Technology In o ma ion and Science Managemen o Jou nal In e na ional The
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Po ugal in e io , Bei a o Uni e si y Fe ei a, M. J. João Edi o -in-Chie :
Edi o s: Main
USA Memphis, o Uni e si y and Po ugal Lisbon, o Ins i u e Uni e si y Fe ei a, F. A. Fe nando
Spain Ba celona, o Uni e si y Lindahl, Me igó M. José
Edi o s: Assis an
Uni e si y, Po ucalense and (UBI) Sciences Business in Uni -Resea ch NECE a Reseache Fe nandes, C is ina
Po ugal
UK Reading, o Uni e si y Co, Jess
Po ugal Lisbon, o Ins i u e Uni e si y Jalali, S. Ma jan
Boa d: Ad iso y Edi o ial
UK Managemen , o School Ca di Lincoln, Adebimpe
Is ael College, Academic Ne anya Tzine , Aha on
USA Pennsyl ania, Uni e si y, Mo is Robe Smi h, D. Alan
Spain Ba celona, o Uni e si y La uen e, G. Ma ia Ana
No way Managemen , o School Oslo Ma iussen, Anas asia
Spain Ba celona, de Au ònoma Uni e si a Ta és, i Se a ols Ch is ian
UK uni e si y, Ci y -Bi mingham School Business Millman, Cindy
Romania Bucha es , o Uni e si y Gh, Popescu R. C is ina
UK School, Business Uni e si y Newcas le I awa i, Dessy
Spain Valencia, o Uni e si y Ribei o, Domingo
USA Business, o Schools Ca ayannis, G. Elias
USA Uni e si y, Technological Michigan Oli ei a, Emanuel
Spain Se ille, o Uni e si y Liñán, F ancisco
UK Uni e si y, Ci y Bi mingham Ma lay, Ha y
Romania S udies, Economic o Uni e si y Bucha es The Pu ca ea, I ina
HK Uni e si y, Poly echnic Kong Hong The Choi, Jason
Spain Valencia, o Uni e si y Vila, Jose
Canada Mon éal, HEC Filion, Jacques Louis
I aly II, Fede ico Naples o Uni e si y Landoli, Luca
B azil Paulo, Säo de Uni e sidade a Resea che Sakuda, Ojima Luiz
Po ugal In e io , Bei a o Uni e si y Raposo, L. Má io
Spain València, de Poli ècnica Uni e si a Pe is-O iz, Ma a
Zealand New Waika o, o Uni e si y The Akoo ie, Michele
Canada T ois-Ri iè es, à Québec du Uni e si é Julien, Pie e-And é
Jo dan Uni e si y, Hashemi e The Ka absheh, Radwan
Zimbabwe Technology, and Science o Uni e si y Na ional Mhlanga, Richa d
B azil – Va gas Ge ulio Fundação Bandei a-de-Mello, Rod igo
Ne he lands The - Uni e si y Tilbe g Ru en, Roel
Ve de Cabo Emp esa iais, e Económicas Ciências de Supe io Ins i u o C uz, Rosa
Ne he lands The Ro e dam, Uni e si y E asmus Thu ik, Roy
India Delhi, Technology o Ins i u e Indian Jain, K. Sudhi
Po ugal In e io , Bei a o Uni e si y Aze edo, G. Susana
Denma k Uni e si y, Business Copenhagen Hollensen, S end
Aus ia Vienna, o Uni e si y F isch, Wal e
USA Uni e si y, S a e Colo ado By ne, S. Zin a
Boa d Re iew Edi o ial
Tu key Tu key, Uni e si y Selçuk Ögü , Adem
G eece A hens, o Uni e si y Ag icul u al Side idis, B. Alexande
Ne he lands The Ams e dam, Uni e si y VU Sha panskykh, Alexei
USA Yo k, -Yo k, Uni e si y S a e Pennsyl ania Ka a, Ali
B azil Rio, G ande Uni e sidade F ei as, Angilbe o
Po ugal In e io , Bei a o Uni e si y Paço, do A minda
Finland Jy äskylä, o Uni e si y Ojala, A o
Po ugal Dou o, Al o e T as-os-Mon es o Uni e si y Ma ques, Ca la
Tu key Uni e si y, Çuku o a Tano a, Cem
B azil Ca a ina, San a de Fede al Uni e sidade Tol o, C is iano
Po ugal B anco, Cas elo o Ins i u e Poly echnic Es e ão, S. C is ina
C oa ia Spli , o Uni e si y Mioce ic, Da io
Zealand New School, Business Auckland o Uni e si y The Aska any, Da ood
USA Washing on, o Uni e si y Re e e, Deb a
USA Ohio, Cincinna i, o Uni e si y Go mley, Kolesa Denise
Kong Hong Technology, and Science o Uni e si y Kong Hong Chiu, K.W. Dickson
Spain Na a a, o Uni e si y Melé, Domènec
B azil School, Business FUCAPE Maina des, Eme son
USA Uni e si y, A izona No he n O enyo, E. E ic
USA Uni e si y, Illinois Sou he n Wa son, W. Geo ge
B azil Ma ia, San a de Fede al Uni e sidade Mou a, de Luiz Gilnei
China Uni e si y, Psychology,Zhejiang o Depa men Zhong, An Jian
Po ugal Lisbon, Uni e si y, Ca holic Po uguese Sciences, Human o Facul y Pin o, Ca nei o Joana
Spain Valencia, o Uni e si y Aleg e, Joaquín
USA Je sey, New Business, o School Anisfield Rako obe, Thie y Joel
USA , FL San o d, Flo ida, Cen al o Uni e si y Ma usi z, Jona han
India Kha agpu , Technology o Ins i u e Indian S i as a a, L. B. Kailash
Ne he lands Twen e,The o Uni e si y Sande s, Ka in
Ge many Koblenz-Landau, o Uni e si y T oi zsch, G. Klaus
China Nanjing, Technology, o Uni e si y Nanjing Shi, Kui an
Po ugal ISLA, Fa ia, Cos a da Liliana
Canada On a io, Wes e n o Uni e si y Cap e z, Fe nando Luiz
USA Business, o College Godkin, Lynn
Canada Winnipeg, o Uni e si y Liu, Chunhui Maggie
Belgium Liège, o Uni e si y Ausloos, Ma cel
USA Texas, Uni e si y,Den on, Woman's Texas Benham-Hu chins, Ma ge
Spain G anada, o Uni e si y Pé ez-A ós egui, Nie es Ma ía
I aly Udine, o Uni e si y Cagnina, Rosi a Ma ia
Uni e si y,Taiwan Hwa Dong Na ional Taba a, Mayumi
Po ugal Uni e si y, Lusíada and Uni e si y Po ucalense Pinho, Micaela
I aly Basilica a, o Uni e si y Renna, Paolo
Po ugal Coimb a, o Uni e si y Cunha, Rupino Paulo
Ge many Uni e si y, Saa land Loos, Pe e
Spain Vigo, de Emp esas de Adminis ación e Economia de F. Ga cía, Piñe o Pila
Romania Bucha es , S udies, Economic o Uni e si y Bucha es Gheo ghe, N. Popescu
Economic o Uni e si y Bucha es The and Sa u-Ma e o Academy Comme cial The Ad iana, Ve onica Popescu
Romania Bucha es , S udies,
India Technology, and Managemen o Ins i u e Singh, Ramanjee
Po ugal o Uni e si y Ca holic Mo ais, Rica do
Spain Rioja, o Uni e si y O iz, Fe nández Ruben
Canada Mani oba, o Uni e si y Thulasi am, K. Ruppa
USA NJ, Uni e si y,Mon clai , S a e Mon clai Kim, Soo
Taiwan Uni e si y, Ya -Sem Sun Na ional Chiou, Wen-Bin
USA GA, ,Augus a, College Paine Lawless, Willaim
Singapo e Uni e si y, Managemen Singapo e Koh, T.H. Wins on
Table o Con en s

This is one pape o
The In e na ional Jou nal o Managemen Science and
In o ma ion Technology (IJMSIT)
Issue 3 - (Jan-Ma 2012)
The In e na ional Jou nal o Managemen Science and In o ma ion Technology (IJMSIT)
Issue 3 - (Jan-Ma 2012) (38 - 62)
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A STUDY OF CONSUMER PREFERENCES FOR E-RETAILERS’
ATTRIBUTES: AN APPLICATION OF CONJOINT ANALYSIS.
BEGOÑA PERAL PERAL
JOAQUINA RODRÍGUEZ-BOBADA REY
ANGEL FRANCISCO VILLAREJO RAMOS
UNIVERSIDAD DE SEVILLA
The In e na ional Jou nal o Managemen Science and In o ma ion Technology (IJMSIT)
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ABSTRACT
The aim o his wo k is o de e mine and analyse consume p e e ences ega ding he p o iles o
an e- e aile ’s web page. Two ypes o p oduc s a e examined, a pleasu e ip and a lap op compu e , o
es whe he he e a e di e ences in he indi iduals’ p e e ences. The e a e wo easons o his choice:
hese wo p oduc s a e pu chased he mos o e he In e ne in Spain and he di e en mo i es o buying
hem hedonic-pleasu able and u ili a ian. We conduc ed an ini ial s udy, om which we iden i ied he
p incipal a ibu es alued by he pa icipan s in he su ey. These a ibu es we e hen used o design he
p o iles o he conjoin analysis. The a iables ha a e mos ele an o he shopping ask a e hose which
ecei e a highe esponse equency. In bo h p oduc s, he a ibu es ha a e mos alued by he
pa icipan s a e he i ual s o e’s secu i y and p i acy policy. Howe e , o a lap op compu e ,
consume s also emphasize he impo ance o he p o ision o he echnical de ails o he p oduc and he
ac ha he supplie also has a physical s o e. We ecommend ha e- e aile s’ web pages need o cla i y
and acili a e access o he mos ele an a iables o he shopping ask. Likewise, public ins i u ions and
e- e aile s need o con inue o wo k owa ds minimising non-buye s’ ejec ion o online pu chasing and
hei ea s ega ding secu i y on he Web. Fi ms wi h bo h physical and online ou le s ha e an impo an
compe i i e ad an age o e pu e-playe s, o ce ain p oduc s a leas .
Keywo ds
e- e aile s, consume beha iou , conjoin analysis.
INTRODUCTION
Elec onic comme ce has made apid and adical changes o he way we make ou pu chases
oday. P oo o his is he €2.322 million -wo h o goods and se ices pu chased o e he In e ne in
Spain in he second imes e (CMT, 2011). Howe e , i ms should no ocus solely on inc easing hei
In e ne sales, bu a he , as Rus e al. (2001) indica e, conside he po en ial sales hey a e missing ou on
because hey a e no o e ing wha he consume wan s. P e ious s udies ha e analysed he ela i e
impo ance o he a ibu es o a websi e, unde s ood as “ hose ac o s bo h unc ional and psychological
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ha exis in an online s o e” (Lim and Dubinsky, 2004, p.501). Palme (2002) and Yun and Good (2007)
ound ha no all o a web page’s a ibu es will ecei e he same a ou able esponse om online
consume s. I is essen ial he e o e o unde s and how cus ome s e alua e di e en a ibu es when hey
decide o make an online pu chase [Iqbal e al. (2003); Ba e al. (2005); Shun and Yunjie (2008)]. In
o he wo ds, elec onic comme ce needs o iden i y and ocus on de eloping a ibu es which inc ease
alue o he cus ome [Han and Han (2002); Su (2007)].
The speci ic aim o his esea ch is o iden i y and analyse consume s’ p e e ences o he
di e en a ibu es o e- e aile s’ web pages. P io s udies ha e ocused on unde s anding whe he a
consume ’s p e e ence o online shopping changes wi h di e en ypes o p oduc s (Ko gaonka e al.,
2006). In his in es iga ion, he p oduc s we ha e chosen a e pleasu e ips and lap op compu e s because
hese a e he i ems ha a e pu chased he mos o e he In e ne in Spain. In 2010, 52.4% and 42.93% o
In e ne shoppe s bough a el icke s and booked accommoda ion espec i ely; and elec onic p oduc s
in gene al we e pu chased by one in ou In e ne shoppe s in Spain (ONTSI, 2011). Ano he eason o
his choice is he di e en mo i es o buying hese wo p oduc s, espec i ely being, hedonic-pleasu able
and u ili a ian. This dis inc ion be ween he ype o p oduc analysed allows us o ca y ou a conjoin
analysis o es whe he he e a e di e ences in he impo ance gi en o di e en a ibu es.
Following a li e a u e e iew, we explain he me hodology used and he easons o ou choice.
We hen se ou he esul s o ou conjoin analysis and p opose a numbe o a gumen s and implica ions
o hei de elopmen . Las ly, he s udy’s limi a ions and u u e lines o esea ch will be discussed.
CONCEPTUAL BACKGROUND
To unde s and a consume ’s choice o e- e aile , we mus conside he ela i e impo ance ha
consume s gi e o i s a ibu es a he ime o pu chase (Lim and Dubinsky, 2004). P io s udies ha e
analysed he a ibu es o he online s o e as p edic o s o he consume s’ in en ion o buy [Ba e al.,
(2005); Su, (2007)], hei sa is ac ion, hei accep ance o new echnology (Song and Zinkhan, 2003),
hei a i ude owa ds online pu chases (Lim and Dubinsky, 2004) and cus ome loyal y [Zei haml e al.,
(2002); Yun and Good, (2007)]. We now discuss he dimensions p oposed in p e ious in es iga ions,
ci ed below: me chandise, con enience, in e ac i i y, na iga ion, eliabili y, p omo ions and design.
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3. Resul s o ini ial s udy
We ecei ed 140 alid ques ionnai es, om s uden s en olled in wo deg ee cou ses, o whom
65% we e women and 92% o he sample was unde 27 yea s o age, 65% o he pa icipan s had made an
online pu chase in he p e ious yea and 78% in ended o do so o e he coming yea . The pa icipan s
indica ed he mos impo an a ibu es o a i ual s o e (Table 2) some o which a e common o bo h
p oduc s, while o he s show s a is ically- signi ican di e ences acco ding o he p oduc ii.
Table 2. Ini ial s udy: Pe cen ages o choice o a ibu es
Pleasu e ip
Lap op compu e
A ibu esiii
% o choice
% o choice
P ice o he p oduc / se ice
77.857
80.714
P oduc gua an ee and e u ns policy
64.286
77.857
Da a secu i y and p i acy policies
61.429
52.857
Paymen in o ma ion
55.000
40.714
P oduc ’s images
46.429
53.571
Exis ence o al e na i e paymen
45.714
46.429
Make phone o e-mail con ac
40.714
38.571
Op ion o ese e p oduc s
39.286
17.857
Company epu a ion
38.571
36.429
Pos ing cus ome e iews
37.857
30.714
In o ma ion on how o buy
36.429
30.714
In o ma ion on pos age and packing cos s
32.143
40.000
Technical p oduc desc ip ion
30.714
61.429
Well-know b ands
20.000
32.143
Physical s o e dis ibu o
17.142
32.143
The esul s show ha he abili y o make a ese a ion (co =-0.237, sig=0.000) and paymen
op ions in o ma ion (co =-1.143, sig=0.017) a e gi en he g ea es alue in he case o a el. Fo a
lap op compu e , he mos equen ly selec ed a ibu es a e he p o ision o echnical in o ma ion
(co =0.308, sig=0.000), ha ing a b icks and mo a p esence (co =0.174, sig=0.003), s ocking well-
known b ands (co =0.138, sig=0.021) and o e ing a p oduc gua an ee and e u ns policy (co =0.150,
sig=0.012).

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Fu he mo e, o each p oduc we analysed whe he he e we e any signi ican co ela ions
be ween a ibu es. This is because a ac ional ac o ial design (Hai e al., 2000) mus be o hogonal, ha
is, he e should be no co ela ion be ween a ibu es. The 2- ailed signi icance o he Fishe s a is ic
indica ed ha he e was a ela ionship be ween some o he a ibu es: o he a el p oduc , he
co ela ed pai s we e paymen in o ma ion and he exis ence o se e al paymen op ions (2- ailed sig.
=0.011); and he i m’s epu a ion and p ice (2- ailed sig. =0.039). In he case o he lap op, he ela ed
a ibu es we e in o ma ion on how o pay and p ice (2- ailed sig. =0.015); and epu a ion and p ice (2-
ailed sig. =0.001). We he e o e emo ed he in o ma ion on how o pay and he i m’s epu a ion,
because, in addi ion o hei s a is ical signi icance, we ecognise he concep ual ela ionship be ween
each espec i e pai .
4. Applica ion o conjoin analysis
The i s s ep was o selec he mos impo an a ibu es – hose which we e mos equen ly
chosen in he pilo s udy [Baue and Scha l, (2000); Ba e al., (2005)]. Then, o limi he amoun o
in o ma ion gi en o he esponden s (a high numbe o a ibu es gene a es a g ea numbe o p o iles o
he pa icipan s o e alua e), we chose a numbe o a ibu es ha would gi e he igh balance. Finally,
because o he signi ican di e ences, we used a di e en lis o a ibu es o each p oduc . The e we e
nine in o al: wi h se en a ibu es in common and wo which we e di e en o each p oduc . The nex
s ep was o decide on he le els o each a ibu e, making hem ealis ic in o de o inc ease he alidi y
o he p e e ences (Table 3).
Table 3: A ibu e and a ibu e le els
A ibu es
A ibu e le els
P ice o p oduc /se ice
High p oduc s p ice
Medium p oduc s p ice
Low p oduc s p ice
P oduc gua an ee and e u ns policy
Yes
No
Da a secu i y and p i acy policies
Bo h policies
Ei he policy
No policy
Common o bo h
p oduc s
P oduc ’s images
Yes
No
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Exis ence o al e na i e paymen
C edi ca d
Cash paymen
Pay by ins almen s
Op ions o make phone o e-mail con ac
Yes
No
In o ma ion on pos age and packing cos s
Yes
No
Op ion o ese e p oduc
Yes
No
Only o pleasu e
ip
Pos ing cus ome e iews
Yes
No
Technical p oduc desc ip ion
Yes
No
Only o lap op
compu e
Physical s o e dis ibu ion
Yes
No
Ou s udy consis s o six a ibu es wi h wo le els and h ee a ibu es wi h h ee le els, o each p oduc
analysed, gi ing a possible 1,728 combina ionsi . Gi en he di icul y o e alua ing such a high numbe o
combina ions we used a ac ional ac o ial design, which p o ides an app op ia e ac ion o all he
possible combina ions o he a ibu e le els. The o hogonal ma ix was designed using he SPSS 17.0
O hoplan p ocedu e, which cap u es he main e ec s o each a ibu e. This ma ix consis s o eigh een
p o iles, six een o which we e used o es ima e he model pa ame e s and he emaining wo we e used o
alida e he esul s.
We used he ull p o ile me hod o da a collec ion. Each pa icipan was asked o ank he
eigh een combina ions i on a scale o 1 (leas p e e ed) o 7 (mos p e e ed). The p e e ence model
chosen is he pa -wo h unc ion model, which is sui able o ca ego ical da a. To es ima e he model’s
pa ame e s, he ela i e impo ance o he a ibu es and he pa ial u ili y o he le els, we used he SPSS
17 Conjoin P ocedu e. Fo he in e nal alida ion measu es we used he Pea son co ela ion coe icien
and he Kendall au coe icien .
The popula ion sample used o ob ain he da a consis ed o uni e si y s uden s, who had no
pa icipa ed in he ini ial ques ionnai e. The con enience sample includes s uden s om i e uni e si y
cou ses, which co e s a b oad spec um o he s uden popula ion. We con ac ed 290 pa icipan s, and
ecei ed 274 esponses in he case o choosing a pleasu e ip and 270 o he lap op compu e . The
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esponden s’ demog aphic da a is shown in Table 4. The sample desc ip i es o bo h he pilo s udy and
he conjoin analysis we e e y simila and we can he e o e assume ha i is accep able o apply he mos
impo an a ibu es o he choice o an e- e aile ha we e iden i ied in he pilo s udy o he conjoin
analysis.
Table 4. Samples cha ac e is ics (in pe cen ages)
Cha ac e is ics
Inicial s udy
(n=140)
Pleasu e ip
(n=274)
Lap op compu e
(n=270)
18 o 27 yea s
92
92
91,5
Age
27 + yea s
8
8
8.5
Female
65
63.6
63.5
Sex
Male
35
36.4
36.5
Yes
65
65.7
67.8
P e ious online
pu chase
No
35
34.7
32.2
Yes
78
78.1
80
Fu u e pu chase
in en ion
No
22
219
20
ANALYSIS OF THE RESULTS OBTAINED FROM THE CONJOINT ANALYSIS
In he example o a pleasu e ip, he a ibu es ha a e mos alued by he pa icipan s a e he
i ual s o e’s secu i y and p i acy policy, ha ing a p oduc p ice lis and a ailabili y o p oduc images
(Table 5). The pa icipan s’ sa is ac ion wi h each le el o he a ibu es enables us o iden i y he
sample’s p e e ed online e aile . This will be he s o e ha combines he le els wi h he g ea es pa ial
u ili y: p o iding low p oduc p ices, a p oduc gua an ee and e u ns policy, p oduc images, da a
secu i y and p i acy policies, he op ion o he cus ome o pay by ins almen s, he op ion o make
elephone o e-mail con ac , clea ly isible pos age and packing cos s, he op ion o ese e p oduc s and
pos ing cus ome e iews. The o al o he pa ial u ili ies o hese le els indica es he o al u ili y
a ibu ed o he p e e ed online s o e, plus he cons an , which gi es a o al alue o 5.836 ii.
Table 5. Rela i e a ibu e impo ance and pa -wo h u ili ies o a ibu e le els. Pleasu e ip.
A ibu es
Rela i e
impo ance
Le els
Pa -wo h u ili y
es ima es
S d.e o
1
-0.201
0.099
2
-0.123
0.130
P ice o p oduc /se ice
13.565
3
0.324
0.130
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1
0.368
0.074
P oduc gua an ee and e u ns policy
11.183
2
-0.368
0.074
1
0.394
0.074
P oduc ’s images
11.364
2
-0.394
0.074
1
0.383
0.117
2
0.182
0.140
Da a secu i y and p i acy policies
17.013
3
-0.565
0.117
1
-0.039
0.099
2
0.001
0.116
Exis ence o al e na i e paymen
10.361
3
0.038
0.116
1
0.370
0.080
Make phone o e-mail con ac
10.621
2
-0.370
0.080
1
0.335
0.074
In o ma ion on pos age and packing
cos s
10.268
2
-0.335
0.074
1
0.259
0.074
Op ion o ese e p oduc
8.390
2
-0.259
0.074
1
0.218
0.080
Pos ing cus ome e iews
7.235
2
-0.218
0.080
Cons an = 3.147. Pea son´s R: Value = 0.990, sig. =0.000.
Kendall´s au: Value = 0.883, sig. =0.000. Kendall´s au o holdou s: Value = 1, sig. =0.000
As o he eliabili y o he esul s, he Pea son co ela ion coe icien is 0.990, and he Kendall
au is 0.883, which indica es ha he esul s ob ained a e eliable. Kendall au coe icien o he wo
holdou p o iles and hei alue o 1 con i ms he alidi y o he esul s.
In he case o he lap op compu e , he a ibu es gi en he highes alue by he pa icipan s a e he da a
secu i y and p i acy policies, he p oduc p ice and he p oduc gua an ee and e u ns policy (Table 6).
The le els ha comp ise he ideal p o ile a e low p oduc p ices, a p oduc gua an ee and e u ns policy,
p oduc images, he implemen a ion o a da a secu i y and p i acy policies, he abili y o pay by
ins almen s, he abili y o con ac he s o e by elephone o e-mail, clea ly isible pos age and packaging
cos s and a physical s o e. The sum o he pa ial u ili ies o hese le els is 5.68 iii, which is he highes
global u ili y ha a p o ile can a ain.
Table 6: Rela i e a ibu e impo ance and pa -wo h u ili ies o a ibu e le els. Lap op compu e .
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A ibu es
Rela i e
impo ance
Le els
Pa -wo h u ili y
es ima es
S d.
e o
1
-0.102
0.118
2
-0.075
0.138
P ice o p oduc /se ice
12.762
3
0.177
0.138
1
0.365
0.088
P oduc gua an ee and e u ns policy
11.356
2
-0.365
0.088
1
0.341
0.088
P oduc ’s images
10.034
2
-0.341
0.088
1
0.315
0.118
2
0.243
0.138
Da a secu i y and p i acy policies
16.003
3
-0.558
0.138
1
-0.064
0.118
2
0.158
0.138
Exis ence o al e na i e paymen
10.565
3
-0.093
0.138
1
0.255
0.088
Make phone o e-mail con ac
8.062
2
-0.255
0.088
1
0.297
0.088
In o ma ion on pos age and packing
cos s
9.315
2
-0.297
0.088
1
0.384
0.088
Technical p oduc desc ip ion
11.270
2
-0.384
0.088
1
0.350
0.088
Physical s o e dis ibu ion
10.633
2
-0.350
0.088
Cons an = 3.038. Pea son´s R: Value= 0.986, sig. =0.000
Kendall´s au: Value= 0.900, sig. =0.000. Kendall´s au o holdou s: Value= 1, sig. =0.000
Wi h ega d o he eliabili y o he esul s, he Pea son co ela ion coe icien alues, Kendall au (0.986;
0.9) and Kendall au o he holdou p o iles show ha he esul s a e eliable.
Finally, we compa ed he esul s om conjoin analysis wi h he eplies om he 257 s uden s who
esponded o bo h p oduc s [Keen, e al. (2004); Chiam e al. (2009)]. Fou o he a ibu es a ained
simila and ha e high e alua ions in bo h cases (Table 7).
Table 7: Rela i e a ibu e impo ance and pa -wo h u ili ies o a ibu e le els o bo h p oduc s.
(Sample size = 257 esponden s).

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Pleasu e ip
Lap op compu e
A ibu es
Rela i e
impo ance
Pleasu e ip
Rela i e
impo an e
Lap op
compu e
Le els
Pa -wo h
u ili y
es ima es
S d.
E o
Pa -wo h
u ili y
es ima es
S d.
E o
1
-0.212
0.098
-0.103
0.119
2
-0.125
0.129
-0.074
0.138
P ice o
p oduc /se ice
13.522
12.765
3
0.337
0.129
0.177
0.138
1
0.381
0.074
0.365
0.089
P oduc gua an ee
and e u ns policy
11.351
11.357
2
-0.381
0.074
-0.365
0.089
1
0.402
0.075
0.342
0.088
P oduc ’s images
11.570
10.038
2
-0.402
0.075
-0.342
0.088
1
0.375
0.116
0.314
0.120
2
0.177
0.140
0.245
0.138
Da a secu i y and
p i acy policies
16.826
16.001
3
-0.552
0.116
-0.559
0.138
1
-0.051
0.099
-0.065
0.118
2
0.002
0.115
0.159
0.139
Exis ence o
al e na i e
paymen
10.412
10.564
3
0.049
0.115
-0.094
0.139
1
0.364
0.081
0.255
0.087
Make phone o e-
mail con ac
10.389
8.063
2
-0.364
0.081
-0.255
0.087
1
0.339
0.074
0.299
0.088
In o ma ion on
pos age and
packing cos s
10,408
9.312
2
-0.339
0.074
-0.299
0.088
1
0.257
0.073
-
-
Op ion o ese e
p oduc
8.35
-
2
-0.257
0.073
-
-
1
0.220
0.079
-
-
Pos ing cus ome
e iews
7.182
-
2
-0.220
0.079
-
-
1
-
-
0.386
0.087
Technical p oduc
desc ip ion
-
11.271
2
-
-
-0.386
0.087
1
-
-
0.350
0.088
Physical s o e
dis ibu ion
-
10.628
2
-
-
-0.350
0.088
The mos impo an a ibu e is he conce n o p i acy and secu i y, which was acco ded he
highes alue in he a el ca ego y. Acco ding o Ba e al. (2005, p.135), p i acy is gi en a g ea e
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alue o p oduc s which in ol e sensi i e da a, such as a ip, since his equi es in o ma ion such as a
cus ome ’s whe eabou s and ac i i ies. In second place is p icing in o ma ion, which is also mo e
impo an in he pleasu e ip example (Chiam e al., 2009). Two o he a ibu es, he p oduc gua an ee
and e u n policy and in o ma ion on paymen me hods, a e gi en simila ela i e impo ance. The e a e
di e ences in he ela i e impo ance gi en o he o he a ibu es. Whe eas o he pleasu e ip, p oduc
images is he hi d mos impo an , o he lap op, he impo an a ibu es a e he echnical desc ip ion o
he p oduc and he ac ha he supplie also has a physical s o e. These esul s may be explained by he
p oduc ype. In he case o he compu e , he indi idual is conside ed o be in a si ua ion o high a ional
in ol emen , which means ha he sea ch o in o ma ion is ocused mo e on he echnical aspec s o he
p oduc .
Fo he ip, on he o he hand, his can be iewed as a si ua ion equi ing high emo ional
in ol emen o he indi idual, in which he e is a sea ch o in o ma ion, bu wi h a g ea e ocus on he
hedonic-pleasu able elemen s. The di e en a ibu e le els p esen simila pa ial u ili ies, excep in he
case o he paymen me hods, since o he pleasu e ip he e is a p e e ence o paying by ins almen s,
whe eas o he compu e he op ion o paying up on has a g ea e u ili y.
In o de o es whe he he e a e signi ican di e ences in he pa icipan s’ choices o he wo p oduc s,
and gi en ha he samples a e ela ed, we applied he Wilcoxon signed- ank es o he se en a ibu es
ha a e common o bo h p oduc ypes and hei le els (Table 8).
Table 8: Wilcoxon signed- ank es .
Rela i e a ibu e impo ance
T ip/lap op
Pa -wo h u ili y es ima es
T ip/lap op
Z
-2.028(a)
-0.071(b)
Asymp. Sig. (2- ailed)
0.043
0.943
a Based on posi i e anks. b Based on nega i e anks.
The esul s show ha he e a e no signi ican di e ences in he pa ial u ili ies o he le els o he
wo p oduc ypes, bu he e a e di e ences in he ela i e impo ance o he a ibu es. The sum o he
ela i e impo ance o he se en a ibu es analysed is 84.48% in he case o he ip and 78.1% o he
lap op compu e . This means ha , in he case o he lap op, he pa icipan s conside ha almos 22% o
an e- e aile ’s o al impo ance is de i ed om he p o ision o he echnical de ails o he p oduc and he
exis ence o a physical s o e. Simila ly, he e a e di e ences in he impo ance gi en o he a ibu es. In
he case o he pleasu e ip, i e ou o he se en a ibu es analysed ob ain highe alues in his espec
han o he lap op. Only he exis ence o al e na i e paymen op ions is conside ed o be sligh ly mo e
impo an han in he case o he ip. Finally, u ning o he cha ac e is ics o he esponden s, we es ed
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o di e ences be ween he consume s’ p e e ences. The a iables used o segmen he sample we e
gende and p e ious expe ience in In e ne pu chasing. Using hese a iables, no s a is ically-signi ican
di e ences we e ound in he ela i e impo ance o he a ibu es o in he pa ial u ili y o he le els, o
ei he p oduc .
DISCUSSION, PRACTICAL IMPLICATIONS AND LIMITATIONS
Fi s ly, om he esul s o he ini ial s udy o de e mine he a ibu es o he p o iles o he
conjoin analysis, i is clea ha he leas impo an a ibu es a e aspec s o a web page’s design o he
a iables which ha e li le ele ance o he ask (E oglu e al., 2001). Howe e , he a iables which assis
he shopping ask, such as p ice, p oduc gua an ee and e u ns policy, he da a secu i y and p i acy
policies, in o ma ion on how o buy, p oduc images o echnical desc ip ion, all achie ed high selec ion
pe cen ages om he pa icipan s. These a iables ha e a u ili a ian mo i e and include all he web page
desc ip o s ( e bal o pic o ial) which appea on he sc een, making i easie o consume s o achie e
hei pu chasing aims. E- e aile s he e o e need o make i easy o iden i y and access he a iables on
hei web pages which a e mos ele an o he ask, gi en ha online consume s need his in o ma ion in
o de o make he decision o buy. Ne e heless, he a iables ela ing o he a ac i eness o he web
page should no be o e looked.
Secondly, al hough he secu i y and p i acy policies a e no he mos impo an a ibu e in he ini ial
s udy, gi en he esponse equency ob ained o he wo p oduc s analysed, he esul s o he conjoin
analysis show ha his is he a ibu e o he web page which is gi en he highes ela i e impo ance. In
ac , acco ding o ONTSI (2008), he isk a ached o da a secu i y and con iden iali y is one o he
speci ic a gumen s ha non-pu chase s main ain agains online pu chasing. The e o e public ins i u ions
wi h policies o suppo elec onic comme ce and he i ms which sell hei p oduc s and se ices on he
In e ne should con inue s i ing o minimise consume s’ ejec ion o online pu chasing and hei
conce ns ega ding he p oblem o online secu i y. They should publicise he ad ances made in
gua an eeing he p i acy o pe sonal da a.
Thi dly, we can e i y ha he e a e di e ences in he ela ionship be ween p oduc ype and he
mos aluable a ibu es o a web page. Thus, in he case o he lap op, he echnical desc ip ion o he
p oduc and he ac ha he e- e aile also ope a es om a physical s o e a e gi en high impo ance. In
ac , one o he undamen al easons o In e ne use s no shopping on he web is hei p e e ence o
physical s o es, whe e hey can see wha hey a e buying and can ga he all he echnical and comme cial
in o ma ion ha hey belie e o be impo an .
The e o e, companies wi h physical s o es and an In e ne ou le ha e signi ican compe i i e
ad an age o e he pu e-playe s, o ce ain p oduc s a leas , since consume s p e e hese websi es.
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Thus, in Ap il 2010, he EU app o ed he new Ve ical Res ain s Block Exemp ion Regula ion, which
allows b and owne s o p e en he sale o hei p oduc s on In e ne si es which do no also ha e a
physical p esence. They could, o example, choose supplie s wi h a b icks and mo a s o e, in o de o
p esen a uni o m sales en i onmen .
Fou hly, al hough –in common wi h Bha naga and Ghose (2004) – we ha e been unable o
p o e any di e ences in he esponden s’ p e e ences acco ding o hei cha ac e is ics. Conjoin analysis
is a me hodology which p o ides manage s wi h use ul in o ma ion which hey can apply o hei web
page design s a egy o di e en consume segmen s. Equally, web pages could be ailo ed acco ding o
aspec s such as he consume ’s p e ious expe ience as an online shoppe (Zhu and Zhang, 2010) o age
(Kim and Fo sy he, 2010). The In e ne is a channel o ma ke ing, in o ma ion and assis ance which
allows a high deg ee o adap a ion o each clien ’s p o ile, o help sa is y all o hei consume needs.
Ano he al e na i e is p oposed by Kamaku a e al. (1994), Bha naga and Ghose (2004) and
Ramaswamy and Cohen (2007), who sugges ha he in o ma ion ega ding pa ial u ili ies ob ained
h ough conjoin analysis could be use ully applied o la en class models. These models could be used in
u u e in es iga ions o iden i y segmen s which desc ibe he In e ne shoppe acco ding o cha ac e is ics
ha a e no known a p io i o he esea che . Once hese segmen s ha e been iden i ied, he socio-
demog aphic cha ac e is ics o he indi iduals ha comp ise each segmen could be analysed. This would
make i possible o iden i y whe he signi ican di e ences exis be ween he cha ac e is ics o he
indi iduals in each segmen , he eby acili a ing web page design imp o emen s and p o iding
app op ia e in o ma ion ela ing o he equi emen s o each segmen .
Finally, he limi a ions o his esea ch a ise om he me hodology used. The numbe o a ibu es
and le els mus be decided by he in es iga o : a conjoin analysis canno be ca ied ou using a high
numbe o hem, since he ac o ial design would p oduce oo many p o iles o he indi idual o e alua e.
Ano he limi a ion e e s o he ac ha we canno gene alize he esul s as we ha e used a con enience
sample o uni e si y s uden s.
CONCLUSION
In his s udy we ha e analysed consume s’ p e e ences ega ding he a ibu es buil in o e-
e aile s’ web pages. Unlike p e ious esea ch, whose dimensions encompass se e al a ibu es, making i
di icul o unde s and he impo ance asc ibed o each one, ou s udy used simple a ibu es which can be
easily e alua ed by indi iduals. This a o ds a be e unde s anding o how hey ac ually make hei
choices, maximising he global u ili y o each a ibu e. We also p o ed ha he ype o p oduc analysed
in luences he impo ance o he e- e aile ’s a ibu es. In bo h p oduc s, he a ibu es ha a e mos alued