Scales to measure andbenchmark service quality

in tourism industryA second-order factor approach

Bindu Narayan, Chandrasekharan Rajendran and L. Prakash SaiDepartment of Management Studies, Indian Institute of Technology Madras,

Chennai, India

Abstract

Purpose – The purpose of this paper is to develop and validate scales to measure and benchmarkservice quality (SQ) in tourism industry.

Design/methodology/approach – The second-order confirmatory factor analysis is employed tovalidate the instrument. SQ dimensions have been modeled which have significant impact on customersatisfaction (CS) separately from those which do not have a significant impact.

Findings – Hospitality, food, logistics, security, and value for money have significant impact onsatisfaction, while amenities, core-tourism experience, hygiene, fairness of price, information centers,culture, distractions, personal information, and pubs do not have a significant impact.

Research limitations/implications – The above pattern may be different in a differentdestination, and in a different context. However, a major implication of the current findings is thata destination need not have natural cutting edges to be developed as a tourist destination. A destinationwith good logistics and assurance for security, value for money, impressive hospitality and food, cansatisfy a customer.

Practical implications – The scale which has been developed by us will be useful for destinationmanagers to measure the SQ perceptions of tourists and benchmark destinations. The distinction ofSQ dimensions with and without the impact on CS could enable a manager to manage these two sets offactors separately.

Originality/value – Unlike previous works, SQ has been modeled in tourism as a second-orderfactor, which appears to be a more appropriate approach. The authors have also modeled factors withand without significant impact on satisfaction separately, and the approach does not seem to haveprecedence in literature. The inclusion of the factor, “Fairness of Price” is also a new contribution toliterature.

Keywords Performance measurement (quality), Customer services quality, Tourism, India

Paper type Research paper

IntroductionBenchmarking is an improvement process in which a company measures itsperformance against that of best-in-class companies, determines how those companiesachieved their performance levels, and uses the information to improve its ownperformance (Bemowski, 1992). Service benchmarking is made more difficult thanbenchmarking in manufacturing because it appears that those things, which are

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The authors are thankful to the referee and the editor for their comprehensive suggestions andcomments to improve the earlier version of the paper. The authors are beholden toDr S. Bhardhwaj for his help in the second-order factor analysis.

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Benchmarking: An InternationalJournal

Vol. 15 No. 4, 2008pp. 469-493

q Emerald Group Publishing Limited1463-5771

DOI 10.1108/14635770810887258

important to a customer, would differ significantly from one service industry toanother (Sower et al., 2001) and also because of the reason that quality is not universalin services (Motwani and Sower, 2006). Benchmarking in tourism industry would callfor a comparison among countries (destinations), and this process is perceived to bedifficult (Kyriakidou and Gore, 2005). In order to benchmark destinations, researchersand practitioners need to know how to measure service quality (SQ) in tourism.The objective of this work is to contribute towards the body of literature pertaining tomeasuring SQ in tourism, so that managers can benchmark destinations moreeffectively.

A “quality tourism product” is the sum of contributions and processes, resultingfrom many stakeholders, both private and public. The notion of quality of the tourismproduct includes assurance of safety and security as a basic factor. Quality alsoincludes a professional approach to do things right at all times and meet legitimateexpectations of consumers (www. unwto.org/quality/index. htm).

Hwang and Lockwood (2006) opined that some of the capabilities necessary forhospitality and tourism SMEs are customer focused goals, planning and control, andachieving consistent standards. In order to deliver and maintain SQ, an organizationmust first identify and comprehend the perceptions and perspectives of customers withreference to SQ (Gronroos, 1984).

It would tremendously benefit destination managers if they had a good instrumentto measure and benchmark SQ of a tour, which has been developed from customers’perspective. The data collected using this instrument could be used in assessingwhether the customers’ expectations have been met or not. Many instruments aimed atmeasuring SQ of a tour have been developed by several researchers. While we take arather different approach, we believe the same would broaden the extant literature.

In this paper, we describe a study undertaken in India, wherein we developed andvalidated an instrument to measure and benchmark SQ in tourism by using a secondorder factor approach. In the first section, we discuss the existing scales to measure SQin tourism and the need for a second – order factor approach. The second section dealswith the research methodology which includes scale development, questionnaire andsampling design, details on data collection and data analysis. In the third and theconcluding section, we discuss the managerial implications of our work and also thedirections for future research.

Literature reviewFor the purpose of literature review, we have reviewed the scales developed to measureboth SQ as well as customer satisfaction (CS) in tourism. This is so because SQ and CSseem to be conceptually distinct, but closely related constructs (Parasuraman et al.,1994; Dabholkar, 1995; Sureshchandar et al., 2002), and we find many researchersemploying SERVQUAL dimensions to study CS (Snoj and Mumel, 2002; Saleh andRyan, 1991; Juwaheer and Ross, 2003).

One of the first scales developed to identify factors of tourist satisfaction by Pizamet al. (1978) was done by using a factor-analytic approach based on data obtained froma survey of tourists vacationing at Cape Cod, Massachusetts. This work empiricallyidentified eight factors of tourist satisfaction: beach opportunities, cost, hospitality,eating and drinking facilities, accommodation facilities, environment, and extent ofcommercialization. Saleh and Ryan (1991) also applied factor analysis, while analyzing

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SQ in the hospitality industry. They used factor analysis to examine whether the33-item scale that they had developed would conform to the factor structure prescribedby the SERVQUAL model (Parasuraman et al., 1988).

The literature reveals that most of the authors have used the exploratory factoranalysis (EFA) in identifying dimensions of SQ or CS in tourism, and a few used theconfirmatory factor analysis (CFA).There are others who have used neither.

Tribe and Snaith (1998) developed an instrument called, HOLSAT, to capture thetourists’ satisfaction which approaches satisfaction-related attributes through the useof expectations/performance analysis. HOLSAT has been tested in Cuba. Tribe andSnaith (1998) did not attempt an EFA to arrive at dimensions of tourist satisfaction, butconfined their analysis to expectation – performance analysis and t-test. Chaudhary(2000), Tsang and Qu (2000), Snoj and Mumel (2002) and Burns et al. (2003) also limitedtheir analyses to gap analyses and t-tests, while studying SQ/CS in tourism.Eraqi (2006) had studied tourism services quality in Egypt from the viewpoints ofexternal and internal customers. Eraqi (2006) studied CS based on the generalevaluation of tourism services; the extent to which tourists are satisfied with hotel’sservices; customer value related to tourism services’ prices; level of services ataccommodations; internal transport quality; the extent top which tourism servicesprices at suitable levels; and tourist’s desire to repeat his/her visit. The possiblelimitation of the above studies is that they fail to empirically arrive at dimensions orfactors of SQ/CS in tourism. The importance of arriving at dimensions or criticalfactors of any scale need not be emphasized. Breaking up a construct into dimensionshelps a manager to understand and manage the service effectively and efficiently.

In a study done in an emerging market, Poon and Low (2005) examined the factorsthat measure different satisfaction levels between the Asian and Western travelersduring their stay in hotels in Malaysia. A questionnaire with a five-point Likert scalewas applied to measure CS, and the data were analyzed by employing the EFA. Theyidentified the factors, namely, hospitality, accommodation, food and beverages,recreation and entertainment, supplementary services, security and safety, innovationand value-added services, transportation, location, appearance, pricing and payment.They also found that there are significant differences between Asian and Westernevaluations of hotel quality. Researchers such as Otto and Ritchie (1996), Heung andCheng (2000), Yuksel and Yuksel (2001), Kozak (2001) and Juwaheer and Ross (2003)identified dimensions of SQ/CS in tourism by applying the EFA.

In a study similar in approach to that of Saleh and Ryan (1991) (in which they hadtested if an EFA would yield a factor structure similar to the SERVQUAL model), Leeand Chen (2006) examined whether service – quality evaluations by guests inTaiwan’s hot-spring hotels would yield the factor structure suggested by Kano et al.(1984). They used the EFA for this purpose.

Millan and Esteban (2004) developed a multiple-item scale for measuring CS intravel agencies services. The objective of their study was to analyze the factors whichdetermine client satisfaction and evaluate their dimensionality. They did a preliminaryfactor analysis to refine the initial scales, and arrived at six dimensions, namely,service encounters, empathy, reliability, service environment, efficiency of advice andadditional attributes. Subsequently, a CFA was carried out to verify this factorstructure. CFA using structural equation modeling (SEM) is a method used by many

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researchers to empirically validate quality management measurement instruments(Singh and Smith, 2006).

Need for a second order factor approachEFA seeks to uncover the underlying structure of a relatively large set of variables.A researcher’s a priori assumption is that any indicator may be associated with anyfactor. There is no prior theory, and we use factor loadings to decide the factor structureof the data (Hair et al., 2003). In comparison, CFA seeks to determine if the number offactors and the loadings of measured (i.e. indicator) variables on them conform to what isexpected on the basis of pre-established theory. A researcher’s a priori assumption isthat each factor (the number and labels of which may be specified a priori) is associatedwith a specified subset of indicator variables (Kim and Mueller, 1978).

The type of CFA done by Millan and Esteban (2004) are called first-order factormodels. In this type of CFA model, the researcher specifies just one level of factors(the first order) that are correlated. But this approach assumes that the factors,although correlated, are separate constructs. What if the researcher had a constructsuch as SQ with several facets or dimensions? It would be necessary to see if thedimensions are correlated, and more than that, what is actually needed is a means ofdemonstrating the structural relationships between the dimensions and the construct.This is accomplished through the specification of a second-order factor model whichposits that the first-order factors estimated are actually sub-dimensions of a broaderand more encompassing second-order factor (Hair et al., 2003) which, in this case, is SQ.This second-order factor is completely latent and unobservable. There are two uniquecharacteristics of the second-order model: first, the second-order factor becomesthe exogenous construct, whereas the first-order factors are endogenous; second, thereare no indicators of the second-order factor.

Olorunniwo et al. (2006) modeled SQ in the context of service factory using thesecond-order confirmatory analysis. They found that tangibles, responsiveness,knowledge and recovery formed the first order factors. In a study done on relating workvalues to societal values in a Turkish business context, Karabati and Say (2005) used thesecond-order factor analysis to analyze the relationship between work value dimensionsand general value dimensions. Rintamaki et al. (2006), in a study done in Finland,decomposed total customer value as perceived by department-store shoppers intoutilitarian, hedonic and social dimensions by using second-order factor analysis. Miereset al. (2006) employed the confirmatory second-order factor analysis to verify whetherthe six specific dimensions of functional, financial, social, physical, psychological andtime risks converged into a second order factor, called the overall perceived risk.

It appears that the existing literature does not offer much evidence of any attemptsto model SQ in tourism as a second-order factor. Our postulation is that modeling SQ asa second order factor is a better approach to establish dimensionality of the constructin the context of tourism industry than the conventional first-order factor models.Further, we strongly feel that the factors which have a significant impact on CS andthose which do not have an impact need to be modeled and managed separately (Kanoet al., 1984). A manager needs to take different perspectives, while dealing with thesetwo sets of factors. This paper is an attempt to separately model SQ dimensions whichhave a significant impact on CS, and the ones which do not have a significant impactby using second-order factor modeling approach.

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Research methodologyThe process that produced the scale in this study involves a sequence of steps consistentwith conventional guidelines for scale development (Churchill, 1979; Anderson andGerbing, 1988) (Figure 1).

Development of a scale to measure and benchmark SQ in tourismThe dimensions of SQ and items to represent these dimensions were generated out of adetailed review of the extant literature and focus group research. Face validity of thescales was established by an expert panel of four independent judges (consistent withthe methodology discussed by Hardesty and Bearden, 2004). The ten dimensions, thusidentified, are described below.

Core-tourism experience. Core-tourism experience refers to the primary essence of atour, which includes natural beauty, good climate, richness of cultural heritage, scope forcultural exchange with local people, closeness to nature, sight seeing, variety of landmarks,privacy and an ambience enabling a relaxed leisure time. Chadee and Mattson (1996), Panton(1999) and Burns et al. (2003), respectively, considered sightseeing, attraction, and recreationexperience as a dimension of SQ/CS. However, we have coined the term “core-tourismexperience” in this present study to refer to all the tourism experience in its totality.

Information. Information in the present study pertains to the information about thedestination available at the airport, place of stay, tourist spots, service of a tour guideor a voice-over, and ease of communicating in a common language (e.g. English).Yuksel and Yuksel (2001), Kozak and Rimmington (2000) and Burns et al. (2003) treated“Information” as a dimension of tourist satisfaction.

Hospitality. Hospitality refers to courteousness and friendliness of:. immigration officials at the port of entry;. tour operator;

Figure 1.Scale development process

Defining dimensions of SQ in tourism (10 factors)

Generating items to represent the dimensions (67 items)

Questionnaire designSampling designData collection

Exploratory Factor Analysis

Reliability Analysis

14 factor structure with 63 items

Scalerefinement

Confirmatory Factor Analysis

323 usable questionnaires

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. hotel staff; and

. the local people.

It also includes trustworthiness and reliability of hotel staff and local people. Womentourists would be deeply concerned about the attitude of residents towards them, andmost tourists would prefer to interact with other tourists so as to exchange viewsand share inputs on places to visit. Yuksel and Yuksel (2001), Kozak (2001) and Poonand Low (2005) are the authors who treated “Hospitality” as a dimension of SQ/CS intourism literature.

Fairness of price. In many of the destination countries, tourists generally experiencedifferential pricing to their detriment not only at tourist spots, but also in shops andlocal conveyances. This unfair system at times becomes a part of therevenue-generating model for many of these destinations, however, by causingvarying degrees of annoyance or dissatisfaction among tourists. Previous worksemanating from the western countries seem to have overlooked the impact ofdifferential pricing scheme present in some Asian countries on tourist satisfactionbecause such a practice is not so prevalent in those countries.

Hygiene. Apart from the cleanliness and hygiene at airports, place of stay, touristspots and restaurants, the cleanliness of streets and hygiene level of food available at adestination would also be a matter of concern for tourists. This factor has receivedattention from other researchers as well (Yuksel and Yuksel, 2001; Kozak, 2001). Whiletraveling in a destination like India, the disturbance and unpleasant atmospherecreated by beggars and hawkers would also be reflective of the general hygiene of theplace.

Amenities. Amenities such as internet, telecommunication services and moneyexchange facilities at critical points are important for a tourist. Tourists may also wantaccess to medical help in case of emergencies. Nightlife is also an aspect quiteimportant to many travelers. Apart from the availability of pubs, the operating hoursof pubs may also be crucial to some. Some tourists may want pubs that are open untillate night. Tribe and Snaith (1998) considered physical resort and facilities as a factoraffecting tourist satisfaction. Yuksel and Yuksel (2001) regarded tourist facilities asone of the components of a satisfying holiday experience. Kozak (2001) used facilitiesand activities as a factor while studying tourist perceptions about Turkey and Spain.

Value for money. As far as a tourist is concerned, a tourist would desire the value formoney with respect to the tour package, accommodation, domestic flight, food atrestaurants, local conveyance, and also while shopping. Many researchers haveconsidered similar factors like, price and value (Yuksel and Yuksel, 2001), level ofprices (Kozak, 2001) and pricing and payment (Poon and Low, 2005) as dimensions ofSQ/CS while studying tourism. We suggest the term “value for money,” as it capturesboth price and quality effectively.

Logistics. A tourist would be concerned about transportation and logistics of adestination (Panton, 1999; Yuksel and Yuksel, 2001; Kozak, 2001). Accessibility oftourist spots as well as the condition of infrastructure is quite important to a tourist.Traffic congestion would obviously be an unwelcome element.

Food. There are tourists who travel to a destination in order to understand andexperience the local cuisine. There is also a set of tourists who want to be served thekind of food they are used to in their home country. The taste of local food and

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the availability of food that the tourist would prefer to eat thus become indicativeof the dimension, namely, food. Panton (1999), Yuksel and Yuksel (2001) and Alampay(2003) considered this dimension as a factor of SQ in their research.

Security. Increasingly of late, security has emerged as an inevitable dimension of SQin tourism related studies (Yuksel and Yuksel, 2001; Weiermair and Fuchs, 1999; Poonand Low, 2005). A tourist would want to feel safe at the hotel where he/she is staying,at the landmarks that he/she is visiting which is more prone to terrorist attacks andwhile traveling.

In short, we are proposing that in tourism, the dimensions of SQ includecore-tourism experience, information, hospitality, fairness of price, hygiene, amenities,value for money, logistics, food, and security.

Some of these dimensions have been taken from literature and some are modified;for example, we find that Yuksel and Yuksel (2001), Kozak and Rimmington (2000) andBurns et al. (2003) used “Information” as a dimension of SQ/CS in tourism, but“Core-tourism Experience” has not been considered by any researcher. At the sametime, we find that sightseeing (Chadee and Mattson, 1996); attraction (Panton, 1999),and recreation experience (Burns et al., 2003) appear synonymous with “Core-TourismExperience.” Hence, in this work, we treat dimensions such as “Core-TourismExperience” as “modified dimensions.” However, “Fairness of Price” or a synonymousfactor does not seem to have been considered by any of the previous author(s). Sincethis dimension has not been studied previously, we propose that it would be interestingto understand how tourists perceive this dimension and how it affects their overallsatisfaction.

Questionnaire designUnder each of the dimensions, we identified appropriate measurement items. There are67 items in all, representing the ten dimensions (for a detailed discussion, Narayan et al.(2007)). The respondents were asked to rate the performance of each of these 67 items.We have adopted the SERVPERF (Cronin and Taylor, 1992) paradigm of measuringSQ as had been done by Arnould and Price (1993), Kozak (2001), Yuksel and Yuksel(2001), Hudson et al. (2004), Burns et al. (2003) and Johns et al. (2004). We use aseven-point semantic differential scale ranging from “Very Low” to “Very High.” Anoption of “Not Applicable” (NA)/“Unable to Answer” is also given. Overall, CS,intention to revisit and intention to recommend are also rated by using the same scale.Demographic details as well as details about the tour are also collected (Appendix fordetails).

Sampling design and data collectionWe chose Kerala as the destination for collecting data as it is one of the muchsought-after destinations in India and the same has been recommended by NationalGeographic as one of the “50 destinations in the world that must be seen in one’slifetime.”

The sampling plan is summarized below:

(1) Target population:. sampling elements – tourists visiting Kerala; and. sampling units – individuals (in the case of couples/families/groups,

everyone in the group could be a sampling unit).

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(2) Sampling frame:. sampling is done at the two major airports and five major tourist spots in

Kerala.

(3) Sampling technique:. convenience sampling.

(4) Sample size requirement:. about 275, consistent with the sample size requirement guidelines for

carrying out; and. SEM (Hair et al., 2003).

(5) Time frame:. January 2006 to May 2006, January-May being the tourist season in Kerala.

A total of 412 respondents were contacted, out of which 369 willingly filled up thequestionnaire. Questionnaires with missing data were removed, which left us with 323usable questionnaires. The sample profile of these 323 respondents is provided as Table I.

Data analysisThe initial refinement of the scale was done through an iterative process of EFA andreliability analysis (Figure 1). A total of 14 factors emerged after the refinementround. All the factors were then regressed on CS to form two groups with andwithout significant bearing or impact on satisfaction. These two groups weremodeled separately with SQ as the second order, and a CFA was run to validate themodels.

EFA was done by using SPSS 12.0. The principal component analysis was used forextraction and Varimax method with Kaiser normalization was used for rotation. Therotation converged in 16 iterations. The Bartlett’s test of sphericity was found to besignificant and the KMO measure of sample adequacy was found to be 0.838. Generallyfor this measure, a value greater than 0.5 is desirable (Malhotra, 2004). Hence, it isconcluded that factor analysis is adequate for analyzing the correlation matrix. Allitems had significant loadings only on one factor, with each factor loading greater than0.4. Four items which did not have significant loadings were dropped. At the end of thescale refinement process, the total number of dimensions became 14. Under the“core-tourism experience” dimension, items pertaining to “Culture” got loaded together;under “Information,” items pertaining to “Personal Information” and those pertainingto “Information Centers” got loaded separately; under “Hygiene,” items with negativeconnotations got loaded together as “Distractions”; and under “Amenities,” itemspertaining to “Pubs” have got loaded separately. Reliability analysis using SPSSshowed that all the 14 dimensions had Chronbach’s a greater than 0.6 except forculture and personal information. Even with these factors, the Chronbach’s a valuesare marginally less than 0.6. While conducting an EFA, factors with Chronbach’s agreater than 0.6 are considered to have good internal consistency (Hair et al., 2003;Malhotra, 2004). The reliability values of all the 14 dimensions are given in Table II.

The 14 factors were regressed on “CS” by using the step-wise procedure in SPSS. Thestep-wise procedure was used to ensure that only factors which have a significant impacton CS enter the model. Five dimensions entered the model: food, logistics, hospitality,security and value for money with an adjusted R 2 of 0.316 and an F-value of 30.819

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Table I.Sample profile

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(p ¼ 0.000). The model is significant with a value of 0.05. This means that hospitality,food, logistics, security, and value for money form the group of SQ dimensions with asignificant impact on CS. Amenities, core-tourism experience, hygiene, fairness of price,information centers, culture, distractions, personal information and pubs form the groupof SQ dimensions with no significant impact on CS. We name the first group as SQ1 andthe second group as SQ2.

Confirmatory factor analysis. We adopted the SEM approach to CFA in order tovalidate our second-order models. SEM is considered the most appropriate techniquefor CFA because of its ability to represent unobserved concepts (i.e. latent variables)and account for measurement error in the estimation process. Moreover, it is possible tomodel and estimate multiple and interrelated cause and effect dependencerelationships (Hair et al., 2003). Anderson and Gerbing (1988) suggested a two-stageapproach to causal modeling using SEM, in which the measurement model is firstconfirmed and then the structural model is built. If the measurement model provides anacceptable fit to the data, the structural model then provides an assessment ofthe extent of relationships between the hypothesized constructs (Byrne, 1994). We havefollowed this two-stage approach with the maximum likelihood estimation usingEQS 5.1.

Estimation of the model. In line with convention, the factor loading of the firstitem in each construct was fixed to 1.00 and the variance of the second order factorwas fixed to 1.00. We did not observe any adverse estimates (such asnon-significant error variances for any construct or standardized coefficientsexceeding or very close to 1.0). Subsequently, the goodness-of-fit for the overallmodel was checked. Goodness-of-fit measures the correspondence of the actual orobserved correlation matrix with that predicted from the proposed model.Goodness-of-fit measures are of three types:

(1) absolute fit measures (they determine the degree to which the measurementmodel and the structural model predicts the observed correlation matrix,without making a distinction as to whether the model fit is better or worse in themodels);

Serial number Dimensions after EFA Chronbach’s a

1 Core-tourism experience 0.8212 Culture 0.6743 Information centers 0.7804 Personal information 0.6815 Hospitality 0.8776 Fairness of price 0.9027 Hygiene 0.8478 Distractions 0.7219 Amenities 0.858

10 Pubs 0.86411 Value for money 0.82212 Logistics 0.86013 Food 0.85514 Security 0.715

Table II.Reliability analysis

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(2) incremental fit measures (they compare the proposed model to some baselinemodel, which in most cases is a single-construct model with all indicatorsperfectly measuring the construct); and

(3) parsimonious fit measures (they diagnose whether model fit has been achievedby “over fitting” the data with too many coefficients).

Mostly, it is observed that some of these measures would indicate an acceptable modelfit while some others will contradict the result. However, if majority of the fit indicesindicate a good fit, then it can be considered that the proposed model enjoys anacceptable overall model fit.

The relevant indices are described below.Absolute fit measures. Standardized root mean-square residual (SRMR) – The root

mean square residual is the square root of the mean of the squared residuals. Valuesabout 0.05 or less have traditionally been considered acceptable.

Root mean square error of approximation (RMSEA) – This value is indicative of thegoodness-of-fit that could be expected if the model were estimated in the population,and not just the sample was used for estimation. Values ranging from 0.05 to 0.08 aredeemed acceptable. If the 90 percent confidence interval limits with respect to RMSEAcontain the value of 0.05, the fit is considered to be good.

Incremental measures. Bentler-Bonnet Non-Normed Fit Index (BBNFI) – Thiscombines a measure of parsimony into a comparative index between the proposed andnull models, resulting in values ranging from 0 to 1.0. A value of 0.90 or greater isrecommended.

Comparative Fit Index (CFI) – It is also a measure of the incremental fit comparingthe estimated and the base model, with the accepted value being 0.90 or greater.

Adjusted Goodness-of-Fit Index (AGFI) – It represents the overall degree of fit(the squared residuals from prediction compared with the actual data), adjusted for thedegrees of freedom. The recommended level is a value of 0.90 or above.

Parsimonious measures. Normed chi-square (x 2/df) – This is the ratio of x 2

statistic divided by the degrees of freedom. A value less than 1.0 typifies an “overfitted” model. A value between 1.0 and 2.0 denotes good fit. A value between 2.0 and 5.0is observed for models that are not yet truly representative and needs improvement.

The fit indices for both measurement model and structural model for the two factorstructures (Figures 2 and 3) are given in Tables III and IV. According to the normdiscussed before, we deem that all the four models have a good fit.

Unidimensionality of the scales. A scale is tested for unidimensionality to make surethat each of the measurement variables measures only a single construct.Unidimensionality is checked by examining the associations between indicatorvariables and constructs (Gerbing and Anderson, 1988). This can be done byexamining the results of the EFA. Each of the indicators would have high loading onlyon one factor and it was found to be so. Refer to Table V for details.

Composite reliability. Composite reliability is a measure of the internal consistencyof the construct indicators, depicting the degree to which they indicate the commonlatent construct. A commonly acceptable cut-off value is 0.70 (Hair et al., 2003). Exceptfor culture, personal information and security, all the other dimensions meet thiscriterion (Table VI). The above three, however, have values reasonably close to 0.70.

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Figure 2.Factor structure of SQ1

Food

Logistics

Security

SQ1

Value formoney

0.676(9.603)

0.682(10.083)

0.639(10.526)

0.866(10.557)

0.564(7.422)

Hospitality

Figure 3.Factor structure of SQ2

SQ2

Amenities

Core-tourismexperience

Culture

Fairness ofprice

Hygiene

Irritants

InformationCenters

Personalinformation

Pubs

0.371(5.044)

0.669(7.406)

0.436(5.694)

0.411(6.067)

0.626(7.246)

0.146(2.114)

0.668(8.196)

0.843(7.527)

0.320(4.947)

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Convergent validity. Convergent validity measures the degree to which the differentapproaches to construct measurement are similar to (or converges on) other approachesthat it is theoretically similar to. This is assessed by reviewing the t-tests for the factorloadings in the EQS output (Anderson and Gerbing, 1988). Each item gave significantt-statistics ( p-value , 0.01), which is a clear indication that all indicator variablesprovide good measures to their respective constructs.

Discriminant validity. Discriminant validity is the degree to which the measure isnot similar to (or diverges from) other measures that it is theoretically dissimilar to.Scales were tested for discriminant validity using a x 2 difference test, for each pair ofconstructs (Anderson and Gerbing, 1988). The differences are all found to bestatistically significant, which indicates a strong evidence for discriminant validity.

Criterion validity. Criterion validity measures the performance of the independentconstructs against some dependent (i.e. criterion) variable (Malhotra, 2004). In thisresearch, CS and customer loyalty (CL) are the criterion variables. CS is measuredusing a single item “Overall, Satisfaction level.” CL is a summated score of “Intention toRevisit” and “Intention to Recommend.” The SQ1 elements are expected to have ahigh correlation with CS and CL and the SQ2 elements are expected to have alow correlation. The result of the correlation analysis is given in Table VII. Thesecorrelations are all as expected (except the correlation between culture and loyalty). Allcorrelations (except those of pubs and distractions) are significant at 0.01 level. The Rof pubs is significant at 0.05 level and that of distractions is not significant.

Nomological validity. Nomological validity measures the performance of theindependent constructs in relation to other constructs in a nomological network(Malhotra, 2004). Previous research has shown that SQ is one of the antecedents of CS

Fit indices Measurement model Structural model

Chi-square/degrees of freedom (x 2/df) 2.06 2.13BBNFI 0.882 0.876CFI 0.935 0.929AGFI 0.855 0.850SRMR 0.052 0.058RMSEA 0.058 0.05990 percent confidence interval (0.050, 0.064) (0.052, 0.066)

Table III.Fit indices for SQ1

Fit indices Measurement model Structural model

x 2/df 1.728 1.85BBNFI 0.830 0.809CFI 0.919 0.901AGFI 0.828 0.817SRMR 0.057 0.078RMSEA 0.048 0.05290 percent confidence interval (0.043, 0.052) (0.047, 0.056)

Table IV.Fit indices for SQ2

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Measurement items Factor loadings

Core-tourism experienceAmbience for having a relaxed leisure time 0.787Quite and peaceful atmosphere 0.778Opportunity to recreate without interference 0.708Closeness to nature 0.578Natural beauty 0.565Sight-seeing and leisure/recreational facilities at tourist spots 0.531CultureScope for cultural exchange with local people 0.710Richness of cultural heritage 0.699Variety of landmarks (e.g. museums, galleries, temples, etc.) 0.590Information centersAvailability of tourist information center at airports 0.771Availability of tourist information center at place of stay 0.759Availability of tourist information center at tourist spots 0.666Personal informationPersonal guidance at tourist spots 0.800Personal guidance in the tourist bus 0.729Ease of communicating with people in a language that both you and the local peopleare comfortable with (e.g. English) 0.554HospitalityCourtesy of staff at place of stay 0.742Trustworthiness of staff at place of stay 0.727Responsiveness of staff at place of stay to solve complaints 0.699Responsiveness of people outside place of stay to help 0.691Attitude of staff at place of stay towards female tourists 0.690Courtesy of people outside place of stay 0.629Trustworthiness of people outside your place of stay 0.563Attitude of people outside place of stay towards female tourists 0.556Co-tourists’ attitude and behavior 0.435Fairness of priceFairness of cost at tourist spots (i.e. Same entrance fee for domestic and foreigntourists) 0.845Fairness of prices of goods in shops (i.e. same prices for residents and tourists) 0.843Fairness of cost at place of stay (i.e. same rates for domestic and foreign tourists) 0.835Fairness of taxi/auto rickshaw/bus fares (i.e. same rates for residents and tourists) 0.788HygieneCleanliness and hygiene of restaurants outside your place of stay 0.807Cleanliness and hygiene at tourist spots/places of visit 0.786Cleanliness and hygienic conditions of streets 0.750Hygiene level of food 0.684Cleanliness and hygiene at airports 0.654Cleanliness and hygiene at the place of stay 0.545DistractionsPossible disturbance by beggars 0.892Possible disturbance by hawkers 0.889Possible disturbance by traffic congestion 0.431AmenitiesTelecom connectivity at the place of stay 0.764Telecom connectivity while traveling 0.760

(continued )Table V.Factor loadings from EFA

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(Zeithaml et al., 2002; Cronin and Taylor, 1994), and that CS leads to CL (Oliver, 1980;Bearden and Teel, 1983; Anderson and Sullivan, 1993). There is also evidence about SQhaving less effect on CL than CS (Cronin and Taylor, 1992; Dabholkar et al., 2000). Wehave considered both SQ1 and SQ2 separately in the place of SQ and carried out a pathanalysis using the SEM (Figure 4). The fit indices are given in Table VIII.

The fit appears to be not a very good one. However, as the value of (x 2/df) is veryclose to 2.0 and RMSEA and the 90 percent confidence interval suggest a good fit, wedeem the proposed model as a moderately fitting one. All the path coefficients, exceptthe path leading from SQ2 to CS, are in line with our expectations. The path coefficientfrom SQ2 to CS is low (0.182) as expected, but contrary to our expectations, it issignificant (3.729 being . 1.96).

DiscussionThe objective of this work is to develop and validate an instrument to measure andbenchmark SQ in tourism by using a second order factor approach. We have chosen tomodel the dimensions that have significant impact on satisfaction separately from

Measurement items Factor loadings

Money exchange facilities outside your place of stay 0.713Money exchange facilities at your place of stay 0.681Money exchange or bank facilities at airports 0.671Internet connectivity at tourist spots/places of visit 0.615Internet connectivity at the place of stay 0.595Internet connectivity at the place of stay 0.460PubsOperating hours of pubs and beer parlors in and around the place of stay 0.867Availability of pubs and beer parlors in and around the place of stay 0.849Value for moneyPrice worthiness of accommodation 0.647Price worthiness of goods in shops 0.646Price worthiness of the tour package 0.644Price worthiness of local conveyance (like buses, taxis, and auto rickshaws) 0.639Price worthiness of food at restaurants outside your place of stay 0.615Price worthiness of domestic flight 0.592LogisticsCondition of infrastructure on the way to tourist spots 0.762Condition of infrastructure at the tourist spots 0.756Accessibility of tourist spots 0.599FoodAvailability of food (that you would prefer to eat) at restaurants outside your place ofstay 0.78Availability of food (that you would prefer to eat) at your place of stay 0.763Taste of local food served at your place of stay 0.723Taste of local food served at restaurants outside your place of stay 0.715SecuritySecurity at the tourist spots/places of visit 0.697Safety of domestic travel (e.g. airlines, trains, buses, taxis, and auto rickshaws) 0.685Security at the place of stay 0.63 Table V.

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those without the impact. The scale that we have developed is found to be both reliableand valid. Further, the approach to model SQ as two separate constructs, contingentupon their impact on satisfaction, is also found to be a valid approach.

As a result of review of the existing literature and focus group interviews, we wereable to identify ten dimensions of SQ in tourism. Manifest variables for each of thedimensions were also identified in a similar manner. The instrument has been tested inKerala, a major tourist destination in India. An EFA of the data has brought out14 factors, in the place of the identified ten dimensions. These 14 factors have beengrouped into two through a step-wise regression analysis: one with significant impacton satisfaction and the other without the impact. We find that hospitality, food,logistics, security and value for money have a significant impact on satisfaction, whileamenities, core-tourism experience, hygiene, fairness of price, information centers,culture, distractions, personal information and pubs do not have a significant impacton CS. Confirmatory factor analyses were done separately on these two groups with SQas the second-order factor and the dimensions as the first-order factors. The scales arefound to be valid.

We have not found a study in the existing literature where SQ in tourism has beenmodeled as a second-order factor. We believe that this is the first time such anapproach has been taken. Second-order factors are typically used in situations wherethe first order factors are correlated. In a study such as the present one, they would becorrelated as they are sub-dimensions of the same construct, namely, SQ. Studies suchas that of Tribe and Snaith (1998), wherein they developed an instrument calledHOLSAT, this is not established. Millan and Esteban (2004), on the contrary,established the correlation of the sub-dimensions through a first-order CFA. Theirpaper, however, did not establish that these sub-dimensions are of a larger construct,namely SQ.

We also believe that our attempt to model SQ dimensions separately, contingentupon their impact on satisfaction, is a new approach. We have included “Fairness ofPrice” as one of the dimensions of SQ in tourism, which has not been explicitlyaddressed by the previous research. We think that this dimension is very relevant inthe Asian context, in which this study has been carried out.

Serial number Construct Composite reliability

1 Core-tourism experience 0.762 Culture 0.623 Information centers 0.704 Personal information 0.575 Hospitality 0.856 Fairness of price 0.847 Hygiene 0.818 Distractions 0.719 Amenities 0.83

10 Pubs 0.7611 Value for money 0.7712 Logistics 0.7313 Food 0.8214 Security 0.67

Table VI.Composite reliabilities

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Cor

rela

tion

sw

ith

CS

SQ1 Hospitality

Food

Logistics

Security

Valueformoney

0.40

90.

455

0.38

10.

356

0.43

9SQ2

Fairnessof

price

Info

centers

Personalinform

ation

Distractions

Amenities

Core

Hygiene

Culture

Pubs

0.19

80.

168

0.24

60.

015

0.28

80.

310

0.28

10.

271

0.13

6C

orre

lati

ons

wit

hC

LSQ1 Hospitality

Food

Logistics

Security

Valueformoney

0.38

20.

493

0.33

30.

309

0.37

2SQ2

Fairnessof

price

Info

centers

Personalinform

ation

Distractions

Amenities

Core

Hygiene

Culture

Pubs

0.26

00.

162

0.27

90.

078

0.20

90.

301

0.16

10.

354

0.13

4

Table VII.Correlation analysis

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However, we concede to certain limitations of the study. One of the limitations is theone that is common to all second-order factor studies. The second-order factor is morelike a “black-box” concept. We chose to address the higher order factor as “SQ,” basedon evidences from literature; it might as well be called “CS” or any other relatedconstruct. There is no known way of conclusively proving this aspect one way or theother. Secondly, we have not compared the structural model (the nomologicalframework) with alternative models (as done in many SEM studies), mainly becausethe objective of this study is primarily the scale development and validation, and not

Figure 4.Nomological framework

SQ1

SQ2

CS

CL0.442(8.851)

0.471(6.843)

0.309(4.879)

0.082(1.602)

0.182(3.729)

Hospitality

Food

Logistics

Security

Value formoney

Amenities

Core-tourismexperience

Culture

Fairnessof price

Hygiene

Irritants

InformationCenters

Personalinformation

Pubs

Fit indices Structural model

x 2/df 2.09BBNFI 0.688CFI 0.807AGFI 0.689SRMR 0.147RMSEA 0.05890 percent confidence interval (0.056, 0.061)

Table VIII.Nomological validity

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theory building. We also feel that the same scales administered in a differentdestination might bring out a different factor structure, especially in terms of thecombinations of SQ1 and SQ2.

Managerial implicationsThe instrument developed in this study can be used by destination managers toobjectively measure the SQ perceptions of tourists. It would also be useful for touroperators in the originating countries who manage travel to these destinations. It willhelp them to evaluate how their customers perceive the SQ dimensions in the hostcountries and also how these dimensions impact CS.

We believe that our attempt to model SQ dimensions with and without a strongimpact on satisfaction would prove particularly appealing to destination mangers as itgives them an insight into the main dimensions of service that need to be carefullylooked at.

An interesting finding of this work is that we have identified five dimensions of SQ,namely, hospitality, food, logistics, security and value for money, which have asignificant impact on CS. It is interesting to note that “Core-Tourism Experience”which embodies the primary component of a tour does not feature in this list. Instead, itis found to be one of the factors not having a significant impact on CS. Given the strongrelationship between CS and loyalty, this piece of finding needs to be given a seriousthought. One implication of this finding is that a destination need not necessarily havenatural cutting edges to be developed as a tourist destination. A destination with goodlogistics, which assures security and value for money and offers impressive hospitalityand food, can also satisfy a customer, resulting in repeat visits and recommendations.

Directions for future researchThis study has been carried out in a state in India. The same instrument can be testedout in other destinations in different countries to examine whether the scales wouldhold good elsewhere as well. It is possible that a different combination of SQ1 and SQ2might emerge. We suggest that in future research, some moderating variables such asthe nationality of the tourist, motivation to travel and type of traveler be alsoconsidered, which may be able to explain any differences that may eventually prop up.

It would also be interesting to find out if SQ1 and SQ2 have other implications, apartfrom what has been established in this study. For example, it would be appealing to seeif they are in fact “Satisfiers” and “Dissatisfiers,” as classified by Herzberg’sHygiene-Motivation theory (Herzberg et al., 1959). This can be accomplished bycarrying out a Critical Incidents Study, similar to the ones conducted by Bitner et al.(1990) and Keaveney (1995).

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Appendix. Instrument for measuring service quality in tourismThe instrument seeks the respondents’ perception of the actual level of performance of thedestination (Kerala) with respect to the 67 items on a seven-point scale where 1 denotes very low,2 – low, 3 – little low, 4 – neutral, 5 – little high, 6 – high, and 7 – very high. The itemscorresponding to the ten factors of service quality in tourism are given below. (In the actualquestionnaire administered to the respondents, details of the factors were not disclosed to avoidany possible bias in the response).

Core-tourism experience. Natural beauty.. Climate.. Variety of landmarks (e.g. museums, galleries, temples, etc.).. Richness of cultural heritage.. Sight-seeing and leisure/recreational facilities at tourist spots.. Scope for cultural exchange with local people.. Closeness to nature.. Scope for excitement (e.g. trekking, forest visits, waterfalls).. Opportunity to recreate without interference.. Quite and peaceful atmosphere.. Ambience for having a relaxed leisure time.

Information. Availability of tourist information center at airports.. Availability of tourist information center at place of stay.. Availability of tourist information center at tourist spots.. Personal guidance in the tourist bus.. Personal guidance at tourist spots.. Ease of communicating with people in a language that both you and the local people are

comfortable with (e.g. English).

Hospitality. Courtesy of immigration officials at the port of entry.. Reception at the airport by the tour operator.. Courtesy of staff at place of stay.. Courtesy of people outside place of stay.. Trustworthiness of staff at place of stay.. Trustworthiness of people outside your place of stay.. Responsiveness of staff at place of stay to solve complaints.. Responsiveness of people outside place of stay to help.. Attitude of staff at place of stay towards female tourists.. Attitude of people outside place of stay towards female tourists.. Co-tourists’ attitude and behavior.

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Fairness of price. Fairness of cost at place of stay (i.e. same rates for domestic and foreign tourists).. Fairness of cost at tourist spots (i.e. same entrance fee for domestic and foreign tourists).. Fairness of prices of goods in shops (i.e. same prices for residents and tourists).. Fairness of taxi/auto rickshaw/bus fares (i.e. same rates for residents and tourists).

Hygiene. Cleanliness and hygiene at airports.. Cleanliness and hygiene at the place of stay.. Cleanliness and hygiene at tourist spots/places of visit.. Cleanliness and hygiene of restaurants outside your place of stay.. Cleanliness and hygienic conditions of streets.. Hygiene level of food.. Possible disturbance by beggars.. Possible disturbance by hawkers.

Amenities. Internet connectivity at the place of stay.. Internet connectivity at tourist spots/places of visit.. Telecom connectivity at the place of stay.. Telecom connectivity while traveling.. Money exchange or bank facilities at airports.. Money exchange facilities at your place of stay.. Money exchange facilities outside your place of stay.. Access to medical help in case of emergencies.. Availability of pubs and beer parlors in and around the place of stay.. Operating hours of pubs and beer parlors in and around the place of stay.

Value for money. Price worthiness of the tour package.. Price worthiness of accommodation.. Price worthiness of domestic flight.. Price worthiness of food at restaurants outside your place of stay.. Price worthiness of local conveyance (like buses, taxis, and auto rickshaws).. Price worthiness of goods in shops.

Logistics. Accessibility of tourist spots.. Condition of infrastructure at the tourist spots.. Condition of infrastructure on the way to tourist spots.. Possible disturbance by traffic congestion.

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Food. Taste of local food served at your place of stay.. Taste of local food served at restaurants outside your place of stay.. Availability of food (that you would prefer to eat) at your place of stay.. Availability of food (that you would prefer to eat) at restaurants outside your place of stay.

Security. Security at the place of stay.. Security at the tourist spots/places of visit.. Safety of domestic travel (e.g. airlines, trains, buses, taxis, and auto rickshaws).

The following three items have been used to measure customer satisfaction and loyalty:

(1) Overall satisfaction level: (on a seven-point scale with 1 – very low, 2 – low, 3 – littlelow, 4 – neutral, 5 – little high, 6 – high, and 7 – very high).

(2) Willingness to revisit Kerala: (on a seven-point scale with 1 – very low, 2 – low, 3 – littlelow, 4 – neutral, 5 – little high, 6 – high, and 7 – very high).

(3) Willingness to recommend Kerala to friends and relatives: (on a seven-point scale with1 – very low, 2 – low, 3 – little low, 4 – neutral, 5 – little high, 6 – high, and 7 – veryhigh).

Corresponding authorChandrasekharan Rajendran can be contacted at: craj@iitm.ac.in

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