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Alternative measures of servicequality: a review
Riadh LadhariDepartment of Business Administration, University of Moncton,
Moncton, Canada
Abstract
Purpose The purpose of this paper is to identify and discuss the key conceptual and empiricalissues that should be considered in the development of alternative industry-specific measurementscales of service quality (other than SERVQUAL).
Design/methodology/approach A total of 30 studies are selected from two well-knowndatabases: Science direct and ABI inform. These studies are subjected to a comprehensive in-depthcontent analysis and theoretical discussion of the key conceptual and empirical issues to be considered
in the development of service-quality measurement instruments.Findings The study identifies deficiencies in some of the alternative service-quality measures;however, the identified deficiencies do not invalidate the essential usefulness of the scales. The studymakes constructive suggestions for the development of future scales.
Originality/value This is the first work to describe and contrast a large number of service-qualitymeasurement models, other than the well-known SERVQUAL instrument. The findings are of value toacademics and practitioners alike.
Keywords Customer services quality, Psychometric tests, SERVQUAL
Paper type General review
1. Introduction
A great deal of service-quality research in recent decades has been devoted to thedevelopment of measures of service quality. In particular, the SERVQUAL instrument(Parasuraman et al., 1988) has been widely applied and valued by academics andpracticing managers (Buttle, 1996). However, several studies have identified potentialdifficulties with the use of SERVQUAL (Carman, 1990; Cronin and Taylor, 1992;Asubonteng et al., 1996; Buttle, 1996; Van Dyke et al., 1997; Llosa et al., 1998). Thesedifficulties have related to the use of so-called difference scores, the ambiguity of thedefinition of consumer expectations, the stability of the SERVQUAL scale over time,and the dimensionality of the instrument. As a result of these criticisms, questionshave been raised regarding the use of SERVQUAL as a generic measure of servicequality and whether alternative industry-specific measures of service quality should bedeveloped for specific service settings.
Over the past 15 years or so, at least 30 industry-specific scales of service qualityhave been published in the literature on service quality including (among others)scales suggested by Saleh and Ryan (1991), Vandamme and Leunis (1993), Jabnoun andKhalifa (2005), Akbaba (2006), and Caro and Garcia (2007). However, no study hasattempted to review and integrate this plethora of research on service-qualitymeasurement. The present study addresses this gap in the literature. Its purpose is toexplore some of the pertinent conceptual and empirical issues involved in thedevelopment of industry-specific measures of service quality.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0960-4529.htm
Alternativemeasures of
service quality
65
Managing Service Quality
Vol. 18 No. 1, 2008
pp. 65-86
q Emerald Group Publishing Limited
0960-4529
DOI 10.1108/09604520810842849
http://www.emeraldinsight.com/0960-4529.htmhttp://www.emeraldinsight.com/0960-4529.htm7/28/2019 Alternative Measures of Service Quality
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The remainder of this paper is organized as follows. Following this introduction, thenext section provides a brief description of the SERVQUAL scale and the critiques thathave been made of it. The paper then presents a summary of 30 alternativeindustry-specific measures of service quality and utilizes these to canvass several
conceptual and empirical issues related to the development of such scales. A discussionof the findings of the review and suggestions for future research are then presented.Finally, the conclusions of the paper and managerial implications are noted.
2. A generic measure of service quality: the SERVQUAL scaleWhen the SERVQUAL scale was developed by Parasuraman et al. (1985, 1988), theiraim was to provide a generic instrument for measuring service quality across a broadrange of service categories. Relying on information from 12 focus groups of consumers,Parasuraman et al. (1985) reported that consumers evaluated service quality bycomparing expectations (of service to be received) with perceptions (of service actuallyreceived) on ten dimensions: tangibles, reliability, responsiveness, communication,credibility, security, competence, understanding/knowing customers, courtesy, andaccess. In a later (Parasuraman et al. (1988)) work, the authors reduced the original tendimensions to five:
(1) tangibles (the appearance of physical facilities, equipment, and personnel);
(2) reliability (the ability to perform the promised service dependably andaccurately);
(3) responsiveness (the willingness to help customers and provide prompt service);
(4) empathy (the provision of individual care and attention to customers); and
(5) assurance (the knowledge and courtesy of employees and their ability to inspiretrust and confidence).
Each dimension is measured by four to five items (making a total of 22 items across thefive dimensions). Each of these 22 items is measured in two ways:
(1) the expectations of customers concerning a service; and
(2) the perceived levels of service actually provided.
In making these measurements, respondents are asked to indicate their degree ofagreement with certain statements on a seven-point Likert-type scale (1 stronglydisagree to 7 strongly agree). For each item, a so-called gap score (G) is thencalculated as the difference between the raw perception-of-performance score (P) andthe raw expectations score (E). The greater the gap score (calculated as G Pminus E), the higher the score for perceived service quality.
SERVQUAL has been used to measure service quality in various service industries;
these have included: the health sector (Carman, 1990; Headley and Miller, 1993; Lam,1997; Kilbourne et al., 2004); banking (Lam, 2002; Zhou et al., 2002); fast food (Lee andUlgado, 1997); telecommunications (Van der Wal et al., 2002); retail chain(Parasuraman et al., 1994); information systems (Jiang et al., 2000); and libraryservices (Cook and Thompson, 2001). SERVQUAL has also been applied in variouscountries; these have included: the United States (Babakus and Boller, 1992; Pitt et al.,1995; Jiang et al., 2000; Kilbourne et al., 2004); China (Lam, 2002; Zhou et al., 2002);Australia (Baldwin and Sohal, 2003); Cyprus (Arasli et al., 2005); Hong Kong (Kettinger
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et al., 1995; Lam, 1997); Korea (Kettinger et al., 1995); South Africa (Pitt et al., 1995; Vander Wal et al., 2002); The Netherlands (Kettinger et al., 1995); and the UK (Pitt et al.,1995; Kilbourne et al., 2004).
Despite the widespread use of the SERVQUAL model to measure service quality,
several theoretical and empirical criticisms of the scale have been raised. These can besummarised as follows:
. The concept and operationalisation of the gap score have been questioned. For
example, it has been suggested that the notion of subtraction contained in the
SERVQUAL model has no equivalent in theories of psychological function
(Ekinci and Riley, 1998). The use of a gap score is said to be a poor choice as a
measure of psychological construct (Van Dyke et al., 1999) because there is littleevidence that customers actually assess service quality in terms of
perception-minus-expectations scores (Peter et al., 1993; Buttle, 1996; Ekinciand Riley, 1998). It has been contended that service quality is more accurately
assessed by measuring only perceptions of quality (Cronin and Taylor, 1992).
Moreover, the validity of the operationalisation of the gap score has beenquestioned because such scores are unlikely to be distinct from their component
scores (Brown et al., 1993).
. The concept of expectations has been criticised for being loosely defined and
open to multiple interpretations (Teas, 1993, 1994). According to this critique,
expectations have been variously defined as desires, wants, what a service
provider should offer, the level of service the customer hopes to receive,
adequate service, normative expectations, and ideal standards. As a result,
it is contended that the operationalisation of SERVQUAL is itself open to
multiple interpretations.
.
The validity of the items and dimensions of the SERVQUAL instrument havebeen questioned. It has been suggested that the factor-loading pattern in a
number of studies (Carman, 1990; Parasuraman et al., 1991; Babakus and Boller,1992; Headley and Miller, 1993; Engelland et al., 2000) indicates a weakness interms of convergent validity because several of the SERVQUAL items had the
highest loadings on different dimensions from those in Parasuraman et al. (1988).
. A number of researchers have suggested that different dimensions are more
appropriate for expectations, perceptions, and gap scores. Suggestions have
included: one dimension (Cronin and Taylor, 1992; Lam, 1997); two dimensions
(Babakus and Boller, 1992; Gounaris, 2005); three dimensions (Chi Cui et al., 2003;Arasli et al., 2005; Najjar and Bishu, 2006); four dimensions (Kilbourne et al.,
2004); six dimensions (Carman, 1990; Headley and Miller, 1993); sevendimensions (Walbridge and Delene, 1993); and nine dimensions (Carman,
1990). Moreover, other studies have reported a poor fit when tested against a
five-factor model with confirmatory factor analysis (CFA) (Chi Cui et al., 2003;Badri et al., 2005).
. It has been contended that perception scores (as in the SERVPERF instrument)
outperform gap scores in predicting overall evaluation of service (Cronin and
Taylor, 1992; McAlexander et al., 1994).
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. It has been argued that SERVQUAL focuses on the process of service deliveryrather than the outcomes of service encounters (Gronroos, 1990; Richard andAllaway, 1993; Brady and Cronin, 2001).
. The fundamental model underlying SERVQUAL has been questioned. Severalresearchers have contended that service quality is an aggregation of variousquality sub-dimensions and that service quality is therefore a multilevelconstruct (as well as being a multidimensional construct) (Dabholakar et al.,1996; Brady and Cronin, 2001; Wilkins et al., 2007).
3. Industry-specific measure of service qualityIn view of the problems outlined above, the applicability of a generic scale formeasuring service quality in all settings has been questioned (Babakus and Boller,1992; Van Dyke et al., 1997; Jabnoun and Khalifa, 2005; Akbaba, 2006; Caro and Garcia,2007). Moreover, it has been argued that a simple adaptation of the SERVQUAL itemsis insufficient to measure service quality across a diversity of service industries
(Carman, 1990; Babakus and Boller, 1992; Brown et al., 1993; Van Dyke et al., 1997). Forexample, Carman (1990) contended that certain dimensions required expansion by theinclusion of 13 additional items to the SERVQUAL instrument in order to captureservice quality adequately across different services. It has also been contended thatservice quality is a simple unidimensional construct in some contexts, but a complexmultidimensional construct in others (Babakus and Boller, 1992). For these reasons, ithas been suggested that industry-specific measures of service quality might be moreappropriate than a single generic scale (Babakus and Boller, 1992; Van Dyke et al.,1997; Caro and Garcia, 2007). Dabholkar et al. (1996, p. 14) summarized this view in thefollowing terms:
. . . it appears that a [single] measure of service quality across industries is not feasible.
Therefore, future research on service quality should involve the development ofindustry-specific measures of service quality.
As a consequence of these arguments, much of the emphasis in recent research hasmoved from attempts to adapt SERVQUAL to the development of alternativeindustry-specific measures. Table I summarizes 30 industry-specific measures ofservice quality taken from two databases: Science direct and ABI inform. Thefeatures of these measures are discussed below.
3.1 Service industries and countriesIt is apparent from Table I that alternative scales have been developed to measureservice quality in a variety of service industries. These have included (among others):
restaurants (Stevens et al., 1995); retail banks (Aldlaigan and Buttle, 2002;Sureshchandar et al., 2002); career centers (Engelland et al., 2000); internet retail(Janda et al., 2002); hotels (Ekinci and Riley, 1998; Akbaba, 2006; Wilkins et al., 2007);hospitals (Sower et al., 2001); and higher education (Markovic, 2006).
Moreover, the scales have been developed in various countries. These have includedTurkey (Akbaba, 2006); Australia (Wilkins et al., 2007); Canada (Saleh and Ryan, 1991);Croatia (Markovic, 2006); India (Sureshchandar et al., 2002); the USA (Dabholkar et al.,1996); Korea (Kang and James, 2004); Hong Kong (Lam and Zhang, 1999); Belgium
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Study
Serviceindustry
(Country)
Sample
Questionnaire
administration
Dataanalysis
procedure
Scale
Dimensions(numberof
items)
Reliability
Knustonetal.
(1990)
Lodgingindustry
(USA)
201adults
Telephone
interviews
Confirmatory
factoranalysis
26items;expectations-only
scores
Seven-pointLikertscale,
rangingfromstronglyagree
(7)tostronglydisagree(1)
5dimensions:
reliability(4items),
assurance(5),
responsiveness(3),
tangibles(6),empathy
(8)
Rangedfrom0.63
to0.80
Salehand
Ryan(1991)
Hospitality
industry(Canada)
200hotelguests,
17management
staff
Self-administered
Exploratory
factoranalysis
32itemsforhotel
guestsand
33itemsformanag
ementstaff;
perceptionminusexpectations
Five-pointLikertscale,
rangingfromhigh
lysatisfied
(1)tohighlydissatisfied(5)
4dimensionsforhotel
guests:tangiblesand
reliability(10),
responsiveness(8),
assurance(8),empathy
(6)5dimensionsfor
managementstaff:
tangibles(7),reliability
(3),responsiveness(8),
assurance(8),empathy
(7)
Rangedfrom0.74
to0.93forhotel
gue
sts;ranged
from0.63to0.80
for
management
staff
Boumanand
vanderWiele
(1992)
Careservice
industry(T
he
Netherlands)
226customersof
careservicefirms
Self-administered
Exploratory
factoranalysis
40items;
Perception-minus-expectations
scores
Seven-pointLikertscale,
rangingfromvery
unimportant(1)to
very
important(7)fore
xpectations
andfrom1
definitelynot
appropriateto7
definitely
appropriateforperceptions
3factors:customer
kindness(19),tangibles
(13),faith(8)
Rangedfrom0.76
to0.92
Vandamme
andLeunis
(1993)
Healthcare
sector
(Belgium)
70patients
Self-administered
Exploratory
factoranalysis
17items;
Perception-minus-expectations
scores
Seven-pointLikertscale,
rangingfromstrongly
disagree(1)tostro
nglyagree
(7)
6dimensions:tangibles
(4),medical
responsiveness(3),
assuranceI(3),
assuranceII(3),
nursingstaff(2),
personalbeliefsand
values(2)
Rangedfrom0.58
to0.82
(continued)
Table I.Review of service-quality
scales
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Study
Serviceindustry
(Country)
Sample
Questionnaire
administration
Dataanalysis
procedure
Scale
Dimensions(numberof
items)
Reliability
Stevensetal.
(1995)
Restaurant
industry(U
SA)
200respondents
forfine-dining,
198for
casual-dining,198
forquick-service
restaurants
Telephone
interviews
Confirmatory
factoranalysis
29items;expectations-only
scores
Seven-pointLikertscale,
rangingfromstronglyagree
(7)tostronglydisagree(1)
5dimensions:tangibles
(10),reliability(5),
responsiveness(3),
assurance(6),empathy
(5)
Rangedfrom0.89
to0.92
TomesandNg
(1995)
Servicequa
lityin
NHSorNHStrust
hospitalser
vices
(England)
132patients
admittedinlarge
generalhospital
intheeastof
England
Self-administered
Exploratory
factoranalysis
49items,
perception-minus-expectations
(factoranalysisis
basedon
expectations-onlyscores)
7dimensions:empathy
(10),relationshipof
mutualrespect(9),
dignity(9),
understandingof
illness(5),religious
needs(1),food(6),
physicalenvironment
(9)
Rangedfrom0.64
to0.92
Dabholkar
etal.(1996)
Retailservice
quality(USA)
227shoppersfor
thefirststudy
and149forthe
cross-validation
study
Self-administered
Confirmatory
factoranalysis
28items;perceptio
n-only
scores
Five-pointLikertscale,
rangingfromstrongly
disagree(1)tostro
nglyagree
(5)
5dimensions:physical
aspects(6items),
reliability(5),personal
interaction(9),problem
solving(3),policy(5)
Rangedfrom0.85
to0.92
Lamand
Zhang(1999)
Travelagen
ts
(HongKong)
209usersoftravel
agents
Self-administered
Exploratory
factoranalysis
23items;
perception-minus-expectations
scores
Seven-pointLikertscales,
rangingfromstronglyagree
(7)tostronglydisagree(1)
5dimensions:
responsivenessand
assurance(6),
reliability(5),empathy
(4),resourcesand
corporateimage(5),
tangibility(3)
Rangedfrom0.67
to0.88
Mentzeretal.
(1999)
Logisticser
vice
quality(USA)
5531defense
logisticsagency
users
Self-
administered
Confirmatory
factoranalysis
25items;perceptio
n-only
scores
Five-pointLikertscale,
rangingfromstrongly
disagree(1)tostro
nglyagree
(5)
9dimensions:
informationquality(2),
orderingprocedures(2),
orderingrelease
quantities(3),
timeliness(3),order
accuracy(3),order
quality(3),order
condition(3),order
discrepancyhandling
(3),personnelcontact
quality(3)
Rangedform0.73
to0.89
(continued)
Table I.
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Study
Serviceindustry
(Country)
Sample
Questionnaire
administration
Dataanalysis
procedure
Scale
Dimensions(numberof
items)
Reliability
Shemwelland
Yavas(1999)
Hospitalservice
quality(USA)
218respondents
residingin
different
neighborhoods
SMSA
Self-administered
Confirmatory
factoranalysis
14items;perceptio
n-only
scores
Seven-pointscale,
ranging
frompoor(1)tooutstanding
(7)
3dimensions:search
attributes(5items),
credenceattributes(4
items),experience
attributes(5items)
Rangedform0.75
to0.83
Engellandetal.
(2000)
Careerservice
centersoncollege
campuses(USA)
262undergraduate
collegestudents
forthe
exploratorystudy
and237forthe
validation
Self-administered
Exploratory
factoranalysis;
Confirmatory
factoranalysis
17items;
perception-minus-expectations
scores
Seven-pointLikertscale,
rangingfromstrongly
disagree(1)tostro
nglyagree
(7)
5dimensions:tangibles
(4),reliability(4),
responsiveness(3),
assurance(3),empathy
(3)
Rangedfrom0.76
to0.89
Frochotand
Hughes(2000)
Servicequa
lity
providedin
historichou
ses
(Englandand
Scotland)
790visitorsfor
thefinalsurvey
Self-administered
Exploratory
factoranalysis
24items;Perception-only
scores
Five-pointLikertscale,
rangingfromstronglyagree
(5)tostronglydisagree(1)
5dimensions:
responsiveness(8),
tangibles(7),
communications(4),
consumables(3),
empathy(2)
Rangedfrom0.70
to0.83
Cookand
Thompson
(2001)
Libraryservice
(USA)
4407participants
Web-based
administration
Exploratory
factoranalysis
34items;perceptio
n-only
scores
Nine-pointscale,rangingfrom
low(1)tohigh(9);and
unnumberedgraphicrating
scale
4dimensions:service
(11items),libraryas
place(9),accessto
collections(7),
reliability(7)
Rangedfrom0.80
to0.94
Soweretal.
(2001)
Hospitalservice
quality(USA)
663recently
discharged
patients
Exploratory
factoranalysis
75items;perceptio
n-only
scores
Seven-pointLikertscale,
rangingfromstronglyagree
(7)tostronglydisagree(1)
8dimensions:respect
andcaring(26),
effectivenessand
continuity(15),
appropriateness(15),
information(7),
efficiency(5),
effectiveness-meals(5),
firstimpression(1),
staffdiversity(1)
Rangedfrom0.87
to0.98
(continued)
Table I.
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Study
Serviceindustry
(Country)
Sample
Questionnaire
administration
Dataanalysis
procedure
Scale
Dimensions(numberof
items)
Reliability
Vaughanand
Shiu(2001)
Voluntarysector
(Scotland)
72disabled
serviceusersand
aparent/carer
groupmembers
Self-administered
Exploratory
factoranalysis;
Correlation
matrixanalysis
27items;perceptio
nscores
andexpectationsscores.
10dimensions:access
(3),responsiveness(4),
communication(4),
humaneness(4),
security(2),
enabling/empowerment
(2),competence(3),
reliability(3),equity(1),
tangibles(1)
Aldlaiganand
Buttle(2002)
Banking(U
K)
975bank
customers
Mailsurvey
Exploratory
factoranalysis
21itemsscale;perception-only
scores
Seven-pointLikertscale,
rangingfromstrongly
disagree(1)tostro
nglyagree
(7)
4dimensions:service
systemquality(11),
behavioralservice
quality(5),machine
servicequality(2),
servicetransactional
accuracy(3)
Rangedfrom0.80
to0.93(total
sam
ple)
Jandaetal.
(2002)
Internetretail
servicequality
(USA)
446respondents
whohadmadeat
leastoneinternet
purchasewithin
thelastsix
months
Administeredby
interviewers
Confirmatory
factoranalysis
22items;perceptio
n-only
scores
Seven-pointLikertscale,
rangingfromstrongly
disagree(1)tostro
nglyagree
(7)
5dimensions:
performance(6items),
access(4items),
security(4items),
sensation(4items),
information(4items)
Rangedfrom0.61
to0.83
Sureshchandar
etal.(2002)
Banking(In
dia)
277bank
customers
Self-administered
Confirmatory
factoranalysis
41items;perceptio
n-only
scores
Seven-pointLikertscale,
rangingfromvery
poor(1)to
verygood(7)
5dimensions:core
serviceorservice
product(5),human
elementofservice
delivery(17),
systemizationof
servicedelivery(6),
tangiblesofservice(6),
socialresponsibility(7)
Rangedfrom0.82
to0.96
Gettyand
Getty(2003)
Lodgingindustry
(USA)
229frequent-traveler
businessowners
Mailsurvey
Exploratory
factoranalysis
26items;perceptio
n-only
scores
Four-pointscale,rangingfrom
low(1)tohigh(4)
5dimensions:
tangibility(8items),
reliability(4),
responsiveness(5),
confidence(5),
communication(4)
Hig
hreliability-
nodetailed
information
(continued)
Table I.
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Study
Serviceindustry
(Country)
Sample
Questionnaire
administration
Dataanalysis
procedure
Scale
Dimensions(numberof
items)
Reliability
Khan(2003)
Ecotourism
324ecotourists
whohadtakenan
ecotripinthepast
18months
Mailsurvey
Exploratory
factoranalysis
29items;expectations-only
scores
Seven-pointLikertscale,
rangingfromstrongly
disagree(1)tostronglyagree
(7)
6dimensions:
ecotangibles(3),
assurance(5),
reliability(5),
responsiveness(4),
empathy(4),tangibles
(8)
Rangedfrom0.86
to0.98
Wolfinbarger
andGilly
(2003)
Onlinee-tail
quality(US
A)
1,013internet
users
Web-based
administration
usinganonlin
e
panel
Exploratory
factoranalysis;
Confirmatory
factoranalysis
14items;
perception-minus-expectations
scores
Seven-pointLikertscale,
rangingfromstrongly
disagree(1)tostronglyagree
(7)
4dimensions:website
design(5),
fulfillment/reliability
(3),security/privacy(3),
customerservice(3)
Rangedfrom0.79
to0.88
YoonandSuh
(2004)
Consulting
service(Korea)
86respondents
fromIT
consultingsites
Self-administered
Exploratory
factoranalysis
36items;perception-only
scores
Seven-pointLikertscale,
rangingfromStro
ngly
disagree(1)toStronglyagree
(7)
6dimensions:
assurance(4),
responsiveness(3),
reliability(12),
empathy(4),process
(9),education(4)
Rangedfrom0.87
to0.95
Gounaris
(2005)
Businessto
businessse
rvice
(Greece)
515senior
management
Mailsurvey
Confirmatory
factoranalysis
(CFA).
22items;perception-only
scores
Seven-pointLikertscale,
rangingfromentirelydisagree
(1)toentirelyagree(7)
4dimensions:potential
quality(6),hard
processquality(5),soft
processquality(6),
output(5)
Rangedfrom0.79
to0.88
Jabnounand
Khalifa(2005)
Bank(United
ArabEmirates)
115customersof
Islamicbanksand
115customerof
conventional
banks
Self-administered
Exploratory
factoranalysis
29items;perception-only
scores
4dimensions:personal
skills(12),reliability
(5),image(6),value(6)
Rangedfrom0.85
to0.94
Karatepeetal.
(2005)
Bankservice
(Cyprus)
1220customers
Self-administered
Exploratory
factoranalysis;
confirmatory
factoranalysis
20items;perception-only
scores
Five-pointLikertscale,
rangingfromstronglyagree
(5)tostronglydisagree(1)
4dimensions:service
environment(4),
interactionquality(7),
empathy(5),reliability
(4)
Rangedfrom0.81
to0.92
Parasuraman
etal.(2005)
Electronicservice
quality(internet
usersnot
identified)
549subjectsfor
thedevelopment
stageand858
customersforthe
validationstage
Web-based
administration
Exploratory
factoranalysis;
confirmatory
factoranalysis
22items;perception-only
scores
Five-pointLikertscale,
rangingfromstrongly
disagree(1)tostronglyagree
(5)
4dimensions:
efficiency(8items);
systemavailability(4);
fulfillment(7);privacy
(3)
Rangedfrom0.83
to0.94
(continued)
Table I.
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service quality
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Study
Serviceindustry
(Country)
Sample
Questionnaire
administration
Dataanalysis
procedure
Scale
Dimensions(numberof
items)
Reliability
Akbaba(2006)Businesshotel
industry(T
urkey)
234hotelguests
Self-administered
Exploratory
factoranalysis
25items;
perception-minus-expectations
scores
5dimensions:tangibles
(6),adequacyinservice
supply(7),
understandingand
caring(5),assurance
(4),andconvenience(3)
Rangedfrom0.71
to0.86
Markovic
(2006)
Higheducation
service(Cro
atia)
444graduate
students
Self-administered
Exploratory
factoranalysis
26items;expectations-only
scores
Five-pointLikertscale,
rangingfromstronglybelieve
thatthestatement
iswrong(1)
tostronglybelieve
thatthe
statementisnotw
rong(5)
7dimensions:
reliability(6),students
inscientificwork(4),
empathy(4),assurance
(3),e-learning(3),
responsiveness(3),
tangibles(3)
Rangedfrom0.53
to0.78
Caroand
Garcia(2007)
Urgenttran
sport
service(Spain)
375subjects
Self-administered
Exploratory
factoranalysis;
Confirmatory
factoranalysis
36items;perceptio
n-only
scores
Five-pointLikertscale,
rangingfromstrongly
disagree(1)tostro
nglyagree
(5)
4dimensions:personal
interaction(3
sub-dimensions,14
items),design(2
sub-dimensions,7
items),physical
environment(2
sub-dimensions,7
items),outcome(2
sub-dimensions,8
items)
Rangedfrom0.74
to0.96
Wilkinsetal.
(2007)
Hospitality
service
(Australia)
664hotelguests
Self-administered
Exploratory
factoranalysis;
confirmatory
factoranalysis
30items;perceptio
n-only
scores
3dimensions:physical
product(3
sub-dimensions,13
items),service
experience(3
sub-dimensions,13
items),qualityfoodand
beverage(4items)
Rangedfrom0.72
to0.90
Table I.
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(Vandamme and Leunis, 1993); the United Arab Emirates (Jabnoun and Khalifa, 2005);and Spain (Caro and Garcia, 2007).
3.2 Dimensional structureAll scales in Table I are multi-dimensional. However, the number of dimensions vary from a minimum of two (Ekinci and Riley, 1998) to a maximum of ten (Vaughan andShiu, 2001). It is apparent that the number of dimensions varied according to theservice context and the country. For example, the factor structure for the lodgingindustry in Australia (Wilkins et al., 2007) was somewhat different from that in NorthAmerica (Knutson et al., 1990; Saleh and Ryan, 1991; Getty and Getty, 2003). Moreover,the factor structure varied within a given country. For example, the factor structure forthe lodging industry in North America varied from five dimensions (Knutson et al.,1990; Getty and Getty, 2003) to four (Saleh and Ryan, 1991).
Despite this variation, it is apparent that the five dimensions of SERVQUAL were,for the most part, retained in the scales examined in this review. For example, the
dimension of tangibles (the appearance of physical facilities, equipment, andpersonnel) was retained in most of the scales (for example, Knutson et al., 1990; Salehand Ryan, 1991; Bouman and van der Wiele, 1992; Dabholkar et al., 1996; Lam andZhang, 1999; Engelland et al., 2000; Frochot and Hughes, 2000; Sureshchandar et al.,2002; Getty and Getty, 2003; Khan, 2003; Akbaba, 2006; Markovic, 2006). Similarly, theempathy dimension (the knowledge and courtesy of employees and their ability toinspire trust and confidence) was retained in numerous studies (for example, Knutsonet al., 1990; Tomes and Ng, 1995; Lam and Zhang, 1999; Engelland et al., 2000; Khan,2003; Yoon and Suh, 2004; Karatepe et al., 2005; Markovic, 2006). Similar observationsapply to the other SERVQUAL dimensions. However, new dimensions were added toaccount for industry-specific characteristics. For example, Janda et al. (2002) addedsecurity as a specific dimension of service quality required in the internet retail
industry.
3.3 Gap scores versus perception scoresThree measurement methods were found in the scales reviewed in Table I:
. performance-only scores (for example, Dabholkar et al., 1996; Ekinci and Riley,1998; Frochot and Hughes, 2000; Janda et al., 2002; Getty and Getty, 2003; Caroand Garcia, 2007; Wilkins et al., 2007);
. expectations-only scores (for example, Knutson et al., 1990; Khan, 2003;Markovic, 2006); and
. perception-minus-expectations scores (for example, Engelland et al., 2000;Wolfinbarger and Gilly, 2003).
It is apparent that, despite the practical difficulties in obtaining information oncustomer expectations, many studies continue to use a gap model. It would seem thatsuch models facilitate the identification of strengths and weaknesses in specific qualityattributes.
3.4 Technical dimension versus functional dimensionAccording to the two-dimensional model of Gronroos (1984), service quality consists of:
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(1) technical (outcome) quality (which refers to the outcome of the serviceperformance); and
(2) functional (process) quality (which refers to the manner in which the service isdelivered).
The SERVQUAL model is based on functional quality (the delivery process) ratherthan technical quality (the outcome of the service encounter).
Most of the studies in the present review focused on the functional quality of theservice-delivery process (for example, Stevens et al., 1995; Engelland et al., 2000;Frochot and Hughes, 2000; Getty and Getty, 2003; Yoon and Suh, 2004; Markovic,2006). Only a limited number of studies incorporated the technical (outcome) dimension(for example, Vaughan and Shiu, 2001; Aldlaigan and Buttle, 2002; Gounaris, 2005;Caro and Garcia, 2007).
3.5 Number of itemsThe number of items in the present review varied from 14 (Shemwell and Yavas, 1999)to 75 (Sower et al., 2001) according to the industry context. For example,Sureshchandar et al. (2002) used 41 items for the banking industry, Vaughan andShiu (2001) used 27 items in the voluntary service sector, Yoon and Suh (2004) used 36items in the consulting service industry, Bouman and van der Wiele (1992) used 40items in the care industry, Markovic (2006) used 26 items in the higher educationindustry, and Akbaba (2006) used 25 items in the business hotel industry.
To determine the number of items, most researchers generated an initial pool ofscale statements from a review of literature. This initial pool was then refined through:
. focus groups (for example, Mentzer et al., 1999; Sower et al., 2001; Vaughan andShiu, 2001; Aldlaigan and Buttle, 2002; Khan, 2003; Wilkins et al., 2007); and/or
. individual interviews with providers or users (for example, Aldlaigan and Buttle,
2002; Janda et al., 2002; Getty and Getty, 2003; Karatepe et al., 2005; Caro andGarcia, 2007).
It is also worthy of note that, in some cases, SERVQUAL was utilised as astarting-point for the development of the item pool (for example, Dabholkar et al., 1996;Frochot and Hughes, 2000; Sureshchandar et al., 2002) or as the fundamental structurefor new instruments (for example, Engelland et al., 2000, Khan, 2003; Markovic, 2006).
3.6 Sample sizesSample sizes in the studies reviewed in Table I varied from 70 (Vandamme and Leunis,1993) to 5,531 (Mentzer et al., 1999) service users. Only three studies had sample sizes ofmore then 1,000: 1,013 internet users (Wolfinbarger and Gilly, 2003), 1,220 customers
(Karatepe et al., 2005), and 5,531 defence logistics agency users (Mentzer et al., 1999).Three studies had sample sizes of fewer than 100 respondents/users and 14 studies hadsample sizes of fewer than 250 respondents/users. Several studies did not providedetails of their samples.
3.7 Analysis methodA total of 16 studies used only exploratory factor analysis (EFA) to assess theirdimensional structure and items. Eight studies used confirmatory factor analysis
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(CFA). Only six studies used a combination of these techniques (Engelland et al., 2000;Wolfinbarger and Gilly, 2003; Karatepe et al., 2005; Parasuraman et al., 2005; Caro andGarcia, 2007; Wilkins et al., 2007).
Item-to-total correlation analysis (that is, correlation between the score on an item
and the sum of the scores of all other items constituting a single factor) was the mostcommonly used methodology to decide which items to retain and which to discard. Inseveral studies, all items were discarded that scored less than ^0.40 on theitem-to-total correlation (for example, Wolfinbarger and Gilly, 2003) or ^0.30 on theitem-to-total correlation (for example, Janda et al., 2002; Aldlaigan and Buttle, 2002).Other studies used loading scores as a basis for item exclusion. For example, somestudies excluded items with factor loadings less than ^0.40 (for example, Engellandet al., 2000; Sower et al., 2001; Janda et al., 2002; Jabnoun and Khalifa, 2005; Caro andGarcia, 2007), others excluded items with factor loadings less than ^0.45 (for example,Markovic, 2006), and others excluded items with factor loadings less than ^0.50 (forexample, Lam and Zhang, 1999; Wolfinbarger and Gilly, 2003; Karatepe et al., 2005). Insome studies, items with cross-loadings greater than ^0.40 were discarded (for
example, Janda et al., 2002).
3.8 Reliability and validityCronbachs alpha was the most commonly used measure of scale reliability (that is, theinternal homogeneity of a set of items composing a scale). Most scales in the presentreview exhibited good reliability (that is, Cronbachs alphas greater than 0.60). Forexample, Frochot and Hughes (2000) used five dimensions with reliability coefficientsranging from 0.70 to 0.83, Akbaba (2006) used five-dimensions with reliabilitycoefficients ranging from 0.71 to 0.86, and Khan (2003) used six dimensions rangingfrom 0.86 to 0.98.
To assess convergent validity (that is, the extent to which a set of items that isassumed to represent a construct does in fact converge on the same construct), most
studies calculated the average variance extracted (AVE) by each dimension (with anAVE of greater than 0.5 being said to support convergent validity). Examples in thepresent review included Gounaris (2005) and Caro and Garcia (2007). Some researchersconsidered the fact that all the items loaded highly on the factor to which they wereassigned as further evidence of convergent validity (for example, Dabholkar et al.,1996; Caro and Garcia, 2007).
To establish discriminant validity (that is, the extent to which measures oftheoretically unrelated constructs do not correlate with one another), severalresearchers used CFA and compared the AVE for each factor with the variance sharedby the remaining factors (for example, Wolfinbarger and Gilly, 2003; Gounaris, 2005;Caro and Garcia, 2007). The two dimensions were confirmed as being distinct fromeach other if the AVE estimates were greater than the shared variance estimates. In
other studies, discriminant validity was demonstrated by simply showing that thescale did not correlate strongly with other measures from which it was supposed todiffer (for example, Sureshchandar et al., 2002).
To demonstrate predictive validity (that is, the extent to which the scores of oneconstruct were empirically related to the scores of other conceptually relatedconstructs) some researcher correlated their service-quality dimensions with overallquality (for example, Sureshchandar et al., 2002; Wolfinbarger and Gilly, 2003;Gounaris, 2005; Jabnoun and Khalifa, 2005; Parasuraman et al., 2005). Others correlatedtheir service-quality dimensions with other dimensions; these included: satisfaction
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(for example, Lam and Zhang, 1999; Janda et al., 2002; Wolfinbarger and Gilly, 2003;Gounaris, 2005); word-of-mouth (for example, Dabholkar et al., 1996; Janda et al., 2002);and loyalty (for example, Janda et al., 2002; Sureshchandar et al., 2002; Wolfinbargerand Gilly, 2003).
Only a few studies tested and supported all three types of validity (convergent,discriminant, and predictive). These included: Dabholkar et al. (1996); Aldlaigan andButtle (2002); Janda et al. (2002); Sureshchandar et al. (2002); Wolfinbarger and Gilly(2003); Gounaris (2005); Karatepe et al. (2005); and Parasuraman et al. (2005). In some ofthe studies, the three types of validity were not evaluated or even discussed.
It is apparent that numerous scales in the present review suffered from incompleteproof of validity. In addition, the methodological assessments of the new alternativeinstruments were not clearly presented in several studies.
4. Discussion and suggestions for future researchThis review has documented a variety of industry-specific measurement scalesproposed in the service-quality literature since the publication of the SERVQUALmodel in 1988. It is apparent that there is ongoing debate about several aspects of suchscales. These include:
. the dimensionality of service quality;
. the hierarchical structure of service quality;
. the relationship of culture to perceptions of service quality;
. comparisons between alternative scales and SERVQUAL;
. validity of service-quality scales; and
. the statistical analysis used.
These aspects are discussed below, together with suggestions for future avenues of
research.
4.1 Dimensionality of service qualityAll of the 30 studies reviewed here posited service quality as a multidimensionalconstruct. However, the number and nature of the dimensions varied, depending on theservice context; indeed, they varied even within the same service industry. It isapparent that the criteria used to evaluate service quality differ among customergroups and circumstances. For example, a businessperson staying in a given hotel hasdifferent service criteria from those of a tourist (Eccles and Durrand, 1997).
Scholars should therefore describe the empirical context in which a particular scalewas developed and the contexts in which it can be applied. In several cases reviewed inthe present study, the authors did not explicitly identify the empirical context in which
the scale was developed. Future studies should replicate these measures in differentcontexts to ascertain whether the number and nature of dimensions are applicable inother settings.
4.2 Hierarchical structure of service qualitySeveral authors have suggested that service quality is a hierarchical constructconsisting of various sub-dimensions (Dabholkar et al., 1996; Brady and Cronin, 2001;Gounaris, 2005; Caro and Garcia, 2007; Wilkins et al., 2007). However, despite this
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theoretical support for a multilevel, multidimensional model of service quality, fewefforts have been made to provide empirical evidence for such a structure. Futureresearch could extend scholarly understanding of service quality by undertakingempirical studies of hierarchical multidimensional conceptions of service quality in
different settings.
4.3 Culture and service qualitySeveral researchers have suggested that there is a need to develop culturally specificmeasures of service quality (Winsted, 1997; Zhou et al., 2002; Raajpoot, 2004; Karatepeet al., 2005). As with other marketing constructs and measures, it has been contendedthat constructs of service quality that are developed in one culture might be notapplicable in another (Kettinger et al., 1995; Karatepe et al., 2005). According to thisview, the meanings, number, and relative importance of service-quality dimensionsdepend on the cultural and value orientations of customers particularly with respectto cultural traditions of power distance and individualism/collectivism (Winsted,1997; Espinoza, 1999; Mattila, 1999; Furrer et al., 2000; Karatepe et al., 2005; Glaveliet al., 2006). Further research in this area is desirable.
4.4 Comparisons between alternative scales and SERVQUALAlthough SERVQUAL has been criticised on theoretical grounds, only one scale in thepresent review (INDSERV) has been empirically shown to outperform SERVQUAL.It is apparent that rigorous empirical studies are needed to substantiate whetheralternative scales really are superior to SERVQUAL. In particular, further studies arerequired to validate and refine the alternative scales. It should also be noted that thesmall sample sizes used in several of the studies proposing alternative scales wereinsufficient to permit a comprehensive psychometric assessment of the proposedscales. There is also a need to compare the new scales with SERVQUAL with regard to
their ability to predict constructs known to be related to service quality such asoverall service quality, satisfaction, word of mouth, and loyalty.
Despite the widespread criticism of SERVQUAL, it is the contention of the presentstudy that the scale continues to be the most useful model for measuring servicequality. In addition, the methodological approach used by Parasuraman et al. (1985,1988, 1991) in developing and refining SERVQUAL was more rigorous than those usedby the authors of the alternative scales.
Finally, it is interesting to note that there are many similarities between thedimensions used in SERVQUAL and those developed in alternative scales. Thissuggests that some service-quality dimensions are generic whereas others are specificto particular industries and contexts.
4.5 Validity of service quality scalesAlthough the measures of service quality reviewed in this study claimed to haveexhibited good reliability, it is important to note that higher alpha values can beindicative of deficiencies (rather than reliability) in a scale (Churchill, 1979; Smith,1999). As Smith (1999) has noted, high alpha values can reflect poor design of themeasurement instrument, poor scale content, or problems of data attenuation. It is thuscritical to establish the validity (the extent to which an instrument measures what it isintended to measure) of any proposed measurement system.
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In this regard, the present review has revealed that validity analysis received farless attention than assessments of reliability; indeed, validity was apparently notexamined at all in several of the studies described in this review. It is thus apparentthat the development of new service-quality scales has suffered from an inadequate
treatment of construct-measurement issues as compared with the procedurerecommended for the development of valid and reliable measures of marketingconstructs (Churchill, 1979; Brown et al., 1993). Future research should provide anassessment of the convergent, discriminant, and nomological validity of proposed newscales. In addition, researchers should indicate under what conditions their scale islikely to be valid or invalid.
It is also of interest that the new industry-specific instruments were not replicated.As a result, their psychometric properties can be questioned.
Finally, although some studies did validate their proposed measurement scales,there remained concerns about generalizability. A generalization from a single study,no matter how large the sample, is always problematic. Future research is certainlyneeded to refine these scales.
4.6 Statistical analysesFrom a methodological perspective, most researchers in the present review used EFAwith varimax (orthogonal) rotation to reduce the items used in their constructs.However, numerous academic researchers have criticized the use of EFA, which is adata-driven method, for this purpose. Indeed, Kwok and Sharp (1998) described the useof EFA as nothing more than a fishing expedition.
EFA has a number of significant shortcomings. First, common factor analysis withvarimax rotation assumes uncorrelated factors or traits; its application to dataexhibiting correlated factors can produce:
. incorrect conclusions regarding the number of factors; and
.
distorted factor loadings (Segars and Grover, 1993).
Second, because the solution obtained is only one of an infinite number of potentialsolutions, the estimates obtained for factor loadings are not unique (Segars and Grover,1993). Finally, given that items are assigned to the factors on which they load mostsignificantly, it is possible for items to load on more than one factor; hence, thedistinctiveness of the factors can be affected and the researcher might lack any soundevidence or theoretical explanation on which to base an interpretation (Ahire et al.,1996; Sureshchandar et al., 2002).
Given these limitations and the potential advantages of using CFA, a combinationof EFA and CFA is desirable. These two approaches to data analysis can providecomplementary perspectives of data.
5. Managerial implicationsThis review should assist service managers to identify the dimensions of servicequality that are appropriate to their particular service industries. Service managers canuse these scales for qualitative and/or quantitative purposes.
In qualitative terms, knowledge of the components of service quality can assistservice managers to identify the strengths and weakness of their own firms and tomake comparisons with other firms in the same service industry. Managers can use
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focus groups of customers to obtain information about their expectations and abouthow well the firm performs on the dimensions identified in appropriateindustry-specific scales.
In addition, because the quality dimensions identified in the literature might not be
exhaustive, managers should also conduct interviews with customers to ascertainwhat they perceive to be the key determinants in their evaluations of service quality. Inconducting such focus groups and interviews, managers should be aware thatexpectations can vary across consumer segments; qualitative data should therefore becollected among different consumer segments. Moreover, the information receivedfrom consumers can be complemented with information obtained in discussion withtheir employees especially service-contact employees who have frequent directinteractions with consumers.
On a quantitative basis, service managers can use industry-specific scales tomeasure:
. customer expectations and perceptions of performance with respect to various
dimensions and attributes and thus identify strengths and weaknesses; and. the importance weighting of each service-quality dimension and attribute.
In undertaking these quantitative assessments, service managers should be aware thatit is inappropriate to measure expectations and perceptions simultaneously after theservice is experienced; rather, customers should respond to the items on expectationsbefore the service is experienced and to the items on perceptions after the service isexperienced. The quantitative analysis should be used to correct weaknesses and tocapitalize on strengths. However, managers should recognize that satisfyingconsumers is not necessarily sufficient to retain them; to ensure loyalty, customersshould be delighted.
Finally, the most obvious implication of the present study for managers is torecognize that each service context is unique. Service providers should be careful inapplying alternative scales to contexts that have few elements in common with theempirical contexts used in their development. In particular, economic and culturalfactors should be taken into consideration when applying these scales to differentcontexts.
6. ConclusionThe measurement of service quality has received significant attention from scholarsand practitioners in recent years. SERVQUAL (Parasuraman et al., 1985, 1988), which
was designed to be a generic instrument applicable across a broad spectrum ofservices, has been extensively used, replicated, and criticised. The most importantcriticism of SERVQUAL has been doubt about its applicability in various specificindustries. As a result, numerous studies in different service sectors have sought todevelop industry-specific service-quality scales. This review, which has documentedand described thirty such industry-specific scales, provides helpful direction toresearchers and practitioners in developing and utilising new industry-specificinstruments.
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Further reading
Cronin, J.J., Brady, M.K. and Hult, G.T.M. (2000), Assessing the effects of quality, value, and
customer satisfaction on consumer behavioral intentions in service environments, Journalof Retailing, Vol. 76 No. 2, pp. 193-218.
Duncan, E. and Elliott, G. (2002), Customer service quality and financial performance amongAustralian retail financial institutions, Journal of Financial Services Marketing, Vol. 7No. 1, pp. 25-41.
Gounaris, S.P., Stathakopoulos, V. and Athanassopoulos, A.D. (2003), Antecedents to perceivedservice quality: an exploratory study in the banking industry, The International Journalof Bank Marketing, Vol. 21 Nos 4/5, pp. 168-90.
Malhotra, N.K., Ulgado, F.M., Agarwal, J.G. and Wu, L. (2005), Dimensions of service quality indeveloped and developing economies: multi-country cross cultural comparisons,
International Marketing Review, Vol. 22 No. 3, pp. 256-78.
Rust, R.T. and Zahorik, A.J. (1993), Customer satisfaction, customer retention and market
share, Journal of Retailing, Vol. 69 No. 2, pp. 193-215.
About the authorRiadh Ladhari is an Assistant Professor of Marketing at the Department of BusinessAdministration, University of Moncton, Canada. His current research is centered on servicequality and customer satisfaction. His work has been published in refereed journals such as
Journal of Business Research and Psychology & Marketing. In addition, he has presented severalpapers at national and international conferences. Riadh Ladhari can be contacted at:[email protected]
To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints
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