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Understanding service quality
Article in Production Planning and Control · January 2012
DOI: 10.1080/09537287.2011.643929
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Production Planning & Control: The Management of
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Understanding service quality
a
A. Prakash & R.P. Mohanty
a
b
ITM-BIT Collaborative Research Programme, Navi Mumbai, Maharashtra, India
b
Siksha O Anusandhan University, Bhubaneswar, Orissa, India
Version of record first published: 10 Jan 2012.
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Production Planning & Control
2012, 1–16, iFirst
Understanding service quality
A. Prakasha and R.P. Mohantyb*
a
ITM-BIT Collaborative Research Programme, Navi Mumbai, Maharashtra, India;
b
Siksha O Anusandhan University, Bhubaneswar, Orissa, India
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(Received 10 May 2011; final version received 15 November 2011)
The purpose of this paper is to capture varied perspectives of one of the important elements in the management
of services known as service quality. This paper proposes that the current key focus for service academics should
provide direction for planning, design and implementation framework to enhance the practical effectiveness
of service quality. It deals with the fundamental concepts that underlie the subject of managing for service
quality, and defines key terms and makes critical distinctions. It helps to identify the key processes through which
service quality is to be managed. It does demonstrate that managing for service quality is a timeless concept,
and it would further undergo frequent evolution in response to the endless emergence of changes and crises to be
faced by human civilisation.
Keywords: customer satisfaction; patronage intensions; service blueprinting; service quality
1. Introduction
The genesis of service quality started with the growing
importance of services in the developed economics
after 1960 (Godfrey 1999). This was the expansion of
the traditional definition of product quality to include
the services surrounding only the product. For many
manufacturing companies, the 1960s and 1970s were
the wake-up calls for this aspect of quality. In the
1970s, there was an important recognition of service
operations and the first two texts to place some
emphasis on the service sector were Johnson et al.
(1972) and Buffa (1976). Both books were entitled
Operations Management ‘to reflect the growing emphasis on the breadth of application of production
management
concepts
and
techniques . . . (in)
non-manufacturing and service industries as well as
manufacturing’ (Buffa 1976).
The period between 1980 and 1985 was a time of
‘high interest and enthusiasm’ in services (Brown et al.
1994), and the epitome of this era was the
well-regarded paper by Parasuraman et al. (1985).
From around 1985 to 1995, most of the research
studies were predominantly concerned with the empirical testing of ideas and frameworks resulting in
underpinned and tested models (see, for example,
Cronin and Taylor 1992, Mattsson 1992, Teas 1993,
Berkley and Gupta 1994). Conceptual frameworks and
ideas continued to emerge to form the basis for fresh
empirical work because of which this period was
*Corresponding author. Email: rpmohanty@gmail.com
ISSN 0953–7287 print/ISSN 1366–5871 online
ß 2012 Taylor & Francis
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certainly an important milestone in the development
of the subject. Chase (1996) referred to the period
between 1985 to 1995 as the ‘theory testing/empirical
era’ where we ‘have been moving from developing
conceptual frameworks to refining their dimensions
and validating them empirically’.
After 1995, a stage was set when much (but not
necessarily all) of the services material can be taken
and applied, and where the outcome of its application
can be predicted (see, e.g., Spreng and Mackoy 1996,
Oh 1999, Dabholkar et al. 2000), and consequently
led to the creation of ‘management of services’ as a
‘mature’ subject with ‘service quality’ as one of the
most researched area (Prakash et al. 2011a).
Dabholkar et al. (2000) have been undertaking empirical works in service quality to understand the links
between operations drivers. It is this type of work that
seems set to continue for some years to come. Behara
et al. (2002) introduced a new approach to modelling
customer evaluation of service quality through the use
of neural networks. Since 2005, AMOS has been found
to be mostly used in place of LISREL for studying the
links between operations drivers.
Many forces have led to the growth of service
quality, and many industries, companies, and individuals have defined the scope of the concepts, and
framework that define the field. The arena of service
quality has evolved as a result of such combined forces.
First, service quality concepts have developed
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2
A. Prakash and R.P. Mohanty
in response to the tremendous growth of service
industries, resulting in their increased importance to
world economies. Second, service quality is being
considered as a business imperative in manufacturing
and information technology. Third, specific demand
for concepts in services marketing has come from the
deregulated industries, and professional services. In the
past, many very large service industries, namely,
airlines, banking, telecommunications, and transportations have been progressively deregulated worldwide.
Providers of professional services have also demanded
new concepts and approaches for their businesses.
As the field of services marketing evolved along with
the arena of service quality, it expanded to address the
concerns and needs of any business in which service is
an integral part of the offering. Concepts and frameworks developed to address the fact that ‘service
quality is distinct and different for diverse background
of services’.
A service may be defined as a change in the
condition of a person, or of a good belonging to some
economic unit, which brought about as the result of the
activity of some other economic unit, with the prior
agreement of the former person or economic unit (Hill
1977). Put in the simplest terms, services are deeds,
processes and performances (Zeithaml et al. 2008).
Compatible with our simple and broad definition is the
one that defines services to include ‘all economic
activities whose output is not just a physical product is
generally consumed at the time it is produced, and
provides added value in forms (with such features as
building blocks, which are considered indicators for
service quality) that are essentially intangible concerns
of its purchaser’. Because of the basic characteristics of
services as intangibility, heterogeneity, perishability
and simultaneous production and consumption,
marketers of services face some very real and distinctive challenges. That is, service(s) are fluid, dynamic
and frequently co-produced in real time by customers,
employees and technology, often with few static
physical properties. Inspired by Lakhe and Mohanty
(1994), answers to questions such as the ones listed
here still elude managers of services:
. What is service quality?
. What are the linkages of service quality?
. What are issues for evaluating array of service
quality models?
. How can service quality be defined and
improved innovatively using service blueprints
when the product is intangible and nonstandardized?
The preceding questions are some of the many
raised by managers and marketers of services.
2. What is service quality?
Scholars from across the academic spectrum have
contributed to an understanding of service quality,
however, with over two decades of study and much
lively debate, concpetual work on service quality can
be best described as divergent. Parasuraman et al.
(1988) presented SERVQUAL as a multi-item scale
developed to assess service quality that is defined
as ‘the degree and direction of discrepancy between
customers’ service perceptions and expectations’.
SERVQUAL require respondents to answer questions
about both their expectations and their perceptions.
The SERVQUAL scale of Parasuraman et al. (1988)
decomposes the notion of service quality into 22 items
comprising of five constructs, namely, tangibles
(physical facilities, equipment, staff appearance, etc.),
reliability (ability to perform service dependably and
accurately), responsiveness (willingness to help and
respond to customer need), assurance (ability of staff
to inspire confidence and trust) and empathy
(the extent to which caring individualized service is
given). Afterwards, Cronin and Taylor (1992) presented SERVPREF as a multi-item scale that considers
the 22 performance items of SERVQUAL to define the
domain of service quality. There is still much debate
and many of the concepts are still in flux (Schneider
and White 2004). This debate continues today, as is
evident from the ongoing and largely failed attempts
either to integrate the SERVQUAL/SERVPERF
conceptualisation into new industries (e.g., Kettinger
and Lee 1995, Dean 1999, Durvasula et al. 1999) or to
replicate its conceptual structure (e.g., Kettinger and
Lee 1995, Asubonteng et al. 1996, Mels et al. 1997,
Van Dyke et al. 1997, Robinson 1999). That is,
service quality has proved to be a difficult concept
to grasp. It has been referred to as ‘elusive’
(Parasuraman et al. 1985, Smith 1999), and research
relative to the construct is still considered ‘unresolved’
(Caruana et al. 2000) and ‘far from conclusive’
(Athanassopoulos 2000).
Researchers generally have adopted one of two
conceptualisations. The first is the ‘Nordic’ viewpoint
(Grönroos 1982, 1984), which defines the dimensions
of service quality in global terms as consisting of functional and technical quality. The second, ‘American’
viewpoint (Parasuraman et al. 1988), uses terms that
describe service encounter characteristics (i.e., reliability, responsiveness, empathy, assurances and tangibles). Although the latter conceptualisation dominates
the literature, a consensus has not evolved as to which,
if either, is the more appropriate approach. The
Nordic/Scandinavian school defines service quality
using overall categorical terms, whereas the American
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Production Planning & Control
school uses descriptive terms (Cronin et al. 2000).
Both schools of thought highlight important aspects of
service quality, but neither fully captures the construct.
Moreover, no attempt has been made to consider how
the differing conceptualisations may be related.
Because the literature has not yet arrived at a real
agreement on many of the issues concerned, it is
important to review many different viewpoints, both
old and new, and from several different concpetual and
empirical approaches.
Service quality is usually defined as the customer’s
impression of the relative superiority/inferiority of a
service provider and its services (Bitner and Hubbert
1994) and is often considered similar to the customer’s
overall attitude towards the company (Parasuraman
et al. 1988, Zeithaml 1988, Bitner 1990). Researchers
have tried to conceptualise and measure service quality
and explain its relation to the overall performance of
companies and organisations. Early conceptualisations
(e.g., Grönroos 1982, 1984, Parasuraman et al. 1985)
are based on the disconfirmation paradigm employed
in the physical goods literature (e.g., Cardozo 1965,
Howard and Sheth 1969, Olshavsky and Miller 1972,
Oliver 1977, 1980, Churchill and Carol 1982).
Grönroos (1982) suggests that the consumers’ expectations are influenced by marketing activities, external
influences and word of mouth and identifies two types
of service quality: ‘technical’ related to what the customer gets from a service and ‘functional’ associated
with how the service is delivered. The disconfirmation
paradigm also is the basis for Parasuraman et al.’s
(1985) SERVQUAL model, which views service quality
as the gap between the expected level of service and
customer perceptions of the level received.
Service organisations need business models that
more accurately account for the effects of service
system designs and the roles of customer and
service-provider choices in creating and delivering
service encounters. In this respect, the development
of a meaningful classification matrix for services
focussing on service quality fundamentals is an important contribution to the management literature. Some
authors (Collier and Meyer 1998, Schmenner 2004)
have tried to develop classification schemes or positioning matrices for services but not for service quality;
however, none of these schemes or matrices is truly
satisfactory to define the relationship between
the service and the service delivery process.
Hence, we give a fresh look to arrive at bases for
classifying services as ‘state of customer involvement’
and ‘state of complexity’. The crossing of these
two dimensions results in four general service
categories.
3
. Type A – It involves services with low degree
of complexity and low level of customer
involvement, for example, mass public transport, teller machine, which are basically about
customer self-service.
. Type B – It involves services with high degree
of complexity and low level of customer
involvement, that is, such services are developed case by case and depend on a great
expertise from the service provider; the
customer has little knowledge of the process and have a rather passive role. An
illustrative example of this kind of service
is a plastic surgery, IT Outsourcing service,
life insurance, etc.
. Type C – It involves services with low degree
of complexity and high level of involvement
due to highly standardized and efficient processes. Call center and fast food restaurants
could be classified under this category.
. Type D – It involves services with high
degree of complexity and high level of
involvement, that is, these services have very
complex processes and therefore they should
be devised case by case considering the
customer’s learning, and the customers need
to have a good knowledge of the process.
They are normally services which give help
or support to the customer. A good example
of this kind of service is buying through
the internet, consulting, and medical
examination.
Involvement, a term first popularised by Krugman
(1965), concerns a customer’s perceived importance of
a purchase situation (Engel et al. 1993). The greater the
involvement, the more effort will be put into the
purchase decision leading to creation of expectation
(see, for example, Foxall 1990, Johnston 1995). That is,
the greater effort will involve a greater search for
information, and a greater expectation about the
service because of which the gap between performance
and expectation would be marginal and vice versa
(Engel et al. 1993). Therefore, we view that there are
two popular forms of service quality involving state
of low involvement and state of high involvement
(Figure 1). Irrespective of complexity, in the state of
low involvement (Type A and Type B), customers will
evaluate perceived service quality based on comparison
of perceived performance with their expectations
(SERVQUAL basis); however, in the state of high
involvement (Type C and Type D), they will evaluate
perceived service quality based on performance
(SERVPREF basis).
4
A. Prakash and R.P. Mohanty
State of complexity
Low
State of
Low
customer
involvement
High
High
Type A
Type B
Examples:
Examples:
Teller machine
IT outsourcing service
Mass public transport
Life insurance
Type C
Type D
Examples:
Examples:
Call center
Consulting
Fast food restaurant
Medical examination
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Figure 1. Service quality classification matrix.
This classification of service quality is our
attempt to theoretically integrate the SERVQUAL/
SERVPERF conceptualisation into new industries
with postulation that expectations start to build
because of low customer involvement.
3. Linkages of service quality
Service quality can pay rich dividends when done well.
Higher levels of service quality produce higher levels of
customer satisfaction that lead to increased patronage
intensions and increased sales. While a price or product
strategy can also yield these outcomes, service quality if
done well is more difficult to imitate and can have a
more enduring competitive advantage; service quality
done well is an asset that has to be managed. After all,
it is more complicated to do service well than to change
the price or to alter the inventory of goods available.
So, competing on price or product may be dangerous
because the price of entry is relatively cheap (Mohanty
et al. 2007).
Some researchers like Johnson and Gustafsson
(2000) aviod addressing the differences between service quality and customer satisfaction and use both
terms interchangeably in practice and in theory. By
contrast, other researchers such as Berry et al. (1988),
Dabholkar et al. (2000), Oliver (1993), Parasuraman
et al. (1986, 1994), Schneider and White (2004) and
Spreng and Mackoy (1996) argue that, while service
quality and customer satisfaction are related, they
are two distinct constructs. Service quality is a total
or inclusive attitude relating to the excellence of
the service, whereas satisfaction is related to a specific
transaction. This imples that statisfaction is more
of situation oriented (Parasuraman et al. 1986).
Schneider and White (2004) suggests that service
quality is a customer’s judgement about the
service itself, that is, it is descriptive and based
on fact; whereas satisfaction is more of a judgement
of how the service afftects the customer emotionally,
that is, it is more evalautive basically based on
emotion.
Both service quality and customer satisfaction are
usually measured through the gap approach, that is,
the difference between percpetions and expectations
(Rust et al. 1995). The difference between service
quality and customer satisfaction arises mainly because
of different definitions of expectations. In the service
quality literature, expectations are regarded as ‘wants’
of consumers, in other words, what customers feel
a service provider should offer them rather than what a
service provider would offer (Parasuraman et al. 1986).
By contrast, customer satisfaction is belived to result
from a comparision between what did happen in a
service expeience on the one hand, and what
customers’ believed (predicted) would happen on the
other (Churchill and Carol 1982, Parasuraman et al.
1986, Bitner 1990, Schneider and White 2004). Since
a consumer’s expectation in a satisfaction context
represents a prediction, it is to be expressed by a mean
expectation value with a degree of uncertainty
surrounding the mean, because the consumer is
unsure about what to expect. However, consumer’s
expectation in a service quality context represents what
one desires, that expectation can be regarded as
a distinct value with little or no uncertainty surrounding it (Parasuraman et al. 1986).
It was originally believed that the two constructs
were related, in that incidents of customer satisfaction
decay over time into overall consumer attitude or
judgement of percpetions of service quality
(Parasuraman et al. 1986, Bitner 1990). Further
research found that it might be more correct to
regard service quality as antecedent of customer
satisfaction (Oliver 1993, Parasuraman et al. 1994,
Spreng and Mackoy 1996, Dabholkar et al. 2000).
Spreng and Mackoy (1996) modified a model originally developed by Oliver (1993) because they found
empirical evidences that illustrate that service quality
is an antecedent of customer satisfaction. That is,
Spreng and Mackoy (1996) model implies that customer statisfaction is a consequence of service quality.
The effect of service quality on customer satisfaction
was further refined by Dabholkar et al. (2000), who
found that customer satisfaction mediates the effect
of service quality on behavioral intensions.
Dabholkar et al. (2000) also found that customer
satisfaction is a much better predictor of behavioral
intensions, whereas service quality is more closely
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Production Planning & Control
5
Figure 2. Linkages of service quality. (a) Patronage intension free insignificant linkage, (b) Patronage intension free insignificant
linkage, (c) Patronage intension constrained insignificant linkage and (d) Patronage intension constrained significant linkage.
related to specific factor evaluations about the service.
Schneider and White (2004) agree that the service
quality construct is for diagnosing the way the organisation performs, while the customer satisfaction
construct is for diagnosing the way customer feel and
their behavioral inensions. Behavioral intensions in the
marketing management literature relate to purchase
intensions, particularly to customer loyalty and the
intension to repurchase in relation to optimising sales
and net profit of the organisation.
Olorunniwo et al. (2006) found that customer
satisfaction fully mediates the influence of service
quality on patronage intentions in retail banking
but only partially mediates it in the lodging industry.
That is, service quality has not been found to be
leading to customer satisfaction significantly in the
lodging industry. A mediating relationship is one in
which the path relating one variable to another is
impacted by a third variable (e.g., service quality leads
to customer satisfaction that drives patronage
intentions). Levitt (1981) suggested that the universal
conceptualisation of the service quality construct may
be futile, while others argue that service quality is
either industry or context specific (Cronbach 1986,
Babakus and Boller 1992). Despite the fact that
service quality is not synonymous with customer
service, Mohanty and Behara (1996) argued that
customer service and customer relations are part of
service quality. We observe that there is no clear-cut
postulated theory for service quality, customer
satisfaction and patronage intention; however, viewing
customers as the future assets of the organisation
(Mohanty and Yadav 1994) following four types of
linkages (Figure 2) can be visualized when the
service provider is entirely represented by service
quality (Olorunniwo and Hsu 2006, Olorunniwo
et al. 2006, Prakash et al. 2011b).
(A) Patronage intension free insignificant linkage –
The service provider is guarded by customer
satisfaction but patronage intension is not
involved in the service, however, due to the
presence of many competitors offering similar products the linkage between service
quality and customer satisfaction are
insignificant.
(B) Patronage intension-free significant linkage –
The service provider is guarded by customer
satisfaction but patronage intension is not
involved in the service, however, due to the
presence of small number of competitors
offering dissimilar products the linkage
between service quality and customer satisfaction are significant.
(C) Patronage intension constrained insignificant
linkage – The service provider is guarded by
patronage intension and customer satisfaction,
however, due to the presence of many competitors offering similar products the linkage
between customer satisfaction and patronage
intension are insignificant.
(D) Patronage intension constrained significant
linkage – The service provider is guarded by
patronage intension and customer satisfaction,
however, due to the presence of small number
of competitors offering dissimilar products the
6
A. Prakash and R.P. Mohanty
linkage between customer satisfaction and
patronage intension are significant.
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This classification on linkages of service quality
assumes that patronage intensions have to be earned as
retaining customers. They are imperative for a bussiness owing to maintaining sales, margins and
profits; increasing loyalty and value of existing
customers; inducing cross-product buying; differentiating brand; preempting entry of new brand; preempting competitor’s loyalty program; and so on. Retaining
customers for patronage intension means repeat
purchases, create profitable customers, more information on customers, reward loyal customers, acquisition
of customers, etc. (Reinartz and Kumar 2002).
4. Assessment of service quality models
Over the past 25 years or more, many industry-specific
models of service quality have been published in the
literature on service quality. In order to derive the issues
for evaluating the service quality models, the present
study attempts to review following 34 popular service
quality models in the light of changed business scenario
and analyze them for suitability/need for modification
in the current context (Table 1).
We find that the growth of literature in the field of
service quality has developed sequentially, providing a
continuous updation and learning from the finding/
observations of predecessors. The following 22 arguments or issues seem to be suitable for comparative
evaluations of the service quality models.
(A) Hierarichal representation to achieve service
quality measurement.
(B) Identification of factors affecting service
quality.
(C) Suitability for variety of services in
consideration.
(D) Flexibility to account for changing nature of
customers perceptions.
(E) Directions for improvement in service quality.
(F) Suitability to develop a link for measurement
of customer satisfaction.
(G) Diagnosing the needs for training and education of employees.
(H) Flexible enough for modifications as per the
changes in the environment/conditions.
(I) Suggests suitable measures for improvements
of service quality both upstream and downstream the organisation in focus.
(J) Identifies futures needs (infrastructure,
resources) and thus provide help in planning.
(K) Accommodates use of IT in services.
(L) Accommodates use of Neural Networks
(Artificial Intelligence) in services.
(M) Involve multiple expectations from various
stakeholders.
(N) Capability to be used as a tool for
benchmarking.
(O) Reporting of the exploratory factor analysis.
(P) Adequate theoretical foundations for the postulated structural relations.
(Q) Accurate description of the measurement
model.
(R) Reporting the psychometric properties of
scales.
(S) Accurate description of the structural model.
(T) Giving a clear delineation of the model
modification process.
(U) Expressing significant relationships in the best
structural model.
(V) Use of second-order factor model.
The findings of evaluation of service quality models
are presented in Table 2. A very thorough and
interesting literature on the measurement of service
quality has emerged over the past 25 years. Some
essential learning points are as follows.
. Several authors have suggested that service
quality is a hierarchical construct consisting
of various sub-dimensions. Future research
could extend scholarly understanding of
service quality by undertaking empirical
studies of hierarchical multi-dimensional conceptions of service quality in different settings.
. Most of the studies reviewed here posited
service quality as a multi-dimensional
construct; however, the number and nature
of the dimensions varied, depending on the
service context; indeed, they varied even
within the same service industry. Scholars
should therefore describe the context in which
a particular factor was developed and in which
it can be applied. Future studies should
replicate these measures in different contexts
to ascertain whether the number and nature of
dimensions are applicable in other settings.
. Very few studies have suitability for variety
of services in consideration to serve as the
generic model having tested in variety of
service contexts.
. The business environment has changed dramatically over the past 25 years, leading to the
need for greater adaptability and flexibility
that is seen in very few studies.
. Some studies have attempted to develop a link
for measuring customer satisfaction.
Production Planning & Control
7
Table 1. Review of service quality models.
SQ01. Technical and functional quality model (Grönroos 1984)
Service quality depends on technical quality, functional quality and corporate image of the organisation in consideration.
Functional quality is more important than the technical quality.The model does not offer an explanation on how to measure
functional and technical quality.
SQ02. GAP model (Parasuraman et al. 1985)
It is based on the exploratory study. The model is an analytical tool. It enables the management to identify systematically service
quality gaps between a numbers of variables affecting the quality of the offering. This model is focused from the viewpoint of the
consumer.
SQ03. Attribute service quality model (Haywood-Farmer 1988)
This model provides a base of segregating service organisation on three dimensions to enhance understanding of the concepts of
service quality and help to guide about targeting towards the right customer segment; however, it does not offer the measurement
of service quality.
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SQ04. Synthesised model of service quality (Brogowicz et al. 1990)
This model identifies key variables that require systematic management attention in planning, implementation and controlling
service-marketing strategies that prevent or minimize service quality gap in any industry. It needs empirical validation.
SQ05. Performance only model (Cronin and Taylor 1992)
Service quality should be conceptualized and measured as an attitude. The performance-based SERVPERF is efficient in
comparison with SERVQUAL, as it directly reduces the number of items by 50% and the results are better. Service quality is an
antecedent of consumer satisfaction and may have a better effect on purchase intentions than service quality. It needs to establish
quantitative relationship between consumer satisfaction and service quality.
SQ06. Ideal value model of service quality (Mattsson 1992)
This model provides a new learning perspective on how an ideal standard can be formed and how it can be sustained mentally.
The model highlights attention to the importance of negative disconfirmation experience as a determinant for satisfaction
outcome.
SQ07. Evaluated performance and normed quality model (Teas 1993)
The model raised a number of issues pertaining to conceptual and operational definitions of expectation and revised expectation.
The criterion and construct validity of the EP model was higher than both the SERVQUAL and NQ model.
SQ08. IT alignment model (Berkley and Gupta 1994)
This model describes how IT can be used to improve customer service along key service quality dimensions including reliability,
responsiveness, competence, access, communication, security and understanding the customer. The model does not offer a way to
measure and monitor service quality. The model is silent about the level of IT use for particular service settings.
SQ09. Attribute and overall affect model (Dabholkar 1996)
The attribute-based model is favored in forming the evaluations of service quality for technology-based self-service options
without considering the effect of demographic variables, price, physical environment, etc.
SQ10. Model of perceived service quality and satisfaction (Spreng and Mackoy 1996)
This model shows that service quality and satisfaction are distinct and desires congruency does influence satisfaction. A key
determinant of service quality and customer satisfaction is meeting customer desires. Rising expectations have a positive effect on
customer satisfaction perceptions of performance, but they also have a negative effect on satisfaction through disconfirmation.
The model does not highlight how the service quality is achieved and operationalized.
SQ11. PCP attribute model (Philip and Hazlett 1997)
It highlights the area of improvements for service quality depending on the frequency of encounter. The model is lacking in
providing general dimensions to three levels of attributes, and also lacks empirical validation.
SQ12. Retail service quality and perceived value model (Sweeney et al. 1997)
The technical service quality is an important contributor to product quality and value perceptions and hence influences
willingness to buy. Functional service quality has indirect influence on willingness to buy. The model considers only one value
construct, that is, value for money, also inadequate or fewer number of items per construct are taken in this study.
SQ13. Service quality, customer value and customer satisfaction model (Oh 1999)
The model can be used as a framework for understanding consumer decision process as well as evaluating company performance.
The model variables are measured through relatively fewer items.
SQ14. Antecedents and mediator model (Dabholkar et al. 2000)
Consumers evaluate different factors related to the service but also form a separate overall evaluation of the service quality
(which is not a straightforward sum of the components). Customer satisfaction is a better predictor of behavioral intentions.
Antecedents of customer satisfaction have not been explored. The model measures behavioural intention.
(continued )
8
A. Prakash and R.P. Mohanty
Table 1. Continued.
SQ15. Internal service quality model (Frost and Kumar 2000)
The perceptions and expectations of internal customers and internal suppliers play a major role in recognizing the level of
internal service quality perceived. Accordingly, effect of changes in external environment on model is not considered.
SQ16. Internal service quality Data Envelope Analysis model (Soteriou and Stavrinides 2000)
Indicates the resources, which can be better utilized to produce higher service quality levels in a bank. Model ignores other bank
performance measures. It does not provide the measurement of service quality.
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SQ17. Internet banking model (Broderick and Vachirapornpuk 2002)
Implication for the management of quality in internet banking service arises in two areas (a) within the service interface and
(b) with the management of increased customer role. The level and nature of customer participation had the greatest impact on
the quality of service experience and issues such as customers’ ‘zone of tolerance’ and the degree of role understanding by
customers and perceived service quality. Not much empirical works were carried out. The model is based on the experience of one
web site only, needs to be validated with other experiences.
SQ18. IT-based model (Zhu et al. 2002)
IT-based services have a direct impact on the reliability, responsiveness and assurance dimensions and an indirect impact on
customer satisfaction and perceived service quality. IT can help service providers achieve higher level of customer satisfaction.
The customer evaluation of IT-based services is affected by preference towards traditional services, past experience in IT-based
services and perceived IT policies. The model uses fewer items have been chosen to measure the feeling of self-control and
comfort in using IT-based services. It does not provide a measure of service quality of IT-based transactions.
SQ19. Reverse SERVQUAL Model (Behara et al. 2002)
It provides a new approach to modelling customer evaluation of service quality through the use of neural networks to suggest
that perception-only model out-performed the gap model in accuracy. Its suitability for developing a link for measurement of
customer satisfaction has not been tested.
SQ20. Model of e-service quality (Santos 2003)
It provides a better understanding of e-service quality and, therefore, to achieve high customer retention, customer satisfaction,
and profitability. This e-service quality model can be of assistance to all companies that engage e-commerce or plan to do so. It is
based on exploratory study. The model did not provide specific measurement scales and no statistical analysis were carried out.
SQ21. E-S-QUAL model (Parasuraman et al. 2005)
Using the means-end framework as a theoretical foundation, it conceptualizes, constructs, refines, and tests a multiple-item scale
(E-S-QUAL) for measuring the service quality delivered by Web sites on which customers shop online.
SQ22. Service quality model on airline image (Park et al. 2005)
This model investigates how individual dimensions of airline service quality determine airline image and passengers’ future
behavioural intentions.
SQ23. Service quality model on service factory (Olorunniwo and Hsu 2006)
This model investigates how operationalized service quality dimensions of a mass service influences customer satisfaction and
behavioural intentions.
SQ24. Service quality model on service factory (Olorunniwo et al. 2006)
This model investigates how operationalized service quality dimensions of a service factory influences customer satisfaction and
behavioural intentions.
SQ25. Service quality model for sports tourism (David 2006)
It purposes a comprehensive set of dimensions of quality in sport tourism services and test a model where perceived quality
in selected dimensions is said to lead to client satisfaction with the experience which, in turn, is said to influence the intent of the
tourist to return to the event in the future.
SQ26. EduQUAL model of service quality (Mahapatra and Khan 2007)
It provides a systematic integrated approach for modelling customer evaluation of service quality applied to technical education
using neural networks. The study reconfirms that the traditional gap model for defining service quality outperforms other
models. Its suitability for developing a link for measurement of customer satisfaction has not been tested.
SQ27. GIQUAL model of service quality (Tsoukatos and Rand 2007)
The dimensionality of service quality proposed by Parasuraman et al. (1988) was not confirmed in Greek insurance. It has
validated that service quality leads to customer satisfaction, and customer satisfaction leads to loyalty. The model has not been
tested for multiple expectations of various stakeholders.
SQ28. A Hierarchical model of health service quality (Dagger et al. 2007)
Although developed in the context of oncology clinics, this model may be of interest to a range of service providers offering high
involvement, high-contact, ongoing services. The cross-sectional design of the research is a limitation because all measures were
collected simultaneously. The model has not been tested for multiple expectations of various stakeholders.
(continued )
Production Planning & Control
9
Table 1. Continued.
SQ29. Chinese banking service quality (CBSQ) model (Guo et al. 2008)
Using SERVQUAL as a starting point for the research, empirical evidence was gained through sampling corporate customers of
banks in China for the CBSQ model. Its suitability for developing a link for measurement of customer satisfaction has not been
tested. Also, the model has not been tested for multiple expectations of various stakeholders.
SQ30. Socially responsible customer (SRC) SERVQUAL (Somyot 2008)
It uses the social responsibility scale extended with 22 items of the SERVQUAL.
SQ31. Service quality model for real estate brokerage industry (Kuo and Tsai 2009)
This study provides an overall model to explore the effect of soft service attributes, hard service attributes, relationship quality
and behavior intention in a high service encounter context, namely in the real estate brokerage industry.
SQ32. Measurement model of sports service quality (Suk and Petersen 2010)
Participants’ satisfaction and attitudes were found to be very significant elements linking perceived service quality and actual
usage of fantasy sports websites. Thus, sport marketers need to comprehend the service quality dimensions that would influence
participants’ satisfaction levels. This study utilized a convenience sampling method from only four fantasy sports websites.
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SQ33. Gap model of service quality in life insurance industry (Siddiqui and Sharma 2010)
The study strives to develop a reliable instrument to measure customer perceived service quality in life-insurance sector.
SQ34. Service quality model for life insurance business (Prakash et al. 2011a, b)
The purpose of this model is to validate the multiple-item scale for measuring service quality in a way that it predicts customer
satisfaction and patronage intentions in life insurance business.
. Some studies accommodate the use of
computerised software’s.
. Recently an artificial intelligence approach
using neural networks has been tried. Such an
approach can be used to model the complex
relationships between inputs and outputs or to
find patterns in data.
. Multiple stakeholders have different backgrounds and varied behavioural patterns.
The service quality items may be likely to
differ amongst stakeholders; however, the
attempt can be made to develop a uniform
construct (minimum number of items) of
service quality that meets the requirement
of important stakeholders.
. Though most of the service quality studies
have reported factors using exploratory factor
analysis, very few have attempted to apply
confirmatory factors analysis in totality for
empirical validation of the developed multiple-item scale.
. Most of the service quality models have
capability to be used as a tool for benchmarking provided the quantitative measures are
agreed and applied.
5. Building a blueprint for service quality
Along with the awakening to the domination of
services in the world’s economies, there is a growing
emphasis in business practice on creating meaningful,
memorable customer experiences. There are a number
of models trying to capture and define ‘service quality’.
They each have their strengths and weaknesses;
however, the core definition of service quality is
simple and consistent, that is, service quality is
customers thinking that they are getting better service
than expected associated with actual delivery, where
expectation is the level of service the customer hopes to
receive.
Time has now come to visualize for developing new
services with acceptable ‘service quality’ based on
traditional engineering approach to be called as service
engineering, which is concerned with the systematic
development and design of service products. A number
of models are available and they can be classified as
follows.
. A product model that describes, what a service
delivers (description of the service, data
models), that is, it deals with ‘what’ aspects
of service quality.
. A process model that describes, how a service
delivers (definition of process steps, definition
of interfaces), that is, it deals with ‘how’
aspects of service quality, say, using service
blueprinting. Visualisation of the service
process is particularly useful for decision
makers, contact personnel, experts and
customers.
. A resource model that plans the resources
needed for service delivery (staff, materials, IT
infrastructure), which is to be the domain for
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84 85 88 90 92 92 93 94 96 96 97 97 99 00 00 00 02 02 03 03 05 05 06 06 06 07 07 07 08 08 09 10 10 11
Issue
SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ SQ
number 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
Table 2. Evaluation of service quality models.
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A. Prakash and R.P. Mohanty
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Production Planning & Control
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practicing trainers. Though the discussion of
this paper is extended only to service bluepriniting, it is well understood that there will
be an ongoing set of activities as progress to
be continuously monitored for results, and
actions through completed work-plan tracking
sheets having bases for classifying the
respondents.
Most of the academic literatures have focussed on
‘what’ aspects relating to service quality; however,
a little explicit coverage have been found on ‘how’
aspects relating to service quality because of underlying
belief that service(s) have no tangible value (Vargo and
Lusch 2004). Despite the dominance of services in
modern economies, and their rapid growth worldwide,
it is surprising how little research and how few
methods and techniques exist to address this unique
challenge. Notably, the manufacturing industries have
a long tradition of design for specification unlike
services, which commonly lack concrete specifications
for which process documentation and analysis tools
have been in use for many years, for example,
flowcharts, or ‘flow process charts’, date back to at
least 1921, when the legendary Frank Gilbreth gave a
presentation titled ‘Process Charts – First Steps in
Finding the One Best Way’ at the Annual Meeting of
the American Society of Mechanical Engineers
(Graham 2004). However, flowcharting and the various flowcharting tools have been useful in their own
right, but limited in depicting distinguishing elements
of service operations (Sampson and Froehle 2006).
In this section, we review the most popular flowcharting framework as applied to services as ‘service
blueprinting’.
The service quality can be improved innovatively
using ‘service blueprinting’ as it is a picture or map that
accurately portrays the service system so that the
different people involved in providing it can understand and deal with it objectively regardless of their
roles or their individual points of view. Buleprints are
particularly useful at the design stage of service
development allowing firms to simultaneously visualize
the service processes, the points of customer contact,
and the physical evidence of service from the
customer’s point of view. While the essentials of service
blueprinting were introduced two decades ago, the
method has evolved significantly as a useful approach
for addressing many of the challenges in services
design and innovation and is particularly open to the
customer experience design. It has been expanded over
the years to consider issues such as organisational
structure, physical evidence and depiction of customer roles in service delivery (Bitner et al. 2008).
Service blueprint components
Physical evidence
Customer actions
Line of interaction
Onstage contact
Employee actions
Line of visibility
Backstage contact
Employee actions
Line of internal interaction
Support processes
Figure 3. Service blueprint components.
Blueprints also illuminate and connect the underlying
support processes throughout the organisation that
drive and support customer-focused service execution.
According to Shostack (1984, 1987), service blueprinting was initially introduced as a process control
technique for services that offered several advantages:
it was more precise than verbal definitions; it could
help solve problems preemptively; and it was able to
identify failure points in a service operation. One early
adaptation was the clarification of service blueprinting
as a process for plotting the customer process against
organisational structure (Kingman-Brundage 1989).
Service blueprinting was further developed to distinguish between onstage and backstage activities. These
key components still form the basis of the technique.
The key components of service blueprints are
shown in Figure 3. They are customer actions,
onstage/visible contact employee actions, backstage/
invisible contact employee actions, support processes
and physical evidence.
The customer actions area includes the steps,
choices, activities and interaction that customers
perform in purchasing, consuming and evaluating the
service delivery process. Customer actions are depicted
chronologically across the top of the blueprint. What
makes blueprinting different from other flowcharting
approaches is that the actions of the customer are
central to the creation of the blueprint, and as such
they are typically laid out first so that all other activities
can be seen as supporting the value proposition offered
to or co-created with the customer.
The next critical component is the onstage/visible
contact employee actions, separated from the customer
by the line of interaction. Those actions of frontline
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12
A. Prakash and R.P. Mohanty
contact employees that occur as part of a face-to-face
encounter are depicted as onstage contact employee
actions. Every time the line of interaction is crossed via
a link from the customer to a contact employee
(or company self-service technology, etc.), a moment
of truth has occurred.
The next important component of the blueprint
is the backstage/invisible contact employee actions,
separated from the onstage actions by the line of
visibility. Everything that appears above the line of
visibility is seen by the customer, while everything
below it is invisible.
The fourth critical component of the blueprint is
support processes separated from contact employees by
the internal line of interaction. These are all of the
activities carried out by individuals and units within
the company who are not contact employees but that
need to happen in order for the service to be delivered.
Vertical lines from the support area connecting with
other areas of the blueprint show the inter-functional
connections and support that are essential to delivering
the service to the final customer.
Finally, for each customer action, and every
moment of truth, the physical evidence that customers
come in contact with is described at the very top of the
blueprint. These are the tangibles that customers are
exposed that can influence their service quality.
Applying blueprinting in practice for service quality
requires following guidelines.
(1) Decide on the company’s service or service
process to be blueprinted and the objective.
(2) Determine who should be involved in the
blueprinting process.
(3) Modify the blueprinting technique as
appropriate.
(4) Map the service as it happens most of the time.
(5) Note disagreements to capture learning.
(6) Be sure customers remain the focus.
(7) Track insights that emerge for future action.
(8) Develop recommendations and future actions
based on blueprinting goals.
(9) If desired, create final blueprints for use within
the organisation.
Referring to a retrospective commentary by
Lovelock (2001) on new tools for achieving service
quality, it is always vital to get to the root cause
of service failures rather than simply dealing with
symptons. Almost all service quality models describe
only ‘what’ aspects of service quality to be referred as
symptoms; however, ‘how’ aspects to be referred
as root cause have been significantly overlooked.
Emphasis on ‘how’ aspects would lead to continuous
efforts in making improvements of service quality
rather than being considered a one-time fix. As every
business has a mix of interactive processes and
independent processing, recently Sampson (2010) has
defined the ‘service science’ as the science of multientity interactive processes and proposed processchain-network (PCN) diagrams to help researchers
and practitioners in documenting, designing, analyzing
and reconfiguring processes of all types by considering
useful features of service blueprinting alongside accommodating a network representation of service processes. It is to be noted that the quest for service quality
through design of a service process is an ongoing
journey rather than a destination, which would still
undergo staged changes.
6. Conclusions
The purpose of this paper was to capture the varied
perspectives of one of the important elements in the
management of services called as service quality.
This paper is a critical appreciation of the diverse
perspectives. We have proposed a clarification scheme.
Thus, the proposed classifications do not intend to be
conclusive, but to contribute to the ongoing debate
about the classification of service and service quality.
The resultant classification has brought new ways of
developing strategies and improvements in the service
delivery process. In this respect, Section 2 discusses
concept for classification of service and service quality, and Section 3 provides linkages of service quality.
In Section 4, we discussed assessment of service
quality models. First, we notice that there is a great
deal of service quality research in recent decades
devoted to the development of measures of service
quality. Second, we notice that recently artificial
intelligence approach using neural networks have
been tried. Third, there is popularity of the gap
approach in estimation of service quality suggesting
that it is always useful to have data on customer
expectations for meeting them. In Section 5, we have
proposed that the current key focus for service research
should be to provide direction for planning, design
and implementation framework to enhance the practical effectiveness of service quality through service
blueprinting such that new innovations in services can
be managed. The uniqueness of the technique when
compared to other process techniques is its unrelenting
focus on the customer as the center and foundation for
innovation and service improvement. Even after blueprinting, the quest for new service innovations involving service quality must go on. The future work should
attempt to make the concept of service quality explicit
and obvious through confirmation of the classification
Production Planning & Control
of service and service quality such that the
SERVQUAL/SERVPERF conceptualisation can be
empirically integrated. Moreover, the findings of
evaluation of service quality models have presented
some essential learning points as future research
agenda.
Acknowledgements
We are grateful to the editors and the anonymous reviewers
for their comments and suggestions which greatly helped us
for making the contents more value adding.
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Notes on contributors
Anand Prakash is an Assistant
Professor in operational management
discipline at Balaji Institute of
Telecom & Management, Pune, India
and a pursuing his research in service
quality with ITM-BIT Collaborative
Research Programme.
R.P. Mohanty is the Vice Chancellor
of Siksha O Anusandhan University,
Bhubaneswar, India. He has 34 years
of academic experiences in institutes
of national (India) importance and in
some foreign universities. He has ten
years of industry experience in top
management positions. He advises
academic institutions and industries,
supervises research scholars and undertakes sponsored
research projects. He has published more than 250 papers
in scholarly peer reviewed international journals and has also
authored eight books. Many professional institutions both in
India and abroad have honoured him.
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