We investigate the incremental predictive power of three consumer dispositions – xenocentrism, cosmopolitanism and ethnocentrism – on domestic and foreign product purchase intentions, after taking the impact of consumer demographics and product category-specific variables into account. Using data from an online survey of 201 Turkish consumers, hierarchical regression analysis reveals that widely used demographic variables (namely, age, gender and education) do not significantly influence consumer intentions to buy either domestic or foreign clothing products. Of the consumer dispositions, xenocentrism exerts a significant negative effect on domestic product purchase intentions, cosmopolitanism has a positive effect on foreign product purchase intentions, while ethnocentrism shows no effect on either domestic or foreign product purchase intentions. Implications of the findings are considered and future research directions identified.

Conventional flipped learning instructional models are operated in a blended learning environment online and offline. In contrast, this study moved onto fully online systems and explored how a sense of presence worked for students’ learning outcomes at university English writing courses. The two research questions for this study are: 1) What is the relationship between a sense of presence (teaching, cognitive, social presence) and learning outcomes (group cohesion, class satisfaction)? and 2) What are the variables among a sense of presence that affect group cohesion and class satisfaction? For the purposes of this study, 46 university students from English composition courses answered student questionnaires in the spring of 2021. Correlation and multiple-regression analyses were conducted to look into the relationships among the variables. Additionally, focus-group interviews were conducted and teaching journals were analyzed. The major findings were revealed as follows: Firstly, a sense of presence was significantly related to group cohesion and satisfaction. Secondly, social presence and cognitive presence only had a predictive power of group cohesion. Thirdly, cognitive presence and teaching presence were significant predictors of class satisfaction. Pedagogical implications are discussed for those interested in applying flipped learning in a fully online setting.

The purpose of this study is to compare short-term price predictive power among ARMA ARMAX and VAR forecasting models based on the MDM test using monthly consumer price data of frozen mackerel. This study also aims to help policymakers and economic actors make reasonable choices in the market on monthly consumer price of frozen mackerel. To analyze this study, the frozen wholesale prices and new consumer prices were used as variables while the price time series data were used from December 2013 to July 2021. Through the unit root test, it was confirmed that the time series variables employed in the models were stable while the level variables were used for analysis. As a result of conducting information standards and Granger causality tests, it was found that the wholesale prices and fresh consumer prices from the previous month have affected the frozen consumer prices. Then, the model with the highest predictive power was selected by RMSE, RMSPE, MAE, MAPE, and Theil’s inequality coefficient criteria where the predictive power was compared by the MDM test in order to examine which model is superior. As a result of the analysis, ARMAX(1,1) with the frozen wholesale, ARMAX(1,1) with the fresh consumer model and VAR model were selected. Through the five criteria and MDM tests, the VAR model was selected as the superior model in predicting the monthly consumer price of frozen mackerel.

수문 시계열의 분석은 수문자료를 활용한 수자원의 효율적인 운영 및 관리에 필수적인 부분이며, 특히 장기적인 수문량 예측에 널리 활용되고 있다. 이러한 수문 시계열 분석은 전통적으로 하나의 자료계열을 하나의 요인으로 파악하여 자료를 분석하고 예측해왔지만 시계열 자료가 여러 가지 요인으로 혼합되 어 하나의 자료계열로 나타내질 수 있다는 가정 하에 각 요인들을 분해하여 분석하는 방법도 널리 연구되고 있다. 본 연구에서는 경험적 모드분해법을 이용하여 주어진 수문 시계열을 다중 성분으로 분해하고 분해된 각 요소를 시계열 모형으로 재구축한 후, 구축된 요소별 시계열 모형으로부터 예측된 값을 합하여 시계열을 예측하는 방법을 이용하였으며 이를 국내 댐 유입량에 적용한 후 그 결과를 나타내었다. 기존 시계열 모형과 경험적 모드분해법을 이용한 방법의 정확도를 비교한 결과, 기존의 시계열 모형을 이용하여 자료를 예측한 결과보다 경험적 모드분해법을 적용하여 자료를 분해한 후 시계열 자료를 예측한 결과가 주어진 시계열 자료를 더 잘 나타내는 것을 알 수 있었다.