• Korean small business review
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• v.42 no.1
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• pp.79-105
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• 2020

While individual enterprises with different objectives each other within supply chains require a variety of resources to achieve their own seeking goals and performances, it is necessary to form interdependent relationships among the enterprises to secure the resources what they need, as the individual enterprises are supposed to have limitations on such as time, space and cost to secure all the resources. In this process, conflict possibilities rise and opportunistic behaviors increase due to those environmental factors such as unbalanced information among enterprises, limited rationality, pursuit of interests, and risk aversion. Those existing studies on conflicts in the field of supply chains have limitations in that they failed to present specific conflict management strategies based on the conflict types from the perspective of the conflict resolution mechanism as the studies have made only focused on investigating the causes of conflicts and the impact of conflicts on performance. In this study, therefore, it used the TKI model of Kilmann and Thomas(1977) to subdivide the conflict management strategies in the process of transactions within supply chains by enterprises, and looked into the impact on partnership and performance according to each strategy. As the results, it showed that those types of conflict management strategies such as concession type and cooperation type had a positive(+) impact on the relationship commitment as a factor of partnership, and it was identified that the relationship commitment had a positive(+) impact on performance. In other words, it can be considered that the enterprises making use of the concession type & the cooperation type conflict management strategies under the situation of conflict would be able to have a very positive impact on their performances if they can make good relationship commitment such as investments in and efforts for the sustainable relationship along with the conflict management, while recognizing the importance of relationship. The most important meaning of this study lies on in terms of that it would be contributable to strengthening the partnership between enterprises and minimizing the risk of supply chains caused by conflicts through these results from the study.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.

• Journal of Korea Port Economic Association
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• v.38 no.2
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• pp.81-93
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• 2022

Since 2008, China's shipping industry has been in a slump, with shipbuilding orders falling sharply, and high-growth excess capacity has become increasingly apparent, leaving many firms with sharply reduced orders at risk of bankruptcy and shutdown. To ensure the development of the shipbuilding industry and enhance the international competitiveness of the shipbuilding industry, it is necessary to analyze the present situation of the shipbuilding industry and the financial situation of the shipbuilding enterprises. And analyzing the problems faced by enterprises from the perspective of capital structure is very meaningful to the shipbuilders with high capital operation. We are trying to analyze the determinants of capital structure of China's shipbuilding listed companies. 30 listed Chinese shipbuilding and listed companies have been designated as sample companies that can obtain financial statements for 13 consecutive years. They also divided 30 sample companies into shipbuilding, shipbuilding-related manufacturing, and shipbuilding-related transportation. Dependent variable is the debt level of the year, independent variable includes the debt level of the previous year, fixed asset ratio, profitability ratio, depreciation cost ratio and asset size. The regression model of the panel used to analyze determinants is capital structure. The results of the empirical analysis are as follows. First, a fixed-effect model for the entire entity showed that the debt-to-equity ratio and the size of the asset in the previous period had a positive effect on the debt-to-equity ratio in the current period. Second, the impact of the profitability ratio on the debt level in the prior term also supports the capital procurement ranking theory rather than the static counter-conflict theory. Third, it was shown that the ratio of the depreciation of the prior term, which replaces the non-liability tax effect, affects the debt-to-equity ratio in the current period.

• The Journal of Bigdata
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• v.8 no.1
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• pp.111-129
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• 2023

With the diffusion of digital innovation, the adoption of innovative medical technologies based on artificial intelligence is increasing in the medical field. This is driving the launch and adoption of AI-based SaMD(Software as a Medical Device), but there is a lack of research on the factors that influence the adoption of SaMD by medical institutions. The purpose of this study is to identify key factors that influence medical institutions' decisions to adopt AI-based SaMDs, and to analyze the weights and priorities of these factors. For this purpose, we conducted Delphi surveys based on the results of literature studies on technology acceptance models in healthcare industry, medical AI and SaMD, and developed a research model by combining HOTE(Human, Organization, Technology and Environment) framework and HABIO(Holistic Approach {Business, Information, Organizational}) framework. Based on the research model with 5 main criteria and 22 sub-criteria, we conducted an AHP(Analytical Hierarchy Process) analysis among the experts from domestic medical institutions and SaMD providers to empirically analyze SaMD adoption factors. The results of this study showed that the priority of the main criteria for determining the adoption of AI-based SaMD was in the order of technical factors, economic factors, human factors, organizational factors, and environmental factors. The priority of sub-criteria was in the order of reliability, cost reduction, medical staff's acceptance, safety, top management's support, security, and licensing & regulatory levels. Specifically, technical factors such as reliability, safety, and security were found to be the most important factors for SaMD adoption. In addition, the comparisons and analyses of the weights and priorities of each group showed that the weights and priorities of SaMD adoption factors varied by type of institution, type of medical institution, and type of job in the medical institution.

• Journal of Nutrition and Health
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• v.56 no.3
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• pp.264-276
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• 2023

Purpose: Food environmental factors related to food insecurity affect household food intake in several socio-ecological aspects. This study explores the relationship between food environment factors and food insecurity in households with married immigrant women. Methods: From November 2018 to February 2020, a survey was conducted enrolling 249 married immigrant women residing in the metropolitan areas of South Korea. In the final analysis, 229 subjects were divided into 2 groups classified as food security (n = 154) and food insecurity (n = 75), as assessed by the score of food security. Three aspects of food environments were measured: built·natural, political·economic, and socio-cultural Results: Food environments were significantly different between food security and food insecurity groups, as follows: the number of foods market and their distance from the home and food status for the last week at home in the built·natural domain; monthly cost of food purchase and experience for food assistance in the political·economic domain; total score of social support, parenting, and cooking skills in the socio-cultural domain. A stepwise multivariate linear regression model showed a negative association between the food insecurity score with social support from family and food inventory status in the last week. After adjusting for confounders, a positive association was obtained between the experience of a food support program. The final regression model explains about 30% of the relationship obtained in the three food environment domains and food insecurity (p < 0.001). Conclusion: Not only economic factors, which are common determinants of household food insecurity, but socio-cultural factors such as social support also affect household food insecurity. Therefore, plans for implementing a food assistance program to improve food insecurity for households with immigrant women should consider financial support as well as other comprehensive aspects, including socio-cultural domain such as social support from family and community.

• Korean Journal of Radiology
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• v.2 no.1
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• pp.28-36
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• 2001

Objective: To provide a systematic overview of the effects of various parameters on contrast enhancement within the same population, an animal experiment as well as a computer-aided simulation study was performed. Materials and Methods: In an animal experiment, single-level dynamic CT through the liver was performed at 5-second intervals just after the injection of contrast medium for 3 minutes. Combinations of three different amounts (1, 2, 3 mL/kg), concentrations (150, 200, 300 mgI/mL), and injection rates (0.5, 1, 2 mL/sec) were used. The CT number of the aorta (A), portal vein (P) and liver (L) was measured in each image, and time-attenuation curves for A, P and L were thus obtained. The degree of maximum enhancement (Imax) and time to reach peak enhancement (Tmax) of A, P and L were determined, and times to equilibrium (Teq) were analyzed. In the computed-aided simulation model, a program based on the amount, flow, and diffusion coefficient of body fluid in various compartments of the human body was designed. The input variables were the concentrations, volumes and injection rates of the contrast media used. The program generated the time-attenuation curves of A, P and L, as well as liver-to-hepatocellular carcinoma (HCC) contrast curves. On each curve, we calculated and plotted the optimal temporal window (time period above the lower threshold, which in this experiment was 10 Hounsfield units), the total area under the curve above the lower threshold, and the area within the optimal range. Results: A. Animal Experiment: At a given concentration and injection rate, an increased volume of contrast medium led to increases in Imax A, P and L. In addition, Tmax A, P, L and Teq were prolonged in parallel with increases in injection time The time-attenuation curve shifted upward and to the right. For a given volume and injection rate, an increased concentration of contrast medium increased the degree of aortic, portal and hepatic enhancement, though Tmax A, P and L remained the same. The time-attenuation curve shifted upward. For a given volume and concentration of contrast medium, changes in the injection rate had a prominent effect on aortic enhancement, and that of the portal vein and hepatic parenchyma also showed some increase, though the effect was less prominent. A increased in the rate of contrast injection led to shifting of the time enhancement curve to the left and upward. B. Computer Simulation: At a faster injection rate, there was minimal change in the degree of hepatic attenuation, though the duration of the optimal temporal window decreased. The area between 10 and 30 HU was greatest when contrast media was delivered at a rate of 2 3 mL/sec. Although the total area under the curve increased in proportion to the injection rate, most of this increase was above the upper threshould and thus the temporal window was narrow and the optimal area decreased. Conclusion: Increases in volume, concentration and injection rate all resulted in improved arterial enhancement. If cost was disregarded, increasing the injection volume was the most reliable way of obtaining good quality enhancement. The optimal way of delivering a given amount of contrast medium can be calculated using a computer-based mathematical model.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.35-48
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• 2010

Today, the fashion market challenged by a maturing retail market needs a new paradigm in the "evolution of brand" to improve their comparative advantages. An important issue in fashion marketing is lifestyle brand extension with a specific aim to meet consumers' specific needs for their changing lifestyle. For fashion brand extensions into lifestyle product categories, Gen Y and Baby Boomer are emerging as "prospects"-Baby Boomers who are renovating their lifestyle, and generation Y experiencing changes in their life stage-with demands for buying new products. Therefore, it is imperative that apparel companies pay special attention to the consumer cohort for brand extension to create and manage their brand equity in a new product category. The purposes of this study are to (a) evaluate brand equity between parent and extension brands; (b) identify consumers' perceived marketing elements for brand extension; and (c) estimate a structural equation model for examining causative relationship between marketing elements and brand equity for brand extensions in lifestyle product category including home fashion items for the selected two groups (e.g., Gen Y, and Baby boomer). For theoretical frameworks, this study focused on the traditional marketing 4P's mix to identify what marketing element is more importantly related to brand extension equity for this study. It is assumed that comparable marketing capability can be critical to establish "brand extension equity", leads to successfully entering the new categories. Drawing from the relevant literature, this study developed research hypotheses incorporating brand equity factors and marketing elements by focusing on the selected consumers (e.g., Gen Y, Baby Boomer). In the context of brand extension in the lifestyle products, constructs of brand equity consist of brand awareness/association, brand perceptions (e.g., perceived quality, emotional value) and brand resonance adapted from CBBE factors (Keller, 2001). It is postulated that the marketing elements create brand extension equity in terms of brand awareness/association, brand perceptions by the brand extension into lifestyle products, which in turn influence brand resonance. For data collection, the sample was comprised of Korean female consumers in Gen Y and Baby Boomer consumer categories who have a high demand for lifestyle products due to changing their lifecycles. A total of 651 usable questionnaires were obtained from female consumers of Gen Y (n=326) and Baby Boomer (n=325) in South Korea. Structural and measurement models using a correlation matrix was estimated using LISREL 8.8. Findings indicated that perceived marketing elements for brand extension consisted of three factors: price/store image, product, and advertising. In the model of Gen Y consumers, price/store image had a positive effect on brand equity factors (e.g., brand awareness/association, perceived quality), while product had positive effect on emotional value in the brand extensions; and the brand awareness/association was likely to increase the perceived quality and emotional value, leading to brand resonance for brand extensions in the lifestyle products. In the model of Baby Boomer consumers, price/store image had a positive effect on perceived quality, which created brand resonance of brand extension; and product had a positive effect on perceived quality and emotional value, which leads to brand resonance for brand extension in the lifestyle products. However, advertising was negatively related to brand equity for both groups. This study provides an insight for fashion marketers in developing a successful brand extension strategy, leading to a sustainable competitive advantage. This study complements and extends prior works in the brand extension through critical factors of marketing efforts that affect brand extension success. Findings support a synergy effect on leveraging of fashion brand extensions (Aaker and Keller, 1990; Tauber, 1988; Shine et al., 2007; Pitta and Katsanis, 1995) in conjunction with marketing actions for entering into the new product category. Thus, it is recommended that marketers targeting both Gen Y and Baby Boomer can reduce marketing cost for entering the new product category (e.g., home furnishings) by standardized marketing efforts; fashion marketers can (a) offer extension lines with premium ranges of price; (b) place an emphasis on upscale features of store image positioning by a retail channel (e.g., specialty department store) in Korea, and (c) combine apparel with lifestyle product assortments including innovative style and designer’s limited editions. With respect to brand equity, a key to successful brand extension is consumers’ brand awareness or association that ensures brand identity with new product category. It is imperative for marketers to have knowledge of what contributes to more concrete associations in a market entry into new product categories. For fashion brands, a second key of brand extension can be a "luxury" lifestyle approach into new product categories, in that higher price or store image had impact on perceived quality that established brand resonance. More importantly, this study increases the theoretical understanding of brand extension and suggests directions for marketers as they establish marketing program at Gen Y and Baby Boomers.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.