• Journal of Information Technology Services
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• v.11 no.sup
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• pp.153-164
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• 2012

There is a belief that timely and precise data are important to decisions and the better decisions are related to better firm performance. However, empirical research investigating the effect of data quality on firm financial performance is still scarce up to recently. Current study empirically explores such an effect of data quality on firm accounting performance in the Korean manufacturing industry during 2008~2010 with secondary data. The results show that better data quality does not impact on sales and operating profit, but positively and significantly impacts on EVA(Economic Value Added). Raising the level of data quality management maturity by one level can increase EVA by about 34% in manufacturing firms.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.2
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• pp.141-152
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• 2015

Our agriculture and rural villages have faced negative conditions in many reasons. To overcome this situation, a new change is needed by the 6th industrialization. Many agriculture and rural villages in Korea are pursuing the 6th industrialization through the convergence of the primary, secondary, and tertiary industries to vitalize agriculture and rural villages. But there are several problems with the 6th industrialization. There is a limit to the capacity building of the members of the rural organization and Korean agricultural base primary, secondary, and tertiary industries are weak all. In addition, it has been insufficient research for value chain management of the region as a whole; there has been no study of information sharing across the region for the 6th industrialization. This study is about value chain management model for successful the 6th industry with Quick Response System and the big data technology. In this study to provide the efficiency of 6th industry value chain management with customer's needs analysis using big data and research for the information share between the industries in the region through the information pipeline theory of the QR System. We hope that our study is helped to proceed successfully on the 6th industrialization in Korea.

• The Journal of Industrial Distribution & Business
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• v.9 no.5
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• pp.7-15
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• 2018

Purpose - The development of Anhui high-tech industry is the key to transforming the mode of economic development and optimizing the industrial structure. Research design, data, and methodology - The data of paper that is the data of Anhui province and the six provinces of central China from 2010 to 2015, which analyzed the present situation of High-tech industry in Anhui Province by means of data comparison and literature analysis, and then explored several possible modes of undertaking. Results - With the analysis of the current situation of the development of high-tech industry in Anhui Province, several possible modes for undertaking the transfer of high-tech industries in Anhui Province were explored: The mode of undertaking of Leading Enterprises + Industrial park, the mode of undertaking of differentiated development + Regional comprehensive exploitation and the mode of undertaking "Dynamic industrial Chain" + industrial cluster. Conclusions - Based on the perspective of industrial upgrading, this paper analyzes the shortcomings of Anhui Province in the traditional mode of undertaking industrial transfer and expounds the inherent requirements of innovation industry undertaking. Finally, on the basis of the above analysis, the author explored three possible modes of acceptance.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.1-9
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• 2014

This research describes strategies to promote the growth of the Big Data industry and the companies within the ecosystem. In doing so, we identify the roles and responsibilities of various objects of this ecosystem and Big Data concepts. We describe the five components of the Big Data ecosystem: governance, data holders, service users, service providers and infrastructure providers. Related to the Big Data industry, the paper discusses 13 business strategies between the five components in the ecosystem. These strategies directly respond to areas of research by the Big Data industry leading experts on its early development. These strategies focus on how companies can gain competitive advantages in a growing new business environment of Big Data. The strategy topics are as follows: 1) the government's long term policy, 2) building Big Data support centers, 3) policy support and improving the legal system, 4) improving the Privacy Act, 5) increasing the understanding of Big Data, 6) Big Data support excavation projects, 7) professional manpower education, 8) infrastructure system support, 9) data distribution and leverage support, 10) data quality management, 11) business support services development, 12) technology research and excavation, 13) strengthening the foundation of Big Data technology. Of the proposed strategies, establishing supportive government policies is essential to the successful growth of thee Big Data industry. This study fosters a better understanding of the Big Data ecosystem and its potential to increases the competitive advantage of companies.

• The Journal of Information Systems
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• v.26 no.4
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• pp.137-161
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• 2017

Purpose In this study, the difficulties were analyzed with the field data from two domestic and interview with industry practitioners. And We presented initiatives with feasibilities to overcome the hurdle for progress of easy-payment. Design/methodology/approach We collected industry data from two domestic credit card companies and analyzed that data to prove 7 proposition in detail. Also We had interview data from industry practitioners who can understand the relationship between stakeholders. For this analysis, we used the causal loop diagram to find activation inhibition and activation elements about easy-payment. Findings The Fintech easy-payment industry has been organically involved in various partners such as customers, merchants, PGs, VANs, credit card issuers, banks, payment providers, terminal manufacturers, etc. and they have been competing against each other to hold leader position in the easy-payment market. Because of the reasons, the easy-payment does not spread out as much as it expects. In this study, the difficulties were analyzed with the field data and interview with industry practitioners and proposed five initiatives with feasibilities to overcome the hurdle for progress of easy-payment. This study helps to understand current situation and issues of Fintech and easy-payment for related research in future.

• The Journal of Fisheries Business Administration
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• v.49 no.4
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• pp.1-17
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• 2018

The Korean fisheries industry is a traditional business, the majority of which are small and medium-sized enterprises (SMEs). It has played an important role in the South Korean economies in the past several decades, but it currently faces the limitations of growth potential and profitability due to declining workforce, aging populations, deteriorating fishery environments, climate changes, and rapid changes in the global industrial ecosystem. Many studies have suggested solutions for the fisheries industry in macro perspective, but there are rarely any studies taking the strategic approaches for the problem. If it is possible for governments to support the companies that are likely to increase their value-added selectively, it will break through the current situation more effectively. This paper introduces a study on the selection method utilizing data envelopment analysis (DEA) to find SMEs with potentials to increase profits and growth. We suggest selecting SMEs with high management efficiency and ability to utilize intangible assets as the target companies. We also suggest policy objectives for SMEs in the domestic fisheries industry based on the results of DEA analysis and propose a data-based method for the policy decisions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.321-324
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• 2022

The Korean halibut farming industry suffer from price instability and demand decrease due to various environmental and social issues. It is urgent to predict the appropriate amount of halibut production. However, it is not easy for employments working in the halibut farming industry to handle statistical tools in order to perform the prediction. In this paper, we implemented a Excel-based regression analysis tool that allows users to get a regression analysis result by just entering historical data in a sheet. Our tool will reduce workloads of employments working in the halibut farming industry by enabling them to perform a regression analysis with Excel on-the-fly. This study expect that by using the tool the halibut farming industry cope actively with the real-time change in the industry.

• Journal of Advanced Research in Ocean Engineering
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• v.3 no.1
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• pp.32-40
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• 2017

Recently, the amount of data to be processed and the complexity thereof have been increasing due to the development of information and communication technology, and industry's interest in such big data is increasing day by day. In the shipbuilding and offshore industry also, there is growing interest in the effective utilization of data, since various and vast amounts of data are being generated in the process of design, production, and operation. In order to effectively utilize big data in the shipbuilding and offshore industry, it is necessary to store and process large amounts of data. In this study, it was considered efficient to apply Hadoop and R, which are mostly used in big data related research. Hadoop is a framework for storing and processing big data. It provides the Hadoop Distributed File System (HDFS) for storing big data, and the MapReduce function for processing. Meanwhile, R provides various data analysis techniques through the language and environment for statistical calculation and graphics. While Hadoop makes it is easy to handle big data, it is difficult to finely process data; and although R has advanced analysis capability, it is difficult to use to process large data. This study proposes a big data platform based on Hadoop for applications in the shipbuilding and offshore industry. The proposed platform includes the existing data of the shipyard, and makes it possible to manage and process the data. To check the applicability of the platform, it is applied to estimate the weights of offshore structure topsides. In this study, we store data of existing FPSOs in Hadoop-based Hortonworks Data Platform (HDP), and perform regression analysis using RHadoop. We evaluate the effectiveness of large data processing by RHadoop by comparing the results of regression analysis and the processing time, with the results of using the conventional weight estimation program.

• 한국체육학회지인문사회과학편
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• v.56 no.4
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• pp.397-413
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• 2017

The purpose of this study is to introduce the case and to provide related information for the physical education major to handle and utilize the social big data through the exploratory study for the application of sports industry in the fourth industrial revolution. For this study, data was collected from the article database, which covers the keyword such as 'Social Big Data', 'Sports' and so on. The analyzed articles were 86 articles. As a results, The research on social big data applied to sports industry are as follows: 1) Analysis of major issues related to sports fans' interests and sports events, 2) A study on media sports engagement, 3) The prediction analysis of sports game based on the sentiment analysis, 4) Development of salary estimation model for professional player in sports, 5) Research trend analysis and so on. In conclusion, the social big data analysis technology in the sports industry and management can be utilized variously. Therefore, the specialists of the sports industry and management field need to learn the techniques, to acquire the know-how for the research project, to convert the convergence thinking.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.156-169
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• 2016

The objective of this study is to investigate patent trends of Daegu city which tries to introduce environment friendly energy and to develop new technology or new industry sprung from technology convergence on smart decentralized energy technology and other technologies. After applying network analysis to corresponding groups of technology or industry convergence, strategy for future energy convergence industry is provided. Patent data applied in Daegu city area are used to obtain research goal. The technology which contains several IPC codes (IPC Co-occurrence) is considered as a convergence technology. Path finder network analysis is used for visualizing and grouping by using IPC codes. The analysis results categorized 13 groups in energy convergence industry and reclassified them into 3 cluster groups (Smart Energy Product Production Technology Group, Smart Energy Convergence Supply Technology Group, Smart Energy Indirect Application Technology Group) considering the technical characteristics and policy direction. Also, energy industry has evolved rapidly by technological convergence with other industries. Especially, it has been converged with IT industry, and there is a trend that energy industry will be converged with service industry and manufacturing industry such as textile, automobile parts, mechanics, and logistics by employing infrastructure as well as network. Based on the research results on core patent technology, convergence technology and inter-industry analysis, the direction of core technology research and development as well as evolution on decentralized energy industry is identified. By using research design and methodology in this study, the trend of convergence technology is investigated based on objective data (patent data). Above all, we can easily confirm the core technology in the local industry by analyzing the industrial competitiveness in the macro level. Based on this, we can identify convergence industry and technology by performing the technological convergence analysis in the micro level.