• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.139-145
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• 2017

The purpose of this study is to establish a predictive causal model of codependency that is a main issue of family problem on the base of Triple ABC-X model which is a kind of family stress model. For the purpose of this study, we reviewed the concept and characteristics of codependency, affecting factors of codependency, and then reviewed the basic concept and logic of Triple ABC-X Model as theoretical viewpoint for the purpose of establishing a predictive causal model of codependency. We established it through examining main variables of codependency from Triple ABC-X Model. Main ingredients of the predictive causal model include boundary ambiguity, internal working model, internal and external locus of control, self-regard, social support, individual maladjustment etc. We established a predictive model of codependency basing on logic inferences among the variables. This study is expected to be used basic data to introduce some implications and for hereafter research.

• Korean Journal of Child Studies
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• v.20 no.4
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• pp.195-211
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• 1999

This study investigated developmental trends and sex differences in the relation between children's self-esteem and relevant variables by proposing and testing the causal model. The 763 children who participated in the study were 3rd, 5th, and 7th grade students. Major findings were that physical appearance was the most powerful determinant of self-esteem. Students with high self-esteem were more learning oriented, used more motivational behaviors and had higher academic achievement. The findings from this analysis of the causal model revealed remarkable developmental differentiation and stability.

• Knowledge Management Research
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• v.13 no.4
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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.

• Science of Emotion and Sensibility
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• v.24 no.1
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• pp.59-72
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• 2021

Concepts and categories offer the basis for inference pertaining to unobserved features. Prior research on category-based induction that used blank properties has suggested that similarity between categories and features explains feature inference (Rips, 1975; Osherson et al., 1990). However, it was shown by later research that prior knowledge had a large influence on category-based inference and cases were reported where similarity effects completely disappeared. Thus, this study tested category-based feature inference when features are connected in a causal chain and proposed a feature inference model that predicts participants' inference ratings. Each participant learned a category with four features connected in a causal chain and then performed feature inference tasks for an unobserved feature in various exemplars of the category. The results revealed nonindependence, that is, the features not only linked directly to the target feature but also to those screened-off by other feature nodes and affected feature inference (a violation of the causal Markov condition). Feature inference model of causal model theory (Sloman, 2005) explained nonindependence by predicting the effects of directly linked features and indirectly related features. Indirect features equally affected participants' inference regardless of causal distance, and the model predicted smaller effects regarding causally distant features.

• Journal of the East Asian Society of Dietary Life
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• v.17 no.1
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• pp.136-142
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• 2007

This study measured the causal relationships among tourist-perceived sacrifice, service quality, service value, and behavioral intention of employee's service. A total of 224 questionnaires were completed. The equation model was used to measure the causal effect. The results demonstrated that the structural analysis result for the data was an excellent model fit. The influences of perceived value and service quality on service value were statistically significant. As expected, service quality and service value had significant effects on behavioral intention. Moreover, overall service quality played a mediating role in the relationship between perceived sacrifice and service value. Service value played a mediating role in the relationship between service quality and behavioral intention.

• 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.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.1
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• pp.13-23
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• 1996

Weld defects degrade the strength and safety of astructure and are resulted from the various cases. The complexity of causal relation of weld defects requires an expert for the analysis of weld defects and the measures counter to them. An expert system has the intelligent functions such as the representation of knowledge and the inference. On this research, weld defect are systematically analysed and their causal model is developed. This information is saved to the knowledge base. The suitable inference algorithm for the diagnosis of weld defects is developed and realized with C++ programming.

• The Journal of Information Systems
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• v.28 no.1
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• pp.1-16
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• 2019

Purpose The purpose of this study is to analyze causal relationship between quantity and quality for deriving ranking model of Online reviews. Thus, we propose implications for deriving the ranking model for retrieving Online reviews more effectively. Design/methodology/approach We collected Online review from Tripadvisor web sites which might be a kind of world-famous tourism web sites. We transformed the natural text reviews to quantified data which consists of quantified positive opinions, quantified negative opinions, quantified modification opinions, reviews lengths and grade scores by using opinion mining technologies in R package. We executed corelation and regression analysis about the data. Findings According to the empirical analysis result, this study confirmed that the review length influenced positive opinion, negative opinion and modification opinion. We also confirmed that negative opinion and modification opinion influenced the grade score.

• The Journal of Industrial Distribution & Business
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• v.11 no.10
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• pp.59-73
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• 2020

Purpose: The ultimate purpose of this work is to investigate gender differences in the relationships between product quality cues and perceived values at a food select shop. Specifically, this study examines the effects of internal and external cues, which are indicators of product quality, on emotional and social values based on gender differences. Research design, data and methodology: In this study, a questionnaire technique was used to collect the data necessary to test the proposed model. 183 data were collected through this technique. PLS SEM (Partial Least Squares Structured Equation Model) was used to test the research model. Results: First, there is no gender difference between intrinsic cue and emotional value. When using male and female data, there was no significant causal relationship between intrinsic cues and emotional values. Second, we found no gender difference between intrinsic cue and social value. When analyzed with female data, there was no significant causal relationship between intrinsic cue and social value. On the other hand, in the case of men, it was found that a weak causal relationship exists. Third, this study found gender difference between extrinsic cue and emotional value. In the case of men, it was found that a weak causal relationship exists, whereas in the case of women, a strong causal relationship exists between extrinsic cue and emotional value. Fourth, we found gender difference between extrinsic cue and social value. In the case of men, there was no causal relationship, whereas in the case of women, there was a strong causal relationship between extrinsic cue and social value. Finally, we found that there are moderating roles of gender in the relationship between external cues and perceived quality. Conclusions: As a result of analysis, it is necessary to focus on extrinsic clues of product in order to increase the perceived emotional and social values of women. On the other hand, in order to improve the perceived emotional and social values of men, it is necessary to pay attention to both intrinsic and extrinsic cues of product. Therefore, it is necessary to consider what clues and values are important to core customers.

• The Journal of Information Systems
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• v.23 no.4
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• pp.75-91
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• 2014

The effective supply chain management(SCM) is a matter of survival in many firms because successful supply chains will effectively coordinate their processes, focus on delivering customer value, eliminate unnecessary costs in key functional areas, and create performance measurement systems. The balanced scorecard(BSC) is widely used to measure the performance of the SCM. The BSC framework suggests that balance is obtained by adopting performance measures from four different areas. In this study, we analyzed the causal relationship of SCM performance based on BSC framework. First, we reviewed the nested causal relationships among four different perspective of the BSC, namely, business process perspective, customer perspective, financial perspective, and innovation and learning perspective. Then, we used the chi-square difference test to identify the best model to fit the causal relationship of SCM performance. Of the 800 questionnaires posted, a total of 265 questionnaires were returned after one follow-up. A total of 66 questionnaires were eliminated due to largely missing values. The major finding says alternative model 3 is dominant to other models to fit causal relationships among four different perspective of the BSC. Innovation and learning perspective positively influence on customer perspective, business process perspective, and financial perspective. Business process perspective also positively influence on customer perspective and financial perspective whereas customer perspective does not influence on financial perspective significantly.