• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.209-222
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• 2015

Time series are used in many studies for model building and analysis. We must be very careful to understand the kind of time series data used in the analysis. In this review article, we will begin with some issues related to the use of aggregate and systematic sampling time series. Since several time series are often used in a study of the relationship of variables, we will also consider vector time series modeling and analysis. Although the basic procedures of model building between univariate time series and vector time series are the same, there are some important phenomena which are unique to vector time series. Therefore, we will also discuss some issues related to vector time models. Understanding these issues is important when we use time series data in modeling and analysis, regardless of whether it is a univariate or multivariate time series.

• Journal of the Ergonomics Society of Korea
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• v.29 no.2
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• pp.241-248
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• 2010

In accident analysis, it is essential to understand the causal pathways of the accident. Although numerous accident models have been developed to help analysts understand how and why an accident occurs, most of them do not include all elements related to the accident in various fields. Thus analysis of human error accidents in railway operations using these existing models may be possible, but inevitably incomplete. For a more thorough analysis of the accidents in railway operations, a more exhaustive model of accident causation is needed. This paper briefly reviews four recent accident causation models, and proposes a new model that overcomes the limitations of the existing models for the analysis of human error accidents in railway operations. In addition, the usefulness and comprehensiveness of the proposed model is briefly tested by explaining 12 railway accident cases with the model. The proposed accident causation model is expected to improve understanding of how and why an accident/incident occurs, and help prevent analysts from missing any important aspect of human error accidents in railway operations

• (The) Korean Journal of Educational Psychology
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• v.22 no.2
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• pp.367-384
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• 2008

The purpose of this study was to develop picture testing instrument for measuring children's causal reasoning ability on events that can occur in daily life. The measurement instrument contains three domain of human development; haman behavior domain, human psychology domain, and natural/physical domain. Through this study, researchers designed a concept model based on theoretical framework and prior studies and investigated the reliability and validity of the measurement instrument which was developed in accordance with the concept model. For the empirical validation research, a pretest was conducted to 336 elementary school students in grade 2 to 4 in Seoul. Considering reliability and validity of the developed measurement instrument and factor loadings, researchers sorted out 18 questions. And then 18 question test and KICE Critical Thinking Ability Test was conducted to 509 elementary school students in grade 1 to 4 in Seoul. According to the result of the tests, the researchers sorted out final 12 questions. The Cronbach's alpha, reliability of the children's causal reasoning ability test consisted of the final 12 question, was significant as .72. Also, the result of exploratory factor analysis showed that factors of this test were haman behavior domain, human psychology domain, and natural/physical domain. Moreover, the correlation between the KEDI Reasoning Ability Test(2003) scores and the scores of the test developed in the current study was significant as .55. Finally, the result of the analysis about children's grade differences, the development by discrepancy of age was significant in total points and that of each domain. The children's causal reasoning ability test developed by this study can be useful not only for the evaluation of children's thinking ability but also for screening of the handicapped children in thinking ability development.

• Journal of the East Asian Society of Dietary Life
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• v.18 no.2
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• pp.172-180
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• 2008

The purpose of this study was to measure the causal relationships among food involvement, health, mood, convenience, sensory appeal, weight control, and fruit consumption. A total of 290 questionnaires were completed. A structural equation model was used to measure the causal effects of the constructs, and the structural analysis results for the data indicated an excellent model fit. The effects of food involvement on health, mood, convenience, sensory appeal, weight control, and fruit consumption were statistically significant. As expected, health, mood, and weight control had significant effects on fruit consumption. Moreover, food involvement had a significant indirect effect on fruit consumption through health, mood, convenience, sensory appeal and weight control. For the future development and testing of conceptual models that integrate the relationships among personality traits, food choice motives, and fruit consumption, this study may approach a deeper understanding of the complex relationships among fruit consumption behavior-related variables. Greater understanding of these complex relationships can improve the managerial diagnosis of the problem and opportunities for different marketing strategies, including fruit production and fruit product development and marketing communications.

• Journal of Distribution Science
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• v.20 no.11
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• pp.79-88
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• 2022

Purpose: The purpose of this study was to determine the effect of agricultural exports and imports on economic growth in Bangladesh and propose an upgraded and customized model of the supply chain for agribusiness growth in Bangladesh to achieve plain sailing and systematic operation and financial gains at home and abroad. Research design, data, and methodology: All data in the research have been collected from secondary sources. Gross domestic product was used as the dependent variable and exports and imports of agricultural products were used as independent variables. Pairwise Granger causality was utilized to see the impact of the variable responsible for the economic growth in Bangladesh and the causal relationship between the variables analyzed was measured using Johansen co-integration test. Results: From the empirical analysis, the researchers observed that agricultural commodity imports and exports have a unidirectional impact on economic growth in Bangladesh and a long-run causal link with economic growth in Bangladesh. The suggested supply chain model of agribusiness aids in achieving smooth operations, systematic management, and monetary gains both domestically and internationally. Conclusions: This paper contributes to the development of a more effective and profitable agribusiness supply chain in Bangladesh systematically through their theoretical and practical implications.

• Journal of the Korean Society of Food Culture
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• v.22 no.3
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• pp.303-308
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• 2007

The purpose of this study was to measure the mediating effects of sacrifice, value and satisfaction on the relationship between restaurant service quality and loyalty. A total of 273 questionnaires were completed. Structural equation model was used to measure the causal effects of service quality, perceived sacrifice, value, satisfaction and loyalty. Results of the study demonstrated that the structural analysis result for the data also indicated excellent model fit. The influences of service quality and perceived sacrifice on value were statistically significant. The influences of service quality and value on satisfaction were statistically significant. The influences of value and satisfaction on loyalty were statistically significant. As expected, the service quality had a significant effect on loyalty. The results indicated that the effect of service quality on loyally was mediated by value. Moreover, the effect of service quality on loyalty w3s mediated by satisfaction.

• 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 Distribution Research
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• v.3 no.1
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• pp.31-54
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• 1998

The purpose of this research is three-fold; 1, establish the causal model of market-orientation, customer satisfaction, employee satisfaction and firm's perfor-mance. 2. testify the hypothesis derived by the model on a service industry of fastfood franchise. 3. develop the diagnostic tool for managing the service firm from the perspective of CS. The results show us; 1. customer satisfaction increases the performance of franchise store. 2. employee satisfaction does not necessarily increases customer satisfaction in a short-run, however the long-run impact of employee satisfaction on customer satisfaction is conjectured to be increased. 3. Satisfaction Portfolio Analysis can be utilized as management tool to improve the customer satisfaction and employee satisfaction for service industry.

• The Journal of Industrial Distribution & Business
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• v.9 no.5
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• pp.63-72
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• 2018

Purpose - This work analyzes, in detail, the specification of vector error correction model (VECM) and thus examines the relationships and impact among seven economic variables for USA - balance on current account (BCA), index of stock (STOCK), gross domestic product (GDP), housing price indices (HOUSING), a measure of the money supply that includes total currency as well as large time deposits, institutional money market funds, short-term repurchase agreements and other larger liquid assets (M3), real rate of interest (IR_REAL) and household credits (LOAN). In particular, we search for the main explanatory variables that have an effect on stock and real estate market, respectively and investigate the causal and dynamic associations between them. Research design, data, and methodology - We perform the time series vector error correction model to infer the dynamic relationships among seven variables above. This work employs the conventional augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root techniques to test for stationarity among seven variables under consideration, and Johansen cointegration test to specify the order or the number of cointegration relationship. Granger causality test is exploited to inspect for causal relationship and, at the same time, impulse response function and variance decomposition analysis are checked for both short-run and long-run association among the seven variables by EViews 9.0. The underlying model was analyzed by using 108 realizations from Q1 1990 to Q4 2016 for USA. Results - The results show that all the seven variables for USA have one unit root and they are cointegrated with at most five and three cointegrating equation for USA. The vector error correction model expresses a long-run relationship among variables. Both IR_REAL and M3 may influence real estate market, and GDP does stock market in USA. On the other hand, GDP, IR_REAL, M3, STOCK and LOAN may be considered as causal factors to affect real estate market. Conclusions - The findings indicate that both stock market and real estate market can be modelled as vector error correction specification for USA. In addition, we can detect causal relationships among variables and compare dynamic differences between countries in terms of stock market and real estate market.

• Journal of Distribution Science
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• v.12 no.6
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• pp.49-57
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• 2014

Purpose - The world now recognizes environmental disruption as a serious issue when regarding growth-oriented strategies; therefore, environmental preservation issues become pertinent. Consequently, green distribution is continuously emphasized. However, studying the prediction and association of distribution and the environment is insufficient. Most existing studies about green distribution are about its necessity, detailed operation methods, and political suggestions; it is necessary to study the distribution service industry and environmental service industry together, for green distribution. Research design, data, and methodology - ARIMA (auto-regressive moving average model) was used to predict the environmental service and distribution service industries, and the Granger Causality Test based on VAR (vector auto regressive) was used to analyze the causal relationship. This study used 48 quarters of time-series data, from the 4th quarter in 2001 to the 3rd quarter in 2013, about each business type's production index, and used an unchangeable index. The production index about the business type is classified into the current index and the unchangeable index. The unchangeable index divides the current index into deflators to remove fluctuation. Therefore, it is easy to analyze the actual production index. This study used the unchangeable index. Results - The production index of the distribution service industry and the production index of the environmental service industry consider the autocorrelation coefficient and partial autocorrelation coefficient; therefore, ARIMA(0,0,2)(0,1,1)4 and ARIMA(3,1,0)(0,1,1)4 were established as final prediction models, resulting in the gradual improvement in every production index of both types of business. Regarding the distribution service industry's production index, it is predicted that the 4th quarter in 2014 is 114.35, and the 4th quarter in 2015 is 123.48. Moreover, regarding the environmental service industry's production index, it is predicted that the 4th quarter in 2014 is 110.95, and the 4th quarter in 2015 is 111.67. In a causal relationship analysis, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. Conclusions - This study predicted the distribution service industry and environmental service industry with the ARIMA model, and examined the causal relationship between them through the Granger causality test based on the VAR Model. Prediction reveals the seasonality and gradual increase in the two industries. Moreover, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. This study contributed academically by offering base line data needed in the establishment of a future style of management and policy directions for the two industries through the prediction of the distribution service industry and the environmental service industry, and tested a causal relationship between them, which is insufficient in existing studies. The limitations of this study are that deeper considerations of advanced studies are deficient, and the effect of causality between the two types of industries on the actual industry was not established.