• International journal of advanced smart convergence
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• v.10 no.4
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• pp.59-65
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• 2021

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.102-108
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• 2019

Big data was associated with three key concepts, volume, variety, and velocity. Securities and investment services produce and store a large data of text/numbers. They have also the most data per company on the average in the US. Gartner found that the demand for big data in finance was 25%, which was the highest. Therefore securities and investment companies produce the largest data such as text/numbers, and have the highest demand. And insurance companies and credit card companies are using big data more actively than banking companies in Korea. Researches on the use of big data in securities and investment companies have been found to be insignificant. We surveyed 22 major securities and investment companies in Korea for activating big data. We can see they actively use AI for investment recommend. As for big data of securities and investment companies, we studied open API. Of the major 22 securities and investment companies, only six securities and investment companies are offering open APIs. The user OS is 100% Windows, and the language used is mainly VB, C#, MFC, and Excel provided by Windows. There is a difficulty in real-time analysis and decision making since developers cannot receive data directly using Hadoop, the big data platform. Development manuals are mainly provided on the Web, and only three companies provide as files. The development documentation for the file format is more convenient than web type. In order to activate big data in the securities and investment fields, we found that they should support Linux, and Java, Python, easy-to-view development manuals, videos such as YouTube.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.45-61
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• 2019

Blockchain technology, which enables transparent transactions among individuals without central control, opens up diverse business possibilities. It is also expected that blockchain will have a ripple effect on the entire area of society including finance, manufacturing, distribution, and the public sector. Previous studies related to the blockchain also deals with its functional features and application to industrial and public fields. In the new technology such as blockchain, it is necessary to know what social perception is in order to create technological development environment, but there is a lack of research on it. Therefore, this study aims to find out the implications for industrial and policy direction by analyzing issues related to the blockchain in South Korea and the US through text mining. From these two countries, we collected text data related to blockchain in online communities and internet articles. Then, we did co-occurrence analysis and topic modeling on them respectively. As a result of this study, we have found common points and differences in keywords and topics extracted from social media in the two countries. Based on them, we can offer helpful suggestions for building a sound blockchain ecosystem, and directions for future research.

• The Korean Journal of Health Service Management
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• v.12 no.4
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• pp.85-102
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• 2018

Objectives: In order to cope with the quality and the substantiality issues in long-term care for the elderly, we should have a wider view of long-term system components based on the understanding of health care organizations, management services, support for care providers and beneficiaries, education of the workforce, and management of finance and resources. Methods: For resolving the issues raised and offering guidance in the area of long-term care, we reviewed 20 reports and documents of the government and government-related institutions using the Internet home pages of the Australian government and the related organizations in the health care sector. These organizations are undergoing a huge system reform to implement consumer-directed care since 2015, in the areas of service, resources, finances, organization, and management. Results and conclusions: The study outcomes can have some implications for the long-term care system in Korea based on the differences in the service components. The results can provide basic information for improving the long-term care service, and can have several other implications for long-term care in Korea.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.157-167
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• 2021

Cyberloafing is activity deviation at the workplace where employees intentionally avoid doing their job during working hours that results in a decrease in productivity. Particularly in the context of this study, cyberloafing activity is the usage of the Internet while working. Yet, studies on the antecedents and consequences of cyberloafing in the context of industrial revolution 4.0 and society 5.0 have not been conducted. This research used a purposive convenient sampling of 280 employees in the business services branches in Indonesia, particularly the representative business service branches located in some cities and regencies, including Yogyakarta City, Sleman Regency, and Bantul Regency (Special Region of Yogyakarta) and its surroundings. The results show 3 antecedents of cyberloafing and 1 consequence of cyberloafing which influence each other. Furthermore, these findings have filled the existing gaps regarding the antecedents and consequences of cyberloafing in service provider industries in the context of industrial revolution 4.0 and society 5.0. From the results of this research, it can be concluded that the five hypotheses proposed in this study are supported. The antecedents and consequences of cyberloafing have been tested and proven in this study as a contribution to science and technology.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.675-684
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• 2021

This paper aims to analyze factors influencing earnings of workers in Vietnam using provincial-level data from 2016 to 2018. We show the important determinants of earnings of workers of more than 15 years old including working hour, labor force, life expectancy, education, regulation measured by Provincial Competitiveness Index (PCI) and especially Industry 4.0, our major depart from literature proxies by government expenditure on science and technology, number of phone lines, and number of internet users. Working hours are a typical measurement of quantity of labor supplied. Labor force represents market size from the supply side. Life expectancy measures the health of laborers, a physical quality measure of workers. PCI stands for institutional status of the locality. Two most important factors of our interest are education, representing qualification of workers, and Industry 4.0, reflecting the new working environment of workers. By estimating a robust standard error fixed-effect model, we have evidence that all factors are significant in explaining earnings of Vietnamese workers. Education and IR4.0 play an important role in earnings of workers of Vietnam. Results also provide an estimation of Vietnam's labor supply in the context of Industry 4.0. In addition, findings contribute to explain the income discrepancy among Vietnamese provinces.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.129-135
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• 2022

This study aims to identify the factors supporting and inhibiting the digitalization of blue economy MSMEs in Bitung, Indonesia. The literature shows little research on digitalization related to the blue economy in Southeast Asia, especially in Indonesia. This indicates that there is a large research gap related to digitalization and the blue economy in the Indonesian context. Data was collected through the distribution of questionnaires with open-ended questions to blue economy MSMEs. Data was also obtained from in-depth interviews with representatives of Aruna, an Indonesian company that focuses on simplifying the supply chain of fishery products by connecting small-scale fishers to the global market through technology. According to the study's findings, two primary factors-motivation to develop their business and efforts to maintain seller-buyer interaction-support SMEs' use of technology in the blue economy. However, digital literacy and technological infrastructure, such as the internet network, are the two main factors that become obstacles in the effort to digitize MSMEs in the blue economy. The role of the government is also a contingent factor that can strengthen the relationship between factors that support digitization and weaken the relationship between factors that hinder digitalization.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.79-90
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• 2021

The purpose of this research is to identify factors affecting the acceptance and use of e-tax services among medium taxpayers in Phnom Penh, Cambodia. The researcher conducted the study based on a quantitative approach by using multi-stage sampling method, which selects a sample size by two or more stages. The first stage sampling was the stratified random sampling and the subsequent stage was purposive sampling. In this study, the stratified random sampling was first used, followed by purposive sampling. The data were collected from 450 medium taxpayers who experienced using e-tax services located in three tax branches in Phnom Penh. This study adapted the confirmatory factor analysis (CFA) and structural equation model (SEM) to analyze the model accuracy, reliability and influence of various variables. The primary result showed that behavioral intention has a significant effect on user behavior of e-tax services among medium taxpayers in Phnom Penh, Cambodia. Moreover, the results revealed that performance expectancy, effort expectancy, social influence, and anxiety have significant impact on behavioral intention. In addition, social influence has the strongest impact on behavioral intention, followed by anxiety, performance expectancy and effort expectancy. Conversely, facilitating conditions, trust in government, and trust in internet do not influence behavioral intention.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.579-589
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• 2021

In the era of tough competition, the customer's emotional attachment to brand plays a vital role to the successes and failures of enterprises. Specifically in the case of doing business online, brands have to cope with the troubles of rising from brand hate as brand avoidance, negative word of mouth and brand retaliation. Traditionally, the brand communication is very hard to control and with online communities, the problems tend to be even more severe. This paper aims to explore and discuss the core concept, the driven factors and the actionable consequences of brand hate among netizens. A total of 358 valid responses were obtained from surveys taken from the internet users across the nation. Partial Least Square - Structural Equation Modeling (PLS-SEM) was conducted using Smart PLS to assess the hypotheses. The result shows that the expression of brand hate among netizen consists of active hate and passive hate. Deficit value, deceptive advertising, negative past experience and ideology incompatibility have been confirmed as influencing factors on customers' brand hate emotion. Then brand hate itself causes the customer's actionable outcomes such as brand avoidance, brand negative word of mouth and brand retaliation. Along with the theoretical contributions and managerial implications have been recommended for enterprises to avoid netizens' brand hate.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.571-578
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• 2021

The outbreak of the COVID-19 pandemic, which began in 2020, had a significant impact on the economy and business activities worldwide. Large companies, as well as small businesses were affected, many of them had to scale down or divert their businesses, and some even had to stop. This extraordinary situation requires business people to make innovations and adjustments to survive during a pandemic. Entering the digital era, business players are helped by the ease of internet access, which will make it easier for SME players to get data from their consumers. Business actors can use this data to innovate and create new creations to improve business performance during this pandemic. This research aims to identify how small and medium enterprises can take advantage of Big Data to improve marketing performance through innovation and value creation. The research methodology used the in this research is quantitative method. The respondents are SME producers of food and beverage, with a total of 150 respondents. The results in the study indicate that all the proposed hypotheses are accepted. The most significant influence is found on the relationship of Big Data to value creation. The lowest effect was obtained from the relationship between Big Data and marketing performance through the mediation variable and innovation capability.