Asymmetric multidimensional scaling is a visualization method that reveals relations between objects using their asymmetric dissimilarity data as input. Such visualization is important in areas such as marketing science since it reveals consumer dynamics and brand competition. Large amounts of asymmetric dissimilarity data can be processed by modern information technology; nevertheless, it remains difficult to interpret the asymmetries between many objects. To overcome this problem, we propose a new visualization method called discriminant coordinates for asymmetric dissimilarity data, which interprets the relations between and within a set of object classes, given asymmetric dissimilarity data. The method easily interprets the estimated asymmetries between and within classes, even when the asymmetric dissimilarity data contain noise if some assumptions for asymmetric dissimilarity data is satisfied.
This paper aims to compare and clarify people's environmental consciousness in China, Japan and South Korea, from a perspective of willingness to sacrifice (WTS) for the environment. Analytical results derived from cross-national survey data indicate that the Chinese, especially Beijing citizens, tend to hold positive WTS, the Japanese tend to hold negative WTS, and South Koreans are located between Chines and Japanese. Based on the revised norm-activation theory, environmental anxiety and environmental responsibility judgments are proposed to explain the formation of WTS on a national level in this study. Positive relationship between WTS and environment anxiety is verified in surveyed regions. However, influence of environmental responsibility judgments to WTS differs from area to area. Generally speaking, people who ascribe the most important environmental responsibility to citizens or corporations are more likely to form positive WTS than those who ascribe it to the government. In addition, the influence of demographic factors to the formation of WTS is also clarified and the attributive features of individuals who are inclined to sacrifice for the environment are clarified on a national level.
The purpose of this paper is to grasp stable and reliable understanding about the attitudes of people in Asia-Pacific relating to international relations by analyzing the results of a longitudinal and cross-national comparative survey: Asia-Pacific Values Survey (APVS: 2010-13), Pacific Rim Values Survey (PRVS: 2004-2009), and East Asia Values Survey (EAVS: 2002-2005). In the analyses, this paper focuses on the three sets of question items: attitudes toward foreign countries, law/contract consciousness, and confidence in the United Nations. Regarding people's attitudes toward foreign countries, changes in the political and social conditions such as the rise of China and the Great East Japan Earthquake (March 11, 2011) are expected to have influenced people's attitudes toward Japan and China between PRVS and APVS by previous studies. However, our analysis shows that this is not the case. Moreover, multidimensional analyses of both law/contract consciousness and confidence in the United Nations show that there are four clusters of countries/areas in the Asia-Pacific region. These patterns of clustering are reliable as well as interesting results, which previous studies have had difficulty explaining.
Setting learning goals enhances motivation and performance. However, a lack of motivation is still nowadays a large cause of education failure because learners often find difficulties to relate to the goals fixed in their formal education. Social Networking Services (SNS) offer a massive source of diverse information and represent an influential factor, including for learning. The purpose of this research consists therefore in 1) the construction of a large-scale dataset containing goal-based messages expressed by peers on SNS, and 2) the analysis of topics making the meaning of the different categories of goals included in the goal-based dataset. The massiveness of information available on SNS calls for a systematic text analysis. This study therefore introduced a Systemic Functional Grammar (SFG) approach to determine the linguistic features used to create the meaning of learning goals in SNS messages. This analysis resulted in the creation of a dataset containing 16,000 goal-based messages.