• Korean Journal of Logic
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• v.8 no.1
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• pp.47-67
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• 2005

Bayesian networks have been used in studying and simulating causal inferences by using the probability function distributed over the variables consisting of inquiry space. The focus of the debates concerning Bayesian networks is the causal Markov condition that constrains the probabilistic independence between all the variables which are not in the causal relations. Cartwright, a strong critic about the Bayesian network theory, argues that the causal Markov condition cannot hold in indeterministic systems, so it cannot be a valid principle for causal inferences. The purpose of the paper is to explore whether her argument on the causal Markov condition is valid. Mainly, I shall argue that it is possible for upholders of the causal Markov condition to respond properly the criticism of Cartwright through the continuous causal model that permits the infinite sequence of causal events.

• Korean Management Science Review
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• v.17 no.2
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• pp.39-54
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• 2000

This paper presents a causal forecasting model using guided genetic algorithm in continuous manufacturing process. The guide genetic algorithm(GGA) is an extended genetic algorithm(GA) using penalty function and population diversity index to increase forecasting accuracy. GGA adds to the canonical GA the concept of a penalty function to avoid selecting the unproductive chromosomes and to make a proper searching direction. Also, GGA modifies the current population using the similarity of chromosomes to avoid falling into the trap of local optimal solution. For investigation GGA performance, we used a set of real data that was collected in local glass melting processes, and experimental results show the proposed model results in the better forecasting accuracy than linear regression model and canonical GA.

• Journal of the military operations research society of Korea
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• v.25 no.1
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• pp.37-54
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• 1999

In this paper, we proposed a modeling method of continuous combat simulation by using VENSIM. VENSIM is a CASE tool for developing continuous simulation. It provides a simple and flexible way of building simulation models from causal loop or influence diagram. As a case model, we developed "a prototype model of battle"incorporating infantry, artillery, air defense weapon, aircraft, and guerrilla engagement.ngagement.

• Journal of Korean Society for Quality Management
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• v.44 no.2
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• pp.409-424
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• 2016

Purpose: Present study was designed to examine the casual relationships among service quality, self-efficacy, perceived usefulness, user satisfaction and Continuous Usage in new technology-based banking system. Also, we intended to testify the moderating effects of user experience period in causal model. We applied path analysis model in order to test the hypotheses and research model. Methods: Survey tool, that is, questionnaire has obtained validity through literature survey, exploratory survey and pretest and sample 279 was selected. For statistical treatment of pretest and main analysis, SPSS20.0 and AMOS 20.0 were employed and structural equation model was employed as analysis method. Results: Result of this study shows as follows. All factors have an effect on user satisfaction and Continuous Usage, and we found that user experience period played moderating effect in causal relationship. Therefore, new technology-based banking system is found that the determinants of continuous usage intention is different according to the user experience period. Conclusion: Present study shows that self-efficacy in via of user experience period, there is a need to emphasize that the main consideration factor new technology-based banking system. However, present study has some limitations to additionally research in the future.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.1
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• pp.47-58
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• 2024

The serving robot is defined as a robot that carries cooked food to a customer table or collects and carries bowls after eating. This study presented a research model to find out the causal relationship between customer satisfaction and continuous use intention through the three factors of serving robot service quality. In addition, in order to empirically verify the above research model, a survey was conducted on customers using serving robots within the last 5 months. 300 copies were analyzed using SPSS 20 as the final analysis, excluding unfaithful responses. The main findings of this study are as follows. First, serving robot service quality (typical) found to have significant effect on customer satisfaction and continuous use intention. Second, it was found that serving robot service quality (reliability) had a significant effect on customer satisfaction and continuous use intention. Third, it was found that serving robot service quality (ease) did not significantly affect customer satisfaction and continuous use intention.

• Journal of Information Technology Services
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• v.10 no.1
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• pp.73-88
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• 2011

Continuous usages of Internet Protocol Television(IPTV) is regarded as one of key factors for IPTV success. Quality improvement and increasing user satisfaction are generally contributed to intention to continue using of innovation technology. We classify the quality constructs into information quality, service quality, system quality, and experience quality and find how these constructs affect on user satisfaction and intention to continue using of IPTV. We perform factor analysis to IPTV users by developing a causal relationship model and defining measurement variables for the constructs. Through regression analysis, we found information quality and experience quality are significant to affect on intention to continue using of IPTV.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.169-174
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• 2018

In general, the methods of the analysis of variance(ANOVA) for the continuous data and the chi-square test for the discrete data are used for statistical analysis of the effect and the association. In multidimensional data, analysis of hierarchical structure is required and statistical linear model is adopted. The structure of the linear model requires the normality of the data. A multidimensional categorical data analysis methods are used for causal relations, interactions, and correlation analysis. In this paper, Bayesian network model using probability distribution is proposed to reduce analysis procedure and analyze interactions and causal relationships in categorical data analysis.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.1-36
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• 2020

This study is to reveal the acceptance factors of the Market Sentiment Index (MSI) created by reflecting the investor sentiment extracted by processing unstructured big data. The research model was established by exploring exogenous variables based on the rational behavior theory and applying the Technology Acceptance Model (TAM). The acceptance of MSI provided to investors in the stock market was found to be influenced by the exogenous variables presented in this study. The results of causal analysis are as follows. First, self-efficacy, investment opportunities, Innovativeness, and perceived cost significantly affect perceived ease of use. Second, Diversity of services and perceived benefits have a statistically significant impact on perceived usefulness. Third, Perceived ease of use and perceived usefulness have a statistically significant effect on attitude to use. Fourth, Attitude to use statistically significantly influences the intention to use, and the investment opportunities as an independent variable affects the intention to use. Fifth, the intention to use statistically significantly affects the final dependent variable, the intention to use continuously. The mediating effect between the independent and dependent variables of the research model is as follows. First, The indirect effect on the causal route from diversity of services to continuous use intention was 0.1491, which was statistically significant at the significance level of 1%. Second, The indirect effect on the causal route from perceived benefit to continuous use intention was 0.1281, which was statistically significant at the significance level of 1%. The results of the multi-group analysis are as follows. First, for groups with and without stock investment experience, multi-group analysis was not possible because the measurement uniformity between the two groups was not secured. Second, the analysis result of the difference in the effect of independent variables of male and female groups on the intention to use continuously, where measurement uniformity was secured between the two groups, In the causal route from usage attitude to usage intention, women are higher than men. And in the causal route from use intention to continuous use intention, males were very high and showed statistically significant difference at significance level 5%.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.39-44
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• 2021

Since the recent Corona 19, the importance of cloud computing is increasing, and at the same time, competition among clouds is intensifying. Cloud companies are competing for survival by promoting various management innovation methods for continuous growth and development amid a rapidly changing business environment. They are also increasingly interested in performance management in their operations and growth. In this paper, we propose Cloud BSC, an IT BSC-based performance measurement model for cloud enterprise performance management. The validity of the proposed model is verified through statistical analysis and causal analysis. Eventually, the proposed model is expected to be utilized as a management evaluation tool that can provide useful performance analysis information to cloud companies.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.346-358
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• 2018

Objectives: The association between obstructive sleep apnea (OSA) and mortality or serious cardiovascular events over a long period of time is not clearly understood. The aim of this observational study was to estimate the clinical effectiveness of continuous positive airway pressure (CPAP) treatment on an outcome variable combining mortality, acute myocardial infarction (AMI), and cerebrovascular insult (CVI) during a follow-up period of 15.5 years ($186{\pm}58$ months). Methods: The data set consisted of 978 patients with an apnea-hypopnea index (AHI) ${\geq}5.0$. One-third had used CPAP treatment. For the first time, a data-driven causal Bayesian network (DDBN) and a hypothesis-driven causal Bayesian network (HDBN) were used to investigate the effectiveness of CPAP. Results: In the DDBN, coronary heart disease (CHD), congestive heart failure (CHF), and diuretic use were directly associated with the outcome variable. Sleep apnea parameters and CPAP treatment had no direct association with the outcome variable. In the HDBN, CPAP treatment showed an average improvement of 5.3 percentage points in the outcome. The greatest improvement was seen in patients aged ${\leq}55$ years. The effect of CPAP treatment was weaker in older patients (>55 years) and in patients with CHD. In CHF patients, CPAP treatment was associated with an increased risk of mortality, AMI, or CVI. Conclusions: The effectiveness of CPAP is modest in younger patients. Long-term effectiveness is limited in older patients and in patients with heart disease (CHD or CHF).