• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.349-355
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• 2022

Today, apartment houses account for a very high proportion of the types of residence. This study aims to present basic data for preparing a reasonable distribution plan by identifying factors considered in calculating the amount of collection of management fees. After reviewing previous studies on common management fees and long-term repair allowances, various data on apartment houses subject to mandatory management in Busan in 2020 were collected from the Apartment Management Information System to analyze the differences in influencing factors according to type of apartment management(self-governing management, consignment management). As a result of statistical analysis, the number of households and many construction elapsed periods had a negative(-) effect on long-term repair allowances, and many construction elapsed periods had a negative(-) effect on common management fees. In addition, the degree to which many construction elapsed periods had a negative(-) effect on long-term repair allowances and common management fees had less impact on consignment management than on self-governing management. And the long-term repair allowances were imposed less by consignment management, common management fees were charged less by self-governing management. The results of this study will serve as basic data for rational distribution of long-term repair allowances and common management fees to residents and managers of apartment houses subject to mandatory management in Busan.

• Journal of Venture Innovation
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• v.6 no.4
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• pp.1-21
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• 2023

This study is related to the performance of open innovation collaboration between startups and large corporations and financial institutions. In the life cycle of a typical company, the growth of a startup is difficult to predict. Startups that possess innovative technology but have only recently been established seek to verify their technology and capabilities by participating in open innovation with large corporations and financial institutions, and further strive to lay the foundation for corporate growth. However, if you approach it only as a theoretical coexistence plan, it will be viewed as a vague attempt from the startup's perspective. The purpose of this study is to differentiately verify the benefits of open innovation by analyzing the difference in sales growth of startups for the purpose of sales performance based on the open innovation participation of large companies and small and medium-sized companies(startups). In verifying this, the analysis was based on the sales results of the actual open innovation collaboration B2C model, and the difference was confirmed by comparing before and after collaboration. Here, the differentiation of the study was added by reflecting the corporate growth stage theory, a growth theory. When the corporate growth stage theory was excluded, it was confirmed that sales growth due to open innovation of startups was applied from the third month, and sales growth depending on participation was confirmed to be significant. On the other hand, when the corporate growth stage theory was applied, sales growth was not significant, but the difference in growth could be confirmed from the fourth month, and it was also confirmed in sales growth depending on participation. As a result, this study objectively confirms the effects that can be gained when startups participate in Open-innovation, and it is expected that Open-innovation led by large corporations, financial institutions, and government agencies will develop into a high-quality program environment.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.309-323
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• 2023

As the role of online reviews has become increasingly crucial, numerous studies have been conducted to utilize helpful reviews. Helpful reviews, perceived by customers, have been verified in various research studies to be influenced by factors such as ratings, review length, review content, and so on. The determination of a review's helpfulness is generally based on the number of 'helpful' votes from consumers, with more 'helpful' votes considered to have a more significant impact on consumers' purchasing decisions. However, recently written reviews that have not been exposed to many customers may have relatively few 'helpful' votes and may lack 'helpful' votes altogether due to a lack of participation. Therefore, rather than relying on the number of 'helpful' votes to assess the helpfulness of reviews, we aim to classify them based on review content. In addition, the text of the review emerges as the most influential factor in review helpfulness. This study employs text mining techniques, including topic modeling and sentiment analysis, to analyze the diverse impacts of content and emotions embedded in the review text. In this study, we propose a review helpfulness prediction model based on review content, utilizing movie reviews from IMDb, a global movie information site. We construct a review helpfulness prediction model by using an explainable Graph Neural Network (GNN), while addressing the interpretability limitations of the machine learning model. The explainable graph neural network is expected to provide more reliable information about helpful or non-helpful reviews as it can identify connections between reviews.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.125-132
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• 2024

The purpose of this study is to discover a drone utilization model tailored to local characteristics, propose directions for building a drone demonstration city based on demand surveys for drone activation, and suggest ways to utilize and support a drone application system. First, according to the survey results, there was a high understanding of and necessity for drone demonstration projects, particularly in addressing urban issues, which were deemed to have a significant impact. Second, based on the analysis of priorities and short- and long-term approaches, disaster-related tasks were evaluated as a priority, requiring an approach through medium- to long-term strategies. Third, it was noted that budgetary considerations emerged as the most critical issue during project implementation. Practitioners and experts expressed willingness to actively introduce drone-based technologies into their work when budget and technology were ready. Budgetary constraints were identified as the most significant obstacle to proper implementation, emphasizing the need for resolution. Fourth, the necessity of demand surveys during project development was identified in certain areas. Demand surveys were deemed essential for drone-based demonstration city construction, and a survey indicated that public leadership in this regard was also necessary. Fifth, concerning approaches in specific areas, the field of safety and disaster management was highlighted as the most crucial for application.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.307-317
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• 2024

The purpose of this study is to analyze the impact of merchants' perceptions of service quality, youth mall creation, and traditional market revitalization on management performance to derive factors that can improve the self-sustainability of the traditional market. In particular, it was intended to predict the practical effect of the youth mall creation project by including merchants' perceptions of the rapidly emerging youth mall to revitalize the traditional market. In this study, 430 small business owners from five private traditional markets in Iksan were surveyed, the research model was verified by analyzing the technical statistics, reliability, and validity of the data collected using the SPSS 21.0 program, and the hypothesis was verified through correlation and regression analysis. Although youth malls are actively promoted at the government level to revitalize traditional markets and improve management performance, this study confirmed that the creation of youth malls in traditional markets does not directly affect traditional market revitalization and management performance, confirming that policies to create youth malls that can actually help revitalize traditional markets and improve management performance in the future need to be promoted.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.915-922
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• 2024

Currently, in Korea, there is a growing interest in improving the learning ability of the education target group due to the low birth rate and aging population. The dilemma of a shrinking population ultimately causes the burden of having to come up with a plan to efficiently maximize the use of available population resources. Accordingly, this study explores the impact of learning motivation (activity-oriented motivation, learning-oriented motivation) on learner characteristics (learning value, learning efficacy) and learning satisfaction, and as a result, intention to continue participating in lifelong learning (recommendation intention, relationship continuation intention). As a results of the analysis, it shows that learning motivation had a significant effect on learning satisfaction, and the emotions formed in this way had a positive effect on recommendation intention and relationship continuation intention. In addition, the results show that learning-oriented motivation had a significant effect on both learning satisfaction and learner characteristics, but that learning efficacy had no effect on recommendation intention. This study is significant in that it presents the basis for an educational system based on relationship maintenance and learner characteristics by considering the learner's orientation, individual achievement direction, recommendation intention, and relationship continuation intention.

• The Journal of Bigdata
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• v.9 no.1
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• pp.31-47
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• 2024

With the advancement of artificial intelligence, the travel and hospitality industry is also adopting AI and machine learning technologies for various purposes. In the tourism industry, demand forecasting is recognized as a very important factor, as it directly impacts service efficiency and revenue maximization. Demand forecasting requires the consideration of time-varying data flows, which is why statistical techniques and machine learning models are used. In recent years, variations and integration of existing models have been studied to account for the diversity of demand forecasting data and the complexity of the natural world, which have been reported to improve forecasting performance concerning uncertainty and variability. This study also proposes a new model that integrates various machine-learning approaches to improve the accuracy of hotel sales demand forecasting. Specifically, this study proposes a new time series forecasting model based on XGBoost that selectively utilizes a local model by clustering with DTW K-means and a global model using the entire data to improve forecasting performance. The hotel demand forecasting model that selectively utilizes global and regional models proposed in this study is expected to impact the growth of the hotel and travel industry positively and can be applied to forecasting in other business fields in the future.

• Journal of the Korean Society of Food Culture
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• v.14 no.4
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• pp.371-383
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• 1999

This report gave analysis of food demand both in Korea and Japan through introducing the concept of cohort analysis to the conventional demand model. This research was done to clarify the factors which determine food demand of the household. The traits of the new model for demand analysis are to consider and quantify those effects on food demand not only of economic factors such as expenditure and price but also of non-economic factors such as the age and birth cohort of the householder. The results of the analysis can be summarized as follows: 1) The comparison of the item-wise elasticities of food demand demonstrates that the expenditure elasticity is higher in Korea than in Japan and that the expenditure elasticity is -0.1 for cereal and more than 1 for eating-out in both countries. In respect to price elasticity, the absolute values of all the items except alcohol and cooked food are higher in the Korea than in Japan, and especially the price elasticities of beverages, dairy products and fruit are predominantly higher in Japan. In this way, both expenditure and price elasticities of a large number of items are higher in Korea than in Japan, which may be explained from the fact that the level of expenditure is higher in Japan than in Korea. 2) In both of Korea and Japan, as the householder grows older, the expenditure for each item increases and the composition of expenditure changes in such a way that these moves may be regarded as due to the age effect. However, there are both similarities and differences in the details of such moves between Korea and Japan. Those two countries have this trait in common that the young age groups of the householder spend more on dairy products and middle age groups spend more on cake than other age groups. In the Korea, however, there can be seen a certain trend that higher age groups spend more on a large number of items, reflecting the fact that there are more two-generation families in higher age groups. Japan differs from Korea in that expenditure in Japan is diversified, depending upon the age group. For example, in Japan, middle age groups spend more on cake, cereal, high-caloric food like meat and eating-out while older age groups spend more for Japanese-style food like fish/shellfish and vegetable/seaweed, and cooked food. 3) The effect of the birth cohort effect was also demonstrated. The birth cohort effect was introduced under the supposition that the food circumstances under which the householder was born and brought up would determine the current expenditure. Thus, the following was made clear: older generations in both countries placed more emphasis upon stable food in their composition of food consumption; the share of livestock products, oil/fats and externalized food was higher in the food composition of younger generation; differences in food composition among generations were extremely large in Korea while they were relatively small in Japan; and Westernization and externalization of diet made rapid increases simultaneously with generation changes in Korea while they made any gradual increases in Japan during the same time period. 4) The four major factors which impact the long-term change of food demand of the household are expenditure, price, the age of the householder, and the birth cohort of the householder. Investigations were made as to which factor had the largest impact. As a result, it was found that the price effect was the smallest in both countries, and that the relative importance of the factor-by-factor effects differed among the two countries: in Korea the expenditure effect was greater than the effects of age and birth cohort while in Japan the effects of non-economic factors such as the age and birth cohort of householder were greater than those of economic factors such as expenditures.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.261-268
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• 2021

Cloud computing is a computing paradigm in which users can utilize computing resources in a pay-as-you-go manner. In a cloud system, resources can be dynamically scaled up and down to the user's on-demand so that the total cost of ownership can be reduced. The Modeling and Simulation (M&S) technology is a renowned simulation-based method to obtain engineering analysis and results through CAE software without actual experimental action. In general, M&S technology is utilized in Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Multibody dynamics (MBD), and optimization fields. The work procedure through M&S is divided into pre-processing, analysis, and post-processing steps. The pre/post-processing are GPU-intensive job that consists of 3D modeling jobs via CAE software, whereas analysis is CPU or GPU intensive. Because a general-purpose desktop needs plenty of time to analyze complicated 3D models, CAE software requires a high-end CPU and GPU-based workstation that can work fluently. In other words, for executing M&S, it is absolutely required to utilize high-performance computing resources. To mitigate the cost issue from equipping such tremendous computing resources, we propose HEMOS-Cloud service, an integrated cloud and cluster computing environment. The HEMOS-Cloud service provides CAE software and computing resources to users who want to experience M&S in business sectors or academics. In this paper, the economic ripple effect of HEMOS-Cloud service was analyzed by using industry-related analysis. The estimated results of using the experts-guided coefficients are the production inducement effect of KRW 7.4 billion, the value-added effect of KRW 4.1 billion, and the employment-inducing effect of 50 persons per KRW 1 billion.