• Journal of Korea Society of Industrial Information Systems
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• v.24 no.2
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• pp.65-80
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• 2019

With the advent of the ICT-based 4th industrial revolution, the convergence of the manufacturing industry and ICT seems to be the new breakthrough for achieving the company's competitiveness and play a role on the key element for accelerating the revival of the manufacturing industry. When the smart factory is implemented, each plant can analyze the quantity of data collected, build the data-driven operation systems which can make decisions, and ultimately discover the correlation among many events in the manufacturing sites. As the customers' needs become diversified more and more, it is required for the company to change its operating method from large quantity batch production systems to customizable and flexible manufacturing systems. For performing this requirements, it is essential for the company to adopt the smart factory. Based on technology acceptance model (TAM), this study investigates the factors influencing continuous use intention and switching intention of the smart factory. To do so, a questionnaire survey is conducted both online and offline. 122 samples are used for the study analysis. The results of this study will provide many implications with many researchers and practitioners relevant smart factories.

• Journal of Distribution Science
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• v.11 no.10
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• pp.27-36
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• 2013

Purpose - This paper empirically studies the effects of advanced design innovation strategy on business performance, to investigate manufacturing industries that can develop design-driven-innovation strategies. Many researchers now recognize the importance of design in a CEO's decision-making process. To analyze these effects, this study deduces the definition of advanced design strategy by reviewing existing studies. The advanced design is a strategy that is applied to improve business performance instead of the appearance of a product for increasing its sales. In terms of business processes, the advanced design strategy is defined as the incorporation of business activities prior to the development of the product, to offer new experiences and values to users, from those designs. Research design/data/methodology - This paper establishes a model for empirical analysis. In this study, we derived factors of the characteristics of advanced design based on previous studies. We tried to investigate whether advanced design innovation strategy and entrepreneur's characteristics could have any impact on business performance. At the same time, we tried to find out the moderating effect of entrepreneurs' characteristics. The advanced design is made up of three elements: precedence, integration, and immersion of design activities. These three elements are independent variables for the model. The dependent variables are: increased rate of sales, R & D performance, and public image of the company. Specifically, this study establishes a CEO's characteristics as a moderating variable between the independent and dependent variables. Results - We proved that the level of entrepreneurs' characteristics has a moderating effect on the business performance. The findings of this study offer the following theoretical implications. The precedence of design activities positively affects the increased rate of sales by offering new experiences to users and creating new values. The integration of design activities also has a positive effect on the R&D performance. In addition, the immersion of design activities positively influences all the elements comprising business performance. The analysis of moderating variables elucidates that CEO's characteristics have a moderating role between precedence, integration, and immersion of design activities, and business performance. Conclusions - The practical implications of the study are as follows. This study contributes to the progression of advance design theories by conducting an empirical study on the advanced design concept. More importantly, the empirical study on the CEO group seeking exploratory innovation supports Verganti's "design-driven innovation" concept, according to which design can make innovation successful by offering useful values to users, as evident in the case of many innovative companies, such as Nintendo and Apple. Future studies need to investigate the reliability of practical examples, including the various activities of business. We suppose that there may be real differences between the results of this study and the applicative situation in the presence of a CEO group.

• Journal of Aerospace System Engineering
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• v.9 no.4
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• pp.62-66
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• 2015

During the conceptual design of turboprop aircraft, the power effect driven from rotating propeller is typically obtained from empirical data. In the present paper, propeller power effect was obtained by using unsteady three-dimensional Navier-Stokes solver with $k-{\omega}$ turbulence model for the accurate prediction of turboprop aircraft performance. In order to simulate the relative motion between propeller and fuselage, unsteady sliding mesh method was used. During simulation, three flow conditions such as climb, cruise and descending flight were selected considering the flight envelop of the real turboprop aircraft. For the correction of aerodynamic coefficients, the thrust effect of engine exhaust gas was included based on the engine manufacturer's data. Using the computational results, the correction table for the aerodynamic coefficient of turboprop aircraft was suggested for the performance analysis of turboprop aircraft.

• Ocean and Polar Research
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• v.31 no.1
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• pp.11-29
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• 2009

Variability of sea surface temperature (SST) in the Japan/East Sea (JES) was studied using complex empirical orthogonal function (CEOF) analysis. Two daily data sets were analyzed: (1) New Generation 0.05o-gridded SST from Tohoku University, Japan (July 2002-July 2006), and (2) 0.25o-gridded SST from the Japan Meteorological Agency (October 1993-November 2006). Linkages with wind stress curl were revealed using 6-h 1o-gridded surface zonal and meridional winds from ancillary data of the Sea- WiFS Project, a special National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) product (1998-2005). SST anomalies (SSTA) were obtained by removing the seasonal signal, estimated as the leading mode of the CEOF decomposition of the original SST. Leading CEOF modes of residual SSTA obtained from both data sets were consistent with each other and were characterized by annual, semiannual, and quasi-biennial time scales estimated with 95% statistical significance. The Semiannual Mode lagged 2 months behind the increased occurrence of the anticyclonic (AC) wind stress curl over the JES. Links to dynamic processes were investigated by numerical simulations using an oceanic model. The suggested dynamic forcings of SSTA are the inflow of subtropical water into the JES through the Korea Strait, divergence in the surface layer induced by Ekman suction, meridional shifts of the Subarctic Front in the western JES, AC eddy formation, and wind-driven strengthening/weakening of large-scale currents. Events of west-east SSTA movement were identified in July-September. The SSTA moved from the northeastern JES towards the continental coast along the path of the westward branch of the Tsushima Current at a speed consistent with the advective scale.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.7 no.3
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• pp.257-264
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• 1995

Various factors are investigated on the bed load transport driven by waves and current, and proper forms of bed load transport formulas mainly used in river hydraulics are chosen for the estimation of combined flow bed load transport after considering the additional factors. The BYO Model is employed for the computation of maximum bed shear stress and mean bed shear stress of the combined flow. The friction factor of uni-directional flow is estimated by using modified Keulegan equation, and equivalent roughness height is determined by obtaining correct answer for the bed shear stress of uni-directional flow. Empirical constant in each bed load formula is determined by applying it to Bijker's laboratory data of bed load transport by waves and current and the formulas obtained are discussed on their final forms with the values of empirical constants.

• Journal of Korea Trade
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• v.23 no.7
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• pp.113-127
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• 2019

Purpose - This paper investigates whether financial crises could be the indicators of capital outflow waves or vice versa in Korea. Korea has experienced two severe financial crises, which are the Asian Crisis and the global financial crisis. Although there were many variables associated with these two remarkable events, one notable variable was gross capital outflows, which had significantly increased around them. Motivated by existing literature which built theoretical frameworks explaining the relationship between capital flight and financial crises, we examine the empirical evidence for this relationship. Design/methodology - We use panel data from 61 countries including Korea from 1980 to 2009 to study the associations between capital flight and diverse financial crises such as banking, currency, debt, and inflation crises. To be specific, we use the complementary log-log model to see whether capital outflow waves are reliable indicators for domestic financial crises. Findings - The results show, first, that banking, currency, and inflation crises are associated with capital flight. Second, debt crises are also associated with capital flight, but the result is not robust to different specifications. And, third, the positive associations between capital flight and crises are mainly driven by banking flows rather than FDI and portfolio flows. Originality/value - This paper is one of a few studies that investigates domestic (not foreign) investors' behavior during financial turmoil. Furthermore, theoretical studies which provide contradictory explanations on the movements of gross capital outflows during financial crises emphasizes the importance of empirical evidence in this paper.

• Health Policy and Management
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• v.14 no.1
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• pp.89-120
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• 2004

The purpose of this empirical study is to test hypotheses in order to identify the cost drivers that drive indirect costs in general hospitals in Korea. In various cases' studies, it has been suggested that overhead costs are driven by volume and complexity variables, how they are structurally related and how the cost impacts of these variables can be A unique feature of the research is the treatment of complexity as an endogenous variable. It is hypothesized that level of hospital complexity in terms of the number of services provided(i.e., “breath" complexity) and the intensity of individual estimated in practice. overhead services(ie., “depth" complexity) are simultaneous determined with the level of costs needed to support the complexity. Data used in this study were obtained from the Database of Korean Health Industry Development Institute, Health Insurance Review Agency and analyzed using simultaneous equation model, path model. The results found those volume and complexity variables are all statistically signi-ficance drivers of general hospital overhead costs. This study has documented that the level of service complexity is a significant determinant of hospital overhead costs, caution should be exercised in interpreting this as supportive of the cost accounting procedures associated with ABC. with ABC.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.4
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• pp.149-165
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• 2016

The objectives of this study is to assess the influence of individual characteristics and economic environment on the entrepreneurship such as entrepreneurial Intention and behavior based on the theory of Planned behaviors. This study used a country-level merged data set composed of GEM(Global Entrepreneurship Monitor) data and the OECD Statistics data. And this used the fixed effect model to analyze the panel data of 31 OECD countries during the period from 2005 to 2014. Our findings show that subjective norm has a significantly positive effect on entrepreneurial intention. In individual characteristics, the perceived opportunities has a significantly positive effect on early-stage entrepreneurial activity(TEA) and improvement-driven opportunity entrepreneurial activity. We identify the differences of between necessity-driven and improvement-driven opportunity entrepreneurial activity. For example, the effect on necessity-driven entrepreneurial activity is significantly negative. We also find the differences of between necessity-driven and improvement-driven opportunity entrepreneurial activity in economic environment variables. While real GDP growth as a demand variable has a significantly positive effect on necessity-driven entrepreneurial activity, unemployment rate as a supply variable has a significantly negative effect on early-stage entrepreneurial activity(TEA) and improvement-driven opportunity entrepreneurial activity. And GDP per capita as a supply variable has a significantly positive effect on early-stage entrepreneurial activity(TEA) and improvement-driven opportunity entrepreneurial activity. But the effect on necessity-driven entrepreneurial activity is significantly negative. We provide an interpretation of these empirical findings, emphasizing the importance of considering individual characteristics and economic environment simultaneously in promoting entrepreneurship.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.37-64
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• 2022

As the social acceptance of artificial intelligence increases, the number of cases of applying machine learning methods to companies is also increasing. Technical factors such as accuracy and interpretability have been the main criteria for selecting machine learning methods. However, the success of implementing machine learning also affects management factors such as IT departments, operation departments, leadership, and organizational culture. Unfortunately, there are few integrated studies that understand the success factors of machine learning selection in which technical and management factors are considered together. Therefore, the purpose of this paper is to propose and empirically analyze a technology-management integrated model that combines task-tech fit, IS Success Model theory, and John Rice's algorithm selection process model to understand machine learning selection within the company. As a result of a survey of 240 companies that implemented machine learning, it was found that the higher the algorithm quality and data quality, the higher the algorithm-problem fit was perceived. It was also verified that algorithm-problem fit had a significant impact on the organization's innovation and productivity. In addition, it was confirmed that outsourcing and management support had a positive impact on the quality of the machine learning system and organizational cultural factors such as data-driven management and motivation. Data-driven management and motivation were highly perceived in companies' performance.