Alternative measures of service quality: a review

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Alternative measures of service quality: a review

Riadh Ladhari

Department of Business Administration, University of Moncton, Moncton, Canada

Abstract

Purpose– The purpose of this paper is to identify and discuss the key conceptual and empirical issues that should be considered in the development of alternative industry-specific measurement scales of service quality (other than SERVQUAL).

Design/methodology/approach– A total of 30 studies are selected from two well-known databases: Science direct and ABI inform. These studies are subjected to a comprehensive in-depth content 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 study makes constructive suggestions for the development of future scales.

Originality/value– This is the first work to describe and contrast a large number of service-quality measurement models, other than the well-known SERVQUAL instrument. The findings are of value to academics and practitioners alike.

KeywordsCustomer services quality, Psychometric tests, SERVQUAL Paper typeGeneral review

1. Introduction

A great deal of service-quality research in recent decades has been devoted to the development of measures of service quality. In particular, the SERVQUAL instrument (Parasuraman et al., 1988) has been widely applied and valued by academics and practicing managers (Buttle, 1996). However, several studies have identified potential difficulties with the use of SERVQUAL (Carman, 1990; Cronin and Taylor, 1992;

Asubontenget al., 1996; Buttle, 1996; Van Dykeet al., 1997; Llosaet al., 1998). These difficulties have related to the use of so-called “difference scores”, the ambiguity of the definition of “consumer expectations”, the stability of the SERVQUAL scale over time, and the dimensionality of the instrument. As a result of these criticisms, questions have been raised regarding the use of SERVQUAL as a generic measure of service quality and whether alternative industry-specific measures of service quality should be developed for specific service settings.

Over the past 15 years or so, at least 30 industry-specific scales of service quality have been published in the literature on service quality – including (among others) scales suggested by Saleh and Ryan (1991), Vandamme and Leunis (1993), Jabnoun and Khalifa (2005), Akbaba (2006), and Caro and Garcia (2007). However, no study has attempted to review and integrate this plethora of research on service-quality measurement. The present study addresses this gap in the literature. Its purpose is to explore some of the pertinent conceptual and empirical issues involved in the development of industry-specific measures of service quality.

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Alternative measures of service quality 65

Managing Service Quality Vol. 18 No. 1, 2008 pp. 65-86 qEmerald Group Publishing Limited 0960-4529 DOI 10.1108/09604520810842849

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When the SERVQUAL scale was developed by Parasuramanet al.(1985, 1988), their aim was to provide a generic instrument for measuring service quality across a broad range of service categories. Relying on information from 12 focus groups of consumers, Parasuraman et al. (1985) reported that consumers evaluated service quality by comparing expectations (of service to be received) with perceptions (of service actually received) on ten dimensions: tangibles, reliability, responsiveness, communication, credibility, security, competence, understanding/knowing customers, courtesy, and access. In a later (Parasuramanet al. (1988)) work, the authors reduced the original ten dimensions to five:

(1) tangibles (the appearance of physical facilities, equipment, and personnel);

(2) reliability (the ability to perform the promised service dependably and accurately);

(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 inspire

trust and confidence).

Each dimension is measured by four to five items (making a total of 22 items across the five 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 of agreement with certain statements on a seven-point Likert-type scale (1¼“strongly disagree” to 7¼“strongly agree”). For each item, a so-called “gap score” (G) is then calculated as the difference between the raw “perception-of-performance” score (P) and the raw “expectations score” (E). The greater the “gap score” (calculated as G¼P minusE), 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; Kilbourneet al., 2004); banking (Lam, 2002; Zhouet al., 2002); fast food (Lee and Ulgado, 1997); telecommunications (Van der Wal et al., 2002); retail chain (Parasuraman et al., 1994); information systems (Jiang et al., 2000); and library services (Cook and Thompson, 2001). SERVQUAL has also been applied in various countries; these have included: the United States (Babakus and Boller, 1992; Pittet al., 1995; Jiang et al., 2000; Kilbourne et al., 2004); China (Lam, 2002; Zhou et al., 2002);

Australia (Baldwin and Sohal, 2003); Cyprus (Arasliet al., 2005); Hong Kong (Kettinger

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der Walet al., 2002); The Netherlands (Kettingeret al., 1995); and the UK (Pittet al., 1995; Kilbourneet 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 be summarised 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 Dykeet al., 1999) because there is little evidence that customers actually assess service quality in terms of perception-minus-expectations scores (Peter et al., 1993; Buttle, 1996; Ekinci and 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 been questioned because such scores are unlikely to be distinct from their component scores (Brownet 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 have been questioned. It has been suggested that the factor-loading pattern in a number of studies (Carman, 1990; Parasuramanet al., 1991; Babakus and Boller, 1992; Headley and Miller, 1993; Engellandet al., 2000) indicates a weakness in terms of convergent validity because several of the SERVQUAL items had the highest loadings on different dimensions from those in Parasuramanet 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 Cuiet al., 2003;

Arasli et al., 2005; Najjar and Bishu, 2006); four dimensions (Kilbourne et al., 2004); six dimensions (Carman, 1990; Headley and Miller, 1993); seven dimensions (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;

Badriet 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; McAlexanderet al., 1994).

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3. Industry-specific measure of service quality

In view of the problems outlined above, the applicability of a generic scale for measuring service quality in all settings has been questioned (Babakus and Boller, 1992; Van Dykeet al., 1997; Jabnoun and Khalifa, 2005; Akbaba, 2006; Caro and Garcia, 2007). Moreover, it has been argued that a simple adaptation of the SERVQUAL items is insufficient to measure service quality across a diversity of service industries (Carman, 1990; Babakus and Boller, 1992; Brownet al., 1993; Van Dykeet al., 1997). For example, Carman (1990) contended that certain dimensions required expansion by the inclusion of 13 additional items to the SERVQUAL instrument in order to capture service quality adequately across different services. It has also been contended that service quality is a simple unidimensional construct in some contexts, but a complex multidimensional construct in others (Babakus and Boller, 1992). For these reasons, it has been suggested that industry-specific measures of service quality might be more appropriate than a single generic scale (Babakus and Boller, 1992; Van Dyke et al., 1997; Caro and Garcia, 2007). Dabholkaret al.(1996, p. 14) summarized this view in the following 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 of industry-specific measures of service quality.

As a consequence of these arguments, much of the emphasis in recent research has moved from attempts to adapt SERVQUAL to the development of alternative industry-specific measures. Table I summarizes 30 industry-specific measures of service quality taken from two databases: “Science direct” and “ABI inform”. The features of these measures are discussed below.

3.1 Service industries and countries

It is apparent from Table I that alternative scales have been developed to measure service 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 (Jandaet al., 2002); hotels (Ekinci and Riley, 1998; Akbaba, 2006; Wilkinset al., 2007);

hospitals (Soweret al., 2001); and higher education (Markovic, 2006).

Moreover, the scales have been developed in various countries. These have included Turkey (Akbaba, 2006); Australia (Wilkinset al., 2007); Canada (Saleh and Ryan, 1991);

Croatia (Markovic, 2006); India (Sureshchandaret al., 2002); the USA (Dabholkaret al., 1996); Korea (Kang and James, 2004); Hong Kong (Lam and Zhang, 1999); Belgium

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Study (Country) Sample administration procedure Scale items) Reliability Knustonet al.

(1990)

Lodging industry (USA)

201 adults Telephone interviews

Confirmatory factor analysis

26 items; expectations-only scores

Seven-point Likert scale, ranging from strongly agree (7) to strongly disagree (1)

5 dimensions:

reliability (4 items), assurance (5), responsiveness (3), tangibles (6), empathy (8)

Ranged from 0.63 to 0.80

Saleh and Ryan (1991)

Hospitality industry (Canada)

200 hotel guests, 17 management staff

Self-administered Exploratory factor analysis

32 items for hotel guests and 33 items for management staff;

perception minus expectations Five-point Likert scale, ranging from highly satisfied (1) to highly dissatisfied (5)

4 dimensions for hotel guests: tangibles and reliability (10), responsiveness (8), assurance (8), empathy (6)

5 dimensions for management staff:

tangibles (7), reliability (3), responsiveness (8), assurance (8), empathy (7)

Ranged from 0.74 to 0.93 for hotel guests; ranged from 0.63 to 0.80 for management staff

Bouman and van der Wiele (1992)

Care service industry (The Netherlands)

226 customers of care service firms

40 items;

Perception-minus-expectations scores

Seven-point Likert scale, ranging from very unimportant (1) to very important (7) for expectations and from 1¼definitely not appropriate to 7¼definitely appropriate for perceptions

3 factors: customer kindness (19), tangibles (13), faith (8)

Vandamme and Leunis (1993)

Health care sector (Belgium)

70 patients Self-administered Exploratory factor analysis

17 items;

Perception-minus-expectations scores

Seven-point Likert scale, ranging from strongly disagree (1) to strongly agree (7)

6 dimensions: tangibles (4), medical

responsiveness (3), assurance I (3), assurance II (3), nursing staff (2), personal beliefs and values (2)

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TableReviewofservice-qualityscales

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Study

Service industry

(Country) Sample

Questionnaire administration

Data analysis

procedure Scale

Dimensions (number of

items) Reliability

Stevenset al.

(1995)

Restaurant industry (USA)

200 respondents for fine-dining, 198 for casual-dining, 198 for quick-service restaurants

Telephone interviews

5 dimensions: tangibles (10), reliability (5), responsiveness (3), assurance (6), empathy (5)

Tomes and Ng (1995)

Service quality in NHS or NHS trust hospital services (England)

132 patients admitted in large general hospital in the east of England

49 items,

perception-minus-expectations (factor analysis is based on expectations-only scores)

7 dimensions: empathy (10), relationship of mutual respect (9), dignity (9), understanding of illness (5), religious needs (1), food (6), physical environment (9)

Dabholkar et al.(1996)

Retail service quality (USA)

227 shoppers for the first study and 149 for the cross-validation study

Self-administered Confirmatory factor analysis

28 items; perception-only scores

Five-point Likert scale, ranging from strongly disagree (1) to strongly agree (5)

5 dimensions: physical aspects (6 items), reliability (5), personal interaction (9), problem solving (3), policy (5)

Lam and Zhang (1999)

Travel agents (Hong Kong)

209 users of travel agents

23 items;

perception-minus-expectations scores

Seven-point Likert scales, ranging from strongly agree (7) to strongly disagree (1)

5 dimensions:

responsiveness and assurance (6), reliability (5), empathy (4), resources and corporate image (5), tangibility (3)

Mentzeret al.

(1999)

Logistic service quality (USA)

5531 defense logistics agency users

Self- administered

9 dimensions:

information quality (2), ordering procedures (2), ordering release quantities (3), timeliness (3), order accuracy (3), order quality (3), order condition (3), order discrepancy handling (3), personnel contact quality (3)

Ranged form 0.73 to 0.89

(continued)

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Study (Country) Sample administration procedure Scale items) Reliability Shemwell and

Yavas (1999)

Hospital service quality (USA)

218 respondents residing in different neighborhoods SMSA

Seven-point scale, ranging from poor (1) to outstanding (7)

3 dimensions: search attributes (5 items), credence attributes (4 items), experience attributes (5 items)

Ranged form 0.75 to 0.83

Engellandet al.

(2000)

Career service centers on college campuses (USA)

262 undergraduate college students for the

exploratory study and 237 for the validation

Self-administered Exploratory factor analysis;

17 items;

5 dimensions: tangibles (4), reliability (4), responsiveness (3), assurance (3), empathy (3)

Frochot and Hughes (2000)

Service quality provided in historic houses (England and Scotland)

790 visitors for the final survey

24 items; Perception-only scores

Five-point Likert scale, ranging from strongly agree (5) to strongly disagree (1)

5 dimensions:

responsiveness (8), tangibles (7), communications (4), consumables (3), empathy (2)

Cook and Thompson (2001)

Library service (USA)

4407 participants Web-based administration

Exploratory factor analysis

Nine-point scale, ranging from low (1) to high (9); and unnumbered graphic rating scale

4 dimensions: service (11 items), library as place (9), access to collections (7), reliability (7)

Soweret al.

(2001)

Hospital service quality (USA)

663 recently discharged patients

Exploratory factor analysis

8 dimensions: respect and caring (26), effectiveness and continuity (15), appropriateness (15), information (7), efficiency (5), effectiveness-meals (5), first impression (1), staff diversity (1)

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Table

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Study

Service industry

(Country) Sample

Data analysis

procedure Scale

items) Reliability

Vaughan and Shiu (2001)

Voluntary sector (Scotland)

72 disabled service users and a parent/carer group members

Self-administered Exploratory factor analysis;

Correlation matrix analysis

27 items; perception scores and expectations scores.

10 dimensions: access (3), responsiveness (4), communication (4), humaneness (4), security (2),

enabling/empowerment (2), competence (3), reliability (3), equity (1), tangibles (1)

Aldlaigan and Buttle (2002)

Banking (UK) 975 bank customers

Mail survey Exploratory factor analysis

21 items scale; perception-only scores

4 dimensions: service system quality (11), behavioral service quality (5), machine service quality (2), service transactional accuracy (3)

Ranged from 0.80 to 0.93 (total sample)

Jandaet al.

(2002)

Internet retail service quality (USA)

446 respondents who had made at least one internet purchase within the last six months

Administered by interviewers

5 dimensions:

performance (6 items), access (4 items), security (4 items), sensation (4 items), information (4 items)

Sureshchandar et al.(2002)

Banking (India) 277 bank customers

Seven-point Likert scale, ranging from very poor (1) to very good (7)

5 dimensions: core service or service product (5), human element of service delivery (17), systemization of service delivery (6), tangibles of service (6), social responsibility (7)

Getty and Getty (2003)

Lodging industry (USA)

229

frequent-traveler business owners

Four-point scale, ranging from low (1) to high (4)

5 dimensions:

tangibility (8 items), reliability (4), responsiveness (5), confidence (5), communication (4)

High reliability- no detailed information

(continued)

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Study (Country) Sample administration procedure Scale items) Reliability Khan (2003) Ecotourism 324 ecotourists

who had taken an ecotrip in the past 18 months

6 dimensions:

ecotangibles (3), assurance (5), reliability (5), responsiveness (4), empathy (4), tangibles (8)

Wolfinbarger and Gilly (2003)

Online e-tail quality (USA)

1,013 internet users

Web-based administration using an online panel

Exploratory factor analysis;

14 items;

4 dimensions: web site design (5),

fulfillment/reliability (3), security/privacy (3), customer service (3)

Yoon and Suh (2004)

Consulting service (Korea)

86 respondents from IT consulting sites

Seven-point Likert scale, ranging from Strongly disagree (1) to Strongly agree (7)

6 dimensions:

assurance (4), responsiveness (3), reliability (12), empathy (4), process (9), education (4)

Gounaris (2005)

Business to business service (Greece)

515 senior management

Mail survey Confirmatory factor analysis (CFA).

Seven-point Likert scale, ranging from entirely disagree (1) to entirely agree (7)

4 dimensions: potential quality (6), hard process quality (5), soft process quality (6), output (5)

Jabnoun and Khalifa (2005)

Bank (United Arab Emirates)

115 customers of Islamic banks and 115 customer of conventional banks

4 dimensions: personal skills (12), reliability (5), image (6), value (6)

Karatepeet al.

(2005)

Bank service (Cyprus)

1220 customers Self-administered Exploratory factor analysis;

confirmatory factor analysis

Five-point Likert scale, ranging from strongly agree (5) to strongly disagree (1)

4 dimensions: service environment (4), interaction quality (7), empathy (5), reliability (4)

Parasuraman et al.(2005)

Electronic service quality (internet users – not identified)

549 subjects for the development stage and 858 customers for the validation stage

Web-based administration

Exploratory factor analysis;

4 dimensions:

efficiency (8 items);

system availability (4);

fulfillment (7); privacy (3)

(continued)

Table

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Study

Service industry

(Country) Sample

Data analysis

procedure Scale

items) Reliability

Akbaba (2006) Business hotel industry (Turkey)

234 hotel guests Self-administered Exploratory factor analysis

25 items;

5 dimensions: tangibles (6), adequacy in service supply (7),

understanding and caring (5), assurance (4), and convenience (3)

Markovic (2006)

High education service (Croatia)

444 graduate students

Five-point Likert scale, ranging from strongly believe that the statement is wrong (1) to strongly believe that the statement is not wrong (5)

7 dimensions:

reliability (6), students in scientific work (4), empathy (4), assurance (3), e-learning (3), responsiveness (3), tangibles (3)

Caro and Garcia (2007)

Urgent transport service (Spain)

375 subjects Self-administered Exploratory factor analysis;

4 dimensions: 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)

Wilkinset al.

(2007)

Hospitality service (Australia)

664 hotel guests Self-administered Exploratory factor analysis;

3 dimensions: physical product (3

sub-dimensions, 13 items), service experience (3 sub-dimensions, 13 items), quality food and beverage (4 items)

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and Spain (Caro and Garcia, 2007).

3.2 Dimensional structure

All 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 and Shiu, 2001). It is apparent that the number of dimensions varied according to the service context and the country. For example, the factor structure for the lodging industry in Australia (Wilkinset al., 2007) was somewhat different from that in North America (Knutsonet al., 1990; Saleh and Ryan, 1991; Getty and Getty, 2003). Moreover, the factor structure varied within a given country. For example, the factor structure for the lodging industry in North America varied from five dimensions (Knutsonet 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, and personnel) was retained in most of the scales (for example, Knutsonet al., 1990; Saleh and Ryan, 1991; Bouman and van der Wiele, 1992; Dabholkaret al., 1996; Lam and Zhang, 1999; Engellandet al., 2000; Frochot and Hughes, 2000; Sureshchandaret al., 2002; Getty and Getty, 2003; Khan, 2003; Akbaba, 2006; Markovic, 2006). Similarly, the

“empathy” dimension (the knowledge and courtesy of employees and their ability to inspire trust and confidence) was retained in numerous studies (for example, Knutson et al., 1990; Tomes and Ng, 1995; Lam and Zhang, 1999; Engellandet al., 2000; Khan, 2003; Yoon and Suh, 2004; Karatepeet al., 2005; Markovic, 2006). Similar observations apply to the other SERVQUAL dimensions. However, new dimensions were added to account for industry-specific characteristics. For example, Jandaet al. (2002) added

“security” as a specific dimension of service quality required in the internet retail industry.

3.3 Gap scores versus perception scores

Three measurement methods were found in the scales reviewed in Table I:

. performance-only scores (for example, Dabholkaret al., 1996; Ekinci and Riley, 1998; Frochot and Hughes, 2000; Jandaet al., 2002; Getty and Getty, 2003; Caro and Garcia, 2007; Wilkinset 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 on customer expectations, many studies continue to use a “gap model”. It would seem that such models facilitate the identification of strengths and weaknesses in specific quality attributes.

3.4 Technical dimension versus functional dimension

According to the two-dimensional model of Gro¨nroos (1984), service quality consists of:

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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 items

The 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 and Shiu (2001) used 27 items in the voluntary service sector, Yoon and Suh (2004) used 36 items in the consulting service industry, Bouman and van der Wiele (1992) used 40 items in the care industry, Markovic (2006) used 26 items in the higher education industry, and Akbaba (2006) used 25 items in the business hotel industry.

To determine the number of items, most researchers generated an initial pool of scale statements from a review of literature. This initial pool was then refined through:

. focus groups (for example, Mentzeret al., 1999; Soweret al., 2001; Vaughan and Shiu, 2001; Aldlaigan and Buttle, 2002; Khan, 2003; Wilkinset al., 2007); and/or

. individual interviews with providers or users (for example, Aldlaigan and Buttle, 2002; Jandaet al., 2002; Getty and Getty, 2003; Karatepe et al., 2005; Caro and Garcia, 2007).

It is also worthy of note that, in some cases, SERVQUAL was utilised as a starting-point for the development of the item pool (for example, Dabholkaret al., 1996;

Frochot and Hughes, 2000; Sureshchandaret al., 2002) or as the fundamental structure for new instruments (for example, Engellandet al., 2000, Khan, 2003; Markovic, 2006).

3.6 Sample sizes

Sample sizes in the studies reviewed in Table I varied from 70 (Vandamme and Leunis, 1993) to 5,531 (Mentzeret al., 1999) service users. Only three studies had sample sizes of more then 1,000: 1,013 internet users (Wolfinbarger and Gilly, 2003), 1,220 customers (Karatepeet al., 2005), and 5,531 defence logistics agency users (Mentzeret al., 1999).

Three studies had sample sizes of fewer than 100 respondents/users and 14 studies had sample sizes of fewer than 250 respondents/users. Several studies did not provide details of their samples.

3.7 Analysis method

A total of 16 studies used only exploratory factor analysis (EFA) to assess their dimensional structure and items. Eight studies used confirmatory factor analysis

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Wolfinbarger and Gilly, 2003; Karatepeet al., 2005; Parasuramanet al., 2005; Caro and Garcia, 2007; Wilkinset 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 most commonly used methodology to decide which items to retain and which to discard. In several studies, all items were discarded that scored less than ^0.40 on the item-to-total correlation (for example, Wolfinbarger and Gilly, 2003) or^0.30 on the item-to-total correlation (for example, Jandaet al., 2002; Aldlaigan and Buttle, 2002).

Other studies used loading scores as a basis for item exclusion. For example, some studies excluded items with factor loadings less than^0.40 (for example, Engelland et al., 2000; Soweret al., 2001; Jandaet al., 2002; Jabnoun and Khalifa, 2005; Caro and Garcia, 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 (for example, Lam and Zhang, 1999; Wolfinbarger and Gilly, 2003; Karatepeet al., 2005). In some studies, items with cross-loadings greater than ^0.40 were discarded (for example, Jandaet al., 2002).

3.8 Reliability and validity

Cronbach’s alpha was the most commonly used measure of scale reliability (that is, the internal homogeneity of a set of items composing a scale). Most scales in the present review exhibited good reliability (that is, Cronbach’s alphas greater than 0.60). For example, Frochot and Hughes (2000) used five dimensions with reliability coefficients ranging from 0.70 to 0.83, Akbaba (2006) used five-dimensions with reliability coefficients ranging from 0.71 to 0.86, and Khan (2003) used six dimensions ranging from 0.86 to 0.98.

To assess convergent validity (that is, the extent to which a set of items that is assumed to represent a construct does in fact converge on the same construct), most studies calculated the average variance extracted (AVE) by each dimension (with an AVE of greater than 0.5 being said to support convergent validity). Examples in the present review included Gounaris (2005) and Caro and Garcia (2007). Some researchers considered the fact that all the items loaded highly on the factor to which they were assigned 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 of theoretically unrelated constructs do not correlate with one another), several researchers used CFA and compared the AVE for each factor with the variance shared by the remaining factors (for example, Wolfinbarger and Gilly, 2003; Gounaris, 2005;

Caro and Garcia, 2007). The two dimensions were confirmed as being distinct from each other if the AVE estimates were greater than the shared variance estimates. In other studies, discriminant validity was demonstrated by simply showing that the scale did not correlate strongly with other measures from which it was supposed to differ (for example, Sureshchandaret al., 2002).

To demonstrate predictive validity (that is, the extent to which the scores of one construct were empirically related to the scores of other conceptually related constructs) some researcher correlated their service-quality dimensions with overall quality (for example, Sureshchandar et al., 2002; Wolfinbarger and Gilly, 2003;

Gounaris, 2005; Jabnoun and Khalifa, 2005; Parasuramanet al., 2005). Others correlated their service-quality dimensions with other dimensions; these included: satisfaction

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proof of validity. In addition, the methodological assessments of the new alternative instruments were not clearly presented in several studies.

4. Discussion and suggestions for future research

This review has documented a variety of industry-specific measurement scales proposed in the service-quality literature since the publication of the SERVQUAL model in 1988. It is apparent that there is ongoing debate about several aspects of such scales. 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 quality

All of the 30 studies reviewed here posited service quality as a multidimensional construct. However, the number and nature of the dimensions varied, depending on the service context; indeed, they varied even within the same service industry. It is apparent that the criteria used to evaluate service quality differ among customer groups and circumstances. For example, a businessperson staying in a given hotel has different service criteria from those of a tourist (Eccles and Durrand, 1997).

Scholars should therefore describe the empirical context in which a particular scale was developed and the contexts in which it can be applied. In several cases reviewed in the present study, the authors did not explicitly identify the empirical context in which the scale was developed. Future studies should replicate these measures in different contexts to ascertain whether the number and nature of dimensions are applicable in other settings.

4.2 Hierarchical structure of service quality

Several authors have suggested that service quality is a hierarchical construct consisting of various sub-dimensions (Dabholkaret al., 1996; Brady and Cronin, 2001;

Gounaris, 2005; Caro and Garcia, 2007; Wilkins et al., 2007). However, despite this

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efforts have been made to provide empirical evidence for such a structure. Future research could extend scholarly understanding of service quality by undertaking empirical studies of hierarchical multidimensional conceptions of service quality in different settings.

4.3 Culture and service quality

Several researchers have suggested that there is a need to develop culturally specific measures of service quality (Winsted, 1997; Zhouet al., 2002; Raajpoot, 2004; Karatepe et al., 2005). As with other marketing constructs and measures, it has been contended that constructs of service quality that are developed in one culture might be not applicable in another (Kettingeret al., 1995; Karatepeet al., 2005). According to this view, the meanings, number, and relative importance of service-quality dimensions depend on the cultural and value orientations of customers – particularly with respect to cultural traditions of “power distance” and “individualism/collectivism” (Winsted, 1997; Espinoza, 1999; Mattila, 1999; Furreret al., 2000; Karatepe et al., 2005; Glaveli et al., 2006). Further research in this area is desirable.

4.4 Comparisons between alternative scales and SERVQUAL

Although SERVQUAL has been criticised on theoretical grounds, only one scale in the present review (“INDSERV”) has been empirically shown to outperform SERVQUAL.

It is apparent that rigorous empirical studies are needed to substantiate whether alternative scales really are superior to SERVQUAL. In particular, further studies are required to validate and refine the alternative scales. It should also be noted that the small sample sizes used in several of the studies proposing alternative scales were insufficient to permit a comprehensive psychometric assessment of the proposed scales. 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 as overall service quality, satisfaction, word of mouth, and loyalty.

Despite the widespread criticism of SERVQUAL, it is the contention of the present study that the scale continues to be the most useful model for measuring service quality. In addition, the methodological approach used by Parasuramanet al.(1985, 1988, 1991) in developing and refining SERVQUAL was more rigorous than those used by the authors of the alternative scales.

Finally, it is interesting to note that there are many similarities between the dimensions used in SERVQUAL and those developed in alternative scales. This suggests that some service-quality dimensions are generic whereas others are specific to particular industries and contexts.

4.5 Validity of service quality scales

Although the measures of service quality reviewed in this study claimed to have exhibited good reliability, it is important to note that higher alpha values can be indicative 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 the measurement instrument, poor scale content, or problems of data attenuation. It is thus critical to establish the validity (the extent to which an instrument measures what it is intended to measure) of any proposed measurement system.

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likely 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 certainly needed to refine these scales.

4.6 Statistical analyses

From a methodological perspective, most researchers in the present review used EFA with varimax (orthogonal) rotation to reduce the items used in their constructs.

However, numerous academic researchers have criticized the use of EFA, which is a data-driven method, for this purpose. Indeed, Kwok and Sharp (1998) described the use of EFA as nothing more than a “fishing expedition”.

EFA has a number of significant shortcomings. First, common factor analysis with varimax rotation assumes uncorrelated factors or traits; its application to data exhibiting 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 potential solutions, 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 most significantly, it is possible for items to load on more than one factor; hence, the distinctiveness of the factors can be affected and the researcher might lack any sound evidence or theoretical explanation on which to base an interpretation (Ahire et al., 1996; Sureshchandaret al., 2002).

Given these limitations and the potential advantages of using CFA, a combination of EFA and CFA is desirable. These two approaches to data analysis can provide complementary perspectives of data.

5. Managerial implications

This review should assist service managers to identify the dimensions of service quality that are appropriate to their particular service industries. Service managers can use these scales for qualitative and/or quantitative purposes.

In qualitative terms, knowledge of the components of service quality can assist service managers to identify the strengths and weakness of their own firms and to make comparisons with other firms in the same service industry. Managers can use

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how well the firm performs on the dimensions identified in appropriate industry-specific scales.

In addition, because the quality dimensions identified in the literature might not be exhaustive, managers should also conduct interviews with customers to ascertain what they perceive to be the key determinants in their evaluations of service quality. In conducting such focus groups and interviews, managers should be aware that expectations can vary across consumer segments; qualitative data should therefore be collected among different consumer segments. Moreover, the information received from consumers can be complemented with information obtained in discussion with their employees – especially service-contact employees who have frequent direct interactions with consumers.

On a quantitative basis, service managers can use industry-specific scales to measure:

. 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 that it is inappropriate to measure expectations and perceptions simultaneously after the service is experienced; rather, customers should respond to the items on expectations before the service is experienced and to the items on perceptions after the service is experienced. The quantitative analysis should be used to correct weaknesses and to capitalize on strengths. However, managers should recognize that satisfying consumers is not necessarily sufficient to retain them; to ensure loyalty, customers should be delighted.

Finally, the most obvious implication of the present study for managers is to recognize that each service context is unique. Service providers should be careful in applying alternative scales to contexts that have few elements in common with the empirical contexts used in their development. In particular, economic and cultural factors should be taken into consideration when applying these scales to different contexts.

6. Conclusion

The measurement of service quality has received significant attention from scholars and practitioners in recent years. SERVQUAL (Parasuramanet al., 1985, 1988), which was designed to be a generic instrument applicable across a broad spectrum of services, has been extensively used, replicated, and criticised. The most important criticism of SERVQUAL has been doubt about its applicability in various specific industries. As a result, numerous studies in different service sectors have sought to develop industry-specific service-quality scales. This review, which has documented and described thirty such industry-specific scales, provides helpful direction to researchers and practitioners in developing and utilising new industry-specific instruments.

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