• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.2
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• pp.99-107
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• 2024

The recent digital transformation in many industries has led to an explosion of data, which has exponentially increased the amount of data that companies need to generate and process. As a result, enterprises are leveraging ERP systems to manage and analyze large amounts of data in real time. However, due to cost and time issues in processing large amounts of data in existing ERP systems, it is essential to apply data archiving technology that can compress and store data in real time in existing systems. Therefore, this paper aims to identify the trends of the target technology by utilizing patent data on ERP-based real-time data archiving technology, analyze the core patents, and create new core patents based on them.

• Journal of Digital Convergence
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• v.14 no.3
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• pp.83-94
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• 2016

The objective of this paper is to examine the determinants of technology transfer for Small and Medium Enterprises. Based on IP-MARKET, KIPRIS, and Wintelips, we employ the logistic regression analysis using all data related with technology transfer in markets for intellectual property rights from 2008 through 2012. Our main results are as followings: (i)the more inventors the higher possibility; and (ii)the more claims and forwards the higher possibility of success in technology transfer in Small and Medium Enterprises.

• Information Systems Review
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• v.10 no.1
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• pp.91-113
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• 2008

Recently, as most functional business activities in an enterprise are supported by computerized information systems, data duplication and inconsistency among functional information systems become serious problems. It brings people to have many interests on data quality management. This paper presents a case study in which a company had improved their data quality by enhancing their data quality management processes. Though the case study, we describe main issues and risk factors in the process of data quality improvement projects as well as solutions to resolve the issues, which can be referred by other companies who pursue data quality improvement. Also, the improvement effects are evaluated by multidimensional perspectives which include quantitative and qualitative measures on data quality, productivity, customer satisfaction, organization, and culture.

• Industry Promotion Research
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• v.2 no.1
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• pp.1-7
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• 2017

In the case of manufacturing companies, various types of data are collected. Many of these data can be used as useful information for product reliability evaluation. In this study, we first look at data that can be collected by a manufacturing company and related to products, technology, finance, and customers. Next, we will look at the company's business management system, scientific journals, test and marketing survey data, etc., as sources of data. Next, look at what kind of data is collected over the product life cycle to evaluate the reliability of the product. In the development stage of the product, reliability test is performed for each component, and reliability data is collected by performing reliability test at the subsystem and system level. On the other hand, at the manufacturing stage, data on the functional test and the design change test of the product are collected, and at the field stage, the problem of the product is detected in the field and collected in the form of data. Finally, let's look at what you need to do to make a reasonable analysis later in your data collection.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.157-175
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• 2022

At this moment, a paradigm shift is taking place across all sectors of society for the transition movements to the digital economy. Various movements are taking place in the global agricultural industry to achieve innovative growth using big data which is a key resource of the 4th industrial revolution. Although the government is making various attempts to promote the use of big data, the movement of the agricultural industry as a key player in the use of big data, is still insufficient. Therefore, in this study, effects of performance expectations, effort expectations, social impact, facilitation conditions, based on the Unified Theory of Acceptance and Use of Technology(UTAUT), and innovation tendencies on the acceptance intention of big data were analyzed using the economic and practical benefits that can be obtained from the use of big data for agricultural-related companies as moderating variables. 333 questionnaires collected from agricultural-related companies were used for empirical analysis. The analysis results using SPSS v22.0 and Process macro v3.4 were found to have a significant positive (+) effect on the intention to accept big data by effort expectations, social impact, facilitation conditions, and innovation tendencies. However, it was found that the effect of performance expectations on acceptance intention was insignificant, with social impact having the greatest influence on acceptance intention and innovation tendency the least. Moderating effects of economic benefit and practical benefit between effort expectation and acceptance intention, moderating effect of practical benefit between social impact and acceptance intention, and moderating effect of economic benefit and practical benefit between facilitation condition and acceptance intention were found to be significant. On the other hand, it was found that economic benefits and practical benefits did not moderate the magnitude of the influence of performance expectations and innovation tendency on acceptance intention. These results suggest the following implications. First, in order to promote the use of big data by companies, the government needs to establish a policy to support the use of big data tailored to companies. Significant results can only be achieved when corporate members form a correct understanding and consensus on the use of big data. Second, it is necessary to establish and implement a platform specialized for agricultural data which can support standardized linkage between diverse agricultural big data, and support for a unified path for data access. Building such a platform will be able to advance the industry by forming an independent cooperative relationship between companies. Finally, the limitations of this study and follow-up tasks are presented.

• The Korean Journal of Financial Management
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• v.27 no.2
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• pp.63-84
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• 2010

We investigate whether FX hedging materially increases firm value by testing the valuation effect of Foreign Exchange Risk Insurance in Korea, using our sample of 84 listed firms with 617 observations between 2000 and 2008, Employing Tobin's Q as a proxy of firm value and foreign exchange risk insurance as a proxy of hedging instrument, we find a positive relation between firm value and the use of foreign exchange risk insurance. The hedging premium is statistically significant and is on average 7.4% of sample firm value. We also find our empirical results consistent with the preceding evidence that firm uses the hedging instrument in order to alleviate economic frictions and then hedging causes an increase in firm value.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.601-613
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• 2005

일반적으로 B2C가 불특정 다수에 대한 서비스라면 B2B는 특정 소수에 대한 서비스라고 할 수 있다. 이러한 특성으로 B2C와 B2B에서 고객의 구매의도는 다르게 평가되어야 한다. 또한 B2B는 협상이라는 단계가 있고, 이것은 B2C와 B2B의 구매의도 평가기준에 영향을 미치게 된다. 본 연구에서는 B2B에서 구매의도 파악을 위한 웹 로그 분석 모형을 제시한다. 제시된 모형을 통해 구매의도 파악을 위한 웹 로그 분석 데이터를 추출하고, 추출된 데이터를 기업의 레거시 시스템 데이터와 통합하는 과정을 보여준다. 또한 분석 데이터를 추출하기 위한 웹마이닝 과정과 추출된 분석 데이터가 데이터베이스에 저장되는 과정을 보여준다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.177-179
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• 2018

기존 산업평가 방법은 산업별로 분류된 기업의 재무, 비재무 관련 정형 데이터를 기반으로 통계적 기법을 이용하여 각 산업을 평가하고 있다. 이러한 정형 데이터 기반의 산업 평가 방법은 산업별 재무 정보의 집계 및 통계에 오랜 시간이 소요된다. 따라서, 현재 시장 상황을 반영하기 어려운 현실이다. 최근에는 빠르게 변화하는 산업 환경을 반영하기 위해 뉴스 기사와 같은 비정형 데이터를 통해 산업 트랜드를 분석하기 위한 연구가 이루어 지고 있다. 이에, 본 논문에서는 실시간으로 변화하는 산업 트렌드를 반영하여 적시에 산업 분석 정보를 제공하기 위해 정형/비정형 데이터 기반의 산업평가 정보 분석 엔진을 구현하고, 산업별로 분석된 산업평가 정보를 활용하여 사용자가 직관적인 판단을 할 수 있도록 산업평가 정보 시각화 서비스를 제안한다.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.73-85
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• 2014

As the importance of big data analysis increases to improve the competitiveness of a corporate, a unified big data project development methodology is required in order to study the problem of a corporate in a systematic way and evaluate the problem w.r.t. a business value after solving the problem. This paper propose Scientific Data Anslysis and Development methodology(SDAD) which are integrated methodology of software development and project management for easier application into a field project. SDAD consisits of 6 stages(problem definition stage, data preparation stage, model design stage, model development stage, result extraction stage, service development state), each stages has detailed processes(47) and productions(93). SDAD, furthermore, unified previous ISP, DW, SW development methodologies in terms of the data analysis and can easily interchange the productions with them. This paper, lastly, introduces a way to assign responsible persons for each process and provide communication procedures in RACI chart to improves the efficiency of the interaction among professionals from different subjects. SDAD is applied to a Bigdata project in Korea Employment Information Services institution and the result turned out to be acceptable when evaluated by the supervision.

• The Journal of Small Business Innovation
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• v.20 no.3
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• pp.49-68
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• 2017

Platform finance is emerging as an alternative finance for SMEs by suggesting a new funding source based on a new technology named FinTech. The essence of this business is the adapting ICT challenges to the financial industry that can adequately reflect risk assessment using Big Data and effectively meet individual risk-return preference. Thus, this is evolving as an alternative to existing finance in the form of P2P loans for Micro Enterprises and supply-chain finance for SMEs that need more working capital. Platform finance in Korea, however, is still at an infant stage and requires policy support. This can be summarized as follows: "Participation of institutional investors and the public sector," meaning that public investors provide seed money for the private investors to crowd in for platform finance. "Negative system in financial regulations," with current regulations to be deferred for new projects, such as Sandbox in the UK. In addition, "Environment for generous use of data," allowing discretionary data sharing for new products," and "Spreading alternative investments," fostering platform finance products as alternative investments in the low interest-rate era.