ArticlePDF Available

The Effect of Website Performance and Online Retailer Status on Consumer Purchase Intention: A Mediator Role of Buyer Perception

Authors:

Abstract and Figures

Purpose: The purpose of this research is to examine the effect of website performance and online seller status on consumer purchase intention. This study also aims to assess the mediation effect of buyer perception on the relationship between website performance and consumer purchase intention in the context of Internet shopping. Design/Methodology/Approach: This study proposed a conceptual framework and collected a total of 255 samples in the Mainland China to test research hypothesis and investigate the relationship among different predictors. Both measurement model and construct model were established and evaluated using AMOS 21. Findings: Results present that website performance and online seller status significantly affect consumer purchase intention. Buyers' perception partially mediates the relationship between the website performance and consumer purchase intention. The effect of website performance on purchase intention is greater than the effect of other constructs. There is a reciprocal relation between seller status and website performance. Implication: Website managers should further enhance website quality, customer service, and well acknowledge consumers about the good performance of their website. Online vendors should devote to strengthen their online status. A partial influencing effect impacts on the relation between website performance and consumer purchase intention, thus the buyers' perception should not be considered as a 'standalone' concept. Originality/Value: This study proposed a conceptual model to predict consumer purchase intention in the context of C2C E-Commerce. The primary value lies in a better understanding of consumer behavior and detailed examination of the critical determinants.
Content may be subject to copyright.
International Journal of Business and Management; Vol. 10, No. 10; 2015
ISSN 1833-3850 E-ISSN 1833-8119
Published by Canadian Center of Science and Education
158
The Effect of Website Performance and Online Retailer Status on
Consumer Purchase Intention: A Mediator Role of Buyer Perception
Nan Jiang1, Mohd Muttaqin Bin Mohd Adnan1, Manmeet Kaur1 & Xue Ying Yang2
1 Taylor’s Business School, Taylor’s University, Malaysia
2 YeahMobi, China
Correspondence: Nan Jiang, Taylor’s Business School, Taylor’s University, Malaysia. E-mail:
nan.jiang@taylors.edu.my
Received: July 14, 2015 Accepted: July 28, 2015 Online Published: September 18, 2015
doi:10.5539/ijbm.v10n10p158 URL: http://dx.doi.org/10.5539/ijbm.v10n10p158
Abstract
Purpose: The purpose of this research is to examine the effect of website performance and online seller status on
consumer purchase intention. This study also aims to assess the mediation effect of buyer perception on the
relationship between website performance and consumer purchase intention in the context of Internet shopping.
Design/Methodology/Approach: This study proposed a conceptual framework and collected a total of 255
samples in the Mainland China to test research hypothesis and investigate the relationship among different
predictors. Both measurement model and construct model were established and evaluated using AMOS 21.
Findings: Results present that website performance and online seller status significantly affect consumer
purchase intention. Buyers’ perception partially mediates the relationship between the website performance and
consumer purchase intention. The effect of website performance on purchase intention is greater than the effect
of other constructs. There is a reciprocal relation between seller status and website performance.
Implication: Website managers should further enhance website quality, customer service, and well acknowledge
consumers about the good performance of their website. Online vendors should devote to strengthen their online
status. A partial influencing effect impacts on the relation between website performance and consumer purchase
intention, thus the buyers’ perception should not be considered as a ‘standalone’ concept.
Originality/Value: This study proposed a conceptual model to predict consumer purchase intention in the
context of C2C E-Commerce. The primary value lies in a better understanding of consumer behavior and
detailed examination of the critical determinants.
Keywords: C2C electronic commerce, online purchase intention, website
1. Introduction
Internet has created a massive paradigm shift of the way consumer purchase, indicating massive changes in
consumer behavioral trends towards shopping. Consumers can be active at any time and place to purchase a
product or services 24/7. In the past decade, China’s C2C electronic market has grown dramatically. In 2014,
Chinese E-commerce giant Alibaba found its way to the New York Stock Exchange. Alibaba is valued at $231.4bn,
making it significantly larger than Amazon and Facebook (BBC News, September, 2014). In 2011, C2C
transaction volume occupies 89% of the whole online shopping transaction (Kwahk et al., 2012). At the same time,
consumers are getting more cautious about online transaction security, business ethics, safety, reliability, and
honesty. These areas have attracted large attention from previous scholars (Eroglu et al., 2001, 2003; Ethier et al.,
2006; Davis et al., 2008; Chang & Wang, 2008). However, certain concerns may be too narrow or too scatted in
scope and not capturing much of the associated effects, thus may not serve the main stakeholders’ interest
efficiently.
Consumer purchasing intentions could be affected by website quality and performance (Corritore et al., 2003;
McKnight et al., 2004; Ethier et al., 2006). However, good website operation and service may not be sufficient to
motivate consumers to place order online (Cho, 2006). On one hand, consumers may not prefer to deal with
unknown vendors (Lim, 2003). On the other hand, consumer’s perception may mediate the effect of website
performance towards their purchase intention. The dimensions of consumer’s emotion may response as an
www.ccsenet.org/ijbm International Journal of Business and Management Vol. 10, No. 10; 2015
159
expected reaction to the objective stimuli (Eroglu et al., 2001, 2003). In this study, the objective stimulus is
reflected by potential buyers’ perception. In addition, online seller (vendor) status should also be considered as a
critical determinant influencing consumer online purchase intention. This study focuses on a total of four
constructs and investigates the associated interrelation among them within a context of C2C Ecommerce. First, a
brief literature review discusses a total of four main constructs, including website performance, seller status,
buyer perception, and consumer purchase intention. Next, methodology addresses the research design, data
collection and analysis strategies, followed by research findings and implications.
2. Literature Review
2.1 Purchase Intention
Online purchase intention has been receiving much attention in the context of E-Commerce. Hsu et al. (2012)
defined the online purchase intention as the strength of a consumer’s intentions to perform a specified
purchasing behavior via Internet. It has been used to predict consumer behavior and correlated with the actual
behavior (Ajzen & Fishbein, 1980). Measuring the intention is an effective way to capture the buyer’s mind due
to constraints that exist during the real purchase (Day, 1969). The intention to purchase online transaction takes
place when the activities involving retrieving information, transferring messages and purchasing product occur.
The intentions of purchasing online through a particular website or platform can be determined by various
factors, such as consumer’s satisfaction (Kuo et al., 2011, 2013), website quality and website brand (Chang &
Chen, 2008), and online vendors performance (Ling et al., 2011; Wang & Dai, 2013) and perceived ethics of
online retailers (Limbu et al., 2011). This study explores the antecedents surrounding and investigates the
associated determinants of consumer purchase intention.
2.2 Seller Status
Online seller status significantly influences the consumer purchase intention (Jun & Jaafar, 2011). In order to
increase sale volume and maximize profit, online vendors tend to establish good reputations or enhance positive
status that can differentiate themselves from other competitors. The existence of uncertainty, ambiguity, and
other concerns of Internet shopping might hinder consumer buying activities. But these concerns can be reduced
or partially eliminated via a positive seller status that acts as an important trust-building mechanism in the
context of E-Commerce. The seller status is normally gained by word of mouth in terms of service quality,
delivery efficiency, responsiveness, product warranty, return policy, and honesty of vendors (Zeithaml et al.,
2002; Yen & Lu, 2008). Consumers are induced to rate seller’s performance (Awad & Ragowsky, 2008),
feedback their own experience, or review the purchased products (Park et al., 2007). All these initiatives build up
a seller’s online status. Several studies stress that higher seller status can result increased sale volume, whereas a
weak status sounds non-attractive to potential buyers (Bente et al., 2012; Wang & Dai, 2013).
Normally, the seller status only have an impact on existing sellers in terms of revenue, prices and transaction
volume, while the new sellers are unable to acquire such benefits (Xiao et al., 2013). Thus, new online vendors
tend to establish their reputations and brand identities through encompassing various activities, such as sales
promotion and switching product categories, or even involving in scam and increasing their status artificially
(Zhang et al., 2013). Wee et al. (2004) point out that seller status is unreliable in the Chinese E-Commerce
context, such as TaoBao. However, the above statement may not precisely reflect the majority online sellers’
practice as most vendors adopt the honest methods to improve their status, and further attract consumers’
attention. Therefore, seller status is worthy to be re-emphasized in this research. It could be an important
predictor for consumer online purchase intention.
H1: Seller status positively influences consumer online purchase intention.
2.3 Website Performance
Consumer interaction with online vendors is mainly facilitated by websites (Luo et al., 2012). Hence, website is
crucial in attracting and motivating the customer purchase intention (Hsu et al., 2012). Well-designed and
comfortable atmosphere of websites enhance consumer purchase intention, purchase actions, and repeat purchase
(Bai et al., 2008; Chen & Cheng, 2008), especially through positive perception of website features, such as ease
of use, website design, web pattern, hyperlinks, icons, and display (Everard & Gallatte, 2006; Zhang et al., 2011).
Website should be designed to increase the usefulness and informativeness, and avoid irritations to the buyers
(Hausman & Siekpe, 2009). A good website performance can be reflected by several perspectives, for example
communication, privacy policies, customer service, security of transactions, flexibility of payment mechanism,
etc. Furthermore, level of communication and website involvement could ultimately affect consumer online
purchase intention (Jiang et al., 2010). Certain communication tools such as live chat or video chat are developed
www.ccsenet.org/ijbm International Journal of Business and Management Vol. 10, No. 10; 2015
160
to enhance online interaction. Online privacy policies are also presented in detailed manner as part of their
practice. However, most information policies are not clearly displayed in website, which may increase consumer
perception of ambiguity and uncertainty. In contrast, a website that displays this information compactly increases
the website quality, and consequently attributes to higher purchasing intention (Tsia et al., 2011). In addition,
with increased online purchase, the demand for better payment mechanisms becomes more critical. A secured
and flexible payment mechanism can clue to better website image thus increasing the likelihood of online
purchase (Kuo & Chen, 2011). Therefore, higher website performance may attribute higher purchase intention.
H2: Website performance positively influences consumer online purchase intention.
2.4 Buyer Perceptions
The online shopping experience is different from those of the brick and mortar mode due to the temporal
distance between the buyers and sellers (Tan, 1999). The online buyers may have several concerns prior to their
purchase action. These concerns create or impede consumer perception towards the particular product or website.
Buyer perception can be determined by past experiences, perceived safety or risk of Internet security (Wiesberg
et al., 2011). Previous purchase experiences serves as an indicator that either reduce or increase consumer’s
anxiety and uncertainty (Ranganathan & Jha, 2007; Ling et al., 2011), and consequently influences their online
purchase intention. Ranganathan and Jha (2007) further emphasized that buyer’s experience is more important
than website quality, security and privacy. However, not every online buyer can assess their perception based on
past experiences, for example first time or impulse buyers. Therefore, other concerns, such as consumer’s
tolerance of risk and perceived safety of online website may emerge. Some individuals may accept ambiguous
situation and tolerating uncertainty of Internet, whereas certain customers are keen to avoid any mistakes during
online purchasing rather than maximizing their own utility (Mitchell, 1999). Risk tolerance may motive or
deterrent consumer purchase intention, which normally coincides with psychological and situational
characteristics (Cho & Lee, 2006). Perceived safety of particular website can be reflected by the degree of
information transparency (Kotler & Armstrong, 2010). All the information obtained by consumers creates their
own perceived dimension in relation to particular website. Previous studies indicate that a positive effect of a
specific or an individual measurement of website performance (such as privacy, security, display, communication)
certainly impacts on general expertise, word of mouth testimonials (Roman & Cuestas, 2008, cited in Limbu et
al., 2011), and perceived trust of website (Yang et al., 2009). Therefore, buyer perception may not play a
‘stand-alone’ role, but a mediator impacting on the relation between website performance and consumer
purchase intention:
H3: Buyer perception mediates the relation between website performance and purchase intention.
Numerous studies investigate similar areas from a dispersed dimensions by addressing the effect of each distinct
factor (such as, trust, risk, privacy, ethics, experience, payment security, attitude, variety of products, website
design, efficiency, communication, service quality, word of mouth), but overlooked the integration or
interrelation among them. New insights may emerge when the measurements and constructs are combined or
re-structured. Next, certain factors are too narrow or too specific in terms of prediction of purchase intention.
Certain existing studies were lack of validation of empirical evidence due to absence of cross-validation in data
analysis and interpretation. Besides that, many literatures explore the effects of customer satisfaction as it is
based on past experience or a holistic evolution of all aspects of consumption (Kuo et al., 2012). However, as a
subjective concept, consumer satisfaction may not have a primary impact on consumer purchase intention,
although numerous studies do mention it as an important predictor. In fact, most consumers purchase decisions
are based on more objective fact rather than their emotional perception. A conceptual model is presented in
Figure 1, where ‘Website’ represents the website performance; ‘Buyer’ indicates buyer perception towards
internet shopping; ‘Seller’ reflects individual vendor’s online status; ‘Intention’ specifies the likelihood of
consumer online purchase intention.
www.ccse
n
3. Metho
d
This stud
y
p
rovinces
324 surve
y
adopted t
o
to H3) w
e
measure
m
SEM mo
d
cited in J
a
in SEM, i
t
an overes
t
Powell &
Jackson e
t
and 1.561
acceptabl
e
violation
o
multivari
a
reliable w
i
Accordin
g
conducte
d
the obser
v
extent to
w
this resea
r
validity in
n
et.org
/
ijbm
d
olo
gy
y
gathered em
p
of Mainland
C
y
s was retur
n
o
measure eac
h
e
re estimated
m
ent model be
f
d
el if the facto
r
a
ckson et al., 2
t
is necessary
t
t
imation of th
e
Schafer, 200
1
t
al., 2009). T
within accep
e
range [-7 a
n
o
f multivariat
e
a
te normality (
M
i
th maximum
l
g
to Anderso
n
d
first in order
t
v
ed variables t
o
w
hich a set of
r
ch assesses t
h
measurement
Inter
n
p
irical data us
i
C
hina in a peri
o
n
ed with 255
v
h
item from ‘s
t
using structu
r
f
ore assessing
r
s specified a
s
009, p. 6). Al
t
t
o assess the
d
e
chi-square st
a
1
) and down
w
h
e univariate n
table range [-
n
d 7] (Pallan
t
e
normality (
M
M
VN) (Fan
&
l
ikelihood (M
L
n
and Gerbin
t
o establish c
o
o
the underlyi
n
measured ite
m
h
e adequacy
o
models (see
T
n
ational Journa
l
Figure 1.
i
ng a self-desi
g
o
d of two mo
n
v
alid questio
n
t
rongly disagr
e
r
al equation
m
the structural
s
part of the
m
t
hough maxi
m
d
ata distributi
o
a
tistics, conse
q
w
ard biased s
t
ormality is ac
h
2 and 2]; ske
w
t
, 2011). M
u
M
VN). Howe
v
&
Wang, 1998,
L
).
g (1988), tw
o
o
nfidence in th
e
n
g constructs.
m
s actually re
fl
o
f each multi-
i
T
able 1).
l
of Business an
d
161
Conceptual
m
g
ned question
n
n
ths. Full ques
n
naires (respo
n
e
e (1)’ to ‘str
o
m
odeling (SE
M
model as “it
m
m
odel are not
w
m
um likelihoo
d
o
n as failure to
q
uently enhan
c
t
andard errors
h
ieved in this
s
w
ness of eac
h
u
ltivariate cri
t
v
er ML estim
a
Hu et al., 19
9
o
-step approa
e measureme
n
One of the p
r
fl
ects the theor
i
tem scale in
d Management
m
odel
n
aire among
4
tionnaire is
p
r
n
se rate, 63.7
5
o
ngly agree (7
)
M
), AMOS 2
1
m
akes little s
e
w
orthy of furt
h
d
(ML) is set a
meet the ass
u
c
e an inflated
(Nevitte &
H
s
tudy as kurto
h
item is bet
w
t
ical ratio is
a
tion is still r
o
9
2), thus resea
r
ch, a confir
m
n
t model, whic
h
r
imary objecti
v
etical latent c
o
capturing co
n
V
4
00 Chinese c
o
esented in Ap
p
5
%). A 7-
p
oi
n
)
’. The hypoth
1
. Researcher
s
e
nse to relate
c
h
er attention”
s a default est
i
u
mption of no
r
Type I error (
C
H
ancock, 200
1
sis of each ite
m
w
een 0.087 an
d
22.64, which
o
bust with mi
r
ch result of t
h
m
atory factor
h specifies th
e
v
es of CFA/S
E
o
nstruct (Hair
n
structs validit
y
V
ol. 10, No. 10;
o
nsumers fro
m
p
endix 1. A to
t
n
t Likert scal
e
esized relatio
n
s
first evaluat
c
onstruct wit
h
(Thompson,
2
i
mation proce
d
r
mality may l
e
C
urran et al.,
1
1
; Bandalos,
2
m
is between
0
d
1.512 withi
n
indicates a
s
ld departures
h
is study sho
u
analysis (CF
A
e
posited relati
o
E
M is to asse
s
et al., 2009).
T
y
and discri
m
2015
m
four
t
al of
e
was
n
(H1
e
the
i
n an
2
004,
d
ures
ad to
1
996;
2
002;
0
.013
n
the
s
light
from
ld be
A
) is
o
n of
s
s the
T
hus,
inant
www.ccsenet.org/ijbm International Journal of Business and Management Vol. 10, No. 10; 2015
162
Table 1. CFA and convergent validity (n=255)
UNSTD S.E. T-value P STD SMC 1-SMC CR AVE
WOM <--- Seller 1 0.656 0.430 0.570 0.824 0.541
Feedback <--- Seller 1.016 0.100 10.160 *** 0.773 0.598 0.402
Credit <--- Seller 1.106 0.105 10.543 *** 0.817 0.667 0.333
SQ <--- Seller 1.092 0.118 9.249 *** 0.685 0.469 0.531
Communication <--- Website 1 0.716 0.513 0.487 0.819 0.534
Payment <--- Website 1.095 0.088 12.393 *** 0.825 0.681 0.319
Variety <--- Website 1.064 0.091 11.670 *** 0.774 0.599 0.401
Privacy <--- Website 0.726 0.082 8.855 *** 0.586 0.343 0.657
Safety <--- Buyer 1 0.844 0.712 0.288 0.819 0.614
Risk <--- Buyer 0.776 0.090 8.603 *** 0.521 0.271 0.729
Experience <--- Buyer 1.155 0.066 17.560 *** 0.927 0.859 0.141
Likelihood <--- Intention 1 0.905 0.819 0.181 0.904 0.825
Possibility <--- Intention 0.900 0.043 20.943 *** 0.911 0.830 0.170
The standardized loading estimates of all items are significant (p<0.001) and higher than 0.5 (Anderson &
Gerbin, 1988; Hair et al., 2009). The average variance extracted (AVE) estimates are between 0.534 and 0.825
(above 0.5, Bagozzi & Yi, 1988; Ping, 2004) and construct reliability (CR) of each construct is between 0.819
and 0.904 (above 0.7, Fornell & Larcker, 1981), which indicates that the convergent validity is achieved.
Discriminant validity assesses the extent to which a construct is truly distinct from other constructs (Hair at al.,
2009). Although the correlation (Pearson’s R) among constructs can be used to detect the issue of
muticollinearity, there is no firm rule that a correlation with other measurements below absolute 0.85 is a cut
point. With Anderson and Gerbin’s first step approach (1988), the correlations among four latent variables (seller,
buyer, website, and intention) are between 0.596 and 0.868. Larger correlations should be tested by examining
the confidence interval of correlation to examine if they include ‘1’ (Anderson & Gerbing, 1988; Ping, 2004). In
addition, parameter estimate method also can be adopted to further confirm the distinctness among constructs
(Bagozzi et al., 1988; Hooper et al., 2008). The discriminant test is presented in Table 2. The Bias-Corrected
confidence interval (95%) does not include ‘1’; so do Percentile CI and the Parameter Estimate Interval. Thus,
discriminant validity among four latent constructs is supported.
Table 2. Discriminant validity
Bias-Corrected Percentile ر*1.96
Parameter Estimate Lower Upper Lower Upper SE Lower Upper
Website <--> Seller 0.758 0.645 0.855 0.641 0.852 0.053 0.651 0.859
Website <--> Buyer 0.830 0.763 0.886 0.767 0.892 0.031 0.771 0.893
Intention <--> Buyer 0.779 0.699 0.844 0.697 0.843 0.036 0.704 0.846
Website <--> Intention 0.868 0.794 0.930 0.793 0.928 0.034 0.799 0.933
Seller <--> Intention 0.754 0.640 0.845 0.641 0.845 0.052 0.649 0.853
Seller <--> Buyer 0.596 0.473 0.692 0.475 0.693 0.056 0.485 0.705
4. Research Result and Discussion
Following the proposed measurement model, a conceptual structural equation model is established to test the
hypothesized relations among constructs. The construct model includes two exogenous latent variables (‘website’
and ‘seller’) and two endogenous variables (‘buyer’ and ‘intention’). The goodness-of-fit indices of this model
are within an acceptable range (chi-square = 112.699, df = 60, p<0.001, chi-square/df = 1.878, GFI = 0.936,
AGFI = 0.903, RMSEA = 0.059, SRMR = 0.037, TLI = 0.965, IFI = 0.973, CFI = 0.973, NFI = 0.944). As a
result, there is no negative error variance of variables or ‘Heywood Case’ occurs (Rindskopf, 1984; Kolenikov &
Bollen, 2012). The standard errors of variance are relatively small between 0.058 and 0.223.
Hypotheses are tested by examining ‘the sign, size, and statistical significance of the structural coefficients’
(Baumgartner & Homburg, 1996, p. 146). All hypotheses tests are statistically significant among latent variables
www.ccse
n
in structu
r
coefficien
t
0.001). T
h
status (H1
)
effects of
w
0.001) ha
s
b
y 1, purc
h
0.334. Th
e
consumer
p
erceptio
n
Tab l e 3. S
t
Buyer
Intention
Intention
Intention
In additi
o
p
erceptio
n
p
<0.001,
H
Based on
exists bet
w
approach
i
quantifica
t
weakness
sampling
d
effect sa
m
resamplin
g
1998). Th
e
b
ootstrap
(
of buyer p
n
et.org
/
ijbm
r
al model an
d
t
s f
r
om websit
e
h
e parameter e
s
)
are also stati
s
w
ebsite perfo
r
s
greater impa
c
h
ase intention
e
exogenous v
a
online purcha
n
, and seller st
a
t
ructural regre
<---
W
<--- B
u
<---
W
<--- S
e
o
n, partial in
f
n
(mediator).
T
H
3_a). Buyer
p
Baron and K
e
w
een website
p
i
s among the l
o
t
ion to test t
h
and is often u
s
d
istribution o
f
m
pling distrib
u
g
to make inf
e
e
standard err
o
(
2000 sample
s
erception bet
w
Inter
n
d
also consist
e
e
performanc
e
s
timates for th
e
s
tical signific
a
r
mance and se
l
c
t than seller s
t
goes up by 0.
6
a
riable of web
s
se intention, t
h
a
tus.
ssion weigh
t
U
N
W
ebsite 0.
8
u
ye
r
0.
2
W
ebsite 0.
6
e
lle
r
0.
3
f
luence of w
e
T
he relation
b
p
erception is
a
e
nny’s (1986)
p
erformance a
n
o
west in pow
e
h
e mediation
e
s
ed as a suppl
e
f
indirect effe
c
u
tion (Bollen
&
e
rences rather
o
r based on t
h
s
with 95% P
C
w
een website
p
n
ational Journa
l
e
nt with the p
r
e
to buyer perc
e
e
relationships
a
nt (p < 0.05)
a
l
ler status on
p
t
atus does (0.3
6
48, while wh
e
s
ite performan
h
is model exp
l
N
STD S.
E
8
98 0.
0
2
62 0.1
6
48 0.1
3
34 0.1
Figure 2
.
e
bsite perfor
m
b
etween webs
i
a
lso positivel
y
causal steps
a
n
d consumer
p
e
r (MacKinno
n
e
ffect. Althou
g
e
ment to Baro
n
c
t is met. Act
u
&
Stine, 1990
than making
a
h
e bootstrap di
C
confidence l
e
p
erformance a
n
l
of Business an
d
163
r
oposed direc
t
e
ption (H3_a)
of purchase i
n
a
nd consistent
p
urchase inten
t
34, t = 3.190,
p
e
n seller status
ce can explai
n
l
ains 78.9% o
f
E
. T-val
u
0
85 10.54
11 2.363
63 3.966
05 3.190
.
Path coeffici
e
m
ance on pur
c
i
te performan
c
y
associated w
a
pproach, the
p
urchase inten
t
n
et al., 2002;
g
h Sobel test
n
and Kenny’
s
u
ally, it is les
; Stone & So
b
a
ssumptions a
b
stribution of t
h
e
vel) is adopt
e
n
d purchase i
n
d Management
t
ion (see Tab
l
and purchase
i
n
tention with b
u
with the prop
o
t
ion, website p
p
= 0.001). W
h
goes up by 1,
p
n
67.8% of the
v
f
its variation
w
u
e P
1 ***
0.018
***
0.001
e
nts
c
hase intenti
o
c
e and buyer
ith purchase i
n
intervening v
t
ion. Howeve
r
Fritz & Mack
i
(Sobel, 1982,
s
approach, So
s possible to
g
b
el, 1990). B
o
b
out the popu
l
h
e mediation
e
e
d in this rese
a
n
tention (see T
a
V
l
e 3 and Figu
r
i
ntention (H2)
u
yer percepti
o
o
sed assumpti
o
erformance (0
h
en website p
e
p
urchase inten
v
ariation in b
u
w
ith website
p
STD
0.824
0.219
0.497
0.250
o
n is distribu
t
perception is
n
tention (0.21
9
ariable effect
r
, Ba
r
on and
K
i
nnon; 2007).
1986) can o
v
o
bel test assu
m
g
et a normal
d
o
otstrap uses
c
l
ation (Lockw
o
e
ffect can be
r
a
rch to test th
e
a
ble 4).
V
ol. 10, No. 10;
r
e 2). All the
are significan
t
o
n (H3_b) and
s
o
ns. Compari
n
.648, t = 3.96
6
e
rformance go
tion just goes
u
u
yer perceptio
n
p
erformance,
b
SMC
0.678
0.789
t
ed through
b
significant (
0
9
, p=0.018, H
(buyer perce
p
K
enny’s causal
It is not base
d
v
ercome the
a
m
es the normal
i
d
istributed in
d
c
omputer inte
n
ood & Macki
n
r
educed. Ther
e
e
intervening
e
2015
path
t
(p <
s
eller
n
g the
6
, p <
e
s up
u
p by
n
. For
b
uyer
H3_a
H3_b
H2
H1
b
uyer
.824,
3
_b).
p
tion)
steps
d
on a
a
bove
i
ty of
d
irect
n
sive
n
non,
e
fore,
e
ffect
www.ccsenet.org/ijbm International Journal of Business and Management Vol. 10, No. 10; 2015
164
Table 4. Bootstrap mediation effect
Product of
Coefficients Bias-Corrected 95% CI Percentile 95% CI
Estimate SE T-Value Lower Upper P (2-tailed) Lower Upper P (2-tailed)
Total Effect
Intention <-- Website 0.883 0.148 5.966 0.591 1.187 0.001 0.588 1.184 0.001
Indirect Effect
Intention <-- Website 0.235 0.118 1.992 0.031 0.494 0.025 0.013 0.472 0.038
Direct Effect
Intention <-- Website 0.648 0.187 3.465 0.300 1.029 0.002 0.307 1.032 0.001
The indirect (mediated) effect of website performance on purchase intention is 0.235 (t = 1.992, p < 0.05). Both
Bias-Corrected 95% confidence interval (CI) and Percentile 95% confidence interval (CI) do not include zero,
which indicate the intervening effect of buyer perception between website performance and consumer purchase
intention is significantly different from zero; so do the total effect (0.883, t = 5.966, p < 0.001) and direct effect
(0.648, t = 3.465, p < 0.001). Both total effect and direct effect are statistically significant. It implies that there is a
partial medication impact exists. The effect of website performance towards purchase intention is partially
medicated by buyer perception with an effect size of 26.61%.
Cross-validation has been employed extensively in order to examine the predictive validity of model (Cudeck &
Brown, 1983). The objective is to identify the model from a set of competing alternative that replicates best
across different population. According to Cudeck and Browne (1983), a random sample can be assumed by
splitting the data samples randomly into two subsamples (50:50): calibration sample and validation sample. The
former is used to develop the model, while the latter is used to test the derived model. As presented in Table 5,
this research has a good model stability (TLI <0.01, CFI < 0.05, p > 0.05). It indicates that the prediction
validity of this model can be generalized to other distribution samples.
Table 5. Cross validation
Model NPAR CMIN DF DF CMIN P TLI CFI
Unconstrained 62 226.169 120
Measurement weights 53 237.402 129 9 11.233 0.260 -0.003 -0.002
Structural weights 49 238.392 133 4 0.990 0.911 -0.004 0.002
Structural covariances 46 244.581 136 3 6.188 0.103 0.000 -0.002
Structural residuals 44 247.456 138 2 2.876 0.237 0.000 0.000
Measurement residuals 31 263.468 151 13 16.012 0.249 -0.004 -0.002
5. Conclusion and Implications
The findings of this research present how website performance and seller status influence consumer online
purchase intention. Buyer perception mediates the relationship between the website performance and consumer
purchase intention. Research results indicate several implications. Firstly, theoretical implication demonstrates
that the structural model with an acceptable model fit and all the proposed hypotheses are supported.
Approximately 79% variation of consumer online purchase intention can be explained by three predictors:
website performance, seller status, and buyer perception. The confirmed measurement model and examined
reliability and validity indicators attest that the proposed instrument validly and reliably measure the constructs
in this model. Acceptable discriminant validity proves that the constructs are truly distinct from each other. The
cross-validation further examines stability and predictive validity of the construct model, thus enhances the
generalizability and managerial implications in practice. Next, research result confirms that there is a partial
mediation effect between website performance and consumer purchase intention, thus the buyer perception is not
a ‘standalone’ concept. This mediator partially distributes the effect of website performance (objective-oriented
entity) towards the consumer’s subjective purchase decision. The estimate of indirect effect is 0.235 (p < 0.05,
see Table 4), occupying 26.61% of total effect from website performance to consumer purchase intention. While,
the estimate of direct effect is 0.648 (p < 0.001) employing 73.39% of total effect. Compared with indirect effect,
the direct effect has greater impacts on purchase intention, which implies that the fact of website performance is
a primary index for consumer’s decision of online purchase. Consumer purchase intention mainly stems from
www.ccsenet.org/ijbm International Journal of Business and Management Vol. 10, No. 10; 2015
165
objective entities of website performance. It implies that the electric marketers should emphasize more on their
own performance as the consumer perception (mediator) is subjective and out of their reach. Thus, website
managers need to allocate more resources and efforts to improve customer services as customer service in an
online context reflects the performance of website itself (Limbu et al., 2012). The website administrators should
ensure that consumers are well acknowledged about the good performance of website, such as efficient
communication, transparent payment, privacy protection, variety of products, ease use, clear instruction, and be
aware of vague statements. Today, the traditional Chinese proverb “Doing well and not wanting others to know it”
is not suitable in this context. The website managers need to demonstrate how well they performance to convince
consumers to place order online. Finally, online seller status also significantly influences consumer purchase
intention. It implies that it is important for vendors to develop or strengthen their own status, for instance gaining
more positive reviewer comments, gaining high ranking position, providing flexible channels of payment,
prompt delivery service, and product warranty, etc. Online vendors should be more explicit when describing the
product information and additional charges, return policies or situation in which item/product are
non-refundable.
6. Limitation and Further Research
Some limitations to the present study are specific whereas others are common to survey research. Although 79%
variation of consumer online purchase intention can be explained by three key determinants presented in this
study, other predictors, such as price, competition (Pan et al., 2002), website brand (Chang & Chen, 2008) and
value creation (Garicano & Kaplan, 2002; Bakker et al., 2008) may also have specific impacts on consumers’
purchase intention. Next, the scope of this research was in China Mainland; therefore, caution might be advised
when generalizing the research finding to different countries or regions. In addition, there is lack of evidence that
similar research results can be discerned in other contexts or different industries, such as Internet Banking, B2B
E-Commerce or retailing sectors. Therefore, further research aims to generalize the conceptual model and
compare the results in relatively broader scopes. Finally, the speed of change in the study context as consumers’
increasing experience with the E-commerce developments may certainly affect their decision making in future.
References
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social. Behaviour. Englewood Cliffs, NJ:
Prentice-Hall.
Anderson, J. C., & Gerbing, D. W. (1982). Some methods for respecifying measurement models to obtain
unidimensional construct measurement. Journal of Marketing Research, 453-460.
Anderson, J. C., & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solutions, and
goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49(2),
155-173.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended
two-step approach. Psychological Bulletin, 103(3), 411. http://dx.doi.org/10.1037/0033-2909.103.3.411
Awad, N. F., & Ragowsky, A. (2008). Establishing trust in electronic commerce through online word of mouth:
An examination across genders. Journal of Management Information Systems, 24(4), 101-121.
http://dx.doi.org/10.2753/mis0742-1222240404
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of
Marketing Science, 16(1), 74-94. http://dx.doi.org/10.1007/bf02723327
Bai, B., Law, R., & Wen, I. (2008). The impact of website quality on customer satisfaction and purchase
intentions: Evidence from Chinese online visitors. International Journal of Hospitality Management, 27(3),
391-402. http://dx.doi.org/10.1016/j.ijhm.2007.10.008
Bakker, E., Zheng, J., Knight, L., & Harland, C. (2008). Putting e-commerce adoption in a supply chain context.
International Journal of Operations & Production Management, 28(4), 313-330.
http://dx.doi.org/10.1108/01443570810861543
Bandalos, D. L. (2002). The effects of item parceling on goodness-of-fit and parameter estimate bias in structural
equation modeling. Structural Equation Modeling, 9(1), 78-102.
http://dx.doi.org/10.1207/s15328007sem0901_5
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological
research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology,
51(6), 1173. http://dx.doi.org/10.1037/0022-3514.51.6.1173
www.ccsenet.org/ijbm International Journal of Business and Management Vol. 10, No. 10; 2015
166
Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and
consumer research: A review. International Journal of Research in Marketing, 13(2), 139-161.
http://dx.doi.org/10.1016/0167-8116(95)00038-0
Bente, G., Baptist, O., & Leuschner, H. (2012). To buy or not to buy: Influence of seller photos and reputation on
buyer trust and purchase behavior. International Journal of Human-Computer Studies, 70(1), 1-13.
http://dx.doi.org/10.1016/j.ijhcs.2011.08.005
Bollen, K. A., & Stine, R. (1990). Direct and indirect effects: Classical and bootstrap estimates of variability.
Sociological methodology, 20(1), 15-140. http://dx.doi.org/10.2307/271084
Chang, H. H., & Chen, S. W. (2008). The impact of online store environment cues on purchase intention: Trust
and perceived risk as a mediator. Online Information Review, 32(6), 818-841.
http://dx.doi.org/10.1108/14684520810923953
Chang, H. H., & Wang, I. C. (2008). An investigation of user communication behavior in computer mediated
environments. Computers in Human Behavior, 24(5), 2336-2356.
http://dx.doi.org/10.1016/j.chb.2008.01.001
Chen, C. W. D., & Cheng, C. Y. J. (2009). Understanding consumer intention in online shopping: A
respecification and validation of the DeLone and McLean model. Behaviour & Information Technology,
28(4), 335-345. http://dx.doi.org/10.1080/01449290701850111
Chen, J., Zhang, C., Yuan, Y., & Huang, L. (2007). Understanding the Emerging C2C Electronic Market in China:
An Experience-Seeking Social Marketplace. Electronic Markets, 17(2), 86-100.
http://dx.doi.org/10.1080/10196780701292468
Cho, J., & Lee, J. (2006). An integrated model of risk and risk-reducing strategies. Journal of Business Research,
59(1), 112-120. http://dx.doi.org/10.1016/j.jbusres.2005.03.006
Corritore, C. L., Beverly, K., & Susan, W. (2003). On-line trust: Concepts, evolving themes, a model.
International Journal of Human-Computer Studies, 58(6), 737-758.
http://dx.doi.org/10.1016/s1071-5819(03)00041-7
Cudeck, R., & Browne, M. W. (1983). Cross-validation of covariance structures. Multivariate Behavioral
Research, 18(2), 147-167. http://dx.doi.org/10.1207/s15327906mbr1802_2
Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification
error in confirmatory factor analysis. Psychological methods, 1(1), 16.
http://dx.doi.org/10.1037/1082-989x.1.1.16
Davis, L., Wang, S., & Lindridge, A. (2008). Culture influences on emotional responses to on-line store
atmospheric cues. Journal of Business Research, 61(8), 806-812.
http://dx.doi.org/10.1016/j.jbusres.2007.08.005
Day, N. E. (1969). Estimating the components of a mixture of normal distributions. Biometrika, 56(3), 463-474.
http://dx.doi.org/10.1093/biomet/56.3.463
Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2001). Atmospheric qualities of online retailing: A conceptual
model and implications. Journal of Business Research, 54(2), 177-184.
http://dx.doi.org/10.1016/s0148-2963(99)00087-9
Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2003). Empirical testing of a model of online store atmospherics
and shopper responses. Psychology & Marketing, 20(2), 139-150. http://dx.doi.org/10.1002/mar.10064
Éthier, J., Hadaya, P., Talbot, J., & Cadieux, J. (2006). B2C web site quality and emotions during online
shopping episodes: An empirical study. Information & Management, 43(5), 627-639.
http://dx.doi.org/10.1016/j.im.2006.03.004
Everard, A., & Galletta, D. F. (2006). How presentation flaws affect perceived site quality, trust, and intention to
purchase from an online store. Journal of Management Information Systems, 22(3), 56-95.
http://dx.doi.org/10.2753/mis0742-1222220303
Fan, X., & Wang, L. (1998). Effects of potential confounding factors on fit indices and parameter estimates for
true and misspecified SEM models. Educational and Psychological Measurement, 58(5), 701-735.
http://dx.doi.org/10.1177/0013164498058005001
Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological
www.ccsenet.org/ijbm International Journal of Business and Management Vol. 10, No. 10; 2015
167
Science, 18(3), 233-239. http://dx.doi.org/10.1111/j.1467-9280.2007.01882.x
Garicano, L., & Kaplan, S. N. (2002). Business-to-business e-commerce: Value creation, value capture and
valuation. Advances in applied microeconomics, 11, 89-125.
http://dx.doi.org/10.1016/s0278-0984(02)11029-7
Hair, J. F. (2009). Multivariate Data Analysis: A Global Perspective (7th ed.). Upper Saddle River: Prentice Hall.
Hausman, A. V., & Siekpe, J. S. (2009). The effect of web interface features on consumer online purchase
intentions. Journal of Business Research, 62(1), 5-13. http://dx.doi.org/10.1016/j.jbusres.2008.01.018
Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining
model fit. Articles, 2.
Hsu, C. L., Chang, K. C., & Chen, M. C. (2012). The impact of website quality on customer satisfaction and
purchase intention: Perceived playfulness and perceived flow as mediators. Information Systems and
e-Business Management, 10(4), 549-570. http://dx.doi.org/10.1007/s10257-011-0181-5
Hu, L. T., Bentler, P. M., & Kano, Y. (1992). Can test statistics in covariance structure analysis be trusted?
Psychological bulletin, 112(2), 351. http://dx.doi.org/10.1037/0033-2909.112.2.351
IRESEARCH. (2006). China C2C E-Commerce Research Report 2005. ShangHai, China, IResearch Consulting
Group.
Jackson, D. L., Gillaspy Jr, J. A., & Purc-Stephenson, R. (2009). Reporting practices in confirmatory factor
analysis: An overview and some recommendations. Psychological methods, 14(1), 6.
http://dx.doi.org/10.1037/a0014694
Jiang, Z., Chan, J., Tan, B. C., & Chua, W. S. (2010). Effects of interactivity on website involvement and
purchase intention. Journal of the Association for Information Systems, 11(1), 1.
Jun, G., & Jaafar, N. I. (2011). A study on consumers’ attitude towards online shopping in China. International
Journal of Business and Social Science, 2(22), 122-132.
Kenny, D. A. (2006). Series editor’s note. In T. A. Brown (Ed.), Confirmatory factor analysis for applied
research (pp. 4-10). New York: Guilford. http://dx.doi.org/10.1177/1094428108323758
Kolenikov, S., & Bollen, K. A. (2012). Testing Negative Error Variances Is a Heywood Case a Symptom of
Misspecification? Sociological Methods & Research, 41(1), 124-167.
http://dx.doi.org/10.1177/0049124112442138
Kuo, H. M., & Chen, C. W. (2011). A study of merchandise information and interface design on B2C websites.
Journal of Marine Science and Technology, 19(1), 15-22.
Kuo, H. M., & Chen, C. W. (2011). Application of quality function deployment to improve the quality of Internet
shopping website interface design. International Journal of Innovative Computing, Information and Control,
7(1), 253-268.
Kuo, Y. F., Hu, T. L., & Yang, S. C. (2013). Effects of inertia and satisfaction in female online shoppers on
repeat-purchase intention: The moderating roles of word-of-mouth and alternative attraction. Managing
Service Quality, 23(3), 168-187. http://dx.doi.org/10.1108/09604521311312219
Kwahk, K. Y., Ge, X., & Lee, J. M. (2012). The Effects of Use of Instant Messenger on Purchase Intention: The
Context of Chinese C2C E-Commerce. Asia Pacific Journal of Information Systems, 22(2).
Lim, N. (2003). Consumers’ perceived risk: Sources versus consequences. Electronic Commerce Research and
Applications, 2(3), 216-228. http://dx.doi.org/10.1016/s1567-4223(03)00025-5
Limbu, Y. B., Wolf, M., & Lunsford, D. (2012). Perceived ethics of online retailers and consumer behavioral
intentions: The mediating roles of trust and attitude. Journal of Research in Interactive Marketing, 6(2),
133-154. http://dx.doi.org/10.1108/17505931211265435
Ling, K. C., Bin Daud, D., Piew, T. H., Keoy, K. H., & Hassan, P. (2011). Perceived risk, perceived technology,
online trust for the online purchase intention in Malaysia. International Journal of Business and
Management, 6(6), 167. http://dx.doi.org/10.5539/ijbm.v6n6p167
Lockwood, C. M., & MacKinnon, D. P. (1998). Bootstrapping the standard error of the mediated effect. In
Proceedings of the 23rd annual meeting of SAS Users Group International (pp. 997-1002). Cary, NC: SAS
Institute, Inc.
www.ccsenet.org/ijbm International Journal of Business and Management Vol. 10, No. 10; 2015
168
Luo, J., Ba, S., & Zhang, H. (2012). The Effectiveness of Online Shopping Characteristics and Well-Designed
Websites on Satisfaction. MIS Quarterly, 36(4), 1131-1144.
MacCallum, R. (1986). Specification searches in covariance structure modeling. Psychological Bulletin, 100(1),
107. http://dx.doi.org/10.1037//0033-2909.100.1.107
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of
methods to test mediation and other intervening variable effects. Psychological Methods, 7(1), 83.
http://dx.doi.org/10.1037/1082-989x.7.1.83
McKnight, D. H., Kacmar, C. J., & Choudhury, V. (2004). Dispositional trust and distrust distinctions in
predicting high-and low-risk internet expert advice site perceptions. E-Service, 3(2), 35-58.
http://dx.doi.org/10.2979/esj.2004.3.2.35
Mitchell, V. W. (1999). Consumer perceived risk: Conceptualisations and models. European Journal of
marketing, 33(1/2), 163-195. http://dx.doi.org/10.1108/03090569910249229
Nevitt, J., & Hancock, G. R. (2001). Performance of bootstrapping approaches to model test statistics and
parameter standard error estimation in structural equation modeling. Structural Equation Modeling, 8(3),
353-377. http://dx.doi.org/10.1207/s15328007sem0803_2
Pallant, J. (2011). SPSS Survival Manual 4th edition: A step by step guide to data analysis using SPSS version
18.
Pan, X., Shankar, V., & Ratchford, B. T. (2002). Price competition between pure play versus bricks-and-clicks
e-tailers: Analytical model and empirical analysis. Advances in Applied Microeconomics, 11, 29-61.
http://dx.doi.org/10.1016/s0278-0984(02)11027-3
Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention:
The moderating role of involvement, International Journal of Electronic Commerce, 11(4), 125-148.
http://dx.doi.org/10.2753/jec1086-4415110405
Ping, R. A., Jr. (2004). On Assuring Valid Measures for Theoretical Models Using Survey Data,
Powell, D. A., & Schafer, W. D. (2001). The robustness of the likelihood ratio chi-square test for structural
equation models: A meta-analysis. Journal of Educational and Behavioral Statistics, 26(1), 105-132.
http://dx.doi.org/10.3102/10769986026001105
Ranganathan, C., & Jha, S. (2007). Examining online purchase intentions in B2C e-commerce: testing an
integrated model. Information Resources Management Journal (IRMJ), 20(4), 48-64.
http://dx.doi.org/10.4018/irmj.2007100104
Rindskopf, D. (1984). Structural Equation Models Empirical Identification, Heywood Cases, and Related
Problems. Sociological Methods & Research, 13(1), 109-119.
http://dx.doi.org/10.1177/0049124184013001004
Román, S., & Cuestas, P. J. (2008). The perceptions of consumers regarding online retailers’ ethics and their
relationship with consumers’ general internet expertise and word of mouth: A preliminary analysis. Journal
of Business Ethics, 83(4), 641-656. http://dx.doi.org/10.1007/s10551-007-9645-4
Sobel, M. E. (1982). Aysmptotic confidence intervals for indirect effects in structural equation models. In S.
Leinhardt (Ed.), Sociological Methodology (pp. 290-212). San Francisco: Jossey-Boss.
http://dx.doi.org/10.2307/270723
Sobel, M. E. (1986). Some new results on indirect effects and their standard errors in covariance structure
models. In N. Tuma (Ed.), Sociological Methodology (pp. 159-186). Washington, DC: American
Sociological Association. http://dx.doi.org/10.2307/270922
Stone, C. A., & Sobel, M. E. (1990). The robustness of estimates of total indirect effects in covariance structure
models estimated by maximum. Psychometrika, 55(2), 337-352. http://dx.doi.org/10.1007/bf02295291
Tan, S. J. (1999). Strategies for reducing consumers’ risk aversion in Internet shopping. Journal of Consumer
Marketing, 16(2), 163-180. http://dx.doi.org/10.1108/07363769910260515
Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications.
American Psychological Association. http://dx.doi.org/10.1037/10694-000
Tsai, J. Y., Egelman, S., Cranor, L., & Acquisti, A. (2011). The effect of online privacy information on
purchasing behavior: An experimental study. Information Systems Research, 22(2), 254-268.
www.ccsenet.org/ijbm International Journal of Business and Management Vol. 10, No. 10; 2015
169
http://dx.doi.org/10.1287/isre.1090.0260
Wang, Q., & Dai, Y. (2013). The Influence of Online Product Presentation and Seller Reputation on the
Consumers’ Purchase Intention across Different Involvement Products.
Wee, C. S., Ariff, M. S. B. M., Zakuan, N., Tajudin, M. N. M., Ismail, K., & Ishak, N. (2014). Consumers
Perception, Purchase Intention and Actual Purchase Behavior of Organic Food Products. Review of
Integrative Business and Economics Research, 3(2), 378.
Weisberg, J., Te’eni, D., & Arman, L. (2011). Past purchase and intention to purchase in e-commerce: the
mediation of social presence and trust. Internet Research, 21(1), 82-96.
Xiao, M., Ju, J., & Fan, Y. (2013). Losing to Win: Reputation Management of Online Sellers. In 2013 Meeting
Papers (No. 192). Society for Economic Dynamics.
Yang, M. H., Chandlrees, N., Lin, B., & Chao, H. Y. (2009). The effect of perceived ethical performance of
shopping websites on consumer trust. Journal of Computer Information Systems, 50(1), 15.
Yen, C. H., & Lu, H. P. (2008). Effects of e-service quality on loyalty intention: An empirical study in online
auction. Managing Service Quality, 18(2), 127-146. http://dx.doi.org/10.1108/09604520810859193
Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2002). Service quality delivery through web sites: A critical
review of extant knowledge. Journal of the Academy of Marketing Science, 30(4), 362-375.
http://dx.doi.org/10.1177/009207002236911
Zhang, Y., Bian, J., & Zhu, W. (2013). Trust fraud: A crucial challenge for China’s e-commerce market.
Electronic Commerce Research and Applications, 12(5), 299-308.
http://dx.doi.org/10.1016/j.elerap.2012.11.005
Zhang, Y., Fang, Y., Wei, K. K., Ramsey, E., McCole, P., & Chen, H. (2011). Repurchase intention in B2C
e-commerce—A relationship quality perspective. Information & Management, 48(6), 192-200.
http://dx.doi.org/10.1016/j.im.2011.05.003
Appendix
Appendix 1. Questionnaire and factor loading
Questionnaires Items Factor
Loading
P
Website Performance
The payment transaction on that particular website is reliable and flexible. Payment 0.825 ***
The websites (e.g. Taobao, Alibaba, Ebay) offer a wide variety of products. Variety 0.774 ***
The websites protect my privacy and personal information. Privacy 0.586 ***
The communication medium and tools in websites are efficient and helpful. Communication 0.716 ***
Seller Status
I use word of mouth to evaluate seller’s practice and honesty. WOM 0.656 ***
I check customer feedbacks and reviews of particular vendors before placing order. Feedback 0.773 ***
I check vendor’s reputation and online ranking status before purchasing. Credit 0.817 ***
I prefer vendors who provide good quality of service. SQ 0.685 ***
Buyer Perception
Internet shopping is safe and secured. Safety 0.844 ***
I can tolerate certain risk when shopping online. Risk 0.521 ***
I have a pleasant experience of Internet shopping. Experience 0.927 ***
Customer Purchase Intention
I intend to place order online in near future. Likelihood 0.905 ***
There is a high possibility for me to shop online. Possibility 0.911 ***
www.ccsenet.org/ijbm International Journal of Business and Management Vol. 10, No. 10; 2015
170
Appendix 2. Covariance matrix
rowtype_ varname_ experience risk safety privacy variety payment SQ credit possibility likelihood WOM feedback communication
cov experience 2.32
cov risk 1.31 3.32
cov safety 1.72 1.24 2.10
cov privacy 0.99 0.57 0.80 1.91
cov variety 1.40 0.93 1.17 1.01 2.35
cov payment 1.39 1.07 1.34 1.01 1.42 2.20
cov SQ 1.24 0.65 0.90 0.90 1.33 1.22 3.04
cov credit 1.07 0.75 0.86 0.55 1.06 1.18 1.36 2.19
cov possibility 1.37 0.92 1.12 0.79 1.38 1.40 1.34 1.28 2.08
cov likelihood 1.74 1.02 1.46 0.94 1.51 1.58 1.34 1.27 1.92 2.60
cov WOM 0.90 0.59 0.73 0.68 1.12 1.01 1.16 1.36 1.15 1.20 2.78
cov feedback 0.82 0.60 0.70 0.58 0.95 1.00 1.37 1.38 1.13 0.99 1.24 2.07
cov communication 1.33 0.73 1.05 1.03 1.32 1.34 1.24 0.95 1.39 1.37 0.72 0.81 2.43
n 255 255 255 255 255 255 255 255 255 255 255 255 255
Copyrights
Copyright for this article is retained by the author(s), with first publication rights granted to the journal.
This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution
license (http://creativecommons.org/licenses/by/3.0/).
... This term states that not necessarily every online search on the internet leads to an online purchase. Reasons could be many, but here in this case according to Jiang et al. (2015), poor interface and experience of the website can lead towards purchase abandonment. According to a survey conducted by Kearney (2001), 82 percent of people who intend to buy online, leave the shopping experience in the middle due to poor interface/lack of features available on the website. ...
... If the risk attached is low, then there are chances that the consumer will intend to buy online, however, if the risk is high there are very low chances of the consumer purchasing online. Furthermore, Jiang et al. (2015) argued that there are various factors that customers tend to ignore while making purchases. Perceived risk is a mediating factor in online shopping, it has a significant effect but not a direct effect on the online buying. ...
Full-text available
Article
Purpose-The motive of this study is to find out the determining factors that encourage and influence the online buying behaviour amongst consumers. In a world where everything is available on the internet and everything is becoming digitised, developing and emerging markets are still behind the developed markets and need to measure up to them in order to stand out. There are various factors that influence online buying behaviour amongst consumers, but this study will focus only on those producing the most impact. Design/Methodology/Approach-This study is based on a quantitative approach, as it helps in testifying and applying the hypothesis. It is a correlation study where it aims to find out the relationship between the variables. The statistical tool used in this study is SPSS and the tests that have been applied were Multiple Regression Analysis and Reliability Analysis. The sample size is 350 and the targeted audience are people living in Malaysia from age 18 and above. Findings-The findings of this study suggest that the hypotheses are accepted and the literature review provided as per the earlier studies is consistent with the hypothesis of our study, which states that perceived benefit and psychological factors (such as security, privacy, and trust) have a direct and significant relationship with online buying behaviour; on the contrary, perceived risks have an inverse and negative relationship with online buying behaviour. Research Limitations/Implications-This study is only applicable in a Malaysian context, as the demographics and economic situation of the market was kept to these conditions while doing this research. Also, the sample of size does not International Journal of Innovation, Creativity and Change. www.ijicc.net Volume 8, Issue 8, 2019 223 necessarily cover every individual in the Malaysian context. The results might vary. Furthermore, this study is beneficial for the marketers and the business managers of e-commerce websites. Also, it should be used as an example for teaching students of business to demonstrate the e-commerce and online shopping behaviour influencing factors, as our future is based on the whole new digital world.
... These usable interactive websites tend to create more positive attitudes towards the online store and increase conversion rates (Becker 2001;Palla, 2017). Jiang (2010) conducted a research with the aim of understanding how website interactivity can impact purchase intention, and results indicated that websites with high level of active control and reciprocal communication increases customer involvement with the website, that ultimately leads to higher purchase intention (Jiang et al. 2010;Jiang, 2015). ...
Full-text available
Conference Paper
With the emergence of new information and communication technology, the presence of concept of interactivity in the field of marketing empirical investigations has dramatically increased. To expand the knowledge of the domain of interactivity, this study was conducted to examine the influence of perceived website interactivity on website usage intentions, and the mediation effect of perceived website credibility on the relationship between interactivity and intention. For this empirical research study, the framework was mainly drawn from TAM and MAIN models, which are related to technology acceptance and adaptation. Moreover, this study aims at enhancing customers' intention to use the hotel's official websites through interactive features rather than the involvement of a third party. The study was conducted by collecting data through close-ended questionnaires from 200 international visitors to Sri Lanka, who also have visited a Sri Lankan Hotel's official website at least once. The results indicate that the more the viewers of the website perceive that the website is interactive, the more the viewers perceived that the website is credible, which in turn leads to several behavioral intentions. Also, this study highlights the importance of each of the three major dimensions of interactivity (active control/user control, two-way communication/reciprocal communication and responsiveness/synchronicity) in the hotel sector. Its general conclusion, theoretical contribution, societal implication, managerial contribution and implications for future research were discussed at the end.
... After it was introduced, it did not become famous right away. People had to go through a lot to understand e-commerce (Jiang et al. 2015); however, in countries like Indonesia, online shopping is a comparatively newer concept than in the western world. Through this study, we are going to identify the online purchase behaviour of the consumers and compare it with the western world. ...
Conference Paper
The objective of this study was to estimate the influence of the perception of the web design of e-commerce retail on the level of risk perceived in online purchases by consumers. For this, a cross-sectional study of causal relationship was carried out through the structural equation model (SEM). The sample consisted of 390 consumers (54.9% women and 45.1% men) in an age range of 18 to 67 years. For the validation of the instrument, the exploratory factorial analysis (EFA) and the confirmatory factorial analysis (CFA) were applied. In turn, the latter was also used to contrast the study hypothesis. An inverse and significant relationship was demonstrated between the research variables (p<0.001 and R= -0.65). This means that the better consumers’ perception of online retail website design, the lower the perceived risk of shopping online.
Full-text available
Article
A Monte Carlo study compared 14 methods to test the statistical significance of the intervening variable effect. An intervening variable (mediator) transmits the effect of an independent variable to a dependent variable. The commonly used R. M. Baron and D. A. Kenny (1986) approach has low statistical power. Two methods based on the distribution of the product and 2 difference-in-coefficients methods have the most accurate Type I error rates and greatest statistical power except in 1 important case in which Type I error rates are too high. The best balance of Type I error and statistical power across all cases is the test of the joint significance of the two effects comprising the intervening variable effect.
Full-text available
Article
In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators. (46 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
This paper reviews prior applications of structural equation modeling in four major marketing journals (the Journal of Marketing, Journal of Marketing Research, International Journal of Research in Marketing, and the Journal of Consumer Research) between 1977 and 1994. After documenting and characterizing the number of applications over time, we discuss important methodological issues related to structural equation modeling and assess the quality of previous applications in terms of three aspects: issues related to the initial specification of theoretical models of interest; issues related to data screening prior to model estimation and testing; and issues related to the estimation and testing of theoretical models on empirical data. On the basis of our findings, we identify problem areas and suggest avenues for improvement.
Article
In online market, consumers tend to make use of other signals when assessing the product quality due to the aggravated information asymmetry. Taking the moderating effects of product involvement into account, this paper developed a research model to study the influence mechanism of product information presentation and seller reputation on consumers' perceived product quality and purchase intension. A lab experiment was designed to prove the research model, in which 57 participants' questionnaire data was collected and eye movement data was recorded by SMI Hi-speed eye tracker when they viewed the website. The results showed that perceived product information presentation and seller reputation positively influenced consumers' perceived product quality which further more affected the willingness to buy, with the moderating effects of product involvement. More specifically, the impact of perceived product information presentation got weaker when seller reputation got higher for low-involvement product.
Article
Electronic commerce has grown rapidly in recent years. However, surveys of online customers continue to indicate that many remain unsatisfied with their online purchase experiences. Clearly, more research is needed to better understand what affects customers' evaluations of their online experiences. Through a large dataset gathered from two online websites, this study investigates the importance of product uncertainty and retailer visibility in customers' online purchase decisions, as well as the mitigating effects of retailer characteristics. We find that high product uncertainty and low retailer visibility have a negative impact on customer satisfaction. However, a retailer's service quality, website design, and pricing play important roles in mitigating the negative impact of high product uncertainty and low retailer visibility. Specifically, service quality can mitigate the negative impacts of low retailer visibility and high product uncertainty in online markets. Website design, on the other hand, helps to reduce the impact of product uncertainty when experience goods are involved.
Article
Trust is a key factor to the success of electronic commerce (EC). Websites facilitate enterprises selling online but also generate many problems, especially ethical issues that make customers hard to trust the sites. This study aims at investigating the effect of shopping websites' perceived ethical performance on consumer trust. Through an experimental study of the simulated shopping website, the result shows that consumers will trust the website if they feel the site keeps a good EC ethical performance such as practicing the privacy policies and stating it explicitly, describing products or services in an appropriate way. This finding suggests EC vendors have a better understanding of the relationship between website ethics and consumer trust. It may also encourage EC vendors to enhance their websites' ethical performance so they could gain trust from customers.
Article
Research on online shopping has taken three broad and divergent approaches viz, human-computer interaction, behavioral, and consumerist approaches to examine online consumer behavior. Assimilating these three approaches, this study proposes an integrated model of online shopping behavior, with four major antecedents influencing online purchase intent: Web site quality, customer concerns in online shopping, self-efficacy, and past online shopping experience. These antecedents were modeled as secondorder constructs with subsuming first-order constituent factors. The model was tested using data from a questionnaire survey of 214 online shoppers. Statistical analyses using structural equation modeling was used to validate the model, and identify the relative importance of the key antecedents to online purchase intent. Past online shopping experience was found to have the strongest association with online purchase intent, followed by customer concerns, Web site quality, and computer self efficacy. The findings and their implications are discussed.
Article
Monte Carlo computer simulations were used to investigate the performance of three χ2 test statistics in confirmatory factor analysis (CFA). Normal theory maximum likelihood χ2 (ML), Browne's asymptotic distribution free χ2 (ADF), and the Satorra-Bentler rescaled χ2 (SB) were examined under varying conditions of sample size, model specification, and multivariate distribution. For properly specified models, ML and SB showed no evidence of bias under normal distributions across all sample sizes, whereas ADF was biased at all but the largest sample sizes. ML was increasingly overestimated with increasing nonnormality, but both SB (at all sample sizes) and ADF (only at large sample sizes) showed no evidence of bias. For misspecified models, ML was again inflated with increasing nonnormality, but both SB and ADF were underestimated with increasing nonnormality. It appears that the power of the SB and ADF test statistics to detect a model misspecification is attenuated given nonnormally distributed data.
Article
Using experimental design and conjoint analysis, this paper studies the risk perception of Singaporean consumers on Internet shopping, and tests the effectiveness of several risk-reducing strategies that Internet marketers could use in promoting online shopping among consumers. The results show that Singaporean consumers with a higher degree of risk aversion than others tend to perceive Internet shopping to be a risky activity. However, Internet marketers could rely on using reference group appeal as the most preferred risk relievers for this group of consumers, particularly by getting expert users to endorse the products involved. In addition, the marketer's reputation, the brand's image, and specific warranty strategies are also effective risk relievers for the potential Internet shoppers.