• Asia pacific journal of information systems
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• v.17 no.2
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• pp.83-95
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• 2007

We are motivated by how offline and online firms compete. The Internet made many conventional offline firms build a dynamic online business as another sales channel using their advantages such as brand equity, an existing customer base with comprehensive purchasing data, integrated marketing, economies of scale, and longtime experience with the logistics of order fulfillment and customer service. Even though the hybrid selling using both offline and online channel seems to have advantages over a pure online retailer, all the conventional offline firms are not seen to create an online business. Many conventional offline firms began to launch online business since the Internet era, however, just being online business is not likely to guarantee success. According to Bizate.com's report whether the hybrid channel strategy is successful is still under investigation. For example, consider the classic case of Barnes and Noble versus Amazon.com, Barnes and Noble was already the largest chain of bookstores in the U,S., when Amazon.com was established in 1995, BarnesandNoble.com followed suit in 1997, After suffering losses in its initial years, Amazon finally turned profitable in 2003. In 2004, Amazon's net income was $588 million on revenues of $6.92 billion, while Barnes and Noble earned $143 million on revenues of $4.87 billion, which included BarnesandNoble.com's loss of $21 million on revenues of $420 million. While these examples serve to motivate our thinking, it does not explain when offline firms should venture online. It also does not provide an analytical framework that can generalized to other competitive online-offline situations. We attempt to do this in this paper and analyze a hybrid channel model where a conventional offline firm competes against online firms using its own direct online channels. We are particularly interested in an optimal channel strategy when a conventional offline firm sells its products through its own direct online channel to compete with other rival online firms. We consider two situations where its direct online channel and other online firms are symmetric and asymmetric in the brand effect. The analysis of this paper presents several findings. In the symmetric model where a hybrid firm's online channel is not differentiated from a pure online firm, (i) a conventional offline firm will not launch its online business. In the asymmetric model where a hybrid firm's online channel is differentiated from a pure online firm, (ii) a conventional offline firm can launch its online business if its brand effect is greater than a certain threshold. (iii) there is a positive relationship between its brand effect and online customer costs showing that a conventional offline firm needs more brand effect in order to launch online business as online customer costs decrease. (iv) there is a negative relationship between its brand effect and the number of customers with access to the Internet showing that a conventional offline firm tends to launch its online business when customers with access to the Internet increases.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.143-150
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• 2021

It introduces the contents of establishing a geostationary satellite-based forest fire monitoring system that can monitor areas of the Korean Peninsula 24 hours a day for forest fire monitoring, and describes how to establish a forest fire monitoring system and use it in various ways. In order to establish a satellite-utilized forest fire monitoring system, we will describe and draw conclusions on literature research, technical principles, forest fire monitoring means, and satellite forest fire monitoring system. The satellite-utilized forest fire monitoring system can consist of one geostationary satellite equipped with infrared detection optical sensors and a ground processing station that processes data received from satellites to spread surveillance information. Forest fire monitoring satellites are located in the country's geostationary orbit and should be operated 24 hours a day, 365 days a day. Forest fire monitoring technology is an infrared detection technology that can be used in national public interests such as forest fire monitoring and national security. It should be operated 24 hours a day, and to satisfy this, it is efficient to establish a geostationary satellite-based forest fire monitoring satellite system.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.5
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• pp.289-296
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• 2009

All over the world more and more attention is being paid to green management which enables environment-friendly sustainable development. To keep up with these global trends and in response to the government's "low carbon green growth" policy, many efforts are being made in the port and logistics in order to drastically reduce carbon emissions and save operating expenses. To this end, the engine power source of the cranes in the container terminal is being replaced with electricity from fossil fuel. Among those cranes, especially e-RTGC has been proved to be practical and excellent in its performance through many previous studies. However, no empirical study has yet been made on its introduction effects when e-RTGC has been introduced to the container terminal. In the meantime, however, many domestic container terminals have introduced e-RTGC, and so some data have been accumulated for further research. Under these circumstances, this study has tried to make an empirical study through the case study of D container terminal, checking the restriction factors to be considered at the time of eURTGC introduction, and analyzing its introduction effects and problems. We have high expectations that this study will provide key consideration factors and empirical analysis data to the decision makers who plan to introduce an e-RTGC system, and also will be used as a reference to the development direction of e-RTGC.

• 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.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Digital Convergence
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• v.16 no.2
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• pp.103-115
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• 2018

The potential competitiveness for new routes and resources has been theoretically discussed with regards to the Arctic route but is gradually becoming a reality as global warming increases. In June of 2017, China officially included the Northern Sea Route (NSR) as part of the Belt and Road Initiative (BRI), and major countries' interests in the NSR are greatly expanding. This paper presents the general characteristics of the NSR, NSR development in China, the expected relationship between the NSR and the BRI, and this relationship's implications for Korea. The NSR has poor facilities and information infrastructure and is not economically viable for commercial navigation due to its high-cost conditions compared to competitive routes. In order to explore the Arctic and develop the NSR, large-scale projects must be funded over a long period of time; this has caused major difficulties in development. However, as the NSR is included in the BRI, there could be an opportunity to utilize BRI funds, such as Asian Infrastructure Investment Bank (AIIB). Further, China's NSR development and the NSR development of partner countries, such as Korea, should be further stimulated. As Korea has strengths in terms of its shipbuilding technology and geographical location, which is located at the core of the NSR, Korea would have chances to expand the economic cooperation and business opportunities with China and Russia.

• Journal of Distribution Science
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• v.13 no.1
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• pp.27-33
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• 2015

Purpose - The ICT market in the EU is lagging behind that of the US; however, algorithm and software development within the EU have grown steadily, and they involve focusing on the creative cultural convergence conceptualized as part of Horizon 2020 and connecting neighboring markets in the EE and the Mediterranean region. It is essential to study the requirements to market the EU's creative ICT development in emerging industrial countries after examining its applicability in these countries. Research design, data, and methodology - This study deals with data pertaining to the EU's creative industry and competitive edge. The global cultural expansion of the EU facilitates a new concept involving not only low-cost IT products to enhance local cultural artifacts through R&D and the construction of efficient infrastructure services, but also information exchange with a realistic commercialization of the technology that can be applied for creative cultural localization. In the European industry, research on algorithms has been applied for the benefit of consumers. We investigated how the process is conducted in the EU. Results - Europe needs to adjust its economic structure to the local culture as part of IT distribution convergence. The convergence has been converted into a production algorithm with IT in the form of low-cost production. This is because there is an attempt to improve the quality of transport infrastructure, workforce availability, and the distribution of the distance to the local industries and consumers, using IT algorithms. Integrated into the manufacturing industry, based on the ICT infrastructure and solutions, smart localized regional clusters are formed with the help of grafting. Europe has own strategy to increase the number of hub-and-spoke cities. Europe is now becoming integrated, with an EPC system for regional cooperation rather than national competition in ICT technology. Europe has also been recognized in this study as changing the step-by-step paradigm for global competitiveness through new creative culture industries. Conclusions - As a result, there are several ways of converging with others through EU R&D intensity; therefore, the EU can be seen as successfully increasing marginal value, which is useful in developing a special industrial cluster or local cultural cities that create converged development by connecting people and objects with IT. In fact, when compared to the US, Europe has a strong culture and the car industries have a tendency to overshadow the IT industries with integration of services in IT distribution. Considering the rapid environmental changes, the convergence of IT services is likely to take place in Europe, similar to the pharmaceutical industry and the automotive industry. This requires a focus on human resources and automated systems management. The trend is to move away from low-wage industries, switched to key personnel centers of the local university-industry. EU emphasizes the creation of IT market demand in Europe involving local cultural convergence for marketing as the second step to strengthen the economic hub-and-spoke areas.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1119-1126
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• 2023

This study measured the social value of social economy enterprises in Incheon Metropolitan City using the Social Value Index (SVI) developed by the Korea Social Enterprise Promotion Agency. The results showed that the social value orientation of the business activities of SSEs averaged 9.3 out of 15 points, and their innovation efforts were 8.0 out of 10 points. The average monetary and non-monetary social contribution efforts of SSEs was 5.1 out of 10. When comparing the average sales and social value scores by industry, the manufacturing sector shows that social enterprises have higher average sales and social value orientation of business activities, but lower social return efforts. Social work facility management and business support services have high average sales, but low social value orientation of business activities and efforts to make monetary or non-monetary social contributions. On the other hand, education services; arts, sports, and leisure-related services; and publishing, video, broadcasting, communication, and information services have lower average revenues but higher social value orientation of business activities. These SVI indicators are well utilized by local governments, but not yet by the central government. In the future, governments and public institutions should reflect the differences between sectors when formulating policies for social enterprises.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.213-226
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• 2011

In global supply chain, the scheduling problems of large products such as ships, airplanes, space shuttles, assembled constructions, and/or automobiles are complicated by nature. New scheduling systems are often developed in order to reduce inherent computational complexity. As a result, a problem can be decomposed into small sub-problems, problems that contain independently small scheduling systems integrating into the initial problem. As one of the authors experienced, DAS (Daewoo Shipbuilding Scheduling System) has adopted a two-layered hierarchical architecture. In the hierarchical architecture, individual scheduling systems composed of a high-level dock scheduler, DAS-ERECT and low-level assembly plant schedulers, DAS-PBS, DAS-3DS, DAS-NPS, and DAS-A7 try to search the best schedules under their own constraints. Moreover, the steep growth of communication technology and logistics enables it to introduce distributed multi-nation production plants by which different parts are produced by designated plants. Therefore vertical and lateral coordination among decomposed scheduling systems is necessary. No standard coordination mechanism of multiple scheduling systems exists, even though there are various scheduling systems existing in the area of scheduling research. Previous research regarding the coordination mechanism has mainly focused on external conversation without capacity model. Prior research has heavily focuses on agent-based coordination in the area of agent research. Yet, no scheduling domain has been developed. Previous research regarding the agent-based scheduling has paid its ample attention to internal coordination of scheduling process, a process that has not been efficient. In this study, we suggest a general framework for agent-based coordination of multiple scheduling systems in global supply chain. The purpose of this study was to design a standard coordination mechanism. To do so, we first define an individual scheduling agent responsible for their own plants and a meta-level coordination agent involved with each individual scheduling agent. We then suggest variables and values describing the individual scheduling agent and meta-level coordination agent. These variables and values are represented by Backus-Naur Form. Second, we suggest scheduling agent communication protocols for each scheduling agent topology classified into the system architectures, existence or nonexistence of coordinator, and directions of coordination. If there was a coordinating agent, an individual scheduling agent could communicate with another individual agent indirectly through the coordinator. On the other hand, if there was not any coordinating agent existing, an individual scheduling agent should communicate with another individual agent directly. To apply agent communication language specifically to the scheduling coordination domain, we had to additionally define an inner language, a language that suitably expresses scheduling coordination. A scheduling agent communication language is devised for the communication among agents independent of domain. We adopt three message layers which are ACL layer, scheduling coordination layer, and industry-specific layer. The ACL layer is a domain independent outer language layer. The scheduling coordination layer has terms necessary for scheduling coordination. The industry-specific layer expresses the industry specification. Third, in order to improve the efficiency of communication among scheduling agents and avoid possible infinite loops, we suggest a look-ahead load balancing model which supports to monitor participating agents and to analyze the status of the agents. To build the look-ahead load balancing model, the status of participating agents should be monitored. Most of all, the amount of sharing information should be considered. If complete information is collected, updating and maintenance cost of sharing information will be increasing although the frequency of communication will be decreasing. Therefore the level of detail and updating period of sharing information should be decided contingently. By means of this standard coordination mechanism, we can easily model coordination processes of multiple scheduling systems into supply chain. Finally, we apply this mechanism to shipbuilding domain and develop a prototype system which consists of a dock-scheduling agent, four assembly- plant-scheduling agents, and a meta-level coordination agent. A series of experiments using the real world data are used to empirically examine this mechanism. The results of this study show that the effect of agent-based platform on coordinated scheduling is evident in terms of the number of tardy jobs, tardiness, and makespan.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.2
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• pp.67-80
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• 2020

This study has been conducted at a time when Korean government continues to extend support for youth startups as part of its policy to create jobs and the focus moves from career and employment to youth startups with a growing interest in the field of youth startups. Against this background, this study aims to identify difficulty factors of youth startups in areas besides the Seoul Metropolitan Area, seek ways to overcome difficulty factors, and propose policy implications. To this end, this study set five criteria and 25 sub-criteria to evaluate the difficulties of youth startups by reviewing previous studies and conducting literature review, and performing brainstorming method. The empirical analysis of the evaluation criteria was performed, using the analytic hierarchy process (AHP) method, on youths aged 19 to 39 in Gunsan area. The analysis results showed that the largest difficulty factors facing local youths include business model establishment, business administration and management, and startup funding in the criteria. As for sub-criteria, the largest difficulty factors are market information acquisition, technology commercialization, project feasibility, technology development, and new market pioneering in descending order. Local youths have much difficulty about the process of turning a business item into a product and commercializing it. According to a comparative analysis by gender, men were a relatively high difficulty in commercializing business models than women. men were a relatively high difficulty in commercializing business models than women. On the other hand, women were higher than men in all factors (management management, entrepreneurship, improvement of entrepreneurship system, and improvement of entrepreneurship awareness) except for factors affecting business model. In addition, the factors of entrepreneurship were found to be relatively different among young people (college students, prospective entrepreneurs, entrepreneurs). In conclusion, it was suggested that in order to revitalize youth entrepreneurship in the region, it is necessary to actively resolve the difficulties of business model commercialization rather than entrepreneurship funds. In addition, it is necessary to strategically support customized entrepreneurship support and situational administrative services because gender and hierarchical difficulties are different than general solutions. This study presented practical priorities and derivation methods for the entrepreneurship difficulties faced by local youth, and suggested measures and improvements for vitalizing local youth entrepreneurship in the future.