• Journal of Information Technology Applications and Management
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• v.17 no.2
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• pp.207-223
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• 2010

Mobile banking is a subset of electronic banking which underlies not only the determinants of the banking business but also the special conditions of mobile commerce. Nowadays wireless networks are being evolved and diversified. In this situation The wireless e-commerce is in the limelight on new profits of Carriers. Especially from current year when Carriers in domestic plans to provide the services using 2.5G networks the service providers choose wireless e-commerce as the main parts of wireless internet strategy and will provide a various of services. Because of this situation, the importance of mobile billing service is being emphasized. This paper searches the definition and service types of mobile banking, and suggests status and prospects of domestic mobile banking. We suggest the basic direction, the stage of development and functions of services by analyzing the cases of Nonghyup's. Finally we derive the critical factors from those and suggest the effect of introduction and the direction of development. From the customer perspective, mobile banking has many strengths. For example, it allows that all customers access banking service at anytime, anywhere more easily than telephone banking or pc banking. And it reduces the time and the effort for using the service. It enables the company to make a business against global customers. On the other hand, from the company perspective, it has a lot of potential that affect market share and reduce the costs of human and material resources which used to operate and support branches. However, it needs many efforts to reach at the stage of completion. And We will have to solve the problems that develop many contents, expend the range of services and raise the service convenience.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.117-137
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• 2024

ESG has become a necessity for all companies, and major Korean banks are actively practicing ESG management. Banks are playing a role in providing ESG finance as intermediaries in the supply of funds. Recently, they have launched ESG digital banking products that offer preferential interest rates for eco-friendly activities in combination with digital technologies. However, indiscriminate provision of preferential interest rates can adversely affect profitability of banks, and they may face the problem of 'Greenwashing' if they do not contribute to improving environmental awareness. Therefore, this study selected ESG digital savings products linked to electricity savings as the subject of the study, and empirically analyzed consumers' environmental awareness and savings behavior through actual data of consumers (N=2,478). The main findings of this study are as follows First, the analysis of the consumer status of ESG digital banking products shows that the 30-50s are the main consumer base, and the MZ generation shows relatively high performance in achieving preferential interest rates through electricity saving practices. Second, consumers' environmental awareness has a significant impact on achieving the goals of ESG banking products. ESG banking products can contribute to environmental awareness while fulfilling the basic function of saving. Third, environmental awareness did not drive consumers' savings contribution behavior, suggesting the need for continued consumer engagement. Based on environmental awareness and the theory of saving behavior, this study provides a theoretical explanation in ESG financial products. The results suggest that the appropriateness of the preferential interest rate design of ESG financial products is important.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.369-382
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(quality of service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.185-196
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.

• Journal of Venture Innovation
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• v.4 no.2
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• pp.77-95
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• 2021

In the midst of the progress of the 4th industrial revolution, the Corona19 Pandemic was forming giant double wave. Companies riding this wave can win, but companies that do not will fall into the wave and struggle. In connection with the 4th industrial revolution, various technologies are emerging and commercialized. At this point, consumers, especially digital natives, who have been with digital since birth, tried to find out what factors affect the intention to use these technologies and which factors have the most important influence. For this purpose, data were collected through a survey on factors affecting the intention to use FinTech technology and AR technology for 150 digital natives in their 20s. Based on this, statistical analysis was conducted and the following results were obtained. As a result of the overall analysis regardless of the type of technology, it was found that performance expectancy, effort expectancy, social influence, and habits have a positive (+) effect on digital natives' intention to use the 4th industrial technology. On the other hand, a significant influence relationship between the facilitating conditions, hedonic motivation and intention to use the 4th industrial technology was not tested. It was found that the influence was greatly influenced by social influence and habits. In the case of FinTech and AR, which were further subdivided into this study, different aspects were revealed as a result of separate analysis. In the case of FinTech technology that emphasizes utilitarian value, performance expectancy, effort expectancy, social influence, and habits had a positive (+) effect on intention to use. It was found that the influence was greatly influenced by habits and social influence. In the case of AR, which emphasizes the hedonic value, all the variables adopted in this study had a positive (+) effect on the intention to use the technology. It was found that hedonic motivation and social influence had a great influence. Combining the results of the analysis, social influence was found to be an important influence variable regardless of the type of 4th industrial technology. FinTech technologies such as mobile banking, where services are becoming more common, are habits, and in the case of AR, which has not yet been universalized and is provided mainly for entertainment, hedonic motivation was found to be an important factor. This study was able to present academic and practical implications based on the above confirmation of factors affecting digital natives' acceptance and use of the 4th industry technology.