e Loyalty6

7/30/2019 e Loyalty6

1/5

Customer Satisfaction in E-commerce: an Exploration

of its Antecedents and ConsequencesZHA Jin-xiang1 JU Fang-hui 2 WANG Li-sheng 3

1 School of management, Zhejiang University, Hangzhou, P.R.China [email protected]

2 School of business, Zhejiang Wanli University, Ningbo, P.R.China [email protected] School of management, Shandong University, Jinan, P.R.China [email protected]

Abstract-This study developed and empirically tested a model

examining the antecedents and consequences of consumer

satisfaction toward e-retailers. Confirmatory Factor Analysis

(CFA) was performed to examine the reliability and validity of the

measurement model, and the structural equation modeling

techniques were used to evaluate the casual model. Based on a

survey in China, this study showed that e-SQ is very important in

generating overall customer satisfaction and loyalty intention

toward an e-retailer. The conclusion also showed that website

design characteristic is not the significant influential factor of

E-satisfaction, and internet security, price advantage and product

quality guarantee are the most important factors of E-satisfaction.

However, the study demonstrated that future e-SQ expectancy

exerted a negative influence on a consumers overall satisfaction

and loyalty intention toward their e-retailers.

Key words-E-retailers, e-SQ, e-satisfaction, e-loyalty

I.INTRODUCTION

Internet popularity is growing at an impressive rate.

E-business is an irresistible general trend for consumers or

companies to trade over Internet. According to the CNNICs

survey, there are more than 97 million Internet users in China

up to January 2005. This shows a potential business in Internet

in China is not ignorable.

The relational management between buyers and sellers isalways the key point for enterprises to run a business

successfully. Loyal customers have affinitive connections for

them to gain profits as well. Most of the scholars mention that

the focal point on marketing has transferred from the

traditional transaction in the past to relationship marketing that

stresses on constructing and maintaining the good

enterprise-customer relationship nowadays. To E-retailer,

customer relationship, rather than technology, is the key to the

success (Keen & McDonald, 2000). Internet consumer can

switch from one E-retailer to another easily, so the E-retailer

who cannot provide more valuable services to consumers only

attracts non-profit customer seeking low price forever

(Reichheld & Schefter, 2000). According to Bain &

Mainspring (2000), in order to obtain profit from a customer;the internet store must keep the customer visiting the website

more than 18 months or trade with this customer more than 4

times. Developing consumer satisfaction and loyalty in the

electronic marketplace may appear a somewhat utopian when

consumers can leave with just a mouse click away (Srinivasan

et al, 2002). Previous investigation shows that only 30% of the

Internet market is exploited and the E-retailers lose more than

half of the customers every 5 years. According to the CNNICs

survey, most internet users are doubtful about the internet and

E-retailers. About 58% of the 3.3 million internet users in

Hong Kong are suspicious of internet and E-retailers. The

proportion in Macao is 57% and 34.41% in Taiwan. So, the

level of satisfaction, trust and loyalty to E-retailers is not high

enough generally In order to increase customer satisfaction

toward e-retailers, it is important to understand the driving

forces of consumer satisfaction, because strong customersatisfaction can help an E-retailer survive fierce competition.

Early investigations to customer satisfaction, focused

primarily traditional environment, there is still a dearth of data

about the influencing elements of E-satisfaction, especially

from Chinese investigation. As a result, much uncertainty

remains regarding the nature of marketing activities most

appropriate on the Internet. The consumer behavior in terms of

the online retail trade (e-commerce) has therefore not yet been

sufficiently researched under the Chinese cultural context. The

purpose of this paper is to develop an empirically tested

framework that provides a deeper understanding of the driving

forces of E-satisfaction and gains a better comprehension of the

relationship between E-satisfaction and E- loyalty.

In this research, the antecedents and consequences ofe-satisfaction in E-business over E-retailers are discussed. The

following section of this paper is structured in four key sections.

The first section is devoted to a review of relevant literature

and investigating the relationships among quality, satisfaction

and loyalty in online environment, including developing the

conceptual framework and hypotheses. Second, how the

sample and the data are collected is presented. Third, the most

important part, the quality of the sample and data is assessed

and the hypotheses are tested. Finally the conclusion, some

managerial implications and concluding remarks are presented.

. THEORETICAL MODEL AND HYPOTHESES

A. Relevant Literature Review

Satisfaction, according to Oliver (1980) is the consumers

fulfillment response and hence a satisfaction judgment,

involves at the minimum two stimulian outcome and a

comparison referent. In this context, both Szymanski and Hise

540

1-4244-0148-8/06/$20.00 c2006 IEEE


2/5

(2000) and this study conceptualize e-satisfaction as the

consumers judgment of their Internet retail experience as

compared to their experiences with traditional retail stores. The

most obvious difference between traditional and electronic

retail services is the replacement of human-to-human

interaction with human-to-machine interaction and therefore,

new or modified approaches to conceptualizing and measuring

satisfaction may be needed for e-commerce settings. However,

the basic importance of satisfaction and its consequent effectsappear to remain intact even in e-commerce settings. Anderson

and Srinivasan (2003) find that the impact of e-satisfaction on

e-loyalty is the greatest in the presence of consumer-level

moderator factors. The theoretical model in this study is

constructed on the basis of the following theories.

(1)Expectation disconfirmation model

Oliver (1980) constructed the famous expectation

disconfirmation model. According to this model, customer

satisfaction is determined by both the customers expectation

and the performance. If the retailers performance surpassed

customers expectation, the positive disconfirmation will

engender and customer will tend to be satisfactory, on the

contrary, if the retailers performance cant meet the

customers expectation, the negative disconfirmation will resultin the dissatisfactory customer. Olivers expectation

disconfirmation model has four core constructs: performance,

expectation, disconfirmation and customer satisfaction. In this

study, customer expectation is to be considered when

discussing the driving factors of e-satisfaction.

(2)Service quality measure theory

Parasuraman, Zeithaml & Berry (1988) investigated the

measurement of the service quality and developed the famous

SERVQUAL instrument. The refined SERVQUAL scale

consists of 5 dimensions: tangible, reliability, empathy,

responsiveness, and assurance. Although limited empirical

studies exist in measuring the effects of e-service quality,

research is beginning to emerge. Shohreh & Christine (2000)

have ever studied internet tourism and summarized an

E-QUAL instrument, which assess e-service quality by content

& purpose, accessibility, navigation, design & presentation,

responsiveness, background and personalization &

customization. Srinivasan, Anderson & Ponnavolu (2002) have

ever found that Customization, Contact interactivity,

Cultivation, Care, Community, Choice, Convenience and

Character (8C) are the key determinants to develop and

increase e-loyalty in business-to-consumer e-commerce.

Notable research by Wolfinbarger and Gilly (2003) employed a

four-dimension measure of e-Service quality and found that

Website design, Fulfillment/reliability, Privacy/security and

Customer service were strongly predictive of satisfaction,

attitudes toward the Website and behavioral intentions.

Alternatively, WebQual (Loiacono et al. 2002) has emerged

into the academic literature as a reliable and rigorousinstrument to operationalism and assess e-service quality.

The purpose of this study is to apply PZBs SERVQUAL

instrument to internet environment. Based on the past research,

The e-SQ is measured mainly by website design, internet

security, customization, internet interactivity, merchandise

quality, convenience, relative price and operation simplicity,

which are corresponding with the SERVQUALs 5 dimensions

respectively (table 1).

TABLE 1

THE MEASUREMENT OF THE E-SQE-SQ construct SERVQUAL

Website design Tangible

Security Reliability

Customization Empathy

Internet interactivity Responsiveness

Merchandise quality Assurance

Relative price Internet characteristic

Convenience Internet characteristic

Operation simplicity Internet characteristic

B. Theoretical Model and Hypotheses

Figure 1 presents the proposed theoretical model illustrating

the effects of e-service quality on e-satisfaction and the

consequences of e-satisfaction in B2C e-commerce. The

framework proposes that the quality dimensions of a Website

can drive the level of customer satisfaction, which can translate

into positive e-loyalty consequences.

H2

H3H1

Website design

Internet security

Internet interactivity

Customization

Merchandise quality

Convenience

Relative price

Operation simplicity

1:

E-satisfaction

1:

E-loyalty

Customer

Expectation

)LJ(VDWLVIDFWLRQPRGHORILQWHUQHWVKRSSLQJ

2006 IEEE International C onf erence on Management of Innovation and Technology 541


3/5

(1)The relationship between e-SQ and E-satisfaction

The available research suggests that dimensions of e-service

quality are strongly predictive of customer satisfaction and

trust. Previous studies by Wolfinbarger and Gilly (2003), Xue

and Harker (2002) and Szymanski and Hise (2000) came to the

conclusion that e-Service Quality, such as Website design,

personalization & customization, Interactivity, plays a

prominent role in developing positive customer satisfaction.Therefore, we suppose that:

H1: There will be a positive relationship between perceived

E-SQ and E-satisfaction.

The sub- hypotheses of H1 are that website design, internet

security, customization, internet interactivity, merchandise

quality, convenience and operation simplicity will be positively

related to e-satisfaction, while relative price will be negatively

related to e-satisfaction

(2)The relationship between e-satisfaction and customer

expectation

Previous research in traditional contexts has considered the

influence of the customer expectation to the customer

satisfaction, such as Oliver(1980) and Parasuraman1988.

They found that the higher the level of the customerexpectation, the lower the level of satisfaction, provided the

retailers performance keeps the same level. Such relationship

between the expectation and satisfaction is supposed the same

in the intermit contexts. So we have the hypotheses H2:

H2: The higher the level of the customer expectation, the

lower the level of e-satisfaction.

(3)The relationship between e-satisfaction, e-trust and e-loyalty

Previous Web-based research has found that a more satisfied

consumer will form positive attitudes towards the Website, is

more likely to revisit a Website, purchase from the Website in

the future, talk about the Website with others and recommend

the Website to those seeking advise (Srinivasan, Anderson &

Ponnavolu, 2002;Loiacono et al. 2002). Therefore, we suppose

that:H3: The higher the level of e-satisfaction, the higher the

level of e- loyalty.

. VARIABLES MEASUREMENT AND DATA COLLECTION

A. Variables Measurement

There are 11 variables to be measured in this study. The

dimensions of e-SQ and customer expectation are exogenous

variables, while e-satisfaction and e-loyalty is the endogenous.

We used the multiple-items method in which each item was

measured on a seven-point Likert scale from strongly disagrees

to strongly agree. The items in our survey instrument were

developed either by adapting the existing measures validated

by other researchers or by converting the definitions of the

constructs into a questionnaire format. Based on our review ofthe previous related literature and the comments gathered from

our interviews, we constructed our survey instrument. Internet

security, internet interactivity, website design, customization,

convenience, customer expectation, e-satisfaction and e-loyalty

were measured by four items. Four items were employed to

measure relative price, operation simplicity, customer

expectation and merchandise quality.

B. Data Collection

The initial version of our survey instrument was

subsequently refined through extensive pretesting with 5

professors in Zhejiang University who have significant

expertise in the study of Internet commerce. The instrumentwas further pilot tested with 40 MBA students enrolled in an

MIS course at Zhejiang University in Hangzhou. After the

Exploratory factor analysis, three items respectively from

website design, customization and convenience were deleted to

improve the scale reliability. The multiple phases of instrument

development resulted in a significant degree of refinement and

restructuring of the survey instrument as well as establishing

the initial face validity and internal validity of the measures

(Nunnally, 1978).

Data was collected using a self-administered questionnaire.

The respondents were undergraduate, postgraduate, doctor

students at universities and the employees in the companies in

Hangzhou. They were asked to evaluate an online retail store

from which they purchased a physical product during the past

twelve months. Among the 614 returned questionnaires, 491

were usable. The average respondent was 26.8 years old.

Fifty-four percent were male. The most frequently purchased

product categories were books and CDs (58%), the next was

computers and electronic products (34%). On the average, a

respondent purchased 1.5 times from the online retail store in

the last twelve months.

. DATA ANALYSIS AND RESULTS

A. Validity and Reliability

Our measures were constructed by adopting the existing

measures of constructs that have been validated by other

researchers and were based on the suggestions in the literature,

so the instruments have good content validity. Confirmatory

factor analysis was performed to verify the factor structure ofall the 11 latent variables and indicator variables included. The

measurement model had good overall fit: Chi-square =1572.02

(d.f. = 574), RMSEA = 0.078, GFI = 0.942, AGFI=0.929,

CFI=0.958, and NFI=0.910. Also, the path coefficients from

latent constructs to their corresponding manifest indicators

were significant at.05, and a pairwise comparison of the

correlations between the respective latent constructs indicated

that all correlations were significantly different from 1.0. As

shown in Table 2, the internal consistency estimates of all

scales were above the cutoff point of 0.7 recommended by

Nunnally (1978). For average variance extracted (AVE) by

measures, a score of 0.5 indicates its acceptable level. Table 2

shows that the average variance extracted (AVE) of all the

latent variables were in the range from 0.5987 to 0.7658, whichexceeded the recommended value. After measure development,

all the items are reliable, valid and undimensional, and then the

model estimation data set was used to test the hypotheses.

TABLE 2

542 2006 IEEE International Conference on M anagement of Innovation and Technology


4/5

THE RESULT OF THE C OMFIRMATORY FACTOR ANALYSISE-SQ construct

Coefficients

AVE Model fit

indexes

Website design 0.8772 0.7658

Security 0.8065 0.6774

Customization 0.7885 0.6678

Internet interactivity 0.7204 0.5987

Merchandise quality 0.8010 0.7019

Relative price 0.8329 0.7213

Convenience 0.7284 0.6276

Operation simplicity 0.8137 0.6741

Customer expectation 0.7412 0.6569

E-satisfaction 0.8541 0.7436

E-loyalty 0.8076 0.7022

Chi-square

=1572.02.

d.f.=574

RMSEA=0.078

AGFI=0.929

CFI=0.958

NFI=0.910

GFI=0.942

B. Hypotheses Results

We estimated the proposed structural model using AMOS

4.0. The results indicate reasonable overall fits between the

model and the observed data. The overall fit of measurement

model were Chi-square = 1669.55, d.f.= 583, CFI=0.968,

NFI=0.925, GFI=0.946, AGFI=0.932, RMSEA=0.068. CFI,

NFI, AGFI, GFI exceed the recommended 0.90 threshold

levels .In addition, RMSEA is lower than 0.08 as

recommended by Hair et al.

TABLE 3

RESULTS OF STRUCTURAL MODEL AND HYPOTHESES TESTS

Structural paths

Standardized

Path

Coefficients

t-valuesHypotheses

testing

H1:e-SQ

e-satisfaction----

Supported

partially

H1.1:website design

e-satisfaction0.017 0.328

Not

supported

H1.2:security

e-satisfaction0.455*** 4.508 Supported

H1.3:Customization

e-satisfaction0.070 0.968

Not

supported

H1.4:internet interactivity

e-satisfaction0.106** 2.124 Supported

H1.5:merchandise quality

e-satisfaction0.189*** 3.954 Supported

H1.6:relative price

e-satisfaction-0.379*** -4.515 Supported

H1.7:conveneice


H1.8:operation simplicity


H2:Customer expectation

e-satisfaction-0.334*** -4.234 Supported

H3:e-satisfaction

e-loyalty0.795*** 16.291 Supported

Model fit indexes: Chi-square = 1669.55 d.f.= 583 RMSEA=0.064

AGFI=0.932 CFI=0.968 NFI=0.925 GFI=0.946

***p


5/5

the report also shows that most internet users regard internet

and E-retailers with great uncertainty. About 58% of the 3.3

million internet users in Hong Kong are suspicious of internet

and E-retailers. The proportion in Macao is 57% and 34.41% in

Taiwan. So, the level of satisfaction, trust and loyalty to

e-retailers is not high enough generally.

Our findings have managerial implications. From a

managerial perspective, e-retailers can take appropriate

remedial action when any of elements of e-SQ is perceived as

falling below an acceptable level. Based on the conclusion of

this study, in order to build up the satisfaction and loyalty to

e-retailers, e-retailers must improve the service quality based

on the internet, including the quality of security, price

advantage, internet interactivity, product quality operation

simplicity and convenience. First, E-retailer must guarantee the

security of payment and privacy. Second, the retailer must

ensure the product quality, which is in accord with the AD,

consumers expectation and contract. Third, e-retailers can take

advantage of internet interactivity to make it appealing to

enhance users satisfaction and will drive the user to visit the

site again in the future. Finally, to satisfy the customers,

e-retailers must offer lower price products and service.

C. Limitations

There are several limitations of this study. First, the model

does not take into account individual-level variables. Thus, to

have a deeper understanding of E-loyalty may require the

inclusion of a personal lifestyle factors and personal

characteristics factors. Secondly, the suitability of the Internet

for e-retailing depends to a large extent on the characteristics of

the products and services being marketed. This study does not

control for such differences across product and service

categories. Researchers can develop richer models that capture

and explain these differences

REFERENCES

[1] Bain & Mainspring (2000), eStrategy BriefThe Value of

Online Customer Loyalty.

(http://www.mainspring.com/press/frameset/1,2406,pressrelease-view-

51)

[2] J.F. Hair, R.E. Anderson, R.L. Tatham, W.C. Black,

Multivariate Data Analysis, 5th ed., Prentice-Hall, New Jersey, 1998.

[3] Lee, J., J. Kim, and J. Y. Moon (2000), What Makes Internet

Users Visit Cyber Stores again? Key Design Factors for Customer

Loyalty, Proceedings of the CHI 2000 conference on Human factors

in computing systems, 2000, pp.305-312.

[4] Loiacono E., Watson R., and Goodhue D. (2002), WebQual: A

measure of website quality, American Marketing Association

Conference Proceedings, pp. 432-8.

[5] Nunnally, J. (1978). Psychometric Theory, 2d Ed. New York,

McGraw-Hill.

[6] Oliver, Richard L., A Cognitive Model of the Antecedents and

Consequences of Satisfaction Dicision [J], Journal of Marketing

Research, 1980,17(Nov.): 460-469.

[7] Parasurman, A., Zeithaml, A., & Berry, L. L. (1988).

Communication and control process in the delivery of service

quality [J], Journal of Marketing, 52(2), 35-48.

[8] Peter Keen G Mark McDonald, The eProcess Edge, Osborne/

McGraw-Hill 2000.

[9] Reichheld, Frederick F. & Phil Schefter (2000), E-Loyalty:

Your Secret Weapon on the Web, Harvard Business Review,

(Jul.-Aug.), pp. 105-113.

[10] Shohreh, A. Kaynama & I. Black Christine (2000), A Proposal

to Assessthe Service Quality of Online Travel Agencies: An

Exploratory Study, Journal of Professional Services Marketing, 21(1),

pp.63-88.

[11] Smith, E. R. (2001), Seven steps to building e-loyalty,

Medical Marketing and Media, 36(3): 94-102.

[12] Srinivasan, S. S., Anderson, R. and Ponnavolu, K. (2002),

Customer loyalty in e-commerce: an exploration of its antecedents

and consequences [J], Journal of Retailing, 78(1): 41-50.

[13] Szymamski, D. M. & R. T. Hise (2000), E-Satisfaction: An

Initial Examination, Journal of Retailing, 76(3), pp. 309-322.

[14] Spreng, Richard A., Scott B. MacKenzie & Richard W.

Olshavsky (1996), Reexamination of the Determinants of Consumer

Satisfaction, Journal of Marketing, 60(July), 15-32.

[15]Xue, M. and Harker, P. (2002), Customer Efficiency: Conceptand Its Impact on E-Business Management, Journal of Service

Research, vol. 4, pp. 253-267.[16]Wolfinbarger M., and Gilly M. (2003), eTailQ:Dimensionalizing, measuring and predicting e-tail quality,Journal of

Retailing, vol. 79, no. 3, pp. 183-198.

544 2006 IEEE International Conference on M anagement of Innovation and Technology

e Loyalty6

Documents

Transcript of e Loyalty6