• Asia pacific journal of information systems
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• v.20 no.3
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• pp.139-166
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• 2010

The supply chain management (SCM) systems have emerged as strong managerial tools for manufacturing firms in enhancing competitive strength. Despite of large investments in the SCM systems, many companies are not fully realizing the promised benefits from the systems. A review of literature on adoption, implementation and success factor of IOS (inter-organization systems), EDI (electronic data interchange) systems, shows that this issue has been examined from multiple theoretic perspectives. And many researchers have attempted to identify the factors which influence the success of system implementation. However, the existing studies have two drawbacks in revealing the determinants of systems implementation success. First, previous researches raise questions as to the appropriateness of research subjects selected. Most SCM systems are operating in the form of private industrial networks, where the participants of the systems consist of two distinct groups: focus companies and vendors. The focus companies are the primary actors in developing and operating the systems, while vendors are passive participants which are connected to the system in order to supply raw materials and parts to the focus companies. Under the circumstance, there are three ways in selecting the research subjects; focus companies only, vendors only, or two parties grouped together. It is hard to find researches that use the focus companies exclusively as the subjects probably due to the insufficient sample size for statistic analysis. Most researches have been conducted using the data collected from both groups. We argue that the SCM success factors cannot be correctly indentified in this case. The focus companies and the vendors are in different positions in many areas regarding the system implementation: firm size, managerial resources, bargaining power, organizational maturity, and etc. There are no obvious reasons to believe that the success factors of the two groups are identical. Grouping the two groups also raises questions on measuring the system success. The benefits from utilizing the systems may not be commonly distributed to the two groups. One group's benefits might be realized at the expenses of the other group considering the situation where vendors participating in SCM systems are under continuous pressures from the focus companies with respect to prices, quality, and delivery time. Therefore, by combining the system outcomes of both groups we cannot measure the system benefits obtained by each group correctly. Second, the measures of system success adopted in the previous researches have shortcoming in measuring the SCM success. User satisfaction, system utilization, and user attitudes toward the systems are most commonly used success measures in the existing studies. These measures have been developed as proxy variables in the studies of decision support systems (DSS) where the contribution of the systems to the organization performance is very difficult to measure. Unlike the DSS, the SCM systems have more specific goals, such as cost saving, inventory reduction, quality improvement, rapid time, and higher customer service. We maintain that more specific measures can be developed instead of proxy variables in order to measure the system benefits correctly. The purpose of this study is to find the determinants of SCM systems success in the perspective of vendor companies. In developing the research model, we have focused on selecting the success factors appropriate for the vendors through reviewing past researches and on developing more accurate success measures. The variables can be classified into following: technological, organizational, and environmental factors on the basis of TOE (Technology-Organization-Environment) framework. The model consists of three independent variables (competition intensity, top management support, and information system maturity), one mediating variable (collaboration), one moderating variable (government support), and a dependent variable (system success). The systems success measures have been developed to reflect the operational benefits of the SCM systems; improvement in planning and analysis capabilities, faster throughput, cost reduction, task integration, and improved product and customer service. The model has been validated using the survey data collected from 122 vendors participating in the SCM systems in Korea. To test for mediation, one should estimate the hierarchical regression analysis on the collaboration. And moderating effect analysis should estimate the moderated multiple regression, examines the effect of the government support. The result shows that information system maturity and top management support are the most important determinants of SCM system success. Supply chain technologies that standardize data formats and enhance information sharing may be adopted by supply chain leader organization because of the influence of focal company in the private industrial networks in order to streamline transactions and improve inter-organization communication. Specially, the need to develop and sustain an information system maturity will provide the focus and purpose to successfully overcome information system obstacles and resistance to innovation diffusion within the supply chain network organization. The support of top management will help focus efforts toward the realization of inter-organizational benefits and lend credibility to functional managers responsible for its implementation. The active involvement, vision, and direction of high level executives provide the impetus needed to sustain the implementation of SCM. The quality of collaboration relationships also is positively related to outcome variable. Collaboration variable is found to have a mediation effect between on influencing factors and implementation success. Higher levels of inter-organizational collaboration behaviors such as shared planning and flexibility in coordinating activities were found to be strongly linked to the vendors trust in the supply chain network. Government support moderates the effect of the IS maturity, competitive intensity, top management support on collaboration and implementation success of SCM. In general, the vendor companies face substantially greater risks in SCM implementation than the larger companies do because of severe constraints on financial and human resources and limited education on SCM systems. Besides resources, Vendors generally lack computer experience and do not have sufficient internal SCM expertise. For these reasons, government supports may establish requirements for firms doing business with the government or provide incentives to adopt, implementation SCM or practices. Government support provides significant improvements in implementation success of SCM when IS maturity, competitive intensity, top management support and collaboration are low. The environmental characteristic of competition intensity has no direct effect on vendor perspective of SCM system success. But, vendors facing above average competition intensity will have a greater need for changing technology. This suggests that companies trying to implement SCM systems should set up compatible supply chain networks and a high-quality collaboration relationship for implementation and performance.

• Journal of the Korean Society of Food Science and Nutrition
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• v.24 no.5
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• pp.816-828
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• 1995

During the industrial preparation and the storage of foods, the side chain of some protein-bound amino acids can react chemically each other or with other molecules present in the food. The following reactions have been described : destruction of amino acids, racemization, protein-protein interactions, reactions of proteins with reducing sugars, oxidizing agents, or polyphenols. Apart from total destruction, the main reacitons are the forming of Maillard reactions products(e.g. fructoselysine) and the crosslinking with other amino acids in the same or in another protein molecule(e.g. lysinoalanine). The most often involved amino acid is lysine because of its free functional ${\varepsilon}-amino$ acid group. Generally derivatives of amino acids or crosslinks in polypeptides influence the bioavailability and the overall digestibility of the protein. This work reviews the technological, analytical, nutritional, and physiological problems related to the formation of fructoselysine and lysinolalnine in human foods, and evaluates the possible health risk for humans. A summary of the available information is of help in considering whether or not the presence of fructoselysine/lysinoalanine in foods represents a danger to man. The reduction in protein quality through these reactions is not a problem for the general population, but it is extremely important in infant foods, since infants are often nourished with a limited number of food product(e.g. formular foods) which are sensitive to the Mailard reaction.

• The Journal of the Korea Contents Association
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• v.13 no.5
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• pp.48-57
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• 2013

Smartphones provide users with environment for communication and sharing information and at the same time play an important role of mobile technology and mobile art development. Smartphone technology-related researches are being accelerated especially with the advent of mobile Augmented Reality(AR) age, but the studies on user participation that is essential for AR content industry were insufficient. In that regard, the assistance from mobile art area that has already developed these characteristics is essential. Thus, this article is to classify mobile art that has not been studied a lot domestically into feature phone usage and smartphone usage and to analyze each example case with the three most used methods. The usage of feature phones which use the sound and images of mobile devices can be divided into three: installation and performing methods, single channel video art method and five senses communication method. On the other hand, the usage of smartphones that use sensors, cameras, GPS and AR can be divided into location-based AR, marker-based AR and markerless AR. Also, as a result of examining mobile AR content utilization technology by industries, combined methods are utilized; tourism and game-related industries use location-based AR, education and medicine-related industries use marker-based AR, and shopping-related industries use markerless AR. The development of AR content industry is expected to be accelerated with mobile art that makes use of combined technology method and constant communication method through active participation of users. The future development direction of mobile AR industry is predicted to have minimized HMD, integration of hologram technology and artificial intelligence and make the most of big data and social network so that we could overcome the technological limitation of AR.

• Journal of Korea Technology Innovation Society
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• v.20 no.2
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• pp.430-457
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• 2017

The purpose of this study is to present meaningful information and policy implications concerning the determinants of royalties of technology transfer to stakeholder. To identify key determinants of royalties in technology transfer, this study conducted AHP survey analysis (Survey period: 01/09~31/10, 2016) of 96 government-funded research centers and 85 companies which were participants of the R&D project "Next Generation BioGreen21" of R.D.A in the "N" center from 2011 to 2015. Research results show that both parties acknowledge 'Technical considerations for determining the profitability of the technologies' and 'The interest and willingness of the management group' as critical factors for the determinants of royalties. The difference of each party is that private companies acknowledge 'Available budget plan' as a critical factor while the government-funded research centers value 'Market competitiveness'. These findings suggest four main policy implications which are the investigation of technological demands reflecting specific needs of industrial sites, the diversification of royalty payments for private companies, the differentiated research evaluation system for the purpose of technology transfer and the planning of public R&D project reflecting research time span of private companies.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.4
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• pp.567-584
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• 2013

This study aims to explore an evolutionary path of the IT service industry in Fairfax County using the Cluster Adaptive Cycle model in economic geography. The analysis is based on detailed historical and industrial information obtained through a variety of data sources including local archival materials, economic census, and interviews. This study also performs a shift-share analysis during the period of 1990 to 2011. Using the adaptive cycle model, the local IT service industry is indicated by a trajectory of constant cluster mutation. The evolution of the local IT service industry has been closely related to federal government policy due to the regional specificity of the National Capital Region and the proximity of the Department of Defense. Although the economic downturn of the late 2000s, the local IT service industry has been notable resilience and adapted to a changing market and technological environment. This constant mutation of the local industry is resulted from not only high resilience which is based on the large government procurement market, the reinforcement of adaptive capacity of the local firms and the network of economic agents such as firm and supporting institutions, but also high flexibility of the knowledge-based service industry to a changing business environment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.403-412
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• 2022

As the 4th industrial revolution is in full swing and next-generation ICT(Information & Communications Technology) convergence technology is being developed, various smart construction technologies are being rapidly introduced in the construction field to respond to technological changes. In particular, since the earth-volume calculation process for site design accounts for a large part of the design cost at the construction site, related researches are being actively conducted to improve the efficiency of the process and accurately calculate the earth-volume. The purpose of this study is to present a method for quickly constructing the topography of a construction site in 3D and efficiently calculating earth-volume using the results. For this purpose, the construction site was constructed as a 3D realistic model using large-scale aerial photos obtained from UAV(Unmanned Aerial Vehicle). At this time, since the constructed 3D realistic model has a surface model structure in which volume calculation is impossible, the structure was converted into a 3D solid model to enable volume calculation. And we devised a methodology to calculate earth-volume based on CAD(Computer-Aided Design and Drafting) using the converted solid model. Automatically calculating earth-volume from the solid model by applying the method. As a result, It was possible to confirm a relative deviation of 1.52% from the calculated earth-volume from the existing survey results. In addition, as a result of comparative analysis of the process time required for each method, it was confirmed that the time required is reduced of 60%. The technique presented in this study is expected to be utilized as a technology for smart construction management, such as periodic site monitoring throughout the entire construction process, as well as cost reduction for earth-volume calculation.

• The Journal of Bigdata
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• v.6 no.2
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• pp.71-84
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• 2021

Since small and medium-sized enterprises fell short of the securement of technological competitiveness in the field of big data and artificial intelligence (AI) field-core technologies of the Fourth Industrial Revolution, it is important to strengthen the competitiveness of the overall industry through technology commercialization. In this study, we aimed to propose a priority related to technology transfer and commercialization for practical use of public research results. We utilized public research performance information, improving missing values of 6T classification by deep learning model with an ensemble method. Then, we conducted topic modeling to derive the converging fields of big data and AI. We classified the technology fields into four different segments in the technology portfolio based on technology activity and technology efficiency, estimating the potential of technology commercialization for those fields. We proposed a priority of technology commercialization for 10 detailed technology fields that require long-term investment. Through systematic analysis, active utilization of technology, and efficient technology transfer and commercialization can be promoted.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.910-914
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• 2023

With the technological innovation owing to the 4th industrial revolution, the maritime transportation is rapidly being developed with autonomous ships and systems. Particularly, autonomous ships will partially replace the manned ships and navigation among them remotely upon the degree of autonomy suggested by IMO. Accordingly, the remote operator and related research have increased as well. However, the data on the minimum required manpower for remote operators are lacking such as considering engage required situations and their co-occurrence probability. Therefore, this study proposes a simulation model that calculates the number of remote engage required situations by defining restricted water area and remote engage required situation as close-quarter situations based on accumulated trajectory data of actual ships. The findings are expected to be used as background materials to establish the appropriate manpower distribution of remote operators in remote operation centers.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.141-156
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• 2023

Due to the unprecedented worldwide pandemic of the new Covid-19 infection, business trends of companies have changed significantly. Therefore, it is strongly required to monitor the rapid changes of innovation trends to design and plan future businesses. Since the pandemic, many studies have attempted to analyze business changes, but they are limited to specific industries and are insufficient in terms of data objectivity. In response, this study aims to analyze business trends after Covid-19 using Crunchbase, a global startup data. The data is collected and preprocessed every two years from 2018 to 2021 to compare the business trends. To capture the major trends, a network analysis is conducted for the industry groups and industry information based on the co-occurrence. To analyze the minor trends, LDA-based topic modelling and word2vec-based clustering is used. As a result, e-commerce, education, delivery, game and entertainment industries are promising based on their technological advances, showing extension and diversification of industry boundaries as well as digitalization and servitization of business contents. This study is expected to help venture capitalists and entrepreneurs to understand the rapid changes under the impact of Covid-19 and to make right decisions for the future.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.