© Copy igh 2006: Se icio de Publicaciones de la Uni e sidad de Mu cia. Mu cia (España)
ISSN edición imp esa: 0212-9728. ISSN edición web (www.um.es/analesps): 1695-2294
anales de psicología
2006, ol. 22, nº 1 (junio), 155-160
De elopmen o an index o assess he b and image o ou is des ina ions
Jesús Va ela Mallou1,*, Albe o Ga cía Ca ei a1, Vicen e Manzano A ondo2 y An onio Rial Boube a1
1Uni e sidad de San iago (España), 2Uni e sidad de Se illa (España)
Tí ulo: Desa ollo de un índice pa a e alua la imagen de ma ga de des i-
nos u ís icos.
Resumen: La imagen de un des ino u ís ico cons i uye un elemen o un-
damen al en los di e en es modelos que analizan el p oceso de elección po
pa e del consumido (Mou inho, 1984; Chon, 1990; Baloglu & McClea y,
1999). Los di e en es es udios consul ados sugie en que aquellos des inos
que poseen una imagen posi i a gozan de una mayo p obabilidad de se
conside ados y inalmen e elegidos en un p oceso de decisión. El obje i o
del p esen e es udio es p opone una me odología que pe mi a e alua la
imagen de ma ca como un cons uc o de ca ác e psicológico, a pa i de la
pe cepción que los suje os ienen de los a ibu os que de inen dis in os
des inos u ís icos. Pa a lle a a cabo es e abajo y llega a p opone un
índice de imagen de ca ác e cuan i a i o, se analiza on los da os co es-
pondien es a una encues a de 916 u is as españoles.
Palab as cla e: Tu ismo; imagen de ma ca; compo amien o del consu-
mido ; análisis mul i a ian e.
Abs ac : A des ina ion’s image cons i u es a cen al ac o in he di e en
models ha analyze a el decision-making and i s selec ion (Mou inho,
1984; Chon, 1990; Baloglu & McClea y, 1999). The s udies consul ed sug-
ges ha des ina ions wi h a posi i e image will ha e a highe p obabili y
o being conside ed and inally chosen in he selec ion p ocess. The aim o
his s udy is o p opose a me hodology o measu ing b and image as an
en i ely psychological cons uc , de i ing om he subjec s’ pe cep ions o
i s componen a ibu es. To de elop and e alua e his measu e, we ana-
lyzed he esul s o a su ey o 916 ou is s.
Key wo ds: Tou ism; b and image; consume beha iou ; mul i a ia e
analysis.
In oduc ion
Inc easing economic de elopmen and massi e a ailabili y
o p oduc s and se ices means ha i is inc easingly di icul
o i ms o each consume s. The p incipal p oblem is o
eme ge om he “communica ions noise” cons i u ed by he
en i onmen , and o achie e he image and ma ke posi ion
desi ed. To his end, i ms aim o p oduc s and se ices
ha can be eadily iden i ied and ecognized. Image hus be-
comes a cen ally impo an ool in he s uggle o a ac he
consume ’s a en ion, and as a esul i ms de o e inc easing
a en ion o i s s udy.
Bu his conce n, he new cul o image, is no exclusi e
o b ands and p oduc s. People aspi e o an a ac i e ap-
pea ance, and in his way o appeal o o he s and be lo ed.
Public o p i a e ins i u ions and o ganiza ions a emp o
o e a be e se ice e e y day, o sell an image o o ganiza-
ional cul u e ha mee s he needs and equi emen s o hei
clien s. And o cou se, coun ies a emp o compe e and o -
e a ou ism p oduc in a ma ke ha is inc easingly sa u-
a ed and in which i is inc easingly di icul o compe e. The
a ious s udies consul ed (Hun , 1975; Mou inho, 1984;
Ga ne , 1986; Woodside & Lysonski, 1989; Chon, 1990;
Jenkins, 1999; Bigné, Sánchez & Sánchez, 2001) sugges ha
hose des ina ions wi h a posi i e image will ha e a highe
p obabili y o being conside ed and inally chosen in he
p ocess o selec ion o he des ina ion o isi .
* Di ección pa a co espondencia [Co espondence add ess]: Jesús
Va ela Mallou, Dp o. Me odología de las Ciencias del Compo amien o,
Facul ad de Psicología, Uni e sidad de San iago de Compos ela, 15782.
San iago de Compos ela (España). E-mail: [email p o ec ed]
Objec i es
The p incipal aim o he s udy p esen ed in wha ollows is
o p opose a me hodology o he measu emen o b and im-
age, unde s ood as an en i ely psychological cons uc
o med om subjec s’ pe cep ions o i s di e en compo-
nen a ibu es.
In he case which conce ns us, and in applica ion o he
ou ism ma ke , he Image Index p oposed should e lec
he Value o he Image o he di e en des ina ions on he
basis o he sum o he indi idual pe cep ions o he a ge
g oup on a lis o a ibu es ha de ine he image.
We a e awa e ha when alking abou he image o a
ou is des ina ion i is no possible o summa ize e e y hing
ha ha des ina ion ep esen s in a single alue. In ac , he
p oposed index is no going o esol e all he p oblems o
image measu emen , bu should p esen i sel as a use ul ool
o comple ing he in o ma ion supplied by o he analyses,
such as o example awa eness, s eng hs and weaknesses, po-
si ioning maps, e c.
Howe e , he p oposed image index should be capable
o esol ing a leas wo ques ions: 1) I should be sensi i e
o he di e en ou is sec o s, which implies es ima ion o
one alue pe subjec ; and 2) i should allow assessmen o
he de elopmen o he image o e ime, and should hus be
easy o apply and o in e p e .
Me hod
De elopmen o scales
The i s s ep in measu emen o he image alue o a
b and consis s o he app op ia e iden i ica ion o he a ibu es
ha pa icipa e in he con igu a ion o he image. To his
- 155 -
156 Jesús Va ela Mallou e al.
end we pe o med a e iew o he exis ing li e a u e on ou -
is des ina ion image (Good ich, 1978; Woodside & Lyson-
ski, 1989; Calan one e al., 1989; Baloglu & McClea y, 1999;
Pike, 2002). In addi ion, and in complemen a y ashion, we
o med wo discussion g oups wi h expe s and consume s.
The esul s ob ained we e pooled and analysed un il we
had d awn up a de ini i e lis comp ised o 21 a ibu es: 1)
Quali y o ho els and accommoda ion; 2) Good es au an
se ice and ood; 3) Gas onomic quali y and a ie y; 4)
Quali y and a ie y o shops; 5) Ease-o -use and quali y o
anspo ; 6) F iendliness and poli eness o local people; 7)
Quali y o se ice om p o essionals; 8) Pleasan clima e; 9)
Na u al a ac ions o beaches; 10) Landscape beau y; 11)
Cul u al and a chi ec u al a ac ions; 12) Va ie y o ou is
a ac ions; 13) Sa e y on he s ee s; 14) Wa e and beach
quali y; 15) Too c owded; 16) En i onmen al noise; 17)
Adequa e signpos ing; 18) Cleanliness and ca e o su ound-
ings; 19) Value o money; 20) Good ou is in o ma ion se -
ices; 21) A ac i e publici y.
De elopmen o he image index
I we assume ha he a ibu es a e posi i e c i e ia, hen
associa ing hem wi h s imuli indica es ha his pa icula
subjec pe cei es ha s imulus wi h a high alue. In his con-
nec ion, he mos highly alued des ina ion (wi h bes image)
will be ha which has he g ea es numbe o associa ions.
In ou case, and gi en ha we used da a on associa ion
be ween s imuli and a ibu es, we had a ma ix o n subjec s,
m a ibu es and s imuli, om which we cons uc ed n mx
associa ion ables. In each cell he esponse was symbolized
by πijk, which ep esen s he esponse o subjec i o a ibu e
j in combina ion wi h s imulus k. In a con en ional associa-
ion able, πijk can ake only wo alues: 1 i he in e iewee
has associa ed ha a ibu e wi h ha s imulus, and 0 i no .
Subjec I
1 2 ... k ...
1 π i11 π i12 … π i1k … π i 1
2 π i21 π i22 … π i2k … π i 2
... … … … … … …
j π ij1 π ij2 … π ijk … π i j
... … … … … … …
m π im1 π im2 … π imk … π i
m
I we g oup hese n ables o associa ion be ween
s imuli and a ibu es, we ob ain an mx equency ma ix
using which we can s udy he s eng hs and weaknesses o a
des ina ion, compa e des ina ions, o apply mul i a ia e
echniques wi h which o s udy posi ioning in he ou ism
ma ke (Va ela, Ga cía, B aña & Rial, 2002; Va ela, Picón &
B aña, 2004).
F om his ma ix, which addi ionally se es as inpu o
he applica ion o da a- educ ion echniques, we p oceeded
o calcula e he Image Index, he de elopmen o which is
desc ibed in wha ollows.
Gi en he way in which he associa ions able has been
cons uc ed (wi h ze oes and ones), and o subjec i, he
image o des ina ion k will be equal o he sum o he alues
o his column, ha is:
∑
=m
jijkik
I
π
(1)
The p incipal d awback o his index is ha i is e y
p obable ha no all a ibu es ha pa icipa e in he image
o a b and will be posi i e. In o he wo ds, he e will be
some a ibu es whose associa ion wi h a pa icula s imulus
will no add alue o he image, bu will a he de ac alue
om i . Conside o example an a ibu e like oo c owded. In
his case, and gi en he way in which i is wo ded, he des i-
na ion associa ed wi h his a ibu e will p esumably de ac
alue in he inal Image Index. In ac , i is e y possible ha
he des ina ion wi h bes b and image will be ha which pos-
sesses some a ibu es and no o he s. The a ibu es “noise”
and “c owded” should de ac good image om he s imulus
i hey a e associa ed wi h i , while he emaining a ibu es,
o example “g een landscape” and “pleasan clima e”,
should add o i .
F om his poin o iew, in addi ion o he associa ions
able, i is necessa y o cons uc an o e all ec o o weigh s
o di ec ions ( o all cases). This ec o con ains a alue o
each a ibu e:
⎩
⎨
⎧
+
−
=posi i eisa ibu e hei
nega i eisa ibu e hei
j1
1
δ
Thus, an Index ha egis e s he alue o he Image o
B and k o subjec i can be w i en as ollows:
S ímuli
j
m
jijkik
I
δπ
∑
=(2)
A ibu es
The minimum alue will occu when he subjec associ-
a es wi h ha s imulus all he nega i e a ibu es and none o
he posi i e a ibu es, in which case exp ession (2) will gi e
he ollowing esul :
∑∑
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛−=
−
=m
j
m
jj
jm
δ
δ
2
1
2
1
mín (3)
The maximum alue will a ise when he subjec associa es
wi h ha s imulus all he posi i e a ibu es and none o he
nega i e a ibu es, in which case exp ession (2) will gi e he
ollowing esul :
anales de psicología, 2006, ol. 22, nº 1 (junio)
De elopmen o an index o assess he b and image o ou is des ina ions 157
∑∑
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛+=
+
=m
j
m
jj
jm
δ
δ
2
1
2
1
max
The index de i es om conside a ion o exp ession (2),
exp essed by i s minimum (3) and maximum (4), in such a
way ha he esul lies wi hin (0,1):
∑∑
∑∑
∑∑
+−=
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛−−
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛+
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛−−
=m
j
m
jjjijk
m
jj
m
jj
m
j
m
jjjijk
ik mm
mm
m
I2
1
2
11
2
1
2
1
2
1
δδπ
δδ
δδπ
Bu gi en ha he wo inal summands o exp ession (5)
a e cons an s, he index can be o mula ed as ollows:
∑+= m
jjijkik p
m
I
δπ
1
2
11
2
1
δ
δ
−
=
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛−−= ∑
m
jjm
m
pwhe e
No e he in ui i e and di ec in e p e a ion o he index.
I all he a ibu es a e posi i e, p=0 and he index gi es he
mean o he associa ion coe icien s, which as no ed can ake
alues o 0 and 1. Wi h inc easing numbe s o nega i e a -
ibu es, he alue o he cons an p will be g ea e . Thus, o
example, i we ha e as many posi i e as nega i e a ibu es,
he alue o he cons an p will be equal o 0.5. A he o he
ex eme, only nega i e a ibu es exis , so ha he bes pos-
sible image will gi e a alue o 0 in he le pa o he sum-
mand and o 1 in he cons an p, wi h he esul ha he im-
age index will again each i s maximum exp ession wi h 1.
Howe e , and e en bea ing in mind ha he e is no sin-
gle image (Ba ich & Ko le , 1991), we need a measu e which
ells us he mean alue o b and k in a gi en segmen . To
his end, we p opose he calcula ion o an Image Index ha
goes beyond he subjec s and p o ides in o ma ion abou a
homogeneous g oup o consume s. This Image Index o
he b and k migh be:
∑
=n
iikk I
n
I1
Thanks o his index we can p o ide in o ma ion o
b and manage s abou he exis ence o di e ences be ween
he di e en segmen s unde s udy. In ou pa icula case,
and conside ing he alue o a b and image o a ou ism
p oduc , he index p oposed will pe mi he p esen a ion o
esul s by egions o esidence, ou is segmen s (sun-and-
sand ou ism, cul u al ou ism, e c.), e c., acili a ing deci-
sion-making o imp o ed managemen o he ou ism p od-
uc .
Ne e heless, o app oach he concep o image e ec-
i ely, i is necessa y o in oduce one inal conside a ion.
The Image Index (Ijk) p oposed abo e indica es absolu e im-
age, unde s anding he image o a b and k as he sum o i s
indi idual pe cep ions. We a e awa e ha when we come o
assess a b and, i is always necessa y o ake in o accoun he
es o he compe ing ma ke . In o he wo ds, a subjec may
gi e Galicia a sco e o 0.64 ou o 1. Fo his subjec Galicia
will ha e a good image i he es o he des ina ions e alu-
a ed ha e sco es below 0.64, o a leas i he mean a ing o
he des ina ions is below his alue. I i is he case ha he
es o he des ina ions a e a ed abo e 0.64, he image o
Galicia o his subjec , e en i i is posi i e, is wo se han
ha o he o he des ina ions.
(4)
(5)
The abo e commen s necessi a e he in oduc ion o a
inal modi ica ion wi h espec o he p oposed index, which
leads us o p opose he Rela i e Image Index. To achie e an
in e p e a ion on he basis o each subjec ’s ela i e image,
as a unc ion o his o he pe cep ion o he ull se o s im-
uli, index (6) can be exp essed in e ms o he subjec ’s mean
o all s imuli, in such a way ha a esul wi h alue 1 indi-
ca es he mean alue:
(6)
i
ik
kik
ik
ik I
I
I
I
I ==
∑
1 (8)
Fo a g oup o subjec s o ou is segmen , he Rela i e
Image index is as ollows:
∑
=n
iikk I
n
I 1
(9)
Wi h ega d o he in e p e a ion o his new index, he
esul ing alues can be g ouped in o h ee bands:
- I ik < 1: subjec i conside s ha b and k has a ela i e im-
age ha is wo se han expec ed (image wo se han he es
o he des ina ions conside ed). The index e e ed o he
o al sample (I k) ells us ha b and k is being badly a ed
by subjec s.
(7) - I ik = 1: subjec i conside s ha b and k has an in e medi-
a e ela i e image; in o he wo ds, o ha subjec b and k
ep esen s a balanced image: he e a e be e - and wo se-
a ed b ands.
- I ik > 1: subjec i conside s ha b and k has a be e ela-
i e image han expec ed (image be e han he es o he
des ina ions conside ed). I he index is e e ed o he o-
al sample, i ells us ha o mos subjec s, his b and was
a ed be e han he o he des ina ions.
Among he ad an ages o his index, we would s ess
anales de psicología, 2006, ol. 22, nº 1 (junio)
158 Jesús Va ela Mallou e al.
ha an index o image by subjec will enable us o segmen
he esul s o di e en segmen s. Thus, and whe eas a
global index o e s e y li le in o ma ion, pa icula ly when
he e is a lo o a iabili y in he sample, an index by subjec
will allow us o p o ide in o ma ion abou image b oken
down by au onomous communi ies, eason o a el, ou is
p o ile, e c.
Da a collec ion
Fo sample selec ion we used a andom p ocedu e wi h
s a i ica ion by au onomous communi y, age and sex. The
size o he sample used allows us o in e p e he esul s wi h
a con idence le el o 95.5% (Z=1.96; p=q=50) and a sam-
pling e o o ±3.25%. S a ing om he lis o selec ed a -
ibu es, a doo - o-doo su ey was pe o med o 916 sub-
jec s esiden in Spanish na ional e i o y and who had a -
elled a leas once in he las wo yea s. The in e iewees
we e asked o s a e which ou is des ina ion o a lis o
ele en possible des ina ions hey associa ed wi h each o he
abo e-men ioned a ibu es. Wi h espec o da a collec ion,
we conside i impo an o s ess wo aspec s. The i s e-
e s o he ype o da a, since al hough he e a e some au-
ho s who p e e o pe o m s udies o image h ough p o-
ile o me ic da a, we conside ha he use o associa ion
da a imp o es he quali y o in o ma ion collec ion (Va ela e
al. 2004), abo e all when he numbe o s imuli and a ib-
u es is high, as in he p esen case. Secondly, he p oposed
me hodology implies he use o mul iple choice o he im-
age s udy, unlike o he s udies which simply no e which
b and is he bes o each o he a ibu es conside ed. In
his way he image co esponds mo e closely o eali y,
whe e we ind b ands which o a subjec sha e he same a -
ibu e. Thus o example, in he case o he ou ism ma ke ,
an a ibu e like quali y o ho els and accommoda ion may be
sha ed by se e al ou is des ina ions. Con e sely, i we
me ely no e which des ina ion is ha which is mos closely
associa ed wi h ha a ibu e (a single esponse), he image
o less well known des ina ions, des ina ions which a e no
a “ he op o he subjec ’s mind”, will be clea ly disad an-
aged, in iew o he g ea e epu a ion o he ma ke lead-
e s.
Da a analysis
In ou case, and gi en ha we ha e a o al o wen y-one
a ibu es, whe e 19 a e posi i e and 2 a e nega i e ( oo
c owded and oo noisy), he alue o he cons an p in exp es-
sion (6) o calcula ion o b and Image by subjec will be
equal o 0.0952.
0952,0
2
8095,01
2
1=
−
=
−
=
δ
p
Resul s
Absolu e Image Index
The ollowing igu e shows he esul s o applying he
p oposed Image Index o he ma ix o da a o associa ion
be ween s imuli and a ibu es. In he i s case he alues
ep esen ed a e Absolu e Image Indices (Ik), whose alues lie in
he in e al (0,1).
AND PV CAT GAL MAD AST IBA ICA CYL VAL CAN
Des ina ions
0,00
0,10
0,20
0,30
Absolu e Image Index
0,32
0,22
0,28 0,29
0,22
0,24
0,18
0,22
0,18
0,22
0,20
Figu e 1: Absolu e Image Indices o he ele en des ina ions conside ed
The Absolu e Image esul s indica e ha he mos highly
a ed des ina ions a e Andalucia (0.32), Galicia (0.29) and
Ca alonia (0.28), while he Balea ic Islands and he Commu-
ni y o Cas illa y León a e among he leas highly a ed.
Howe e , a de ailed analysis o he abo e igu e shows
ha he absolu e Image Index is no capable o disc imina -
ing adequa ely be ween he image alue o he di e en des-
ina ions. In ac , he di e ence be ween he maximum alue
ob ained, which co esponds o Andalucia, and he mini-
mum, o he Balea ic Islands and Cas illa y León wi h 0.18,
sca cely exceeds 10%. This is due o he ac ha he uppe
and lowe limi s o he in e al conside ed (0,1) a e ic i ious
alues, whe e 0 ep esen s he wo s image possible (s imu-
lus associa ed by all subjec s wi h nega i e a ibu es, in no
case wi h a posi i e a ibu e), and 1 he ideal image (s imu-
lus associa ed by all subjec s wi h posi i e a ibu es, in no
case wi h a nega i e a ibu e). O cou se, we a e awa e ha
i is e y di icul o ob ain hese alues, so ha i is neces-
sa y o pe o m a ans o ma ion, in iew o each subjec ’s
a ings.
Likewise, he Absolu e Image Index is no capable o
disc imina ing be ween des ina ions wi h good image and
des ina ions wi h an image wo se han expec ed. To explo e
he image concep in g ea e dep h, he Image Index p o-
posed should ake accoun o his ype o in o ma ion.
anales de psicología, 2006, ol. 22, nº 1 (junio)
De elopmen o an index o assess he b and image o ou is des ina ions 159
As ega ds he image o he p incipal ou is des ina ions
o Spain, he abo e esul s p o ide much clea e in o ma-
ion. F om he poin o iew o Rela i e Image (i.e. aking
in o accoun he o he des ina ions conside ed), Andalucia is
he mos highly a ed des ina ion, wi h an Image Index o
1.423, ollowed by Ca alonia and Galicia wi h 1.218 and
1.192 espec i ely. As u ias is he ou h des ina ion in e ms
o absolu e image, wi h a alue sligh ly g ea e han 1
(1.008). In he case o he leas highly a ed des ina ions, i
can be no ed ha he Balea ic Isles (0.790), Cas illa y León
(0.793) and Can ab ia (0.834) a e he des ina ions wi h low-
es image alues.
Rela i e Image Index
The second image p oposed: he Rela i e Image Index, is
capable o supplying in o ma ion ha mo e ai h ully ep e-
sen s subjec s’ opinions wi h espec o image. Recall ha
his second index, in addi ion o o e ing an image alue o
each des ina ion, o e s he e e ence alue (1) which allows
us o s a e which des ina ions ha e a posi i e image and
which ha e a poo image. The esul s a e shown in he ol-
lowing igu e.
Al hough he abo e da a ell us abou he o e all image
ha ou is s ha e o he di e en des ina ions conside ed, i
is wo h s essing ha one o he majo ad an ages o his
index is p ecisely he possibili y o p esen ing esul s o di -
e en segmen s. F om his iewpoin , and in line wi h
Ba ich & Ko le (1991), we conside ha a b and does no
ha e only one image, bu a he se e al, implying a need o
p esen he abo e esul s as a unc ion o he segmen s ha
a e o in e es o he managemen o he ou is p oduc in
ques ion. In ou case, and by way o example, we ha e de-
cided o di e en ia e he esul s in e ms o he di e en a -
eas unde s udy.
AND PV CAT GAL MAD AST IBA ICA CYL VAL CAN
Des ina ions
0,800
1,000
1,200
1,400
Rela i e Image Index
1,423
0,945
1,218 1,192
0,932
1,008
0,790
0,938
0,793
0,927
0,834
The abo e able allows us o assess a ia ion in image
depending on egion o esidence. O pa icula in e es a e
he esul s o Ca alonia, whose image is posi i e in all e-
gions excep Andalucia and he no h, whe e i ob ains a
balanced image. The case o Andalucia is especially in e es -
ing. Though i showed a good image (i.e. i was he mos
highly a ed des ina ion), analysing he esul s by a ea we
ind ha in he communi ies o he no h o Spain, Anda-
lucia ob ained a sligh ly wo se a ing han he emaining des-
ina ions (0.9).
Figu e 2: Rela i e Image Indices o he ele en des ina ions conside ed
Table 1:
Rela i e Image
Indices as a unc ion o egion o esidence
ANDALUCÍA
PAIS VASCO
CATALUÑA
GALICIA
MADRID
ASTURIAS
I.BALEARES
I.CANARIAS
CASTILLA Y LE-
ÓN
C. VALENCIANA
CANTABRIA
ANDALUCIA 2.4 0.7 1.0 1.1 1.0 0.9 0.8 1.0 0.7 0.8 0.7
VALEN-
CIA/MURCIA 1.2 0.8 1.2 1.1 1.0 1.0 0.8 0.9 0.7 1.6 0.7
ZONA CENTRO 1.2 0.8 1.1 1.2 0.9 1.0 0.7 1.0 1.1 1.0 0.9
MADRID 1.1 0.9 1.1 1.2 1.3 1.0 0.8 0.9 0.8 0.8 1.0
CATALUÑA 1.5 0.9 1.8 1.2 0.7 0.9 0.8 1.0 0.7 0.7 0.7
ZONA NORTE 0.9 1.6 1.0 1.3 0.8 1.3 0.7 0.9 0.8 0.7 1.0
TOTAL 1.423 0.945 1.218 1.192 0.932 1.008 0.790 0.938 0.793 0.927 0.834
anales de psicología, 2006, ol. 22, nº 1 (junio)

160 Jesús Va ela Mallou e al.
Conclusions
One o he opics ha has acqui ed g ea es impo ance in
ecen yea s in publica ions abou ma ke ing, and speci ically
in he ou is sec o , is ha o image. The a ious models
ha s udy he a el decision p ocess ha e conside ed image
as one o he mos impo an ac o s (Hun , 1975;
Mou inho, 1984; Ga ne , 1986; Woodside & Lysonski,
1989; Chon, 1990; Tapachai & Wa yszak, 2000). Na u ally,
he e a e o he impo an ac o s, such as p ice, dis ance o
ype o p oduc o e ed, bu in gene al we can say ha hose
des ina ions wi h a posi i e image will ha e a highe p ob-
abili y o being conside ed and inally chosen in he p ocess
o selec ion o he des ina ion o isi . These commen s
should encou age us o e lec on he impo ance o conce n
o image, since as no ed by Hebe (1988) “image exis s e-
ga dless, whe he o no he i m wan s i [...]. I a i m does
no cons uc i s own image, o he s will ake i upon hem-
sel es o do so; and no necessa ily in he way we wan ”.
The g owing conce n abou he s udy o image has ans-
la ed in o a majo de elopmen o echniques (Hun , 1975;
Good ich, 1977; C omp on, 1979; Richa dson & C omp on,
1988; Calan one e al., 1989; Ech ne & Ri chie, 1993), no a-
bly quali a i e echniques and mode n s udies o image and
posi ioning by he applica ion o mul i a ia e echniques.
Howe e , i we wish o unde s and image de elopmen , and
quan i y he changes ha image unde goes o e ime and in
esponse o di e en ma ke ing s a egies, we need o ha e a
sui able ool. This Image Index should e lec each b and’s
associa ions in a se ies o a ibu es ha cons i u e he im-
age, and should be sensi i e o changes occu ing.
In he cou se o he p esen s udy we ha e p esen ed a
ool wi h which o comple e analyses o image and posi ion-
ing, and which allows us o quan i y he B and Image o
ou is des ina ions. The s a ing da a ma ix is cons i u ed
by da a on associa ion be ween s imuli (des ina ions) and a -
ibu es, whe e he subjec s mus decide which des ina ion
o des ina ions om a lis hey associa e wi h each o he a -
ibu es indica ed.
S a ing ou om his associa ion da a ma ix, he image
index quan i ies he associa ions ha ha b and shows wi h
each o he a ibu es e alua ed. To his end i conside s he
loading o he a ibu es and he ela i e a ing o each
b and, allowing us o compa e all o he b ands. Among he
ad an ages o his index, we would s ess he ollowing:
1. I compa es he image o he di e en ou is des ina-
ions, since i ela es he image o a des ina ion wi h ha o
i s mos di ec compe i o .
2. I pe o ms an objec i e measu emen o a subjec i e
concep : image. I is capable o summa izing in a single alue
he opinion o he subjec s abou a se ies o a ibu es de-
e mining image.
3. I pe mi s segmen a ion o he esul s by g oups o
ou is s, so ha ma ke ing ac ions can be a ge ed o pa icu-
la subjec g oups and ou is p o iles, ensu ing mo e e ec-
i e esou ce use.
4. Finally, and gi en ha image is dynamic - in o he
wo ds, i changes o e ime - he p oposed Image Index al-
lows us o assess de elopmen o e ime, and design u u e
ma ke ing s a egies in e e ence o ou b and.
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(A ículo ecibido: 1-3-05; acep ado: xx-x-06)
anales de psicología, 2006, ol. 22, nº 1 (junio)