The purpose of this study was to investigate what was important for clothing behavior. The subjects were Japanese males and females in their 20's and 50's. The subjects were asked to imagine some scenes and reply what was important for clothing in the specific situations. As a result, the females had higher level of awareness in clothing behavior than males did. Focusing on the generation difference, the subjects in their 20's scored higher in fashion than 50's in formal scenes. The subjects in their 50's scored higher on social norm than 20's. Focusing on the situational difference, the scores on social norm and social conformity in formal scenes were higher than informal scenes. The scores on personal preference and practicality in informal scenes were higher than formal scenes. Focusing on the priority in formal scenes, the score on social norm was highest than others. The scores on personal preference and practicality were higher than others in informal scenes. These results suggest that priorities in clothing changes depending on gender, generation and scenes.
Information on risk of detergents in China was analyzed. First, 65,546 papers were searched at an internet search site CNKI using ‘detergents' as a keyword, and 366 papers were extracted from them according to the titles of papers and the abstracts. The data were arranged from the viewpoints of skin related toxicity, fluorescent agents, EDC (endocrine-disrupting chemicals), human health risk relevant to experimental data on toxicity test with mammals and that on toxicity test on living organisms except for mammals. The results show that the ratio and the amount of information against synthetic detergents increased after 1990. EDC problem has gradually attracted attention in China since it attracted worldwide attention in 1997. By comparing the history of detergent dispute in China with that in Japan using the chronology, it was clarified that a lot of risk information in China had been influenced by that in Japan, and the risk information in China tends to lag behind because of the slow data update rate.