• Journal of Preventive Veterinary Medicine
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• v.42 no.4
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• pp.143-147
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• 2018

The present study aimed to develop growth prediction models of Listeria monocytogenes in processed meat products, such as mixed pressed hams, to perform accurate microbial risk assessments. Considering cold storage temperatures and the amount of time in the stages of consumption after opening, the growth of L. monocytogenes was determined as a function of temperature at 0, 5, 10, and $15^{\circ}C$, and time at 0, 1, 3, 6, 8, 10, 15, 20, 25, and 30 days. Based on the results of these measurements, a Baranyi model using the primary model was developed. The input parameters of the Baranyi equation in the variable temperature for polynomial regression as a secondary model were developed: $SGR=0.1715+0.0199T+0.0012T^2$, $LT=5.5730-0.3215T+0.0051T^2$ with $R^2$ values 0.9972 and 0.9772, respectively. The RMSE (Root mean squared error), $B_f$ (bias factor), and $A_f$ (accuracy factor) on the growth prediction model were determined to be 0.30, 0.72, and 1.50 in SGR (specific growth rate), and 0.10, 0.84, and 1.35 in LT (lag time), respectively. Therefore, the model developed in this study can be used to determine microorganism growth in the stages of consumption of mixed pressed hams and has potential in microbial risk assessments (MRAs).

• Asian Journal of Business Environment
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• v.11 no.1
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• pp.17-25
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• 2021

Purpose: This study extends previous empirical work on the threshold effects of institutions on the relationship between infrastructure and economic growth. It does so by using three sub-indices of institutions as the threshold variable in place of aggregate index. This is with a view to determining the roles of the sub-indices in the nexus between infrastructure and economic growth. Research design, data and methodology: The analysis is based on a dynamic panel threshold regression model using a panel data set comprising 41 countries in Sub-Saharan Africa over the sample period of 1996-2015. Data are obtained from Ogbaro (2019). Results: The study finds that infrastructure exerts significant positive effects on economic growth below and above the threshold values of the three sub-indices, with higher effects above the threshold values. Results also show that on average, the Sub-Saharan African countries are not able to satisfy any of the threshold conditions, which accounts for their poor growth experience. Conclusion: The study concludes that countries with weak institutions do not benefit maximally from infrastructure development policies. The paper, therefore, recommends that countries in Sub-Saharan Africa need to focus on improving their institutional patterns if they are to reap the optimum benefits from their infrastructure development efforts.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.709-716
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• 2021

The main objective of this research is to find out the effect of Non-Performing Loan (NPL) of Nepalese conventional banks. The population of this study is major commercial banks in Nepal and the data obtained for this study was from the period 2015-2019. This research used secondary data and it is collected from each bank's annual report and GDP and Inflation taken from the World Bank database. The method used for data analysis in this study is multiple regression analysis. The study used NPL as a dependent variable and Return on Asset (ROA), Capital Adequacy Ratio (CAR), Bank Size, GDP growth, and Inflation as independent/explanatory variables. The result of this research shows that ROA, Bank Size, GDP, and Inflation have a significant effect on NPL but CAR does not have a significant effect on the NPL of banks. In other words, the GDP effect on NPL in this study shows a positive and significant effect while most studies show a negative effect. It demonstrates that when GDP growth increases, there is a significant increase in the growth of Nepalese banks even though there were no significant changes in income growth. Therefore, GDP growth has a positive and significant effect on the NPL of commercial banks. Thus, the bankers and policymakers need to consider GDP growth carefully while taking NPL-related decisions.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.203-211
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• 2022

Foreign direct investment (FDI) has increased at an exponential rate during the last two decades. It is now a feature of emerging market economies as well. Foreign direct investment and financial development are important factors in an economy's growth. Various studies have examined the impact of foreign direct investment and financial development on economic growth in different countries and areas. However, the findings are currently inconclusive. Using updated data from 1970 to 2020, this study will examine the relationships between FDI, financial development, and economic growth in 30 rising economies.GDP is the dependent variable, while FDI, financial development, trade openness, infrastructure, exchange rate, and GDP growth are the independent factors. To estimate the panel data, we used the most recent econometric models. The study's major findings suggest that FDI and financial development are critical determinants in emerging economies' economic progress. Furthermore, multiple robustness checks supported the study's empirical findings. The results of this study include various practical recommendations for investors, governments, and policymakers, given the increased interest in global economic integration and member states' reliance on FDI as a critical aspect of sustaining prosperity.

• The Journal of Industrial Distribution & Business
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• v.8 no.5
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• pp.87-93
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• 2017

Purpose - This paper investigates whether managerial overconfidence is associated with firm-specific crash risk. Overconfidence leads managers to overestimate the returns of their investment projects, and misperceive negative net present value projects as value creating. They even use voluntary disclosures to convey their optimistic beliefs about the firms' long-term prospects to the stock market. Thus, the overconfidence bias can lead to managerial bad news hoarding behavior. When bad news accumulates and crosses some tipping point, it will come out all at once, resulting in a stock price crash. Research design, data and methodology - 7,385 firm-years used for the main analysis are from the KIS Value database between 2006 and 2013. This database covers KOSPI-listed and KOSDAQ-listed firms in Korea. The proxy for overconfidence is based on excess investment in assets. A residual from the regression of total asset growth on sales growth run by industry-year is used as an independent variable. If a firm has at least one crash week during a year, it is referred to as a high crash risk firm. The dependant variable is a dummy variable that equals 1 if a firm is a high crash risk firm, and zero otherwise. After explaining the relationship between managerial overconfidence and crash risk, the total sample was divided into two sub-samples; chaebol firms and non-chaebol firms. The relation between how I overconfidence and crash risk varies with business group affiliation was investigated. Results - The results showed that managerial overconfidence is positively related to crash risk. Specifically, the coefficient of OVERC is significantly positive, supporting the prediction. The results are strong and robust in non-chaebol firms. Conclusions - The results show that firms with overconfident managers are likely to experience stock price crashes. This study is related to past literature that examines the impact of managerial overconfidence on the stock market. This study contributes to the literature by examining whether overconfidence can explain a firm's future crashes.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.585-600
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• 2011

South Korean central government has launched the first comprehensive climate change policies in 1999, and they have been renewed every three year. The third policies ended in 2007. However, it is quite rare to analyze whether the climate change policies are effective against climate change. In this context, this paper aims at analyzing the effectiveness of climate change policy which was launched for seven years from 1999 to 2007 in South Korea. The effectiveness analysis of policy can be done in terms of the individual policy and/or all policies being synthesized as a comprehensive unit. Employing the latter methodology, this paper analyzed the effectiveness on the basis of economic growth as independent variable, greenhouse gas emission as dependent variable, and energy use and its process as intervening variable. Seven analytic indicators covering the three variables were selected on the basis of two points in time before and after climate change policy having been launched. The seven indicators were analyzed in terms of three aspects. They were the change in the state of each indicator, the effectiveness of climate change policy from 1999 to 2007, and the effectiveness process from 1999 to 2007. The effectiveness process was analyzed in terms of the relational context and its flow processing path. Economic growth was advanced remarkably with increase in the total consumption of energy. As a result, greenhouse gas emission increased. However, energy efficiency increased with significant decrease in energy intensity, carbon intensity, and energy elasticity. The expansion of new and renewable energy over total energy supply was not effective significantly on the decrease in greenhouse gas emission. The processing path of climate change policy being effective advanced toward increase in energy efficiency through energy intensity rather than toward sustainable development. Such a way of the effectiveness of climate change policy implies that most policies focused on adaptation rather than on mitigation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.51-59
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• 2017

Due to the rapid change of global business environment, the growth of China's steel industry and the inflow of cheap products, domestic steel industry is faced on downward trend. The change of business paradigms from a quantitative growth to a qualitative product is needed in this steel industry. In this environment, it is very important for domestic steel distribution companies to secure their competitiveness by selecting good supply companies through a efficient procurement strategy and effective method. This study tried to find out the success factors of steel distribution industry based on survey research from experts. Weighted values of each factors were found by using AHP (analytic hierarchy process) analysis. The weighted values were applied to DEA(data envelopment analysis) model and eventually the best steel supply company were selected. This paper used 29 domestic steel distribution firms for case example and 5 steps of decision process to select good vendors were suggested. This study used quality, price, delivery and finance as a selection criteria. Using this four criterions, nine variable were suggested. Which were product diversity, base price, discount, payment position, average delivery date, urgency order responsibility and financial condition. These variables were used as a output variable of DEA. Sales and facilities were used as an input variable. Pairwise comparison was conducted using these variables. The weighted value calculated by AHP pairwise comparison were used for DEA analysis. Through the analysis of DEA efficiency process, good DMU (decision making unit) were recommended as a steel supply company. The domestic case example was used to show the effectiveness of this study.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.2
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• pp.192-197
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• 2009

This study was conducted to investigate the effect of site-specific variable fertilization following digital fertility map generated from soil analysis on rice growth and yield. The site-specific application of fertilizer was implemented by using rice transplanter equipped with side dressing applicator and global positioning system (GPS). Coefficient of variation (C.V.) of soil nitrogen content was reduced after the experiment, and spatial variation of semivariogram was reduced. Rice growth from tillering to ripening stage, plant height, tiller and panicle number increased at site-specific variable fertilization treatment, and coefficient variation (C.V.) of each growth characteristics was lower than those of conventional fertilization treatment. As a result, fertility in the rice field was more uniform become of site-specific fertilizer application. Head rice yield of site-specific application plot increased by 9% (i.e., to from 450 kg/10a to 492 kg/10a of the control plot) and its CV was significantly reduced to 3.5 compared to 7.8 of the control plot. In addition, there was no significant difference in amylose, protein contents and whiteness of milled rice, but its CV was reduced.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.6
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• pp.111-122
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• 2016

Support and efforts are being strengthened at the national level for entrepreneurial activities that are expected to revitalize our economy. Generally, according to the previous studies, it is known that entrepreneurship positively affects economic growth and the reduction of unemployment rate through employment creation. The analysis of the impact of entrepreneurial activities on economic growth and job creation is mainly based on national comparative analysis. In this paper, however, the impact of start-up activities in the Busan metropolitan city on regional economic growth and employment improvement. In this study, the dummy variables were assigned to firms within three years, five years, and ten years after start-up according to the period since the start-up of the firm. As a result of the empirical analysis, the value of dummy variable of the start - up firms was found to be significant in the analysis of the sales growth rate as the dependent variable according to the preceding study by Evans(1987). Therefore, it can be seen that the growth rate of the start - up company is higher than that of the other companies and it is positive for the regional economic growth. In addition, the coefficient of the dummy variable decreases from the enterprise analysis to the latter within three years, five years, and ten years after the start-up, and confirms that the coefficient of the firm's start-up years has a negative value. I could see more clearly. On the other hand, in the analysis of the effect of start-up firms on unemployment rate reduction, the growth rate of regular workers was higher than that of other firms. This suggests that it positively affects the reduction of the unemployment rate in the region. In addition, in the dynamic analysis using the Almon estimation formula and the Koyck model, the effect of log-transformed net start-up firms on the growth rate of new workers is very significant. As a result of the above model, It is reaffirmed that start-up activity has a positive effect on employment growth.

• Agribusiness and Information Management
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• v.6 no.1
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• pp.1-11
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• 2014

The purpose of this study is to develop the growth prediction model that can predict growth and development information influencing on the production of citrus fruits. The growth model was developed to predict the floral leaf ratio, number of fruit sets, fruit width, and overweight fruits depending on the main period of growth and development by considering the weather factors because the fruit production is influenced by weather depending on the growth and development period. To predict the outdoor-grown citrus fruit production, the investigation result for the standard farms is used as the basic data; in this study, we also understood that the influence of weather factors on the citrus fruit production based on the data from 2004 to 2013 of the outdoor-grown citrus fruit observation report in which the standard farms were targeted by the Agricultural Research Service and suggested the growth and development information prediction model with the weather information as an independent variable to build the observation model. The growth and development model for outdoor-grown citrus fruits was assumed by using the Ordinary Least Square method (OLS), and the developed growth prediction model can make a prediction in advance with the weather factors prior to the observation investigation for the citrus fruit production. To predict the growth and development information of the production of citrus fruits having a great ripple effect as a representative crop in Jeju agriculture, the prediction result regarding the production applying the weather factors depending on growth and development period could be applied usefully.