• The Journal of Asian Finance, Economics and Business
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• 제9권10호
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• pp.97-104
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• 2022

The purpose of the existing work is to inspect the impact of knowledge management on organizational performance. Business experts now appreciate how important knowledge management is for organizational performance. Earlier studies have investigated the research model with causal linkages, however, only a few of them have considered sample-selecting bias problems when analyzing the model of knowledge management on organizational performance. The number of 312 executives related to knowledge management from 312 enterprises that have been approved with quality management systems offered suitable responses for analyses. The data was employed to investigate the effect of knowledge management on organizational performance, considering sample-selecting bias. The empirical outcomes indicate that sample-selecting bias exists in the causal impact of knowledge management on organizational performance. The empirical findings are helpful to scholars of knowledge management as well as business executives by giving an insight into the casual effect of knowledge management on organizational performance with the intervention of sample-selecting bias. The acceptance of knowledge management should be tailored to improve competitive advantages that will lead to better organizational performance.

• 지식경영연구
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• 제10권2호
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• pp.49-63
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• 2009

In this study, sub domain of flow was investigated on literature survey, and suggested of the measuring method of web-site flow and its simulation analysis. Constructing of measuring method of flow, and using this method what-if analysis was simulated when several condition changed. Using causal map approach to extract knowledge from web-site domain experts and to derives a causal relationship of knowledge. Specially, in our study, describes method of developing and building causal map, and suggests guide line of this method on practical application. This research results show that web-site flow starts "direct searching" or "interesting of special issue(domain)", and when challenges of web-site were accorded with user's skills web-site flow grows. Further, in the web-site, information searching intention results in increase of user's duration time and experience flow to discovery new interesting issues in this process. If user's web-site of interaction is increased, awareness of environment conditions decreased, finally, user's telepresense results in increased web-site flow. This paper contained thai this method make used of measuring flow in the web-site and developing of practical strategy.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1998년도 추계학술대회 논문집
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• pp.228-231
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• 1998

Artificial Intelligence literatures have recognized that stock market is a highly unstructured and complex domain so that it is difficult to find knowledge that belongs to that domain. This paper demonstrates that the proposed QCOM can derive global knowledge about stock market on the basis of a set of local knowledge and express it as a digraph representation. In addition, inference mechanism using quantitative causal reasoning can describe the qualitative and quantitative effects of exogenous variables on stock market.

• 지식경영연구
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• 제9권4호
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• pp.35-49
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• 2008

The purpose of the study is to develop and validate an instructional design model from the perspective of the knowledge creation. To serve the purpose, the researcher conducted 1) literature review to find causal relationship model among knowledge creation factors and to propose a hypothetical instructional design model, 2) data analysis with 50 senior level e-Learning instructional designers, and 3) testing the fitness of the proposed model and relevant causal-relational hypotheses. Results indicate; 1) the proposed model fit to the empirical evidence, 2) 6 hypotheses among 11 were validated. A typical instructional designer's personal competency was evidenced as the most powerful independent variable that predicted knowledge acquisition, knowledge sharing, and the application of the instructional models. However, the expected effect of instructional design models toward other dependent variable was not be found. In addition, further suggestions for the future research are addressed.

• 지식경영연구
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• 제13권4호
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• pp.55-69
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• 2012

This paper developed measurement indices for intellectual capital of public R&D organizations and investigated causal relationships among the components. We developed 10 measurement factors and 37 indicators and confirmed the reliability of these measurements. We offered an alternative to the existing model for searching causal relationships. From our survey research, using the structural equation model, we found a new relationship. In contrast to the existing model, we found a cycling relationship among three variables: human capital causes structural capital, structural capital causes relational capital, and relational capital causes human capital.

• 한국지능시스템학회논문지
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• 제14권3호
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• pp.342-348
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• 2004

As the advent of the Internet, B2B negotiation process on the Internet has been given attention from both researchers and practitioners. However, literature still shows that only structured conditions have been explicitly considered, despite the fact that unstructured conditions should be rendered as well. In this sense, this paper proposes a new negotiation support mechanism to incorporate causal relationships between structured and unstructured conditions in the process of B2B negotiation. Fuzzy cognitive map was used as a main source of causal knowledge as well causal inference engine. A prototype named CAKES-NEGO was developed to perform experiments with an illustrative example. Results revealed the robustness of our proposed negotiation support mechanism.

• 한국시뮬레이션학회논문지
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• 제23권4호
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• pp.85-95
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• 2014

제품 설계를 위한 시뮬레이션을 위해서는 다양한 인과지식이 필요하다. 따라서 현존하는 다양한 지식으로부터 새로운 지식을 획득하기 위해서는 이러한 지식들을 통합하는 것이 필요하다. 예를 들어, 사용자가 가열이 되는 컵을 설계하고자 하지만 기존의 설계 지식 베이스에는 가열되는 컵에 대한 설계 지식은 없고 가열 기구에 대한 지식과 컵에 대한 지식이 따로 존재한다. 이러한 상황에서 가열 기구에 대한 지식과 컵에 대한 지식을 통합할 수 있는 자동화된 방법론이 필요하다. 이를 통해 사용자는 가열이 되는 컵을 설계하는 지식을 획득할 수 있다. 따라서 본 연구의 목적은 이러한 제품 설계에 관련한 지식을 통합하여 새로운 지식을 만드는 방법론을 제안 한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권4호
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• pp.253-258
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• 2010

The causal network represents the knowledge related to the dependency relationship between all attributes. If the causal network is available, the dependency relationship can be employed to estimate the missing values for improving the estimation performance. However, the previous method had a limitation in that it did not consider the bidirectional characteristic of the causal network. The proposed method considers the bidirectional characteristic by applying prior and posterior conditions, so that it outperforms the previous method.

• Computers and Concrete
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• 제31권4호
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• pp.277-292
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• 2023

Much of our experiments are designed to uncover the cause(s) and effect(s) behind a phenomenon (i.e., data generating mechanism) we happen to be interested in. Uncovering such relationships allows us to identify the true workings of a phenomenon and, most importantly, to realize and articulate a model to explore the phenomenon on hand and/or allow us to predict it accurately. Fundamentally, such models are likely to be derived via a causal approach (as opposed to an observational or empirical mean). In this approach, causal discovery is required to create a causal model, which can then be applied to infer the influence of interventions, and answer any hypothetical questions (i.e., in the form of What ifs? Etc.) that commonly used prediction- and statistical-based models may not be able to address. From this lens, this paper builds a case for causal discovery and causal inference and contrasts that against common machine learning approaches - all from a civil and structural engineering perspective. More specifically, this paper outlines the key principles of causality and the most commonly used algorithms and packages for causal discovery and causal inference. Finally, this paper also presents a series of examples and case studies of how causal concepts can be adopted for our domain.

• 제어로봇시스템학회논문지
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• 제10권5호
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• pp.450-457
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• 2004

In this research, the automatic synthesis of knowledge models is proposed. which are the basis of the methods using qualitative models adapted widely in fault diagnosis and hazard evaluation of chemical processes. To provide an easy and fast way to construct accurate causal model of the target process, the Role-Behavior modeling method is developed to represent the knowledge of modularized process units. In this modeling method, Fault-Behavior model and Structure-Role model present the relationship of the internal behaviors and faults in the process units and the relationship between process units respectively. Through the multiple modeling techniques, the knowledge is separated into what is independent of process and dependent on process to provide the extensibility and portability in model building, and possibility in the automatic synthesis. By taking advantage of the Role-Behavior Model, an algorithm is proposed to synthesize the plant-wide causal model, Fault-Causality Graph (FCG) from specific Fault-Behavior models of the each unit process, which are derived from generic Fault-Behavior models and Structure-Role model. To validate the proposed modeling method and algorithm, a system for building FCG model is developed on G2, an expert system development tool. Case study such as CSTR with recycle using the developed system showed that the proposed method and algorithm were remarkably effective in synthesizing the causal knowledge models for diagnosis of chemical processes.