• Knowledge Management Research
• /
• v.21 no.1
• /
• pp.61-78
• /
• 2020

Recently, in the global financial transaction field, global transactions using computer systems and server hubs between foreign exchanges beyond the one-dimensional offline transactions and two-dimensional online transactions have been actively conducted. In the previous research by Lee Hyung-wook and Lee Min-jae (2018), as the globalization has accelerated following the development of digital technology and the expansion of ubiquitous communication network, the role of companies, the attribute of economic value creation and economic structure are being reorganized. It is said that O2O (Online to Offline) transactions are increasing due to the development. As a result, a new financial transaction paradigm is emerging that solves the inconveniences of existing financial services and enhances speed and convenience. Considering the global network trend and the rapidly developing and evolving digital bank environment, the necessity of utilizing the business platform model is emerging. However, despite this necessity, there are very limited cases in which such an attempt has been applied in practice. Accordingly, this study seeks to explore the business platform of the new financial transaction system. Specifically, the case study systematically examines the actual implementation of a unique network connection model with Koscom's global investment bank, which is currently in charge of the domestic financial transaction system, and improves ICT innovation performance and process through this. I would like to suggest a solution. In particular, this study analyzed a variety of business model construction and use cases by pursuing a platform connection with digital banks, which has recently been increasingly in demand. Therefore, this study intends to pursue the original and long-term profitability of the company by utilizing ICT innovation and platform business model, and also analyzes the convenience and excellence of trading for institutional and individual investors using the platform of digital bank. The implications of this study are significant in that it explores and explores the actual cases of ICT innovation and additional digital bank platform-connected business models based on this, and suggests a unique and preemptive business strategy of the company in the future.

• Asian Journal of Innovation and Policy
• /
• v.1 no.2
• /
• pp.148-167
• /
• 2012

This paper analyzes the impact of the change in telecommunication regulation changes including the unification of telecommunication service on network investment. The unification of telecommunication service plays a role of separating behavior regulation from entrance regulation and reducing entrance constraints. Therefore, it is expected that the market spillover effect is high through the improvement of behavior regulation. In addition, the effects of the other regulation changes in the 2010 Telecommunications Business Act revision are analyzed. This paper discusses critical factors affecting the decision making process in respect to the firm level and analyzes the impact path guiding investment and innovation. The key findings are as follows. First, the impact of entrance deregulation depends on the intensity of deregulation. If the intensity is not high, this regulation increases the incentive on investment and innovation. However, if the intensity is high as shown in abolishing of licensing, it affects the incentive negatively. Second, if interconnection regulation focuses on existing facilities or the intensity is not strong, this light handed regulation might increase investment and innovation. However, if interconnection obligation is expanded to the facility not constructed or the facility applying new technology, this regulation might deteriorate investment. Third, price deregulation increases the competition of service but it also increases the business opportunity, which means positive effect on investment. Finally, the paper proposes the guideline for telecommunications policy.

• Journal of Information Technology Services
• /
• v.16 no.3
• /
• pp.167-183
• /
• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 2008.10b
• /
• pp.479-491
• /
• 2008

A "task network" is a type of social network that consists of experts who exchange professional help and advice regarding executing tasks. In this study, we investigate the task network used within the IS department of a national bank in Korea. We identify how this network moderates the influence of computer-mediated communication (CMC) technology on an individual s task performance. Size, density, and centrality were measured as the characteristics of a personal task networks. Size equates to the total number of colleagues who work with a specific member for a certain project. Density is the ratio of the number of actual relationships to the total number of available relationships. Centrality defines whether an individual s position is in the exact center of whole network, and is measured by betweenness centrality, meaning the position one member holds between others in a network. Our findings conclude that the conditions - the larger the size of the task network, the smaller its density and the higher its level of centrality - lead to more benefits of using CMC media. Further, this positive effect of CMC is more noticeable when it provides synchronicity.

• Journal of Korean Society for Quality Management
• /
• v.45 no.1
• /
• pp.65-92
• /
• 2017

Purpose: The performance of global major airports is evaluated and several research questions are examined relative to the measures characterizing airport performance. Methods: The two-stage internal structure of airport performance is considered by decomposing it into physical operations and revenue generation. In the physical operations stage, operating costs, number of runways, terminal area and number of employees are used as inputs, while passenger throughput, cargo throughput and aircraft movements are taken as outputs. Subsequently, in the revenue generation stage, the outputs from the preceding stage are taken as inputs, while revenue is used as output. Results: Based upon this two-stage modeling of airport performance, a multiplicative two-stage network data envelopment analysis model is employed to calculate the overall and stage efficiencies of 59 airports using the recent data in the 2014 Airport Benchmarking Report published by the Air Transport Research Society. Several internal and external factors are also considered such as airport size, airport geographical location, proportion of international passengers, ownership (listed or not) and management style, and statistical analysis is performed to examine their impacts on airport performance. Conclusion: It is shown that the airports exhibit statistically significant difference across regions, and also some statistically significant factors affecting airport performance are identified.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.43 no.3
• /
• pp.87-94
• /
• 2020

In this study, the factors affecting the efficiency of 48 projects of private R&D institutes were analyzed using the Tobit model. Influencing factors were selected as open R&D network size, IT industry, interaction between R&D network size and IT industry, and type of R&D network cooperation. As a result of Tobit analysis, the R&D network size, the IT industry, and the type of R&D network cooperation were found to be significant. The larger the open R&D network size, the lower the efficiency, and the IT industry showed lower R&D efficiency than other industries. In addition, cooperation with universities and research institutes showed lower R&D efficiency than cooperation with companies. As a result of these studies, companies will be able to select and focus on cooperation with the outside in relations and investment allocation.

• STI Policy Review
• /
• v.5 no.1
• /
• pp.57-74
• /
• 2014

This review organises the fragmented management literature on platform business according to a conceptual map and a meta-theoretical scheme. Since the early 2000s, numerous researchers have examined platform business and two-sided networks with platform business and strategy being an important business innovation model for many industries, creating value primarily by enabling direct interactions. Platforms such as Google or Amazon contain a common set of rules and components in most user transactions. Thirty-two core papers and books on Strategic Management Journal, Industrial Economics and Operation Management-related disciplines are reviewed, with further observations on how cumulative research streams on the platform are carried out independently from each academic perspective. The first of the two arguments in this paper is that because interactive relationships bridge the platform and stakeholders such as end-users and developers, it is crucial for platform companies to be aware of their relationship with stakeholders in order to support and sustainably provide content to their platform. The second is that integrative perspectives are essential due to the low number of interdisciplinary investigations conducted thus far. The paper's final section deals with implications for theory and practice, concluding that integrative studies and interactive relationship studies should be the main research streams in future platform research.

• Journal of Information Technology Applications and Management
• /
• v.24 no.1
• /
• pp.169-185
• /
• 2017

This study proposes a novel recommendation algorithm that reflects the results from trust/distrust network analysis as a solution to enhance prediction accuracy of recommender systems. The recommendation algorithm of our study is based on memory-based collaborative filtering (CF), which is the most popular recommendation algorithm. But, unlike conventional CF, our proposed algorithm considers not only the correlation of the rating patterns between users, but also the results from trust/distrust relationship network analysis (e.g. who are the most trusted/distrusted users?, whom are the target user trust or distrust?) when calculating the similarity between users. To validate the performance of the proposed algorithm, we applied it to a real-world dataset that contained the trust/distrust relationships among users as well as their numeric ratings on movies. As a result, we found that the proposed algorithm outperformed the conventional CF with statistical significance. Also, we found that distrust relationship was more important than trust relationship in measuring similarities between users. This implies that we need to be more careful about negative relationship rather than positive one when tracking and managing social relationships among users.

• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.190-194
• /
• 2023

By looking the importance of communication, data delivery and access in various sectors including governmental, business and individual for any kind of data, it becomes mandatory to identify faults and flaws during cyber communication. To protect personal, governmental and business data from being misused from numerous advanced attacks, there is the need of cyber security. The information security provides massive protection to both the host machine as well as network. The learning methods are used for analyzing as well as preventing various attacks. Machine learning is one of the branch of Artificial Intelligence that plays a potential learning techniques to detect the cyber-attacks. In the proposed methodology, the Decision Tree (DT) which is also a kind of supervised learning model, is combined with the different cross-validation method to determine the accuracy and the execution time to identify the cyber-attacks from a very recent dataset of different network attack activities of network traffic in the UNSW-NB15 dataset. It is a hybrid method in which different types of attributes including Gini Index and Entropy of DT model has been implemented separately to identify the most accurate procedure to detect intrusion with respect to the execution time. The different DT methodologies including DT using Gini Index, DT using train-split method and DT using information entropy along with their respective subdivision such as using K-Fold validation, using Stratified K-Fold validation are implemented.

• Journal of Industrial Convergence
• /
• v.18 no.1
• /
• pp.1-15
• /
• 2020

This study aims at examining the effect of strategic orientation on IT startup performance and analyzing the mediating effect of network in the relationship. The study was conducted on the three dimensions that make up strategic orientation: technological orientation, customer orientation, competitive orientation on firm performance, and the understanding of the role of the network. Empirical research on 94 IT startups as residents of startup support institutions was conducted. The suitability of the research model was evaluated with PLS-SEM. As a result of the empirical analysis, customer orientation and competitive orientation was verified to have a positive effect on network, while Technological orientation was confirmed to have no positive effect. and network mediated the relationship between customer orientation and competitive orientation, while Technological orientation was confirmed to have no positive effect. The results of this study provide startup practitioners with strategic direction and the importance of network establishment and utilization.