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Çмú´ëȸ¸í Á¦ 46Â÷ Çѱ¹È£Åڿܽİ濵ÇÐȸ Ãß°èÇмú´ëȸ
ÀϽà 2013.12.14
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ÃÊ·Ï A variety of events are currently being held all over the world. Of those different events, in order to have a chance
to organize mega sports events, multiple countries are competing with each other only severely. In this thesis, ¡®F1 Grand
Prix¡¯, one of the three mega sports events in the world, was investigated as a research subject. The study conducted its
research targeting visitors to ¡®F1 Korean Grand Prix¡¯ held in County Yeongam, Jeollanam-do, South Korea, and the study
was carried out for a total of two years from 2011 to 2012. The motivations of the visitors in ¡®F1 Korean Grand Prix¡¯
were used to perform the cluster analysis, and the study came up with sub-groups ultimately to work on market segmentation
which would understand features of the groups. According to the results from the analysis, there had been four visit
motivation factors (the recreation factor, the driver/skill factor, the arts factor, the socialization factor) and the other four
sub-groups (the multi-purpose participation group, the recreation participation group, the simple group(the group that
participates with no particular purposes), the arts participation group). The study looked into demographic features (gender,
age and the club membership status) in connection with those features of the sub-groups, and no signifiant results were
found. How the sports identification is related to the features of the sub-groups was observed, and all those nine items
of the sports identification presented significant results. In the light of the data gained from the analysis, the study determined
the ¡®multi-purpose participation group¡¯ and the ¡®arts participation group¡¯ as loyal customers, and learned that those groups
have a possibility of being a main target of F1 Korean Grand Prix when this field tries to increase the demand for itself.
In terms of this study¡¯s expectations for any following research, the study believes that if the researches can extract more
various results by working on even more variables, such as the amount that each group would consume, the level of affection,
the level of satisfaction and others, what they learn from all those variables will have greater value as marketing data.