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Transcription:

111 n=1151 8, 2011; 2012,2012 1 3,1 2 50 60 3 30 3 6 2005a, 2005b, 2007a, 2007b, 2009a, 2009b, 2010, 2011, 2012b

112 22,,,,,,,,,2,, 5% 5 2% 2 1.3 1.4% 1.3 1.4% 5% 5% 6% 6 20% 20 30% national, family,, individual social 3C 2012 pp.33 2012a 1 social,2009;, 2010, 2012 64% 2009 1996;

113 JMR 2008 3 1 2008 7 DINKS 7 2,, =20 2008, 1936 45,1946 51 DC 1952 58 1959 64 1965 70, 1971 76 1977 86 1987 92 9 1 3 2012a social 2008 5 1 119961971 76 7 8, 2011

114 22 2008,pp.203 2 2012 2 2012

115 3 4 10 20 30 30 40 40 50 50 60 300 300 500 500 700 700 1000 1000 / / / / / / / 1 / / / / / / / 1 5 3 26 4 3 2011 5

116 22 2 Cross 20 60 1151 =1000+ 3 6 1151 100.0 1151 100.0 1 20 60 5.2 1 300 255 22.2 2 20 128 11.1 2 300 500 324 28.1 3 30 114 9.9 3 500 700 259 22.5 4 30 118 10.3 4 700 1000 191 16.6 5 40 132 11.5 5 1000 122 10.6 6 40 96 8.3 7 50 179 15.6 8 50 89 7.7 9 60 235 20.4 / 11 100 10 50 100 9 10 50 8 5 10 7 3 5 6 1 3 5 5 1 4 3 5 3 1 3 2 1 1 0 0 / / / / / / / 8 1 6 Web 60

117 { {5 2} {5 2},{ 2 2} {5 2} {5 2} {3 2} {3 2} {3 2} } 1 1 3 1 3 / / 2, { {5 3} {5 2} {4 2} {5 2} {6 4} {5 2} {7 4} {6 2} } 2 6.124 Mathematica FindCluster 7 k 4 5~10 k 8 8 k k= k=5 259 281 255 212 144 1.60944 k=6 102 246 251 218 196 138 1.79176, k=7 116 255 250 200 156 119 55 1.94591. k=9 73 169 169 259 80 125 126 103 47 2.04319 k=10 88 183 160 199 155 59 91 74 92 50 2.30259 Mathematica 7 Mathematica 8 k 9 k=8

118 22 2 5 1 L H 2 2 L 3 4 8 3 L 4 5 6 6 7 2 L H H L L 168,007 143,707 24,300. % L 37,900 6,278 32,314 85.3% 2 L 92,699 4,852 87,213 94.1% L 126,293 13,389 125,859 99.7% L 19,975 1,907 18,540 92.8% L 430,795 386,466 56,524. % L 221,933 114,611 114,769. % 2 L H 86,342 84,875 79,471 92.0% L

119 7 10 1 9.4% 300 500 500 700 1000 =10.6% 20 60 60 300 500 2009b 9 2 13.6% 300 500 1 500 700 20 1000 3 15.5% 30 12% 30 88% 60 8 4 17.3% 3 30 46% 50 12% 300 500 300 1000 10 5 15.2%300 1/3 500 7 60 6 13.6% 5 700 9,, 23 2009b 10 2011 OL 5

120 22 4 30 50 40 30 40 19% 50 11 7 11.8% 300 700 1000 1000 6 60 30 40 36% 6 8 3.8%60 3 30 16% 300 500 700 56% 32.8% 37.0% 28.2% 33.7% 34.2% 25.1% 41.0% 33.1% 27.9% 1 6 2 5 8 5 12 6 1 9.4% 37.0% 2 13.6% 28.2% 3 15.5% 33.7% 4 17.3% 34.2% 5 15.2% 25.1% 6 13.6% 41.0% 7 11.8% 33.1% 8 3.7% 27.9% 11 40 12

121

122 22

123

124 22

125 Bourdieu [1979]1989 FV 40 3 1 8 FV FV 11 X Y X 0.638 Y 0.131 1 FV 2 3 5 FV FV 1 3 5 8 4 7 FV

126 22 Kawaii 6 NetMiner3.0 Mathematica NetMiner3.0 C1 C2 C3 C4 Kawaii C5 C6 C7 C8

127 1 9.4% 20 60 60 300 500 13 2 13.6% 300 500 3 15.5% 30 60 4 17.3%Kawaii 30 46% 50 12% 5 15.2% 60 14 6 13.6% 700 4 30 50 40 13 20 60, 1 14 5 3

128 22 7 11.8% 700 1000 1000 60 30 40 36% 8 3.7% 60 3 30 16% 300 500 700 56% 8 4 Mathematica BubbleChart X Y 5

129 Bourdieu 1979 1989 Mathematica BubbleChart X X Y

130 22 1 20 60 20 20 30, Kawaii Kawaii,,,, 30% 2 2

131 30 10 2,3 50 20 BP, /, 2012 2008 2011 BP Bourdieu, P. 1979 La Distinction: Critique sociale du jugement, Paris: Edition de Minuit,1989, 2010 1996 PHP JMR 2008 2008 http://www.marketing.co.jp/marketing/ronbun/index.html 2009 2015 2012 vol.20,pp.93-109 2005a 2005b 2007a 2 2007b 2009a

132 22 2009b PHP 2010 2011 3 2012a 2012b! 2012 3 88 2008 2009 2012 AD STUDIES Vol.39, pp.18-23. Mapping Clusters of Japanese Female Consumers in the Age of The Forth Consumption characterized by social consumption: A Cluster Analysis of Consumers based on Purchase Patterns of High/ Low Fashion Brands in 8 Categories Jun KANAMITSU ABSTRACT In this paper we explored into purchasing patterns of fashion brands among Japanese female consumers, who were strongly shaken by the great earthquake, in the age of The Fourth Consumption characterized by the emergence of social consumption. We mapped clusters of Japanese female consumers on the plane spanned by an alter-oriented fashion value (x axis) and purchase amount of fashion brands (y axis), based on a cluster analysis of consumers conducted on purchasing patterns of high/low fashion brands in 8 categories. We detected 8 clusters varying in distributions of age, income, urbanization and fashion values. One of the interesting clusters, that are expected to be leaders in fashion trends, is Kawaii Women composed of young and relatively urban females, who like to be kawaii and prominently prefer low brands, purchasing tons of low brands. Another is Fashionable Women composed of middle-aged high-income urban females who like to go in style, enjoying plenty combinations of high/low fashion brands. We projected a distribution of female fashion clusters considering female marriage/work trends in the near future.