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Developing e-service quality scales: A literature review

Riadh Ladhari n

Faculty of Business Administration, Laval University, Quebec, Canada

a r t i c l e i n f o

Keywords:

E-service quality

Scale development

Dimensionality

Psychometric properties

a b s t r a c t

This study reviews the literature on e-service quality (e-SQ), with an emphasis on the methodological

issues involved in developing measurement scales and issues related to the dimensionality of the e-SQ

construct. We selected numerous studies on e-SQ from well-known databases and subjected them to a

thorough content analysis. The review shows that dimensions of e-service quality tend to be contingent

on the service industry. Despite the common dimensions often used in evaluating e-SQ, regardless of

the type of service on the internet (reliability/fulfilment, responsiveness, web design, ease of use/

usability, privacy/security, and information quality/benefit), other dimensions are specific to

e-service contexts. The study also identifies several conceptual and methodological limitations

associated with developing e-SQ measurement such as the lack of a rigorous validation process, the

problematic sample size and composition, the focus on functional aspects, and the use of a data-driven

approach. This is the first study to undertake an extensive literature review of research on the

development of e-SQ scales. The findings should be valuable to academics and practitioners alike.

& 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Online service quality has a significant influence on manyimportant aspects of electronic commerce (e-commerce). These

include consumer trust in an online retailer (Gefen, 2002; Hsu,

2008; Hwang and Kim, 2007); site equity (Yoo and Donthu, 2001);

consumer attitudes towards the site (Hausman and Siekpe, 2009;

Yoo and Donthu, 2001); attitude toward e-shopping (Ha and Stoel,

2009); perceived value of the products/services (Hsu, 2008);

willingness to pay more (Fassnacht and Kose, 2007), user online

satisfaction (Cristobal et al., 2007; Fassnacht and Kose, 2007; Ho

and Lee, 2007; Lee and Lin, 2005); site loyalty intentions (Ho and

Lee, 2007; Yoo and Donthu, 2001); site recommendation inten-

tions (Long and McMellon, 2004); and cross-buying (Fassnacht

and Kose, 2007). In view of the apparent importance of electronic

service quality (e-SQ),Hsu (2008)contends that the achievement

of superior online service quality should be the crucial differ-

entiating strategy for all e-retailers; indeed, e-SQ has been

increasingly recognised as the most important determinant of

long-term performance and success for e-retailers (Fassnacht

and Koese, 2006; Holloway and Beatty, 2003; Santos, 2003;

Wolfinbarger and Gilly, 2003; Zeithaml et al., 2000, 2002). An

understanding of how consumers evaluate e-SQ is thus of the

utmost importance for scholars and practitioners alike (Fassnacht

and Koese, 2006; van Riel et al., 2001). However, despite the

obvious importance of the issue, the conceptualisation and

measurement of e-SQ are still at an early phase of development

(Cristobal et al., 2007; Fassnacht and Koese, 2006; Santos, 2003; vanRiel et al., 2001) and studies in this field are still somewhat limited

and disparate (Gounaris and Dimitriadis, 2003; Parasuraman et al.,

2005). AsZeithaml et al. (2002, p. 371)note: Rigorous attention to

the concept of service quality delivery through Web sites is needed.

This would involve a comprehensive examination of the ante-

cedents, composition, and consequences of service quality.

Against this background, the present study undertakes a

comprehensive review of the current state of knowledge regard-

ing e-SQ. In doing so, the study reviews the literature on e-SQ

measurement models with a view to (i) analysing the key

methodological issues involved in the development of such scales,

and (ii) discussing the dimensional structure of the e-SQ

construct. As a result of these considerations, the paper provides

valuable insights and implications for the development and

application of e-SQ scales.

2. Definition and nature of e-SQ

Parasuraman et al. (2005, p. 217)define e-SQ as y the extent

to which a web site facilitates efficient and effective shopping,

purchasing and delivery. This definition makes it clear that the

concept of e-SQ extends from the pre-purchase phase (ease of use,

product information, ordering information, and personal informa-

tion protection) to the post-purchase phase (delivery, customer

support, fulfilment, and return policy). The online environment

Contents lists available atScienceDirect

journal homepage:ww w.elsevier.com/locate/jretconser

Journal of Retailing and Consumer Services

0969-6989/$ - see front matter & 2010 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jretconser.2010.06.003

n Tel.: +1 418 6562131x7940; fax: +1 418 6562624.

E-mail address: [email protected]

Journal of Retailing and Consumer Services 17 (2010) 464477
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differs from the traditional retail context in several ways. These

can be summarised as follows:

Convenience and efficiency: Consumers using the online

environment have the convenience of saving time and effort

in comparing the prices (and some technical features) of

products more efficiently (Santos, 2003).

Safety and confidentiality: Participation in the online environ-

ment involves users in distinctive issues regarding privacy,safety, and confidentiality.

Absence of face-to-face contact: Customers in the online

environment interact with a technical interface (Fassnacht

and Koese, 2006). The absence of person-to-person interaction

means that the traditional concepts and ways of measuring

service quality, which emphasise the personal interaction of

the conventional service encounter, are inadequate when

applied to e-SQ (van Riel et al., 2001).

Co-production of service quality: Customers in the online

environment play a more prominent role in co-producing the

delivered service than is the case in the traditional retail

context (Fassnacht and Koese, 2006).

3. Literature review

3.1. Issues of adequacy of dimensions of e-SQ

Several measures of e-SQ are described by Zeithaml et al.

(2002)as being ad hoc. These measures, which have attempted

to assess e-SQ mainly in terms of the design and quality of

websites, include factors that induce satisfaction with a website

and/or repeat visits (Alpar, 2001; Muylle et al., 1999; Rice, 1997;

Szymanski and Hise, 2000). In this regard,Alpar (2001)identifies

four attributes of satisfaction with a website: (i) ease of use

(response speed, navigation support, use of new web technolo-

gies); (ii) information content (quantity, quality, accuracy,

customised information); (iii) entertainment (amusement, excite-

ment); and (iv) interactivity (e-mail, live-chats, notice boards).

Liu and Arnett (2000)suggest that the determinants of website

success included the following: (i) information and service

quality; (ii) system use; (iii) playfulness; and (iv) system design

quality.Szymanski and Hise (2000) report four dominant factors

in consumer assessments of e-satisfaction: (i) convenience

(shopping times, ease of browsing); (ii) merchandising (product

offerings and information available online); (iii) site design

(uncluttered screens, easy search paths, fast presentations); and

(iv) financial security.

Apart from thead hocuse of website parameters (as described

above), other authors attempt to develop more direct and

comprehensive measures of the construct of e-SQ. Some research-

ers (such as Gefen, 2002) modify or replicate the well-knownSERVQUAL scale (Parasuraman et al., 1988, 1991), whereas others

develop their own scales to measure the construct (e.g., Ho and

Lee, 2007; Loiacono et al., 2002; Parasuraman et al., 2005).

According toParasuraman et al. (1991, p. 445), SERVQUAL is a

generic instrument with good reliability and validity and broad

applicability. Parasuraman et al. (1988) find that consumers

evaluate perceived service quality in terms of five dimensions:

tangibility (the appearance of physical facilities, equipment, and

personnel); responsiveness (the willingness to help customers

and provide prompt service); reliability (the ability to perform the

promised service accurately and dependably); empathy (the level

of caring and individualised attention the firm provides to its

customers); and assurance (the knowledge and courtesy of

employees and their ability to inspire trust and confidence).

These dimensions are measured by a total of 22 items, where each

item is measured according to the performance of the service

actually provided (P) and the expectations for the service (E). The

gap score (G) is therefore calculated as the difference between

performance and expectations (PE). The greater the gap scores,

the higher the perceived service quality.

It is true that SERVQUAL has been successfully applied in a

wide variety of traditional service settingsincluding (among

others) insurance services, library services, information systems,healthcare settings, bank services, hotel services, and dental clinic

services. However, several difficulties exist with regard to the

conceptualisation and operationalisation of the SERVQUAL scale

(e.g., Buttle, 1996; Ladhari, 2009). In particular, questions have

been raised about the applicability of the five generic SERVQUAL

dimensions in several service industries. As a result, adaptations

of SERVQUAL have been proposed for various industry-specific

contexts, and the findings suggest that the attributes of service

quality are context-bounded (e.g., Cai and Jun, 2003; Ladhari,

2009). Similar doubts have been raised regarding the applicability

of the five SERVQUAL dimensions in the e-service context. In this

regard,Gefen (2002) apply an adapted SERVQUAL instrument to

the online service context and reported that the five dimensions

collapsed into three: (i) tangibles; (ii) a combined dimension of

responsiveness, reliability, and assurance; and (iii) empathy. The

tangibles dimension is the most critical for inducing customer

loyalty whereas the combination dimension (responsiveness,

reliability, and assurance) is the most important for promoting

customer trust.

Parasuraman et al. (2005)acknowledge these difficulties when

they suggest that y studying e-SQ requires scale development

that extends beyond merely adapting offline scales. In a similar

vein,Parasuraman and Grewal (2000, p. 171)state that studies are

needed on whether the definitions and relative importance of the

five service quality dimensions change when customers interact

with technology rather than with service personnel. SERVQUAL

was developed in the context of services provided through

personal interaction between customers and service providers;

as a result, its dimensions might not transpose directly to the e-SQ

context (Fassnacht and Koese, 2006; Hsu, 2008).Hsu (2008)notes

that the SERVQUAL model does not consider such dimensions as

security and ease of navigation, andGefen (2002)contends that

the dimension of empathy is less important in the e-SQ context

because the online environment lacks personal human interac-

tion. In addition,van Riel et al. (2001) argue that the tangibility

dimension of SERVQUAL could be replaced by a dimension of web

design or user interface.

In view of these difficulties, it is apparent that the traditional

SERVQUAL model does not constitute a comprehensive instru-

ment for assessing e-SQ. Several studies attempt to develop

specific measurement scales for online service quality, but the

task is neither simple nor straightforward. AsAladwani and Palvia

(2002)observe: Construct measurementy

in the context of webtechnologies and applicationsy is a challenging task.

Despite the difficulties, several studies endeavour to identify

and measure the dimensions of the e-SQ construct. These studies

are the subject of the literature review that forms the substance of

the present study. The studies for review are summarised in

Table 1.

3.2. Methodological issues in developing e-SQ scales

The studies inTable 1come from well-known databasessuch

as ScienceDirect, ABI/INFORM, and EBSCOhost. Only studies

focusing on developing an instrument for measuring e-service are

included and are subjected to a comprehensive in-depth content

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store for electronic

equipment.

Ibrahim et al. (2006) E-banking service

quality

135 UK banking

customers.

e-bank services 26 items 5 point scale

Offline administration

Exploratory factor

analysis

25 items

Cristobal et al. (2007) E-service quality 461 internet users who

visited, bought or used

Internet service at least

once during the

previous three months.

NA 25 items 7 point scale

Offline administration

Exploratory factor

analysis; Confirmatory

factor analysis

17 items

Ho and Lee (2007) E-travel service quality 289 online purchasers

for the development

stage and 382 online

purchasers for thevalidation stage.

E-travel services 30 items 7 point scale

Online administration

Exploratory factor


factor analysis

18 items

Sohn and Tadisina

(2008)

E-service quality 204 customers

experienced with

internet-based

financial services.

Internet based

financial services

30 items Online and

offline administration

Exploratory factor


factor analysis

25 items

a NA: Not addressed/Wide variety of sites categories.b NI: No information about the number of original items.c IP: internet purchasers; INP: internet non-purchasers.

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analysis of the key methodological aspects of the development of

the various e-SQ scales and their proposed dimensions. This

review of the studies listed in Table 1 reveals: (i) several

methodological issues related to the development of e-SQ mea-

surement; and (ii) several pertinent observations regarding the

dimensionality of the e-SQ construct (including the identification

of several common dimensions of various e-SQ scales and certain

limitations associated with the development of e-SQ scales).

A detailed discussion of these two subjects is presented below.The methodological issues identified in this review can be

summarised as follows: research methods; sampling methods;

service industries considered; survey administration; generation

of items; purification and assessment of items; analysis of

dimensionality; scale reliability and validity.

3.2.1. Research methods

Studies of e-SQ measurement use a variety of methodolo-

giesqualitative (Santos, 2003; Zeithaml et al., 2000), quantita-

tive (Bauer et al., 2006); and mixed (Wolfinbarger and Gilly, 2003;

Yang et al., 2004). Using qualitative methods, Zeithaml et al.

(2000)identify 11 dimensions of e-SQ: (i) reliability; (ii) access;

(iii) ease of navigation; (iv) efficiency; (v) responsiveness; (vi)

flexibility; (vii) price knowledge; (viii) assurance/trust; (ix)

security/privacy; (x) site aesthetics; and (xi) personalization.

Santos (2003) also uses qualitative methods in conducting 30

offline focus groups to investigate e-SQ dimensions. Yang et al.

(2004)use a mixed approach that combines content analysis of

critical incidents of online banking services and a web-based

survey of these services. They analyze two online consumer

review web sites (Gomez.com and ratingwonders.com) to obtain

848 consumer anecdotes about their banks. Their analysis of the

reviews finds 17 dimensions of online service quality that they

groups into three categories: customer service quality, online

system quality, and product or service variety.

In developing electronic service quality measurement scales,

researcher should use qualitative research at the earliest stage

possible of their work, using one of several methods. One method

that researchers seldom use is the critical incident technique

(CIT), a qualitative interview method to study significant

processes, incidents and events identified by respondents (Chell,

1998). The goal is to understand significant incidents from the

consumer perspective, taking into account behavioural, affective,

and cognitive aspects (Chell, 1998). The technique allows

respondents to indentify which events are the most important

to them (Gremler, 2004). In service research, the respondents

recall specific events they experienced with the service used. They

are asked to think of a time when they felt very satisfied

(dissatisfied) with the service received, to describe the service and

why they felt so happy (unhappy) (Johnston, 1995). The CIT

technique has a several advantages for electronic service quality

measure development. First, the respondents can use their ownlanguage and terms to express their perceptions. Second,

respondents can classify the critical incidents into satisfactory

and unsatisfactory occurrences (Gremler, 2004; Johnston, 1995).

Previous studies report that determinants of satisfactory online

service quality are not the same as the determinants of

unsatisfactory online service quality (e.g., Yang and Fang, 2004).

Third, CIT can serve as an exploratory method to increase

knowledge about online service quality and identify the relevant

dimensions in a given online context (e.g., banking, travel agent

services, education, grocery shopping, bookstores, and libraries).

Previous studies show that traditional service quality components

vary depending on the service industry (Ladhari, 2008). In that

case, a purely quantitative approach can complement the CIT

technique. Fourth, CIT can be used both qualitatively and

quantitatively to identify the type and nature of incidents and

the frequency of occurrence (Gremler, 2004).

In one of the few studies applying the CIT to the online

environment, Holloway and Beatty (2003) address service

failure and service recovery. Using a combination of qualitative

(30 in-depth interviews) and quantitative methods (a survey

of 295 online shoppers who had experienced at least one

service failure within the past 6 months), they report six types

of service problems encountered by online shoppers: websitedesign, payment, delivery, product quality, and customer

service. When asked about service recovery, respondents reported

several reasons for dissatisfaction: lengthy delays, poor commu-

nication, poor quality customer service support, and generic

recoveries.

3.2.2. Sampling methods

The samples for research into e-SQ are drawn from a variety of

populations. Several studies use convenience sampling (e.g., Cai

and Jun, 2003; Lee and Lin, 2005; Long and McMellon, 2004),

whereas only few use random sampling (e.g., Fassnacht and

Koese, 2006; Parasuraman et al., 2005). Several of the studies use

students for their surveys (e.g.,Aldwani and Palvia, 2002; Lee and

Lin, 2005; Loiacono et al., 2002; Yoo and Donthu, 2001), despite a

major limitation being that these respondents are not usually

actual internet purchasers, but students who are merely invited to

visit websites and rate them.Yoo and Donthu (2001)gather data

from convenience samples of students who were asked to visit

and evaluate internet shopping sites over a period of 2 days.

Loiacono et al. (2002)also use a convenience sample of students

to visit and evaluate websites. They told undergraduate students

to explore a designated website and asked them to imagine that

they are searching for a book. Only few studies use non-student

samples. For example, Long and McMellon (2004) use respon-

dents who were about to purchase items online. These respon-

dents were asked to complete a questionnaire on expectations

before going on the internet, followed by a questionnaire on

perceptions after purchasing a product online.Parasuraman et al.

(2005)utilise only respondents who had visited the internet on at

least 12 occasions and made at least three purchases during the

preceding 3 months.

The studies reviewed have some limitations. First, the samples

used in most of the studies consist of student populations, which

may limit the generalisability of the scales and reduce their

applicability to the broader population of online users. Loiacono

et al. (2002, p. 435) question their own use of student samples:

While these subjects are typical of a substantial body of web

users, they are not a representative sample of all users. Kim and

Stoel (2004, p. 112) criticize the use of student samples: By

having students visit and rate websites with which they were not

familiar or interested in, those studies may have suffered

limitations in the accuracy of findings with regard to perceptionsof actual users. In addition, most of these respondents are not

regular customers or users of the websites selected (Loiacono

et al., 2002).

Second, several studies use mostly US respondents. For

instance, all the respondents in Cai and Juns (2003) study are

from the southwest and the midwest regions in the US.

Ranganathan and Ganapathy (2002)use a sample of respondents

from Illinois. Seventy-four percent of the respondents in theYang

et al. (2004)study are US residents. The reasons for internet use

and the behavior of these participants may differ from those in

other countries. Therefore, future studies should use more

diversified samples. The literature on traditional service quality

shows that dimensions of service quality differ from one country

to another (Ladhari, 2008).

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Third, many respondents in these studies use the internet as an

information source and not for commercial transactions (e.g.,

Yang et al., 2004). They may have different perceptions of service

quality dimensions. Respondents who have not engaged in

commercial transactions on the internet may have concerns

about security compared to experienced internet buyers (Yang

et al., 2004). For instance,Cai and Jun (2003)reported differences

between online buyers and information searchers with respect to

their e-service quality perceptions. Information searchers rate thefour dimensions of e-service quality (trustworthiness, web site

design/content, communication, and prompt/reliable service)

lower than online buyers do. In addition, these two groups

differ on the relative importance of the four e-service quality

dimensions.

Finally, several studies used limited sample sizes. For instance,

Cai and Jun (2003) use a sample of only 171 respondents,

including 61 online searchers and 110 online buyers. In their

study on electronic banking service quality, Ibrahim et al. (2006)

use a sample of 131 customers. In another study, Aldwani and

Palvia (2002)use a sample of 101 students in their first study and

127 students in their second study. These sample sizes are

relatively small for developing new scales. Future studies should

use more larger and diversified samples.

3.2.3. Service industries considered

Some studies collect data across several industries

(Ranganathan and Ganapathy, 2002; Yoo and Donthu, 2001),

whereas other studies focus on particular sectors. Ranganathan

and Ganapathy (2002) examine the key dimensions of B2C

websites and retain in their sample individuals who completed

at least one online purchase in the last 6 months. Yoo and Donthu

(2001)ask their student respondents to evaluate a broad range of

websitesincluding sites offering books, music and videos,

department stores, electronics, computers, sports and fitness,

flowers and gifts, health and beauty, and travel and auto. In

contrast, other studies focus on such specific sectors as library

services (ONeill et al., 2001), books (Barnes and Vidgen, 2002),apparel (Kim and Stoel, 2004), financial services (Sohn and

Tadisina, 2008), and travel services (Ho and Lee, 2007).

A few studies focus on support services related to purchasing

goods on the internet (e.g., Francis and White, 2002; Janda et al.,

2002; Kim and Stoel, 2004) while other studies focus on pure

service offers such as Web portal quality (e.g., Gounaris and

Dimitriadis, 2003; Yang et al., 2005). Other researchers develop

scales for measuring service quality for support services and pure

information web sites (e.g., Fassnacht and Koese, 2006; Yoo and

Donthu, 2001). For instance, Fassnacht and Koese (2006) discuss

three types of electronic service: online shopping for electronic

equipment (i.e., support services), the creation and maintenance

of home pages (i.e., pure service offer), and sports coverage (i.e.,

content offer).

3.2.4. Survey administration

Scholars use both online and offline approaches for collecting

data in their studies. In qualitative research, researchers use

online and offline focus group studies (Wolfinbarger and Gilly,

2003), and offline in-depth interviews (Cristobal et al., 2007). In

quantitative studies, researchers use mail surveys (Kim and Stoel,

2004; Sohn and Tadisina, 2008), website surveys (Parasuraman

et al., 2005; Sohn and Tadisina, 2008), and in-person surveys

(ONeill et al., 2001; Cristobal et al., 2007). For example,Kim and

Stoel (2004)collect data via a mail survey sent to a mailing list of

1000 randomly selected female shoppers who had purchased

apparel online in the preceding 3 years.Parasuraman et al. (2005)

utilise a research firm to administer an online survey to a random

sample of internet users. Cristobal et al. (2007) collect data

through personal interviews among a random sample of 461

internet users who had used the services provided by online

shops. Sohn and Tadisina (2008) use a combination of a mail

survey and a website survey by mailing a hardcopy of a

questionnaire to 2000 potential respondents and posting the

same questionnaire on the web to accommodate respondents

who were more familiar with the online interface.

Researchers should report more details about the mode ofadministration of their surveys. They should also describe why

they choose one mode rather than another. For instance,

Ranganathan and Ganapathy (2002)give no information on how

they administer their survey. Considering that the object of the

research is e-service quality, researchers are expected to use web-

based or e-mail-based surveys. Using other modes of administra-

tion may cause a disparity between the target population and the

framed population. In addition, web surveys have several

advantages (van Selm and Jankowski, 2006): convenience for

participants, no interviewer bias, direct data entry to electronic

files, easier recruitment of respondents and lower cost. Therefore,

studies using other modes of administration need to justify an

alternate choice.

3.2.5. Generation of items

Because e-SQ is a relatively new concept, scale items are

generated using both inductive methods (such as literature

reviews) and deductive methods (such as exploratory research),

but most came through deductive methods. A review of the

literature on service quality and electronic commerce byLoiacono

et al. (2002)generated 142 items.Cristobal et al. (2007)also use a

literature review (in combination with in-depth interviews) to

generate an extensive list of 86 potential items. Exploratory

studies for the generation of items utilise focus groups (ONeill

et al., 2001; Wolfinberger and Gilly, 2003), in-depth interviews

(Bauer et al., 2006; Cristobal et al., 2007; Yang and Jun, 2002), and

content analysis of customer reviews (Yang et al., 2004). For

example, Yang et al. (2004)access two online review websites to

collect and analyse 848 customer reviews in generating potential

attributes of online banking services. Focus groups were con-

ducted with university students (ONeill et al., 2001) and with

online shoppers (Wolfinberger and Gilly, 2003). In some studies,

experts or managers are asked to comment on the wording of

items and/or to propose items (Gounaris and Dimitriadis, 2003;

Fassnacht and Koese, 2006; Yang et al., 2005). Other studies do not

state exactly how they generate their items or the number of items

initially generated (e.g.,Ranganathan and Ganapathy, 2002).

There is no agreement in the literature about the exact nature

and definition of e-service quality dimensions. For example, the

web site design dimension is conceptualized and operationalized

in different ways. Ranganathan and Ganapathy (2002) include

delay and ease of navigation and the presence of visualpresentation aids in the web design construct. Loiacono et al.

(2002) report a dimension they call entertainment which

includes visual appeal (aesthetics of the web site), innovativeness

(the uniqueness and creativity of site design), and flow (the

emotional effect of using the web site).Cristobal et al. (2007)state

that web design consists of user-friendliness, content layout, and

content updating. Wolfinberger and Gilly (2003) maintain that

the construct refers to navigation, order processing, appropriate

personalization, information search, and product selection.

Fassnacht and Koese (2006) conclude that dimensions reported

in one study cannot be compared to those in other studies on

e-service quality.

These different constructs highlight the lack of consensus

about the components of e-service quality. In fact, most of the


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papers analyzed do not include clear definitions of e-service

dimensions. In addition, several studies adopt data-driven

approaches to the construct of e-service quality and components.

The scale items are derived from exploratory factor analysis and

the dimensions identification is based on which items have the

most important loading scores for each factor. The luck of

consensus leads to differences among studies on the number

and nature of the items generated and those finally retained.

In several studies, items generated are based solely onqualitative research such as in-depth interviews and focus groups.

Future research should develop a more specific theoretical

framework that defines the e-service quality construct and

its dimensions more consistently and identifies pertinent

scale-items.

3.2.6. Assessment and purification of items

In several studies, total item correlation serves as a criterion

for initial assessment and purification. Various cut-off points are

adopted: 0.30 by Cristobal et al. (2007), 0.40 by Loiacono et al.

(2002), and 0.50 byFrancis and White (2002)and Kim and Stoel

(2004). Items are rejected byLoiacono et al. (2002)if they possess

a high correlation with items on other putative constructs (that is,

discriminant validity problems). Cristobal et al. (2007) useconfirmatory factor analysis to eliminate indicators whose

standardised coefficients are less than 0.5. Wolfinberger and Gilly

(2003)are rigorous in retaining only items that (i) load at 0.50 or

more on a factor, (ii) do not load at more than 0.50 on two factors,

and (iii) have an item to total correlation of more than 0.40.

3.2.7. Analysis of dimensionality

The dimensionality of the scale is assessed using exploratory

factor analysis (EFA) and/or confirmatory factor analysis (CFA).

Exploratory analysis is used by several researchers such asFrancis

and White (2002), Loiacono et al. (2002), Ranganathan and

Ganapathy (2002), Kim and Stoel (2004), Ibrahim et al. (2006),

Cristobal et al. (2007),Ho and Lee (2007), andSohn and Tadisina

(2008). Confirmatory factor analysis is utilised by Janda et al.

(2002),Loiacono et al. (2002),Kim and Stoel (2004),Lee and Lin

(2005), Cristobal et al. (2007), Ho and Lee (2007), andSohn and

Tadisina (2008).

As noted, most of the studies reviewed use EFA to reduce the

number of items in their constructs, but many researchers still

criticized its use.Kwok and Sharp (1998)describe the use of EFA

as a fishing expedition. In fact, this technique has a number of

shortcomings. First, the estimates obtained for factor loadings are

not unique and the factor structure obtained is only one of an

infinite number of potential solutions (Segars and Grovers, 1993).

Second, the CFA provides goodness-of-fit indicators to evaluate

whether the factors structure fits the data, which is not the case

for EFA (Marsh and Hocevar, 1985). Third, when applied to data

exhibiting correlated factors, common factor analysis withvarimax rotation can produce distorted factor loadings and

incorrect conclusions on the factor solution (Segars and Grovers,

1993). Fourth, it is possible for items to load on more than one

factor in EFA, which may affect their distinctiveness and the

interpretation (Sureshchandar et al., 2002). Finally, contrary to

EFA, CFA allows researcher to compare several model specifica-

tions and to examine invariance of a specific parameter in the

factor solution (Marsh and Hocevar, 1985). Given the limitations

of EFA, researchers should use a combination of EFA and CFA.

Other studies use multinational samples of internet users,

which may also create bias. Previous research reports that

cultural differences in response styles, such as extreme responses

and use of mid-points, are sources of bias that can threaten the

validity of scales (Diamantopoulos et al., 2008). Since the response

style cannot be completely eliminated through research design, so

researchers should establish measurement invariance via multi-

group confirmatory factor analysis (Steenkamp and Baumgartner,

1998).

3.2.8. Scale reliability and validity

The reliability of scales (that is, the internal homogeneity of a

set of items) is usually assessed by Cronbachs a coefficient or byJorskogs r coefficient. Most scales in the present review exhibit

good reliability in terms of Cronbachs a coefficient, with values

greater than 0.70 (Fornell and Larcker, 1981;Nunally, 1978). For

example, Loiacono et al. (2002) report 12 dimensions with

Cronbachs a ranging from 0.72 to 0.93; Ranganathan and

Ganapathy (2002)find four dimensions with Cronbachs aranging

from 0.87 to 0.89; and Yang et al. (2004) report six dimensions

with r coefficients ranging from 0.77 to 0.91 (see Table 1).

However, in a few studies the reliability coefficients were under

the recommended level (as reported in Table 1). For example,

Long and McMellon (2004) find that reliability coefficient values

are only 0.51 and 0.59 for purchasing process and responsive-

ness dimensions, respectively. Yang and Jun (2002) find Cron-

bachs a value of the credibility dimension to be only 0.59.

Ibrahim et al. (2006)report Cronbachs a values at 0.33 and 0.57,

with friendly/responsive customer service and targeted custo-

mer service dimensions, respectively. This means that certain

scales reported in the literature are problematic.

Convergent validity (that is, the extent to which a set of items

assumed to represent a construct does in fact converge on the

same construct) is verified in various ways. Gounaris and

Dimitriadis (2003) evaluate this by calculating the average

variance extracted for each factor and confirming convergent

validity when the shared variance accounted for 0.50 or more of

the total variance. Other studies assess convergent validity by

correlating their scales with a measure of overall service quality.

For example, Loiacono et al. (2002) establish the convergent

validity of their WebQual scale by correlating the total score of

36 items with an overall quality single item measure.

Discriminant validity (that is, the extent to which measures of

theoretically unrelated constructs do not correlate with one

another) is established byGounaris and Dimitriadis (2003)when

the average variance extracted for each factor is greater than the

squared correlation between that construct and other constructs

in the model. Discriminant validity is also evaluated by comparing

the fit of two correlated factors with the fit of a single-factor

model for each pair of dimensions (Kim and Stoel, 2004; Loiacono

et al., 2002); discriminant validity is established when the fit of

the two-factor model is better than the fit of the one-factor model

for each pair of factors. In other studies, discriminant validity is

examined by constraining the inter-factor correlations between

pairs of dimensions (one at a time) to unity, and repeating

confirmatory factor analysis (Parasuraman et al., 2005; Yang et al.,2005); discriminant validity is confirmed when the constrained

model produces an increase in the chi-square statistic compared

with the non constrained model.

To assess predictive/nomological validity (that is, the extent to

which the scores of one construct are empirically related to the

scores of other conceptually related constructs), authors examine

the impact of particular e-SQ dimensions on (i) users overall

quality rating (Aladwani and Palvia, 2002; Bauer et al., 2006;

Parasuraman et al., 2005; Yang et al., 2004), (ii) satisfaction (Bauer

et al., 2006; Fassnacht and Koese, 2006; Kim and Stoel, 2004;

Yang et al., 2004), (iii) perceived value (Bauer et al., 2006;

Parasuraman et al., 2005), (iv) relationship duration (Bauer et al.,

2006), and (v) behavioural intentions (Bauer et al., 2006; Francis

and White, 2002; Loiacono et al., 2002; Parasuraman et al., 2005 ).


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The psychometric propertiesthe three types of validity (i.e.,

convergent, discriminant, and predictive)of most of the scales in

this review are not examined. Long and McMellon (2004) prove

only the predictive validity of their newly developed scale.

The same observation applies to Yang and Yuns study (2002).

Few studies examine and confirm the convergent, discriminant,

and predictive validity of their newly developed scale (e.g.,

Aladwani and Palvia, 2002; Cristobal et al., 2007; Kim and Stoel,

2004; Loiacono et al., 2002; Parasuraman et al., 2005). The researchagenda in e-service quality should consider the validation process a

major issue. Future studies addressing the measurement of

e-service quality scale should rigorously test and report on the

psychometric properties of their newly developed scales.

3.3. Dimensionality and structure of the e-SQ construct

It is apparent from this review that certain general observa-

tions can be made regarding the dimensionality and structure of

the e-SQ construct as presented in these studies: (i) there is no

consensus on the number and the nature of the dimensions in

the e-SQ construct but globally six dimensions recur more

consistently (reliability/fulfilment, responsiveness, ease of

use/usability, privacy/security, web design, and informationquality/benefit); (ii) some of the e-SQ dimensions in this review

are identical (or at least similar) to those reported for traditional

service quality; (iii) the studies reviewed here concentrate on

functionalquality. Only a few studies deal with outcome quality;

and (iv) despite the general support for a hierarchical multi-

dimensional model of service quality, little effort is made by the

authors reviewed here to examine such a structure for e-SQ. These

observations are discussed in greater detail below.

3.3.1. Dimensionality of the e-SQ construct

All of the studies in Table 1 find the construct of e-SQ to be

multidimensional, with the number of reported dimensions

ranging from three (Gounaris and Dimitriadis, 2003) to 12

(Loiacono et al., 2002). It is apparent that there is no consensus

on the number and the nature of the dimensions of the e-SQ

construct identified in previous research. It is true that some

dimensions (such as reliability and ease of use) appear

consistently in the various models, which indicates that there

are some common dimensions used by customers in evaluating

e-SQ regardless of the type of service being delivered on the

Internet (Fassnacht and Koese, 2006; Zeithaml et al., 2000).

However, other dimensions mentioned in the various studies

appear to be specific to particular e-service contexts. These

observations mirror the debate regarding generic or specific

measures in assessing traditional/physical service quality (e.g.,

Karatepe et al., 2005; Ladhari, 2008). E-service quality dimensions

tend to be contingent on the service industry involved. Even in the

same industry, these dimensions depend on the type of userservice (Barrutia et al., 2009). For instance, informational content

is essential to portal web and internet banking services and less

important for companies such Amazon.com that produce physical

products (Barrutia et al., 2009).Kim and Stoels study (2004)uses

the 36-item scale developed byLoiacono et al. (2002)and reports

a different number of dimensions for the apparel industry. In their

study,Loiacono et al. (2002)report 12 dimensions.

The benefit electronic web sites may yield depends on the

service setting. Each industry deals with different basic and

supplementary services and user needs. For instance, Fassnacht

and Koese (2006) distinguish between stand-alone services

(where the electronic service provided represents the main

benefit for users) and support services (where the electronic

service facilitates the purchase of goods or services such online

reservations or online shopping). Stand- alone services are also

grouped into pure service offerings (e.g., online banking) and

content offerings (e.g. news and sport coverage).

Among the various dimensions the literature review cites, six

appear consistently: (i) reliability/fulfilment; (ii) responsiveness;

(iii) ease of use/usability; (iv) privacy/security, (v) web design;

and (vi) information quality/content.

The first of these, reliability/fulfilment, which is also one of the

prominent dimensions in the traditional SERVQUAL instrument,refers to the performance of a promised service in an accurate and

timely manner and to the delivery of intact and correct products

(or services) at times convenient to customers (Yang and Jun,

2002). In the studies reviewed here, this dimension is a significant

determinant of (i) overall service quality (Lee and Lin, 2005;

Parasuraman et al., 2005; Sohn and Tadisina, 2008; Wolfinbarger

and Gilly, 2003; Yang and Jun, 2002), (ii) satisfaction (Lee and Lin,

2005; Wolfinbarger and Gilly, 2003), (iii) perceived value (Bauer

et al., 2006; Parasuraman et al., 2005), (iv) intention to purchase

(Lee and Lin, 2005; Wolfinbarger and Gilly, 2003), and (v)

repurchase intentions (Bauer et al., 2006).

The second of the dimensions that appears consistently in the

studies reviewed here is responsiveness, which refers to a

willingness to help users (Li et al., 2002; ONeill et al., 2001),

prompt responses to customers enquiries and problems (Bauer

et al., 2006; Yang and Jun, 2002; Yang et al., 2004), and the

availability of alternative communication channels (Bauer, 2006).

In this regard, Lee and Lin (2005) report that responsiveness

influences overall service quality and satisfaction.

Ease of use/usability refers to user friendliness, especially with

regard to searching for information (Yang et al., 2005; Yoo and

Donthu, 2001). Ease of access to available information is an

important reason for consumers choosing to purchase through the

internet (Cristobal et al., 2007; Wolfinbarger and Gilly, 2003).

Such usability is an important aspect of e-SQ because the

e-business environment can be intimidating and complex for

many customers (Parasuraman et al., 2005).

The fourth dimension, privacy/security, refers to the protection of

personal and financial information (Yoo and Donthu, 2001) and the

degree to which the site is perceived by consumers as being safe from

intrusion (Parasuraman et al., 2005). This dimension is relevant

because of the perceived risk of financial loss and fraud in the online

environment (Parasuraman et al., 2005). Security has been identified

as the most important factor in determining e-SQ for consumers of

online banking services (White and Nteli, 2004). Security is the most

important influence on intentions to revisit a site and make purchases

(Ranganathan and Ganapathy, 2002; Yoo and Donthu, 2001).

The fifth common dimension, web design, refers to aesthetics

features and content as well as structure of online catalogues ( Cai

and Jun, 2003). According toSohn and Tadisina (2008), a website

design similar to a physical store environment influences

customer perceptions of the online service provider and subse-

quent behavioural intentions. The design of a web site plays animportant role in attracting and retaining visitors and is as

important as its content (Ranganathan and Ganapathy, 2002).

The sixth dimension, information quality/benefit, refers to the

adequacy and accuracy of the information users get when visiting

a web site (e.g.,Collier and Bienstock, 2006; Fassnacht and Koese,

2006; Ho and Lee, 2007; Yang et al., 2005). This dimension

becomes important for pure service offerings such as web portal

services (Gounaris and Dimitriadis, 2003; Yang et al., 2005).Yang

et al. (2005) find that two of the five dimensions refer to the

quality of information: its adequacy and usefulness. The ade-

quacy-of-information dimension was measured by items referring

to comprehensiveness, content completeness, sufficiency, and

detailed contact. Usefulness of content refers to relevance,

uniqueness and whether it is up-to-date, as perceived by the user.


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privacy and security, website design, quality of information,

and personalization.

The review shows that several e-SQ dimensions are relevant

across several industries, whereas others are more or less specific

to particular online service industries. Any attempt to promote a

global (or generic) measure of e-SQ could be subject to similar

criticisms to those directed at generic measurement instruments

in traditional service quality (such as SERVQUAL). It is apparent

from this review that the generic e-SQ dimensions identified inthis study should be complemented by sector-specific dimensions

in particular contexts. The development of valid industry-specific

quality measurement scales would seem to be a fruitful avenue

for future research (just as it is proving to be in the case of

traditional service quality).

Apart from industry-specific scales, future research could also

seek to develop and compare specific e-SQ measurement scales

for different functional types of e-business. In undertaking

this research, the two-dimensional classification scheme of

internet businesses Francis and White (2004) devise could be

useful. According to this model, two dimensions can be used to

classify e-businesses: (i) fulfilment process (which can be

distinguished into electronic delivery and offline delivery); and

(ii) product (which can be distinguished into the purchase of

services and the purchase of goods). By combining these two

dimensions (and the two sub-divisions of each), four types of

internet retailing can be identified: (i) offlinegoods (that is,

offline delivery of purchased goods); (ii) offline-services (offline

delivery of purchased services); (iii) electronicgoods (electronic

delivery of purchased goods); and (iv) electronicservices

(electronic delivery of purchased services). It is likely that the

quality factors considered by internet users will differ across

these various categories of internet retailing. It could be interest-

ing for future studies to examine the relative importance

of service quality dimensions across these four categories of

e-business.

The present study also finds that several newly developed

scales, such as SITEQUAL (Yoo and Donthu, 2001), WEBQUAL

(Loiacono et al., 2002), E-S-QUAL (Parasuraman et al., 2005),

eTransQual (Bauer et al., 2006), and PeSQ (Cristobal et al., 2007),

lack specific application and validation. A possible avenue of

future research is to replicate these e-SQ scales across different

contexts with a view to enhancing their external validity. Indeed,

Table 1 shows that studies are largely confined to business-to-

consumer relationships. Developing measurement scale of elec-

tronic service quality in business-to-business industry would be

valuable. It is clear that the type of user (individual or

organizational) and the nature of the service setting should

determine the e-service quality dimensions retained.

The present study also finds that the dimensionality of the

e-SQ construct is not stable across studies, which probably

reflects the diversity of the scope of the studies examined in this

paper. Some studies examine websites that sell goods or serviceswhereas other studies examine non-selling sites. Moreover,

some studies develop generic e-SQ scales whereas others

develop industry-specific scales. In addition, different methodo-

logical approaches are adopted for the identification of the

dimensions of e-SQ and/or the generation (and number) of

potential items within those dimensions. The small samples

used in several of the studies reported in this review do not

permit an adequate assessment of the validity of the scales;

moreover, several studies use convenience samples of students,

which limits the generalisability of the findings. To ensure

external validity, it is recommended that future research should

use random samples of appropriate internet shoppers to identify

the key dimensions and their relative influence on online

consumer behaviour.

Most of the studies in this review focus on functional quality

(that is, the service-delivery process that takes place on the

internet) rather than technical quality (the outcome of the service

process). According to Brady et al. (2002), such a reliance on

functional quality can constitute a misspecification of service

quality (at least with regard to traditional service quality). If this

contention also applies in the online context, it would seem that

the conceptualisation of e-SQ requires further development with

a view to paying appropriate attention to technical quality, as wellas functional quality.

Finally, most of the studies reviewed here describe e-SQ in

terms of reflective attributes rather than formative attributes.

Jarvis et al. (2003) cite four rules for determining whether a

construct is reflective or formative: (i) direction of causality from

construct to measure; (ii) interchangeability of the indicators;

(iii) co-variation among the indicators; and (iv) nomological net

among the construct indicators. For instance, in reflective

measurement models, the direction of causality goes from

construct to measure, while the opposite is true for formative

measurement models. With formative measurement models,

indicators do not necessarily co-vary with each other, while they

are expected to co-vary with each other in reflective measure-

ment models. In reflective models, indicators are supposed to

have the same antecedents and consequences, which is not

required in formative models (Jarvis et al., 2003). The formative

model has been proposed as an alternative approach for

measuring traditional service quality (Rossiter, 2002; Ladhari,

2009) and electronic service quality (Parasuraman et al., 2005;

Collier and Bienstock, 2006). Collier and Bienstock (2006), who

question the use of reflective indicators to conceptualize electro-

nic service quality, support the use of formative indicators.

Parasuraman et al. (2005)state that calling scale items formative

or reflective indicators of latent constructs is a challenging issue.

Further studies are needed to examine the formative conceptua-

lization of e-SQ in greater depth.

4.2. Managerial implications

In accordance with these findings, we propose several

recommendations for consideration by e-business managers.

First, given the fact that responsiveness and reliability are

identified as key dimensions in e-SQ, online retailers and service

providers must ensure that they are able to perform the promised

services accurately and on time; moreover, all information about

products and services (characteristics, price, warranty, and return

policy) should be easy to locate and understand. The online

provider should provide accurate customer information on such

issues as billing and account balance. Second, to ensure that the

key dimension of responsiveness is fulfilled, internet retailers

should respond promptly to all enquiries from their customers

and ensure that their e-mail systems perform well at all times.Third, given the importance of the dimension of ease of use,

the structure of the website and any online catalogues should be

logical and easy to navigate. Fourth, consumers must be made to

feel secure in providing personal and sensitive information (such

as credit card details). Managers should provide an explicit and

reassuring guarantee that their websites respect and protect

personal information at all times.

Finally, website managers should recognise that each online

business is unique and that it is therefore necessary for each

business to identify how its particular internet users define e-SQ.

Managers should then design their websites to ensure that they

deliver e-SQ in a manner that meets the expectations of their

cohort of customers. These expectations can be identified in depth

using online focus groups of their customers. Internet service


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providers should also ensure that they continuously track their

customers perceptions of service quality in terms of appropriate

dimensions and attributes. These dimensions, which can be

initially identified in Table 1 of the present study, should be

complemented by specific dimensions and attributes that are

identified from the managers own focus groups. Managers should

always be careful to utilise e-SQ scales that are appropriate to the

particular context in which they are applied.

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