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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.

INTERNATIONAL JOURNAL OF BUSINESS RESEARCH, Volume 8, Number 5, 2008 127

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.


(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.


Information Quality

Price Advantage

Web Security Security

Customer Service Quality

System Quality

Web interactivity

Shopping Convenience

Corporate Image

Distribution Efficiency

customer satisfaction

Attitude to shopping

online

Intention to shopping

online

Online customer behavior

H1

H2

H3

H4

H5

H6

H7

H8

H9

H10 H12

H11

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.


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

1824.571

df 276 Sig. .000


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.


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


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


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.


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. REFERENCES: [1]China Network Information Center.China Internet development report [W]. www.cnnic.net/ html/Dir/2006/01/17/3508.htm/2006.1.17. [2] Mary Wolf inbarger,Mary.C.Gilly.EtailQ: dimensionalizing, measuring and redicting [J].Journal of Retailing,2003,(07):183-198. [3] Liu,C.&Arnett,K.P. Exploring the factors associated with Web site success in the context of electronic commerce[J].Information&Management,2000,38(10):23-34.


[4] Wang shu-chuan. Individual/organizational characteristics and intention to adopt e-commerce: A study based on innovation adoption theory [D].The Chinese University of Hong Kong,2001. [5] Aron O’Cass, Tino Fenech .Web retailing adoption :Exploring the nature of internet users web retailing behavior[J].Journal of Retailing and Consumer Services, 2003,10(2):81-94. [6] Terry L. Childersa,*, Christopher L. Carrb, Joann Peckc, Stephen Carsond. Hedonic and utilitarian motivations for online retail shopping behavior[J]Journal of Retailing 77 (2001) 511–535. [7] C.Ranganathan, Shobha Ganapathy.Key dimensions of business-to-consumer web sites[J]. Information & Management,2002,39(6):457-465. [8] Sheng-Uei Guan, Yang Yang. SAFE: Secure Agent roaming for E-commerce[J]. Computers & industrial engineering, 42(2002):481-493 [9] Xixi Wang Research on consumer shopping online.[D].Zhejiang:Zhejiang University,2001. [10] Zhicheng Li,Meilian Liu. Research on consumer behavior under electronic commerce environment[J]. Chinese Journal of management science ,2002,10(6): 88-91. [11] Hua Cheng,Gongmin Bao. Empirical study on factors related to Internet shopping intention [J]. Journal of econometrics and technology economics,2003,(11):510-153. [12] Kun liu. Latent Web consumer behavior study based on electronic commerce [D].Xi an: university of science and technology,2004. [13] Fengjie Jing, Fu e Zeng. Review on customer satisfaction to customer behavior [J].Journal of Business economy and management,2004,(10):21-25. [14] Jiwen Zhu. Research on Factors related to consumer web shopping behavior [D].South west jiao tong University,2001. [15] Linfen Cheng, Chongming Wang. Relation Between Web consumer behavior and electronic commerce service quality [J].Journal of consumption economics,2005,(06):78-81. [16] Beard WO, Teel J E. Selected determinants of consumer satisfaction and complaint reports [J]. Journal of Marketing Research,1983(20):21-28. [17] Fornell,Claes and BIrger Wernerfelt. Defensive Marketing Strategy by Customer Complaint Management: A Theoretical Analysis [J]. Journal of Marketing Research,24(November),1987:337-346. [18] McAlister , Debbie Thorne. A content analysis of outcomes and responsibilities for consumer complaints to third – party organizations [J]. Journal of Business Research, 2003 56 (4),:341-351 [19] Walsh, John P. and Todd Bayma. 1996. Computer Networks and Scientific Work[J] Social Studies of Science Vol. 26, No. 4: 661-703. 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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