Officially assessed land price has been the index of South Korea since 1989 throughout different sectors of tax and welfare. Officially assessed land price is used as a tax valuation for the tax on property holdings, and the equity of such is the most important factor in the fair taxation for the people of South Korea. On this wise, this research analyzed and verified the horizontal and vertical inequity of officially assessed land price in Seoul by using the real transaction data between 2016 and 2018. In fact, Seoul's assessment ratio for the entire three-year period was 60.64% and it showed to increase each year. Horizontal equity was found to be most favorable in 2017, and the horizontal equity of each borough of Seoul appeared to improve each year. Vertical inequity was found to have reverse inequality in most boroughs of Seoul, however, some parts of Gangnam districts such as Gangnam-gu, Seocho-gu, and Gangdong-gu presented progressive inequality. Such example showed the need for improvement in terms of balance by each borough. The use of quantile regression demonstrated reverse inequality in most quantile, but, the differences in the value of the coefficient by each quantile showed the need for improvement of officially assessed land price with the equity of each quantile. Through the equity verification of officially assessed land price, it was analyzed that the lack of equity was found by year, by borough, and by use district. In order to redeem the lack of equity, the government must systematically supplement the real-estate disclosure system by initiating ratio studies to verify horizontal and vertical equity.
The pandemic age came to us after the COVID19 broke out in the early of 2020, which resulted in many changes in our everyday life. One of the changes is non-contact transactions in economic activities, especially contact-free financial transactions are ever-increasing activities in the pandemic age. In non-contact financial transactions, security and trust are important issues that users are concerned about the most. So the paper examines how security and trust have effects on the attitudes of bank customers who use mobile banking applications on their smartphones. The study conducted factor ananlysis to extract research constructs using survey data gathered during Nobember of 2020 through February of 2021 across the nation. After extracting the constructs, the study checked validity, sampling adequacy, and internal reliability with KMO, Bartlett's test, and Crombach Alpha respectively. To make sure the purpose of the study, I do regression analysis. All factors of attitude had impacts on willingness on both of transactions and purchasing financial products. The results could give guidelines banks which want to attract more customers in open-banking environment through ever-more secure and trusty mobile banking application.
Due to COVID-19 and soaring participation of individual investors, large-scale transactions exceeding system capacity limits have been reported frequently in the capital market. The capital market IT systems, which the impact of system failure is very critical, have encountered unexpectedly tremendous transactions in 2020, resulting in a sharp increase in system failures. Despite the fact that many companies maintained large-scale system capacity planning policies, recent transaction influx suggests that a new approach to capacity planning is required. Therefore, this study developed capital market IT system capacity planning models using machine learning techniques and analyzed those performances. In addition, the performance of the best proposed model was improved by using sentiment index that can promptly reflect the behavior of investors. The model uses empirical data including the COVID-19 period, and has high performance and stability that can be used in practice. In practical significance, this study maximizes the cost-efficiency of a company, but also presents optimal parameters in consideration of the practical constraints involved in changing the system. Additionally, by proving that the sentiment index can be used as a major variable in system capacity planning, it shows that the sentiment index can be actively used for various other forecasting demands.
Based on prior studies on real estate policy, tax policy, and financial policy, this study examined how tax policy and financial policy affected real estate prices using monthly data from January 2014 to December 2021. We performed a VAR model using unit root tests, cointegration tests, as well as conducted impulse response analysis and variance decomposition analysis. The results are as follows. First, the tax regulation index and the financial regulation index had no discernible impact on housing prices. Specifically, a one-sided stabilizing regulatory policy was ineffective and, instead, led to unintended side effects, such as price increases resulting from reduced transaction volume. Secondly, mortgage rates had a negative impact on the housing sale price index. In other words, an increase in interest rates might led to a decrease in housing prices. Thirdly, an increase in the transfer difference, which involves capital gains tax, has a positive effect on housing prices. This led to rising housing prices because the transfer taxes were shifted to buyers, causing them to hesitate to make purchases due to the increased tax burden. Fourthly, both acquisition taxes and mortgage loans had relatively little impact on housing prices.
In case of a specific sector being divided into sub-sectors, this study presents a process for estimating an input-output table, which is frequently used as basic data in fields of energy and environment economics. RAS method, which is universally used for this case, requires information on production, intermediate input sum, and intermediate demand sum for each sector in the new table. But in many cases, it is difficult to secure information on intermediate demand sum by sector. This study suggests a process for estimating a new input-output table without using information of intermediate demand sum in the case of sector separation, under the assumption that information of production value and intermediate input sum by sector are available. The key idea is that the values of many elements in the input-output table after disaggregation are the same as those in the table before disaggregation and that the sum of the elements after disaggregation, equals the values of the elements before disaggregation. The process of estimating the intemediate transaction matrix or the input coefficient matrix is presented by using these information instead of intermediate demand sum information. A small-scale simulation shows that the average error rate of the process proposed in this study is about 11.23% in estimating input coefficients, which is smaller than the 11.30% estimation error of RAS using the information of intermediate demand sum. However, since it is known in the literature that using additional information does not always improve estimation performance compared to not using it, additional research on various simulations is needed to apply the method of this study to reality.
This study was conducted to confirm the relationship between the proactive accounts receivable management of SMEs on credit sales decision making and business performance, and to derive effective accounts receivable management plan and systematic credit sales decision making plan. Based on 455 copies of data collected through a survey targeting SMEs, it was confirmed through factor analysis, reliability analysis, confirmatory factor analysis, and model fit verification, and the research hypothesis was verified with a structural equation model. As a result of the verification, credit rating had a positive effect on financial performance, sales performance and credit sales decision, while credit control had a positive effect on financial performance, while negative effect on sales performance and credit sales decision. In the mediating effect hypothesis test, credit sales decision had a positive effect between credit rating and business performance and a negative effect between credit control and business performance. The study suggests that if small and medium-sized enterprises improve their business performance through effective accounts receivable management, they can create a synergistic effect in enhancing the business performance of companies if they simultaneously improve their proactive accounts receivable management and credit sales decision ability. Future research is required to study the impact of factors such as segmentation of research subjects and credit transaction motives and accounts receivables management.
Recently, enterprise information management activities have been applied to promote electronic trade, while changing the paradigm of cross-border trade and overall trade business processes. L/C, which facilitates payment from the trade transaction in a company, has been able to eliminate the high cost and inefficient element of the trade process by utilizing the electronic letter of credit (e-L/C). This study examines the influential relationship among the e-trade utilizing factor (such as the perceived ease of use and the volition of CEO), the e-L/C and corporate performance, and the study aimed to verify the moderating effect of customer service level by organizations utilizing e-trade. For the purpose of the research, we conducted a survey implementing the e-L/C and analyzed the 338 data collected. The results of this research are as follows. First, the perceived ease of use and volition of CEO have positive impact on the e-L/C. Second, the e-L/C has positive impact on the business performance. Third, a moderating effect shows on the customer expectation level. The main implication of this study is that the perceived ease of use is to be considered preferentially than the volition of CEO in order to utilize electronic trading, and the volition of CEO shows synergy effect with customer service level by organization utilizing e-trade.
The major purpose of this study is to investigate the impact of environmental uncertainties and logistics resources capabilities mediated by relational norms and logistics services on logistics performance in the industrial products. The 272 data were collected from the key informants who were working at the logistics-related departments in the H Heavy Industries & Construction and HSD Engine. The following results were verified using structural equation modeling. First, environmental uncertainties such as dynamism and heterogeneity unexpectedly had insignificant effects on relational norms such as information exchange and flexibility and logistics services such as product availability and on-time delivery. Second, logistics resource capabilities showed unique effects based upon its component's characteristics. For example, Logistics Information Systems did not have direct impact on logistics services but had indirect effect on logistics services via relational norms. On the other hand, logistics resources such as logistics specific assets and transportation service competencies had direct impact on logistics services but not on relational norms. Third, relational norms between transaction partners significantly affected logistics services but had insignificant effects on logistics performance such as logistics costs reduction and delivery qualities. Fourth, consistent with several studies, excellent logistics services between industrial purchaser and suppliers based upon relational norms did have significant effect on logistics performance such as delivery consistency and delivery qualities. Finally, the empirical results in this study could be strategic logistics management guidelines based upon the theoretical relationships among the environmental uncertainties, logistics information systems, logistics resources, relational norms, logistics services, and logistics performance.
Mobile shopping goes beyond the level of a tool that simply supports consumers' shopping in-store and creates a new level of experience for consumers through offline connection. It is important to acquire new customers in order to gain a competitive advantage through mobile shopping, but it is more important to maintain a continuous transaction relationship with the secured customers. Existing mobile shopping-related studies can be largely classified into three categories such as a study on the characteristics of mobile consumers, a study on the characteristics of a mobile shopping mall, and a study on the characteristics of mobile shopping itself. Therefore, this study aimed to analyze the impact of perceived value, perceived risk, and innovativeness on trust in mobile shopping. In order to investigate the impact of perceived value, perceived risk, consumer innovativeness on the trust in mobile shopping, consumers who have experience in purchasing products through mobile were investigated. The data collected in this study were verified the reliability and validity of the measurement items based on the measurement validation process. In this study, regression analysis was performed by selecting perceived value, perceived risk, innovativeness as independent variables, and trust as dependent variables. As a result of the analysis, perceived value, innovativeness had a positive impact on trust and perceived risk had a negative impact on trust. As a result of analysis, three hypotheses were supported. Finally, implications of the research are presented, and limitations and directions for future research are described.
Yeon Joo Lee;Hobin Jang;Sujung Jo;GyeHyun Jang;Geontae Noh;Ik Rae Jeong
Journal of the Korea Institute of Information Security & Cryptology
/
v.34
no.1
/
pp.129-141
/
2024
Amidst the acceleration of digital transformation across various sectors, the financial market is increasingly focusing on the development of digital and electronic payment methods, including currency. Among these, Central Bank Digital Currencies (CBDC) are emerging as future digital currencies that could replace physical cash. They are stable, not subject to value fluctuation, and can be exchanged one-to-one with existing physical currencies. Recently, both domestic and international efforts are underway in researching and developing CBDCs. However, current CBDC systems face scalability issues such as delays in processing large transactions, response times, and network congestion. To build a universal CBDC system, it is crucial to resolve these scalability issues, including the low throughput and network overload problems inherent in existing blockchain technologies. Therefore, this study proposes a solution based on reinforcement learning for handling large-scale data in a CBDC environment, aiming to improve throughput and reduce network congestion. The proposed technology can increase throughput by more than 64 times and reduce network congestion by over 20% compared to existing systems.
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