• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.5
• /
• pp.749-759
• /
• 2021

We investigate the relationship between ownership control disparity and future firm value in high-technology industries, and whether the effect of ownership control disparity on future firm value is differentiated when high-tech industry firms belong to chaebol groups. Using 11,848 firm-year observations of Korean firms listed on the stock market from 2006 to 2019, we employ univariate analysis and Heckman 2 stage analysis to test our hypotheses. We define high-technology industries as ICT industries based on the Korean Standard Industrial Classification. We measure future firm value using average Tobin's q for the next three years and ownership control disparity using the shareholding ratio of affiliated companies. Our univariate test results show that mean of Tobin's q is higher in ICT firms than non-ICT firms and firms largely owned by affiliates. In multivariate test, we find that the ICT firms with higher ownership control disparity are positively associated with future firm value. However, this association is lessened when firms belong to a chaebol group. Based on our findings, we suggest ownership control disparity has an additional positive effect on future firm in high-technology industries. The negative impact of chaebol groups on the association suggests the possibility of diversification discount in business group.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.3
• /
• pp.185-198
• /
• 2017

Since the year 2000, the growth rate of domestic manufacturing has declined and the sales and employment have decreased. Major developed countries have established a variety of strategies to strengthen their manufacturing competitiveness, and promote manufacturing innovation through the convergence of technology and ICT. The key to manufacturing innovation is to reduce the time and cost for developing new products using modeling and simulation (M&S) technology in the product design stage. M&S industries, which belong to the top sector of the industry value chain, have a huge ripple effect across other industries. On the other hand, the competitiveness of the domestic M&S industry is weak compared to developed countries and even the definition and classification of domestic M&S companies have not been made. In this paper, by analyzing the Korea Standard Industry Classification (KSIC), five fine industry classifications included in M&S service companies were derived. In addition, the 307 M&S service companies were derived in accordance with the selection procedure of 3 steps from the 11,822 related companies. To analyze the capabilities of domestic M&S service companies, the current status of the selected M&S service companies was investigated. Considering the Korean economy's high dependence on the manufacturing industry, strengthening the competitiveness of manufacturing industry is required by enhancing the capacities and building an ecosystem in domestic M&S services for future sustainable economic growth.

• Journal of Information Technology Services
• /
• v.19 no.2
• /
• pp.23-35
• /
• 2020

The development of digital technology is changing the paradigm of the healthcare industry to preventive and consumer-oriented. The combination of the ICT industry and the bio-healthcare industry is emerging as a core industry in the era of the Fourth Industrial Revolution. The Korean government has also selected the bio-healthcare industry as one of the three key future development industries. In May, the government announced its bio-health industry innovation strategy and set a goal of 300,000 employees. Therefore, analyzing the effects of employment on the related industries of the digital bio-healthcare industry is very important for the establishment of future industrial and technology development policies. The research method restructures the integrated classification of 32 industries into 34, including the digital bio-healthcare industry, using the classification criteria of the government and professional institutions, and then reorganizes the digital bio-healthcare industry into eight industries classified as one industry group. The analysis data was taken from the Bank of Korea's 2019 data. Various trigger coefficients and ripple effects coefficients were rewritten using the analysis method of the Input-output Statistics. The analysis of the results compares the employment-induced effects of the digital bio-healthcare industry and the ripple effects of related industries in production, investment and value-added. In addition, in terms of investment effect, the effects of in-house and related industries were compared. It is hoped that the results of this study will be used to establish employment and industrial policies.

• Journal of Internet Computing and Services
• /
• v.14 no.5
• /
• pp.119-128
• /
• 2013

As ICT technology becomes advanced, the importance of future internet is emphasized and in part of that, M2M (Machine-to Machine communications) is magnified in terms of importance and usage in public and private sector. M2M is emerging as a next generation strategic industry but there is no existing analyzed data or market classification, so it disrupts establishing policies on the M2M industry. As the technology is progressing, the evolution from M2M to IoT (Internet of Things) has started and many countries actively try to find technological trend through market analysis in order to develop new growth engine. Therefore, in order to strengthen competitiveness, we should secure differentiated capabilities in industry and service. This article examines Korea's domestic market and international market trends in IoT and analyses the economic impact of the IoT industry using quantitative methodology and evaluates relations between the IoT industry and other relevant industries. As a result, the effect of IoT industry on production inducement is KRW474.6 billion; the effect on value-added inducement is KRW314.7 billion; and it is measured that 3,628 jobs will be created by the IoT industry.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.99-122
• /
• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Journal of Platform Technology
• /
• v.11 no.3
• /
• pp.45-55
• /
• 2023

This paper presents a model that supports decision-makers in the Korean film industry to maximize the success of online movies. To achieve this, we collected historical box office movies and clustered them into types to propose a model predicting each type's online box office performance. We considered various features to identify factors contributing to movie success and reduced feature dimensionality for computational efficiency. We systematically classified the movies into types and predicted each type's online box office performance while analyzing the contributing factors. We used automated machine learning (AutoML) techniques to automatically propose and select machine learning algorithms optimized for the problem, allowing for easy experimentation and selection of multiple algorithms. This approach is expected to provide a foundation for informed decision-making and contribute to better performance in the film industry.

• Journal of Platform Technology
• /
• v.10 no.4
• /
• pp.56-61
• /
• 2022

Various convergence technologies discovered through the 4th industrial revolution are permeating the industry. Drones are being used in industries such as construction, transportation, and national defense based on convergence technology. Quart-copter drone control is being used in a wide range of fields from the visual field of operation with the naked eye to the remote field of view using GCS. If we classify those who operate industrial drones, there are general pilots who directly use drones, instructors who train drone pilots, and mechanics who check the status of drones and use them for a long time. Depending on the shape of the screen of the drone GCS, a user's quick response or key data can be acquired. Accordingly, in this study, GUI characteristics were analyzed for the mission planner GCS and a screen composition method according to the user was proposed.

• Information Systems Review
• /
• v.22 no.4
• /
• pp.185-203
• /
• 2020

With the advent of 4th industrial revolution, the manufacturing industry is converging with ICT and changing into the era of smart manufacturing. In the smart factory, all machines and facilities are connected based on ICT, and thus security should be further strengthened as it is exposed to complex security threats that were not previously recognized. To reduce the risk of security incidents and successfully implement smart factories, it is necessary to identify key security factors to be applied, taking into account the characteristics of the industrial environment of smart factories utilizing ICT. In this study, we propose a 'hierarchical classification model of security factors in smart factory' that includes terminal, network, platform/service categories and analyze the importance of security factors to be applied when developing smart factories. We conducted an assessment of importance of security factors to the groups of smart factories and security experts. In this study, the relative importance of security factors of smart factory was derived by using AHP technique, and the priority among the security factors is presented. Based on the results of this research, it contributes to building the smart factory more securely and establishing information security required in the era of smart manufacturing.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.26 no.2
• /
• pp.551-558
• /
• 2016

The recent convergence in ICT industry has created new businesses as well as other opportunities. However, it entails new convergence threat accompanied by security risks. Even though there are security professionals who are dealing with the situation, there is not enough human resource in risk management. Moreover, the amount of research that studies quality of education and training security personnel is not sufficient. This paper explores the curriculum of information security education in the private sector and reasons out fifteen standard curriculums in four professional fields categorized by job classification. In addition, it provides a weighted score table based on the evaluation indicator for the effective security education certificates in the private sector.

• The Journal of Society for e-Business Studies
• /
• v.26 no.1
• /
• pp.1-28
• /
• 2021

FinTech has been discussed as an important business area towards technology-driven financial innovation. The term fintech is a combination of finance and technology, which means ICT technology currently associated with all finance areas. The popularity of the fintech industry has significantly increased over time, with full investment and support for numerous startups. Therefore, both academia and practice tried to analyze the trend of the fintech area. Despite the fact, however, previous research has limitations in terms of collecting relevant databases for fintech and identifying proper application areas. In response, this study proposed a new method for analyzing the trend of Fintech fields by expanding Fintech's terminology and using network analysis and topic modeling. A new Fintech terminology list was created and a total of 18,341 patents were collected from USPTO for 10 years. The co-classification analysis and network analysis was conducted to identify the technological trends of patent classification. In addition, topic modeling was conducted to identify the trends of fintech in order to analyze the contents of fintech. This study is expected to help both managers and investors who want to be involved in technology-driven financial services seize new FinTech technology opportunities.