• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.150-162
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• 2016

This study empirically analyze the effect of government R&D investment to find out whether it complements or substitutes for the firm R&D. In order to do it panel data set was constructed for the period of three years from 2012 to 2014 based on the number of 1301 data by utilizing national technology information service(NTIS) and publicly announced financial statement. Analysis was implemented in consideration of size of the firm(large corporation, small and medium sized firm) of which sample was obtained from only listed company. The result of two-way fixed effect model and two-way random effect model is as follows. In case of large corporation, government R&D investment has an effect of substitute for the company's R&D on the other hand, small and medium sized firm shows an complementary effect. It verifies that current R&D policy is appropriate. Therefore government's direct subsidy is expected to be successful to fertilize firm's innovation by allocating government R&D budget efficiently.

• Management & Information Systems Review
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• v.36 no.4
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• pp.53-80
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• 2017

The purpose of this study is to examine the determinants of the number of bank relationships in Korea. Firm-specific determinants considered here include leverage, size, age, return on asset, investment grade, tangibility, liquidity, R&D expenditure. We estimate the effects of these variables, and compare the results with those from previous studies performed for other economies. Concerning the effects of business cycle, we find that the business cycle is an important factor in determining the number of bank relationships. The number of bank relationships varies over the business cycle, and we notice a counter-cyclical behavior, which means the number decreases during economic expansions and increases during contractions. This result can be interpreted as a result of firms' diversification of borrowings into multiple banks in order to reduce the liquidity risk during the recession. In the subsets, however, the number of bank relationships for large firms is stable regardless of the business cycle. Unlisted firms, non-chaebol, and low credit quality firms which have relatively limited access to alternative sources of financing show counter-cyclical behavior. Finally, such phenomena is not observed in the non-competitive credit market, while they show a counter-cyclical behavior in the competitive credit market.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.1-14
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• 2013

Annual average daily traffic (AADT) serves the important basic data in transportation sector. Despite of its importance, AADT is estimated through permanent traffic counts (PTC) at limited locations because of constraints in budget and so on. At most of locations, AADT is estimated using short-term traffic counts (STC). Though many studies have been carried out at home and abroad in an effort to enhance the accuracy of AADT estimate, the method to simplify average STC data has been adopted because of application difficulty. A typical model for estimating AADT is an adjustment factor application model which applies the monthly or weekly adjustment factors at PTC points (or group) with similar traffic pattern. But this model has the limit in determining the PTC points (or group) with similar traffic pattern with STC. Because STC represents usually 24-hour or 48-hour data, it's difficult to forecast a 365-day traffic variation. In order to improve the accuracy of traffic volume prediction, this study used the geostatistical approach called co-kriging and according to their reports. To compare results, using 3 methods : using adjustment factor in same section(method 1), using grouping method to apply adjustment factor(method 2), cokriging model using previous year's traffic data which is in a high spatial correlation with traffic volume data as a secondary variable. This study deals with estimating AADT considering time and space so AADT estimation is more reliable comparing other research.

• KDI Journal of Economic Policy
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• v.13 no.4
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• pp.141-153
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• 1991

The optimal choice of the tax rate and the inflation rate framework is extended to yield relevant interpretations for the optimal fiscal and monetary policy regime in Korea. To study the relationship between the government budget and monetary growth in different environments of policy coordination, two models assuming different degrees of fiscal dominance are developed. By modelling differing institutional arrangements of the fiscal and the monetary authority from an optimal government finance viewpoint, we find the optimal relationship among some important fiscal and monetary variables. By testing the existence of the relationship empirically, we find the characteristics of the optimal policy-mix regime in Korea. The first model-the strong from of fiscal dominance-studies the optimal collection of seigniorage in a period-by-period optimization with standard assumptions on the income velocity of money, deriving a general testable result: the optimal inflation/tax rate ratio co-vary with the marginal revenue ratio. The second model-the weak form of fiscal dominance-studies an implication of the inflationary bias of discretionary monetary policy in the presence of fiscal side distortions. This model shows that the tax rate and the inflation rate can have a positive correlation. Empirical tests of the theoretical results are done for the Korean economy for 1972-1989 period. The test results show that the macroeconomic policy regime in Korea can be characterized by the strong form of fiscal dominance, implying the importance of the government budget in explaining money growth and inflation.

• Korean Journal of Construction Engineering and Management
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• v.16 no.3
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• pp.113-122
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• 2015

VE workshop is performed based on VE expert' experiences without retrieving VE data of similar previous projects. Moreover, it usually omitted or applied for the sake of formality due to insufficiently understanding VE function, limited time, space and budget. Even though many studies have established VE databases for retrieving and reusing VE data, VE workshop is still inefficient and ineffective to improve projects' values. With this regard, this study proposes a preliminary prototype for improving VE workshop, which utilizes the state-of-the-art information communication technologies(ICTs) including Building Information Modeling(BIM), Mobile Computing(MC), Network Service System(NSS), and Database Management System(DBMS) for better managing, storing and reusing VE data. The prototype was developed to evaluate advantages and limitations. The results show that the proposed prototype can support visual VE data retrieval from similar previous projects, enhance communication among VE team and save much time and cost comparing to traditional VE. Through this, the productivity of VE workshop can improve efficiently and effectively.

• Journal of the Korea Institute of Building Construction
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• v.18 no.3
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• pp.295-303
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• 2018

In the DBB delivery system, the design stage and the construction stage are separated. Because of this, design changes frequently occur, and problems such as construction cost overrun, schedule delay, and quality deterioration happen as well. Recently, in the construction industry CM at Risk(CM@R) delivery system, which can systematically solve the above-mentioned problems of DBB delivery system, meet various demands of clients, and overcome the limited cost and period. In the CM@R delivery system, the contractor negotiates for a maximum guaranteed price(GMP) with the client at the design stage, and the CM performer carries out the construction within the GMP. However, uncertainties are inherent in the GMP calculation because the calculation is based on unfinished drawings and documents. In this study, a Probabilistic Earlier GMP Calculation Method by combining a probabilistic tool of Monte Carlo simulation with a case based reasoning is proposed so that the uncertainty in GMP calculation is reflected. After the earlier GMP is calculated, a process to calculate the $2^{nd}$ GMP at the time of around 80 % of detailed deign and to negotiate with the client to fix the final GMP is proposed. The Probabilistic Earlier GMP Calculation Method is verified through the case study. In this study, researchers set the range of GMP through the proposed probabilistic GMP calculation and tried to reduce the risk through negotiation between the client and the CM performer. The proposed method and process would contribute to the successful introduction of CM@R in Korea.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.137-153
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• 2003

Comparison shopping is the most popular functionality in the e-Marketplace. Most of their revenue has been generated kent the Internet advertisement, but the ad earning was declined as the ad costing per action method widespread. Seller less familiar to the customer shrinks from chances for advertising and exposing their products. So, we need an efficient methodology subject to the seller's ad budget and other constraints, and it also has to increase comparison broker's earning in the e-Marketplace. Our research proposed and developed an ad planning methodology using comparison challenge approach which can be applied by 3$^{rd}$ party comparison brokers. Comparison challenge planning is organized with challenge policy of competitor level, product level and specification level. With that policies and basic challenge propositions, we measure the quantified value of functional distance between the specifications of my product and competitor's product. My product challenges the comparison using the comparative ad format to the similar but inferior competitor's product based on quantified valuation. Comparison challenge planning system has two phases of comparative value generation and optimization. We developed a prototype system and applied it to the desktop PC market of five major manufacturers. Our performance was emphasized by comparing to other comparative ad methods such as random display method and minimum distance method..

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.15-31
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• 2009

The next generation IT environment is expected to be an open computing environment based on Grid computing technologies, which allow users to access to any type of computing resources through networks. The open computing environment has benefits in aspects of resource utilization, collaboration, flexibility and cost reduction. Due to the variation in performance of open computing resources, however, resource allocation simply based on users' budget and time constraints often fails to meet the Service Level Agreement(SLA). This paper proposes the Mean-Absolute Deviation(MAD) portfolio optimization approach, in which service brokers consider the uncertainty of performance of resources, and compose resource portfolios that minimize the uncertainty. In order to investigate the effect of this approach, we simulate an open computing environment with varying uncertainty levels, users' constraints, and brokers' optimization strategies. The simulation result concludes threefolds. First, the MAD portfolio optimization improves the success ratio of delivering the required performance to users. Second, the success ratio depends on the accuracy in predicting the variability of performance. Thirdly, the measured variability can also help service brokers expand their service to cost-critical users by discounting the access cost of open computing resources.

• Korean Journal of Heritage: History & Science
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• v.55 no.2
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• pp.132-144
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• 2022

The representative cultural heritage showing relatively clearly the growth and development of the Mahan culture in Jeollabuk-do is the tombs in Sangun-ri, Wanju. The tombs were excavated in the early 2000s; however, little effort has since been made to investigate or preserve the wider area around the tombs. Thus, only the status quo has been maintained except for the conservation of a few relic parks. In recent years, active cultural management has become necessary to maintain the historic areas in the Mahan cultural region, so these tombs are once again receiving attention. To further the preservation efforts in the area, it is important to actively discuss budget adjustments, consider additional investigation, and re-examine the academic value of the historic site. Practical limitations mean that it is necessary to approach the conservation strategy in stages. The first step is to have the areas of cultural interest and any adjacent lands urgently designated as provincially protected cultural heritage. Consequently, historically important areas on private land should be identified and purchased. Thereafter, a preservation plan should be established to reinforce the historical value of the sites as nationally designated historic sites. This can be achieved through the restoration of the landscape adjacent to the historic sites, as well as additional investigations and designation of sites in surrounding areas. These goals can only be achieved with the active interest and support of relevant institutions.

• Journal of Korean Library and Information Science Society
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• v.53 no.1
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• pp.231-263
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• 2022

The understanding and capability to utilize artificial intelligence (AI) incorporated technology has become a required basic skillset for the people living in today's information age, and various members of the university have also increasingly become aware of the need for AI education. Amidst such shifting societal demands, both domestic and international university libraries have recognized the users' need for educational content centered on AI, but a user-centered service that aims to provide personalized recommendations of digital AI educational content is yet to become available. It is critical while the demand for AI education amongst university students is progressively growing that university libraries acquire a clear understanding of user intention towards an AI educational content recommender system and the potential factors contributing to its success. This study intended to ascertain the factors affecting acceptance of such system, using the Extended Technology Acceptance Model with added variables - innovativeness, self-efficacy, social influence, system quality and task-technology fit - in addition to perceived usefulness, perceived ease of use, and intention to use. Quantitative research was conducted via online research surveys for university students, and quantitative research was conducted through written interviews of university librarians. Results show that all groups, regardless of gender, year, or major, have the intention to use the AI-related Educational Content Recommendation System, with the task suitability factor being the most dominant variant to affect use intention. University librarians have also expressed agreement about the necessity of the recommendation system, and presented budget and content quality issues as realistic restrictions of the aforementioned system.