• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.16 no.4
• /
• pp.1-24
• /
• 2021

The Korean government is currently expanding the business startup incubator support program and funds for new ventures with innovative technology in order to spread the second venture boom. However, despite the fact that entrepreneurial education and mentoring that entrepreneurs should have are important parts for the sustainable growth of the startup, some companies selected for government support programs are reluctant to participate in programs such as entrepreneurship education and mentoring for the sole purpose of funding commercialization. This research addressed the effects of entrepreneurial alertness with opportunity awareness as its medium and the small business mentoring service along with the number of times the mentoring has taken place, on the corporate performances. The results of empirical research are as follow: the first one is that scanning-search and evaluation-judgment can influence a company's performance (financial, non-financial) through opportunity recognition, with the exception of association-connection, which is a sub-factor of entrepreneurial alertness. Secondly, it was found to affect a company's financial and non-financial performance through opportunity recognition for financing mentoring, technical support mentoring, and management support mentoring. Thirdly, it was found that the number of mentoring also affects the financial and non-financial performance of a company through opportunity recognition. The implications of this study are that it should be revisited that program managers consider rooms that do not violate the startup founder's strategic decision-making opportunities when designing and operating the program as entrepreneurial alertness sub-factor association-connection does not affect corporate performance through opportunity recognition. This study also emphasizes the need for customized mentoring to meet the outcome goals of each startup, as it has been empirically clarified that the mentoring provided to the startup by the government's support is important. The contribution of this research is that entrepreneurial alertness and opportunity recognition that are treated as important components in research for entrepreneurship, and the factors of mentoring and mentoring frequency that are recognized as important elements in the practical aspect of startup business are clarified theoretically and empirically as an influential factor in corporate performance. And this study also provide a rationale for the startup business support agency supplying mentoring.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.99-122
• /
• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.3
• /
• pp.208-220
• /
• 2019

The seasonality of leaf fall has important implications for understanding the response of trees' phenology to climate change. In this study, we quantified the leaf fall pattern with a model to estimate the timing and speed of leaf litter according to species and considered the nutrient use strategy of canopy species. In the autumns of 2015 and 2016, leaf litter was collected periodically using 36 litter-traps from the deciduous forests in Gwangneung and sorted by species. The seasonal leaf fall pattern was estimated using the non-linear regression model of Dixon. Additionally, the resorption rate was calculated by analyzing the nitrogen concentration of the leaf litter at each collection time. The leaf litter generally began in early October and ended in mid-November depending on the species. At the peak time (T50) of leaf fall, on average, Carpinus laxiflora was first, and Quercus serrata was last. The rate of leaf fall was fastest (18.6 days) for Sorbus alnifolia in 2016 and slowest (40.8 days) for C. cordata in 2015. The nitrogen resorption rates at T50 were 0.45% for Q. serrata and 0.48% for C. laxiflora, and the resorption rate in 2015 with less precipitation was higher than in 2016. Since falling of leaf litter is affected by environmental factors such as temperature, precipitation, photoperiod, and $CO_2$ during the period attached foliage, the leaf fall pattern and nitrogen resorption differed year by year depending on the species. If we quantify the fall phenomena of deciduous trees and analyze them according to various conditions, we can predict whether the changes in leaf fall timing and speed due to climate change will prolong or shorten the growth period of trees. In addition, it may be possible to consider how this affects their nutrient use strategy.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.22 no.3
• /
• pp.82-98
• /
• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Economic and Environmental Geology
• /
• v.54 no.3
• /
• pp.373-387
• /
• 2021

In this study, the impact of clustered groundwater usage facilities and the proper amount of groundwater usage in the Daejeong-Hangyeong watershed of Jeju island were evaluated based on the data-driven analysis methods. As the applied data, groundwater level data; the corresponding precipitation data; the groundwater usage amount data (Jeoji, Geumak, Seogwang, and English-education city facilities) were used. The results show that the Geumak usage facility has a large influence centering on the corresponding location; the Seogwang usage facility affects on the downstream area; the English-education usage facility has a great impact around the upstream of the location; the Jeoji usage facility shows an influence around the up- and down-streams of the location. Overall, the influence of operating the clustered groundwater usage facilities in the watershed is prolonged to approximately 5km. Additionally, the appropriate groundwater usage amount to maintain the groundwater base-level was analyzed corresponding to the precipitation. Considering the recent precipitation pattern, there is a need to limit the current amount of groundwater usage to 80%. With increasing the precipitation by 100mm, additional groundwater development of approximately 1,500m³-1,900m³ would be reasonable. All the results of the developed data-driven estimation model can be used as useful information for sustainable groundwater development in the Daejeong-Hangyeong watershed of Jeju island.

• Korean Journal of Environment and Ecology
• /
• v.36 no.5
• /
• pp.481-495
• /
• 2022

This study was designed to identify the actual state of naturalized plants and invasive alien species that cause disturbances to the ecosystem, plants which are introduced after forest restoration, and explore the implications resulting from the project. Onsite examination included 29 sites which have been subjected to forest restoration by the Korea Forest Service. Once these were chosen, activity took place twice a year in the spring (May-June) and in the summer (August-September) in 2020 and 2021. Areas not relevant to the project sites were excluded from this activity so that we could identify the plants that could be understood to have been introduced or brought into the site after the actual forest restoration. And the correlation was analyzed, between the naturalized flora within the project sites and the working types applied to the site through confirmation of completion of the restoration project. The naturalized plants appearing on the entire site cover a total of 109 taxa, which includes 29 families, 80 genera, 108 species and 1 subspecies, while invasive plants included 3 families, 7 genera and 8 species. The number of classifications and the naturalization rate gradually decreased over time, after the project. While there was no significant difference between the number of classification groups and the naturalization rate for naturalized plants between project sites, given the number of taxa of naturalized plants, organized by type of damage, there were relatively more naturalized plants that appeared in the severed section of the Baekdudaegan Mountain Range, as well as at quarry and facility sites. Seeding apparently results in naturalization rates as high as 15.545%, on average, based on comparisons of naturalization rates by sowing, seeding, planting, herb planting, and sod pitching channels, all of these being methods of vegetation for planting/greening of bareland and slopes within the project areas. With no seeding, it was 9.167%, higher than the average. As for other vegetation, there was no significant difference depending on application of the working type. This means that unlike the plants subjected to planting, the working type of seed planting which makes it difficult to identify whether a certain plant is a naturalized plant greatly affects the introduction of naturalized plants to the restoration sites, even when using herb planting and sod pitching to control plants and results. Therefore the study suggests that there be inspection by experts of seeds when sowing within restoration sites. The results of this study suggest good practices that will help to direct effective vegetation restoration and follow-up management.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1285-1300
• /
• 2022

Turbidity, the measure of the cloudiness of water, is used as an important index for water quality management. The turbidity can vary greatly in small river systems, which affects water quality in national rivers. Therefore, the generation of high-resolution spatial information on turbidity is very important. In this study, a turbidity retrieval model using the Korea Multi-Purpose Satellite-3 and -3A (KOMPSAT-3/3A) images was developed for high-resolution turbidity mapping of Han River system based on eXtreme Gradient Boosting (XGBoost) algorithm. To this end, the top of atmosphere (TOA) spectral reflectance was calculated from a total of 24 KOMPSAT-3/3A images and 150 Landsat-8 images. The Landsat-8 TOA spectral reflectance was cross-calibrated to the KOMPSAT-3/3A bands. The turbidity measured by the National Water Quality Monitoring Network was used as a reference dataset, and as input variables, the TOA spectral reflectance at the locations of in situ turbidity measurement, the spectral indices (the normalized difference vegetation index, normalized difference water index, and normalized difference turbidity index), and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived atmospheric products(the atmospheric optical thickness, water vapor, and ozone) were used. Furthermore, by analyzing the KOMPSAT-3/3A TOA spectral reflectance of different turbidities, a new spectral index, new normalized difference turbidity index (nNDTI), was proposed, and it was added as an input variable to the turbidity retrieval model. The XGBoost model showed excellent performance for the retrieval of turbidity with a root mean square error (RMSE) of 2.70 NTU and a normalized RMSE (NRMSE) of 14.70% compared to in situ turbidity, in which the nNDTI proposed in this study was used as the most important variable. The developed turbidity retrieval model was applied to the KOMPSAT-3/3A images to map high-resolution river turbidity, and it was possible to analyze the spatiotemporal variations of turbidity. Through this study, we could confirm that the KOMPSAT-3/3A images are very useful for retrieving high-resolution and accurate spatial information on the river turbidity.

• Korean Journal of Mineralogy and Petrology
• /
• v.35 no.3
• /
• pp.333-344
• /
• 2022

The quarrying and utilization of natural building stones such as granite and marble are rapidly emerging in developing countries. A huge amount of wastes is being generated during the processing, cutting and sizing of these stones to make them useable. These wastes are disposed of in the open environment and the toxic nature of these wastes negatively affects the environment and human health. The growth trend in the world stone industry was confirmed in output for 2019, increasing more than one percent and reaching a new peak of some 155 million tons, excluding quarry discards. Per-capita stone use rose to 268 square meters per thousand persons (m²/1,000 inh), from 266 the previous year and 177 in 2001. However, we have to take into consideration that the world's gross quarrying production was about 316 million tons (100%) in 2019; about 53% of that amount, however, is regarded as quarrying waste. With regards to the stone processing stage, we have noticed that the world production has reached 91.15 million tons (29%), and consequently this means that 63.35 million tons of stone-processing scraps is produced. Therefore, we can say that, on a global level, if the quantity of material extracted in the quarry is 100%, the total percentage of waste is about 71%. This raises a substantial problem from the environmental, economical and social point of view. There are essentially three ways of dealing with inorganic waste, namely, reuse, recycling, or disposal in landfills. Reuse and recycling are the preferred waste management methods that consider environmental sustainability and the opportunity to generate important economic returns. Although there are many possible applications for stone waste, they can be summarized into three main general applications, namely, fillers for binders, ceramic formulations, and environmental applications. The use of residual sludge for substrate production seems to be highly promising: the substrate can be used for quarry rehabilitation and in the rehabilitation of industrial sites. This new product (artificial soil) could be included in the list of the materials to use in addition to topsoil for civil works, railway embankments roundabouts and stone sludge wastes could be used for the neutralization of acidic soil to increase the yield. Stone waste is also possible to find several examples of studies for the recovery of mineral residues, including the extraction of metallic elements, and mineral components, the production of construction raw materials, power generation, building materials, and gas and water treatment.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.49 no.6
• /
• pp.27-36
• /
• 2021

At a time when the equitable use of urban parks is gradually emerging as a social issue, this study was initiated to expand the influence of urban parks by improving the quality of park services, thereby resolving areas not covered by urban park services. This study targeted the Hangang Park in Seoul, where the qualitative service of parks shows the greatest difference. The influence relationship between the qualitative services of the park and the user's sphere of influence, which indicates the distribution of park users, was proposed to assess the influence of improvements in the quality of service. As a research method, the top three districts and the bottom three districts were selected through the Han River Park user satisfaction survey conducted from 2017 to 2019, and a qualitative service evaluation was carried out. It was derived using the data acquired in September. Afterward, by performing a spatial autocorrelation analysis on the user's sphere of influence, additional verification of the user's sphere of influence was performed numerically and visually. As a result of the study, the user influence in the top three districts, with high-quality service, was stronger and wider than that of the lower three districts. It was confirmed that the quality of service of the park affects the user influence. This shows that to realize park equity, it is necessary to improve the quality of services through continuous management and improvement of individual parks and the creation of new parks. This study has significance in that it recognizes the limitations of research on park services from a supplier's point of view and evaluates the qualitative services of parks from the perspective of actual park users. We propose an alternative to deal with the lower the park deprivation index.

• Journal of Korea Water Resources Association
• /
• v.56 no.4
• /
• pp.285-299
• /
• 2023

Estimating the evapotranspiration is very important factor for effective water resources management, and FAO Penman-Monteith (FAO P-M) model has been applied for reference evapotranspiration estimation by many researchers. However, because various input data are required for the application of FAO P-M model, understanding the effect of each input data on FAO P-M model is necessary. Therefore, in this study, for 56 study stations located in South Korea, the effects of 8 meteorological factors (maximum and minimum temperature, wind speed, relative humidity, solar radiation, vapor pressure deficit, net radiation, ground heat flux), energy and aerodynamic terms of FAO P-M model, and elevation on FAO P-M reference evapotranspiration (RET) estimation were analyzed. The relative sensitivity analysis was performed to determine how 10% increment of each specific independent variable affects a reference evapotranspiration under given set of condition that other independent variables are unchanged. Furthermore, to select the 5 representative stations and perform the monthly relative sensitivity analysis for those stations, 56 study stations were classified into 5 clusters using cluster analysis. The study results showed that net radiation was turned out to be the most sensitive factor in 8 meteorological factors for 56 study stations. The next most sensitive factor was relative humidity, solar radiation, maximum temperature, vapor pressure deficit and wind speed, followed by minimum temperature in order. Ground heat flux was the least sensitive factor. In case of ground surface condition, elevation showed very low positive relative sensitivity. Relativity sensitivities of energy and aerodynamic terms of FAO P-M model were 0.707 for energy term and 0.293 for aerodynamic term respectively, indicating that energy term was more contributable than aerodynamic term for reference evapotranspiration. The monthly relative sensitivities of meteorological factors showed the seasonal effects, and also the relative sensitivity of elevation showed different pattern each other among study stations. Therefore, for the application of FAO P-M model, the seasonal and regional sensitivity differences of each input variable should be considered.