, multi-channel, and offline. These were variously subdivided according to the characteristics of the MZ generations. Gen Z's journey was categorized into types: fashion platform app, Youtube, multi-channel supplement, multi-channel non-planned store visit, offline loyalty store, and impulsive offline store. Gen M's journey was categorized as: an online community bond, portal site, online loyalty store, multi-channel brand involvement, multi-channel efficiency, a multi-channel conversion, offline efficiency and offline task. The difference in mobile journey between generations was found in the time and length of the purchase. Gen M recognized both online and offline search processes to be tiring, while Gen Z enjoyed the search process using the online path. In the offline journey Gen Z began with their own intention to purchase, while Gen M sometimes recognized that purchasing fashion products necessary for work was a cumbersome task.

• 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.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.124-132
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• 2019

Scheduling problems can be classified into offline and online ones. This paper considers an online scheduling problem to minimize makespan on the identical parallel machines. For dynamically arrived jobs with their ready times, we show that the sequencing order according to the ERD (Earliest Ready Date) rule is optimal to minimize makespan. This paper suggests an algorithm by using the MIP(Mixed Integer Programming) formulation periodically to find a good periodic schedule and evaluates the required computational time and resulted makespan of the algorithm. The comparition with an offline scheduling shows our algorithm makes the schedule very fast and the makespan can be reduced as the period time reduction, so we can conclude that our algorithm is useful for scheduling the jobs under online environment even though the number of jobs and machines is large. We expect that the algorithm is invaluable one to find good schedules for the smart factory and online scheduler using the blockchain mechanism.

• Human Ecology Research
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• v.53 no.6
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• pp.641-654
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• 2015

This study identifies and tracks changes gender differences in adolescent users' problematic online behavior. This study used Korea Youth Panel Survey (KYPS), which has tracked respondents over 7 years, with self-control theory and social learning theory applied as a theoretical framework. The model included individual-level variables such as self-control and respondent's experience of problematic behavior (offline), as well as socialization variables such as the number close friends who engaged in problematic offline behavior, parent-child relationships, and parental monitoring. Dependent variables included problematic online behavior, unauthorized ID use (ID theft) and cyberbullying (cursing/insulting someone in a chat room or on a bulletin board). Control variables consisted of academic performance, time spent on a computer, monthly household income, and father's educational attainment. Random and fixed effects models were performed by gender. Results supported self-control theory even for the within-level analysis (fixed effects models) regardless of gender, while social learning theory was partially supported. Only peer effects were found significant (except for unauthorized ID use) among girls. Year dummy variables showed significant negative associations; however, academic performance and time spent using computers were significant in some models. Father's educational attainment and monthly household income were found insignificant, even in the random effects models. We also discuss implications and suggestions for future research and policy makers.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.199-209
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• 2022

Typical anomaly detection algorithms are trained by using prior data. Thus the batch learning based algorithms cause inevitable performance degradation when characteristics of newly incoming normal data change over time. We propose an online anomaly detection algorithm which can consider the gradual characteristic changes of incoming normal data. The proposed algorithm based on one-class classification model includes both offline and online learning procedures. In offline learning procedure, the algorithm learns the prior data to be close to centroid of the latent space and then updates the centroid of the latent space incrementally by new incoming data. In the online learning, the algorithm continues learning by using the updated centroid. Through experiments using public underwater acoustic data, the proposed online anomaly detection algorithm takes only approximately 2 % additional learning time for the incremental centroid update and learning. Nevertheless, the proposed algorithm shows 19.10 % improvement in Area Under the receiver operating characteristic Curve (AUC) performance compared to the offline learning model when new incoming normal data comes.