• 한국경영과학회지
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• 제23권2호
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• pp.103-129
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• 1998

Corporate level decision making with multiple decision makers in a consistent way is essential in Decision Support System. However, since the decision makers have different interests and knowledge, the models used by them are also different in their level of abstraction. This makes decision makers waste a lot of efforts for an integrated decision making. The purpose of this paper is to propose an integration mechanism so that collaborative decision making models may be used synthetically in multi-abstraction level. Models are classified as multimedia model, mathematical model, qualitative model, causal ＆ directional model, causal model, directional model and relationship model according to the level of abstraction. The proposed integration mechanism consists of model interpretation phase. model transformation phase, and model integration phase. Specifically, the model transformation Phase is divided into (1) model tightening mode which gather information to make a model transformed into upper level model, and (2) model relaxing mode which makes lower level model. In the model integration phase, models of same level are to be integrated schematically. An illustrative M＆A-decision example is given to show the possibility of the methodology.

• Structural Engineering and Mechanics
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• 제23권4호
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• pp.409-430
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• 2006

The aim of this study is to investigate the reliability of strong motion records processed by causal and acausal Butterworth filters in comparison to the results obtained from a synthetic accelerogram. For this purpose, the fault parallel component of the Bolu record of the Duzce earthquake is modeled with a sum of exponentially damped sinusoidal components. Noise-free velocities and displacements are then obtained by analytically integrating the synthetic acceleration model. The analytical velocity and displacement signals are used as a standard with which to judge the validity of the signals obtained by filtering with causal and acausal filters and numerically integrating the acceleration model. The results show that the acausal filters are clearly preferable to the causal filters due to the fact that the response spectra obtained from the acausal filters match the spectra obtained from the simulated accelerogram better than that obtained by causal filters. The response spectra are independent from the order of the filters and from the method of integration (whether analytical integration after a spline fit to the synthetic accelerogram or the trapezoidal rule). The response spectra are sensitive to the chosen corner frequency of both the causal and the acausal filters and also to the inclusion of the pads. Accurate prediction of the static residual displacement (SRD) is very important for structures traversing faults in the near-fault regions. The greatest adverse effect of the high pass filters is their removal of the SRD. However, the noise-free displacements obtained by double integrating the synthetic accelerogram analytically preserve the SRD. It is thus apparent that conventional high pass filters should not be used for processing near-fault strong-motion records although they can be reliably used for far-fault records if applied acausally. The ground motion parameters such as ARIAS intensity, HUSID plots, Housner spectral intensity and the duration of strong-motion are found to be insensitive to the causality of filters.

• 논리연구
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• 제18권1호
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• pp.83-133
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• 2015

이 글에서는 상반된 이중 원인 효과를 해명하는 데 히치콕(Hitchcock 2001a)이 제시한 두 가지 원인 효과를 비판적으로 평가하고 그 비판을 상쇄할 대안을 제시한다. 이를 위해 다섯 가지를 논의한다. 첫째, 상반된 이중 원인 효과에 관한 사례를 소개하고 이에 대한 오테(Otte 1985)와 엘스(Eells 1987)의 논쟁을 검토한다. 이 검토는 이중 원인 효과에 대한 이해와 그와 관련해 논의할 히치콕, 카트라이트(Cartwright 1979, 1989, 1995), 엘스(Eells 1991, 1995)의 이론들을 비교, 평가하는 준비가 된다. 둘째, 구조 방정식 모형에서 두 원인 효과에 착안하여, 히치콕이 제시한 원인 효과의 두 개념, 망 효과와 요소 효과를 소개한다. 셋째, 이중 원인 효과를 해명하는 데 카트라이트와 엘스의 논쟁을 논의하면서, 동질적 부분 집단의 역할, 인과 상호 작용의 중요성에 주목한다. 넷째, 이중 원인 효과를 해명하는 데 히치콕이 적용한 두 원인 효과 개념을 비판적으로 검토하고, 엘스의 이론에 대한 히치콕의 비판을 다시 비판한다. 다섯째, 구조 방정식 모형의 조절 효과 개념을 인과에 대한 확률 이론에 응용하여, 이중 원인 효과에서 초래된 인과 상호 작용을 선언(disjunctive) 관계와 반 사실적 요인으로 형식화하여 보여준다. 조절 효과에 대한 논의는 최근에 많은 주목을 받은 구조 방정식 모형에 토대한 인과 모형 이론들이 인과의 문제들을 해결하는 데 기여할 수 있다. 다른 한편으로, 선언 관계, 반 사실적 조건을 이용한 인과 상호 작용에 대한 논의는 통계적 방법론으로서 구조 방정식 모형을 제고하는 데 도움을 줄 수 있다.

• 한국의류학회지
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• 제16권4호
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• pp.443-455
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• 1992

The purpose of this study is to verify the theoretical model on the clothing satisfaction. Research problems are as following; 1. To identify a causal model on the clothing satisfaction. 2. To examine the causal model by the brand levels. 3. To examine the causal model by the consumer characteristics. The empirical study of the above research problems is carried out by the longitudinal survey. The subjects selected for the final analysis are 362 women living in Seoul and Pusan. The results of our analysis are as following; 1. The main causal course of the clothing satisfaction is that the brand level and the consumer expectation $\rightarrow$ the expectation $\rightarrow$ the perceived performance ($\rightarrow$ the disconfirmation) $\rightarrow$ the clothing satisfaction. Those relevant variables explain $70\%$ of the clothing satisfac-tion variance. Especially, the influence of the perceived performance appears to be greater than that of the disconfirmation. 2. According to our analysis, the expectation influences the clothing satisfaction indirectly through the perceived performance. Especially, the normative expectation exhibits the contrast effect on the disconfirmation, while the predictive expectation exhibits the assimilation effect on the perceived performance. 3. The clothing satisfaction model differs by the brand levels (high price brand vs. moderate price brand) and by the consumer involvement levels (high involvement vs. low involvement). The relevant variables explain $65\%$ of the clothing satisfaction variance in the high price brand, while they explain $77\%$ in the moderate price brand. In the high involvement group, the relevant variables explain $78\%$ of the clothing satisfaction variance and $60\%$ in the low involvement group. In both involvement groups, the most critical direct variable is the perceived perfor-mance. In conclusion, we find that the clothing satisfaction can be explained by three constructs, the expectation, the perceived performance and the disconfirmation. The hypothesis that the two dimensions of the expectation explain the clothing satisfaction better is empirically supported in our study. Finally, we find that the clothing satisfaction models differ between two brand levels and consumer involvement levels.

• 한국조리학회지
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• 제13권4호
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• pp.45-56
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• 2007

The purpose of this study was to measure the causal relationships among affective belief, ambivalence, subjective norm, attitude, intention to consume and meat consumption. A total of 318 questionnaires were completed. The structural equation model was used to measure the causal effects among constructs. The results demonstrated that the confirmatory factor analysis model provided excellent model fit. The proposed model yielded a significantly better fit to the data than the baseline model. The effects of affective belief, ambivalence and subjective norm on attitude were statistically significant. The effect of subjective norm on intention was statistically significant. As expected, subjective norm and attitude had significant effects on meat consumption. Moreover, affective belief, ambivalence and subjective norm had indirect influences on meat consumption. Subjective norm also had an indirect influence on intention. The overall findings offered strong empirical support for the intuitive notion that improving the level of attitude toward eating meat can increase favorable intentions and decrease unfavorable intentions to reduce future meat consumption.

• Journal of Information Technology Applications and Management
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• 제30권4호
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• pp.29-38
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• 2023

A single-learner model which integrates the user's positive and negative perceptions is proposed by augmenting counterfactual data to the interaction data between users and items, which are mainly used in collaborative filtering in this study. The proposed CausRec showed superior performance compared to the existing NCF model in terms of F1 value and AUC in experiments using three published datasets: MovieLens 100K, Amazon Gift Card, and Amazon Magazine. Compared to the existing NCF model, the F1 and AUC values of CausRec showed 1.2% and 2.6% performance improvement in MovieLens 100K data, and 2.2% and 10% improvement in Amazon Gift Card data, respectively. In particular, in experiments using Amazon Magazine data, F1 and AUC values were improved by 11.7% and 21.9%, respectively, showing a significant performance improvement effect. The performance of CausRec is improved because both positive and negative perceptions of the item were reflected in the recommendation at the same time. It is judged that the proposed method was able to improve the performance of the collaborative filtering because it can simultaneously alleviate the sparsity and imbalance problems of the interaction data.

• STI Policy Review
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• 제2권2호
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• pp.45-54
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• 2011

This paper develops a recursive multinomial probit model and describes its estimation method. The recursive multinomial probit model is an extension of a recursive bivariate probit model. The main difference between the two models is that a single decision among two or more alternatives can be considered in each choice equation in the proposed model. The recursive multinomial probit model is developed based on a standard framework of the multinomial probit model and a Bayesian approach with a Gibbs sampling is adopted for the estimation. The simulation exercise with artificial data sets is showed that the model performed well. Since the recursive multinomial probit model can be applied to analyze the causal relationship between discrete dependent variables with more than two outcomes, the model can play an important role in extending the methodology of the causal relationship analysis in innovation research.

• 한국산업정보학회논문지
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• 제22권6호
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• pp.71-84
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• 2017

Over the past decade, we have witnessed Serious Debates in MISQ and Other Journals Between Two Camps that have Differing Views on the use of Causal Indicators to Measure Constructs. There is the Camp that advocates Causal Indicators (ADVOCATE) and the Camp that opposes Their Usage (OPPONENT). The Debates have been primarily centered on the OPPONENT's Argument that the Meaning of a Latent Variable is determined by its Outcome Variables. However, Little Effort has been made to Validate the ADVOCATE's Dispute (Against the OPPONENT's Arguments) that the Meaning of a Latent Variable is decided by its Causal Indicators if there is no Misspecification. Our Study precisely examines the Integrity of the Argument. For this, we empirically examine how the two Primary Psychometric Properties-Comprehensiveness and Interrelationship-of Causal Indicators Influence Theory Testing between Latent Variables through Three Different Tests (i.e., Comprehensive Test, Interrelationship Test, and Mixed Test). Conducted on Two Different Datasets, Our Analysis Consistently Reveals that Structural Path Coefficients are Hardly Sensitive to the Changes (i.e., Misspecification) in the Properties of Causal Indicators. The Discovery offers Important Evidence that the Sound Theoretical Logic of a Causal Model is not in Sync with the Empirical Mechanism of Parameter Estimation. This Underscores that a Latent Variable Formed by Causal Indicators is empirically an elusive notion that is Difficult to Operationalize. As Our Results have Significant Implications on the Integrity of Numerous IS studies which have conducted Theory or Hypothesis Testing Using Causal Indicators, we strongly advocate Open Discussions among Methodologists regarding Our Findings and Their Implications for Both Published IS Research and Future Practices.

• 대한골대사학회지
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• 제25권4호
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• pp.251-266
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• 2018

Background: The causal networks among genes that are commonly expressed in osteoblasts and during bone metastasis (BM) of breast cancer (BC) are not well understood. Here, we developed a machine learning method to obtain a plausible causal network of genes that are commonly expressed during BM and in osteoblasts in BC. Methods: We selected BC genes that are commonly expressed during BM and in osteoblasts from the Gene Expression Omnibus database. Bayesian Network Inference with Java Objects (Banjo) was used to obtain the Bayesian network. Genes registered as BC related genes were included as candidate genes in the implementation of Banjo. Next, we obtained the Bayesian structure and assessed the prediction rate for BM, conditional independence among nodes, and causality among nodes. Furthermore, we reported the maximum relative risks (RRs) of combined gene expression of the genes in the model. Results: We mechanistically identified 33 significantly related and plausibly involved genes in the development of BC BM. Further model evaluations showed that 16 genes were enough for a model to be statistically significant in terms of maximum likelihood of the causal Bayesian networks (CBNs) and for correct prediction of BM of BC. Maximum RRs of combined gene expression patterns showed that the expression levels of UBIAD1, HEBP1, BTNL8, TSPO, PSAT1, and ZFP36L2 significantly affected development of BM from BC. Conclusions: The CBN structure can be used as a reasonable inference network for accurately predicting BM in BC.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1998년도 추계학술대회 논문집
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• pp.224-227
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• 1998

This paper proposes inter-level causal reasoning to implement synergistic approach. We decompose KOSPI prediction model into economy and industry level. Two kinds of intra-level QCOM are combined in inter-level QCOM via Inter-level relations. Downward reasoning is achieved by propagating the disturbance in the higher level to lower level while upward reasoning is to analyze the reverse cases.