• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.5
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• pp.15-29
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• 2021

This study is based on ERIS model for start-up performance, and aims to derive the main reason for start-up sustainability centered on organizational resilience. To this end, systematic literature examination and modified Delphi method were used to investigate start-up sustainability factors based on ERIS Model focused on organizational resilience. The results showed that ERIS model-based entrepreneurial continuity factors were divided into four categories: entrepreneur, resource, industrial environment, strategy, subdivision 8 and detailed factors 54. In addition, the ERIS model-based continuity factors were structured around organizational resilience, and the continuity factors were structured according to ERIS model under five categories: leadership, culture, people, system and environment. The results of this study are as follows. First of all, the results of existing research and analysis show that the concept of successful start-up and sustainability of start-up are used in various fields. Second, it is confirmed that there are common factors of influence on start-up performance and start-up sustainability based on ERIS model. Third, Delphi method's results showed that the general characteristics of entrepreneurs, such as academic background, education level, gender, age, and business experience did not affect the sustainability of entrepreneurship. This study is significant in that it is based on ERIS model focused on organization resilience, and ERIS-R, which integrates Strategy into System and Organization resilience into R in the field of gradually expanding start-up development and support. It is expected that the results of this study will improve the sustainability of start-up that can predict, prevent, and overcome various crises at any time.

• Land and Housing Review
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• v.13 no.4
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• pp.55-71
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• 2022

Recently, the domestic department store industry is growing into a complex shopping cultural space, which is advanced and differentiated by changes in consumption patterns. In addition, competition is intensifying across 70 places operated by five large companies. This study investigates the determinants of the visits to department stores using the big data concept's automatic vehicle access system (pos) and proposes how to strengthen the competitiveness of the department store industry. We use a negative binomial regression test to predict the frequency of visits to 67 branches, except for three branches whose annual sales were incomplete due to the new opening in 2021. The results show that the demand for visiting department stores is positively associated with airport, terminal, and train stations, land areas, parking lots, VIP lounge numbers, luxury store ratio, F&B store numbers, non-commercial areas, and hotels. We suggest four strategies to enhance the competitiveness of domestic department stores. First, department store consumers have a high preference for luxury brands. Therefore, department stores need to form their own overseas buyer teams to discover and attract new luxury brands and attract customers who have a high demand for luxury brands. In addition, to attract consumers with high purchasing power and loyalty, it is necessary to provide more differentiated products and services for VIP customers than before. Second, it is desirable to focus on transportation hub areas such as train stations, airports, and terminals in Gyeonggi and Incheon. Third, department stores should attract tenants who can satisfy customers, given that key tenants are an important component of advanced shopping centers for department stores. Finally, the department store, a top-end shopping center, should be developed as a space with differentiated shopping, culture, dining out, and leisure services, such as "The Hyundai", which opened in 2021, to ensure future growth potential.

• Journal of the Korean Geographical Society
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• v.42 no.4
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• pp.540-559
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• 2007

The purpose of this paper is to analyse the locations and to interpret the landscapes of the local towns in Joseon Dynasty from Feng-shui's point of view. As a result of analysing the locations of towns in Gyeongsang Province, the towns which have typical Feng-shui landscapes make up to 58% of the total. Historically, the local towns that were established in the early period of the Joseon Dynasty didn't reveal Feng-shui's landscape, but those that were established in the late period of the Joseon Dynasty revealed the Feng-shui's landscape clearly. In this article, I classify the local towns of the Gyeongsang Province into 3 types: 1. Non Feng-shui type These towns are located near the seashore. The main reason that these towns were located at the seashore was defense against an enemy. 2. Semi Feng-shui type. These towns don't have natural location but have a man-made landscape, based on the principles of Feng-shui. 3. Typical Feng-shui type. These towns were typically administrational towns which were located at the center of a local region.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1053-1066
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• 2020

Sulfur dioxide (SO₂) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO₂ can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO₂ crucial. In South Korea, SO₂ monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO₂ concentrations. Thus, this research estimated spatially continuous ground-level SO₂ concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO₂ estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO₂ concentrations based on the proposed model agreed with the general seasonality of SO₂ and the temporal patterns of emission sources.