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1 - - A S tu dy on th e Im pact of th e P lac e M ark etin g on th e Citiz en 's W ay of T hinkin g

2 - - A S tu dy on th e Im pact of th e P lac e M ark etin g on th e Citiz en 's W ay of T hinkin g

3

4 1990,,,,., (Place M arketing ).,.,.,,., (PIF AN ), (W F EK ). 97, 90,.,,.,,,, Chi- squ are..,, - i -

5 .,,.,., , ,.,.,,...,.. (Place M arketin g ),.. :,,,, Chi- Squa re,, t- test,,, (CVM),, (P IFAN),, (WFEK) - ii -

6 < > (Place M arketing ) (CVM ) 32 - iii -

7 (Contin gent V aluation M ethod ) iv -

8 A b s trac t v -

9 < > < 2-1> 12 < 2-2> 20 < 2-3> 21 < 2-4> 23 < 2-5> 24 < 2-6> 25 < 2-7> 26 < 3-1>, 37 < 3-2> 38 < 3-3>, 39 < 3-4>, 39 < 3-5> 5 43 < 3-6> 43 < 3-7>, 48 < 4-1> 55 < 4-2> 57 < 4-3> 58 < 4-4> 59 < 4-5>, 60 < 4-6>, 61 < 4-7> 62 < 4-8> 63 < 4-9>, 65 < 4-10>, 66 < 4-11>, Chi- squ are 67 < 4-12> 69 < 4-13> 70 < 4-14> 71 < 4-15> 77 < 4-16> (t - t est ) 77 - v i -

10 < 4-17>, 78 < 4-18>, 78 < 4-19> 79 < > < 1-1> 7 < 2-1> 9 < 2-2> 16 < 2-3> 18 < 2-4> 24 < 2-5> 25 < 2-6> 25 < 2-7> 26 < 3-1> 34 < 3-2> 36 < 3-3> 36 < 3-4> 5 (2001) 42 < 3-5> 2 (2000) 46 < 3-6> 50 < 4-1> ( ) 59 < 4-2> ( ) 59 < 4-3> 60 < 4-4> 60 < 4-5> / 61 < 4-6> 61 < 4-7>, 62 < 4-8> 72 < 4-9> ( ) 73 < 4-10> ( ) 74 - v ii -

11 ,.,.., 2.,,...,.,, - 1 -

12 ,, 1),,,,.,..,. 2..,.,.,, 1), 1998,,,,, p

13 ..,..,.,,.,.,

14 1..,., , (PIFAN), (WFEK).,, 97, ,

15 ..,, ,.,.,, ,. 2,,,., - 5 -

16 ,, CVM ( ). 3,,.,. 4,,.,. 5.,,

17 1 2 CVM 3 4 CVM, 5 < 1-1> < 1-1> - 7 -

18 (P lac e M ark etin g ) 1 ) (P l ac e ) (th e S en s e of P la c e )..,.,,,. (T uan, Yi- Fu ) 2),,, (Luckerman),., 3).,,.,., (the Sense of Place) 2) T uan, Yi- Fu, 1979, Space and P lace, Minneapolis : Univer sity of Minnesota 3), 1999,,,, CI,, p

19 .,,..,.,.. < 2-1> :, 1997,, 32 p.181. < 2-1>, - 9 -

20 ,. ( ) ( ).,,. 2 ) (Place Marketing ) 4).,.,.,., (Place Marketing ),, (Kearns & Philo, 1993)., 5)., 4) (Place Marketing), (, 1998, :, 1998), (, 1998)

21 , ( ).. (City Boosterism ) 6) CI(City Identity ) 7). (Paddison, 1993).,.,.,., 5), 1998,,, 6) (City Boosterism). 7) CI Corporate Identity,. CI CI(City Identity)

22 ,.,.. < 2-1> / :, 1997,, 2. 1 )

23 ., 1960,.,., ).., 9). 2 3.,.,. 8) 1979 (Karl R. Popper).,,.,,.. 9), 1999,,,, 11, p

24 ,. 2 ).,.. (1) ,.,., (city vision )

25 (2) , 1998 IMF,.,,.,. (3).,.,., (, 1998).,

26 (4),,.,....,. < 2-2> < 2-2>

27 3..., (, 1998, p.186.). 1 ). 3 (Meffert, 1989, p.275.). (1)..... (2)

28 ,.. SWOT. (3).. < 2-3>. < 2-3> (feedback ) < 2-3> 4. 1 ),,

29 . (Griffiths ) 10),,. (political priority ), (conception of cultural domain ) (spatial emphases).. (Cultural Industries Model), (Integration Model), (Promotion or Consumerist Model) (Griffiths, 1995)., (Cultural Industries Model)., (affinity )., (Integration Model),., (Promotion or Consumerist Model).,.,. 10) Griffiths, 1995, Cultural S trategy and N ew M odes of Urban I ntervention, Cities, Vol.12, pp

30 , < 2-2>. < 2-2> (Cultural Indus tries M odel) (Integ ration M odel) (Prom otion or Cons um eris t Model) - : - : - : : Griffiths, 1995, Cultural S trategy and new m odes of urban intervention, Cities, Vol.12 pp :, 1998, :,,,, 33 6, p ) 6. < 2-3>.,,,

31 < 2-3>,,,,, , : :, 1997,,,.,

32 5..,,. 1 ).. ( ).,,, (, 1999).,,, (, 1998, p.62.).,,

33 < 2-4> , :, 2000,,, p ) ) ). 11), 1999, 12), 1999,,,

34 . (1) /, (52), (51) (49) 10%.,,. < 2-5> % :, 1999, < 2-4>, %.,

35 < 2-6> ( ) ( ) ( ) / ( ) (% ) :, 1999, (2), 10 (189 ).. < 2-5> < 2-6>,, % , 2 3. (3),

36 . 4,.,. < 2-7> ( ) (% ) :, 1999, < 2-7> 6..,,.,,.,,.,

37 , 10 1.,,,.,,. 2,, (CVM )..,., (CVM )

38 (1997) 13),,.,. (1998) 14).,, 3.,,. (1999) 15).,. (1998) 16),,.,., 13), 1997,,,, 8,, pp ), 1998,,,, 15), 1999,,,, 11, pp ), 1998, :,

39 ,. (1998) 17). 4,,.,,. (1999) 18).,.,,, 4. (2000) 19).,., 17), 1998,, 18), 1999,, 19), 2000, :,

40 . (2001) 20)..,.,. 2. (1999) 21),.,., Chi- square.,,,,. (1999) 22) 20), 2001,, 21), 1999, :, 22), 1999, :

41 .,.,,,,,,, 6,. (1999) 23),.,,, t - test /.,, /. (2000) 24),.,, 153,. (2000) 25), 28, pp ), 1999,,, 11 1, pp ), 2000, :, 25), 2000,,

42 .,,. 3. (CV M ) (Contingent Valuation Method), (environmental good). (1997) 26) (CVM ),,. (1998) 27) CVM.,,,., 8,799, 3,641, 6,088, 47 : 20 : 33.,,, (- ). (1997) 28). - 26), 1997,,, 15, pp ), 1998, : CVM, 28), 1997, (CVM) :, 9 1, pp

43 . 1 50,000 1,500, ,000 9,000. (1998) 29).,,. 1, , , 3,.,.., (CVM ),.,. 29), 1998, : (CVM),, 12, pp

44 3 1, 1990 (Bucheon City ) (Koyang City ).. (Puchon International F antastic Film Festival, PIFAN ) (World Flower Expo Koyang, WFEK). < 3-1>

45 , 1990,,, ),.,.,,,,, %.,.,, % 30)., ,. 30) 1999, 10,189.24,

46 2 ) (1) , , 1999, , , , 77 3.,,. < 3-2> < 3-3>

47 < 3-1>, ( :, ) ,712 65,080 32,504 32, , ,955 64,001 62, , ,471 82,686 80, , ,519 75,294 73, , , , , , ,515 85,585 80, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , :, 2000, :, 2000,.,,, UR., (18 ), (10 ), (7 ) 3, 35, (17 ) (18 ) 2, 35. (2)

48 ., 3, 4, 80.6% 87.8% 31)., 1. < 3-2> ( : ) 1 ( ) 778, , , , , , , , , , , , , , , , , , , , , : ,. 1995,,. < 3-3>. 31) 96.3%, 25.1%

49 < 3-3>, ( :, ) , ,128 20,064 83, , ,350 22,440 99, , ,139 24, , , ,155 25, , , ,057 29, ,619 :, 2000, :, 2000,, 1999 (18.67%) (10.47%)., /., < 3-4>.. < 3-4>, ( : ) , % 29, % % % % % % % 8, % 3, %, % % % %

50 12, % 7, % 8, % 5, % 3, % 1, % % % /, 2, % 2, %, % % 1, % 1, % % %, 2, % 1, %, 5, % 3, % :, 2000, :, 2000, 2. 1 ) ,., ), 32), (, )

51 . 1999, (, 2001)., 1999, (PISAF ),., (PIFAN). 2001, 5 33). (1) (PIFAN ),. 5 34). (bed town )., 33) 5, , 1, (, 2001, 5, p.97.). 34) 5, (PIFAN), (PISAF ),

52 .,,, 1, 2. < 3-4> 5 (200 1) / / ( ) < 3-4> 5 (200 1) :, 2001, 5,, p.61. (PIFAN). < 3-4> ( ) ,, ,

53 , -. 5, ,762. < 3-5> 5 ( ) 2,451, % 1,030, % 500, % 170, % 11, % 45, % 695, % :, 2001, 5,, p.70. < 3-6> ( :, %) ,313 38,739 (42.42) 52,574 (57.58) ,646 60,703 (55.87) 47,943 (44.13) ,250 38,022 (25.31) 112,228 (75.69) ,419 40,102 (19.24) 117,317 (80.76) ,762 38,779 (44.70) 21,658 (55.30) :, 2000, :, 2001, 5,, p.3. (2)

54 , 9. (European F antasy Film F estivals Federation, EFFFF )., (F anta F estival), (F antas Porto- Oporto International Film Festival), (International Film Festival of Catalonia), SF (T he Brussels International Festival of Fantasy, T hriller and Science Fiction Films) 4.,, (Amsterdam Fantasy Film Festival) (Cinenygma), (Espoo Cine International), (T he F antastic Film Festival Lund), 1999 (San Sebastian Horror and Fantasy Film F estival) 35). (PIFF ) (JIFF ). 6 (PIFF ),.,, 35) http :// korean/ pifaninfo/ EFFFF.html

55 36)., (JIFF ) 2000,. 2 ),.,.,,.,. (1) (WFEK) (WFEK).., 37). 36) , 8.7% 150, (, 1999, p.27.) 37), 1997, 97, pp

56 , ( ) ). 4, 3. < 3-5> 2 (2000) < 3-5> 2 (2000) : 2000 ( 38) 2000 (

57 , , 80. (2) % ,000,,,,, 39).,, (Flower and New Civilization ) AIPH A2/ B2. 793,000 40),. 21, 39), 2000,,, p ) http :// exhibition/ htm/ intro_2.htm

58 .,,. 3 ).,. < 3-7>,, ,

59 ,.,, 21.,,., ,..,, Chi- square.,.,.,

60 < 3-6>.,. < 3-6> 1 : (,,,,, ).,. 1 :. 2 :. 3 :

61 4 :. 5 :. 2 : (,,,,, ). 3 : (,,,,, ).. 1,,., Chi- squarerjawjd,,

62 )... 2 ) , 574 ( 349, 225 ) 72%., 330, )

63 ,. 2., < 4-1>.,,,,,, 7. 1 ),. 2 ),,,

64 3 ),,,,,.,. 4 ),.,,, ),.,,,,,,

65 < 4-1> ,

66 2 1..,.,.,,. (ANOVA ),, x (Chi- square) ( 1 ).,, (Cronbach s alpha)., ( 2 ).,..,, ( 3 )

67 . < 4-2> x,,,, 2. 1 ), < 4-2>

68 < 4-3> ( ) (%) ( ) (%) / / / / / / / / /

69 2 ),.,., 2. < 4-4> ( : ) (3 27 ) (2 14 ) % % 4 1.2% % % % % % % 1 0.5% % 4 1.9% 6 1.8% % 4 1.2% 3 1.4% % < 4-1> ( ) < 4-2> ( )

70 ,, (56.1%), (93.0%). < 4-5>, ( : ) (3 28 ) (2 15 ) % 5 2.3% % % % 6 2.8% % 4 1.9% % 4.., 46.06%, 56.75%,., 3. < 4-3> < 4-4>

71 ,,., (85.40%, 72.94%). < 4-5> / < 4-6>, (36.7%),. < 4-6>, ( : ) (33 0 ) (2 15 ) % % % 8 3.7% % % % % % % % % 4 1.2%

72 < 4-7>,,, (56.83%), (35.35%)., PPP. < 4-7> ( : %) / / /

73 ,. 3 ) (1)., (Reliability Analysis). (Cronbach ' s alpha). < 4-8>, Cronbach 's alpha

74 0.6,, 0.6. (2) 1 1 : (,,,,, ).,.,.,., Duncan Grouping., 5%,,.,.,

75 ,..,.( ) < 4-9>, s u m of s qu are s F v alu e P r > F * * * * * p < 0.05 *, 5%,,,,,

76 ,..( ) < 4-10>, s u m of s qu are s F v alu e P r > F * * * <.0001 * * p< 0.05 *,,.,

77 ,.,,.,,, (x )., x (Chi- square) 5%,.,.,.,. < 4-11>, Chi- s qua re ( ) ( ) missing = 10 missing =

78 Chi- Square value Prob value Prob < missing = 102 missing = 48 Chi- Square value Prob value Prob < <.0001 (3) 2 2 : (,,,,, )., 4.,..,

79 .. < 4-12> W ald Log Likelihood Goodness of Fit Cox & Snell - R.226 Nagelkerke - R.420, 5%,., 10%.,,.,

80 < 4-13> W ald Log Likelihood Goodness of Fit Cox & Snell - R.325 Nagelkerke - R.543, 5%,,.,, (- ), (- ).,.. 4 )

81 , 30.70%, 28.97%., 2. < 4-14> ( ) (% ) ( ) (% ) )., (e) (j) 41),.. a ) e ) i ) m ), b ) f ) j ) n ) c ), g ) k ) o ) d ) h ) l ) p )

82 ., (a), (e), (j) (m ). < 4-8>,, 42), 16 a p,, A, B, C, D. 42) (Quantification Method),,.,, (, 2000, pp )

83 Plot of dim1*dim2. Symbol is value of region di m l B 1 + g h + i nk p D d c ba f o j C a e di m2 < 4-9> ( ), (m ). (A ) (b), (B) (h), (j) (p).,

84 Plot of dim 1*dim2. Symbol is v alue of region di m1 2 + e + C o 1 + l + k d hn b f p i B g D j a di m2 < 4-10> ( ) 3 (A ) (c), (m )., (A ) (c) (m), (B) (b) (f), (i).,

85 ) (Contin g e nt V alu ation M eth od ) (CVM ) (public good), (environmental good) (, 1997, p.61.)..,.,,.. (survey )., (CVM ). 44) 43). (1998), (1998)., (1997)

86 .,,,. (PIFAN ) (WFEK). 2..,.,., 4, ) (Willingness- to- pay model), (survey method), (the interview method), (the direct interview method), (the question method), (the hypothetical demand curve estimation method), (the difference mapping method), (the preference elicitation method) (, 1997, p.61)

87 1 ) , < 4-15>., 1,000,. < 4-15> ( ) (% ) ( ) (% ) 0-1, ,000-4, ,000-10, , ,. t - test,. < 4-16> (t- te s t) T P rob > T 2, ,

88 ,, 36.3%, 41.3%. < 4-17>, ( ) (% ) ( ) (% ) , < 4-16> 2,107.9, 2, < 4-18>, (P M V ) = (H ) (C ) PMV = 253,661( ) ( ) = 534,692,022( ) PMV = 253,831( ) ( ) = 724,509,823( ) : (PIFAN) (WFEK) 5 3 4,

89 . 2 ) 3 : (,,,,, ).., 4,,,. Logistic(Value)= + 1( )+ 2( )+ 3( ) + 4( )+ 5( )+ 6( )+ 7( ) + 8( ), 5%., 5%,. < 4-19> W ald nagelkerke = p < 0.05 *

90 : 1=, 0=, 1=, 0=,,,,,., (- ),. 4,,.,.,, 5%,,,.,, 5%,,,,,.,, x (Chi- square),

91 , 4,., 1-2.,,,,.,.,. 36.3%, 41.3%, , , (PIFAN) 5 3 4, (WFEK)

92 5 1.,.,..,..,,.,,.,.,,

93 ,.,,,. 5%,,,. 5%,,,,,.,,,.,,.,.,,, (Chi- square),.,,..,,,

94 ,.,., 4,.,,,., 4 1-2,.,,,.,,,..,,,.,,

95 , 2, ,..,.,..,,.,.,

96 ..,.,,,,,.,..,. 3 1.,,

97 .,,.,. 2...,,

98 [ ] 1. 1), 1997, 97, 2000,, 2001, 5,, 1999,, 2000,, 2001, VISION BUCHEON 2010, 1999,,, 1999,,,, 2001, SAS,, 2000, :, 2), 2000,,, 2001,,, 2000, :,, 1999, :,, 1999, :,

99 , 2000, :,, 1998, :,, 1998, : CVM,, 1998,,,,, 1999, :,, 2000,,, 2000,,, 1998,, 3), 1999,,,, 11, pp , 1997,,, 15, pp , 1999, :, 34, pp , 1998, :,, 2 2, pp

100 , 2001, :,, 5, 1997,,, 1997,,, 32, pp , 1999, :,, 28, pp , 1997, (CVM ) :,, 9 1, pp , 1999,,, 11 1, pp , 1999,,,, CI., pp , 1998, : (CVM ),, 12, pp , 1998,,,, pp ) Up & Up ( ) ( ) ( city.koyang.kyonggi.kr/ ) ( ) ( korean/ index.asp) ( )

101 Achworth, G. J. and Voogd, H., 1990, S elling the city : m ark eting app roaches in p ublic s ector urban p lann ing, Belhaven Press Griffiths, R., 1995, Cultural S trateg ies and N ew M od es of Urban I ntervention, Cities 12(4), pp Paddison, R., 1993, City M ark eting, I m ag e R econs truction and Urban R eg eneraion, Urban Studies Vol. 30, No. 2 T uan, Yi- Fu, 1979, Sp ace and P lace, Minneapolis : University of Minnesota

102 [ ]?..,,., (02) E- Ma il : le o ppa il.ne t,. : ( ), ( ) ( ) : ( ) 1)? ( ) ( ) 2)?

103 1) (, )? 2)? 3) ( )? 4)? 5)? ( ) ( ) 1).. ( ).... ( )

104 ( ).. (,, ). ( ) )? ( ) ( ) 2),? 1)?, ( ) 2)? 1, 2, 3. 1 :, 2 :, 3 :

105 1) 5) 9) 13), 2) 6) 10) 14) 3) 7) 11) 15) 4) 8) 12) 16). 4,000.? >,? >,?. 1)? 2)? ( ) 3)? / / / ( ) 4)? (, ) 5)? )? !!

106 [ ]?..,,., (02) E- Ma il : le o ppa il.ne t,. : ( ), ( ) ( ) : ( ) 1)? ( ) ( ) 2)? ( )

107 1) ( )? 2)? 3) ( )? 4)? 5)? ( ) ( ) 1).. ( ).... ( )

108 ( ).. (, ). ( ) )? ( ) ( ) 2),? 1)? ( ) 2)? 1, 2, 3. 1 :, 2 :, 3 :

109 1) 5) 9) 13), 2) 6) 10) 14) 3), 7) 11) 15) 4) 8) 12) 16). 4,000.? >,? >,?. 1)? 2)? ( ) 3)? / / / ( ) 4)? (, ) 5)? )? !!

110 [ ], Chi- s quare, 1. 1) The GLM Pr ocedur e Dependent Var i abl e : PART par t ( ) Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 318 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means 2 Cr i t i cal Range Duncan Gr oupi ng Mean N SEX A B Dependent Var i abl e : PART par t Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 316 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Gr oupi ng Mean N AGE A A A A A A A Dependent Var i abl e : PART par t Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom

111 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Gr oupi ng Mean N EDU A A A A A A A Dependent Var i abl e : PART par t Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 315 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Gr oupi ng Mean N INCOME A A B A B A B A B B Dependent Var i abl e : PART par t Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 315 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Groupi ng Mean N DWELLING A A B A B A B A B B B B Dependent Var i abl e : PART par t

112 Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 316 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means 2 Cr i t i cal Range Duncan Gr oupi ng Mean N ADDRESS A B The GLM Pr ocedur e Dependent Var i abl e : INTEND i nt end( ) Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 232 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means 2 Cr i t i cal Range Duncan Gr oupi ng Mean N SEX A A A Dependent Var i abl e : INTEND i nt end Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 230 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Gr oupi ng Mean N AGE A A A A A A A

113 Dependent Var i abl e : INTEND i nt end Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 229 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Gr oupi ng Mean N EDU A B B B B B Dependent Var i abl e : INTEND i nt end Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 230 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Gr oupi ng Mean N INCOME A A A A A A A Dependent Var i abl e : INTEND i nt end Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 229 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Groupi ng Mean N DWELLING A A

114 A A A A A A A Dependent Var i abl e : INTEND i nt end Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 230 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means 2 Cr i t i cal Range Duncan Gr oupi ng Mean N ADDRESS A A A ) The GLMPr ocedur e Dependent Var i abl e : PART par t ( ) Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 210 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means 2 Cr i t i cal Range Duncan Gr oupi ng Mean N SEX A B Dependent Var i abl e : PART par t Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 208 Er r or Mean Squar e Har moni c Mean of Cel l Si zes

115 Number of Means Cr i t i cal Range Duncan Gr oupi ng Mean N AGE A A B A B B B B Dependent Var i abl e : PART par t Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 208 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Gr oupi ng Mean N EDU A A A A A A A ependent Var i abl e : PART par t Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 208 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Gr oupi ng Mean N INCOME A A A A B A B B Dependent Var i abl e : PART par t Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model < Er r or

116 Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 207 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Groupi ng Mean N DWELLING A A A A B A B B C Dependent Var i abl e : PART par t Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 209 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means 2 Cr i t i cal Range Duncan Gr oupi ng Mean N ADDRESS A B The GLM Pr ocedur e Dependent Var i abl e : INTEND i nt end( ) Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 168 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means 2 Cr i t i cal Range Duncan Gr oupi ng Mean N SEX A A A Dependent Var i abl e : INTEND i nt end

117 Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 166 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Gr oupi ng Mean N AGE A A A A A A A Dependent Var i abl e : INTEND i nt end Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 166 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Gr oupi ng Mean N EDU A A A A A A A Dependent Var i abl e : INTEND i nt end Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 166 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Gr oupi ng Mean N INCOME A A A A

118 A A A Dependent Var i abl e : INTEND i nt end Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 165 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means Cr i t i cal Range Duncan Groupi ng Mean N DWELLING A A A A A A A A A Dependent Var i abl e : INTEND i nt end Sum of Sour ce DF Squar es Mean Squar e F Val ue Pr > F Model Er r or Cor r ect ed Tot al Al pha Er r or Degr ees of Fr eedom 167 Er r or Mean Squar e Har moni c Mean of Cel l Si zes Number of Means 2 Cr i t i cal Range Duncan Gr oupi ng Mean N ADDRESS A A A

119 2. Chi- square p r o c f r e q ; t ab l e s know*p a r t (p a r t *i nt end ) / nop e r c ent nocol chi s q c e l l chi 2 ; r u n ; 1) know(know) * par t (par t ) Fr equency Cel l Chi - Squar e Row Pct par t (No) par t (Yes ) Tot al know (No) know (Yes ) Tot al Fr equency Mi ssi ng = 10 St at i st i cs f or Tabl e of know by par t St at i st i c DF Val ue Pr ob Chi - Squar e Li kel i hood Rat i o Chi - Squar e Cont i nui t y Adj. Chi - Squar e Mant el - Haenszel Chi - Squar e Phi Coef f i ci ent Cont i ngency Coef f i ci ent Cr amer ' s V Fi sher ' s Exact Test Cel l (1, 1) Fr equency (F) 183 Lef t - si ded Pr <= F Ri ght - si ded Pr >= F E- 04 Tabl e Pr obabi l i t y (P) E- 04 Two- si ded Pr <= P Ef f ect i ve Sampl e Si ze = 320 Fr equency Mi ssi ng = 10 par t (par t ) * i nt end (i nt end)

120 Fr equency Cel l Chi - Squar e i nt end i nt end Row Pct (No) (Yes ) Tot al par t (No) par t (Yes ) Tot al Fr equency Mi ssi ng = 102 St at i st i cs f or Tabl e of par t by i nt end St at i st i c DF Val ue Pr ob Chi - Squar e < Li kel i hood Rat i o Chi - Squar e < Cont i nui t y Adj. Chi - Squar e < Mant el - Haenszel Chi - Squar e < Phi Coef f i ci ent Cont i ngency Coef f i ci ent Cr amer ' s V Fi sher ' s Exact Test Cel l (1, 1) Fr equency (F) 31 Lef t - si ded Pr <= F Ri ght - si ded Pr >= F E- 06 Tabl e Pr obabi l i t y (P) E- 06 Two- si ded Pr <= P E- 06 Ef f ect i ve Sampl e Si ze = 228 Fr equency Mi ssi ng = 102 WARNING: 31% of t he dat a ar e mi ssi ng. 2) know(know) * par t (par t ) Fr equency Cel l Chi - Squar e Row Pct par t (No) par t (Yes ) Tot al know (No) know (Yes ) Tot al Fr equency Mi ssi ng = 3 St at i st i cs f or Tabl e of know by par t St at i st i c DF Val ue Pr ob

121 Chi - Squar e < Li kel i hood Rat i o Chi - Squar e < Cont i nui t y Adj. Chi - Squar e < Mant el - Haenszel Chi - Squar e < Phi Coef f i ci ent Cont i ngency Coef f i ci ent Cr amer ' s V Fi sher ' s Exact Test Cel l (1, 1) Fr equency (F) 91 Lef t - si ded Pr <= F Ri ght - si ded Pr >= F E- 10 Tabl e Pr obabi l i t y (P) E- 10 Two- si ded Pr <= P E- 10 Ef f ect i ve Sampl e Si ze = 212 Fr equency Mi ssi ng = 3 par t (par t ) * i nt end (i nt end) Fr equency Cel l Chi - Squar e i nt end i nt end Row Pct (No) (Yes ) Tot al par t (No) par t (Yes ) Tot al Fr equency Mi ssi ng = 48 St at i st i cs f or Tabl e of par t by i nt end St at i st i c DF Val ue Pr ob Chi - Squar e < Li kel i hood Rat i o Chi - Squar e < Cont i nui t y Adj. Chi - Squar e < Mant el - Haenszel Chi - Squar e < Phi Coef f i ci ent Cont i ngency Coef f i ci ent Cr amer ' s V Fi sher ' s Exact Test Cel l (1, 1) Fr equency (F) 36 Lef t - si ded Pr <= F Ri ght - si ded Pr >= F E- 07 Tabl e Pr obabi l i t y (P) E- 07 Two- si ded Pr <= P E- 07 Ef f ect i ve Sampl e Si ze = 167 Fr equency Mi ssi ng = 48 WARNING: 22% of t he dat a ar e mi ssi ng

122 3. 1) pr oc i ml ; st ar t quant i i i ; r eset nol og; f =f ; dr = f [, +] ; dc=f [+, ] ; n=sum(f ) ; g=di ag(1/ sqr t (dr ) ) *f *di ag(1/ sqr t (dc ) ) ; cal l svd(u, q, V, G) ; ei genval =(q#q) `; c=mi n(nr ow(f ), ncol (f ) ) ; ei gen=ei genval [2 :c ] ; cor r = q[2 :c ] ; per cent = ei gen/ (sum(ei genval )- 1 ) *100 ; X=di ag(sqr t (n/ dr ) ) *U; X1_x2=x[, 2 :3 ] ; y= di ag(sqr t (n/ dc ) ) *V; y1_y2=y[, 2 :3 ] ; pr i nt,, "Quant i f i cat i on Met hod I I I usi ng sas/ i ml Pr ogr am"; pr i nt, "Response Pat t er n Mat r i x", f [f or mat =7. 0 ], ; pr i nt, "ei genval ues and per cent age (%) " ; pr i nt, cor r [f or mat =6. 4 ] ei gen[f or mat =6. 4 ] per cent [f or mat = 6. 2 ] ; pr i nt, "r ow scor e ", X1_X2[f or mat = 8. 4 ], ; pr i nt, "col umn scor e ", Y1_Y2[f or mat = 8. 4 ], ; r eset l og; f i ni sh; f ={ , , , , , , , , , , , , , , , }; r un quant i i i ; Quant i f i cat i on Met hod I I I usi ng sas/ i ml Pr ogr am Response Pat t er n Mat r i x F

123 ei genval ues and per cent age (%) CORR EIGEN PERCENT r ow scor e X1_X col umn scor e Y1_Y Input r egi on $ di m1 di m2; CARDS; a b c d e f g h i j k l m n o p A B C D Pr oc PLOT; PLOT di m1 * di m2 = r egi on/ box vaxi s = - 4 t o 2 haxi s = t o 9 vpos = 40 hpos = 70 vr ef = 0. 0 hr ef = 0. 0; r un; 2) pr oc i ml ; st ar t quant i i i ; r eset nol og; f =f ; dr = f [, +] ; dc=f [+, ] ; n=sum(f ) ;

124 g=di ag(1/ sqr t (dr ) ) *f *di ag(1/ sqr t (dc ) ) ; cal l svd(u, q, V, G) ; ei genval =(q#q) `; c=mi n(nr ow(f ), ncol (f ) ) ; ei gen=ei genval [2 :c ] ; cor r = q[2 :c ] ; per cent = ei gen/ (sum(ei genval )- 1 ) *100 ; X=di ag(sqr t (n/ dr ) ) *U; X1_x2=x[, 2 :3 ] ; y= di ag(sqr t (n/ dc ) ) *V; y1_y2=y[, 2 :3 ] ; pr i nt,, "Quant i f i cat i on Met hod I I I usi ng sas/ i ml Pr ogr am"; pr i nt, "Response Pat t er n Mat r i x", f [f or mat =7. 0 ], ; pr i nt, "ei genval ues and per cent age (%) " ; pr i nt, cor r [f or mat =6. 4 ] ei gen[f or mat =6. 4 ] per cent [f or mat = 6. 2 ] ; pr i nt, "r ow scor e ", X1_X2[f or mat = 8. 4 ], ; pr i nt, "col umn scor e ", Y1_Y2[f or mat = 8. 4 ], ; r eset l og; f i ni sh; f ={ , , , , , , , , , , , , , , , }; r un quant i i i ; Quant i f i cat i on Met hod I I I usi ng sas/ i ml Pr ogr am Response Pat t er n Mat r i x F ei genval ues and per cent age (%) CORR EIGEN PERCENT r ow scor e X1_X

125 col umn scor e Y1_Y Dat a quant i i i ; Input r egi on $ di m1 di m2; CARDS; a b c d e f g h i j k l m n o p A B C D Pr oc PLOT; PLOT di m1 * di m2 = r egi on/ box vaxi s = - 4 t o 2 haxi s = t o 9 vpos = 40 hpos = 70 vr ef = 0. 0 hr ef = 0. 0; r un;

126 A b s tract A S tu dy on th e Im pac t of th e P lac e M ark etin g on th e Citiz e n 's W ay of T hin kin g : A Case Study on Bucheon and Koy ang City in Kyonggi- Do K y u n g, D o - H y un D e part m en t of U rb an E n g in e erin g, Gradu at e S ch ool, U n iv ers it y A dv i s e d by of S e ou l P rof. K im, Ch an g - S e ok T he purpose of this study is to analyze the impacts and values of the Place Marketing based on city image and economic regeneration through the citizen 's w ay of thinking. Now aday s, the Place Marketing promoted as a new city management strategy on which have much effected the region s. So, this study is compared and analyzed that the place marketing comes up to the regions in a case of Bucheon city 's Puchon International F antastic Film F estivals (PIFAN) and Koyang city ' s World Flower Exhibition Koyang (WFEK) in Kyonggi- Do. T he analysis carried out a ANOVA, Chi- square(x ) test and Logistic Regres sion Analy sis and compared with Bucheon and Koyang city u sing the basic statistics. T he major finding s of this study are as follow s; First, the more a knowledge on the place marketing is low, the more a participation is low and the more a participation is low, the more a

127 participation intention is low. It needs to operate Public Relations (PR) and Marketing on festivals and events. Second, a much problem of the place marketing is a low participation of the citizen. It needs to effort for citizen 's participation by the local governments. T he results as compared with Bucheon and Koyang city on the Place Marketing, the former operated on the sense of place based on marketing by cultural policy, the latter operated on marketing based on the sen se of place by flow ering plant industry. F urther, the Place Marketing v alues of Koyang city is larger than Bucheon city by Contingent Valuation Method(CVM ). In conclusion s, for the successful place marketing, it must achieve the Place marketing 's purpose through citizen participation as w ell as attract and raise of allied indu stries. In order to solv e these, it must be con solidated the Place Marketing 's Public Relation s (PR). Key Words : the P lace M ark eting, the S ens e of P lace, Cultural S trategy, City M anag em en t, A N OVA, T - tes t, Chi- S quare T es t, Quan tif ication M ethod, L og is tic R eg ress ion A naly s is, CVM, B ucheon, P I FA N, K oy ang, W F E K

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