• Korean Journal of Organic Agriculture
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• v.20 no.4
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• pp.491-502
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• 2012

The world's food production systems are becoming an area of great concern for both human and ecological health. Research has identified that one of the greatest threats to sustainability is conventional industrial agricultural systems and the high energy and material cost they require to function. The organic food movement is contributing as promising alternative to the current dominant model. Over the last 50 years it has developed into the most visible brand for a healthier and more environmentally sustainable food system. However, to achieve its full potential there are still a number of hurdles which must be overcome to make organic products a more viable and appealing option for consumers. This paper provides an overview of key research that has been conducted on why consumers are buying organic products, what they are buying and what is preventing them from purchasing more. It concludes that the key challenge is for the organic food movement to convince existing consumers of the superior 'value' of its products. In addition there are a number of methodological issues associated with analysing the market for organic products as well as issues of limited distribution, intermittent availability and high prices that are currently preventing increases in sales. Recognition and management of these barriers could contribute to more effective targeted research into consumer food purchasing motivations and subsequently the development of more sophisticated marketing strategies that assist in maintaining integrity with consumers and fending off challenges from conventional as well as other complementary food systems such as local food movement. And finally to achieve these market growth strategies the organic food movement will need to cope with its diverse constituency - ranging from global corporates through to local production and consumption - and provide attractive opportunities to individuals and business at all stages in supply chain whilst retaining credibility with government to ensure ongoing policy support.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.5
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• pp.150-162
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• 2006

A research model was developed to examine the relationships between the anticipated benefit factors of Internet shopping malls selling physical products and their competitive strategies when the shopping malls are located in their supply chain clusters. A survey questionnaire was implemented and 99 usable responses were obtained from the internet shopping malls located in major industrial clusters in Korea. The six major benefit factors were found: sharing information with offline suppliers, obtaining customers, improvement of trust, efficiency of operation processes, value-added service, management of suppliers. The six empirically derived benefit factors were then used to examine how they affect the firms' competitive strategies and strategic alliance/merger. The regression results shows that the benefit factors influence on firms' choosing one or some combination of the competitive strategies and strategic alliance to survive and succeed in the market.

• Journal of the Korean Institute of Navigation
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• v.19 no.1
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• pp.49-69
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• 1995

What is the national competitive power\ulcorner What are the determinants of national competitive advantage\ulcorner In order to find these problems, this study was initiated theoretically through the international trade theory and international managerial strategic theory. The flow of studies, in connection with these competitive advantage, come into industries competitive advantage from national competitive advantage. It is understood that the flow of studies is based on the change of paradigm about the international industry environment. Today, the international industry environment has been changed into vigorous situation with the new trade system such as globalization, UR negotiation, WTO system instead of GATT. Among these environments, all enterprises in any industries should try to enhance their benefit, income profits and develop their management strategy, because it is essential for them to keep and make a profit in order to be competitive against all other rivaling enterprises. According to researchers and the ages of history, the concept and determinants of competitive advantage have been dissimiliar as followings. 1) Theory of absolute advantage : Absolute Product Cost. 2) Theory of comparative advantage : Comparative Product Cost. 3) Theory of reciprocal demand : the Reciprocal Demand Structure. 4) Theory of Hechsher-Ohlin : Factor Endowment. 5) Paradox of Leontief : the Quality of Factor Endowment. 6) Theory of technological gap : R & D. 7) Technological gap model : The Technological Gap. 8) Theory of the product life cycle : Product Life Cycle. 9) Christensen's study : Firm's character & manager's character. 10) Monopolistic Advantage Theories : Monopolistic Advantage 11) Electic theory : Ownership, location and internalization specific advantages. 12) Theory of value chain : Cost advantage, Differentiation, Focus. However all these theories have the limit of illustration regarding today's competitive situation, the Porter's theory shows well the determinants of competitive advantage with competitive strategy. Therefore, the analysis of the competitive advantage for maritime industry should be studied by the determinants of competitive advantage of nations

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.1
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• pp.16-22
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• 2021

This study investigated the reduction in human norovirus (HNV) GII. 4 count in pacific oyster Crassostrea gigas using electron beam irradiation. Infectious HNV GII. 4 was detected using RT-qPCR (real time reverse transcription-quantitative polymerase chain reaction) with PMA (propidium monoazide)/sarkosyl. At electron beam doses 1, 5, 7, and 10 kGy, the count of HNV GII. 4 was 2.74, 2.37, 2.06, and 1.55 log copies/μL (control, 3.01 log copy/μL), respectively, confirming that as the irradiation dose increased, norovirus count reduced significantly (P<0.05). After PMA/sarkosyl treatment, the counts further reduced at the same irradiation dose, and 10 kGy showed significant differences between the non-treated and PMA/sarkosyl-treated samples (P<0.05). The Ed (decimal reduction dose of electron beam) value based on the first-order kinetic model was 7.33 kGy (R2=0.98). No significant difference was observed in the pH values of the control (6.2) and electron beam-irradiated samples at all doses (6.1). For sensory evaluation, the non-treated sample scored the highest in all categories (5.25-6.17), while the samples treated with 10 kGy showed the lowest score (4.67-5.33), although without statistical significance (P>0.05). Overall, our results suggest that 7 kGy electron beam is sufficient for the non-thermal sterilization of oysters without causing significant changes in quality.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.177-189
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• 2020

The OECD has assessed Korea as the third highest in trade facilitation worldwide. The paperless trade of Korea is world class based on uTradeHub : national e-trade service's infrastructure for trade community. Over 800 trade-related document standards provide interoperability of message exchange and trade process automation among exporters, importers, banks, customs, airlines, shippers, forwarders and trade authorities. Most one-to-one unit processes are perfectly paperless & online; however, from the perspective of process flow, there is a lack of streamlining end-to-end trade processes spread over many different parties. This situation causes the trade community to endure repetitive-redundant load for handling trade documents. The trade community has a strong demand for seamless trade flow. For streamlining the trade process, processes with data should flow seamlessly to multilateral parties. Flowing data with an optimized process is the critical success factor to accomplish seamless trade. This study proposes four critical digital trade infrastructures as a platform service : (1) data-centric Intelligent Document Recognition(IDR), (2) data-driven Digital Document Flow (DDF), (3) platform based Digital Collaboration & Communication(DCC), and (4) new digital Trade Facilitation Index (dTFI) for precise assessment of K-Trade Digital Trade Framework. The results of new dTFI analyses showed that redundant reentry load was reduced significantly over the whole trade and logistics process. This study leads to the belief that if put into real-world application can provide huge economic gains by building a new global value chain of the K-trade eco network. A new digital trade framework will be invaluable in promoting national soft power for enhancing global competitiveness of the trade community. It could become the advanced reference model of next trade facilitation infrastructure for developing countries.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.791-797
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• 2022

Through recent metaverse marketing case studies, success factors and activation methods were analyzed from the perspective of content, platform, network, and device of the metaverse ecosystem in each industry. The importance of contents and platform of metaverse could be found in entertainment, fashion, office space and real estate, education, advertisement and commerce industries. In order to vitalize the metaverse, firstly, it is necessary to strengthen active participation and retention by providing a stable revenue model for market participants. Secondly, the importance of attractive content to expand subscribers is a key trigger for metaverse activation. Thirdly, it is necessary to increase the convenience of using metaverse service by using a light and simple device for the user. Fourthly, a win-win cooperation strategy should be supported in the value chain of the industry through ecosystem scalability. In addition, business opportunities for market participants and additional revenue models should be continuously provided.

• Asian Journal of Innovation and Policy
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• v.10 no.1
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• pp.108-131
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• 2021

The increase of international trade across countries and borders results in increased risks associated with the inflow of new pests and diseases. These risks are likely to be increased more rapidly due to climate change. Some countries implement strict regulations on imports to prevent these risks and protect biosecurity, food safety, and public health. However, the problems arise when the diseases and pests are found in a country where their economic structure largely depends on agricultural exports and cause ripple effects on other industries and ecosystems. Therefore, establishing an effective quarantine system is essential to protect and recover from the damage caused by non-native diseases and pests. This study's objectives are 1) analyzing the agricultural policies relate to the quarantine system on diseases and pests in Korea, 2) evaluating the Korea plant quarantine system's value, and 3) simulating plant quarantine policy strategies. We estimated the Korean quarantine system's benefits on diseases and pests to reach these objectives. The benefits are measured with a willingness to pay from respondents surveyed by the contingent valuation method (CVM). The CVM approach directly asks people how much they would willingly pay for food security. Finally, the Korean quarantine system's values are simulated with several policy scenarios and different scales of infection at the regional level. The results of this study can deliver policy implications on the quarantine system innovation in developing countries including Asia.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Transactions of the Korean hydrogen and new energy society
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• v.33 no.6
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• pp.845-855
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• 2022

In response to climate change, the world is continuing efforts to reduce fossil fuels, expand renewable energy, and improve energy efficiency with the goal of achieving carbon neutrality. In particular, R&D is being made on the value chain covering the entire cycle of hydrogen production, storage, transportation, and utilization in order to shift the energy supply system to focus on hydrogen energy. Hydrogen-based energy sources can produce heat and electricity at the same time, so it is possible to utilize heat energy, which can increase overall efficiency. In this study, calculation of the optimal scale for hydrogen-based cogeneration and the composition of heat sources were reviewed. It refers to a method of the optimal heat source size according to the external heat supply and heat storage to be considered. The results of this study can be used as basic data for establishing a hydrogen-based energy supply model in the future.