• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.373-378
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• 2005

The diagrammatic language for pathways is widely used for representing systems knowledge as a network of causal relations. Biologists infer and hypothesize with pathways to design experiments and verify models, and to identify potential drug targets. Although there have been many approaches to formalize pathways to simulate a system, reasoning with incomplete and high level knowledge has not been possible. We present a qualitative formalization of a pathway language with incomplete causal descriptions and its translation into propositional temporal logic to automate the reasoning process. Such automation accelerates the identification of drug targets in pathways.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.54-61
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. By using the FCM, authors have proposed FCM-based fault diagnostic algorithm. However, it can offer multiple interpretations for a single fault. In process engineering, as experience accumulated, some form of quantitative process knowledge is available. If this information can be integrated into the FCM-based fault diagnosis, the diagnostic resolution can be further improved. The purpose of this paper is to propose an enhanced FCM-based fault diagnostic scheme. Firstly, the membership function of fuzzy set theory is used to integrate quantitative knowledge into the FCM-based diagnostic scheme. Secondly, modified TAM recall procedure is proposed. Considering that the integration of quantitative knowledge into FCM-based diagnosis requires a great deal of engineering efforts, thirdly, an automated procedure for fusing the quantitative knowledge into FCM-based diagnosis is proposed by utilizing self-learning feature of neural network. Finally, the proposed diagnostic scheme has been tested by simulation on the two-tank system.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.5-8
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• 2008

This study proposed the Knowledge Base Building method reflecting the user's preferences based on the fuzzy set theory to develop information contents which support pedestrian's navigation. This research evaluated subject's preferences on the commercial spaces set to the hypothetical destination. Also it surveyed the causal relationship between the visual characteristics and the emotional characteristics to propose the methods of Navigation Knowledge Base (NKB). The NKB was composed by three elements; 1.the correlation model between emotional characteristics, 2.the causal relationship between visual characteristics and emotional characteristics, 3.the transformation model between visual characteristics and the physical characteristics.

• The Journal of Korean Academic Society of Nursing Education
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• v.11 no.2
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• pp.161-168
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• 2005

Purpose: The purposes are to identify the factors that influence work-based stressor experienced by clinical nurses and to provide a perceived causal structural model among these factors. Method: Data was collected and analyzed in 2 steps to apply a perceived causal structure : network analysis which was developed by Kelley(1983). Results: 1. The extracted causes from qualitative data were identified 10 categories ; over loaded work, relative feelings of deprived, inefficient duty schedule, negative attitudes of patient, burden of extra affair, inadequate administrative support, negative attitudes of physician, conflict with other personnels in hospital, lack of professional knowledge and skill, nursing service marketing burden. 2. Construction of the perceived causal structural model ; 1) The most central cause is over loaded work and the distal causes were inadequate administrative support, lack of professional knowledge and skill in the systems of causation. 2) The causes that have a number of outgoing link were over loaded work, inadequate administrative support, negative attitudes of physician. 3) The cause that have a number of incoming link was relative feelings of deprived. Conclusion: The network suggests that the first centre cause was related on over loaded work.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.329-347
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• 2017

Early research into category-based feature inference reported various phenomena in human thinking including typicality, diversity, similarity effects, etc. Later research discovered that participants' prior knowledge has an extensive influence on these sorts of reasoning. The current research tested the effects of causal knowledge on feature inference and conducted modeling on the results. Participants performed feature inference for categories consisted of four features where the features were connected either in common cause or common effect structure. The results showed typicality effects along with violations of causal Markov condition in common cause structure and causal discounting in common effect structure. To model the results, it was assumed that participants perform feature inference based on the difference between the probabilities of an exemplar with the target feature and an exemplar without the target feature (that is, $p(E_{F(X)}{\mid}Cat)-p(E_{F({\sim}X)}{\mid}Cat)$). Exemplar probabilities were computed based on causal model theory (Rehder, 2003) and applied to inference for target features. The results showed that the model predicts not only typicality effects but also violations of causal Markov condition and causal discounting observed in participants' data.

• Korean Journal of Cognitive Science
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• v.21 no.1
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• pp.47-75
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• 2010

Two experiments were conducted to explore whether the Or structure works as a default causal model in inferring the causal structure from the contingency data. The contingencies of three unfamiliar variables were used in Experiment 1. Participants inferred the Or structure quite well from the OR data, but incorrectly inferred the Or structure from the And data for about a little less than half of the time, and almost always inferred the Or structure from the chain data. The results suggested that the Or interpretation can be the default causal model. The prevalence of the Or interpretation from the contingency data was reported even when the three variables were familiar ones in Experiment 2. Multinomial modeling performed on the results of the two experiments strongly suggested that the Or interpretation work as a default causal model.

• Journal of Distribution Science
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• v.15 no.1
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• pp.51-56
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• 2017

Purpose - The study verified the effects of consumers' knowledge perception upon word-of-mouth intention and purchase intention of consumers who were exposed to a lot of information, and examined consumer's behavior from multi-dimensional points of view. Research design, data, and methodology - The study conducted the test of difference between consumer's cognition on importance and satisfaction of HMR product by gap of HMR (Home Meal Replacement) product for IPA analysis. The consumer's reliability and words-of-mouth were measured by the questionnaire method with 4 questions according to Likert 7-point scale. Conversion into z-score removed the difference of variables. Results - The causal relation model for importance, satisfaction and gap, not relying upon multi-dimensional scaling and others, could construct causal relation model to give implications. Difference (d) of the products could lessen consumer's reliability to increase consumer's knowledge perception, word-of-mouth intention, knowledge perception, and purchase intention. Therefore, enterprises should make an effort to lessen consumers' complaint for the products and to elevate consumers' reliability. Enterprises also try to give consumers exact information and to promote purchase intention. Conclusions - Difference (d) of consumers' complaint and/or disappointment decreased consumers' reliability to increase knowledge perception. Enterprises should supply consumers with products according to their requirements to minimize the gap and to give them proper information.

• Journal of the Korean Home Economics Association
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• v.51 no.1
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• pp.89-95
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• 2013

The purpose of this study is to identify a causal relationship among green consumer behavior, environmental knowledge and environmental attitudes of students at the university of education. A total of 366 copies of questionnaires were used for the data analysis; 31 copies were excluded due to lack of response to any given question. To ensure the reliability and validity of the questions, technical statistics were performed, such as frequency, ratio, average, standard deviation, skewness, and kurtosis via SPSS 15.0, item-total correlation and the totality, and reliability analysis. A structural analysis was undertaken via AMOS 7.0 in a bootstrapping method in order to perform a path analysis among variables as well as to assess the suitability of the model. The findings of the study were led to the following conclusions: First, the causal model among green consumer behavior, environmental knowledge and environmental attitudes of students at the university of education is suitable to the empirical analysis on research variables. Second, the environmental attitudes of students at the university of education has a direct, positive effect on green consumer behavior. Third, the environmental knowledge of students at the university of education has an indirect, positive effect on green consumer behavior.

• The Journal of Information Systems
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• v.26 no.3
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• pp.91-118
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• 2017

Purpose The purpose of this research is to investigate the effect of social capitals on organizational knowledge sharing characteristics and individual innovation activities in community of practice (CoP) of manufacturing company. Design/methodology/approach For this purpose, we divide social capitals as three dimensions, i.e. structural, relational, and cognitive dimension. Structural dimension also consists of closure and Brokerage. Relational social capital is defined as trust about colleagues, superior authorities, and organization. Then, cognitive social capital is defined as a shared understanding among individuals, such as a shared language and codes within CoP. Knowledge Sharing is defined as quantity and quality of shared knowledge. We also defines the cause and effect relationships among social capitals, organizational knowledge sharing characteristics, and individual innovation activities in CoP of manufacturing company as follows. The social capitals will have positive effects on quality of shared knowledge. Then the quality of shared knowledge will have positive effects on the individual innovation activities. This paper tested the validity of these hypothesized casual effects and the sub-hypothesized causal relationships. For the purpose, we used the Partial Least Squares (PLS) for analyzing the causal relationships. Findings Our empirical results show that social capitals of CoP mostly have effects on organizational knowledge sharing characteristics (quantity and quality of shared knowledge) and knowledge sharing activities also have effects on individual innovative activities in the workplace. In this study, these result have a significant implication that a private company will be able to gain organizational innovative performance much better by strengthening CoP supporting activities.

• Journal of Korea Technology Innovation Society
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• v.19 no.2
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• pp.324-357
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• 2016

As the transition to the knowledge-based economy has been accelerated in the 21st century, the importance of the service industry has been highlighted. As the proportion of knowledge service industry in the economy and the related employment rate are continuously growing, it is necessary to bring innovation to the industry in order to increase competitiveness. In this study, the innovation types are diversified into product, process, organization, and marketing and the influencing factors have been analyzed with knowledge service firms. The complex causal relationship that is linked to the innovation performance has been analyzed by the structural equation with each innovation types as the intervening variables. The results of this study can be summarized as follows. The innovation capacity of firms in knowledge service industry has very strong positive effects either directly or indirectly on product innovation, process innovation, organizational innovation, marketing innovation, and the revenue and employment of the firm. On the other hand, innovation support policy through government intervention produce negative impact on product innovation and they do not create meaningful impact on the total effect on the revenue nor the employment growth. The innovation should ultimately create effects on the revenue and the employment of the firm. And the government support policies should be carefully designed in consideration of the final destination point of this complex causal structure.