Asia-Pacific Journal of Business Venturing and Entrepreneurship
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v.14
no.3
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pp.13-26
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2019
There is a growing interest in the entrepreneurial activity that has long been considered essential for sustainable economic development and value creating. Although it is strongly encouraged by focusing on the positive aspects of venturing, less has been paid attention to entrepreneurial failure, which is the biggest cause of hesitation in starting a business. The uncertain and risky nature of entrepreneurship implies a considerable possibility of failure. Even if it fails, the experience and knowledge of entrepreneurs acquired through entrepreneurship indeed offers valuable lessons for the re-venturing, which can serve as an important social asset that should not be lost. It has been argued that re-entering the same industry for the subsequent venture maximizes the learning effect through utilizing potential benefits from industry-specific knowledge. Although the re-startup after entrepreneurial failure is a very important topic in the studies on serial entrepreneurs, there is a paucity of systematic empirical investigation. This study responds to calls for more research on the re-startup after entrepreneurial failure, and specifically complements existing studies on serial entrepreneurs. Focusing on the entrepreneurs' attribution for the failure, we conducted an empirical analysis of how this affects the re-startup process. Moreover, we also examined the moderating effects of entrepreneurial self-efficacy and resilience. For the analyses, we surveyed the entrepreneurs who tried to re-start the subsequent business after the entrepreneurial failure through the "Revitalization Center for Strained Entrepreneur". The results found that failed entrepreneurs who blamed internal factors for their previous venture failures were likely to keep the same industry for their subsequent business. In addition, the positive effect of internal attribution on maintaining the same industry for the re-startup was found to be stronger when entrepreneurial self-efficacy and resilience were high.
This study was aimed to investigate the education needs for prevention and control of infectious diseases by lifecycle based on age group and to provide the fundamental data to develop the educational programs. A research was conducted with 328 adults over 19 years old for a month of February 2021 through online and mobile survey by Gallup Korea. Research contents include the general characteristics, personal hygiene practices related to infection, perceived risks related to infection, importance and level of knowledge on infectious diseases, and education needs for prevention and control of infectious diseases. For the research data analysis, PASW Statistics Ver 20.0 was used as a statistical program. Ranks from analysis upon conversion as the formula of Borich needs to sum up with importance and knowledge level showed first (Borich 3.11) with treatments for infectious diseases; second (Borich 2.15) with process in case of suspicion and diagnosis of infectious diseases; third (Borich 1.75) with transmission routes of infectious diseases; fourth (Borich 1.73) with preventive ways of infectious diseases; fifth (Borich 1.50) with diagnostic and test methods of infectious diseases; sixth (Borich 1.45) with characteristics of infectious diseases; and seventh (Borich1.38) with main symptoms of infectious diseases. It is anticipated that development of educational programs applying education needs for prevention and control of infectious diseases in this research can contribute to enhance the physical health, mental health, and psychological well-being of the subjects.
Asia-Pacific Journal of Business Venturing and Entrepreneurship
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v.15
no.6
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pp.43-54
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2020
This paper investigates the causal impact of the increase in institutional distance between two geographic regions on the flow of cross-border Venture Capital (VC) between the regions. While cross-border VCs are believed to have competitive advantages at identifying and managing promising startups in a local market compared to local counterparts, the discrepancy in institutional characteristics between two markets exacerbates the difficulty of credible information exchange and negotiation, significantly increasing transaction cost related to a cross-border venture capital investment. This study conducts a difference-in-difference analysis to examine the relationship between institutional distance and the flow of cross-border VC investment using the fact that the official adoption of the Euro currency by member countries of the European Union except the UK created an institutional chasm between the UK and other EU member countries. The outcomes of the analysis suggests that UK-based VCs significantly decreased the VC investment into EU-based startups and that EU-based VCs reduced the investment into UK-based startups. The results have meaningful implications for understanding the impact of the change in institutional difference on cross-border VC investment, which seems to increasingly take place with the recent trend of de-globalization and the rise of protectionism.
The purpose of this study was to investigate the effects of 12-week training on changes in physical fitness and cardiovascular factors for firefighters. For this purpose, 40 men in their 20s and 30s who agreed to participate voluntarily were recruited. They were divided into four groups: the firefighters' physical fitness test training group (hereinafter referred to as PT group), firefighters' physical fitness test and aerobic training group (hereinafter referred to as PT+AR group), firefighters' physical fitness test and both aerobic and anaerobic training group (hereinafter referred to as PT+CO group). Physical fitness factors (grip strength, back muscle strength, seated forward bend, standing long jump, sit-ups, 20-meter shuttle run), cardiovascular factors (total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, glucose, waist circumference, systolic blood pressure, diastolic blood pressure) and the relationship between Framingham Heart Risk Score and physical/cardiovascular factors were compared and analyzed, and the following conclusions were obtained. Aerobic training, anaerobic training, and combined training, including 12 weeks of firefighter physical examinations, all had positive effects on fitness and cardiovascular factors, which would be an appropriate way for firefighter examinees to improve physical strength and reduce the risk of cardiovascular disease.
The Journal of the Institute of Internet, Broadcasting and Communication
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v.22
no.2
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pp.83-88
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2022
The purpose of this study is to present an analysis and implications for the metaverse-based virtual real estate transaction service. Through blockchain-based technology and metaverse, the world we live in is expanding naturally. Therefore, changes in the environment and perceptions of market participants are also very important factors. The concept and thinking about the existing asset value change and investment are also changing. This means that you can generate profits through value and investment in intangible assets. The service user aspect is a case of investing in the future value of virtual real estate that if more users participate rather than the present value, the principle of supply and demand will be applied to increase the number of consumers and the price will naturally rise according to the principle of scarcity. The service provider provides a technical platform for the service to directly transact the portion of the virtual area considered of interest directly through the virtual real estate purchase business. As the number of participants increases as well as funds and transaction fees, various revenue models such as advertisements can be discovered and provided. It plays the role of providing jobs and information through new services. As a stakeholder, governments can exploit the emergence of new technologies and products to create people and services and secure economic benefits. Of course, various institutional supports should be provided so that new services can settle in the market while mitigating risk factors. This study is meaningful in that it contributes to the establishment of a domestic metaverse-based environment and related research and is utilized in the study of virtual space real estate services.
This study is to investigate the factors affecting post-traumatic stress disorder (PTSD) symptoms among hospital nurses during the COVID-19 pandemic in Korea. Cross-sectional, descriptive design is used in this study. Data collection was completed through an online self-administered survey from December 2020 to January 2021 among 180 registered nurses dealing with COVID-19 patients at hospitals. This survey includes socio-demographic questions, including a 22-item PTSD questionnaire, a 14-item type D personality questionnaire, a 25-item resilience questionnaire, and a 23-item Social Support Scale questionnaire. 56.1% of the subjects in this study were at risk of PTSD. In the high-risk group for PTSD, resilience and social support were lower than those in the low-risk group for PTSD. But there was no statistically significant difference in both variables (resilience t=0.21, p=.836, social support t=1.07, p=.287). However, education (OR = 2.23, p= .041) and type D personality (OR = 3.67, p < .001) were significant factors for PTSD symptoms. The results of the study can be utilized to recognize PTSD in nurses by identifying factors influencing PTSD during epidemics such as COVID-19, and to apply management systems such as psychological programs to help overcome them.
As cases of social and ethical problems caused by artificial intelligence technology have occurred, artificial intelligence ethics are drawing attention along with social interest in the risks and side effects of artificial intelligence. Artificial intelligence ethics should not just be known and felt, but should be actionable and practiced. Therefore, this study proposes an artificial intelligence ethics education model to strengthen the practical ability of artificial intelligence ethics. The artificial intelligence ethics education model derived educational goals and problem-solving processes using artificial intelligence through existing research analysis, applied teaching and learning methods to strengthen practical skills, and compared and analyzed the existing artificial intelligence education model. The artificial intelligence ethics education model proposed in this paper aims to cultivate computing thinking skills and strengthen the practical ability of artificial intelligence ethics. To this end, the problem-solving process using artificial intelligence was presented in six stages, and artificial intelligence ethical factors reflecting the characteristics of artificial intelligence were derived and applied to the problem-solving process. In addition, it was designed to unconsciously check the ethical standards of artificial intelligence through preand post-evaluation of artificial intelligence ethics and apply learner-centered education and learning methods to make learners' ethical practices a habit. The artificial intelligence ethics education model developed through this study is expected to be artificial intelligence education that leads to practice by developing computing thinking skills.
COVID-19, which occurred in 2019, has a strong contagious power, has serious symptoms of infection and after-effects, and death in severe cases depending on the underlying disease and symptoms. As COVID-19 is highly contagious, in Korea, screening clinics have been set up across the country to determine whether or not to be positive for COVID-19 and isolate the infected to prevent the spread of COVID-19. However, there are cases where COVID-19 test applicants flock to screening clinics and cannot receive tests due to longer waiting times, and there is a risk that secondary infections may occur in the atmosphere. In this study, the reservation and notification system can be applied from the existing screening care system to solve spatial constraints, reducing waiting time with screening appointments, and solving population bottlenecks to screening clinics. Taking the COVID-19 pandemic as an experience, we propose a system that can present directions in future pandemic situations. To process real-time data, we use Google's Firebase to use Realtime Database in the cloud environment. Because a real-time database is used, users can check the status of screening clinics in real time through the app, make reservations, and receive notifications about test reservations.
This study analyzes whether investor sentiment and liquidity explain the momentum phenomenon in the Korean stock market and whether it is a risk factor for the asset pricing model. The empirical analysis used the monthly returns of non-financial companies listed on the stock market during the period 2000-2021. As a result of the analysis, first, it was found that there is a momentum effect in Korea. This is the same result as the previous study, and since 2000, the momentum effect has been accepted as a general phenomenon in the Korean stock market. Second, if we look at the portfolio based on investor sentiment, investor sentiment is influencing momentum. In particular, when investor sentiment is negative, the return on the winner portfolio is high. Third, as a result of the analysis based on liquidity, the momentum effect disappears and a reversal effect appears. Fourth, it was found that investor sentiment and liquidity influence the momentum effect. This is a result of the strong momentum effect in the illiquid stock group with negative investor sentiment. Fifth, as a result of analyzing the effect of each factor on stock returns, it was found that both investor psychology and liquidity factors have a significant impact on returns. The estimated results provide evidence that the inclusion of these two factors in the Carhart four-factor model significantly increases the predictive power of the model. Therefore, it can be said that investor sentiment factors and liquidity factors are important factors in determining stock returns.
Seo, In-Yong;Shin, Ho-Cheol;Park, Moon-Ghu;Kim, Seong-Jun
Journal of the Korea Society for Simulation
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v.18
no.3
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pp.103-112
/
2009
In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.
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