This document summarizes a research study that aimed to identify factors influencing customer satisfaction with online shopping in China. The researchers developed a model of the online shopping satisfaction process and hypothesized that factors like information quality, website design, product attributes, transaction capabilities, security, payment, delivery and customer service would predict satisfaction. A survey of 1,001 online customers in China was conducted to test these hypotheses using regression analysis. The results suggested that all factors except response time significantly predicted satisfaction, providing insights into what drives satisfaction for Chinese online shoppers.

1. International Journal of Retail & Distribution Management
An empirical study of online shopping customer satisfaction in China: a holistic
perspective
Xia Liu, Mengqiao He, Fang Gao, Peihong Xie,
Article information:
To cite this document:
Xia Liu, Mengqiao He, Fang Gao, Peihong Xie, (2008) "An empirical study of online shopping customer
satisfaction in China: a holistic perspective", International Journal of Retail & Distribution Management, Vol.
36 Issue: 11, pp.919-940, https://doi.org/10.1108/09590550810911683
Permanent link to this document:
https://doi.org/10.1108/09590550810911683
Downloaded on: 22 January 2018, At: 08:35 (PT)
References: this document contains references to 72 other documents.
To copy this document: permissions@emeraldinsight.com
The fulltext of this document has been downloaded 13531 times since 2008*
Users who downloaded this article also downloaded:
(2000),"An examination of the relationship between service quality, customer satisfaction, and store loyalty",
International Journal of Retail &amp; Distribution Management, Vol. 28 Iss 2 pp. 73-82 <a href="https://
doi.org/10.1108/09590550010315223">https://doi.org/10.1108/09590550010315223</a>
(2003),"A descriptive model of online shopping process: some empirical results", International
Journal of Service Industry Management, Vol. 14 Iss 5 pp. 556-569 <a href="https://
doi.org/10.1108/09564230310500228">https://doi.org/10.1108/09564230310500228</a>
Access to this document was granted through an Emerald subscription provided by emerald-srm:233725 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for
Authors service information about how to choose which publication to write for and submission guidelines
are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as
providing an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee
on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive
preservation.
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

2. *Related content and download information correct at time of download.
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

3. An empirical study of online
shopping customer satisfaction
in China: a holistic perspective
Xia Liu
Department of Marketing, Shanghai Jiaotong University, Shanghai,
People’s Republic of China and
Research Center of Hospital Management, Shanghai Sixth People’s Hospital,
Shanghai, People’s Republic of China, and
Mengqiao He, Fang Gao and Peihong Xie
Department of Marketing, Shanghai Jiaotong University, Shanghai,
People’s Republic of China
Abstract
Purpose – The purpose of this study is to identify factors that may influence Chinese customers’
online shopping satisfaction, including those which are ignored by prior studies, from the perspective
of total online shopping experience.
Design/methodology/approach – In this paper, the authors propose a model of the satisfaction
process in the e-commerce environment, identifying key constructs proposed by prior studies and
developing hypotheses about which dimensions of online retailer constructs are significant predictors
of online shopper satisfaction. The authors test the hypotheses through multiple regression analysis
based on a survey of 1,001 online customers.
Findings – The analysis suggests that eight constructs – information quality, web site design,
merchandise attributes, transaction capability, security/privacy, payment, delivery, and customer
service – are strongly predictive of online shopping customer satisfaction, while the effect of response
time is not significant.
Research limitations/implications – This study does not control the differences across product
categories; the use of self-reported scales to measure both independent and dependent variables may
imply the possibility of a common method bias for the results.
Originality/value – This research contributes to the study of online shopping customer satisfaction
by: developing a model of the satisfaction process in the e-commerce environment, and identifying
factors that may influence Chinese customers’ online shopping satisfaction including those which are
ignored by prior studies.
Keywords Electronic commerce, Customer satisfaction, China
Paper type Research paper
1. Introduction
After years of development of China’s online shopping market, there has been a drastic
increase in the number of online shopping web sites in China. Statistics released by the
PRC Ministry of Information Technology indicate that the number of retail web sites in
2001 totaled 2,046 and reached 2,219 by 2004. Retail web sites accounted for
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0959-0552.htm
The authors would like to thank Acme Translations Co., Ltd for their support of the research.
Online shopping
customer
satisfaction
919
Received 31 August 2007
Revised 3 January 2008
Accepted 28 February 2008
International Journal of Retail &
Distribution Management
Vol. 36 No. 11, 2008
pp. 919-940
q Emerald Group Publishing Limited
0959-0552
DOI 10.1108/09590550810911683
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

4. 49.5 per cent of China’s Ecommerce in 2004 with transaction volume of online shopping
estimated at 4.2 billion RMB. By 2005, the transaction turnover of online
shopping amounted to 5.6 billion RMB, a 33 per cent increase. With China’s online
shopping environment maturing and offering improved levels of service, it is predicted
that the volume could reach 46 billion RMB by 2010[1].
The rapid growth of online shopping in China underscores the importance of
focusing on the issue of customer satisfaction as a key factor to address when
designing any online retail outlet. Customers must be satisfied with their online
shopping experience; otherwise they will not return (Kim and Stoel, 2004). A recent
survey conducted by CNNIC found that only 3.5 per cent of the Chinese respondents
were very satisfied with the online shopping experience. Dissatisfaction usually
leads to lost customers and income. Compared with American customers, Chinese
customers have several characteristics: their perception of safety is lower; they
undertake higher purchasing risks because of fake goods and false advertising; the
buyer sometimes is not the consumer; they are more sensitive to price; they are more
cautious in making purchase decision (Lu, 2005). Therefore, a fundamental
understanding of the factors affecting Chinese online shopping customers’ degree of
satisfaction is of great importance to e-commerce.
If companies can better understand their customers, they can present products or
services more effectively and continuously improve them in order to strengthen their
competitive advantage. Market orientation and customer satisfaction research show
there is direct connection between customer satisfaction and organizational
performance (Garver and Gagnon, 2002). Consequently, in the turbulent e-commerce
environment, Internet companies need to know how to satisfy customers. This will
enable them to sustain their growth and market share (McKinney et al., 2002).
It is known that online shopping environment and behavior is fundamentally
different from that of a conventional retail environment (Degeratu et al., 2000; Lynch
and Ariely, 2000; Shankar et al., 2001, 2003; Ranganathan and Ganapathy, 2002; Heiner
et al., 2004). It can be expected that the key drivers of customer satisfaction and
retention in the Internet economy may be also different from those in the traditional
economy. From different perspectives, researchers have developed and tested
instruments to measure customer satisfaction with online shopping (Szymanski and
Hise, 2000; Koivumaki, 2001; Heiner et al., 2004; Kim and Stoel, 2004). These
researchers have done some innovative and pioneering work, but revealed conflicting
findings, such as Szymanski and Hise (2000) find that web site design has the
secondary significant impact on e-satisfaction, the study conducted by Kim and Lim
(2001) shows that information quality has relationship with online shoppers’
satisfaction, but Kim and Stoel’s (2004) study indicate that web appearance and
information attribute have little impact on customer satisfaction.
In addition, China differs from foreign countries greatly in cultural tradition, logistic
infrastructure and credit system. Chinese culture belongs to eastern culture system,
and the infrastructure of logistics in China lags behind other developed countries.
Moreover, there has not sound credit system in China. Prior researches indicate
that culture (David, 2007), logistics (Sharma et al., 1995) and credit (Gentry, 1982)
have important impact on customer behavior and satisfaction. Therefore, it is
understandable that key drivers of online shopping customer satisfaction in China may
be different from that of other countries. Just as de Mooij and Hofstede (2002) indicated,
IJRDM
36,11
920
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

5. converging technology and disappearing income difference across countries will not
lead to homogenization of consumer behavior. Rather, consumer behavior will become
more heterogeneous because of cultural differences (de Mooij and Hofstede, 2002). Few
studies, however, have explored online shopping customer satisfaction on Chinese
context, so little is known about the construct of Chinese online shopping customer
satisfaction.
Based on earlier research (Szymanski and Hise, 2000; Koivumaki, 2001; Heiner et al.,
2004; Kim and Stoel, 2004), the purpose of this research is to identify key constructs
and corresponding measurement scales for examining Chinese online shopping
customer satisfaction. This is done by developing a satisfaction process model in
e-commerce environment, identifying key constructs proposed by prior studies and
designing a group of hypotheses.
Our research contributes to the study of online shopping customer satisfaction by:
.
Developing a model of the satisfaction process in the e-commerce environment.
.
Identifying factors that may influence Chinese customers’ online shopping
satisfaction, including payment which are ignored by prior studies.
The outcome of the study may be relevant to businesses, consumers and researchers.
The paper proceeds as follows. Section 2 is literature review. Section 3 proposes a
satisfaction process model in the e-commerce environment and designs a group of
hypotheses. Section 4 describes the survey used to test the above model. Section 5
presents the results of statistical data analysis. Section 6 discusses the study’s
implications for research and practice and outlines the study’s limitations.
2. Literature review
While the subject of satisfaction has been discussed extensively in the traditional
retailing literature (Mason and Bearden, 1979; Oliver, 1981; Anderson et al., 1994;
Terblanche and Boshoff, 2001a,b; Johan, 2006; Ofir and Simonson, 2007), the
exploration of dimensions and determinations of satisfaction under e-commerce
context is at a nascent stage (Heiner et al., 2004). Recently, some researchers
have started to investigate how the attributes of a web site will influence customers’
satisfaction. These studies have presented various characteristics as important factors
for an effective B2 C e-commerce web site; however, up to now there is no consensus on
how this affects online customer satisfaction (Schaupp and Bélanger, 2005).
Table I summarizes several previous studies of the determinants of online shopping
customer satisfaction.
There have been several other attempts to build an evaluation framework or
identify dominant factors concerning customer satisfaction with online shopping from
the perspective of web site quality (Ranganathan and Ganapathy, 2002), purchasing
behavior (Koivumaki, 2001; Park and Kim, 2003), consumer attitude (Elliott and Speck,
2005), customer value Shun and Yunjie, 2006 and service quality (Zhilin et al., 2003).
The foregoing studies provide a broad basis for gaining insight into customer
satisfaction with online shopping. Few empirical studies have been conducted to
uncover the underlying constructs of customer satisfaction with online shopping, from
the perspective of the total retail experience and following the purchase process.
What’s more, the impact of payment was seldom touched on. A consumer buying
process can be viewed as a sequence of several stages (Kotler, 1997; Oliver, 1981;
Online shopping
customer
satisfaction
921
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

6. Ranganathan and Ganapathy, 2002), thus, satisfaction is a consequence of the
customer’s experiences during various purchasing stages (Kotler, 1997). Berman and
Evans (1998) define total retail experience as all the elements that encourage or inhibit
consumers during their contact with the retailer. In this perspective, dimensions tested
by prior studies, such as information quality, web site design are only one component
of the online shopping customers’ total retailing experience. From a management
perspective, it seems preferable to develop an instrument that covers all the dimensions
of an online shopping experience that can be controlled by an online retailer. If only one
component of the total retailing experience is considered at a time, it might be
detrimental to our understanding of customers’ experience and this in turn could lead
to strategies that either overemphasize or under appreciate the importance of one or
more of such components (Terblanche and Boshoff, 2001a,b).
Although some other studies touch on consumers’ shopping experience and their
evaluation are based on perceptions of the online shopping web sites (Jarvenpaa and
Todd, 1997; Griffith et al., 2001; Koivumaki, 2001; Park and Kim, 2003), these research
mainly discuss the consumer’s information processing style, shopping pattern, etc.
Chen and Chang (2003) innovatively proposed an overall model of Internet shopping
process and identified three common online shopping components: interactivity,
transaction and fulfillment, but their study lack theoretical background.
Independent variable Key references Dependent variable
Convenience Szymanski and Hise (2000) E-satisfaction
Kim and Lim (2001) Consumers’ satisfaction with internet shopping
Heiner et al. (2004) E-satisfaction
Web site design Szymanski and Hise (2000) E-satisfaction
Heiner et al. (2004) E-satisfaction
Kim and Stoel (2004) Shopper satisfaction of apparel web site
Hsuehen (2006) Customer satisfaction
Financial security Szymanski and Hise (2000) E-satisfaction
Heiner et al. (2004) E-satisfaction
Trust Kim and Stoel (2004) Shopper satisfaction of apparel web site
Assurance Devaraj et al. (2002) Consumer attitudes and satisfaction
Reliability Kim and Lim (2001) Consumers’ satisfaction with internet shopping
Information Szymanski and Hise (2000) E-satisfaction
Kim and Lim (2001) Consumers’ satisfaction with internet shopping
Heiner et al. (2004) E-satisfaction
Kim and Stoel (2004) Shopper satisfaction of apparel web site
Ballantine (2005) Customer satisfaction
Hsuehen (2006) Customer satisfaction
Merchandise variety Szymanski and Hise (2000) E-satisfaction
Heiner et al. (2004) E-satisfaction
Perceived ease of use Devaraj et al. (2002) Consumer attitudes and satisfaction
Perceived usefulness Devaraj et al. (2002) Consumer attitudes and satisfaction
Entertainment Kim and Lim (2001) Consumers’ satisfaction with internet shopping
Kim and Stoel (2004) Shopper satisfaction of apparel web site
Speed Kim and Lim (2001) Consumers’ satisfaction with internet shopping
Response time Kim and Stoel (2004) Shopper satisfaction of apparel web site
Transaction capability Kim and Stoel (2004) Shopper satisfaction of apparel web site
Table I.
Empirical research on
determinants of online
shopping customer
satisfaction
IJRDM
36,11
922
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

7. There have been several other attempts to study user satisfaction in the context
e-services or online services (Zhang et al., 2006; Ha, 2006; Yang and Fang, 2004). For
instance, Zhang et al. (2006) examine several factors (user computer proficiency,
perceived convenience, site characteristics, perceived security, user satisfaction)
affecting user satisfaction with e-services and find that perceived convenience, users’
skills and experiences, and perceived security have impact on user satisfaction with
e-services.
In the proceeding section, we will reorganize the web site quality studied in the prior
research from the perspective of total retail experience of online shoppers. In addition,
delivery and payment are examined in our study as unique features in the context of
Chinese online shopping.
3. Research model and hypotheses
Satisfaction is the consequence of the customer’s experiences during various
purchasing process: need arousal, information search, alternatives evaluation,
purchase decision, and post-purchase behavior (Kotler, 1997). In the increasingly
technology-oriented online shopping environment, it is quite safe to assume that online
shopping consumer satisfaction can be affected at every stage. The model of Turban
et al. (2000) suggests that, in addition to selection and different properties of the goods,
various features are related to the web shop, such as speed of operation, ease of use,
that determine the navigation experience of the customer.
Based on Kotler’s (1997) study, this study divides the purchasing process into three
stages: information search and alternatives evaluation stage, purchase stage and
post-purchase stage, and proposes a model of the satisfaction process in the
e-commerce environment outlined in Figure 1.
As indicated by the above model, customers’ overall satisfaction can be affected by
all factors relevant to the process of online shopping. Based on the studies of
Figure 1.
Model of the satisfaction
process in e-commerce
environment
Transaction
capability
Overall satisfaction
Information
quality
H1
H2
H3
H6
H4
H5
H8
H9
H7
Website design
Merchandise
attributes
Response
Security/privacy
Payment
Delivery
Customer service
Stage 3: Post-purchase
Stage 1: Information search
and alternatives evaluation
Stage 2: Purchase
Online shopping
customer
satisfaction
923
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

8. Oliver (1980), Cadotte et al. (1987), and Spreng et al. (1996), we define overall
satisfaction as an affective state representing an emotional reaction to the entire online
shopping experience. This definition focuses on the process evaluation associated with
online shopping behavior. In the following analysis, the concept of overall satisfaction
is referred to simply as “satisfaction”, referring to a customer’s degree of satisfaction
upon completion of an online shopping experience.
3.1 Information search and alternatives evaluation stage
At the stage of information search and alternatives evaluation, information quality, the
ambience associated with the site itself and how it functions, variety of merchandise
and price all play roles in whether consumers are satisfied or dissatisfied with their
online shopping experiences. It is argued that, since a primary role of an online store is
to provide price-related information and product information to help reduce
consumers’ search cost (Bakos, 1997), more extensive and higher quality information
available online leads to higher levels of customer satisfaction (Peterson et al., 1997).
Manes (1997) indicates that good web site design lies in good organization and easy
search. Shopping is thought to be pleasurable and satisfying to consumers when the
retailing sites are uncluttered and easy-to-navigate (Pastrick, 1997). In the alternatives
evaluation stage, merchandise attribute including merchandise variety and price is
important for decision making. Szymanski and Hise (2000) indicate that wider
assortment of products may be attractive to customers and e-satisfaction would be
more positive when online stores offer superior product assortments. Although some
scholars think price sensitivity may actually be lower online than offline (e.g., Degeratu
et al., 2000; Lynch and Ariely, 2000; Shankar et al., 2001), one most commonly cited
reason for online shopping is price, and many early online marketers used price as bait
to lure consumers to their sites (Chen and Chang, 2003). Moreover, the Chinese are quite
sensitive to price (Lu, 2005). Therefore, we hold the view that product price has a lot to
do with Chinese customers’ satisfaction.
Based on the discussion above, we propose the following hypotheses:
H1. Higher level of information quality will improve customer satisfaction in
online shopping.
H2. Good web site design will have a positive effect on online shopping customer
satisfaction.
H3. Wider merchandise variety and low price will have a positive effect on online
shopping customer satisfaction.
3.2 Purchase stage
At the purchase stage, privacy/security, payment mechanisms, transaction capabilities
and speed of operation may affect satisfaction. Compared with the traditional economy,
online consumers are more keenly aware of the need for privacy/security (Culnan, 1999;
Friedman et al., 2000; Grewal et al., 2004). Inadequate infrastructure, lack of trust,
and privacy and security concerns often lead to lost sales (Yianakos, 2002;
Grabner-Kraeuter, 2002). Moreover, online shoppers are known for low tolerance (Chen
and Chang, 2003), it is estimated that, on average, online shoppers only wait for eight
seconds for system feedback before bailing out (Dellaert and Kahn, 1999). A web page
designer has to consider not only appearance and functionality, but also loading time
IJRDM
36,11
924
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

9. (Weinberg, 2000). A commonly cited reason for online shopping is convenience (Chen
and Chang, 2003). Therefore, it will raise the customer’s degree of satisfaction to
improve the web site’s transaction capability, design a secure and convenient payment
mechanism, ensure completion of all online shopping operations and save the
customer’s operation time.
Hence, the study designs the following hypotheses:
H4. Great transaction capability will have a positive effect on online shopping
customer satisfaction.
H5. Rapid response time will have a positive effect on online shopping customer
satisfaction.
H6. Security/privacy will have a positive effect on online shopping customer
satisfaction.
H7. A convenient payment mechanism will have a positive effect on online
shopping customer satisfaction.
3.3 Post-purchase stage
Post-purchase evaluation can be influenced by the efficiency of logistics and customer
service. The most common types of complaints about Internet transactions include
refund and billing disputes, return and exchange policies, defective products, and poor
customer service (Chen and Chang, 2003). Consumers want careful, continuous, useful
communication across geographic barriers (Lohse and Spiller, 1998). In the e-commerce
environment, not only is the consumption of goods separated from production, thus
making it necessary for goods to be delivered to consumers before consumption, there
is also a delay in the delivery of goods. Delayed delivery may have a negative effect on
satisfaction. As shown by the 2004 China Online Shopping Report by CNNIC 25 per cent
of Chinese customers were not satisfied due to delayed delivery or wrong product
delivery (CNNIC, 2004). One customer complained that he bought a digital camera from
an online company but received sports equipment instead.
Thus, we have the following hypotheses:
H8. Safe and rapid delivery will have a positive effect on online shopping
customer satisfaction.
H9. A higher level of customer service will result in greater satisfaction.
4. Research methodology
4.1 Survey instrument
All constructs were measured using multiple items, seven-point, Likert scales ranging
from strongly disagree to strongly agree. Wherever possible, initial scale items were
taken from previously validated measures in e-satisfaction, online shopping customer
satisfaction and web site quality literature and then reorganized and adapted to the
current context.
Information quality was evaluated from four dimensions: information accuracy,
information comprehensibility, information completeness and information relevancy.
The items were adapted from the items initially developed by Jeong et al. (2003) and
Muyllea et al. (2004). Web site design was evaluated from four aspects: navigation,
Online shopping
customer
satisfaction
925
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

10. web site structure, color combination and ease of use. The items were taken from
Muyllea et al. (2004), Jeong et al. (2003) and Kim and Stoel (2004). Merchandise
attribute was evaluated from two dimensions: product variety and product price.
This thesis consulted the study of Szymanski and Hise (2000) which evaluates
product variety from two perspectives: number of offerings and variety of offerings.
Based on this we designed four items. We designed another two items to evaluate
product price through in-depth discussion with 30 online shoppers and site
administrators, who all agree that these two items can reflect price level. The
transaction capability and response time scale were adapted from Kim and Stoel
(2004). Based on the in-depth interview with online shoppers and managers of B2C
web sites such as www.joyo.com, www.dangdang.com, we develop two items to
evaluate payment: the web site has complete payment options; I accept the payment
options provided by the web site. Security/privacy and customer service scale were
taken from Wolfinbargerhe and Gilly (2003). Delivery scale consists of four items,
among which two are taken from Wolfinbargerhe and Gilly (2003). The other two
are developed on the basis of in-depth interviews: the items sent by the site are well
packaged and perfectly sound I am satisfactory with the delivery mode of the web
site. Satisfaction scale consists of four items taken from Oliver (1981), referring to a
customer’s degree of satisfaction upon completion of an online shopping experience.
The survey instrument consists of two sections. In the first section, respondents
were asked to fill in their gender, age, educational level and latest purchased product.
In the second section, they were asked to identify the extent to which they
agree/disagree with the items related to their latest online shopping experience. Each
item was measured on a seven-point Likert scale from 1 (strongly disagree) to
7 (strongly agree).
4.2 Sample and data
Prior to the survey, the survey instrument was pilot tested using five doctoral students
and 20 others who had online shopping experience. Pre-test interviews were
undertaken to refine the questionnaire. The items were revised based on the feedback.
Since we want to gather data of customers’ perception of the online shopping
experience, we asked only those subjects with some prior web shopping experience.
Data was collected as follows:
.
Field survey. Field surveys were conducted mainly in big cities such as Beijing,
Shanghai, Nanjing, Hefei, Changsha and Jinan. We interviewed shoppers at some
large shopping malls in these cities, and inquired if they had online shopping
experience. The incentive was a lottery ticket.742 respondents answered the
questionnaire with 709 usable respondents.
.
E-mail survey. The e-mail was sent to a sample of 542 valid e-mail addresses,
drawn from a large e-mail list provided by a company. The number of each
e-mail address was randomly produced by computer. Internet users (115) agreed
to participate in the study. The e-mail containing questionnaire was then sent to
these 115 Internet users. A total of 98 respondents provided usable answers to
the questionnaire. This was a return of 18.08 per cent on the original mailing and
of 85.2 per cent among the people that agreed to cooperate.
IJRDM
36,11
926
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

11. .
Online survey. We put the questionnaire on a web site (www.acmetranslation.
com/diaocha.php), which is linked to a company’s BBS web site which has a
daily visit volume of 3,000 persons. Respondents online answered the
questionnaire, and the data was saved in the database of the company. This
campaign produced 211 usable responses. Online surveys have several
advantages over traditional surveys: not restricted to a particular geographical
location (Shankar et al., 2003), lower costs (Shankar et al., 2003), faster responses
(Shankar et al., 2003), more effective in identifying and seeking online shoppers
(Szymanski and Hise, 2000), and more interesting (Edmonson, 1997).
This survey altogether collected 1,018 responses, deleted 17 outliers and kept 1,001
valid responses. Before combining the three sub-samples, we performed one-way
ANOVA analysis and the result indicates that there have not significant differences in
the three sub-samples.
The demographic characteristics of respondents were as follows: respondents’ age
averaged 32 years and ranged from 19 to 56; 64.3 per cent of respondents have a college
degree and 15.7 per cent have master’s degree or above; 47.9 per cent were female and
52.1 per cent were male.
4.3 Validity and reliability
In this study, construct, convergent and discriminant validity are assessed by
exploratory factor analysis with SPSS 13.0 and confirmatory factor analysis with
LISREL 8.7.
The main sample is divided into two sub-samples, one (501 respondents) for
exploratory factor analysis and second (500 respondents) for confirmatory factor
analysis. By exploratory factor analysis (principal component analysis and varimax
rotation is used), we have nine factors for the importance with the eight values greater
than one as shown in Table II. The total variance explained by the nine factors is
67.434 per cent. Five items with large standardized residuals were removed, resulting
in the retention of 44 items, with two to ten items per construct (Table II and
Appendix 1).
A second study was carried out in order to further asses the factor structure as well
as to establish convergent and discriminant validity through confirmatory factor
analyses. Confirmatory factor analysis provides satisfactory support for the
nine-dimension model (x 2
¼ 1739.97; df ¼ 704; p , 0.001; RMR ¼ 0.1;
GFI ¼ 0.85; AFGI ¼ 0.83; CFI ¼ 0.97; RMSEA ¼ 0.054). Following the
procedures suggested by Fornell and Larcker (1981) and Bagozzi and Yi (1988),
convergent validity was assessed by verifying the significance of the t values
associated with the parameter estimates (Table II). All t values were positive and
significant ( p , 0.01). Discriminant validity was tested by comparing the square root
of average variance extracted (Ave) by each construct to the correlations between the
construct and all other variables. For each construct, the square root of Ave exceeded
all the correlations (Tables III and IV). In order to verify the reliability of the
instrument, a statistical reliability analysis was conducted using Cronbach’s as.
Cronbach’s as value for each of the factors ranged from 0.705 to 0.908 (Table II) and
clearly exceed the 0.70 cutoff recommended by Nunnally (1978) for basic research.
Online shopping
customer
satisfaction
927
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

12. Factor and items
Exploratory factor
loading
(n ¼ 501)
Confirmatory factor
loading (t-value)
(n ¼ 500) Cronbach’s a
Factor 1: web site design(j1) 0.878
Color combination 1 0.716 0.72 (18.87)
Color combination 2 0.716 0.66 (15.88)
Ease of use 1 0.696 0.69 (17.02)
Ease of use 2 0.669 0.67 (16.32)
Ease of use 3 0.591 0.67 (16.19)
Web site structure 3 0.583 0.55 (12.79)
Navigation 1 0.623 0.55 (12.66)
Navigation 2 0.726 0.62 (14.60)
Navigation 3 0.734 0.65 (15.52)
Navigation 4 0.680 0.59 (13.70)
Factor 2: information quality (j2) 0.797
Understandablity 1 0.693 0.68 (15.92)
Understandability 2 0.739 0.71 (17.01)
Accuracy 1 0.635 0.58 (18.15)
Accuracy 2 0.567 0.62 (14.32)
Completeness 1 0.578 0.59 (13.48)
Completeness 2 0.630 0.44 (9.66)
Relevancy 1 0.591 0.55 (12.32)
Factor 3: merchandise attributes (j3) 0.908
Merchandise variety 1 0.884 0.82 (21.83)
Merchandise variety 2 0.861 0.80 (21.04)
Merchandise variety 3 0.848 0.83 (22.08)
Merchandise variety 4 0.816 0.74 (18.59)
Price level 1 0.787 0.71 (17.65)
Price level 2 0.780 0.71 (17.74)
Factor 4: transaction capability (j4) 0.716
Transaction capability 1 0.724 0.74 (15.78)
Transaction capability 2 0.743 0.71 (15.17)
Factor 5: response time (j5) 0.705
Response time 1 0.856 0.76 (15.50)
Response time 2 0.841 0.72 (14.89)
Factor 6: security/privacy (j6) 0.829
Security/privacy 1 0.658 0.65 (15.10)
Security/privacy 2 0.768 0.79 (19.84)
Security/privacy 3 0.752 0.81 (20.60)
Security/privacy 4 0.749 0.70 (16.80)
Factor 7: payment (j7) 0.896
Payment 1 0.847 0.90 (21.47)
Payment 2 0.838 0.87 (20.64)
Factor 8: delivery (j8) 0.895
Delivery 1 0.770 0.83 (22.74)
Delivery 2 0.791 0.80 (20.78)
Delivery 3 0.829 0.86 (23.39)
Delivery 4 0.768 0.82 (21.80)
Factor 9: customer service (j9) 0.886
Customer service 1 0.815 0.90 (25.04)
Customer service 2 0.822 0.83 (21.96)
(continued)
Table II.
Construct measurement
summary: exploratory
and confirmatory factor
analysis
IJRDM
36,11
928
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

13. 4.3 Regression results
Then, multiple regression analysis (SPSS13.0) was used to estimate the effect of nine
constructs on online shopping consumer satisfaction level. The regression results are
presented in Table V.
The data in Table IV shows that the regression coefficient for delivery, customer
service, security/privacy, transaction capability, merchandise attributes, information
quality, payment and web site design are all statistically significant. Response time has
no effect on customer satisfaction. Additionally, we find that delivery has the greatest
impact on satisfaction (b ¼ 0.214). The data also indicates that transaction capability
is the second most important element driving satisfaction levels (b ¼ 0.191).
Meanwhile, security/privacy, customer service, information quality, merchandise
attributes and payment are statistically significant, but have less impact on
satisfaction compared with delivery and transaction capability. Finally, the data
indicates that web site design (b ¼ 0.055) is of less practical significance to satisfaction
assessment.
Factor and items
Exploratory factor
loading
(n ¼ 501)
Confirmatory factor
loading (t-value)
(n ¼ 500) Cronbach’s a
Customer service 3 0.755 0.82 (21.49)
Dependent variable: satisfaction 0.826
Satisfaction 1 0.788 0.67
Satisfaction 2 0.831 0.76
Satisfaction 3 0.836 0.79
Satisfaction 4 0.811 0.73 Table II.
Factor 1 2 3 4 5 6 7 8 9
Ave 0.41 0.36 0.59 0.53 0.55 0.55 0.78 0.69 0.72
Sqrt 0.64 0.60 0.77 0.73 0.74 0.74 0.88 0.83 0.85
Table III.
The average variance
extracted by each
construct
Dimensions j1 j2 j3 j4 j5 j6 j7 j8 j8
j1 1.00
j2 0.467 * * 1.00
j3 0.250 * * 0.213 * * 1.00
j4 0.428 * * 0.452 * * 0.183 * * 1.00
j5 0.313 * * 0.280 * * 0.078 * 0.206 * * 1.00
j6 0.421 * * 0.462 * * 0.201 * * 0.359 * * 0.279 * * 1.00
j7 0.415 * * 0.312 * * 0.232 * * 0.345 * * 0.138 * * 0.322 * * 1.00
j8 0.421 * * 0.401 * * 20.054 0.410 * * 0.230 * * 0.356 * * 0.395* * 1.00
j9 0.451 * * 0.407 * * 0.173 * * 0.376 * * 0.337 * * 0.488 * * 0.267* * 0.455* * 1.00
Notes: *Correlation is significant at the 0.05 level (two-tailed); * *correlation is significant at the 0.01
level (two-tailed)
Table IV.
Pearson correlations
matrix
Online shopping
customer
satisfaction
929
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

14. Thus, our hypotheses that delivery, customer service, security/privacy, transaction
capability, merchandise attribute, information quality, payment and web site design
are positively correlated with online shopping customer satisfaction were supported.
The hypothesis that rapid response time will have a positive effect on online shopping
customer satisfaction was rejected.
An examination of excessive multicollinearity and autocorrelation was performed.
First, the value of Durbin-Watson was 1.914. Second, the variance inflation factors
(VIF) were scrutinized and all were found to be within the range of 1.206-1.665. Myers
(1990) indicates that only if the rpramila VIF is above ten is there cause for concern
about multicollinearity. Therefore, multicollinearity and autocorrelation were well
within acceptable limits and not unduly influencing the regression estimates.
5. Discussion and implications
This study offers several important findings in the Chinese context, summarized as
follows:
.
Delivery has a positive impact on customer satisfaction, which is consistent with
the study of Sharma et al. (1995).
.
Transaction capability has a significant effect on customer satisfaction. This
finding is consistent with the study of Kim and Stoel (2004).
.
The effect of security/privacy on satisfaction is in a positive direction. This
finding is consistent with the studies of Szymanski and Hise (2000) and Schaupp
and Bélanger (2005), but contradictory to Kim and Stoel’s (2004) finding.
.
As hypothesized, customer service is found to exert a significant positive
influence on customer satisfaction. This finding is consistent with the study of
Wolfinbargerhe and Gilly (2003).
.
Information quality has significant impact on customer satisfaction as well,
which is consistent with the research conclusion by Kim and Stoel (2004), Kim
and Lim (2001) and McKinney et al. (2002).
Constructs Proposed effect
Standard coefficient
(SE) t-value VIF Hypothesis result
Delivery þ 0.214 (0.021) 8.667 * * 1.665 H8 was supported
Transaction capability þ 0.191 (0.021) 8.237 * * 1.468 H4 was supported
Security/privacy þ 0.187 (0.025) 7.869 * * 1.539 H6 was supported
Customer service þ 0.186 (0.023) 7.688 * * 1.601 H9 was supported
Information quality þ 0.153 (0.029) 6.365 * * 1.592 H1 was supported
Merchandise attribute þ 0.119 (0.019) 5.654 * * 1.206 H3 was supported
Payment þ 0.105 (0.019) 4.687 * * 1.385 H7 was supported
Web site design þ 0.055 (0.030) 2.230 * 1.652 H2 was supported
Response time H5 was rejected
Fmodel 218.514 *
R 2
(R 2
adjusted) 0.639 (0.636)
Durbin-Watson 1.914
Notes: *Statistically significant at the 0.01 level; * *statistically significant at the 0.001 level
Table V.
Regression results
IJRDM
36,11
930
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

15. .
The role of merchandise attributes in customer satisfaction is evident, unlike the
finding reported by Szymanski and Hise (2000).
.
The data also reveals that payment has an impact on online shopping customer
satisfaction. Previous research on customer satisfaction seldom touches on it.
This study uncovers its effect on customer satisfaction.
.
The effect of web site design on satisfaction is in a positive direction. This
finding is consistent with the study of Szymanski and Hise (2000), but somewhat
contradictory to Kim and Stoel’s (2004) finding.
.
Nevertheless, the effect of response time on customer satisfaction is not
significant. This finding differs from previous studies (Kim and Lim, 2001; Kim
and Stoel, 2004).
Our findings have both managerial and research implications:
.
Delivery and customer service play a critical role in Chinese customer
satisfaction. To satisfy customers in today’s competitive e-marketplace, online
retailers must keep a close eye on delivery and customer service. Prompt delivery
and prompt response to customers’ concerns and inquiries are crucial because
order fulfillment still remains a weak spot for Chinese online retailers. In the
online shopping environment, delayed delivery and ignorance of customers’
concerns and inquiries will cause customer dissatisfaction. Especially, in the
Chinese context, which can be worse because in China the logistic infrastructure
lags behind other countries and the sense of customer service is relatively weak.
Because customers lack direct, face-to-face interaction with service providers,
many issues still demand human intervention. Therefore, company
representatives must be able to answer customer inquiries and solve problems
as soon as they occur.
.
Detailed and complete product information should be provided. In the online
shopping environment, products are intangible. Customers cannot touch, taste,
observe, smell or listen to the goods as they do in traditional ways. To know the
quality and functionality of a commodity, customers can only rely on the pictures
and descriptions of the goods on the web pages. Therefore, to enhance customers’
degree of satisfaction, B2C web sites should provide clear and understandable
information to online shoppers. Meanwhile, web sites should provide descriptive
information of the goods that is as complete as possible, including the color,
functionality, producer, model, etc. This is to ensure the customers can make
purchase decisions. Koivumaki (2001) finds that displaying pictures of the goods’
has greater influence on customer’s purchase decision than just listing out the
product description.
.
Providing varied types of commodities and preferential price is important.
Customer satisfaction still depends on product variety and product price. With
this in mind, smart online retailers will offer abundant choices to customers, as
well as provide competitive product prices. The Chinese are especially sensitive
to price and are accustomed to comparing prices (Lu, 2005). Many are not willing
to spend extra money in shopping. Moreover, on the Internet, customers have
access to more information. Their ability to compare and analyze forces web sites
to revalue the products and allows customers to gain material benefits.
Online shopping
customer
satisfaction
931
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

16. .
Expending more effort on web page design and making online shopping
enjoyable is also important. The visitor’s first impression on the web site is
derived from the page design, the layout and color match. Then, it is the content.
Comfortable and pleasing pages can attract customers and prolong their stay,
which increases the possibility of purchase and helps to improve the customer’s
degree of satisfaction.
.
It is also vital to strengthen the web site’s transaction capability and make sure
all operations can be completed online. This saves the customer’s time and fully
reflects the convenience of online shopping.
.
Because China lacks network ID authentication and online transaction credit
system, credit, security and privacy become the spotlight of online shopping. It is
important for online retailers to create a safe online shopping environment,
provide convenient and safe payment methods, protect customers’ privacy and
guarantee financial security.
Some limitations of this research are noteworthy:
.
Some researchers suggest that web site attributes considered important by
shoppers may differ by product (Peterson et al., 1997; McGoldrick et al., 1999;
Elliot and Fowell, 2000). This study does not control such differences across
product categories.
.
This study dose not control for tangible versus intangible products/services in
the analysis.
.
The use of self-reported scales to measure both independent and dependent
variables may imply the possibility of a common method bias for the results.
Future research can develop more detailed models that can capture and explain the
differences across product categories or focus on one category, such as books, CDs or
airline tickets. Furthermore, the relationship between customer satisfaction, e-loyalty
and continuous shopping intention in the Chinese online shopping environment should
be scrutinized.
Note
1. http://market.ccidnet.com/pub/report/show_8380.html
References
Anderson, E.W., Fornell, C. and Lehmann, D.R. (1994), “Customer satisfaction, market share, and
profitability: findings from Sweden”, Journal of Marketing, Vol. 58 No. 3, pp. 53-66.
Bagozzi, R.P. and Yi, Y. (1988), “On the evaluation of structural equation models”, Academy of
Marketing Science, Vol. 16 No. 1, pp. 74-94.
Bakos, J.Y. (1997), “Reducing buyer search costs: implications for electronic marketplaces”,
Management Science, Vol. 43 No. 12, pp. 1676-92.
Ballantine, P.W. (2005), “Effects of interactivity and product information on consumer
satisfaction in an online retail setting”, International Journal of Retail & Distribution
Management, Vol. 33 No. 6, pp. 461-71.
Berman, B. and Evans, J.R. (1998), Retail Management, 7th ed., Prentice-Hall, Upper Saddle River,
NJ, p. P19.
IJRDM
36,11
932
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

17. CNNIC (2004), “CNNIC statistic report of the development of internet in China”, available at:
www.cnnic.net.cn (accessed July 2004).
Cadotte, E.R., Robert, B.W. and Roger, L.J. (1987), “Expectations and norms in models of
consumer satisfaction”, Journal of Marketing Research, Vol. 24 No. 3, pp. 305-14.
Chen, S.J. and Chang, T.Z. (2003), “A descriptive model of online shopping process: some
empirical results”, International Journal of Service Industry Management, Vol. 14 No. 5,
pp. 557-69.
Culnan, M.J.G. (1999), Internet Privacy Policy Study: Privacy Online in 1999: A Report to the FTC,
Georgetown University, Washington, DC.
David, M. (2007), “Culture, context, and behavior”, Journal of Personalit, Vol. 75 No. 6,
pp. 1285-320.
de Mooij, M. and Hofstede, G. (2002), “Convergence and divergence in consumer behavior:
implications for international retailing”, Journal of Retailing, Vol. 78 No. 1, pp. 61-9.
Degeratu, A., Rangaswamy, A. and Wu, J. (2000), “Consumer choice behavior in online and
traditional supermarkets: the effects of brand name, price, and other search attributes”,
International Journal of Research in Marketing, Vol. 17 No. 1, pp. 55-78.
Dellaert, B. and Kahn, B. (1999), “How tolerable is delay? consumers’ evaluations of internet web
sites after waiting”, Journal of Interactive Marketing, Vol. 13 No. 1, pp. 41-54.
Devaraj, S., Fan, M. and Kohli, R. (2002), “Antecedents of B2C channel satisfaction and
preference: validating e-commerce metrics”, Information System Research, Vol. 13 No. 3,
pp. 316-33.
Edmonson, B. (1997), “The wired bunch”, American Demographics, Vol. 19 No. 6, pp. 10-15.
Elliot, S. and Fowell, S. (2000), “Expectations versus reality: a snapshot of consumer experiences
with internet retailing”, International Journal of Information Management, Vol. 20 No. 5,
pp. 323-36.
Elliott, M.T. and Speck, P.S. (2005), “Factors that affect attitude toward a retail web site”, Journal
of Marketing Theory and Practice, Vol. 13 No. 1, pp. 40-51.
Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable
variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.
Friedman, B., Peter, H.K. Jr and Howe, D.C. (2000), “Trust online”, Communications of the ACM,
Vol. 43 No. 12, pp. 34-40.
Garver, M.S. and Gagnon, G.B. (2002), “Seven keys to improving customer satisfaction
programs”, Business Horizons, Vol. 45 No. 5, pp. 35-42.
Gentry, J.W. (1982), “The impact of credit decisions on shopping behavior”, Advances in
Consumer Research, Vol. 9 No. 9, pp. 385-8.
Grabner-Kraeuter, S. (2002), “The role of consumers’ trust in online-shopping”, Journal of
Business Ethic, Vol. 39 Nos 1/2, pp. 43-51.
Grewal, D.I., Gopalkrishnan, R. and Levy, M. (2004), “Internet retailing: enablers, limiters and
market consequences”, Journal of Business Research, Vol. 57 No. 7, pp. 703-13.
Griffith, D.A., Krampf, R.F. and Palmer, J.W. (2001), “The role of interface in electronic commerce:
consumer involvement with print versus online catalogs”, International Journal of
Electronic Commerce, Vol. 5 No. 4, pp. 135-53.
Ha, H.Y. (2006), “An integrative model of consumer satisfaction in the context of e-services”,
International Journal of Consumer Studies, Vol. 30 No. 2, pp. 137-49.
Heiner, E., Gopalkrishnan, R.I., Josef, H. and Dieter, A. (2004), “E-satisfaction: a re-examination”,
Journal of Retailing, Vol. 80 No. 3, pp. 239-47.
Online shopping
customer
satisfaction
933
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

18. Hsuehen, H. (2006), “An empirical study of web site quality, customer value, and customer
satisfaction based on e-shop”, The Business Review, Vol. 5 No. 1, pp. 190-3.
Jarvenpaa, S.L. and Todd, P.A. (1997), “Consumer reactions to electronic shopping on the world
wide web”, International Journal of Electronic Commerce, Vol. 1 No. 2, pp. 59-88.
Jeong, M., Oh, H. and Gregoire, M. (2003), “Conceptualizing web site quality and its consequences
in the lodging industry”, Hospitality Management, Vol. 22 No. 2, pp. 161-75.
Johan, A. (2006), “Sources of customer satisfaction with shopping malls: a comparative study of
different customer segments”, International Review of Retail, Distribution & Consumer
Research, Vol. 16 No. 1, pp. 115-38.
Kim, S. and Stoel, L. (2004), “Apparel retailers: website quality dimensions and satisfaction”,
Journal of Retailing and Consumer Services, Vol. 11 No. 2, pp. 109-17.
Kim, S.Y. and Lim, Y.J. (2001), “Consumers’ perceived importance of and satisfaction with
internet shopping”, Electronic Markets, Vol. 11 No. 3, pp. 148-54.
Koivumaki, T. (2001), “Customer satisfaction and purchasing behavior in a web-based shopping
environment”, Electronic Markets, Vol. 11 No. 3, pp. 186-92.
Kotler, P. (1997), Marketing Management: Analysis, Planning, Implementation, and Control,
Prentice-Hall, Engelwood Cliffs, NJ.
Lohse, G.L. and Spiller, P. (1998), “Electronic shopping”, Communications of ACM, Vol. 41 No. 7,
pp. 81-9.
Lu, H.T. (2005), The Behavior Differences of Chinese Consumers, Chinese Social Science Press,
Beijing.
Lynch, J. Jr and Ariely, D. (2000), “Wine online: search costs affect competition on price, quality
and distribution”, Marketing Science, Vol. 19 No. 1, pp. 83-104.
McGoldrick, P., Vasquez, D., Lim, T.Y. and Keeling, K. (1999), “Cyberspace marketing: how do
surfers determine website quality”, in Broadbridge, A. (Ed.), Tenth International
Conference on Research in the Distributive Trades. Institute for Retail Studies, University
of Stirling, Stirling, pp. 603-13.
McKinney, V., Kanghyun, Y. and Zahedi, F.M. (2002), “The measurement of web-customer
satisfaction: an expectation and disconfirmation approach”, Information System Research,
Vol. 13 No. 3, pp. 296-315.
Manes, S. (1997), “Web sites: slow by design?”, Information Week, Vol. 4 No. 642, p. 124.
Mason, J.B. and Bearden, W.O. (1979), “Satisfaction dissatisfaction with food shopping among
elderly consumers”, Journal of Consumer Affairs, Vol. 13 No. 2, pp. 359-69.
Muyllea, S., Moenaert, R. and Despontin, M. (2004), “The conceptualization and empirical
validation of web site user satisfaction”, Information & Management, Vol. 41 No. 5,
pp. 543-60.
Myers, R. (1990), Classical and Modern Regression with Applications, 2nd ed., Duxbury Press,
Boston, MA.
Nunnally, J.C. (1978), Psychometric Theory, 2nd ed., McGraw-Hill, New York, NY, p. 245.
Ofir, C. and Simonson, I. (2007), “The effect of stating expectations on customer satisfaction and
shopping experience”, Journal of Marketing Research, Vol. 164 No. 1, pp. 164-74.
Oliver, R.L. (1980), “A cognitive model of the antecedents and consequences of satisfaction
decisions”, Journal of Marketing Research, Vol. 17 No. 4, pp. 460-9.
Oliver, R.L. (1981), “Measurement and evaluation of satisfaction processes in retail settings”,
Journal of Retailing, Vol. 57 No. 3, pp. 25-48.
IJRDM
36,11
934
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

19. Park, C.H. and Kim, Y.G. (2003), “Identifying key factors affecting consumer purchase behavior
in an online shopping context”, International Journal of Retail & Distribution Management,
Vol. 31 No. 1, pp. 16-29.
Pastrick, G. (1997), “Secrets of great site design”, Internet User, Fall, pp. 80-7.
Peterson, R.A., Sridhar, B. and Bart, J.B. (1997), “Exploring the implications of the internet for
consumer marketing”, Journal of the Academy of Marketing Science, Vol. 25 No. 4,
pp. 329-46.
Ranganathan, C. and Ganapathy, S. (2002), “Key dimensions of business-to-consumer web site”,
Information & Management, Vol. 39 No. 6, pp. 457-65.
Schaupp, L.C. and Bélanger, F. (2005), “A conjoint analysis of online consumer satisfaction”,
Journal of Electronic Commerce Research, Vol. 6 No. 2, pp. 95-111.
Shankar, V., Amy, K. and Rangaswamy, A. (2003), “Customer satisfaction and loyalty in online
and offline environments”, International Journal of Research in Marketing, Vol. 20 No. 2,
pp. 153-75.
Shankar, V., Rangaswamy, A. and Pusateri, M. (2001), “The online medium and price
sensitivity”, working paper, University of Maryland, College Park, MD.
Sharma, A., Grewal, D. and Levy, M. (1995), “The customer satisfaction/logistics interface”,
Journal of Business Logistics, Vol. 16 No. 6, pp. 1-21.
Shun, C. and Yunjie, X. (2006), “Effects of outcome, process and shopping enjoyment on online
consumer behavior”, Electronic Commerce Research and Applications, Vol. 5 No. 4,
pp. 272-81.
Spreng, R.A., MacKenzie, S.B. and Olshavsky, R.W. (1996), “A Reexamination of the
determinants of consumer satisfaction”, Journal of Marketing, Vol. 60 No. 3, pp. 15-32.
Szymanski, D.M. and Hise, R.T. (2000), “E-satisfaction: an initial examination”, Journal of
Retailing, Vol. 76 No. 3, pp. 309-22.
Terblanche, N.S. and Boshoff, C. (2001a), “Measuring customer satisfaction with the controllable
elements of the instore shopping experience”, South African Journal of Business
Management, Vol. 32 No. 4, pp. 11-19.
Terblanche, N.S. and Boshoff, C. (2001b), “Measuring customer satisfaction with some of the
controllable elements of the total retail experiences: an exploratory study”, South African
Journal of Business Management, Vol. 32 No. 2, pp. 35-41.
Turban, E., Lee, J., King, D. and Chung, M.H. (2000), Electronic Commerce: A Managerial
Perspective, Prentice-Hall, Upper Saddle River Hall, NJ.
Weinberg, B. (2000), “Don’t keep your internet customers waiting too long at the (virtual) front
door”, Journal of Interactive Marketing, Vol. 14 No. 1, pp. 30-9.
Wolfinbargerhe, M. and Gilly Mary, C. (2003), “EtailQ: dimensionalizing, measuring and
predicting etail quality”, Journal of Retailing, Vol. 79 No. 3, pp. 183-98.
Yang, Z. and Fang, X. (2004), “Online service quality dimensions and their relationships with
satisfaction: a content analysis of customer reviews of securities brokerage services”,
International Journal of Service Industry Management, Vol. 15 Nos 3/4, pp. 302-26.
Yianakos, C. (2002), “Nameless in cyberspace: protecting online privacy”, Journal of Banking and
Financial Service, Vol. 116 No. 6, pp. 48-9.
Zhang, X., Prybutok, V. and Huang, A. (2006), “An empirical study of factors affecting e-service
satisfaction”, Human Systems Management, Vol. 25 No. 4, pp. 279-91.
Zhilin, Y., Peterson, R.T. and Cai, S. (2003), “Services quality dimensions of internet retailing:
an exploratory analysis”, The Journal of Service Marketing, Vol. 17 Nos 6/7, pp. 685-98.
Online shopping
customer
satisfaction
935
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

20. Further reading
Carman, J.M. (1990), “Consumer perceptions of service quality: an assessment of the SERVQUAL
dimensions”, Journal of Retailing, Vol. 66 No. 1, pp. 33-55.
Helson, H. (1964), Adaptation-Level Theory, Harper & Row, New York, NY.
Loiacono, E.T. (2000), “WebQualTM: a website quality instrument”, unpublished doctoral
dissertation, University of Georgia, Athens.
Solomon, R.L. (1980), “The opponent process theory of acquired motivation: the costs of pleasure
and the benefits of pain”, American Psychologist, Vol. 35 No. 8, pp. 713-28.
Solomon, R.L. and Corbit, J.D. (1974), “An opponent-process theory of motivation: I. Temporal
dynamics of affects”, Psychological Review, Vol. 81 No. 2, pp. 119-45.
Corresponding author
Xia Liu can be contacted at: liuxia1213@163.com
IJRDM
36,11
936
To purchase reprints of this article please e-mail: reprints@emeraldinsight.com
Or visit our web site for further details: www.emeraldinsight.com/reprints
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

21. Appendix 1. Questionnaire Online shopping
customer
satisfaction
937
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

22. IJRDM
36,11
938
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

23. Online shopping
customer
satisfaction
939
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

24. Appendix 2. Exploratory factor loading
Components
Items
Factor
1
Factor
2
Factor
3
Factor
4
Factor
5
Factor
6
Factor
7
Factor
8
Factor
9
Color
combination
1
0.716
0.101
0.094
0.040
0.155
0.085
0.054
0.005
0.048
Payment
2
0.245
0.127
0.130
0.228
0.157
0.080
0.054
0.005
0.048
Delivery
1
0.140
20.091
0.171
0.770
0.178
0.185
0.061
0.149
20.022
Delivery
2
0.252
20.104
0.054
0.791
0.114
0.151
0.093
0.009
0.030
Delivery
3
0.144
20.080
0.135
0.829
0.006
0.126
0.149
0.128
0.063
Delivery
4
0.166
20.058
0.230
0.768
0.080
0.117
0.137
0.111
0.068
Customer
service
1
0.213
0.086
0.103
0.186
0.258
0.815
0.017
0.035
0.128
Customer
service
2
0.129
0.082
0.138
0.202
0.255
0.822
0.008
0.053
0.026
Customer
service
3
0.192
0.073
0.192
0.202
0.198
0.755
0.095
0.145
0.090
Navigation
4
information
0.680
0.070
0.055
0.068
0.002
0.069
0.136
0.030
0.101
Understandability
1
information
0.141
0.103
0.693
0.085
20.053
0.181
0.108
0.145
0.056
Understandability
2
information
0.167
0.094
0.739
0.063
20.070
0.159
0.046
0.184
0.024
Information
accuracy
1
0.085
0.085
0.635
0.023
0.317
20.059
0.170
0.022
0.149
Information
accuracy
2
20.006
0.083
0.567
0.128
0.366
0.043
20.059
0.178
0.160
Information
completeness
1
20.007
20.033
0.578
0.130
0.145
0.080
20.064
0.321
0.054
Information
completeness
2
0.157
20.004
0.630
0.060
0.099
0.052
0.055
20.194
20.048
Information
relevancy
1
0.118
0.069
0.591
0.191
0.195
0.018
0.037
20.034
20.050
Merchandise
variety
1
0.081
0.884
0.051
20.007
0.053
0.027
0.036
0.041
20.033
Merchandise
variety
2
0.051
0.861
0.029
20.049
0.024
0.046
0.101
0.035
20.039
Merchandise
variety
3
0.110
0.848
0.098
20.025
0.005
0.023
0.110
0.022
0.028
Merchandise
variety
4
0.040
0.816
0.063
20.065
20.018
0.065
0.017
0.016
20.055
Price
level
1
0.104
0.787
0.027
20.104
0.111
20.010
0.003
20.004
0.054
Price
level
2
0.129
0.780
0.050
20.003
0.153
0.069
20.011
0.094
20.003
Transaction
capability
1
0.251
0.109
0.201
0.204
0.099
0.060
0.175
0.724
0.076
Transaction
capability
2
0.161
0.106
0.162
0.176
0.210
0.134
0.029
0.743
20.047
Response
time
1
0.107
0.011
0.070
0.085
0.030
0.092
20.005
20.027
0.856
Response
time
2
0.103
20.065
0.076
0.010
0.117
0.071
0.015
0.051
0.841
Security/privacy
1
0.206
0.013
0.123
0.012
0.658
0.172
0.063
0.113
0.130
Security/privacy
2
0.164
0.060
0.138
0.106
0.768
0.190
0.124
0.007
0.008
Security/privacy
3
0.125
0.166
0.200
0.065
0.752
0.199
0.119
0.104
0.003
Security/privacy
4
0.097
0.086
0.134
0.227
0.749
0.085
0.015
0.068
0.031
Payment
1
0.242
0.140
0.126
0.216
0.160
0.028
0.847
0.075
0.003
Table AI.
IJRDM
36,11
940
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

25. This article has been cited by:
1. Sabina-Cristiana Necula. Getting Meaning in the Online Environment of E-Commerce by Using
Semantic Web Technologies 130-137. [Crossref]
2. Feng Zhen, Xiaojuan Du, Jason Cao, Patricia L. Mokhtarian. 2018. The association between spatial
attributes and e-shopping in the shopping process for search goods and experience goods: Evidence from
Nanjing. Journal of Transport Geography 66, 291-299. [Crossref]
3. Thi Song Hanh Pham, Mohammad Faisal Ahammad. 2017. Antecedents and consequences of online
customer satisfaction: A holistic process perspective. Technological Forecasting and Social Change 124,
332-342. [Crossref]
4. Mohammed Shafiq Obeidat, W. Daniel Young. 2017. An Assessment of the Recognition and Use of
Online Shopping by Digital Immigrants and Natives in India and China. Journal of International Consumer
Marketing 29:5, 293-302. [Crossref]
5. . Chapter 8: Enhanced Online Service Quality: The Contribution of Local Search Engines 245-266.
[Crossref]
6. McLeanGraeme J., Graeme J. McLean. 2017. Investigating the online customer experience – a B2B
perspective. Marketing Intelligence & Planning 35:5, 657-672. [Abstract] [Full Text] [PDF]
7. TandonUrvashi, Urvashi Tandon, KiranRavi, Ravi Kiran, SahAsh, Ash Sah. 2017. Analyzing customer
satisfaction: users perspective towards online shopping. Nankai Business Review International 8:3, 266-288.
[Abstract] [Full Text] [PDF]
8. KangJu-Young M., Ju-Young M. Kang, KimJieun, Jieun Kim. 2017. Online customer relationship
marketing tactics through social media and perceived customer retention orientation of the green retailer.
Journal of Fashion Marketing and Management: An International Journal 21:3, 298-316. [Abstract] [Full
Text] [PDF]
9. Xun Xu, Shuo Zeng, Yuanjie He. 2017. The influence of e-services on customer online purchasing
behavior toward remanufactured products. International Journal of Production Economics 187, 113-125.
[Crossref]
10. JainNikunj Kumar, Nikunj Kumar Jain, GajjarHasmukh, Hasmukh Gajjar, ShahBhavin J., Bhavin J. Shah,
SadhAshish, Ashish Sadh. 2017. E-fulfillment dimensions and its influence on customers in e-tailing: a
critical review. Asia Pacific Journal of Marketing and Logistics 29:2, 347-369. [Abstract] [Full Text] [PDF]
11. Edward Shih-Tse Wang. 2017. Different Effects of Utilitarian and Hedonic Benefits of Retail Food
Packaging on Perceived Product Quality and Purchase Intention. Journal of Food Products Marketing 23:3,
239-250. [Crossref]
12. Urvashi Tandon, Ravi Kiran, Ash N. Sah. 2017. The influence of website functionality, drivers and
perceived risk on customer satisfaction in online shopping: an emerging economy case. Information Systems
and e-Business Management 10. . [Crossref]
13. Na Xu, Shi-zhen Bai, Xiang Wan. 2017. Adding pay-on-delivery to pay-to-order: The value of two
payment schemes to online sellers. Electronic Commerce Research and Applications 21, 27-37. [Crossref]
14. Ou Liu, Zhonghui Shi, Woonkian Chong, Ka-Lok Man, Chi-On Chan. College Students’ Online
Purchase Intention in Big Data Era 61-71. [Crossref]
15. Ramanayaka Kokila Harshan, Xianqiao Chen, Bing Shi. 2016. Analytic Hierarchy Process (AHP) Based
Model for Assessing Performance Quality of Library Websites. Information Technology Journal 16:1,
35-43. [Crossref]
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

26. 16. Qihua Liu, Shan Huang, Liyi Zhang. 2016. The influence of information cascades on online purchase
behaviors of search and experience products. Electronic Commerce Research 16:4, 553-580. [Crossref]
17. Najma Imtiaz Ali, Suhaila Samsuri, Muhamad Sadry, Imtiaz Ali Brohi, Asdullah Shah. Online Shopping
Satisfaction in Malaysia: A Framework for Security, Trust and Cybercrime 194-198. [Crossref]
18. Farid Huseynov, Sevgi Özkan Yıldırım. 2016. Behavioral Issues in B2C E-commerce. Information
Development 32:5, 1343-1358. [Crossref]
19. Rama Mohana Rao Katta, Chandra Sekhar Patro. 2016. Online Shopping Behavior. International Journal
of Sociotechnology and Knowledge Development 8:4, 21-36. [Crossref]
20. Fengyangzi Zhang, Guang Song. Can delivery speed affect sale in ecommerce: Evidence from household
appliance 1-5. [Crossref]
21. Divya Mittal, Shiv Ratan Agrawal. 2016. Price transparency reflects assurance and reliability. Journal of
Retailing and Consumer Services 31, 43-51. [Crossref]
22. Hyeon Gyu Jeon, Kun Chang Lee. 2016. A Study on the Trust Tansference and Continuance Intention
to Purchase in Offline-Online Multi-Channel Shopping Contexts : Emphasis on the Moderating Effects
of Neuroticism. Korean Management Science Review 33:2, 89-115. [Crossref]
23. Mingyao Hu, Fang Huang, Hanping Hou, Yong Chen, Larissa Bulysheva. 2016. Customized logistics
service and online shoppers’ satisfaction: an empirical study. Internet Research 26:2, 484-497. [Abstract]
[Full Text] [PDF]
24. Maryam Ghasemaghaei, Khaled Hassanein. 2016. A macro model of online information quality
perceptions: A review and synthesis of the literature. Computers in Human Behavior 55, 972-991.
[Crossref]
25. Francisco Liébana-Cabanillas, Francisco Muñoz-Leiva, Juan Sánchez-Fernández, María I. Viedma-del
Jesús. 2016. The moderating effect of user experience on satisfaction with electronic banking: empirical
evidence from the Spanish case. Information Systems and e-Business Management 14:1, 141-165. [Crossref]
26. Arpita Khare, Anshuman Khare, Sourjo Mukherjee, Tanuj Goyal. 2016. Do consumer shopping styles
influence consumer attitudes toward services offered by shopping websites?. Journal of International
Consumer Marketing 28:1, 28-41. [Crossref]
27. Maryam Ghasemaghaei, Khaled Hassanein. 2015. Online information quality and consumer satisfaction:
The moderating roles of contextual factors – A meta-analysis. Information & Management 52:8, 965-981.
[Crossref]
28. Arunima Rana, Anil Bhat, Leela Rani. 2015. A classificatory scheme for antecedents of the sources of
“online brand equity”. Journal of Research in Interactive Marketing 9:4, 262-298. [Abstract] [Full Text]
[PDF]
29. C. N. Noorshella, A. M. Abdullah, A. R. Nursalihah. 2015. Examining the Key Factors Affecting e-
Service Quality of Small Online Apparel Businesses in Malaysia. SAGE Open 5:2. . [Crossref]
30. Yun Kyung Cho. 2015. The relationship between resources and market coverage in small local internet
retailing. International Journal of Retail & Distribution Management 43:7, 597-616. [Abstract] [Full Text]
[PDF]
31. Moez Ltifi, Jameleddine Gharbi. 2015. Impact de la qualité perçue du site web marchand sur le bonheur
du cyberconsommateur. La Revue Gestion et Organisation 7:1, 33-43. [Crossref]
32. Jae-Ahm Park, Bo Li, Stephen W. Dittmore. 2015. What Motivates Chinese Sports Fans to Subscribe
to Athletes' Social Networking Service Accounts?. Journal of the Korea Academia-Industrial cooperation
Society 16:2, 1064-1072. [Crossref]
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

27. 33. Shiu-Wan HUNG, Min-Jhih Cheng, Shiu-Chun Hsieh. 2015. Consumers’ satisfaction with online group
buying – an incentive strategy. International Journal of Retail & Distribution Management 43:2, 167-182.
[Abstract] [Full Text] [PDF]
34. Jie YU, Nachiappan Subramanian, Kun Ning, David Edwards. 2015. Product delivery service provider
selection and customer satisfaction in the era of internet of things: A Chinese e-retailers’ perspective.
International Journal of Production Economics 159, 104-116. [Crossref]
35. Bernd Philipp, David B. Grant. 2015. Does B2C online logistics service quality impact urban logistics?.
Logistique & Management 23:2, 45-54. [Crossref]
36. Sanjeev Prashar, T. Sai Vijay, Chandan Parsad. 2015. Antecedents to Online Shopping. International
Journal of E-Business Research 11:1, 35-55. [Crossref]
37. Michael D. Clemes, Christopher Gan, Junli Zhang. 2014. An empirical analysis of online shopping
adoption in Beijing, China. Journal of Retailing and Consumer Services 21:3, 364-375. [Crossref]
38. Hao Wang, Zhou-Jing Wang. An approach to deriving interval weights from interval fuzzy preference
relations based on multiplicative transitivity 1371-1375. [Crossref]
39. Arpita Khare, Amrut Sadachar. 2014. Collective Self-Esteem and Online Shopping Attitudes among
College Students: Comparison between the U.S. and India. Journal of International Consumer Marketing
26:2, 106-121. [Crossref]
40. Ilias O. Pappas, Adamantia G. Pateli, Michail N. Giannakos, Vassilios Chrissikopoulos. 2014. Moderating
effects of online shopping experience on customer satisfaction and repurchase intentions. International
Journal of Retail & Distribution Management 42:3, 187-204. [Abstract] [Full Text] [PDF]
41. Chun-Cheng Hsu, Yi-Shian Lee. 2014. Exploring the Critical Factors Influencing the Quality of Blog
Interfaces Using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) Method. Behaviour
& Information Technology 33:2, 184-194. [Crossref]
42. Nachiappan Subramanian, Angappa Gunasekaran, Jie Yu, Jiang Cheng, Kun Ning. 2014. Customer
satisfaction and competitiveness in the Chinese E-retailing: Structural equation modeling (SEM)
approach to identify the role of quality factors. Expert Systems with Applications 41:1, 69-80. [Crossref]
43. Wann-Yih Wu, Chia-Ling Lee, Chen-Su Fu, Hong-Chun Wang. 2013. How can online store layout
design and atmosphere influence consumer shopping intention on a website?. International Journal of
Retail & Distribution Management 42:1, 4-24. [Abstract] [Full Text] [PDF]
44. Jiyoung Kim, Kiseol Yang, Bu Yong Kim. 2013. Online retailer reputation and consumer response:
examining cross cultural differences. International Journal of Retail & Distribution Management 41:9,
688-705. [Abstract] [Full Text] [PDF]
45. Su-Young Pi, Hye-Jung Park, Young-Jik Kwon. 2013. An Analysis of customer satisfaction for shopping
mall using multi LS-SVM : Focused on the Perception of Chinese Students in Korea. Journal of the Korea
Society of Computer and Information 18:6, 81-89. [Crossref]
46. Jae Ik Shin, Ki Han Chung, Jae Sin Oh, Chang Won Lee. 2013. The effect of site quality on repurchase
intention in Internet shopping through mediating variables: The case of university students in South
Korea. International Journal of Information Management 33:3, 453-463. [Crossref]
47. Sapna Rakesh, Arpita Khare. 2012. Impact of promotions and value consciousness in online shopping
behaviour in India. Journal of Database Marketing & Customer Strategy Management 19:4, 311-320.
[Crossref]
48. Arpita Khare, Anshuman Khare, Shveta Singh. 2012. Attracting Shoppers to Shop Online—Challenges
and Opportunities for the Indian Retail Sector. Journal of Internet Commerce 11:2, 161-185. [Crossref]
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

28. 49. Shveta Singh, Arpita Khare. 2012. Focus Group Technique to Study Customer Attitude Towards Online
Travel Services in India. International Journal of Information Systems in the Service Sector 4:2, 33-47.
[Crossref]
50. Zhao Chen, Kwek Choon Ling ., Guo Xiao Ying ., Tang Chun Meng. 2012. Antecedents of Online
Customer Satisfaction in China. International Business Management 6:2, 168-175. [Crossref]
51. Arpita Khare, Anshuman Khare. 2011. Blending Information Technology in Indian Travel and Tourism
Sector. Services Marketing Quarterly 32:4, 302-317. [Crossref]
52. Arpita Khare, Sapna Rakesh. 2011. Antecedents of Online Shopping Behavior in India: An Examination.
Journal of Internet Commerce 10:4, 227-244. [Crossref]
53. 최최최, 최최최, ChungKiHan. 2011. The Factors Affecting Customer Satisfaction and Customer Loyalty to
Enhance Profitability of Non-profit Store. Productivity Review 25:3, 83-114. [Crossref]
54. Sanjukta Pookulangara, Jana Hawley, Ge Xiao. 2011. Explaining consumers’ channel-switching behavior
using the theory of planned behavior. Journal of Retailing and Consumer Services 18:4, 311-321. [Crossref]
55. Boris Otto, Yang W. Lee, Ismael Caballero. 2011. Information and data quality in business networking:
a key concept for enterprises in its early stages of development. Electronic Markets 21:2, 83-97. [Crossref]
56. Arpita Khare, Shveta Singh, Anshuman Khare. 2010. Innovativeness/Novelty-Seeking Behavior as
Determinants of Online Shopping Behavior Among Indian Youth. Journal of Internet Commerce 9:3-4,
164-185. [Crossref]
57. Cheolho Yoon. 2010. Antecedents of customer satisfaction with online banking in China: The effects of
experience. Computers in Human Behavior 26:6, 1296-1304. [Crossref]
58. Ki‐Han Chung, Jae‐Ik Shin. 2010. The antecedents and consequents of relationship quality in internet
shopping. Asia Pacific Journal of Marketing and Logistics 22:4, 473-491. [Abstract] [Full Text] [PDF]
59. Xiao Tong. 2010. A cross‐national investigation of an extended technology acceptance model in the online
shopping context. International Journal of Retail & Distribution Management 38:10, 742-759. [Abstract]
[Full Text] [PDF]
60. Arpita Khare. 2010. Online banking in India: An approach to establish CRM. Journal of Financial Services
Marketing 15:2, 176-188. [Crossref]
61. Adam P. Vrechopoulos. 2010. Who controls store atmosphere customization in electronic retailing?.
International Journal of Retail & Distribution Management 38:7, 518-537. [Abstract] [Full Text] [PDF]
62. Sylvie Rolland, Ina Freeman. 2010. A new measure of e‐service quality in France. International Journal of
Retail & Distribution Management 38:7, 497-517. [Abstract] [Full Text] [PDF]
63. Arpita Khare, Anshuman Khare. 2010. Travel and tourism industry yet to exploit the Internet fully in
India. Journal of Database Marketing & Customer Strategy Management 17:2, 106-119. [Crossref]
64. Md Mahbubur Rahim, JieYing Li. An empirical assessment of customer satisfaction with Internet Banking
applications: An Australian experience 314-320. [Crossref]
65. Hye-Sun Park, Yeon Lee, Hyun-Sook Kim. 2009. Clothing Shopping Motivation on Internet and
Customer e-Loyalty among Korean and Chinese College Students. Journal of the Korean Society of Clothing
and Textiles 33:11, 1744-1754. [Crossref]
66. Rama Mohana Rao Katta, Chandra Sekhar Patro. Online Shopping Behavior 1413-1429. [Crossref]
67. Bo Liang, Yanbin Tu, Thomas Cline, Zhongyu Ma. China's E-Tailing Blossom 1530-1555. [Crossref]
68. Mark Arjun Muthukumaran, Sajad Rezaei, Yoke Moi Oh, Gu Manli. Antecedents of Apps Channel
Selection 252-273. [Crossref]
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)

29. 69. Bo Liang, Yanbin Tu, Thomas Cline, Zhongyu Ma. China's E-Tailing Blossom 72-98. [Crossref]
70. Noorshella Che Nawi, Arthur Tatnall, Michelle W.L. Fong. Online Customer Satisfaction at Point-of-
Purchase and Post-Purchase Phases 216-228. [Crossref]
Downloaded
by
UNIVERSITY
OF
VIRGINIA
At
08:35
22
January
2018
(PT)