• Journal of Information Technology Services
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• v.23 no.1
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• pp.97-121
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• 2024

As data usage grows in importance, ensuring individual control over personal information becomes critical. The emergence of the 'MyData' concept addresses this, particularly in financial services. Although the institutional and technological framework for financial MyData services is in place, there's a need to establish consumer understanding and perception of its usefulness and safety for successful activation. This study focuses on investigating the impact of trust on the intention to use the new mobile banking service, financial MyData. This study has three objectives. Firstly, to analyze whether trust in financial MyData services and trust in financial MyData service providers affect the intention to use financial MyData services. Secondly, to analyze the process of forming trust in financial MyData services based on the phenomenon of transferring trust in service providers to trust in services. Thirdly, to identify the process by which trust transfer occurs between service providers and financial MyData services. Ultimately, the goal of this study is to promote the intention to use financial MyData services based on the concept of trust and to activate these services. In summary, this study emphasizes the significance of trust in financial MyData services, exploring its impact on user intention and the transfer of trust from providers to services. By promoting consumer trust, the research aims to contribute to the activation of financial MyData services.

• Journal of Information Technology Services
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• v.21 no.1
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• pp.1-19
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• 2022

With the revision of the Credit Information Use and Protection Act in August 2020, the MyData service based on open banking policy will take effect in January 2022. Nonetheless, the previous studies focused on the legal system or security-related issues of such service. Therefore, this paper conducted an empirical study on financial consumers aged 20 or older nationwide to analyze the factors which influence the intention to use MyData services based on open banking. Five characteristics representing open banking-based MyData service were derived through prior research, and a research model that combined value-based adoption model and privacy calculus theory was presented. The proposed research model and the relationship of its variables was analyzed using a sample of 400 users that is randomly selected. The results of empirical analysis showed that personalization had the greatest influence on benefits and reliability on sacrifice among service characteristics. They also suggested that MyData operators should devote themselves to providing customized services optimized for customers and establishing trust relationships. It was confirmed that both usefulness and enjoyment had a great influence on perceived value, and in terms of sacrifice, the burden of financial costs had a greater influence than privacy concerns. This study is meaningful in that it explored the psychological propensity of financial consumers to identify service utilization factors and presented a new approach that can contribute to the successful settlement of the domestic MyData industry.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.223-233
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• 2021

MyData industry became possible with the revision of the three data-related bills on August 2020, and from February 2021, each individual can make MyData financial service through the app provided by MyData providers. In this study, in order to understand the user experience trend of MyData-based financial asset management apps in the user-centered financial service era, the usability evaluation of 11 apps from 8 MyData providers was conducted for 300 adults, then average value comparison, radial graph analysis, and heatmap analysis were conducted. In app design preference, asset list type was the most preferred type, followed by asset comparison·list type. As for the expected perception of the future benefits that can be enjoyed through My Data, 'diversification of convenient services' was the highest at 45.3%, and as a negative factor felt by users, personal information-related factors were the highest at 71.4%. The results of this study can be used as basic data for the development and improvement of user interfaces for MyData platforms.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.6
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• pp.373-380
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• 2022

Today, customer satisfaction is the most important issue for Vietnamese banks. There are numerous factors that influence bank customers' satisfaction. The study investigates the factors that influence customer satisfaction with deposit services at the Vietnam Bank for Agriculture and Rural Development (Agribank) in Bac Giang province, Vietnam, and analyses the impact of each component. The authors used 5-point Likert scales to assess whether elements were related to the factors determining consumer satisfaction with Agribank's deposit service. To perform this study, exploratory factor analysis (EFA) and multiple regression were used to find factors determining customer satisfaction with Agribank services. Customers that use deposit services at Agribank in Bac Giang province, Vietnam, provided samples of 300 survey questionnaires for this study. SPSS software is used to examine the data. Customers' satisfaction with Agribank's deposit service is influenced by six factors, including reliability, responsiveness, service capabilities, empathy, tangible means, and service price, according to the study. This finding also offers some recommendations for improving service quality in local Vietnamese banks to gain a competitive advantage.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.