a critical review of the e-satisfaction literature
TRANSCRIPT
http://abs.sagepub.com/American Behavioral Scientist
http://abs.sagepub.com/content/52/1/38The online version of this article can be found at:
DOI: 10.1177/0002764208321340
2008 52: 38American Behavioral ScientistQimei Chen, Shelly Rodgers and Yi He
A Critical Review of the E-Satisfaction Literature
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38
A Critical Review of theE-Satisfaction LiteratureQimei ChenShelly RodgersYi HeUniversity of Hawaii at Manoa
User satisfaction is essential to the success of any Web site. Satisfaction with electronicenvironments, or e-satisfaction, drives traffic to Web sites and encourages repeated use ofa site. However, the e-satisfaction literature has not been critically examined to assess e-satisfaction scales that currently exist in an effort to determine potential gaps or oppor-tunities for future research. The present study is intended to critically review the existinge-satisfaction literature. We identify problems in defining and measuring e-satisfactionand offer directions for future research.
Keywords: e-satisfaction; customer satisfaction; electronic commerce; web site quality; satisfaction measure.
The World Wide Web has more than 65.4 million domain names to date, includ-ing .com, .org, .gov, and .edu addresses (Zooknic Internet Intelligence, 2006).
Attracted by the tremendous potential afforded by an online presence, an increas-ing number of businesses and organizations have built Web sites as important com-munication and dissemination channels. The success and failure of these onlinesites depend on how well customers are satisfied. Given that online environmentscreate different experiences than offline environments (Wolfinbarger & Gilly,2003), traditional customer satisfaction models may not be appropriate when eval-uating consumers’ satisfaction with nontraditional environments such as theInternet.
To address this issue, a growing number of studies have begun to conceptualize,measure, and model user satisfaction in an e-environment. Known as electronic sat-isfaction, or e-satisfaction, the literature in this area has not been critically examinedor scrutinized (Szymanski & Hise, 2000) but represents a potentially fruitful areathat the online component of every business or organization could benefit from. It istherefore essential to assess e-satisfaction scales that currently exist in an effort todetermine potential gaps or opportunities for future research.
The present study is intended to critically examine the existing e-satisfaction lit-erature. In the present review, we examine the definition, dimensionality, and instru-ments of e-satisfaction, followed by a discussion of conceptual and methodologicalissues of past e-satisfaction measures. We then offer suggestions for future research.
American Behavioral ScientistVolume 52 Number 1
September 2008 38-59© 2008 Sage Publications
10.1177/0002764208321340http://abs.sagepub.com
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Chen et al. / E-Satisfaction Literature Review 39
An Overview of E-Satisfaction Scales
A useful starting point in assessing the e-satisfaction literature is to identify generaltrends across existing e-satisfaction scales. To accomplish this, we conducted a broadsearch of the literature across a variety of disciplines to identify studies that aimed tocreate e-satisfaction and e-satisfaction–related scales. During our search, we discov-ered that efforts to develop e-satisfaction measures were predominantly centered inthe fields of marketing and e-commerce. Our final sample was therefore limited to thetop 20 journals in these two areas (Bharati & Tarasewich, 2002; Hult, Neese, &Bashaw, 1997). The sampling strategy was similar to network sampling, which is fea-tured by locating key e-satisfaction instruments by examining and analyzing the ref-erence list of journal articles. Network sampling is a reliable and efficient samplingprocedure particularly when it is difficult to access the population using probabilitysampling methods (e.g., Spreen & Zwaagstra, 1994; Watters & Biernacki, 1989), as inthe case of e-satisfaction measures. Our search process continued until the emerginge-satisfaction instruments were interchangeable and saturated, suggesting that thesearch successfully identified key e-satisfaction scales (see Heckathorn, 1997). Thisprocess resulted in 20 e-satisfaction instruments, outlined in Table 1.
The scales are listed in chronological order in Table 1 starting with Bailey andPearson’s (1983) User Information Satisfaction (UIS) Scale and ending with Bauer,Falk, and Hammerschmidt’s (2006) eTranQual scale. The scales were created byscholars from a variety of disciplines, including e-commerce, business, and infor-mation sciences, thus suggesting that e-satisfaction is a complex phenomenon thathas drawn attention from multiple disciplines. The number of items per scale rangedfrom 6 (Chen & Wells, 1999) to 156 (Bailey & Pearson, 1983). Two scales (Liu &Arnett, 2000, and Novak, Hoffman, & Yung, 2000) did not offer scale items. Morethan 100 factors or dimensions were identified by the e-satisfaction scales. Of these,six dimensions appeared across several scales, including design, convenience, trans-action, security, information or content, and function or usability. To a lesser extent,dimensions such as interactivity (Barnes & Vidgen, 2002), customization (Srinvasan,Anderson, & Ponnavolu, 2002), playfulness (Liu & Arnett, 2000), and entertainment(Chen & Wells, 1999) were identified as dimensions of e-satisfaction.
The impact (times cited) of each scale was noted and ranged from 0 (Yang, Cai,Zhou, & Zhou, 2005; Bauer et al., 2006) to 328 (Bailey & Pearson, 1983). To con-trol for the year the scale was published, we created an impact ratio by dividing thetotal number of citations by the number of years since the study had been published.For instance, because the Web Customer Satisfaction Scale (McKinney, Yoon, &Zahedi, 2002) was published in Information Systems Research in 2002, it has beencited 31 times, resulting in an impact ratio of 6.2 (31/5). This provides an apples-to-apples comparison of the scales and helps to equalize the impact of each scale acrosstime. Impact ratios ranged from 0 (Bauer et al., 2006; Yang et al., 2005) to 15.5(Novak et al., 2000), with an average impact ratio of 5.17 (see Table 1).
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40
Inst
rum
ent
Use
rIn
form
atio
nSa
tisfa
ctio
n
End
Use
r C
ompu
ting
Satis
fact
ion
Dim
ensi
ons
Ele
ctro
nic
data
pr
oces
sing
staf
f,se
rvic
e in
form
atio
npr
oduc
t,ve
ndor
supp
ort,
info
rmat
ion
prod
uct,
know
ledg
e or
in
volv
emen
t
Con
tent
,ac
cura
cy,
form
at,
ease
of u
se,
timel
ines
s
Num
ber
ofIt
ems
156
12
Rel
iabi
lity
.75 (low
est)
.93 (a
vera
ge)
.92
Fit I
ndex
(if
CFA
Perf
orm
ed)
Five
fi
rst-
orde
rfa
ctor
s,on
e se
cond
-or
der
fact
ors
mod
el:
χ2=
126.
16df
= 6
1χ2 /d
f =
2.07
NFI
= .9
08G
FI =
.916
AG
FI =
.875
RM
SR =
.052
(Dol
l,R
aghu
nath
an,L
im,&
Gup
ta,
1995
)
Five
fi
rst-
orde
rfa
ctor
s,on
e se
cond
-or
der
fact
ors
mod
el:
Impa
ct(T
imes
Cite
d)
328
249
Rat
io(C
ites/
Yea
r)
13.7
13.1
Rep
licat
ion
(s)
Ives
,Ols
on,
& B
arou
di
(198
3),
Bar
oudi
&O
rlik
owsk
i(1
988)
,G
alle
tta &
L
eder
er
(198
9),
Ket
tinge
r &
Lee
(19
94),
Dol
l et a
l. (1
995)
,Se
ngup
ta &
Zvi
ran
(199
7),
Jian
g &
K
lein
(2
000)
,W
hitte
n (2
004)
Tork
zade
h &
Dol
l (2
001)
;D
oll e
t al.
(199
4);
Scop
e
Com
pute
r us
er
satis
fact
ion
Satis
fact
ion
of a
n en
d us
er w
ith a
syst
em
Aut
hor(
s)
Bai
ley
&
Pear
son
Dol
l &
Tork
zade
h
Yea
r
1983
1988
Jour
nal
Man
agem
ent
Scie
nce
MIS
Q
uart
erly
Are
a
E- co
mm
erce
E- co
mm
erce
Dis
cipl
inea
Info
rmat
ion
syst
ems
Info
rmat
ion
syst
ems
Tabl
e 1
Sum
mar
y an
d E
valu
atio
n of
E-S
atis
fact
ion
Inst
rum
ents
(con
tinu
ed)
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41
Inst
rum
ent
Dim
ensi
ons
Num
ber
ofIt
ems
Rel
iabi
lity
Fit I
ndex
(if
CFA
Perf
orm
ed)
χ2= 18
5.81
df=
50
χ2 /df
=3.
72N
FI =
.940
GFI
= .9
29A
GFI
=.8
89R
MSR
=.0
35(D
oll,
Xia
,& To
rkza
deh,
1994
)χ2
= 6
1.08
df=
48
p=
.10
χ2 /df=
1.2
7C
FI =
.99
RM
SEA
=.0
4(A
bdin
nour
-H
elm
,C
hapa
rro,
& Farm
er,
2005
)
Impa
ct(T
imes
Cite
d)
Rat
io(C
ites/
Yea
r)R
eplic
atio
n (s
)
McH
aney
,H
ight
ower
,&
Pea
rson
(2
002)
;D
oll,
Den
g,R
aghu
nath
an,
Tork
zade
h,&
Xia
(2
004)
;So
mer
s,N
elso
n,&
K
arim
i (2
003)
;A
bdin
nour
-H
elm
et a
l. (2
005)
Scop
eA
utho
r(s)
Yea
rJo
urna
lA
rea
Dis
cipl
inea
Tabl
e 1
(con
tinu
ed)
(con
tinu
ed)
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42
Inst
rum
ent
Atti
tude
To
war
d th
e Si
te
Fact
ors
Ass
ocia
ted
With
W
ebsi
te
Succ
ess
Ant
eced
ents
to
E-
Satis
fact
ion
Dim
ensi
ons
Ent
erta
inm
ent,
info
rmat
ive-
ness
,org
a-ni
zatio
n
Info
rmat
ion
and
serv
ice
qual
ity,
syst
em u
se,
play
fuln
ess,
syst
emde
sign
qual
ity
Con
sum
er
perc
ep-
tions
ofon
line
con-
veni
ence
,m
erch
andi
s-in
g (p
rod-
uct
offe
ring
san
d pr
od-
uct i
nfor
-m
atio
n),
site
des
ign,
Num
ber
ofIt
ems
6
No ite
ms
offe
red
11
Rel
iabi
lity
.92−
.97
.63−
.92
Not
repo
rted
Fit I
ndex
(if
CFA
Perf
orm
ed)
Thr
ee fi
rst-
orde
rfa
ctor
s,on
ese
cond
-or
der
fact
ors
mod
el:
GFI
= .6
6N
NFI
= .8
3C
FA n
otpe
rform
ed
χ2=
143.
1df
= 35
p <
.05
GFI
= .9
8A
GFI
= .9
5R
MSE
A =
.06
NFI
= .9
7C
FI =
.97
Impa
ct(T
imes
Cite
d)
44
55 60
Rat
io(C
ites/
Yea
r)
5.5 7.
9
8.6
Rep
licat
ion
(s)
Che
n,C
liffo
rd,&
W
ells
(2
002)
Scop
e
Atti
tude
to
war
d a
Web
site
Fact
ors
asso
ciat
ed
with
Web
si
te
succ
ess
unde
r el
ectr
onic
co
mm
erce
co
ntex
tC
onsu
mer
satis
fact
ion
with
e-
taili
ng
expe
rienc
es
Aut
hor(
s)
Che
n &
W
ells
Liu
&
Arn
ett
Szym
ansk
i&
His
e
Yea
r
1999
2000
2000
Jour
nal
Jour
nal
of
Adv
erti
sing
R
esea
rch
Info
rmat
ion
and
Man
agem
ent
Jour
nal
of
Ret
aili
ng
Are
a
Mar
ketin
g
E- co
mm
erce
Mar
ketin
g
Dis
cipl
inea
Bus
ines
s
Info
rmat
ion
syst
ems
Bus
ines
s
Tabl
e 1
(con
tinu
ed)
(con
tinu
ed)
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43
Inst
rum
ent
Con
stru
cts
Rel
ated
toC
ompe
lling
Onl
ine
Exp
erie
nce
Cus
tom
er
Info
rmat
ion
Satis
fact
ion
Dim
ensi
ons
Eas
e of
cont
act,
easy
ord
erin
g,pa
ymen
tre
turn
s,ea
se o
fca
ncel
latio
n,cu
stom
ersu
ppor
t,cu
tting
edg
e,va
riety
,qu
ality
info
rmat
ion,
relia
bilit
y,se
curit
y,lo
wpr
ice
Cus
tom
ersu
ppor
t,se
curit
y,ea
se o
f use
,di
gita
lpr
oduc
ts/s
ervi
ces,
trans
actio
nan
dpa
ymen
t,in
form
atio
nco
nten
t,in
nova
tion
Num
ber
ofIt
ems
No
item
sof
fere
d
21
Rel
iabi
lity
Not re
port
ed
.84−
.93
Fit I
ndex
(if
CFA
Perf
orm
ed)
Not
re
port
ed
Seve
n fir
st-
orde
r fac
-to
rs,o
nese
cond
-or
der
fact
ors
mod
el:
χ2=
287
df=
182
χ2 /df=
1.5
8
Impa
ct(T
imes
Cite
d)
108
15
Rat
io(C
ites/
Yea
r)
15.5
2.5
Rep
licat
ion
(s)
Wan
g &
Tang
(200
4)
Scop
e
Cus
tom
erex
peri
-en
ce in
onlin
een
viro
n-m
ent
Cus
tom
ersa
tisfa
ctio
nto
war
dW
eb s
ites
that
mar
ket
digi
tal
prod
ucts
and
ser-
vice
s
Aut
hor(
s)
Nov
ak,
Hof
fman
,&
Yun
g
Wan
g,Ta
ng,&
Tang
Yea
r
2000
2001
Jour
nal
Mar
keti
ngSc
ienc
e
Jour
nal
ofE
lect
roni
cC
omm
erce
Res
earc
h
Are
a
Mar
ketin
g
Mar
ketin
g
Dis
cipl
inea
Bus
ines
s
Info
rmat
ion
syst
ems
Tabl
e 1
(con
tinu
ed)
(con
tinu
ed)
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44
Inst
rum
ent
SIT
EQ
UA
L
Ele
ctro
nic
Com
mer
ceU
ser-
Con
sum
erSa
tisfa
ctio
nIn
dex
Dim
ensi
ons
Eas
e of
use
,ae
sthe
ticde
sign
,pr
oces
sing
spee
d,se
curit
y
Prod
uct
info
rmat
ion,
cons
umer
serv
ice,
purc
hase
resu
lt an
dde
liver
y,si
tede
sign
,
Num
ber
ofIt
ems
9 51
Rel
iabi
lity
.72−
.78
.69−
87
Fit I
ndex
(if
CFA
Perf
orm
ed)
CFI
= .9
7N
FI =
.91
RM
R =
.049
RM
SEA
=.0
47G
FI =
.92
AG
FI =
.90
PGFI
= .7
2
χ2=
32.0
7df
= 21
CFI
= .9
7IF
I = .9
8G
FI =
.97
AG
FI =
.93
NN
FI =
.96
SRM
R =
.044
RM
SEA
=.0
54C
FA n
otpe
r-fo
rmed
Impa
ct(T
imes
Cite
d)
17 7
Rat
io(C
ites/
Yea
r)
2.8
1.1
Rep
licat
ion
(s)
Scop
e
Perc
eive
dqu
ality
of
an Inte
rnet
shop
ping
site
Satis
fact
ion
inde
x fo
rIn
tern
etsh
oppi
ng
Aut
hor(
s)
Yoo
&D
onth
u
Cho
&Pa
rk
Yea
r
2001
2001
Jour
nal
Qua
rter
lyJo
urna
l of
Ele
ctro
nic
Com
mer
ce
Indu
stri
alM
anag
em
ent
and
Dat
aSy
stem
Are
a
E- co
mm
erce
E- co
mm
erce
Dis
cipl
inea
Bus
ines
s
Bus
ines
s
Tabl
e 1
(con
tinu
ed)
(con
tinu
ed)
at Afyon Kocatepe Universitesi on May 22, 2014abs.sagepub.comDownloaded from
45
Inst
rum
ent
Web
Qua
l 4.0
Use
r-Pe
rcei
ved
Web
Qua
lity
Web
C
usto
mer
Sa
tisfa
ctio
n
Key
D
imen
sion
s of
Bus
ines
s-to
-Con
sum
er
Web
Site
s
Dim
ensi
ons
purc
hasi
ng
proc
ess,
prod
uct
mer
chan
dis
ing,
deliv
ery
time
and
char
ge,
paym
ent
met
hod,
ease
of u
seU
sabi
lity,
info
rmat
ion,
serv
ice
inte
ract
ion
Spec
ific
con-
tent
,con
tent
qual
ity,
appe
aran
ce,
tech
nica
lad
equa
cyU
nder
stan
dabi
lity,
relia
bil-
ity,u
sefu
l-ne
ss,a
cces
s,us
abili
ty,
navi
gatio
nIn
form
atio
nco
nten
t,de
sign
,se
curit
y,pr
ivac
y
Num
ber
ofIt
ems
22 25 16 15
Rel
iabi
lity
.70−
.90
.85−
.91
.79−
.98
.76−
.90
Fit I
ndex
(if
CFA
Perf
orm
ed)
CFA
not
per-
form
ed
CFA
not
per-
form
ed
χ2 /df
= 2
CFA
not
per-
form
ed
Impa
ct(T
imes
Cite
d)
5 20 31 27
Rat
io(C
ites/
Yea
r)
1 4 6.2
5.4
Rep
licat
ion
(s)
Bar
nes
&V
idge
n(2
001)
Scop
e
Qua
lity
ofIn
tern
etbo
oksh
opW
eb s
ites
Web
site
qual
ity
Cus
tom
ersa
tisfa
c-tio
n w
ithth
e on
line
purc
hasi
ngpr
oces
sB
-to-
C
Web
site
Aut
hor(
s)
Bar
nes
&V
idge
n
Ala
dwan
ian
dPa
lvia
McK
inne
y,Y
oon,
&Z
ahed
i
Ran
gana
than
& Gan
apat
hy
Yea
r
2002
2002
2002
2002
Jour
nal
Jour
nal
ofE
lect
roni
cC
omm
erce
Res
earc
hIn
form
atio
nan
dM
anag
emen
t
Info
rmat
ion
Syst
ems
Res
earc
h
Info
rmat
ion
and
Man
agem
ent
Are
a
E- co
mm
erce
E- co
mm
erce
E- co
mm
erce
E- co
mm
erce
Dis
cipl
inea
Info
rmat
ion
syst
ems
Info
rmat
ion
syst
ems
Info
rmat
ion
syst
ems
Info
rmat
ion
syst
ems
Tabl
e 1
(con
tinu
ed)
(con
tinu
ed)
at Afyon Kocatepe Universitesi on May 22, 2014abs.sagepub.comDownloaded from
46
Inst
rum
ent
Ant
eced
ents
to
E
-Loy
alty
eTai
l
Web
Site
U
ser
Satis
fact
ion
Dim
ensi
ons
Cus
tom
izat
ion,
cont
act i
nter
-ac
tivity
,cu
ltiva
tion,
care
,com
-m
unity
,ch
oice
,con
-ve
nien
ce,
char
acte
r
Web
site
desi
gn,
fulfi
llmen
t/rel
i-ab
ility
,pr
ivac
y/se
curit
y,cu
stom
er
serv
ice
Info
rmat
ion
rele
vanc
y,in
form
atio
nac
cura
cy,
Num
ber
ofIt
ems
39 14 34
Rel
iabi
lity
>.7
>.4
8
.74−
.89
Fit I
ndex
(if
CFA
Perf
orm
ed)
Unr
estri
cted
eigh
t firs
t-or
der f
ac-
tors
,one
seco
nd-
orde
r fac
-to
rsm
odel
χ2=
1165
.02
df=
674
GFI
= .7
5Fo
ur fi
rst-
orde
r fac
-to
rs,o
nese
cond
-or
der f
ac-
tors
mod
el:
χ2=
334
df=
73N
FI =
.97
CFI
= .9
7R
MR
= .0
3G
FI =
.95
AG
FI =
.94
11 fi
rst-o
rder
fact
or-
corre
late
dm
odel
:
Impa
ct(T
imes
Cite
d)
19 13 3
Rat
io(C
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Yea
r)
3.8
3.3 1
Rep
licat
ion
(s)
Scop
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Cus
tom
erlo
yalty
inan
onl
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B-t
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cont
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Onl
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e-ta
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ality
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tude
to
war
d a
Web
site
by
a
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s)
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vasa
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finba
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lle,
Moe
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espo
ntin
Yea
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2003
2004
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nal
Jour
nal
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etai
ling
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nal
ofR
etai
ling
Info
rmat
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and
Man
agem
ent
Are
a
Mar
ketin
g
Mar
ketin
g
E- co
mm
erce
Dis
cipl
inea
Bus
ines
s
Bus
ines
s
Bus
ines
s
Tabl
e 1
(con
tinu
ed)
(con
tinu
ed)
at Afyon Kocatepe Universitesi on May 22, 2014abs.sagepub.comDownloaded from
47
Inst
rum
ent
E-S
-Qua
l
Dim
ensi
ons
Iinfo
rmat
ion,
com
preh
en-
sibi
lity,
info
rmat
ion,
com
preh
en-
sive
ness
,co
nnec
tion
ease
of u
se,
conn
ectio
nen
try,g
uid-
ance
,co
nnec
tion
stru
ctur
e,co
nnec
tion
hype
rlink
;co
nnot
atio
n,co
nnec
tion
spee
d,la
y-ou
t,la
ngua
ge,c
us-
tom
izat
ion
Effi
cien
cy,
syst
em a
vail-
abili
ty,
fulfi
llmen
t,pr
ivac
y
Num
ber
ofIt
ems
22
Rel
iabi
lity
.83−
.94
Fit I
ndex
(if
CFA
Perf
orm
ed)
CFI
= .9
5T
LI
= .9
4R
MSE
A =
.05
χ2= 81
3.06
df=
203
CFI
= .9
9N
FI =
.98
RFI
= .9
8T
LI
= .9
8R
MSE
A =
.07
Impa
ct(T
imes
Cite
d)
8
Rat
io(C
ites/
Yea
r)
4
Rep
licat
ion
(s)
Scop
e
hand
s-on
user
of
the
orga
-ni
zatio
n's
Web
site
Serv
ice
qual
ityde
liver
edby
a W
ebsi
te
Aut
hor(
s)
Para
sura
man
,Z
eith
aml,
& Mal
hotra
Yea
r
2005
Jour
nal
Jour
nal
ofSe
rvic
eR
esea
rch
Are
a
Mar
ketin
g
Dis
cipl
inea
Bus
ines
s
Tabl
e 1
(con
tinu
ed)
(con
tinu
ed)
at Afyon Kocatepe Universitesi on May 22, 2014abs.sagepub.comDownloaded from
48
Inst
rum
ent
E-R
ecS-
Qua
l
Use
rPe
rcei
ved
Serv
ice
Qua
lity
ofIn
form
atio
nPr
esen
ting
Web
Port
als
Dim
ensi
ons
Res
pons
iven
ess,
com
pens
atio
n,co
ntac
t
Usa
bilit
y,us
eful
ness
of c
onte
nt,
adeq
uacy
of
info
rma-
tion,
acce
ssib
ility
,in
tera
ctio
n
Num
ber
ofIt
ems
11 19
Rel
iabi
lity
.77−
.88
.66−
.89
Fit I
ndex
(if
CFA
Perf
orm
ed)
χ2= 15
0.32
df=
41
CFI
= .9
9N
FI =
.98
RFI
= .9
8T
LI
= .9
9R
MSE
A =
.07
Five
firs
t-or
der
fact
or-
corr
elat
edm
odel
:χ2 /d
f= 3
.58
CFI
= .9
5G
FI =
.95
NN
FI =
.93
SRM
R =
.04
RM
SEA
=.0
5
Impa
ct(T
imes
Cite
d)
8 0
Rat
io(C
ites/
Yea
r)
4 0
Rep
licat
ion
(s)
Scop
e
Rec
over
yse
rvic
equ
ality
deliv
ered
by a
Web
site
Perc
eive
dse
rvic
equ
ality
of
Web
por
-ta
ls th
atpr
ovid
eus
ers
with
info
rma-
tion
abou
tan
org
ani-
zatio
n's
prod
ucts
or
serv
ices
Aut
hor(
s)
Para
sura
man
et a
l.
Yan
g et
al.
Yea
r
2005
2005
Jour
nal
Jour
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ofSe
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rch
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and
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Are
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ketin
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Tabl
e 1
(con
tinu
ed)
(con
tinu
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at Afyon Kocatepe Universitesi on May 22, 2014abs.sagepub.comDownloaded from
49
Not
e:C
FA =
conf
irm
ator
y fa
ctor
ana
lysi
s; C
FI =
com
para
tive
fit
inde
x; G
FI =
good
ness
-of-
fit
inde
x; A
GFI
=ad
just
ed g
oodn
ess-
of-f
it in
dex;
IFI
= in
cre-
men
tal
fit
inde
x; N
FI =
nor
med
fit
inde
x; N
NFI
= n
on-n
orm
ed f
it in
dex;
RFI
=re
lativ
e fi
t in
dex;
TL
I =
Tuc
ker-
Lew
is i
ndex
; R
MSR
=ro
ot m
ean
squa
rere
sidu
al;
RM
SEA
=ro
ot m
ean
squa
re e
rror
of
appr
oxim
atio
n; R
MR
=ro
ot m
ean
resi
dual
; SR
MR
=st
anda
rdiz
ed r
oot
mea
n re
sidu
al;
B-t
o-C
=bu
sine
ss t
oco
nsum
er.
Inst
rum
ent
eTra
nQua
l
Dim
ensi
ons
Func
tiona
lity
/des
ign,
enjo
ymen
t,pr
oces
s,re
liabi
lity,
resp
onsi
ve-
ness
Num
ber
ofIt
ems
25
Rel
iabi
lity
.83−
.89
Fit I
ndex
(if
CFA
Perf
orm
ed)
χ2=
653
df=
270
CFI
= .9
9R
MR
= .0
7G
FI =
.98
AG
FI =
.97
RM
SEA
=.1
0
Impa
ct(T
imes
Cite
d)
0
Rat
io(C
ites/
Yea
r)
0
Rep
licat
ion
(s)
Scop
e
Ele
ctro
nic
serv
ice
enco
unte
r
Aut
hor(
s)
Bau
er,
Falk
,&H
amm
ersc
hmid
t
Yea
r
2006
Jour
nal
Jour
nal
ofB
usin
ess
Res
earc
h
Are
a
Mar
ketin
g
Dis
cipl
inea
Bus
ines
s
Tabl
e 1
(con
tinu
ed)
at Afyon Kocatepe Universitesi on May 22, 2014abs.sagepub.comDownloaded from
The quality of the scale was examined using scale reliability and fit indices of con-firmatory factor analysis (CFA) reported in each study. Table 1 shows that all but twoscales (Szymanski & Hise, 2000; Novak et al., 2000) reported alpha coefficients as evi-dence of the scale’s reliability. Most of the e-satisfaction instruments were reliable,with an acceptable Cronbach’s alpha higher than .7. To investigate the factor structureof a construct, both exploratory factor analysis and CFA are crucial steps. Table 1shows that 14 of 20 (70%) instruments performed CFA and these CFAs yielded accept-able fit indices. Central to the process of scale development is to replicate the study toverify the scale’s credibility. Of the 20 scales reviewed here, only 5 were replicated bysubsequent research (i.e., Bailey & Pearson, 1983; Barnes & Vidgen, 2002; Chen &Wells, 1999; Doll & Torkzadeh, 1988; Wang, Tang, & Tang, 2001).
In sum, the accumulating body of research on user satisfaction has led to the devel-opment of a variety of instruments to measure e-satisfaction. However, our evaluationreveals that in spite of these scholarly efforts, it appears that e-satisfaction instrumentsto date have had only a limited impact on the overall e-commerce and business areas.The fact that scales ranged markedly on the number of dimensions and items measuredsuggests that scholars cannot agree on how, or possibly what, to measure to determinee-satisfaction. Many of the dimensions reviewed were of a functional versus emotionalnature, suggesting that the literature has not advanced past early conceptualizations ofWeb site usage and design. E-satisfaction scales reviewed here were cited an averageof five times per year, further revealing the narrow impact of most instruments.Although appropriate statistical analyses were conducted on the majority of the scales,replications are lacking and therefore warrant caution against the use of scales thathave not been appropriately tested and validated.
We argue that the slow adoption of e-satisfaction scales is because of inherentlimitations in this stream of research. Specifically, two primary arguments areadvanced: (a) Conceptual and operational definitions of e-satisfaction are inconsis-tent, leading to a general lack of understanding of what e-satisfaction is and how tomeasure it; and (b) the dimensions of e-satisfaction scales are redundant and/orignore aspects of Web sites that are important to e-satisfaction, resulting in poten-tially inadequate measures of e-satisfaction.
Defining E-Satisfaction
Our first argument pertains to defining e-satisfaction. We argued that conceptualand operational definitions of e-satisfaction are inconsistent. First, the definition ofe-satisfaction is grounded in the customer satisfaction literature, and many defini-tions are based on consumer satisfaction with traditional retail channels. Forexample, using Oliver’s (1997) definition of customer satisfaction as the overall sub-sequent psychological state following the appraisal of the consumer experienceagainst the prior expectations, Anderson and Srinvasan (2003) suggest a definition
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of e-satisfaction as “the contentment of the customer with respect to his or her prior pur-chasing experience with a given electronic commerce firm” (p. 125). Similarly,Wang et al. (2001) propose a construct called “customer information satisfaction” (CIS)for Web sites that market digital products and services. The authors define CIS as “a sum-mary affective response of varying intensity that follows consumption, and is stimulatedby focal aspects of sales activities, information systems (websites), digital products/services, customer support, after-sales service, and company culture” (p. 91).
These definitions of e-satisfaction are arguably grounded in an affect-basedmodel, which emphasizes the emotional aspect of Web site use. For instance, simi-lar to Oliver’s (1997) use of the term emotion, Wang et al. (2001) use the term affec-tive response, and Anderson and Srinvasan (2003) refer to experiences, a term thatis arguably affect derived and based on feelings, emotion, and so on. However, thereare inconsistencies between conceptual and operational definitions. Whereas theconceptual definitions emphasize an affective aspect of e-satisfaction, the opera-tional extensions of those definitions seem to emphasize a rational evaluative per-spective. Specifically, the dimensionality or factor structure of e-satisfaction representsthe attributes of the online shopping experience that “rational” customers consider inevaluating their encounter in e-environments. As shown in Table 1, Wang et al.(2001) and Wang and Tang (2004) identify seven dimensions in their CIS instrument,such as customer support, security, and ease of use, which emphasize a cognitiverather than affective experience. Therefore, instead of emphasizing an affectiveresponse, as defined at the conceptual level, e-satisfaction is usually operationalizedvia an expectancy-value approach, as a benchmarking outcome based on the evalu-ation of each attribute. With its roots on Fishbein and Azjen’s (1975) theory of rea-soned action, expectancy-value approach usually operationalizes user satisfactionbased on the evaluation of product-service attributes, and overall satisfaction isexpressed mathematically assuming that all attributes will be accessible and evalu-ated by the customer at the point of evaluation (Melone, 1990).
There are several potential reasons for these trends. First, the inconsistencybetween conceptual and operational definitions may be attributed to the fact thatmost e-satisfaction studies do not adequately define the e-satisfaction constructbefore developing the instrument (Wolfinbarger & Gilly, 2003). The satisfaction lit-erature as a whole has been criticized for its inconsistent use of consumer satisfac-tion definitions (Giese & Cote, 2000). Given that most e-satisfaction definitionswere derived from the general satisfaction literature, it is logical to think that e-satisfaction definitions suffer from the same issues and challenges identified byearlier scholars (e.g., Giese & Cote, 2000; McQuitty, Finn, & Wiley, 2000).Furthermore, the operationalization of e-satisfaction appears to overlap with otherdefinitions, such as e-quality, further blurring the meaning of e-satisfaction.Consider the following definition of “e-SERVQUAL”: the extent to which a Web sitefacilitates efficient and effective shopping, purchasing, and delivery of products and ser-vices (Zeithaml, Parasuraman, & Malhotra, 2000, 2002). This definition stresses the
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evaluation of the overall process of the online shopping experience, which is based ona rational and computational model. As a result, existing studies that aim to develop e-satisfaction instruments by drawing on related definitions or existing definitions inthe traditional literature have yielded contradictory definitions, resulting in confusionabout the definition and dimensions of e-satisfaction, our next argument.
Dimensions of E-Satisfaction
Our second argument is that the lack of consensus on how to define e-satisfactionhas led to disagreement on the number and types of dimensions represented bye-satisfaction scales. Two related trends that support this argument are revealed inTable 1. First, many dimensions are redundant across e-satisfaction scales, and second,different labels are used for the same construct or, alternatively, the same label isused for different constructs. For example, design is a recurring factor in a numberof scales. Returning to Table 1, note that the construct that captures design is labeledsite design (Szymanski & Hise, 2000), aesthetic design (Yoo & Donthu, 2001),design (Ranganathan & Ganapathy, 2002), and system design (Liu & Arnett, 2000)in the four e-satisfaction scales cited. The term design is used redundantly but ismeasured differently and therefore means something different than what is impliedby the label. For instance, the site design dimension identified by Szymanski andHise (2000) consists of three items: “uncluttered screens,” “easy search paths,” and “fastpresentations” (p. 316), whereas design in the scale by Ranganathan and Ganapathy(2002) is represented by “ease of navigation for information search,” “time taken fornavigation,” and “presence of visual presentation aids (graphics, audio, video)” (p. 463).The items that compose the former dimension emphasize convenience, whereas thelatter dimension emphasizes convenience and multimedia, which is a completely dif-ferent interpretation of design.
In some cases, the design dimension is given a different label, such as format (Doll& Torkzadeh, 1988), appearance (Aladwani & Palvia, 2002), and layout (Muylle,Moenaert, & Despontin, 2004). Additionally, whereas many of the dimensions identi-fied by the e-satisfaction scales capture features or characteristics of an e-environment,some dimensions appear to capture the “personality” of Web sites (e.g., see the dimen-sion character in the scale by Srinvasan et al., 2002), which is arguably a separate con-struct that requires a separate scale (see Chen & Rodgers, 2006).
In addition to these limitations, there are aspects of Web sites that are largelymissing or that are ignored entirely by current e-satisfaction scales. For instance,emotional dimensions, such as playfulness (Liu, & Arnett, 2000) and enjoyment(Bauer et al., 2006), are captured by only two scales. Other emotional aspects,such as a Web site’s ability to entertain, also are missing with rare exceptions (seeChen & Wells, 1999; Chen, Clifford, & Wells, 2002). With the exception ofAladwani and Palvia (2002) and Yang et al. (2005), other important features such
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as interactivity are not represented, and for those that contain interactive ele-ments, the definitions are somewhat limited (see McMillan & Downes, 2000).Ignoring affective components of Web sites as well as features that may be impor-tant to Web site users limits existing scales to inform only the rational compo-nents of Web sites and further reduces the generalizability and applicability of theinstruments. In sum, we believe not only that the use of redundant factors andmixed labels has created confusion about which dimensions constitute a soundmeasure of e-satisfaction, but also scales that are cyclic in nature do little toadvance our understanding of e-satisfaction.
There are several explanations for the redundant use of dimensions. First, thedevelopment of e-satisfaction instruments often draws from the end-user satis-faction literature on information systems and quality measures in the marketingliterature. Following both traditions, these scholarly attempts focused on generat-ing a list of attributes that online customers use in evaluating the online experi-ence. These attributes are usually considered the dimensions of e-satisfaction.These two literature streams, however, adopt different perspectives on the user ofthe e-environment. The information systems literature usually views users as endusers of information systems. From the end-user perspective, Web sites areviewed through an engineering lens that analyzes the functional characteristics ofa Web site, focusing on attributes such as ease of use—the degree to which asystem is “user friendly” (Doll & Torkzadeh, 1998)—information quality andtimeliness—whether information on the Web site is up-to-date (Cheung & Lee,2005)—Web site security, and so on.
In contrast, the marketing literature adopts a customer focus, which affords abroader interpretation of the functions, features, and characteristics of Web sites. Tointegrate the customer perspective into e-satisfaction measures, numerous studieshave drawn from the marketing literature and have added dimensions or items thatmeasure the general aspects of goods or services quality related to the e-tailingexperience; for example, merchandising (variety, newness, and assortment of prod-ucts that are available for an online store; Cho & Park, 2001), transaction proce-dure, payment system, and so on. In addition to the generic aspects of onlineshopping, the marketing literature has examined the interactive nature of servicequality (i.e., SERVQUAL; Parasuraman, Zeithaml, & Berry, 1988). In line with thistradition, consumer-centric measures of e-satisfaction have attempted to accountfor the interactive aspect of service quality (e.g., Parasuraman et al., 1988) and tan-gible dimensions—which refers to physical environments offline and Web siteappearance online (Cheung & Lee, 2005; Gefen, 2002). In sum, the customer per-spective requires e-satisfaction instruments to encompass measurements that cap-ture the actual product and service and the process of receiving those products andservices online, implying that scale dimensions are influenced by the traditionsfrom which they are derived as well as assumptions about the end user.
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Future Research on E-Satisfaction
The gaps identified above provide abundant opportunities for future research. First,as argued by Bagozzi, Gopinath, and Nyer (1999), although emotion can perform asantecedents, moderators, or mediators in individuals’ evaluation formation, it has beengiven minimal consideration in the e-satisfaction literature (for an exception, seeÉthier, Hadaya, Talbot, & Cadieux, 2006) and therefore warrants future investigation.As noted earlier, although the construct of satisfaction has both emotional (Bagozzi et al., 1999; Éthier et al., 2006; Wang et al., 2001) and cognitive aspects (Muylle et al.,2004), most studies on e-satisfaction follow a rational-evaluative approach and ignorethe emotional aspect of e-satisfaction. This limitation inherent in the definitions of e-satisfaction has led to a myopic approach to constructing e-satisfaction models anddeveloping e-satisfaction scales. By simply listing the attributes of objects to be eval-uated without examining subjective attributes (individual factors and situational factors), existing research provides “a limited, if not entirely distorted, picture” of usersatisfaction (Melone, 1990 p. 85).
One way to alleviate this criticism is to center the operationalization of e-satisfactionon the well-developed and established model expectancy–disconfirmation para-digm, where the actual performance is compared to a reference point and satisfac-tion is considered the result of the discrepancy between the actual and the referencepoint (Oliver, 1997; Parasuraman, Zeithaml, & Berry, 1988). The disconfirmationparadigm, based on an individual psychological appraisal process, thus far, is ubiq-uitous in the customer satisfaction area. Therefore, examining its generalizabilityunder an online context not only offers a stronger theoretical foundation for build-ing e-satisfaction models, it also helps to identify the similarities and differencesbetween online versus offline customers’ formation of satisfaction (for initialefforts, please refer to McKinney et al., 2002; Wolfinbarger & Gilly, 2003; andZeithaml et al., 2002).
Next, by adopting the disconfirmation paradigm and by taking the emotionalaspects of e-satisfaction into consideration, future investigations should focus on theprocess that consumers adopt in forming their satisfaction with e-environments. Asnoted by Bagozzi et al. (1999), it is more important to examine the processes andexperiences constituting an individual’s response and reaction to an event rather thanfocusing solely on antecedents. A process-based e-satisfaction model will more real-istically reflect how users form evaluations in actuality and will provide greater sen-sitivity to individual and contextual variables, hence will serve as a more usefulmodel to gauge the effectiveness of the online channel than existing models basedon expectancy and value.
The foregoing discussion also calls for multidisciplinary efforts in integrating theexisting literature to eliminate redundant dimensions and clarify items that representand measure those dimensions. As e-commerce is an ever-changing field,researchers need to be sensitive about the constant need to update the measurement
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scale of e-satisfaction. Specifically, new features offered through technologicaladvancement call for the need to take these features into consideration when examin-ing the dimensionality of e-satisfaction, particularly, innovative features of technology,such as interactivity, personalization, and customization. Additionally, future researchshould investigate the predictability of existing dimensions and factors identified ine-satisfaction scales and the relationship of these dimensions to key outcome variablesin an effort to help businesses identify their strategic priorities. Last, more research isneeded to understand not just the what of e-satisfaction, as measured by existingscales, but also the why of e-satisfaction, which attempts to understand underlyingprocesses that drive and consequences that result from e-satisfaction.
Conclusions
The present review has identified a number of gaps in the existing e-satisfaction lit-erature that warrant future study. Inconsistent definitions and redundant dimensionsare but two limitations noted here. We argue that these two limitations are related.Specifically, confusion about how to define e-satisfaction has led to disagreementabout the number and types of dimensions of e-satisfaction. In view of these limita-tions, we have identified several opportunities for future research. One direction is toexamine e-satisfaction from the cognitive and affective perspectives by taking emotioninto consideration when investigating consumers’ e-satisfaction formation processbased on expectancy–disconfirmation instead of expectancy–value paradigm. Theseconsiderations should help to clarify the definition of e-satisfaction and encourageprogress in the creation of scales that more comprehensively measure e-satisfaction.Another direction is to examine the dimensionality of e-satisfaction from both end-user and customer perspectives. These multidisciplinary efforts will ultimately helpeliminate redundancy in identifying the dimensionality of e-satisfaction and help toprioritize the dimensions based on their predictive power in relationship to outcomevariables.
The present review informs practice in many ways. First, as e-satisfaction hasbecome the primary driver of success or failure of a Web site (Zeithaml, 2002),many businesses and organizations have shifted their focus from how to attractcustomers to a Web site to how to keep customers coming back. The inconsisten-cies and trends identified in this study caution organizations to be aware of the lim-itations of current e-satisfaction instruments and present future directions andopportunities in gauging this important concept. Second, as Web sites usuallyserve very different functions, e-satisfaction by its definition applies to nearlyevery type of business, organization, or group represented online, including gov-ernment, education, nonprofit, and political Web sites. This suggests a need todevelop more comprehensive measures of e-satisfaction that can be used acrossdisciplines to assess the effectiveness of Web sites of all varieties. By linking the
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new dimensions of e-satisfaction with outcome variables that are relevant and specificto a particular site, more fruitful results can be found with regard to users’ satisfactionwith those Web sites. For instance, for .com Web sites, repeated patronage might be animportant consequence, whereas for .org, compliance or volunteerism may be impor-tant outcomes. Thus, constructing contingency frameworks of e-satisfaction within thespecific contexts in which e-satisfaction measures are used will provide greater empir-ical potential for evoking rich insights. Replicating current e-satisfaction scales acrosssite types will also help to overcome the lack of validation currently existing with moste-satisfaction scales. Last, Web sites in such fields as health care and education willbenefit from taking the affective aspects of e-satisfaction into consideration, becauseaffect and emotion-laden constructs might be dominant in customer e-satisfactionformation toward such sites.
In sum, the area of e-satisfaction has yet to incorporate the collective intelligence inall related disciplines such as psychology, education, and marketing to offer a compre-hensive picture of users’ online experience and satisfaction with that experience.Consequently, there is tremendous research potential for behavioral scientists who wishto explore e-satisfaction and to advance the e-satisfaction literature as a whole.
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Qimei Chen is the Shidler Distinguished Professor, associate professor of marketing, and chair of theMarketing Department in the Shidler College of Business at the University of Hawaii at Manoa and anadvisory professor at Fudan University in Shanghai, China. She received her PhD from the University ofMinnesota, Twin Cities, and prior to that, she has years of industrial experience in China, Germany, andHong Kong. Her current research interests include online and offline health care marketing and innova-tion and knowledge. Her research has been widely published in journals such as Journal of Advertising,Journal of Advertising Research, Journal of Business Research, Journal of Product InnovationManagement, and Journal of Retailing.
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Shelly Rodgers is an associate professor of strategic communication at the Missouri School of Journalism.She teaches in the areas of strategic communication and health communication with expertise on Internethealth advertising, marketing, and communication. Rodgers is nationally ranked as one of the most pro-ductive Internet advertising scholars and is among the top 10 most cited Internet advertising researchers.Rodgers’ research examines the effects of interactive communications on audience processing withemphasis on how to use the Internet to promote healthy behaviors.
Yi He is an assistant professor of marketing in the College of Business and Economics at California StateUniversity, East Bay. She completed her PhD in international management (marketing concentration) atthe University of Hawaii at Manoa. She received her master’s degree from University of Cincinnati. Hercurrent research interests include consumer behavior and psychology, cross-cultural consumer behavior,psychology of social technology, and advertising effectiveness. Her studies have appeared in journalssuch as Journal of International Marketing, International Marketing Review, and International Journalof Advertising.
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