• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.35-46
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• 2017

This study analyzed the role of innovation intermediaries on the performance of technology-based firms in Korea. Technology-based firms are important to the economy because they contribute to regional economic development and national competitiveness. In Korea, various types of intermediaries, such as Techno-parks and incubators have been established to foster technology-based firms. Researchers analyzed various factors influencing the performance of technology-based firms. On the other hand, there have been few studies on the relationship between the innovation intermediaries and the performance of technology-based firms in Korea. This study identified the firms' capabilities, institutional and environmental factors in the light of the literature. A total of 2,313 technology-based firms in Techno-parks, business incubator of public institutes and universities were surveyed. Of these, 110 respondents were used for empirical analysis. OLS techniques were applied to analyze the data. The empirical results showed that the marketing competence, R&D capacity, which is a firms' innovation capacity, have a positive effect on the performance. The support of intermediaries positively affects the performance of technology-based firms. The economic aspects of regional innovation infrastructure, and cooperation with the customer has a positive effect on the performance of technology-based firms.

• Journal of Industrial Convergence
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• v.18 no.5
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• pp.14-22
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• 2020

In this study, in addition to non-technical innovation activities such as organizational innovation and marketing innovation, the purpose of this study was to verify through empirical analysis what mechanism appropriation affects the business performance of a company. Data were collected from the 2018 Korean Business Innovation Survey: Manufacturing Sector and used as analysis data. As a result of the analysis, it was verified through empirical analysis that non-technical innovation improves management performance, appropriability is affected by non-technical innovation, and acts as a mediating factor between management performance. Through this empirical analysis, it was derived that considering appropriation along with non-technical innovation on the recently studied non-technical innovation can be a meaningful study, and since securing such appropriation is an important factor in determining the business performance of a company. Companies need insight into the strategic use of technology protection measures to secure appropriation. As a limitation, it was limited to studies that verify that it has a mediating effect of non-technical innovation and appropriation, and it is considered that it is possible to develop a study that affects the management performance of a company through more various verification methods for future studies.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.317-323
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• 2022

The purpose of this study was to analyze how innovative facility investment and innovative research manpower capabilities of venture companies related to the 4th industrial smart farm affect the technological performance of patents and design registrations, and the financial performance of sales and operating profit. As a research method, a total of 47 venture companies were selected as a sample and regression analysis was performed. Research Results This study analyzes the technological commercialization factors of venture companies related to the 4th industrial smart farm and proposes to expand the budget for R&D government tasks for financial and technological success. In the future research direction, I believe that more discussion is needed on the contribution of companies to quantitative and qualitative growth.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1293-1304
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• 2022

The personalization recommendation system means analyzing each individual's interests or preferences and recommending information or products accordingly. These personalized recommendations can reduce the time consumers spend searching for information by accessing the products they need more quickly, and companies can increase corporate profits by recommending appropriate products that meet their needs. In this study, products are recommended to consumers using collaborative filtering, matrix factorization, and deep learning, which are representative personalization recommendation techniques. To this end, the data set after purchasing shopping mall products, which is raw data, is pre-processed in the form of transmitting the data set to the input of the recommended system, and the pre-processed data set is analyzed from various angles. In addition, each model performs verification and performance comparison on the recommended results, and explores the model with optimal performance, suggesting which model should be used when building the recommendation system at the mall.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.31-39
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• 2022

Stock prices are influenced by a number of external factors, such as laws and trends, as well as number-based internal factors such as trading volume and closing prices. Since many factors affect stock prices, it is very difficult to accurately predict stock prices using only fragmentary stock data. In particular, since the value of a company is greatly affected by the perception of people who actually trade stocks, emotional information about a specific company is considered an important factor. In this paper, we propose a deep learning-based stock price prediction model using sentiment analysis with news data considering temporal characteristics. Stock and news data, two heterogeneous data with different characteristics, are integrated according to time scale and used as input to the model, and the effect of time scale and sentiment index on stock price prediction is finally compared and analyzed. Also, we verify that the accuracy of the proposed model is improved through comparative experiments with existing models.

• The Journal of Bigdata
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• v.7 no.2
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• pp.43-73
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• 2022

In order to improve productivity, reduce costs, and improve decision-making efficiency, which are one of the main contents of the digital transformation promotion goal, many companies are promoting the introduction of various IT for digital transformation. Information technology (IT) is a key means of determining competitiveness, and the IT adoption worldwide is increasing every year. The financial industry is also actively introducing huge amounts of IT every year to generate profits, improve work efficiency, and secure a strategic competitive advantage. Compared to some studies on the IT adoption in the public and corporate sectors, empirical studies that reflect the characteristics of the financial industry are insufficient. In this study, the purpose of this study was to derive factors affecting the IT adoption in the financial industry for the promotion of digital transformation, and to analyze weights and priorities. By revealing through data analysis that there is a difference in the relative priorities of factors in the financial IT adoption for each group, it can be used as a reference model for which factors should be considered prior to IT adoption from the perspective of each group. It will be meaningful in that it exists.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.915-925
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• 2024

As non-face-to-face work increases due to the COVID-19 pandemic, companies have introduced collaboration tools to perform work without spatial constraints. The rapidly growing collaboration tool market continues to show high utilization rates even after the endemic due to the increase in demand for hybrid work that combines face-to-face and non-face-to-face work. The use of collaboration tools increases work efficiency, facilitates smooth collaboration, and increases data integration, generating various data. However, at the same time, it also increases the risk of exposure of corporate confidential information due to the possibility of external access by internal users. In response to this, an analysis method is needed to collect and acquire data during digital investigations targeting collaboration tools. In this paper, we identified local artifacts targeting JANDI, a collaboration tool in a Windows environment, and explained how to collect and analyze data through API reconstruction. Finally, we presented a digital forensic utilization method through scenario and chat room reconstruction.

• Korean small business review
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• v.40 no.3
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• pp.1-24
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• 2018

The purpose of this study is verifying with corporate financial data that the required investment amount flow shows a similar pattern as times passed, in new product development by start-up company. In the previous paper, the same authors proposed the required investment amount flow as a 'New Product Investment Curve (NPIC)'. In this study, we have studied further in various types of companies. The samples used are accounting data of 462 companies selected from 5,873 Korean companies which were finished external audit in 2015. The results of this study are as follows; The average investment period was 3 years for the listed companies, while 6 years for the unlisted companies. The investment payback period was 6 years for listed companies, while 17 years for unlisted companies. The investment payback period of the company supported by big affiliate company (We call 'greenhouse company') was 14~15 years, while 17 years for real venture companies. When we divide all companies into 4 groups in terms of R&D cost and variable cost ratio, NPIC explanatory power of 'high R&D and high variable cost ratio group (Automobile Assembly Business) is best. Among the eight investment cost indexes proposed to estimate the investment amount, the 'cash 1' (operating cash flow+fixed asset excluding land & building+intangible asset, deferred asset change)/year-end total assets) turned out to be the most effective index to estimate the investment flow patterns. The conclusion is that NPIC explanatory power is somewhat reduced when we estimate all companies together. However, if we estimate the sample companies by characteristics such as listed, unlisted, greenhouse, and venture company, the proposed NPIC was verified to be effective by showing the required investment amount pattern.

• The Journal of Society for e-Business Studies
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• v.13 no.3
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• pp.51-66
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• 2008

The Business Process Management System(BPMS) has received more attentions as companies increasingly realize the importance of business processes. However, traditional BPMS has focused mainly on correct modeling and exact automation of process flow, and paid little attention to the achievement of final goals of improving process efficiency and innovating processes. BPMS usually generates enormous amounts of log data during and after execution of processes, where numerous meaningful rules and patterns are hidden. In the present study we employ the data mining technique to find out useful knowledge from the complicated process log data. A data model and a system framework for process mining are provided to help understand the existing BPMS. Experiments with the simulated data demonstrate the effectiveness of the model and the framework.

• Annual Conference of KIPS
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• 2018.10a
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• pp.155-157
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• 2018

사용자의 개인정보 및 기업의 기밀정보와 같인 데이터의 안전한 이동 및 저장을 위하여 저장장치 보안 기술이 등장하였으며, 보안 USB와 보안 디스크 제품이 대표적으로 등장하였다. 이러한 제품은 저장되는 데이터를 안전하게 보호하기 위하여 사용자 인증 기술 및 데이터 암호 기술, 접근 제어 기술 등의 보안 기술을 적용한다. 특히, 사용자 인증 기술은 비밀번호 인증 기술이 대표적으로 활용되며, 인증을 강화하기 위하여 지문 인증 및 홍체 인증이 활용되고 있다. 따라서 본 논문에서는 보안 USB 제품, 특히 B 제품을 기반으로 적용된 사용자 인증 기술을 분석하고 이를 통하여 발생 가능한 보안 취약점을 분석한다. 분석 결과, 제품 B에 적용된 비밀번호 인증에서 발생 가능한 취약점을 도출하였으며, 이를 통하여 사용자 인증을 우회하여 저장장치 내부에 저장된 데이터의 탈취 가능함을 검증하였다.