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Scales to measure and benchmark service quality in tourism industry A second-order factor approach

Scales to measure and benchmark SQ 469

Bindu Narayan, Chandrasekharan Rajendran and L. Prakash Sai Department 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 benchmark service quality (SQ) in tourism industry. Design/methodology/approach – The second-order confirmatory factor analysis is employed to validate the instrument. SQ dimensions have been modeled which have significant impact on customer satisfaction (CS) separately from those which do not have a significant impact. Findings – Hospitality, food, logistics, security, and value for money have significant impact on satisfaction, 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 different destination, and in a different context. However, a major implication of the current findings is that a destination need not have natural cutting edges to be developed as a tourist destination. A destination with good logistics and assurance for security, value for money, impressive hospitality and food, can satisfy a customer. Practical implications – The scale which has been developed by us will be useful for destination managers to measure the SQ perceptions of tourists and benchmark destinations. The distinction of SQ dimensions with and without the impact on CS could enable a manager to manage these two sets of factors separately. Originality/value – Unlike previous works, SQ has been modeled in tourism as a second-order factor, which appears to be a more appropriate approach. The authors have also modeled factors with and without significant impact on satisfaction separately, and the approach does not seem to have precedence in literature. The inclusion of the factor, “Fairness of Price” is also a new contribution to literature. Keywords Performance measurement (quality), Customer services quality, Tourism, India Paper type Research paper

Introduction Benchmarking is an improvement process in which a company measures its performance against that of best-in-class companies, determines how those companies achieved their performance levels, and uses the information to improve its own performance (Bemowski, 1992). Service benchmarking is made more difficult than benchmarking in manufacturing because it appears that those things, which are The authors are thankful to the referee and the editor for their comprehensive suggestions and comments to improve the earlier version of the paper. The authors are beholden to Dr S. Bhardhwaj for his help in the second-order factor analysis.

Benchmarking: An International Journal Vol. 15 No. 4, 2008 pp. 469-493 q Emerald Group Publishing Limited 1463-5771 DOI 10.1108/14635770810887258

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important to a customer, would differ significantly from one service industry to another (Sower et al., 2001) and also because of the reason that quality is not universal in services (Motwani and Sower, 2006). Benchmarking in tourism industry would call for a comparison among countries (destinations), and this process is perceived to be difficult (Kyriakidou and Gore, 2005). In order to benchmark destinations, researchers and 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 to measuring SQ in tourism, so that managers can benchmark destinations more effectively. A “quality tourism product” is the sum of contributions and processes, resulting from many stakeholders, both private and public. The notion of quality of the tourism product includes assurance of safety and security as a basic factor. Quality also includes a professional approach to do things right at all times and meet legitimate expectations of consumers (www. unwto.org/quality/index. htm). Hwang and Lockwood (2006) opined that some of the capabilities necessary for hospitality and tourism SMEs are customer focused goals, planning and control, and achieving consistent standards. In order to deliver and maintain SQ, an organization must first identify and comprehend the perceptions and perspectives of customers with reference to SQ (Gro¨nroos, 1984). It would tremendously benefit destination managers if they had a good instrument to measure and benchmark SQ of a tour, which has been developed from customers’ perspective. The data collected using this instrument could be used in assessing whether the customers’ expectations have been met or not. Many instruments aimed at measuring SQ of a tour have been developed by several researchers. While we take a rather different approach, we believe the same would broaden the extant literature. In this paper, we describe a study undertaken in India, wherein we developed and validated an instrument to measure and benchmark SQ in tourism by using a second order factor approach. In the first section, we discuss the existing scales to measure SQ in tourism and the need for a second – order factor approach. The second section deals with the research methodology which includes scale development, questionnaire and sampling design, details on data collection and data analysis. In the third and the concluding section, we discuss the managerial implications of our work and also the directions for future research. Literature review For the purpose of literature review, we have reviewed the scales developed to measure both SQ as well as customer satisfaction (CS) in tourism. This is so because SQ and CS seem to be conceptually distinct, but closely related constructs (Parasuraman et al., 1994; Dabholkar, 1995; Sureshchandar et al., 2002), and we find many researchers employing SERVQUAL dimensions to study CS (Snoj and Mumel, 2002; Saleh and Ryan, 1991; Juwaheer and Ross, 2003). One of the first scales developed to identify factors of tourist satisfaction by Pizam et al. (1978) was done by using a factor-analytic approach based on data obtained from a survey of tourists vacationing at Cape Cod, Massachusetts. This work empirically identified eight factors of tourist satisfaction: beach opportunities, cost, hospitality, eating and drinking facilities, accommodation facilities, environment, and extent of commercialization. Saleh and Ryan (1991) also applied factor analysis, while analyzing

SQ in the hospitality industry. They used factor analysis to examine whether the 33-item scale that they had developed would conform to the factor structure prescribed by the SERVQUAL model (Parasuraman et al., 1988). The literature reveals that most of the authors have used the exploratory factor analysis (EFA) in identifying dimensions of SQ or CS in tourism, and a few used the confirmatory factor analysis (CFA).There are others who have used neither. Tribe and Snaith (1998) developed an instrument called, HOLSAT, to capture the tourists’ satisfaction which approaches satisfaction-related attributes through the use of expectations/performance analysis. HOLSAT has been tested in Cuba. Tribe and Snaith (1998) did not attempt an EFA to arrive at dimensions of tourist satisfaction, but confined 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 limited their 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 of external and internal customers. Eraqi (2006) studied CS based on the general evaluation of tourism services; the extent to which tourists are satisfied with hotel’s services; customer value related to tourism services’ prices; level of services at accommodations; internal transport quality; the extent top which tourism services prices at suitable levels; and tourist’s desire to repeat his/her visit. The possible limitation of the above studies is that they fail to empirically arrive at dimensions or factors of SQ/CS in tourism. The importance of arriving at dimensions or critical factors of any scale need not be emphasized. Breaking up a construct into dimensions helps 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 factors that measure different satisfaction levels between the Asian and Western travelers during their stay in hotels in Malaysia. A questionnaire with a five-point Likert scale was applied to measure CS, and the data were analyzed by employing the EFA. They identified the factors, namely, hospitality, accommodation, food and beverages, recreation and entertainment, supplementary services, security and safety, innovation and value-added services, transportation, location, appearance, pricing and payment. They also found that there are significant differences between Asian and Western evaluations of hotel quality. Researchers such as Otto and Ritchie (1996), Heung and Cheng (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 had tested if an EFA would yield a factor structure similar to the SERVQUAL model), Lee and Chen (2006) examined whether service – quality evaluations by guests in Taiwan’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 in travel agencies services. The objective of their study was to analyze the factors which determine client satisfaction and evaluate their dimensionality. They did a preliminary factor analysis to refine the initial scales, and arrived at six dimensions, namely, service encounters, empathy, reliability, service environment, efficiency of advice and additional attributes. Subsequently, a CFA was carried out to verify this factor structure. 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 approach EFA 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 any factor. There is no prior theory, and we use factor loadings to decide the factor structure of the data (Hair et al., 2003). In comparison, CFA seeks to determine if the number of factors and the loadings of measured (i.e. indicator) variables on them conform to what is expected on the basis of pre-established theory. A researcher’s a` priori assumption is that each factor (the number and labels of which may be specified a` priori) is associated with a specified subset of indicator variables (Kim and Mueller, 1978). The type of CFA done by Millan and Esteban (2004) are called first-order factor models. 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 construct such as SQ with several facets or dimensions? It would be necessary to see if the dimensions are correlated, and more than that, what is actually needed is a means of demonstrating the structural relationships between the dimensions and the construct. This is accomplished through the specification of a second-order factor model which posits that the first-order factors estimated are actually sub-dimensions of a broader and 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 unique characteristics of the second-order model: first, the second-order factor becomes the exogenous construct, whereas the first-order factors are endogenous; second, there are no indicators of the second-order factor. Olorunniwo et al. (2006) modeled SQ in the context of service factory using the second-order confirmatory analysis. They found that tangibles, responsiveness, knowledge and recovery formed the first order factors. In a study done on relating work values to societal values in a Turkish business context, Karabati and Say (2005) used the second-order factor analysis to analyze the relationship between work value dimensions and general value dimensions. Rintama¨ki et al. (2006), in a study done in Finland, decomposed total customer value as perceived by department-store shoppers into utilitarian, hedonic and social dimensions by using second-order factor analysis. Mieres et al. (2006) employed the confirmatory second-order factor analysis to verify whether the six specific dimensions of functional, financial, social, physical, psychological and time 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 attempts to model SQ in tourism as a second-order factor. Our postulation is that modeling SQ as a second order factor is a better approach to establish dimensionality of the construct in 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 and those which do not have an impact need to be modeled and managed separately (Kano et al., 1984). A manager needs to take different perspectives, while dealing with these two sets of factors. This paper is an attempt to separately model SQ dimensions which have a significant impact on CS, and the ones which do not have a significant impact by using second-order factor modeling approach.

Research methodology The process that produced the scale in this study involves a sequence of steps consistent with conventional guidelines for scale development (Churchill, 1979; Anderson and Gerbing, 1988) (Figure 1).

Scales to measure and benchmark SQ

Development of a scale to measure and benchmark SQ in tourism The dimensions of SQ and items to represent these dimensions were generated out of a detailed review of the extant literature and focus group research. Face validity of the scales was established by an expert panel of four independent judges (consistent with the methodology discussed by Hardesty and Bearden, 2004). The ten dimensions, thus identified, are described below. Core-tourism experience. Core-tourism experience refers to the primary essence of a tour, which includes natural beauty, good climate, richness of cultural heritage, scope for cultural 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 recreation experience as a dimension of SQ/CS. However, we have coined the term “core-tourism experience” 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 the destination available at the airport, place of stay, tourist spots, service of a tour guide or 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;

473

Defining dimensions of SQ in tourism (10 factors)

Generating items to represent the dimensions (67 items)

Questionnaire design Sampling design Data collection 323 usable questionnaires Exploratory Factor Analysis Scale refinement Reliability Analysis 14 factor structure with 63 items Confirmatory Factor Analysis

Figure 1. Scale development process

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. .

hotel staff; and the local people.

It also includes trustworthiness and reliability of hotel staff and local people. Women tourists would be deeply concerned about the attitude of residents towards them, and most tourists would prefer to interact with other tourists so as to exchange views and share inputs on places to visit. Yuksel and Yuksel (2001), Kozak (2001) and Poon and Low (2005) are the authors who treated “Hospitality” as a dimension of SQ/CS in tourism literature. Fairness of price. In many of the destination countries, tourists generally experience differential pricing to their detriment not only at tourist spots, but also in shops and local conveyances. This unfair system at times becomes a part of the revenue-generating model for many of these destinations, however, by causing varying degrees of annoyance or dissatisfaction among tourists. Previous works emanating from the western countries seem to have overlooked the impact of differential pricing scheme present in some Asian countries on tourist satisfaction because such a practice is not so prevalent in those countries. Hygiene. Apart from the cleanliness and hygiene at airports, place of stay, tourist spots and restaurants, the cleanliness of streets and hygiene level of food available at a destination would also be a matter of concern for tourists. This factor has received attention from other researchers as well (Yuksel and Yuksel, 2001; Kozak, 2001). While traveling in a destination like India, the disturbance and unpleasant atmosphere created by beggars and hawkers would also be reflective of the general hygiene of the place. Amenities. Amenities such as internet, telecommunication services and money exchange facilities at critical points are important for a tourist. Tourists may also want access to medical help in case of emergencies. Nightlife is also an aspect quite important to many travelers. Apart from the availability of pubs, the operating hours of pubs may also be crucial to some. Some tourists may want pubs that are open until late night. Tribe and Snaith (1998) considered physical resort and facilities as a factor affecting tourist satisfaction. Yuksel and Yuksel (2001) regarded tourist facilities as one of the components of a satisfying holiday experience. Kozak (2001) used facilities and 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 for money with respect to the tour package, accommodation, domestic flight, food at restaurants, local conveyance, and also while shopping. Many researchers have considered similar factors like, price and value (Yuksel and Yuksel, 2001), level of prices (Kozak, 2001) and pricing and payment (Poon and Low, 2005) as dimensions of SQ/CS while studying tourism. We suggest the term “value for money,” as it captures both price and quality effectively. Logistics. A tourist would be concerned about transportation and logistics of a destination (Panton, 1999; Yuksel and Yuksel, 2001; Kozak, 2001). Accessibility of tourist 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 and experience the local cuisine. There is also a set of tourists who want to be served the kind of food they are used to in their home country. The taste of local food and

the availability of food that the tourist would prefer to eat thus become indicative of 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 SQ in tourism related studies (Yuksel and Yuksel, 2001; Weiermair and Fuchs, 1999; Poon and 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 and while traveling. In short, we are proposing that in tourism, the dimensions of SQ include core-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) and Burns 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 same time, we find that sightseeing (Chadee and Mattson, 1996); attraction (Panton, 1999), and recreation experience (Burns et al., 2003) appear synonymous with “Core-Tourism Experience.” Hence, in this work, we treat dimensions such as “Core-Tourism Experience” as “modified dimensions.” However, “Fairness of Price” or a synonymous factor does not seem to have been considered by any of the previous author(s). Since this dimension has not been studied previously, we propose that it would be interesting to understand how tourists perceive this dimension and how it affects their overall satisfaction. Questionnaire design Under each of the dimensions, we identified appropriate measurement items. There are 67 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 measuring SQ 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 a seven-point semantic differential scale ranging from “Very Low” to “Very High.” An option 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 for details). Sampling design and data collection We chose Kerala as the destination for collecting data as it is one of the much sought-after destinations in India and the same has been recommended by National Geographic as one of the “50 destinations in the world that must be seen in one’s lifetime.” 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 the questionnaire. Questionnaires with missing data were removed, which left us with 323 usable questionnaires. The sample profile of these 323 respondents is provided as Table I. Data analysis The initial refinement of the scale was done through an iterative process of EFA and reliability analysis (Figure 1). A total of 14 factors emerged after the refinement round. All the factors were then regressed on CS to form two groups with and without significant bearing or impact on satisfaction. These two groups were modeled separately with SQ as the second order, and a CFA was run to validate the models. EFA was done by using SPSS 12.0. The principal component analysis was used for extraction and Varimax method with Kaiser normalization was used for rotation. The rotation converged in 16 iterations. The Bartlett’s test of sphericity was found to be significant and the KMO measure of sample adequacy was found to be 0.838. Generally for this measure, a value greater than 0.5 is desirable (Malhotra, 2004). Hence, it is concluded that factor analysis is adequate for analyzing the correlation matrix. All items had significant loadings only on one factor, with each factor loading greater than 0.4. Four items which did not have significant loadings were dropped. At the end of the scale 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 pertaining to “Information Centers” got loaded separately; under “Hygiene,” items with negative connotations got loaded together as “Distractions”; and under “Amenities,” items pertaining to “Pubs” have got loaded separately. Reliability analysis using SPSS showed that all the 14 dimensions had Chronbach’s a greater than 0.6 except for culture and personal information. Even with these factors, the Chronbach’s a values are marginally less than 0.6. While conducting an EFA, factors with Chronbach’s a greater 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. The step-wise procedure was used to ensure that only factors which have a significant impact on 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

Duration of stay

Reason to travel

Gender Age

Type of accommodation Repeat visits

Nationality Type of tour

Culture seeking: 126 Less than a week: 69

For relaxation: Both: 65 81 1 week: 88 2 weeks: 108

30-39: 66 Visiting friends/relatives: 31 3 weeks: 26

Female: 148 40-49: 60

Repeat visitors: 109

First-time visitors: 208 Male: 175 19 and below: 5 20-29: 91

Foreigners: 222 Independent travelers: 189 Budget: 134

Indians: 101 Package tourists: 134 Up-market: 189

4 weeks and more: 32

Others: 15

50-59: 58

60 and above: 38 Missing entries: 5

Missing entries: 6 Missing entries: 5

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Table I. Sample profile

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Table II. Reliability analysis

Serial number

Dimensions after EFA

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Core-tourism experience Culture Information centers Personal information Hospitality Fairness of price Hygiene Distractions Amenities Pubs Value for money Logistics Food Security

Chronbach’s a 0.821 0.674 0.780 0.681 0.877 0.902 0.847 0.721 0.858 0.864 0.822 0.860 0.855 0.715

( 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 a significant impact on CS. Amenities, core-tourism experience, hygiene, fairness of price, information centers, culture, distractions, personal information and pubs form the group of SQ dimensions with no significant impact on CS. We name the first group as SQ1 and the second group as SQ2. Confirmatory factor analysis. We adopted the SEM approach to CFA in order to validate our second-order models. SEM is considered the most appropriate technique for 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 to model and estimate multiple and interrelated cause and effect dependence relationships (Hair et al., 2003). Anderson and Gerbing (1988) suggested a two-stage approach to causal modeling using SEM, in which the measurement model is first confirmed and then the structural model is built. If the measurement model provides an acceptable fit to the data, the structural model then provides an assessment of the extent of relationships between the hypothesized constructs (Byrne, 1994). We have followed this two-stage approach with the maximum likelihood estimation using EQS 5.1. Estimation of the model. In line with convention, the factor loading of the first item in each construct was fixed to 1.00 and the variance of the second order factor was fixed to 1.00. We did not observe any adverse estimates (such as non-significant error variances for any construct or standardized coefficients exceeding or very close to 1.0). Subsequently, the goodness-of-fit for the overall model was checked. Goodness-of-fit measures the correspondence of the actual or observed 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 measurement model and the structural model predicts the observed correlation matrix, without making a distinction as to whether the model fit is better or worse in the models);

(2) incremental fit measures (they compare the proposed model to some baseline model, which in most cases is a single-construct model with all indicators perfectly measuring the construct); and (3) parsimonious fit measures (they diagnose whether model fit has been achieved by “over fitting” the data with too many coefficients).

Scales to measure and benchmark SQ

Mostly, it is observed that some of these measures would indicate an acceptable model fit while some others will contradict the result. However, if majority of the fit indices indicate a good fit, then it can be considered that the proposed model enjoys an acceptable 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. Values about 0.05 or less have traditionally been considered acceptable. Root mean square error of approximation (RMSEA) – This value is indicative of the goodness-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 are deemed acceptable. If the 90 percent confidence interval limits with respect to RMSEA contain the value of 0.05, the fit is considered to be good. Incremental measures. Bentler-Bonnet Non-Normed Fit Index (BBNFI) – This combines a measure of parsimony into a comparative index between the proposed and null models, resulting in values ranging from 0 to 1.0. A value of 0.90 or greater is recommended. Comparative Fit Index (CFI) – It is also a measure of the incremental fit comparing the 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 the degrees 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 “over fitted” model. A value between 1.0 and 2.0 denotes good fit. A value between 2.0 and 5.0 is 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 factor structures (Figures 2 and 3) are given in Tables III and IV. According to the norm discussed before, we deem that all the four models have a good fit. Unidimensionality of the scales. A scale is tested for unidimensionality to make sure that each of the measurement variables measures only a single construct. Unidimensionality is checked by examining the associations between indicator variables and constructs (Gerbing and Anderson, 1988). This can be done by examining the results of the EFA. Each of the indicators would have high loading only on 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 consistency of the construct indicators, depicting the degree to which they indicate the common latent construct. A commonly acceptable cut-off value is 0.70 (Hair et al., 2003). Except for culture, personal information and security, all the other dimensions meet this criterion (Table VI). The above three, however, have values reasonably close to 0.70.

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Hospitality 0.676 (9.603)

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Food 0.682 (10.083) Logistics

0.639 (10.526)

SQ1

0.866 (10.557) Value for money

Figure 2. Factor structure of SQ1

0.564 (7.422)

Security

Amenities

Core-tourism experience

0.371 (5.044) 0.669 (7.406)

Hygiene 0.436 (5.694) Fairness of price

Culture Irritants

0.411 (6.067) 0.626 (7.246) 0.146 (2.114) 0.668 (8.196)

Information Centers

Personal information

Figure 3. Factor structure of SQ2

Pubs

0.843 (7.527) 0.320 (4.947)

SQ2

Convergent validity. Convergent validity measures the degree to which the different approaches to construct measurement are similar to (or converges on) other approaches that it is theoretically similar to. This is assessed by reviewing the t-tests for the factor loadings in the EQS output (Anderson and Gerbing, 1988). Each item gave significant t-statistics ( p-value , 0.01), which is a clear indication that all indicator variables provide good measures to their respective constructs. Discriminant validity. Discriminant validity is the degree to which the measure is not 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 of constructs (Anderson and Gerbing, 1988). The differences are all found to be statistically significant, which indicates a strong evidence for discriminant validity. Criterion validity. Criterion validity measures the performance of the independent constructs against some dependent (i.e. criterion) variable (Malhotra, 2004). In this research, CS and customer loyalty (CL) are the criterion variables. CS is measured using a single item “Overall, Satisfaction level.” CL is a summated score of “Intention to Revisit” and “Intention to Recommend.” The SQ1 elements are expected to have a high correlation with CS and CL and the SQ2 elements are expected to have a low correlation. The result of the correlation analysis is given in Table VII. These correlations are all as expected (except the correlation between culture and loyalty). All correlations (except those of pubs and distractions) are significant at 0.01 level. The R of pubs is significant at 0.05 level and that of distractions is not significant. Nomological validity. Nomological validity measures the performance of the independent 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 2

Chi-square/degrees of freedom (x /df) BBNFI CFI AGFI SRMR RMSEA 90 percent confidence interval

Fit indices

x 2/df BBNFI CFI AGFI SRMR RMSEA 90 percent confidence interval

Measurement model

Structural model

2.06 0.882 0.935 0.855 0.052 0.058 (0.050, 0.064)

2.13 0.876 0.929 0.850 0.058 0.059 (0.052, 0.066)

Measurement model

Structural model

1.728 0.830 0.919 0.828 0.057 0.048 (0.043, 0.052)

1.85 0.809 0.901 0.817 0.078 0.052 (0.047, 0.056)

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Table III. Fit indices for SQ1

Table IV. Fit indices for SQ2

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Table V. Factor loadings from EFA

Measurement items Core-tourism experience Ambience for having a relaxed leisure time Quite and peaceful atmosphere Opportunity to recreate without interference Closeness to nature Natural beauty Sight-seeing and leisure/recreational facilities at tourist spots Culture Scope for cultural exchange with local people Richness of cultural heritage Variety of landmarks (e.g. museums, galleries, temples, etc.) Information centers 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 information Personal guidance at tourist spots Personal guidance in the tourist bus Ease of communicating with people in a language that both you and the local people are comfortable with (e.g. English) Hospitality Courtesy of staff at place of stay Trustworthiness of staff at 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 Courtesy of people outside place of stay Trustworthiness of people outside your place of stay Attitude of people outside place of stay towards female tourists Co-tourists’ attitude and behavior Fairness of price 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 cost at place of stay (i.e. same rates for domestic and foreign tourists) Fairness of taxi/auto rickshaw/bus fares (i.e. same rates for residents and tourists) Hygiene Cleanliness and hygiene of restaurants outside your place of stay Cleanliness and hygiene at tourist spots/places of visit Cleanliness and hygienic conditions of streets Hygiene level of food Cleanliness and hygiene at airports Cleanliness and hygiene at the place of stay Distractions Possible disturbance by beggars Possible disturbance by hawkers Possible disturbance by traffic congestion Amenities Telecom connectivity at the place of stay Telecom connectivity while traveling

Factor loadings 0.787 0.778 0.708 0.578 0.565 0.531 0.710 0.699 0.590 0.771 0.759 0.666 0.800 0.729 0.554 0.742 0.727 0.699 0.691 0.690 0.629 0.563 0.556 0.435 0.845 0.843 0.835 0.788 0.807 0.786 0.750 0.684 0.654 0.545 0.892 0.889 0.431 0.764 0.760 (continued)

Measurement items Money exchange facilities outside your place of stay Money exchange facilities at your place of stay Money exchange or bank facilities at airports Internet connectivity at tourist spots/places of visit Internet connectivity at the place of stay Internet connectivity at the place of stay Pubs Operating hours of pubs and beer parlors in and around the place of stay Availability of pubs and beer parlors in and around the place of stay Value for money Price worthiness of accommodation Price worthiness of goods in shops Price worthiness of the tour package Price worthiness of local conveyance (like buses, taxis, and auto rickshaws) Price worthiness of food at restaurants outside your place of stay Price worthiness of domestic flight Logistics Condition of infrastructure on the way to tourist spots Condition of infrastructure at the tourist spots Accessibility of tourist spots Food Availability of food (that you would prefer to eat) at restaurants outside your place of stay Availability of food (that you would prefer to eat) at your place of stay Taste of local food served at your place of stay Taste of local food served at restaurants outside your place of stay Security Security at the tourist spots/places of visit Safety of domestic travel (e.g. airlines, trains, buses, taxis, and auto rickshaws) Security at the place of stay

Factor loadings 0.713 0.681 0.671 0.615 0.595 0.460

Scales to measure and benchmark SQ 483

0.867 0.849 0.647 0.646 0.644 0.639 0.615 0.592 0.762 0.756 0.599 0.78 0.763 0.723 0.715 0.697 0.685 0.63

(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 SQ having less effect on CL than CS (Cronin and Taylor, 1992; Dabholkar et al., 2000). We have considered both SQ1 and SQ2 separately in the place of SQ and carried out a path analysis 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 very close to 2.0 and RMSEA and the 90 percent confidence interval suggest a good fit, we deem the proposed model as a moderately fitting one. All the path coefficients, except the path leading from SQ2 to CS, are in line with our expectations. The path coefficient from SQ2 to CS is low (0.182) as expected, but contrary to our expectations, it is significant (3.729 being . 1.96). Discussion The objective of this work is to develop and validate an instrument to measure and benchmark SQ in tourism by using a second order factor approach. We have chosen to model the dimensions that have significant impact on satisfaction separately from

Table V.

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Table VI. Composite reliabilities

Serial number

Construct

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Core-tourism experience Culture Information centers Personal information Hospitality Fairness of price Hygiene Distractions Amenities Pubs Value for money Logistics Food Security

Composite reliability 0.76 0.62 0.70 0.57 0.85 0.84 0.81 0.71 0.83 0.76 0.77 0.73 0.82 0.67

those without the impact. The scale that we have developed is found to be both reliable and valid. Further, the approach to model SQ as two separate constructs, contingent upon 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 were able to identify ten dimensions of SQ in tourism. Manifest variables for each of the dimensions were also identified in a similar manner. The instrument has been tested in Kerala, a major tourist destination in India. An EFA of the data has brought out 14 factors, in the place of the identified ten dimensions. These 14 factors have been grouped into two through a step-wise regression analysis: one with significant impact on satisfaction and the other without the impact. We find that hospitality, food, logistics, security and value for money have a significant impact on satisfaction, while amenities, core-tourism experience, hygiene, fairness of price, information centers, culture, distractions, personal information and pubs do not have a significant impact on CS. Confirmatory factor analyses were done separately on these two groups with SQ as the second-order factor and the dimensions as the first-order factors. The scales are found to be valid. We have not found a study in the existing literature where SQ in tourism has been modeled as a second-order factor. We believe that this is the first time such an approach has been taken. Second-order factors are typically used in situations where the first order factors are correlated. In a study such as the present one, they would be correlated as they are sub-dimensions of the same construct, namely, SQ. Studies such as that of Tribe and Snaith (1998), wherein they developed an instrument called HOLSAT, this is not established. Millan and Esteban (2004), on the contrary, established the correlation of the sub-dimensions through a first-order CFA. Their paper, 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, contingent upon their impact on satisfaction, is a new approach. We have included “Fairness of Price” as one of the dimensions of SQ in tourism, which has not been explicitly addressed by the previous research. We think that this dimension is very relevant in the Asian context, in which this study has been carried out.

SQ1 Hospitality 0.382 SQ2 Fairness of price 0.260

SQ1 Hospitality 0.409 SQ2 Fairness of price 0.198 Logistics 0.333 Personal information 0.279

Info centers 0.162

Personal information 0.246

Info centers 0.168 Food 0.493

Logistics 0.381

Food 0.455

Distractions 0.078

Security 0.309 Amenities 0.209

Value for money 0.372

Amenities 0.288

Value for money 0.439

Distractions 0.015 Correlations with CL

Security 0.356

Correlations with CS

Core 0.301

Core 0.310

Hygiene 0.161

Hygiene 0.281

Pubs 0.136

Pubs 0.134

Culture 0.271

Culture 0.354

Scales to measure and benchmark SQ 485

Table VII. Correlation analysis

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Hospitality Food SQ1

Logistics

486

0.442 (8.851)

Value for money

0.309 (4.879)

CL

0.082 (1.602)

SQ2 0.182 (3.729)

Security

CS

Pubs

Amenities

Culture

Irritants

Hygiene

Personal information Fairness of price

Figure 4. Nomological framework

Fit indices 2

Table VIII. Nomological validity

0.471 (6.843)

x /df BBNFI CFI AGFI SRMR RMSEA 90 percent confidence interval

Core-tourism experience

Information Centers

Structural model 2.09 0.688 0.807 0.689 0.147 0.058 (0.056, 0.061)

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

theory building. We also feel that the same scales administered in a different destination might bring out a different factor structure, especially in terms of the combinations of SQ1 and SQ2. Managerial implications The instrument developed in this study can be used by destination managers to objectively measure the SQ perceptions of tourists. It would also be useful for tour operators in the originating countries who manage travel to these destinations. It will help them to evaluate how their customers perceive the SQ dimensions in the host countries and also how these dimensions impact CS. We believe that our attempt to model SQ dimensions with and without a strong impact on satisfaction would prove particularly appealing to destination mangers as it gives them an insight into the main dimensions of service that need to be carefully looked 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 a significant 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, it is found to be one of the factors not having a significant impact on CS. Given the strong relationship between CS and loyalty, this piece of finding needs to be given a serious thought. One implication of this finding is that a destination need not necessarily have natural cutting edges to be developed as a tourist destination. A destination with good logistics, which assures security and value for money and offers impressive hospitality and food, can also satisfy a customer, resulting in repeat visits and recommendations. Directions for future research This study has been carried out in a state in India. The same instrument can be tested out in other destinations in different countries to examine whether the scales would hold good elsewhere as well. It is possible that a different combination of SQ1 and SQ2 might emerge. We suggest that in future research, some moderating variables such as the nationality of the tourist, motivation to travel and type of traveler be also considered, 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, apart from what has been established in this study. For example, it would be appealing to see if they are in fact “Satisfiers” and “Dissatisfiers,” as classified by Herzberg’s Hygiene-Motivation theory (Herzberg et al., 1959). This can be accomplished by carrying out a Critical Incidents Study, similar to the ones conducted by Bitner et al. (1990) and Keaveney (1995). References Alampay, R.B.A. (2003), “Visitors to Guam: modeling satisfaction, quality and intentions”, dissertation, Michigan State University, East Lansing, MI. Anderson, E.W. and Sullivan, M. (1993), “The antecedents and consequences of customer satisfaction for firms”, Marketing Science, Vol. 12 No. 2, pp. 125-43. Anderson, J.C. and Gerbing, D.W. (1988), “Structuring equation modeling in practice: a review and recommended two-step approach”, Psychological Bulletin, Vol. 103 No. 3, pp. 411-23.

Scales to measure and benchmark SQ 487

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Arnould, E.J. and Price, L.L. (1993), “River magic: extraordinary experience and the extended service experience”, Journal of Consumer Research, Vol. 20, pp. 24-45. Bearden, W.O. and Teel, J.E. (1983), “Selected determinants of consumer satisfaction and complaint reports”, Journal of Marketing Research, Vol. 20 No. 1, pp. 21-8. Bemowski, K. (1992), “The quality glossary”, Quality Progress, Vol. 25 No. 2, pp. 18-29.

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Heung, V.C.S. and Cheng, E. (2000), “Assessing tourists’ satisfaction with shopping in the Hong Kong special administrative region of China”, Journal of Travel Research, Vol. 38 No. 4, pp. 396-405. Hudson, S., Hudson, P. and Miller, G.A. (2004), “The measurement of service quality in the tour operating sector: a methodological comparison”, Journal of Travel Research, Vol. 42 No. 3, pp. 305-12. Hwang, L-J.J. and Lockwood, A. (2006), “Understanding the challenges of implementing best practices in hospitality and tourism SMEs”, Benchmarking: An International Journal, Vol. 13 No. 3, pp. 337-54. Johns, N., Avci, T. and Karatepe, O.M. (2004), “Measuring service quality of travel agents: evidence from northern Cyprus”, The Service Industries Journal, Vol. 24 No. 3, pp. 82-100. Juwaheer, T.D. and Ross, D.L. (2003), “A study of hotel guest perceptions in Mauritius”, International Journal of Contemporary Hospitality Management, Vol. 15 No. 2, pp. 105-15. Kano, N., Seraku, K., Takahashi, F. and Tsuji, S. (1984), “Attractive quality and must-be quality”, Hinshitsu (Quality, The Journal of the Japanese Society for Quality Control), Vol. 14 No. 2, pp. 39-48. Karabati, S. and Say, A.I. (2005), “Relating work values to societal values: evidence from the Turkish business context”, Cross Cultural Management, Vol. 12 No. 2, pp. 85-107. Keaveney, S.M. (1995), “Customer switching behavior in service industries: an exploratory study”, Journal of Marketing, Vol. 59 No. 2, pp. 71-82. Kim, J-O. and Mueller, C.W. (1978), Factor Analysis: Statistical Methods and Practical Issues, Quantitative Applications in the Social Sciences Series, No. 14, Sage, Thousand Oaks, CA. Kozak, M. (2001), “Comparative assessment of tourist satisfaction with destinations across two nationalities”, Tourism Management, Vol. 22 No. 4, pp. 391-401. Kozak, M. and Rimmington, M. (2000), “Tourist satisfaction with Mallorca, Spain, as an off-season holiday destination”, Journal of Travel Research, Vol. 38 No. 3, pp. 260-9. Kyriakidou, O. and Gore, J. (2005), “Learning by example benchmarking organizational culture in hospitality, tourism and leisure SMEs”, Benchmarking: An International Journal, Vol. 12 No. 3, pp. 192-206. Lee, Y-H. and Chen, T-L. (2006), “A Kano two-dimensional quality model in Taiwan’s hot spring hotels service quality evaluations”, Journal of American Academy of Business, Vol. 8 No. 2, pp. 301-6. Malhotra, N.K. (2004), Marketing Research, 4th ed., Pearson Education India, New Delhi. Mieres, C.G., Martin, A.M.D. and Gutie´rrez, J.A.T. (2006), “Antecedents of the difference in perceived risk between store brands and national brands”, European Journal of Marketing, Vol. 40 Nos 1/2, pp. 61-82. Millan, A. and Esteban, A. (2004), “Development of a multiple-item scale for measuring customer satisfaction in travel agencies services”, Tourism Management, Vol. 25 No. 5, pp. 533-46. Motwani, J.G. and Sower, V.E. (2006), “Benchmarking in services”, Benchmarking: An International Journal, Vol. 13 No. 3, Guest Editorial. Narayan, B., Gopalan, R., Rajendran, C. and Prakash Sai, L. (2007), “Dimensions of service quality in tourism – an Indian perspective”, working paper, IIT Madras, Chennai. Oliver, R.L. (1980), “A cognitive model of the antecedents and consequences of satisfaction decisions”, Journal of Marketing Research, Vol. 17, pp. 460-9.

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Appendix. Instrument for measuring service quality in tourism The instrument seeks the respondents’ perception of the actual level of performance of the destination (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 items corresponding to the ten factors of service quality in tourism are given below. (In the actual questionnaire administered to the respondents, details of the factors were not disclosed to avoid any 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.

Scales to measure and benchmark SQ 491

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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 . Price worthiness . Price worthiness . Price worthiness . Price worthiness . Price worthiness

of of of of of of

the tour package. accommodation. domestic flight. food at restaurants outside your place of stay. local conveyance (like buses, taxis, and auto rickshaws). 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.

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.

Scales to measure and benchmark SQ 493

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 – low, 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 – low, 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 1 – very low, 2 – low, 3 – little low, 4 – neutral, 5 – little high, 6 – high, and 7 – high).

Corresponding author Chandrasekharan Rajendran can be contacted at: [email protected]

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