• Journal of Venture Innovation
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• v.5 no.1
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• pp.1-18
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• 2022

There is growing empirical evidence that passion is an important part of entrepreneurship and influences the intentions, behaviors and performance of entrepreneurs, employees and startups. Passion is especially important in an entrepreneurial context, given the effort and challenge that entrepreneurs starting a startup must overcome. The purpose of this study was to confirm the effect of the passion of startup entrepreneurs participating in the accelerator incubation program and the passion of accelerator entrepreneurs and managers on the entrepreneurial performance of incubator startups. In addition, we tried to confirm whether entrepreneurial self-efficacy plays a mediating role in this influence relationship. The survey was conducted online by startups entrepreneur who completed the accelerator incubation program. A total of 330 questionnaires were used for the analysis. As a result of the empirical analysis, it was confirmed that the passion of startup entrepreneurs and the passion of accelerator entrepreneurs and managers all had a positive (+) effect on the entrepreneurial performance of incubator startups. The influence of passion was found to be high in the order of startup entrepreneurs, accelerator entrepreneurs, and accelerator managers. It was confirmed that entrepreneurial self-efficacy plays a mediating role between the passion of startup entrepreneurs, the passion of accelerator entrepreneurs, and the entrepreneurial performance of incubator startups, respectively. However, no significant mediating role was identified between the passion of accelerator managers and the entrepreneurial performance of incubator startups. This study is significant in empirically confirming for the first time that the passion of accelerator entrepreneurs and managers has a positive effect on the entrepreneurial performance of incubator startups. The passion of accelerator entrepreneurs and managers is playing an important role as a hidden lynchpin in creating the entrepreneurial performance of incubator startups. In particular, since the passion of accelerator entrepreneurs has a great influence on the performance of incubator startups, it is necessary to recognize this fact and carefully examine their passion reputation when startups select accelerators.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.147-175
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• 2020

This study analyzed the effects that digital experience factors influence on purchase intention and the purchase. The study targeted an online shopping mall with a strong digital experience value among industries. The research model was derived by adding variables to independent and mediating variables according to the industry context of online shopping which is based on the theoretical background and previous studies. Product variety, price efficiency, convenience and conversation were used by terms of digital marketing mix as independent variables. Personalization has been very important factor in online shopping malls, and therefore added as a independent variable. Flow has been added as a mediating variable. Purchase and purchase intention has been used as dependent variables. For empirical testing of established research models and generalization of research results, research was conducted on online shopping malls where digital experiences are important. To do this, a survey was conducted for existing users of online shopping malls. In hypothesis testing, the hypothesis was established that product diversity, price efficiency, convenience, conversation and personalization influenced the intention to purchase online shopping. In particular, the product diversity and conversation variable were tested as the most influential factors on purchase intention. For price efficiency and personalization there were no statistically significant effect. Flow has been shown to be a partial mediator between Product variety and purchase intention in online shopping. In particular, in the case of personalization, it was tested to have a significant influence on purchase intention only when there was a flow experience called pleasure and immersion. This is because the flow experience of pleasure and immersion has played a full mediating role and significantly has affected the purchase intention, because the consumers themselves have to carry out the overall purchase journey without human help due to the nature of online. In the digital experience economy, since consumers are mostly digital consumers, where communication and sharing are the basics, they have been conducting digital word-of-mouth communication and sharing naturally before purchasing. Based on these results, theoretical and practical implications were suggested.

• Journal of Venture Innovation
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• v.6 no.4
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• pp.41-62
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• 2023

Given that SME workers are the driving force of national competitiveness and the basis and cornerstone of the industry, it is meaningful to study workers' job satisfaction and the factors that affect job satisfaction. In addition to variables related to corporate competitiveness and organizational justice, this study introduced variables such as environmental(E) activities, social(S) activities, and governance(G) activities, which th national government uses as major management evaluation indicators. Therefore, a literature study and empirical analysis were conducted on how self-efficacy affects job satisfaction when workers are faced with a changed work environment. To conduct this study, 300 copies of data were collected from workers in small and medium-sized enterprises and used for analysis. For data analysis, the SPSS statistical program (Ver. 25.0) was used. The study finds, first, that product or service quality and employee competency among corporate competitiveness had a significant positive(+) effect on job satisfaction. Secondly, among ESG management activities, social(S) activities and governance(G) activities were found to have a significant positive(+) effect on job satisfaction. Third, among organizational justice, distribution justice and procedural justice were found to have a positive(+) effect on job satisfaction. Fourth, self-efficacy was found to mediate the effect of product or service quality, employee competency, social(S) and governance(G) activities among ESG management activities, and procedural justice among organizational justice on job satisfaction. The academic value of this study is that it empirically analyzed the factors that ESG management activities affect workers' jobs,. As a result, it was confirmed that workers were satisfied with their jobs by actively showing interest in social(S) activities and governance(G) activities among ESG management activities and participating in corporate management. In addition, workers sensitive to changes in the external environment can become satisfied with their jobs through self-efficacy when SMEs actively enhance corporate competitiveness, execute ESG management activities, and provide a fair organizational culture. Finally, this study suggests that there's a possibility of improving the competitiveness of SMEs through a virtuous cycle created by a change in perception of job conversion and a decrease in turnover.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.53-73
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• 2023

Although it is mandatory to wear a seat belt that can minimize human injury when traffic accident occurs, the number of traffic accident casualties not wearing seat belts still accounts for a significant proportion.The seat belt wearing detection system for all seats is a system that identifies whether all seat passengers wear a seat belt and encourages their usage, also it can be a useful technical countermeasure. Firstly, this study established the viability of system implementation by assessing the factors influencing the severity of injuries in traffic accidents through the development of an ordered probit model. Analysis results showed that the use of seat belts has statistically significant effects on the severity of traffic accidents, reducing the probability of death or serious injury by 0.054 times in the event of a traffic accident. Secondly, a meta-analysis was conducted based on prior research related to seat belts and injuries in traffic accidents to estimate the expected reduction in accident severity upon the implementation of the system.The analysis of the effect of accident severity reduction revealed that wearing seat belts would lead to a 63.3% decrease in fatal accidents, with the front seats showing a reduction of 75.7% and the rear seats showing a reduction of 58.1% in fatal accidents. Lastly, Using the results of the meta-analysis and traffic accident statistics, the expected decrease in the number of traffic accident casualties with the implementation of the system was derived to analyze the traffic safety effects of the proposed detection system. The analysis demonstrated that with an increase in the adoption rate of the system, the number of casualties in accidents where seat belts were not worn decreased. Specifically, at a system adoption rate of 60%, it is anticipated that the number of fatalities would decrease by more than three times compared to the current scenario. Based on the analysis results, operational strategies for the system were proposed to increase seat belt usage rates and reduce accident severity.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1043-1060
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• 2023

An acceleration of climate change in recent years has led to increased attention towards 'blue carbon' which refers to the carbon captured by the ocean. However, our comprehension of marine ecosystems is still incomplete. This study classified and analyzed global marine eco-provinces using k-means clustering considering carbon cycling. We utilized five input variables during the past 20 years (2001-2020): Carbon-based Productivity Model (CbPM) Net Primary Production (NPP), particulate inorganic and organic carbon (PIC and POC), sea surface salinity (SSS), and sea surface temperature (SST). A total of nine eco-provinces were classified through an optimization process, and the spatial distribution and environmental characteristics of each province were analyzed. Among them, five provinces showed characteristics of open oceans, while four provinces reflected characteristics of coastal and high-latitude regions. Furthermore, a qualitative comparison was conducted with previous studies regarding marine ecological zones to provide a detailed analysis of the features of nine eco-provinces considering carbon cycling. Finally, we examined the changes in nine eco-provinces for four periods in the past (2001-2005, 2006-2010, 2011-2015, and 2016-2020). Rapid changes in coastal ecosystems were observed, and especially, significant decreases in the eco-provinces having higher productivity by large freshwater inflow were identified. Our findings can serve as valuable reference material for marine ecosystem classification and coastal management, with consideration of carbon cycling and ongoing climate changes. The findings can also be employed in the development of guidelines for the systematic management of vulnerable coastal regions to climate change.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1135-1144
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• 2023

Many agricultural reservoirs in South Korea, constructed before 1970, have become aging facilities. The majority of small-scale reservoirs lack measurement systems to ascertain basic specifications and water levels, classifying them as unmeasured reservoirs. Furthermore, continuous sedimentation within the reservoirs and industrial development-induced water quality deterioration lead to reduced water supply capacity and changes in reservoir morphology. This study utilized Light Detection And Ranging (LiDAR) sensors, which provide elevation information and allow for the characterization of surface features, to construct high-resolution Digital Surface Model (DSM) and Digital Elevation Model (DEM) data of reservoir facilities. Additionally, bathymetric measurements based on multibeam echosounders were conducted to propose an updated approach for determining reservoir capacity. Drone-based LiDAR was employed to generate DSM and DEM data with a spatial resolution of 50 cm, enabling the display of elevations of hydraulic structures, such as embankments, spillways, and intake channels. Furthermore, using drone-based hyperspectral imagery, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated to detect water bodies and verify differences from existing reservoir boundaries. The constructed high-resolution DEM data were integrated with bathymetric measurements to create underwater contour maps, which were used to generate a Triangulated Irregular Network (TIN). The TIN was utilized to calculate the inundation area and volume of the reservoir, yielding results highly consistent with basic specifications. Considering areas that were not surveyed due to underwater vegetation, it is anticipated that this data will be valuable for future updates of reservoir capacity information.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.3
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• pp.121-133
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• 2023

The purpose of this study was to improve weed management systems under varying carbon dioxide concentrations and temperatures by evaluating the growth of Acalypha australis and observing the efficacy of four foliar and four soil herbicides, as well as measuring phytotoxicity in soybean crops treated with these herbicides. In both growth chamber and greenhouse conditions, plant height and shoot fresh weight of Acalypha australis increased as temperature increased. The variable to maximum fluorescence ratio (Fv/Fm), relative electron transport rate (ETR), plant height, leaf area, and shoot fresh weight of Acalypha australis were higher at carbon dioxide concentrations of 800 ppm than at 400 ppm. The efficacy of a foliar herbicide, glufosinate, on Acalypha australis was lower at 30℃ than at 20℃ and 25℃ in the growth chamber condition and was also lower at 29℃ than at 21℃ and 25℃ in greenhouse conditions. In contrast, mecoprop efficacy on Acalypha australis was lower at 20℃ and 25℃ than at 30℃ in growth chamber conditions and lower at 21℃ and 25℃ than at 29℃ in greenhouse conditions. Glyphosate efficacy was lower at 21℃ than at 25℃ and 29℃ under greenhouse conditions. With soil herbicides, metolachlor and ethalfluraline, efficacies were higher at relatively high temperatures under both growth chamber and greenhouse conditions. However, in the case of linuron, the difference in efficacy was not observed under varying temperatures in both growth chamber and greenhouse conditions. When ¼ of the recommended glyphosate rates were applied to Acalypha australis, efficacy was lower under 800 ppm carbon dioxide concentrations than under 400 ppm. In contrast, when ¼ of the recommended rate of bentazone was applied to Acalypha australis, efficacy was higher under 800 ppm carbon dioxide concentrations than under 400 ppm. Despite application rates, glufosinate efficacy differed insignificantly under different carbon dioxide concentrations. When applied at ¼ of the recommended rate, the efficacy of ethalfuralin was higher under 800 ppm carbon dioxide concentrations than under 400 ppm. However, efficacies of other herbicides were not different despite varying carbon dioxide concentrations. Soybean phytotoxicity in crops treated with the recommended rate and twice the recommended rate of soil herbicides was not significantly different regardless of temperature and carbon dioxide concentrations. Overall, weed efficacy of some herbicides decreased in response to different temperatures and carbon dioxide concentrations. Therefore, new weed management methods are required to ensure high rates of weed control in conditions affected by climate change.