• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.3
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• pp.77-87
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• 2022

Previous literatures have provided growing evidence regarding the impact of leadership in enhancing follower creativity. Despite these ample evidences, a noticeable omission in this body of study is entrepreneurial leadership. The present study extends leadership·creativity research by developing a mediation-moderation model and investigating the key roles that innovation climate and psychological empowerment may play in the mechanism between entrepreneurial leadership and followers' individual creativity. Using a data of 161 SME's members, the results show that: (a) followers' psychological empowerment positively mediates the relationship between entrepreneurial leadership and individual creativity; (b) the impact of entrepreneurial leadership on followers' psychological empowerment through innovation climate is high (in other words, innovation climate positively moderates the relationship between entrepreneurial leadership and followers' psychological empowerment). This study gain more comprehensive insight of individual creativity and leader's entrepreneurial leadership, to foster more creative venture organizations. The findings of this study make several important implications to present venture·leadership·creativity knowledge. Future studies should examine a broad range of mediating or moderating processes through which context for innovation effects positively on individual creativity. Therefore, additional study is needed to test if the results of this research can be generalized across industry·firm types.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• Information Systems Review
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• v.24 no.2
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• pp.1-20
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• 2022

Researchers are making contradictory claims through the concept of superstars and long tails about how the development of IT technology affects demand distribution. Unlike previous studies that focused on changes in demand from a macro point of view, this study explored whether the relationship between a company's marketing activities and consumer response differs depending on the product location (i.e., superstar vs. long tail) from a micro point of view. Based on the marketing mix framework, hypotheses were developed based on the relevant literature. In the case of empirical analysis, 2,835 daily data from 63 Korean films were tested using the quantile regression method. As a result of the analysis, it was found that the influence of marketing mix factors on sales varies depending on the location of the product. Specifically, the appeal breadth of the film and the effect of owned media are enhanced in superstar products, and the effect of acquisition media in long-tail products is enhanced and the negative effects of competition are mitigated. Unlike previous studies that focused on macroscopic changes in demand distribution, this study suggested marketing activities suitable for practitioners through microscopic analysis.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.

• Korean Journal of Construction Engineering and Management
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• v.25 no.1
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• pp.32-41
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• 2024

In the construction industry, not only safety accidents, but also various complex risks such as construction delays, cost increases, and environmental pollution occur, and management technologies are needed to solve them. Among them, process risk management, which directly affects the project, lacks related information compared to its importance. This study tried to develop a MATM tunnel process risk classification system to solve the difficulty of risk information retrieval due to the use of different classification systems for each project. Risk collection used existing literature review and experience mining techniques, and DB construction utilized the concept of natural language processing. For the structure of the classification system, the existing WBS structure was adopted in consideration of compatibility of data, and an RBS linked to the work species of the WBS was established. As a result of the research, a risk classification system was completed that easily identifies risks by work type and intuitively reveals risk characteristics and risk factors linked to risks. As a result of verifying the usability of the established classification system, it was found that the classification system was effective as risks and risk factors for each work type were easily identified by user input of keywords. Through this study, it is expected to contribute to preventing an increase in cost and construction period by identifying risks according to work types in advance when planning and designing NATM tunnels and establishing countermeasures suitable for those factors.

• Industry Promotion Research
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• v.9 no.2
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• pp.23-28
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• 2024

The study investigated the impact of ESG management activities on the non-financial performance of organizations, focusing on small and medium-sized manufacturing companies. Using data from 78 survey responses, the following results were summarized. Firstly, ESG management activities positively influenced internal customer satisfaction. The correlation coefficient was .679, indicating a moderately strong correlation, and the coefficient of determination (R²) was .461, explaining 46.1% of the variance. Additionally, with a beta value of .679, a t-value of 8.058, and a p-value of .000, ESG management activities had a statistically significant impact on internal customer satisfaction. Secondly, ESG management activities also had a positive impact on corporate trust. The correlation coefficient was .695, indicating a moderately strong correlation, and the coefficient of determination (R²) was .483, explaining 48.3% of the variance. The beta value was .695, the t-value was 8.429, and the significance probability was .000, indicating a significant influence on corporate trust.The study aimed to shed light on the relationship between ESG management activities of small and medium-sized enterprises and their non-financial performance. These results suggest that companies can enhance internal customer satisfaction and corporate trust through fulfilling social responsibilities and practicing sustainable management.

• Industry Promotion Research
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• v.9 no.2
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• pp.85-93
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• 2024

This study examines the direct effects of infant temperament, infant happiness, playfulness, and self-regulation and the mediating effect of playfulness and self-regulation in the relationship between infant temperament and infant happiness, and examines the relationship of influence on infant happiness according to infant temperament. The purpose is to provide theoretical and practical information for promotion. The main results of this study are as follows. First, the activity of infant temperament was found to have a positive effect on infant happiness. Second, the adaptability, activity, and approach-avoidance of children's temperament were found to have a positive effect on playability. Third, the physiological regularity of infant temperament was found to have a positive effect on self-regulation. Fourth, playfulness and self-regulation were found to have a positive effect on children's happiness. Fifth, playfulness was found to play a mediating role between infant temperament adaptability, activity, approach avoidance, and happiness. Sixth, self-regulation was found to play a mediating role between the physiological regularity of infant temperament and infant happiness. In conclusion, this study can improve children's happiness by analyzing the direct effects on children's happiness, playfulness, and self-regulation according to the sub-factors of children's temperament, and the indirect influence of the mediating variables, playability and self-regulation, on children's happiness. It is meaningful in providing theoretical and practical basic data for early childhood care and education by understanding what direction there is.

• Journal of Environmental Health Sciences
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• v.50 no.3
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• pp.181-190
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• 2024

Background: Air pollutants have been reported to have harmful effects on human health. Busan is a vulnerable area in terms of air quality due to the installation of various industrial complexes, particularly the port industry. However there is limited research data on the ambient air quality of residential areas near ports and industrial complexes. Objectives: This study aimed to determine the quarterly levels of air pollutants near industrial complexes and ports and to identify trends and characteristics of air pollutant exceedances. Methods: Air measurements were conducted quarterly. The measured air pollutants included O₃, SO₂, CO, NO₂, PM₁₀, and PM_2.5. PM₁₀ and PM_2.5 were measured using BAM-1020 equipment, while O₃, SO₂, CO, and NO₂ were measured using AP-370 Series equipment. The quarterly concentration levels of air pollutants were determined, and the influence of precipitation and commuting hours on fine particulate matter was examined. Analysis of variance (ANOVA) was conducted to determine if there was significance between the concentrations of fine particulate matter during commuting hours and non-commuting hours. Results: The concentrations of air pollutants were generally higher in the first and second quarters. Furthermore, the concentrations of PM₁₀ and PM_2.5 tended to decrease continuously following consecutive rainfall, with concentrations at the end of rainfall periods lower than those observed at the beginning. The frequency of exceeding average concentrations of PM₁₀ and PM_2.5 was higher on weekdays. Moreover, the average concentrations of PM₁₀ and PM_2.5 during weekday commuting hours were higher compared to non-commuting hours. Conclusions: The concentrations of air pollutants in the survey area were found to be higher than the overall average in Busan. Based on this study, continuous air quality monitoring is necessary for residential areas near industrial complexes and ports. For further research, health biomonitoring of residents in these areas should be conducted to assess their exposure levels.

• Journal of Industrial Convergence
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• v.22 no.6
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• pp.51-59
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• 2024

The purpose of the paper is to suggest government policies that are practically helpful to small business owners in pandemic situations such as COVID-19. To this end, keyword frequency analysis and word cloud analysis of text mining analysis were performed by crawling news articles centered on the keywords "COVID-19 Support for Small Businesses", "The Impact of Small Businesses by Response System to COVID-19 Infectious Diseases", and "COVID-19 Small Business Economic Policy", and major issues were identified through LDA topic modeling analysis. As a result of conducting LDA topic modeling, the support policy for small business owners formed a topic label with government cash and financial support, and the impact of small business owners according to the COVID-19 infectious disease response system formed a topic label with a government-led quarantine system and an individual-led quarantine system, and the COVID-19 economic policy formed a topic label with a policy for small business owners to acquire economic crisis and self-sustainability. Focusing on the organized topic label, it was intended to provide basic data for small business owners to understand the damage reduction policy for small business owners and the policy for enhancing market competitiveness in the future pandemic situation.

• Food Science and Preservation
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• v.30 no.2
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• pp.358-368
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• 2023

Euonymus alatus (Thunb.) Sieb., also known as the arrow tree in Korea, is a plant in East Asia used in traditional medicine and food. In particular, the wings of E. alatus are rich in phenolic compounds. This study evaluated the antioxidant, α-glucosidase inhibition, and anti-cancer activities of E. alatus wing extracts. The radical and hydrogen peroxide scavenging acitvities and reducing the power of 1,000 ㎍/mL E. alatus wing extracts, were similar to those of the positive control (0.1% BHT, 0.1% α-tocopherol). In addition, ethanol and methanol extract at 250 ㎍/mL showed 95.70 and 94.99% of α-glucosidase inhibition activity, respectively. The ethanol extract of E. alatus wings had the highest total polyphenol and flavonoid contents (867.8 mg% and 521.7 mg%, respectively). The E. alatus wing extracts significantly decreased the cell viability of LNCaP human prostate cancer cells (p<0.001), MDA-MB-231 human breast cancer cells (p<0.001), and HT-29 human colon cancer cells (p<0.001) in a dose-dependent manner. However, there was no significant effect on B16 mouse melanoma cells. Notably, the ethanol extracts showed higher cancer cell growth inhibitory activity in LNCaP and HT-29 cells than the other extracts. These results suggest that E. alatus wing extracts could have significant clinical applications, and our results can be used as basic data for future functional food material development.