• Journal of Korea Trade
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• v.24 no.1
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• pp.99-112
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• 2020

Purpose - With the increasing uncertainty of China's domestic political and economic environment in recent years, Korean MNC subsidiaries in the Chinese market face greater challenges and competition. Based on the insufficiency of existing research and the need for enterprise management practices, this paper uses the Chinese subsidiaries of Korean MNCs as an example to study and explore how knowledge management and dynamic capabilities affect ambidextrous innovation and the relationship between ambidextrous innovation and subsidiary performance. Design/methodology - From January to March 2019, this study collected 341 valid questionnaires using a survey company specializing in China for the members of the Chinese subsidiaries of Korean MNCs to verify the hypotheses. Using the collected data, the study model was verified using the Smart PLS 3.0 statistical package. Findings - Knowledge transfer and knowledge sharing have positive effects on dynamic capabilities and ambidextrous innovation, and dynamic capabilities have a positive impact on ambidextrous innovation. Ambidextrous innovation has been shown to have a significant effect on subsidiary performance. In addition, a partial mediating effect of dynamic capabilities on the relationship between knowledge management and ambidexterity innovation was found. Originality/value - In the academic context, this paper contributes theoretically to the relationship between knowledge management and ambidextrous innovation, as well as the mechanism of dynamic capability, and to verify the relationship between ambidextrous innovation and corporate performance. Against the background of MNC management, the results of this study provide further enlightenment for managers of subsidiaries.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.145-154
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• 1999

An intelligent system embedded with multiple sources of knowledge may provide more robust intelligence with highly ill structured problems than the system with a single source of knowledge. This paper proposes the hybrid knowledge integration mechanism that yields the cooperated knowledge by integrating expert, user, and machine knowledge within the fuzzy logic-driven framework, and then refines it with a genetic algorithm (GA) to enhance the reasoning performance. The proposed knowledge integration mechanism is applied for the prediction of Korea stock price index (KOSPI). Empirical results show that the proposed mechanism can make an intelligent system with the more adaptable and robust intelligence.

• Asia pacific journal of information systems
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• v.11 no.1
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• pp.139-154
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• 2001

Recently, more firms have shown an interest in implementing knowledge management methods. However, few companies are capable of adopting knowledge management methods effectively to improve organizational performance because it is still unclear how these methods improve corporate performance. To find this relationship between knowledge management methods and organizational performance, this paper analyses effects of knowledge management methods on corporate performance empirically. 51 Korean firms are selected as a sample base for empirical tests. Knowledge management methods can be categorized into four styles; harmony, codification, personalization, and ignorance. For example, the emphasis of harmony style is on both knowledge reusability using information technologies and knowledge sharing through informal intimacy among employees. Corporate performance varies depending on styles. Harmony style is founded to be most effective for corporate performance. Personalization and codification styles don't show any difference. This outcome is in line with the general observation that both tacit knowledge and explicit knowledge are important for effective knowledge management.

• Asia pacific journal of information systems
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• v.14 no.3
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• pp.57-75
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• 2004

The knowledge management is increasingly an important strategic weapon for sustaining competitive advantage of firms. Firms are undertaking knowledge management initiatives and making significant investments. However, there is relatively little empirical support for the impact of knowledge management on performance of firms. Understanding of the impact of knowledge management, this paper explores how knowledge management strategy influences firms' market value. We examine this issue using event study methodology and evaluate the cumulative abnormal returns for knowledge management strategy announced by firms from 1998 to 2002. The results show that firms' announcements of knowledge management strategy are positively related with firms' market value. Specially, dynamic style-which emphasizes both (i) knowledge reusability through information technologies and (ii) knowledge sharing through informal discussions among employees-has higher performance. This outcome presents empirical support to argument that the emphasis on both tacit and explicit knowledge results in better market value.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.145-154
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• 1999

An intelligent system embedded with multiple sources of knowledge may provide more robust intelligence with highly ill structured problems than the system with a single source of knowledge. This paper proposes th hybrid knowledge integration mechanism that yields the cooperated knowledge by integrating expert, user, and machine knowledge within the fuzzy logic-driven framework, and then refines it with a genetic algorithm (GA) to enhance the reasoning performance. The proposed knowledge integration mechanism is applied for the prediction of Korea stock price index (KOSPI). Empirical results show that the proposed mechanism can make an intelligent system with the more adaptable and robust intelligence.

• Knowledge Management Research
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• v.18 no.4
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• pp.119-136
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• 2017

This paper focuses on proactive personality as one of the main personality traits relevant to knowledge sharing and examines how this personal trait influences knowledge sharing behavior. Moreover, in order to consider the interactive effect between proactive personality and a contextual factor, this study utilized the construct of LMXSC which can reveal restoration of resource losses due to knowledge sharing. Because LMXSC can have opposite directions depending on used theories, this study investigates how LMXSC moderates the relationship between proactive personality and knowledge sharing behavior by using conservation of resources theory and trait activation theory. This study tests hypotheses with the data of supervisor-employee dyads in various industries. An empirical results showed that proactive personality increased knowledge sharing behavior and LMXSC strengthened the positive relationship between proactive personality and knowledge sharing behavior as conservation of resources theory predicts. Based on these theoretical arguments and empirical findings, this study suggests theoretical and practical implications, limitations, and the directions of future research.

• Knowledge Management Research
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• v.10 no.4
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• pp.75-91
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• 2009

Many prior researchers have repeatedly emphasized the importance of utilizing external knowledge as a critical factor for the success of organizational innovation. But they seem to have ignored the importance of the practical methods to advance the ability of finding new way of applying external knowledge to innovation activities. This paper suggests the exploitation of firm's knowledge base in innovative way as a practical method to utilize external knowledge for organizational innovation, because it could be possible to find out a common factor in external knowledge with organizational knowledge base by exploiting it. According to the empirical test with data of 1,143 manufacturing firms, all of the hypothesis were strongly supported.

• Proceedings of the Korean Society of Computer Information Conference
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• 2008.06a
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• pp.243-249
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• 2008

Based on a comprehensive framework that reflects lifecycle requirements of KMs and BPMs, we propose an PKMS(Process based KMS) for integrating KMs and BPMs in order to combine the advantages of the two paradigms. This paper first defines the priority order of knowledge according to knowledge type and classifies it into three groups which consist of Basic KM, Practical KM and Reference KM. Then, it suggests PKMS knowledge map composed of much core-knowledge each of which has information about a unit of the related business process. Using the PKMS Knowledge map, we can directly provide related core-knowledge related for the business process while a person is working a unit of business process. This paper designed the PKMS applied to employment insurance business part. Long term goal of the concepts is to concern a change management organization of knowledge on PKMS.

• Journal of the Korean Society of Food Science and Nutrition
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• v.24 no.1
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• pp.10-18
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• 1995

Nutrition knowledge of mothers who had middle-school studients was investigated using questionnaire about nutrition knowledge and food habit. The results were summerized as follows ; The mean score of nutrition knowledge was 25.3 (out of possible-80-80points), which score was slightly lower than those of other studies. the percentage of perceived knowledge and accuracy of the knowledge were 77.7% and 79.6%, respectively. these scores were influenced by general characteristics such as age, education, occupation, income, food expense. With decreasing age and increasing education level, family income, nutritional knowledge score, the percentage of perceived knowledge and accuracy were significantly getting higher. The levels of both perceived knowledge and accuracy on the necessi쇼 of vitamins and minerals were above 90%. However, the accuracy on protein quality and dietary fiber was below 40%. The better food habit a subject had, the higher was the nutrition knowledge score.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.