This document discusses research on the relationship between consumer satisfaction and online shopping behavior. It presents a hypothesized relationship model between consumer satisfaction and online consumer behavior. The model is based on theories of customer satisfaction, consumer behavior, technology acceptance, and innovation diffusion. The researchers conducted statistical analyses to test the model using SPSS software. The results showed that web service quality, security, interaction and other factors influence consumer satisfaction. Higher consumer satisfaction, positive attitudes toward online shopping, and higher perceived usefulness were found to increase shopping intentions, and satisfaction had a positive relationship with intentions to shop online.

1. RESEARCH ON RELATIONSHIP BETWEEN CONSUMER SATISFACTION AND WEB CONSUMER
SHOPPING BEHAVIOR
Meilian Liu, Guilin University of Electronic and Technology, Guilin, China
Yuefeng Xiao, Guilin University of Electronic and Technology, Guilin, China
ABSTRACT
The paper focuses on the relation between consumer satisfaction and consumer behavior in Internet.
Based on the customer satisfaction and consumer behavior theory, a hypothesized relation model
between consumer satisfaction and consumer behavior in Internet has been set forward, according to
the present conditions of electronic commerce and Internet marketing. After statistics analysis have
been done, such as factor analysis, relation analysis and hypothesis test, results show that web
service quality, web safe, web interaction and others are important to consumer satisfaction by means
of SPSS software. Further studies show that consumer satisfaction, attitude to Internet shopping and
perceived usefulness are significant to shopping intention, while consumer satisfaction is positively
related to web shopping intention.
Key words: Consumer satisfaction; Internet shopping intention; Internet shopping behavior
1. INTRODUCTION
With the rapid development of Internet, many E-commerce websites have appeared. However,
consumer is not very satisfied. According to China Network Information Center (CNNIC) report at the
end of 2005, only six percent of netizen is very satisfied with shopping online, while twenty-two percent
netizen is unsatisfied with it.
As a new kind of business mode, e-commerce provides product display, communication, payment
method in a new way. For consumers, shopping online is not restricted by shopping time and physical
place, but it also brings such troubles as anxiety of website reliability, product quality, and after-sale
service. For suppliers, Web consumers belong to a special group which is different from traditional
consumers. To discuss related factors to the Web consumer satisfaction is helpful to improve website
service and to build up their trust to the advancement of the network corporation.
This paper is oriented to Web consumers. A correlation model between consumer satisfaction and
Web consumers has been built up and has been tested by empirical analysis according to satisfaction
and shopping behavior theory in a view of Web consumer satisfaction to website.
2. RELATED WORK REVIEW
Relevant researches abroad mainly focuses on two aspects: one is to analyze the factors related to
consumer behavior and consumer satisfaction to combine the website characteristic and Internet
technology, the other is to research on the search intelligent Agent technology of the E-Commerce
Websites and electronic payment security in view of pure technology adoption.
Mary Wolf(2003) defined customer satisfaction as consumers’ perception to their online shopping
experience, and his empirical research indicates that four factors—website design, convenience,
security and customer service—are positively related to customer satisfaction to the website. Liu,
C.and Arnett, K.P t set up an integrated framework from logistics support, customer service, product
price priority and other website advantages. Wang shu-chuan discussed the probability that potential
consumers go shopping in Internet from consumer characteristics, cognition and psychology, and
results suggested consumer cognition and psychology have greater impact on consumer attitude and
intention, but .demographic characteristics does little. Aron and Tino built up a path dependence chart
of consumer purchase behavior based on previous research on consumer acceptance of new
technology and Internet shopping systems.
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2. Terry (2001) put forward an attitude model by technology acceptance model, which has been tested by
empirical analysis. Results show consumers in Internet pursue more hedonic as well as utilitarian
motivations compared with consumers in traditional business mode. In addition, product attributes,
convenience, and electronic shopping atmosphere are very important to improve interactive online
efficiency. Ranganathan and Keeney examined the key characteristics of B2C website based on a call
of more than one hundred online shoppers, and they suggested that ten key factors such as privacy
protection, lower cost, security, and so on. Sheng-Uei Guan, Yang Yang suggest network security is a
bottleneck of the development of e-commerce and designed a safe mobile Agent SAFE to provide
secure protection to consumers, which help users locate the correct piece of information and improve
customer satisfaction in Internet.
Many studies on online consumer satisfaction and Web consumer shopping behavior focus on the
qualitative analysis in China, while a few quantitative researches focus on factors related to shopping
behavior and network satisfaction.
Xixi Wang analyzed related factors which have impact on consumer purchase behavior and suggested
that four factors: demographics, online shopper characteristics, trade mode and network retail supplier
attribute[9 ]. Meilian Liu and Zhicheng Li set up a consumer behavior model based on theory of
planned behavior and Web consumer characteristics[10]. According to technology acceptance model,
Hua Cheng and Gongmin Bao established a structural equation model on shopping behavior online
which has been tested by AMOS 4.0 software, in which perceived usefulness, perceived convenience
and perceived safety of shopping in Internet [11].
After latent online consumers have been segmented, Kun Liu proposed a framework model based on
potential online consumers’ attitude according to theory of reasoned action and theory of planned
behavior, but only consumer perceived usefulness is included in the model while perceived ease is
ignored [12]. Fengjie Jing proposed that customer satisfaction should be considered as a continuous
variable and study it from three different aspects: dissatisfactory customer behavior, satisfactory
customer behavior and varied satisfactory customer behavior. They believed that the medium variable
can adjust or even control customer behaviors and such interference may magnify, shrink or even
distort customers’ true actions [13].
Based on Technology acceptance model, Zhu jiwen set up customer behavior intention model
according to related theory on consumer behavior. Empirical study shows that online shops should
provide abundant and accurate information, satisfactory service, high quality hardware/software
software to attract customers [14]. Grounded on Howard- Sheth model, Lin fen Chen and Chong ming
Wang set up a simplified B-S online shopping model to study the relation among E-commerce service,
attitude, intention and online consumer behavior [15].
Although many studies from home and abroad related on Internet shopping behavior appeared, but
pays little attention to relation between consumer satisfaction and online consumer behavior, and
especially empirical study is ignored.
3. CORRELATION MODEL CONSTRUCTION
3.1Theoretical Foundation of Model Construction
(1)Theory of Reasoned Action
In an attempt to discuss the relation among beliefs, attitudes, intentions and behaviors, Fishbein and
Ajzen (1975) built up theory of reasoned action. TRA believes that individual behavior is driven by
intention while intention is a weight function of an individual's attitude and subjective norms. Attitude is
determined by one's beliefs and perceived income.
(2)Theory of Planned Behavior
Ajzen (1988) built up theory of planned behavior (TPB) based on theory of reasoned action. TPB
discusses attitude, intention and behavior on condition that individual behavior is not controlled by
oneself. Compared with TRA, TPB supposes perceived behavioral control is related to intention. Both
TRA and TPB make an assumption that human beings are rational to make decisions to make full use
of available information.
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3. (3)Technology acceptance model
In response to the limitations associated with TRA and diffusion of innovation literature, Davis
proposed technology acceptance model (TAM), which is designed especially to understand
acceptance of information technologies. This model highlights two key factors which is perceived ease
of use (PEOU) and perceived usefulness (PU). TAM defines individual behavior to adopt information
technology is determined by his intention, while intention is determined by attitude to information
system and perceived usefulness. PU stands for how much performance can be improved for an
individual to use a given information system , while PEOU stands for how much effort can be reduced
by using a particular system. Both PU and PEOU predict attitude toward using the system, and TAM
model can be shown as fig.1.
TAM is one of the most popular information system acceptance models, which is now extensively used
in technology acceptance in different background, such as Email, Web technology and so on.
FIG.1 TECHNOLOGY ACCEPTANCE MODEL (TAM)
(4)Innovation diffusion theory
IDT is concerned with innovation diffusion, in which an innovation such as a new idea, new invention,
or an innovative product is gradually accepted by people or organizations. At the same time, innovation
characteristics, mass media and human relation have effect on the diffusion speed. IDT although is
strong in its explanatory power to the innovation adoption of likes and dislikes, how attitude is turned
into decision behavior is omitted. So IDT needs to integrated TRA, TAM with TPB to predict and
explain relation between consumer satisfaction and web shopping behavior.
3.2 The relation model between consumer satisfaction and consumer behavior on the Internet.
Based on evaluation index of consumer satisfaction and relevant theory of consumer behavior, we
have proposed a relation model shown in Fig.2 between consumer satisfaction and consumer behavior
in Internet.
Eleven variables are included in the model, of which nine variables are related to web quality as well as
consumer satisfaction and attitude to shop online. For quality index, this research focuses on
determine factors based on information quality, price advantage, web security, system quality,
consumer service quality, web interactivity, convenience, distribution efficiency and corporate image, of
which information quality, price advantage and web security belong to perceived usefulness; while
website system quality, consumer service quality, web interactivity and convenience are included in
consumer perceived ease of use and distribution efficiency and corporate image are classified as other
factors.
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4. Information Quality
Price Advantage
Web Security Security
Customer Service
Quality
System Quality
Web interactivity
Shopping Convenience
Corporate Image
Distribution Efficiency
customer
satisfaction
H11
Attitude to
shopping
online
Intention to
shopping
online
Online
customer
behavior
H1
H2
H3
H4
H5
H6
H7
H8
H9
H10 H12
H13
FIG.2 A HYPOTHESIZED RELATION MODEL BETWEEN ONLINE CUSTOMER SATISFACTION
AND BEHAVIOR
3.3 Hypothesis
Further test is needed to verify the validity of the model. This research proposes relevant hypotheses
and testes them by hypothesis test.
H1: Website information quality is positively related to online customer satisfaction.
H2: There will be a positive relationship between price priority and online customer satisfaction.
H3: The better the website security is, the more satisfied Web consumer is.
H4: There will be a positive relationship between customer service quality and online customer
satisfaction.
H5: There will be a positive relationship between system quality and online customer satisfaction.
H6: The better the web interaction is, the more satisfied the Web consumer is.
H7: The better the web convenience is, the more satisfied the Web consumer is.
H8: There will be a positive relationship between corporate image and online customer satisfaction.
H9: There will be a positive relationship between distribution efficiency and online customer
satisfaction.
H10: There will be a positive relationship between online customer satisfaction and attitude internet
shopping
H11: There will be a positive relationship between online customer satisfaction and behavioral intention
to internet shopping.
According to TAM, there will be a strong correlation between customer attitude and intention. And
attitude will take effect on information processing as well as intention and behavior.
H12: There will be a positive relationship between attitude to internet shopping and behavioral
intention to internet shopping..
According to TAM, perceived ease of use does effect on perceived usefulness. So hypothesis is
proposed as follows:
H13: There will be a positive relationship between perceived ease of use and perceived usefulness.
According to TAM, perceived usefulness is a determined factor of behavioral intention. This research
discusses perceived usefulness in the light of information quality, web security and price advantage.
So hypothesis is proposed as follows:
There will be a positive relationship between perceived usefulness and behavioral intention to internet
shopping.
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5. Consumer expectation online is consumer’s anticipation to overall service quality based on his
experience, individual specified need and the reputation of the brand. Consumer expectation id
uncontrolled to online corporations, while they can improve Web consumer satisfaction by betterment
on information quality, price advantage, web security, system quality, consumer service quality, web
Interactivity, convenience, distribution efficiency and corporate image. Therefore, this research focuses
on the factors related to customer satisfaction and the relationship between consumer satisfaction and
behavioral intention, except for customer expectations.
4 DATA ACQUISITION AND DATA ANALYSIS
All gathered data from questionnaire will be processed by SPSS, and three methods have been
adopted based on the purpose of the paper.
One is factor analysis which is used in all measurement items such as website quality, consumer
satisfaction, consumer attitude to Internet shopping and shopping intention and so on, which makes
several problems summarized into one or more synthesized index and index system simplified.
The second is relation analysis which is to judge whether relations exist such as relation between
website quality and consumer satisfaction, relation between consumer satisfaction and attitude to
Internet shopping, and so on.
The third is hypothesis test which is to verify whether the established model and the proposed
hypothesis are correct or not.
4.1 Sample Selection
According to China Internet Network Information Center report in January 17,2006, 35.1percent of all
netizen are from eighteen to twenty four year old is the biggest population and is up to, the second is
19.3 percent netizen who are from twenty five to thirty years old. In view of occupation background,
students are the biggest population who is up to 35.1 percent. So BBS and Email are the mainly
research type to obtain data, and valid questionnaire number is 290.
4.2 Questionnaire design
The questionnaire is made up of three parts. The first part consists of measurement items which take
Likert five scales and each scale corresponds to 1,2,3,4,5 points respectively. The second part is to
discuss what is related to Web consumer satisfaction and what the consumers worry about in Internet
shopping The third is about consumers information.
4.3 Reliability Analysis and validity test
Reliability analysis is often made by Cronbachα. The total scale reliabilityαis 0.8618,and it can be seen
from the test result that will be 0.8624 if information quality which is to provide timely information is
deleted. Because αlies between 0.7 and 0.9, the questionnaire is credible.
Validity is often made up of three parts: face validity, content validity and construct validity. As for face
validity and content validity, we have consulted some experts to modify the questionnaire to make it
proper. For construct validity, we will verify it by factor analysis.
The precondition of factor analysis is the relation between variables, only relation strength is up to 0.5.
KMO and Bartlett's Test have been used in the paper and results are shown in the table 1.
TABLE 1: KMO AND BARTLETT'S TEST
Kaiser-Meyer-Olkin Measure of
Sampling Adequacy.
.752
Bartlett's Test of
Sphericity
Approx.
Chi-Square
1824.57
1
df 276
Sig. .000
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6. From the table above, KMO is 0.752, which shows the factor analysis is proper and Bartlett's Test of
Sphericity ‘s approximation Chi-Square is 1824.571, whose significant level is very small, which shows
the gathered data can be used in factor analysis.
Initial eigenvalues and total variance explained appear in the following table 2. It can be seen from the
table that the extracted nine factors whose eigenvalues are all bigger than 1 and the nine factors
explain 70.727 percent of the total variance, which illustrates that the relation strength among initial
indexes is bigger.
TABLE 2: TOTAL VARIANCE EXPLAINED
Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Component Total % of
Variance Cumulative % Total % of
VarianceCumulative %Total % of
Variance Cumulative %
1 4.854 22.062 22.062 4.854 22.062 22.062 2.138 9.717 9.717
2 1.621 7.368 29.430 1.621 7.368 29.430 1.841 8.368 18.085
3 1.563 7.107 36.537 1.563 7.107 36.537 1.837 8.351 26.436
4 1.482 6.735 43.272 1.482 6.735 43.272 1.763 8.013 34.450
5 1.434 6.519 49.790 1.434 6.519 49.790 1.743 7.924 42.374
6 1.261 5.731 55.521 1.261 5.731 55.521 1.618 7.353 49.727
7 1.244 5.656 61.178 1.244 5.656 61.178 1.607 7.303 57.030
8 1.080 4.910 66.088 1.080 4.910 66.088 1.561 7.093 64.123
9 1.021 4.639 70.727 1.021 4.639 70.727 1.453 6.604 70.727
According to factor analysis, nine factors are extracted which are website information quality (IQ), price
priority (PP), website safety (WS), website system quality (SQ), Customer Service Quality（SEQ）, Web
Interactivity（WI）, shopping convenience (C), Corporate Image（IE）and Distribution Efficiency（DE）.
4.4 Hypothesis Test
Hypothesis test is taken by relation analysis. Although relation analysis can not distinguish the reasons
and results, we can judge the casual relation based on existent behavioral theories, and only relation
analysis is used in the paper.
TABLE 3: CORRELATIONS BETWEEN WEBSITE QUALITY AND CONSUMER
SATISFACTION
relevant variables
Online Customer Satisfaction to
website
Pearson Correlation Sig.
Information Quality（IQ） .152(**) .010
Price Priority（PP） .253(**) .000
Web Security（WS） .259(**) .000
Customer Service
Quality（SEQ） .369(**) .000
System Quality（SQ） .201(**) .001
Shopping Convenience
（C） .097 .099
Web Interactivity（WI） .331(**) .000
Distribution Efficiency
（DE） .270(**) .000
Corporate Image（IE） .280(**) .000
Note ：* means significant level is 0.05, ** means significant level is 0.01.
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7. 4.4.1 website quality and consumer satisfaction
To further probe the correlations between website quality and consumer satisfaction, relation analysis
has been done and Pearson correlation coefficients have been shown in the table 3.
It can be seen from table 4 above, the rest 8 factors are positively related to network satisfaction
except shopping convenience in 0.01 significant level, which proves the hypothesizes from hypothesis
1 to hypothesis 9 to be true except for hypothesis 7 which is also true in 0.1 significant level.
4.4.2 Relation between consumer satisfaction and attitude to shopping online
To probe the correlation between consumer satisfaction and attitude to Internet shopping, Pearson
analysis has been done and results are shown in the table 4.
TABLE4: CORRELATIONS BETWEEN CONSUMER SATISFACTION AND ATTITUDE TO
SHOPPING ONLINE
Relevant
variables Attitude to shopping online
Pearson
Correlation Sig.
Satisfaction .223(**) .000
It can be seen from table 5 above, consumer satisfaction is positively related to attitude to shopping
online in 0.01 significant level, which proves that the more satisfied the consumers feel, the more
active attitude to shopping online and hypothesis 10 proves to be true.
4.4.3 Correlation analysis between PU and PEOU
According to TAM model, consumer perceived usefulness is determined by perceived ease of use and
other external variables, so relation analysis between perceived shopping website usefulness (website
information, quality price priority and website safety included) and perceived ease of use (customer
service quality ,website system quality ,website interactivity and shopping convenience included) has
been done , and results are shown in the table 6. From the results in the table 5, we can also prove the
hypothesis 13 is true
TABLE5: CORRELATIONS BETWEEN PERCEIVED USEFULNESS AND PERCEIVED EASE
OF USE
Relevant
Variables Perceived Usefulness
Pearson
Correlation Sig.
Perceived Ease of
Use .480(**) .000
4.4.4 Other hypothesis test
In order to prove the rest hypothesis are true or not, relation analysis has been taken a method and
results are shown in the table 6.
TABLE 6: CORRELATION COEFFICIENTS
Relevant Variables
Shopping intention online
Pearson correlation Sig.
Attitude to shopping
online .585(**) .000
Consumer satisfaction .169(**) .004
Perceived usefulness for
shopping online .195(**) .001
It can be seen from table 6, the hypothesis 10, 11 and 12 are proved to be true in significant level 0.01.
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8. According to the results above, the hypothesized model can be modified as the following figure 3.
FIG.3 THE MODIFIED CORRELATION MODEL BETWEEN CONSUMER SATISFACTION AND
WEB SHOPPING BEHAVIOR
4.6 Result analysis
From the modified correlation model between consumer satisfaction and web shopping behavior, the
nine factors proposed in the paper are all positively related to consumer satisfaction except for
shopping convenience online, and customer service quality does greatest effect on the consumer
satisfaction among nine factors and in turn is website interactivity, corporation image, distribution
efficiency, network safety, price priority, website system quality and website information quality. Results
show that consumer satisfaction, attitude to shopping online and perceived usefulness for shopping
online determine the intention to internet shopping, while consumer satisfaction is positively related to
attitude to shopping online and intention. What’s more, consumer perceived ease of use for shopping
website does effect on the perceived usefulness in a way.
5. CONCLUSION AND REMARKS
This paper is oriented to Web consumers to research the correlation between consumer satisfaction
and web shopping behavior, which is helpful for corporations in Internet to make proper E-marketing
decisions.
A relation model has been built up based on TAM and other behavioral theories, in which the website
information quality, price priority, and network safety are classified into perceived usefulness of
shopping website, and website system quality, customer service quality, website interactivity and
shopping convenience are categorized to perceived ease of use of shopping online, while the
distribution efficiency and network image belong to other factors.
The paper researches consumer satisfaction restricted by website itself, while consumer psychology
and other social factors have been ignored. Web consumer characteristics and web shopping behavior
will be integrated in further studies in view of an individual consumer and random behavior so as to
provide more practical guide to E-marketing.
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AUTHOR PROFILES:
Meilian Liu has earned her Ph.D at Huazhong University of Science and Technology, in China in 2005.
Currently she is an assistant professor of management college of Guilin University of Electronic
Technology.
Yuefeng Xiao is an assistant professor of management college of Guilin University of Electronic
Technology, and he is interested in business and marketing research.
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