• Journal of Information Science Theory and Practice
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• v.1 no.4
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• pp.12-37
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• 2013

Organizational climate and organization culture can be some of the leading factors in hindering knowledge sharing within the organization. It is generally accepted that successful knowledge management practice, including knowledge sharing, comes as a result of a conducive and knowledge sharing friendly environment. Organizations that promote and reward collective work generate a trustful and a more collaborative learning culture. The perception of fairness in an organization has been considered an important indicator of employee behavior, attitude, and motivation. This study investigates organizational justice perception and its impact on knowledge sharing practices among forensic experts in the Turkish National Police. The study findings revealed that senior officers, who are experts in the field, have the strongest organizational justice perception. Meanwhile, noncommissioned officers or technicians bear positive but comparatively weaker feelings about the existence of justice within the organization. The study argues that those who satisfy their career expectations tend to have a higher organizational justice perception.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Asia pacific journal of information systems
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• v.10 no.2
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• pp.23-49
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• 2000

We proposed a conceptual design of the web-based agent model for global supply chain management(GSCM), where agents representing autonomous operational units, such as suppliers, factories, distribution center and customers, cooperate and are coordinated through the information exchange. The agent model assumed the hierarchical federated system. In the federated system, the agents of the same region are grouped and linked to the region-specific facilitator only through which communication between agents is allowed. The facilitator is responsible for monitoring and controlling the conversations consisting of the message flows across the agents. A web-based user presentation was also designed so that human users could involve in collaborative settings into the GSCM multi-agent system. In the conversation protocols which allow for complex coordinated behavior among agents, the KQML was extended to represent the messages. A GSCM scenario where the supply chain is formed upon customer order and supply decision is made was used to demonstrate the dynamics of the conversation protocols.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.2
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• pp.409-434
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• 2023

This study analyzes the authorship of all articles published in four domestic LIS journals over a 20-year period from 2002 to 2021 to examine the current status of scholarly communication through Korean LIS journals and suggest future prospects. To achieve this purpose, the study analyzed the number of co-authors, the proportion of returning authors, the publishing preference index (PPI), the author group change trend, and the researcher attraction index (RAI). The analysis revealed the level of collaborative research in each journal, the degree of formation of related author groups by journal, the inflection point of author group changes, the characteristics of emerging researchers, and the degree of author sharing between journals. Overall, 2015 was found to be an inflection point where the author community of Korean LIS journals changed. The newer generation of researchers showed a slightly different behavior of publishing papers than the older generation, as they mainly conduct collaborative research. These quantitative results could be triangulated with the qualitative interview data of previous studies to further strengthen the development strategy of Korean LIS journals.

• Journal of KIISE:Software and Applications
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• v.30 no.5_6
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• pp.540-549
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• 2003

In collaborative recommendation two models are generally used: the user model and the article model. A user model learns correlation between users preferences and recommends an article based on other users preferences for the article. Similarly, an article model learns correlation between preferences for articles and recommends an article based on the target user's preference for other articles. In this paper, we investigates various combination methods of the user model and the article model for better recommendation performance. They include simple sequential and parallel methods, perceptron, multi-layer perceptron, fuzzy rules, and BKS. We adopt the multi-layer perceptron for training each of the user and article models. The multi-layer perceptron has several advantages over other methods such as the nearest neighbor method and the association rule method. It can learn weights between correlated items and it can handle easily both of symbolic and numeric data. The combined models outperform any of the basic models and our experiments show that the multi-layer perceptron is the most efficient combination method among them.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.463-474
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• 2023

Advances in technology have made it easier for organizations to share information and collaborate. However, collaboration systems where multiple entities share and access information are vulnerable to security. The concept of Software Bill Of Materials (SBOM) has emerged as a way to strengthen information security by identifying and transparently managing the components of software programs. To promote the adoption of SBOM in Korea, this study investigated the intention to use of collaboration system managers. This study was based on the theory of planned behavior and the integrated technology acceptance theory. The results of this study confirmed that performance expectations from SBOM adoption were an important variable for intention to use, and positive attitudes toward security also had an indirect effect through performance expectations. We found that SBOM adoption has an important causal relationship with performance due to the fact that it is targeted at enterprises, and that positive attitudes toward security and social climate can have a strong effect on intention to use.

• Journal of Information Processing Systems
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• v.3 no.1
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• pp.1-7
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• 2007

In mobile ad hoc networks, an application scenario requires mostly collaborative mobility behavior. The key problem of those applications is scalability with regard to the number of multicast members as well as the number of the multicast group. To enhance scalability with group mobility, we have proposed a multicast protocol based on a new framework for hierarchical multicasting that is suitable for the group mobility model in MANET. The key design goal of this protocol is to solve the problem of reflecting the node's mobility in the overlay multicast tree, the efficient data delivery within the sub-group with group mobility support, and the scalability problem for the large multicast group size. The results obtained through simulations show that our approach supports scalability and efficient data transmission utilizing the characteristic of group mobility.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.437-438
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• 2019

스마트폰을 중심으로 한 모바일 기기의 보급과 온라인 소셜 네트워크 서비스의 이용자들이 증가하면서 사용자들은 많은 콘텐츠를 소비하고 공유한다. 이는 콘텐츠 사용자들의 개별적 기호에 맞지 않거나 만족도가 떨어지는 콘텐츠를 소비하게 한다. 이와 같은 문제를 해결하기 위해 소셜 네트워크 사용자에게 적합한 콘텐츠를 추천하기 위한 기법에 대한 연구가 활발하게 진행되고 있다. 본 논문에서는 온라인 상에 존재하는 다양한 정보 중에서 공연과 관련한 콘텐츠들을 중심으로 사용자 성향별로 추천을 해줄 수 있는 협업필터링 방법에 대하여 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.213-217
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• 2008

협업시스템은 분산된 작업그룹의 구성원들이 정보를 쉽게 교환하고 공유할 수 있도록 지원하는 공동작업환경이다. HTTP/1.1 을 확장한 WebDAV는 사용자가 원거리 서버의 자원을 수정하고 관리하는 기능을 제공하는 웹 통신 프로토콜이다. CoSlide 협업시스템은 효과적으로 협업을 지원하기 위하여 WebDAV 프로토콜을 확장하여 개발된 협업시스템으로서 협업을 위한 가상공 간을 제공하며 가상공간에 다양한 자원을 등록하고 관리할 수 있다. 하지만, CoSlide 협업시스템이 제공하는 기존의 작업장들은 그룹 구성원들간의 효과적인 협업을 위한 구성원들의 행위 인식 (Behavior Awareness) 을 지원하지 않고 있다. 본 논문에서는 CoSlide 협업시스템을 이용하여 협업을 수행할 때, 협업의 효율성을 증대시키기 위하여 구성원이 수행하는 행동을 인식할 수 있도록 자바 메시지 서비스를 이용하여 CoSlide 서버의 Awareness를 지원하기 위한 시스템을 설계하였다. 또한 CoSlide 서버의 Awareness 지원을 위하여 CoSlide 클라이언트인 CoSpace의 확장을 설계하였다. 사용자는 CoSpace 협업 클라이언트를 이용하여 협업을 수행하면 CoSlide 서버는 구성원이 수행하는 작업을 인식하고, 인식한 정보의 공개수준 여부와 수신여부를 참조하여 관련된 구성원에게 알려줌으로써 협업의 효율성이 증대된다.

• Information Systems Review
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• v.17 no.2
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• pp.179-192
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• 2015

Collaborative research has been actively done on the basis of academic relationships among various study area. The importance of collaboration has also been increased. Collaborative researchers can reduce time, cost, and research risk to maximize research productivity. This study aims to develop a framework for understanding the behavior of professional groups through network characteristics. To achieve the goal, we collected data of the co-authored network and that of the reviewer network from from 2006 to 2012. Total 230 submitted papers were analyzed on the views of research performance and productivity. Various analytical methods such as centrality analysis, sub-group analysis, correlation, and regression were conducted for assuring the reliability and validity of our research. The results shows that the productivity of the co-authored network was increased and the efficiency of the reviewer network was also identified through several network indexes.