Int. Journal of Business Science and Applied Management, Volume 1, Issue 1, 2006

Tourist Satisfaction and Destination Loyalty intention:

A Structural and Categorical Analysis

Patricia Oom do Valle

Faculty of Economics, University of Algarve

9, Campus de Gambelas, 8005-139 Faro, Portugal

Tel: +351 289800915

Fax: +351 289818303

Email: pvalle@ualg.pt

João Albino Silva

Faculty of Economics, University of Algarve

9, Campus de Gambelas, 8005-139 Faro, Portugal

Tel: +351 289800915

Fax: +351 289818303

Email: jsilva@ualg.pt

Júlio Mendes

Faculty of Economics, University of Algarve

9, Campus de Gambelas, 8005-139 Faro, Portugal

Tel: +351 289800915

Fax: +351 289818303

Email: jmendes@ualg.pt

Manuela Guerreiro

Faculty of Economics, University of Algarve

9, Campus de Gambelas, 8005-139 Faro, Portugal

Tel: +351 289800915

Fax: +351 289818303

Email: mmguerre@ualg.pt

Abstract

This study explores the relationship between travel satisfaction and destination loyalty intention. The

research was conducted with 486 tourists visiting Arade, a Portuguese tourist destination. Taking as the

basis the use of structural equation modelling (SEM), the results substantiate the importance of tourism

satisfaction as a determinant of destination loyalty. Also, a categorical principal components analysis

(CATPCA) provides a detailed analysis of this cause-effect relationship by establishing that greater

levels of satisfaction (measured by overall satisfaction in terms of holiday experience, destination

attributes and met expectations) result in increased likelihood of future repeat visits and a keen

willingness to recommend the destination to others. Clusters of tourists were also identified and

characterized in relation to satisfaction levels and loyalty intentions. These analyses provide a useful

background in the planning of future tourist marketing strategies.

Keywords: tourism, satisfaction, loyalty, SEM, CATPCA, clusters

Int. Journal of Business Science and Applied Management / Business-and-Management.com

1 INTRODUCTION

Tourism represents a key industry in the Portuguese economy. In 2004, Portugal received more

than 12 million tourists with tourism representing approximately 8% of the GDP. Tourism also plays an

important role in the Portuguese employment marketplace since more than 10% the population is

employed in tourism-related sectors. Located in the south of Portugal, Algarve belongs to the top 20

travel destinations worldwide with the local economy relying mostly on the tourism-related activities.

Despite the exceptionally favourable conditions for tourism (quality beaches, warm climate, hospitable

and friendly community and multiculturally-attuned), Algarve has recently experienced some difficulty

in maintaining its position as a preferred travel destination. Compared to 2004, the number of tourists

entering Algarve decreased by 0.8% with lodging demand decreasing by 4.8% (AHETA, 2005).

Although several external factors could be mentioned as passive reasons for this occurrence, the current

condition of tourism in Algarve is much the result of emerging new holiday destinations that offer

lower prices and, in some cases, higher quality facilities (AHETA, 2005).

Even though the study of consumer loyalty has been pointed out in the marketing literature as one

of the major driving forces in the new marketing era (Brodie et al., 1997), the analysis and exploration

of this concept is relatively recent in tourism research. Some studies recognise that understanding

which factors increase tourist loyalty is valuable information for tourism marketers and managers

(Flavian et al., 2001). Many destinations rely strongly on repeat visitation because it is less expensive

to retain repeat tourists than to attract new ones (Um et al., 2006). In addition, Baker and Crompton

(2000) show that the strong link between consumer loyalty and profitability is a reality in the tourism

industry.

The study of the influential factors of destination loyalty is not new to tourism research. Some

studies show that the revisit intention is explained by the number of previous visits (Mazurki, 1989;

Court and Lupton, 1997; Petrick et al., 2001). Besides destination familiarity, the overall satisfaction

that tourists experience for a particular destination is also regarded as a predictor of the tourist’s

intention to prefer the same destination again (Oh, 1999; Kozak and Rimmington, 2000; Bowen, 2001;

Bigné and Andreu, 2004; Alexandros and Shabbar, 2005; Bigné et al., 2005). Other studies propose

more comprehensive frameworks. Bigné et al. (2001) model return intentions to Spanish destinations

through destination image, perceived quality and satisfaction as explanatory variables. Yoon and Uysal

(2005) use tourist satisfaction as a moderator construct between motivations and tourist loyalty.

Recently, Um et al. (2006) propose a model based on revisiting intentions that establishes satisfaction

as both a predictor of revisiting intentions and as a moderator variable between this construct and

perceived attractiveness, perceived quality of service and perceived value for money.

More complex models have the advantage of allowing a better understanding of tourist behaviour

since more variables and their interactions can be taken into account. However, for more effective

marketing interventions it is important to assess whether the destination models also consider the

tourist’s personal characteristics (Woodside and Lysonski, 1989; Um and Crompton, 1990). In fact,

despite the use of more comprehensive models, so far, they have left unspecified the main personal

characteristics (socio-demographic and motivational) of the more potentially loyal and satisfied

tourists. The contribution of this study lies in bridging this research gap. This study integrates the main

stream of previous research on destination loyalty intention proposing a causal relationship between

this construct and satisfaction. However, besides estimating this causal model, the paper aims to

identify how observed variables of the latent constructs are related and, next, find and describe

segments of tourists based on these relations.

The study relies on the use of a structural equation model (SEM) procedure, through a categorical

principal components analysis (CATPCA) and a cluster analysis. The model is estimated using data

from a questionnaire answered by tourists visiting Arade, a Portuguese tourism destination, located in

Algarve, in the western part of the province, which includes four municipalities Portimão, Lagoa,

Monchique and Silves (Figure 1). On the one hand, this type of approach can help destination managers

to determine segments of tourists which require special attention in the definition of future tourism

intervention strategies. On the other hand, the complementary use of CATPCA and cluster analysis can

be applied in further research in order to develop more complex models in which an increased number

of latent variables and relations among them are considered.

This study is organised as follows. The next section provides an overview of previous research

that has focused on destination loyalty and tourist satisfaction. Section 3 proposes a structural model

that establishes the causal relationship between these constructs and defines the set of research

hypotheses. Section 4 describes the research methods adopted. The final two sections discuss the

results obtained and summarises the more important conclusions and implications of the study.

Patrícia Oom do Valle, João Albino Silva, Júlio Mendes and Manuela Guerreiro

Figure 1: Algarve, Arade and its municipalities

2 LITERATURE REVIEW

The concept of loyalty has been recognised as one of the more important indicators of corporate

success in the marketing literature (La Barbara and Mazursky, 1983; Turnbull and Wilson, 1989; Pine

et. al., 1995; Bauer et. al., 2002). Hallowell (1996) provides evidence on the connection between

satisfaction, loyalty and profitability. The author refers that working with loyal customers reduces

customer recruitment costs, customer price sensitivity and servicing costs. In terms of traditional

marketing of products and services, loyalty can be measured by repeated sales or by recommendation

to other consumers (Pine et al., 1995). Yoon and Uysal (2005) stress that travel destinations can also be

perceived as a product which can be resold (revisited) and recommended to others (friends and family

who are potential tourists).

In his study about the desirability of loyal tourists, Petrick (2004) states that loyal visitors can be

less price sensitive than first time visitors. This study shows that less loyal tourists and those visiting

the destination for the first time tend to spend more money during the visit. However, these tourists

report a high value in the measure “risk-adjusted profitability index”, proposed by the author, and as

such are not as desired as loyal tourists.

The determining factors of loyalty have been studied in the marketing literature. Bitner (1990),

Dick and Basu (1994) and Oliver (1999) show that satisfaction from products or services affect

consumer loyalty. Flavián et al. (2001) add that loyalty to a product or service is not the result of the

absence of alternative offers. Instead, loyalty occurs because consumers increasingly have less free

time available and therefore try to simplify their buying decision process by acquiring familiar products

or services.

As referred to above, research shows that the satisfaction that tourists experience in a specific

destination is a determinant of the tourist revisiting. Baker and Crompton (2000) define satisfaction as

the tourist’s emotional state after experiencing the trip. Therefore, evaluating satisfaction in terms of a

travelling experience is a post-consumption process (Fornell, 1992; Kozak, 2001). Assessing

satisfaction can help managers to improve services (Fornell, 1992) and to compare organisations and

destinations in terms of performance (Kotler, 1994). In addition, the ability of managing feedback

received from customers can be an important source of competitive advantage (Peters, 1994).

Moreover, satisfaction can be used as a measure to evaluate the products and services offered at the

destination (Ross and Iso-Ahola, 1991; Noe and Uysal, 1997; Bramwell, 1998; Schofield, 2000).

Recently, more holistic models have been used to explain destination loyalty in tourism research.

Yoon and Uysal (2005) propose a model which relates destination loyalty with travel satisfaction and

holiday motivations. This study finds a significant cause-effect relationship between travel satisfaction

and destination loyalty as well as between motivations and travel satisfaction. Oh (1999) establishes

service quality, perceived price, customer value and perceptions of company performance as

determinants of customer satisfaction which, in turn, is used to explain revisit intentions. Bigne et al.

(2001) identify that returning intentions and recommending intentions are influenced by tourism image

and quality variables of the destination. Kozak (2001) model intentions to revisit in terms of the

following explanatory variables: overall satisfaction, number of previous visits and perceived

performance of destination. In a recent paper, Um et al. (2006) propose a structural equation model that

explains revisiting intentions as determined by satisfaction, perceived attractiveness, perceived quality

of service and perceived value for money. In this study repeat visits are determined more by perceived

attractiveness than by overall satisfaction.

Monchique

Silves

Portimão

Lagoa

Monchique

Silves

Portimão

Lagoa

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Another important conclusion from the study carried out by Um et al. (2006) is that the revisit

decision-making process should be modelled in the same way as modelling a destination choice

process. This implies that the personal characteristics of tourists, such as motivations and socio-

demographic characteristics also play an important role in explaining their future behaviour. Despite

sharing equal degrees of satisfaction, tourists with different personal features can report heterogeneous

behaviour in terms of their loyalty to a destination (Mittal and Kamakura, 2001).

Motivations form the basis of the travel decision process and therefore should also be considered

when analysing destination loyalty intentions. Beerli and Martín (2004) propose that “motivation is the

need that drives an individual to act in a certain way to achieve the desired satisfaction” (Beerli and

Martín, 2004:626). Motivations can be intrinsic (push) or extrinsic (pull) (Crompton, 1979). Push

motivations correspond to a tourist’s desire and emotional frame of mind. Pull motivations represent

the attributes of the destination to be visited. Yoon and Uysal (2005) take tourist satisfaction to be a

mediator variable between motivations (pull and push) and destination loyalty.

The effect of socio-demographic variables in the tourist decision process is also an issue

which has received some attention. Some studies propose that age and level of education influence the

choice of destination (Goodall and Ashworth, 1988; Woodside and Lysonski, 1989; Weaver et al.,

1994; Zimmer et al., 1995). Font (2000) shows that age, educational level, nationality and occupation

represent determinant variables in the travel decision process.

3 CONCEPTUAL MODEL AND RESEARCH HYPOTHESES

The proposed structural equation model of the tourist loyalty intention is presented in Figure 2.

The model establishes a direct causal-effect relationship of tourist satisfaction on destination loyalty

intention. This connection is supported by earlier studies as those carried out by Kozak and

Rimmimington (2000), Bigné et al. (2001, 2005), Gallarza and Saura (2005), Yoon and Uysal (2005)

and Um et al (2006).

Figure 2: The proposed hypothetical model

The model also shows the observed variables used to measure the latent constructs tourist

satisfaction and destination loyalty intention. As will be described in the following Section, the

observed variables were chosen based on previous research. In addition, the application of the

structural equation modelling procedure will demonstrate that these variables adequately represent the

corresponding constructs.

As stressed by Yoon and Uysal (2005), satisfaction should be perceived from a multidimensional

perspective, i.e., more than one observed variable should be considered. Chon (1989) demonstrates that

both the perceived evaluative outcome of the holiday experience at the destination and associated

expectations are important elements in shaping tourist satisfaction. Customer satisfaction can be

estimated with a single item, which measures the overall satisfaction (Fornell, 1992; Spreng and

Mackoy, 1996; Bigné et al., 2001). Besides the global perception about the outcome alone, the degree

of satisfaction can be evaluated through specific service attributes (Mai and Ness, 2006). Additionally,

satisfaction can be evaluated using the theory of expectation/confirmation in which expectations and

the actual destination outcome are compared (Oliver, 1980; Francken and Van Raaji, 1981; Chon,

1989; Bigné et al., 2001). That is, if expectations exceed perceived outcome then a positive

disconfirmation is obtained, leaving the tourist satisfied and willing to repeat the visit; if a negative

disconfirmation occurs the tourist feels dissatisfied and will look for alternative travel destinations.

Based on these studies, three observed variables (also referred to as indicators) are used in order to

measure tourist satisfaction in this paper: (1) general destination satisfaction; (2) mean satisfaction

level in terms of destination attributes; and (3) whether destination expectations were met.

Tourist

satisfaction

Destination

loyalty

intention

Intention to

return

Willingness to

recommend

General

satisfaction

Met

expectations

Attribute

satisfaction

Patrícia Oom do Valle, João Albino Silva, Júlio Mendes and Manuela Guerreiro

Oliver (1999) states that loyalty is a construct that can be conceptualised by several perspectives.

Cronin and Tayler (1992), Homburg and Giering (2001) measure the construct “future behavioural

intention” by using two indicators: the intention of repurchase and the intention to provide positive

recommendations. In tourism research, similar approach is adopted and tourist loyalty intention is

represented in terms of the intention to revisit the destination and the willingness to recommend it to

friends and relatives (Oppermann, 2000; Bigné et al., 2001; Chen and Gusoy, 2001; Cai et al., 2003;

Niininen et al., 2004; Petrick, 2004). Therefore, two indicators, “revisiting intention” and “willingness

to recommend” are used as measures of destination loyalty intention.

As referred to in the literature review, socio-demographic variables and motivational variables can

influence the travel decision. This study also aims to analyse whether this relationship is true when

considering revisiting a destination. In specific, besides estimating the conceptual model proposed in

Figure 2, this study looks to show that tourists, stating a more favourable revisiting intentions and

recommendation behaviour, are expected to be the most satisfied, possessing different socio-

demographic characteristics and motivations to travel.

Accordingly to the above considerations, the following research hypotheses are formulated:

: Tourist satisfaction holds a positive influence on tourist loyalty

: “General destination satisfaction”, “mean satisfaction level in terms of destination attributes”

and “the extent to which expectations were met” are adequate measures of tourist satisfaction

: “Revisiting intention” and “willingness to recommend” are adequate measures of destination

loyalty intention

: Destination loyalty intention is different according to socio-demographic characteristics of

tourists

: Destination loyalty intention is different according to travel motivations

4 METHODOLOGY

The questionnaire

The data for this study were collected from 486 personal interviews based on a structured

questionnaire carried out from March to July 2004. The questionnaire, comprising five sections, was

designed to analyse tourist motivations and perceptions towards Arade. Section 1 enquired about the

basic background data on the tourist’s vacation at this destination, that is, lodging municipality

(Portimão, Lagoa, Monchique or Silves), type of lodging (hotel, apartment, private home, other), length

of the stay, main push motivation to travel to Arade (leisure/recreation/holidays, visiting friends,

business, health) and main form of transportation used in the region (rental car, private car, public

transports, other).

Sections 2 and 3 involved thirty attributes of the destination that were assessed in terms of

importance (section 2) and satisfaction (section 3). The assessed attributes, which represent the

attributes of the destination (pull factors) included: beaches, spas, hospitality, authenticity,

accessibilities, historical centres, traffic, forms of transportation, sports facilities, landscape,

monuments, urban planning, restaurants, traditional architecture, animation, lodging, shopping areas,

cultural events, tourist information, food, leisure areas, public safety, gardens/green spaces, pedestrian

areas, competence and kindness, parking, water supply system, waste recovery system, cleanliness and

traffic signs. These attributes were selected because they are the most quoted in the tourism literature

(Uysal, Mclellan and Syrakaya, 1996; Iso-Ahola and Mannel, 1987; Fodness, 1994; Mohsin and Ryan,

2003; Shoemaker, 1989; Cossens, 1989). In both cases, the attributes were assessed with a five-point

Likert type scale. This scale ranged from “totally irrelevant” (1) to “extremely important” (5) in terms

of importance and from “very unsatisfied” (1) to “very satisfied” (5) in terms of satisfaction.

Section 4 looked to measure the overall tourism experience in Arade by asking respondents about

the overall satisfaction with the journey, intention to revisit and recommendation intention, and

whether the expectations about the journey were met or not. Finally, section 5 draws on questions about

socio-demographic characteristics: gender, age, marital status, occupation, educational qualification

and nationality.

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Sample procedures and participants

The target population of this study involves Portuguese and foreigner tourists visiting Arade and

stating in one of the four municipalities of this tourist region. From this population, a sample was

selected using a quota sampling method with interviews performed by trained interviewers, instructed

to select respondents as randomly as possible (not based on personal preferences), at different locations

and at different times. This sampling method was applied because it is not possible to obtain a list of all

tourists visiting Arade during this period, which would enable the use of a stratified sampling method

(the random version of the quota sampling method). The number of tourists to be included in each

quote was defined proportionally to the type of tourist in the target population (Portuguese and

foreigner) and its distribution according to the four municipalities. A minimum of 30 interviews in the

smallest quote (Portuguese tourists lodged in Monchique) was anticipated in order to perform statistical

tests, if necessary. The sample dimension for the remaining quotes was determined proportionally

giving rise to a total of 486 interviews.

Since non-random sampling does not ensure a representative sample, the main socio-demographic

features of the target population of tourists (INE-National Institute of Statistics, 2004) were compared

with the analogous features of the sample. Three socio-demographic characteristics of the target

population were available for this comparison: gender, age and educational qualifications. This analysis

shows that the sample is not significantly different from the target population in terms of gender

because in both cases the majority of tourists were female (around 51% of the population; around 54%

of the sample). In terms of age and educational qualifications, older tourists with lower qualifications

were expected. In fact, the proportion of tourists older than 65 in the target population was 16.5%

although this percentage represents only 3.2% in the sample. Similarly, 19.4% of target tourists have a

degree whereas in the sample this percentage was much higher (50.6%). Note that the sample

represents a target population for both Portuguese nationals and foreign tourists according to the

municipality where they were lodged. Around 30% of respondents were Portuguese tourists, around

59% were lodged in Portimão, 29% in Lagoa, 6% in Silves and 6% in Monchique. Table 1 shows the

main socio-demographic characteristics of respondents and also some features of the visit. Most

tourists were female, possessed college or high school qualifications, belonged to the 25-44 age

interval, were foreign (mainly English), and married. In the majority of cases, tourists were lodged in

Portimão, in a hotel, motivated mainly by reasons related to leisure/recreation and holidays and

travelled by rental car during their stay.

“Runs tests” were carried out in order to assess whether the observations for each variable could

be considered as having a random pattern. For all variables in the table, this hypothesis was not rejected

(runs tests: p > 0.05). This observation is required in order to form statistical inferences, though absent

in sub-represented groups, namely, older tourists with lower education qualifications.

Table 1: Demographic characteristics of the sample and journey features

Characteristic Distribution of Answers

Tourist’s gender Female: 53.6 %; male: 46.4%

Tourist’s age 15 – 24: 19.1%; 25 – 44: 50.0%; 45 – 64: 27.7% ; older than 65: 3.2%

Tourist’s educational qualification Elementary: 6.2%; Secondary: 44.2%; College or higher: 50.6%

Tourist’s nationality Portuguese tourists: 28%; Foreign tourists: 72% (45% English)

Tourist’s marital status Married: 62.4%; single: 32.2%; divorced: 4.5%; widowed: 0.8%

Tourist’s occupation Managerial and professional occupations: 20.6%; associate professional

and technical: 18.3%; students; 17%; sales and customer services or

administration and secretarial: 14%; skilled trades: 13.3%; other: 16.8%

Lodging municipality Portimão: 59%; Lagoa: 29%; Monchique: 6%; Silves: 6%

Type of lodging Hotel: 48.3%; apart hotel: 9.6%; private house: 18%; other: 24.1%

Length of the stay Mean = 12 days; standard deviation = 6 days

Main travel motivation to Arade Leisure/recreation/holidays: quoted by 91.6% of respondents; visiting

friends: quoted by 10.9% of respondents; business: quoted by 3.7% of

respondents; health: quoted by 3.9% of respondents

Main form of transportation used in

the journey

Rental car: 39.8%; private car: 29.3%; public transports: 26.8%; other:

4.1%

Patrícia Oom do Valle, João Albino Silva, Júlio Mendes and Manuela Guerreiro

Latent constructs and observed variables

Table 2 shows the latent constructs, observed variables and questionnaire items used to measure

each observed variable of the proposed model and the corresponding scales.

Table 2: Latent construct, observed variables, questions and scales

Latent

Constructs

Observed Variables Questions Scale

General satisfaction What is your overall satisfaction

level as a tourist experiencing

Arade?

1 – very unsatisfied

2 – unsatisfied

3 – not satisfied nor unsatisfied

4 – satisfied

5 – very satisfied

Tourist Attribute satisfaction In terms of satisfaction, how would

satisfaction you rate the following Arade

attributes? (*)

1 – very unsatisfied

2 – unsatisfied

3 – not satisfied nor unsatisfied

4 – satisfied

5 – very satisfied

Met expectations Were your expectations met?

1 – no

2 – yes

Intentions in revisiting

Destination

Do you intend to revisit Arade in the

future?

1 – no

2 – maybe; 3 – yes

loyalty Willingness to recommend

Would you recommend ARADE to

your friends and family?

1 – no

2 – maybe; 3 – yes

(*) Mean of satisfaction level with the thirty attributes.

Statistical data analysis procedures

This study applies three methods of multivariate statistical analysis: structural equation modelling

(SEM), categorical principal components analysis (CATPCA) and cluster analysis. The research

hypotheses H1 to H3 are tested according to the SEM procedure. By describing the tourist segments

produced by the cluster analysis, H4 e H5 are assessed.

Firstly, the proposed hypothetical model is estimated by using a SEM procedure via the Analysis

of Moment Structures software (AMOS 5) (Arbuckle and Wothke, 1999). This software package is

used because it works inside the software SPSS 14, which was available to the research team and used

to treat the data. AMOS has a simple interface, and only requires the path diagram to specify the

model, generating indexes and tests that are necessary to assess the estimated model.

Questionnaire items described in Table 2 represent observed variables for tourist satisfaction and

destination loyalty intention. To correct for non-normality of the observed variables, the Weighted

Least Squares (WLS) method of estimation (Schumacker and Lomax, 1996) is adopted. The model fit

analysis follows similarly to Hair et al’s approach (1995). According to this study, the measurement

model and the structural model should be evaluated separately, after examining the overall model fit.

Three types of overall model fit measures are examined: absolute fit, incremental fit and parsimonious

fit. The Chi-square goodness-of-fit test is the best known index of absolute fit and used as a general

indicator of how well the proposed model complies with the available data. Chi-square values should

be low and not statistically significant for the purpose of goodness of fit. In addition to the Chi-square

test, other measures of overall model fit are also used. Excluding the cases of the root mean square

residual (RMSR) (Steiger, 1990) and the root mean square residual of approximation (RMSEA)

(Steiger, 1990), in which lower values are considered desirable (zero suggesting a perfect fit), the

remaining measures range from 0 (no fit) to 1 (perfect fit) and the normed Chi-square measure

(Joreskog, 1969) range from 1 to 5, ideally.

The measurement model specifies the relationship between the latent constructs and the

corresponding observed variables. The measurement model fit assesses the reliability and validity of the

latent variables (Hair et al., 1995; García and Martinez, 2000). Reliability analysis refers to whether the

observed variables, chosen to indicate the construct, are really measuring the same (unobserved)

concept. In this study, we determine two measures of reliability for each construct: the construct

composite reliability and the variance extracted from each construct. Scharma (1996) considers 0.7 as

the adequate minimum acceptance level for the composite reliability and 0.5 for the variance extracted.

On the other hand, validity focuses on whether one observed variable truly measures the construct

Int. Journal of Business Science and Applied Management / Business-and-Management.com

intended by the researcher. The validity of the observed variables holds true if these are significant, or

at least moderately significant, on hypothesised latent variables (Bollen, 1989).

The structural model specifies the relationships between the latent constructs. In analysing the

structural model fit, we test the standardised parameter estimate that links the two latent constructs in

terms of its sign and statistical significance. In addition, the squared multiple correlation coefficient for

the structural equation associated to the latent variables is examined. This coefficient is similar to the

coefficient of determination used in multiple regression analysis and shows how well the data supports

the proposed relationship.

Next, using CATPCA, we explore the relationship between each observed variable measuring the

latent constructs tourist satisfaction and destination loyalty intention. The use of this technique

complements information taken from the structural equation model. As reported in Table 2, all

observed variables are qualitative (categorical) and CATPCA is a multivariate technique developed to

analyse categorical variables (Meulman and Heiser, 2004). This method is basically an exploratory

technique that uncovers the associations among the categories of qualitative variables in large

contingency tables. CATPCA uses a mathematical algorithm that provides an optimal quantification to

each category of the qualitative variables that allows for their graphical representation. As the name of

the method suggests, CATPCA performs a principal components analysis (PCA) for categorical

variables. Through this method, each category of the qualitative variables have an optimal

quantification in each dimension (or component) produced by this special type of PCA. For each

category, the optimal quantifications in the retained dimensions are the coordinates that allow the

representation of the category in the geometrical display. These geometrical displays make data

interpretation easier since they reveal similar variables or categories. Specifically, categories that are

related are represented as points close together on the graph. Unrelated categories appear distant on the

graph.

As the classic PCA, CATPCA produces dimensions which are quantitative variables that capture

the information (variability) contained in the initial observed variables. Standard outputs of both

methods include the eigenvalue associated to each retained dimension and the total amount of

explained variance. Each eigenvalue is perceived as a measure of the importance of the corresponding

dimension in capturing the information provided by the original observed variables. In turn, the total

amount of explained variance informs how well the set of retained dimensions captures, as a whole, the

initial set of qualitative variables. In this study we follow the Kaiser (1960) criterion that suggests that

only dimensions with eigenvalues higher than 1 should be retained.

Lastly, the graph produced by CATPCA suggests distinct groups of tourists based on scores

obtained from this method. We validate these groups via a cluster analysis through a k-means cluster

optimisation method. The use of a cluster analysis in this context is recommended because although

CATPCA can identify specific groups present in the data it is unable to specify their common features

(Maroco, 2003). The statistical analysis concludes with a description of the main features for each

group (segment) of tourists. In this study, CATPCA and cluster analysis were performed with SPSS 14.

5 RESULTS

Structural equation modelling

Figure 3 shows the estimated standardised path coefficients on the model itself. All estimates are

statistically significant (p = 0.000). The selected overall fit indices are reported in Table 3. As can be

observed, the Chi-square statistic is low and non-statistically significant (p > 0.01), suggesting that the

model is a good description of the data. Auxiliary measures of overall fit also report the desired levels,

indicating a good overall model fit: the GFI is high and exceeds the recommended level of 0.9; the

RMSR and the RMSEA are close to 0. In addition, the proposed model reports high levels for the

remaining measures (close to 1), suggesting an adequate incremental and parsimonious fit.

Figure 3: Standardised estimates of hypothetical model

0.79

Tourist

satisfaction

Destination

loyalty

intention

Revisiting

intention

0.71

0.70

0.33

0.53

0.26

0.12

0.13

0.32

Willingness to

recommend

0.07

General

satisfaction

Met

expectations

Attribute

satisfaction

0.84

Patrícia Oom do Valle, João Albino Silva, Júlio Mendes and Manuela Guerreiro

Table 3: Goodness-of-fit indices for the estimated structural model

Absolute fit measures Incremental fit measures Parsimonious fit measures

Chi-square = 13.34 (p = 0.015) AGFI

= 0.93 Normed Chi-square

= 3.085

RMSR

= 0.02 NFI

= 0.879

RMSEA

= 0.067 TLI

= 0.774

IFI

= 0.915

CFI

= 0.909

RMSR: root mean square residual (Steiger, 1990);

RMSEA: root mean square residual of approximation (Steiger,

1990);

AGFI: adjusted goodness of fit index (Joreskog and Sorbom 1986);

NFI: normed fit index (Bentler and

Bonnet, 1980);

TLI: Tucker and Lewis index (Tucker and Lewis, 1973);

IFI: incremental fit index (IFI) (Bollen,

1988);

CFI: comparative fit index (Bentler, 1990);

Normed Chi-square measure (Joreskog, 1969).

Table 4 shows the results of the measurement model in terms of the constructs’ reliability and

variance extracted. These measures exceeded the recommended levels of 0.7 and 0.5, respectively, for

both tourist satisfaction and destination loyalty intention. This means that the latent constructs are

reliable, that is, the observed variables selected to indicate each construct measure the same

(unobserved) concept (Scharma, 1996). As seen in Figure 3, significant standardised loadings of each

observed variable on the corresponding constructs (p = 0.000) were reported, thus validating the

proposed constructs. As explained in the methods section, validity refers to whether the observed

variables truly measure the latent construct intended by the researcher (Bollen, 1989). In short,

hypotheses H2 and H3 should not be rejected.

Table 4: Results of the measurement model

Latent constructs Construct reliability Variance extracted

Tourist satisfaction 0.84 0.66

Destination loyalty 0.81 0.75

After assessing the measurement model, we observed the structural model. As presented in Figure

3, the findings indicate a positive relationship between tourist satisfaction and destination loyalty

intention, as shown by a high and statistically significant loading between the two constructs (0.785; p

= 0.000). This implies that satisfaction has a positive influence on the tourist loyalty intention, i.e., H1

is supported. The squared multiple correlation for the structural equation relating the two constructs is

moderately high (0.616), suggesting that 61.6% of the variability of loyalty destination intention is

explained by the variability of tourist satisfaction.

Categorical principal components analysis

In general terms, CATPCA is traditionally used to reduce the dimensionality of an original set of

categorical variables (nominal and ordinal) into a smaller set of quantitative variables (components or

dimensions) which account for most of the information (variance) in the original variables. As

explained above, once this method has been applied, each category of each qualitative variable will

have an optimal quantification in the retained dimensions. These quantifications are coordinates that

allow the categories to be represented in a geometrical display, making data interpretation easier.

In having estimated the structural model, CATPCA was performed to explore the joint

relationships among the five observed variables of the model: general satisfaction, attribute

satisfaction, met expectations, revisiting intention and willingness to recommend. Based on the

observation of the eigenvalues in a higher number of dimensions, we retained only the first two

dimensions (those with eigenvalues higher than 1) which account for 62.1% of the total variance of the

original data.

Figure 4 is the geometrical display that allows a visual interpretation of the how the categories of

the observed variables are related. The horizontal axis represents dimension 1 and the vertical axis

shows dimension 2. In the graph, the variables measuring tourist satisfaction are indicated by solid

lines and the variables measuring destination loyalty intention are captured by the dashed lines. In each

line, the displayed points represent the categories of variables. As can be observed, the graph shows

that the categories indicating higher level of satisfaction (general satisfaction: 5 – very satisfied;

attribute satisfaction: 5 – very satisfied; met expectations: 2 – yes) and higher level of loyalty intention

Int. Journal of Business Science and Applied Management / Business-and-Management.com

Yes

(revisiting intention: 3 – yes; willingness to recommend: 3 – yes) are represented close to eachother (on

the right-hand side of the graph). These results show that tourists generally satisfied with their

experience in terms of specific attributes of the location and whose expectations were met are more

likely to return to Arade and recommend it to family and friends.

Another aspect that the graph clarifies is that the direction of the line representing willingness to

recommend is not very different than the directions of the lines representing level of satisfaction. When

comparing these lines, however, the line indicating revisiting intention has a somewhat different

direction. Since in the graphs produced by CATPCA, similar points/lines suggest related

categories/variables, this study reveals that higher levels of satisfaction are more related to willingness

to recommend than intention to return.

Figure 4: Joint plot of category points for tourist satisfaction and destination loyalty intention

Cluster analysis

The graph produced by CATPCA suggests that two groups of tourists can be determined as a

result of the relations between the categories of variables measuring tourist satisfaction and variables

measuring destination loyalty intention. As indicated by the map, these groups display the following

characteristics: on the right-hand side of the graph, we can observe more satisfied tourists willing to

return and recommend Arade; the left-hand side shows tourists who are less satisfied and uncertain

about revisiting or recommending Arade as a holiday destination.

In order to validate these groups, a cluster analysis was performed. Final cluster centres are

presented in Table 5. Figure 5 displays these centres (also referred to as centroids) on the graph

produced by CATPCA (dark square and lined square). These centroids are clearly at the centre of the

groups suggested by CATPCA, establishing the presence of these groups. The centroid of Custer 1

appears on the right-hand side of the graph and the centroid of Custer 2 is represented on the left-hand

side. Thus, the clusters can be referred to as “more satisfied and more loyal tourists” (cluster 1) and

“less satisfied and less loyal tourists” (cluster 2). Note that 349 (72%) tourists were included in cluster

1 and 137 (28%) in cluster 2.

Patrícia Oom do Valle, João Albino Silva, Júlio Mendes and Manuela Guerreiro

Cluster 1

Cluster 2

Cluster 1

Cluster 2

Yes

Table 5: Final cluster centres and number of tourists in each cluster

Dimensions from CATPCA Cluster 1 Cluster 2

Dimension 1 0.50 -1.27

Dimension 2 -0.15 0.37

Figure 5: Joint plot of category points and clusters centres

An advantage of running a cluster analysis after CATPCA is that it allows us to create a new

variable that identifies which tourist belongs to which cluster. In particular, the tourists included in

cluster 1 were identified with code 1 and code 2 was used to identify tourists belonging to cluster 2.

This new variable (named as cluster membership) can then be related with other variables measured in

the questionnaire in order to provide a detailed description of the groups.

Table 6 shows the distribution of tourists for each group across the categories of variables used in

the CATPCA. As expected, there is a significant dependence relationship reported between each of

these variables and cluster membership (chi-square independence tests: p > 0.000). The values in bold

allow us to identify the tourist profile in each cluster according to these variables. As expected, the first

cluster includes the most satisfied (70.3%) and very satisfied tourists (98.6%), whose travel

expectations were met (79.6%) and whose intentions to recommend and return to Arade were stated

(93.6% and 92.3%). The second cluster displays opposing characteristics in terms of these variables.

Cluster 1

Cluster 2

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Table 6: Frequency distribution of variables used in the CATPCA in the two clusters solution

Variables used in the CATPCA Cluster 1 Cluster 2 Total

Overall satisfaction with holiday experience

1 – very unsatisfied 25.0%

75.0%

100.0%

2 – unsatisfied 8.3%

91.7%

100.0%

3 – not satisfied nor unsatisfied 8.6%

91.4%

100.0%

4 – satisfied

70.3%

29.7% 100.0%

5 – very satisfied

98.6%

1.4% 100.0%

Mean satisfaction with the attributes of the destination

1 – unsatisfied 0.0%

100.0%

2 – not satisfied nor unsatisfied 6.5%

93.5%

100.0%

3 – satisfied

69.3%

30.7% 100.0%

4 – very satisfied

90.7%

9.3% 100.0%

Were your expectations met?

1 – no 31.6%

68.4%

100.0%

2 – yes

79.6%

20.4% 100.0%

Do you intend to revisit Arade in the future?

1 – yes

93.6%

6.4% 100.0%

2 – maybe 54.5% 45.5% 100.0%

3 – no 3.8%

96.2%

100.0%

Would you recommend Arade to friends and family?

1 – yes

92.3%

7.7% 100.0%

2 – maybe 10.4%

89.6%

100.0%

3 – no 0.0%

100.0%

Clusters were also described in terms of socio-demographic characteristics. In this analysis, no

significant dependence relationships are identified between cluster membership and the variables:

“gender”, “occupation”, “marital status” and “type of lodging” (chi-square independence tests: p > 0.1).

This means that tourists in each cluster have approximately the same demographic profile reported in

table 1 according to these variables. Besides these variables, the groups do not report significant

differences in terms of “age” (independent samples t-test: p = 0.268), despite the average age being

higher in cluster 1 (37.19 years; standard deviation = 13.2 years) than in cluster 2 (35.72 years;

standard deviation = 13.68 years).

Table 7 clarifies the variables in which the clusters report significant differences. For a 10%

significance level, tourists in both clusters are statistically different in terms of “educational

qualification level” (chi-square independence test: p = 0.058). As can be observed in the Table, 58.1%

of tourists belonging to cluster 2 hold a degree. This percentage decreases to 46.2% with tourists

included in cluster 1. “Nationality” is an important variable that differentiates the groups (chi-square

independence test: p = 0.000): cluster 1 includes 77% of foreigner tourists whereas this proportion is

59.6% in cluster 2. That is, the weight of Portuguese tourists is higher in cluster 2 (40.4%) than in

cluster 1 (23%). The analysis shows that H4 is only partially demonstrated. Another variable that

distinguishes clusters is the “length of the stay”. Tourists in cluster 1 stay, on average, 12.56 days in

Arade, whereas tourists in cluster 2 remain, on average, 10.56 days (independent samples t-test: p =

0.001). In both cases, the “length of the stay” has a standard deviation of around 6 days. Finally, the

clusters also differ in terms of the main form of transportation mainly used during stay (chi-square

independence test: p = 0.072). Around 40% of tourists in cluster 2 use a private car, whereas most

tourists in cluster 1 rent a car (43.8%) or use public transports (26.8%).

Patrícia Oom do Valle, João Albino Silva, Júlio Mendes and Manuela Guerreiro

Table 7: Frequency distribution of selected variables in the two clusters solution

Selected variables Cluster 1 Cluster 2

Educational qualification level

Elementary 6.9% 4.4%

Secondary 46.8% 37.5%

College or higher 46.2% 58.1%

Total 100% 100%

Nationality

Portuguese 77% 59.6%

Foreigner 23% 40.4%

Total 100% 100%

Length of the journey (mean and standard deviation) 12.56 (6) 10.56 (6)

Main mean of transportation used in the journey

Rental car 43.8% 30.1%

Private car 25.6% 39.0%

Public transports 26.8% 19.8%

Other 3.8% 11.1%

Total 100% 100%

Another finding that deserves attention is the fact that the push motivations behind travelling to

Arade do not differentiate the groups (chi-square independence tests: p > 0.1). In both clusters, only

10.9% of tourists indicate “visiting friends” as the main motivation for visiting this destination. The

same occurs with respect to the remaining motivations: only around 4% of tourists in the two clusters

indicate reasons relating to “business” or “health”. For both groups, “leisure/recreation and holidays” is

the main motivation for travelling to this destination (reason indicated by 92% of tourists in cluster 1

and by 90.5% of tourists in cluster 2).

Figure 6 shows the thirty attributes of Arade that were graded by the respondents in terms of

importance, i.e., the pull motives for visiting this destination. This analysis was done by each cluster.

Regarding importance, a first finding reveals that tourists in both clusters do not report significant

differences for any of the attributes (independent samples t-tests: p > 0.15). In other words, pull

motivations do not distinguish the clusters. Figure 6 also clarifies the attributes that tourists in both

clusters consider more important (beaches, hospitality, landscape, restaurants, lodging, food, public

safety, competence and kindness, water supply system, waste recovery system and cleanliness) and

those that are less valued (spas, sports facilities and monuments). Because motivations (whether pull or

push) do not differentiate the clusters, H5 is not supported.

Figure 6: Mean importance of the attributes (by cluster)

Legend: 1 – totally irrelevant; 2 – little important; 3 – indifferent; 4 – important; 5 – extremely important

Int. Journal of Business Science and Applied Management / Business-and-Management.com

Figure 7 provides a similar analysis as figure 6 but includes the satisfaction with the attributes.

The first aspect that should be noted is that tourists in cluster 1 report a higher level of satisfaction in all

attributes than tourists in cluster 2. All differences between groups are statistically significant

(independent samples t-tests: p = 0.000). The figure also clarifies the attributes in which the differences

between groups are higher (such as, hospitality, urban planning, competence and kindness and

cleanliness) and those that are perceived more similarly (beaches, food and monuments). For both

clusters, the attributes more positively perceived are beaches, hospitality, landscape, restaurants, food,

lodging and the competence and kindness of the locals. The attributes more negatively assessed are

traffic, urban planning, parking zones and traffic signs. Attributes such as spas, traditional architecture,

cultural events, waste recovery system and cleanliness also report low levels of satisfaction, especially

among tourists in cluster 2.

Figure 7: Mean satisfaction according to the attributes (by cluster)

Legend: 1 – very unsatisfied, 2 – unsatisfied, 3 – not satisfied nor unsatisfied, 4 – satisfied, 5 – very satisfied

6 DISCUSSION AND CONCLUSIONS

As living standards increase around the world, more people find themselves able to travel to

different destinations. This study establishes the direct causal relationship between tourist satisfaction

and destination loyalty intention by exploring the case of tourists visiting Arade, a Portuguese tourism

destination.

The results of this study validate the research hypothesis that tourist satisfaction is one

contributing factor to destination loyalty intention. This conclusion is mainly based on the findings of

the estimated structural equation model. Through CATPCA and cluster analyses, results were fully

explored establishing that two clusters of tourists could be identified and then described. Cluster 1

includes the most satisfied tourists who are more determined in revisiting and suggesting the

destination; cluster 2 embraces those with worst perceptions of the destination and with weak intentions

of returning and recommending. Moreover, observation of the graph produced by CATPCA allows us

to conclude that a higher level of satisfaction is more associated to willingness to recommend than to

intention to return. This information could not be provided by the SEM procedure. In fact, the

estimated model only indicates that tourist satisfaction and loyalty intentions are adequately measured

(which is informed by the measurement model results) and are related (which is informed by the

structural model results) but do to put forward how the observed variables are jointly correlated. Thus,

the sequential data analysis procedures used in this study enables an indepth look at the relationship

between satisfaction and loyalty in the tourism framework.

The results of this study have important implications for marketers and managers of Arade as a

travel destination. In specific, there is a need to improve the perceived quality of the tourist offer,

which is the basis of tourist satisfaction (Bigné et al., 2001). Most attributes of the destination services

may be controlled and improved by tourism suppliers. The improvement of these services is important

and worthwhile because, as this study shows, tourists experiencing higher satisfaction levels reveal

favourable intentional behaviour, that is, the willingness to return to Arade and to recommend it to

others. Moreover, this study also shows that the most satisfied tourists (cluster 1) spend more time, on

Patrícia Oom do Valle, João Albino Silva, Júlio Mendes and Manuela Guerreiro

average, in this destination than the least satisfied tourists with weaker intentions of returning or

recommending the region (cluster 2). This is an important finding because a longer stay brings

potentially added economic advantages to the region.

Figure 7, and in particular the line representing tourists in cluster 2 (the least satisfied), provides

useful indications in improving Arade’s competitiveness. The weaknesses of the destination can be

summarised, in decreasing order of importance, into four areas: (1) urban planning problems (indicated

by the attribute ‘urban planning’); (2) traffic problems (indicated by the attributes ‘traffic’, ‘parking’

and ‘traffic signs’); (3) cleanliness problems (indicated by the attributes ‘cleanliness’ and ‘waste

recovery system’); and (4) cultural initiative problems (indicated by the attributes ‘traditional

architecture’ and ‘cultural events’). The most critical attributes can be considered those related to

traffic and cleanliness because these are important pull motivations that go beyond destination choice

(Figure 6). The Destination Management Organization (DMO) of Arade should consider a priority

trying to establish solutions for these problems. Some of these weaknesses can be resolved in the short

term through the involvement of the municipalities. The lack of traffic signs, inadequate waste recovery

system, the need for old building renovation and the development of the absence of cultural initiatives

are good examples. The provision of more and ideally located parking spaces also deserves urgent

attention. Finally, it is of strategic importance to review and improve the region’s urban planning in

order to enhance the overall attractiveness of this tourism region.

Taking into account the country’s natural conditions, Portugal, and in particular Arade, has all the

requirements necessary to be at the forefront in tourism of the future. Figure 7 also clearly shows is that

tourists in cluster 1 provide a very good evaluation of the natural conditions of the destination

(‘beaches’, ‘landscape’), as well as of the social environment (‘hospitality’, ‘authenticity’, ‘public

safety’ and ‘competence and kindness’). Facilities more related to tourism activity are also greatly

appreciated (‘restaurants’, ‘lodging’, ‘shopping zones’, ‘food’, ‘leisure spaces’). These are also the

most positively assessed attributes by tourists in cluster 2, even lower levels of average satisfaction are

observed. It is fundamental that marketers of this destination take advantage of this information in

order to project the region’s image, either nationally or internationally. In general, the perceptions

about this destination (Figure 7) surpass expectations (Figure 6), a characteristic that may be further

explored in future marketing communication plans.

By evaluating each attribute individually Figure 7 exhibited statistically significant differences

between the two clusters for all attributes, more positively graded by tourists belonging to cluster 1.

Despite the attributes being different in terms of perceptions, tourists in both groups assess them

similarly when focus is on importance rather than satisfaction. This means that the groups are not

significantly different in terms of the pull motivations behind the destination (Figure 6). In addition,

this study shows that tourists in the two clusters present a quite similar profile in what concerns the

push motivations behind the Arade region. In both cases, the main and almost single intrinsic

motivation in choosing this destination is associated to the need for a vacation/holiday. Arade,

therefore, should focus on this global segment – tourists that choose the destination for leisure motives

– taking advantage of the unique natural and social conditions of the region, offering recreation and rest

and at the same time work out the problems mentioned above that threaten the destination’s image.

This study also establishes that no significant socio-demographic differences exist between the

two groups of tourists in terms of gender, age, marital status and occupation. By working with a

significance level of 10%, we can conclude that clusters differ in terms of qualification level. As

mentioned, around 60% of tourists belonging to cluster 2 hold a degree (the least satisfied). This

percentage is lower in cluster 1. This result suggests that higher qualification levels may be related to

higher demanding levels I terms of services offered by the destination. It is not atypical that tourists

with higher qualification levels are potentially more judgmental when assessing places they are visiting

since, very likely, they are already aware of alternative holiday destinations and, therefore, more

critical in terms of assessment. However, this is a characteristic that clearly deserves further research.

Another relevant finding is that cluster membership and nationality are significantly dependent. In

specific, cluster 2 registers an increased proportion of Portuguese tourists than cluster 1. This may be a

consequence of the generalised feeling among Portuguese citizens that foreign tourists are better

welcomed and treated than Portuguese tourists. This sentiment has some foundation because some

cities of Algarve – those most dependent on tourism-related activities – resemble foreign surroundings.

There are many English pubs, restaurants displaying English cable television, eateries selling only

familiar English food and tourist information only in English. Moreover, most Portuguese come to

Algarve at least once a year, and so are very familiar with the region. One consequence of this fact is

that national tourists do not perceive the region’s strengths as positively as foreign tourists. For

example, the English tourist more easily appreciates the warmer climate and high quality beaches in

Algarve than the national tourists do. The latter tend to be more intolerant and criticizing.

Int. Journal of Business Science and Applied Management / Business-and-Management.com

This characteristic provides empirical evidence of the need for a more careful marketing approach

towards national tourists. Centring promotional campaigns on sun and beach is not enough to attract

Portuguese tourists. Instead, the DMO should invest in employing more highly qualified staff in the

tourism and hospitality industry, and become more involved with those responsible for arising regional

problems (those depicted in Figure 7), stimulating and supporting initiatives that induce positive

changes in the more critical aspects of the tourism product. Moreover, the DMO should develop

specific promotional actions leading to an upgrading of the destination image since this is always an

important segment of the market. First, marketing messages can be directed to show that destination

problems are being addressed, demonstrating that effort is being made by municipalities to answer to

expectations by visitors. Second, it becomes equally important to stimulate greater participation from

those involved in the evaluation process on the tourist experience as well as more efficient management

of opinions, complaints, and suggestions. Finally, because image change is a slow process, DMO

should consider stakeholder involvement in developing publicity campaigns commitment aimed at the

mass media at regular interval periods. Aside from major campaigns initiatives, it would be essential to

represent the region under the “friendly destination concept” with marketing messages aimed at low

season tourism, when the region is less congested and less marked by some of the drawbacks (such as

traffic and litter problems).

Furthermore, we can also observe in the CATPCA graph that high satisfaction levels are more

related to willingness to recommend than intentions to return. This result is understandable. If a tourist

classifies the tourism experience as positive and pleasant it is expected that he/she recommends the

destination to friends and relatives. However, revisiting destinations carries some costs, even when a

previous visit was highly satisfactory. These costs can be financial, if the tourist feels that the overall

travel expenses are too high and, therefore, conditioning him/her to return, or they can be opportunity-

related. In fact, the tourism offer is so large that returning to an already familiar place can imply not

visiting a different destination, a high opportunity cost.

This study has some limitations whose overcoming provides directions for further research.

As shown in Section 5, the data matched the estimated model. Nevertheless, and because any

model is always an approximate description of reality, a different model with other observed variables

could produce a similar or even improved global fit. In the proposed model, the latent constructs are

measured by observed variables dictated by the previous research. As described, the analysis of the

measurement model shows that, in general, they are reliable and valid measures of the corresponding

constructs, even though the observed variable met expectations had reported a low loading (0.33) on

tourist satisfaction (although statistically significant), especially when compared to those associated

with the satisfaction variables (0.71 and 0.70, respectively). It would certainly be preferable to achieve

a higher loading in this variable. However, as explained in Section 3, assessing whether tourist

expectations are met or not should be considered in terms of satisfaction with the destination

experience. Moreover, removing met expectations from the model yields worse results in almost all

indices produced by the SEM analysis. Therefore, future research should contemplate met expectations

on a more detailed scale, rather than the adopted binary approach.

Based on the SEM results, we can conclude that the first three proposed research hypotheses

cannot be rejected. Some care, however, should be taken when interpreting the first hypothesis. In

effect, this study only shows that tourist satisfaction is one contributing factor to tourism loyalty

intentions. In other words, what is being evaluated is “destination loyalty intentions” and not “actual

destination loyalty” because the observed variables only consider revisiting and recommending

intentions. This aspect of how “destination loyalty intentions” leads to “actual destination loyalty”

(measured for instance by a revisiting experience and whether the destination was effectively

recommended as a result of a previous visit) is another topic of considerable ground for further

investigation.

A final underlying detail of this study is the moderate squared multiple correlation value which

was reported in the structural equation model (61.6%). Despite the model’s goodness-of-fit evidenced

by all analysed indicators, there is empirical support that destination loyalty intention is explained by

additional constructs besides satisfaction. This finding suggests that further work on the predictors of

destination loyalty is necessary. By extending the proposed model to include other constructs in the

satisfaction-loyalty relationship (such as motivations, perceptions, expectations and destination image),

further examination can be made, through the use of combined statistical data analysis procedures, to

better understand the tourist behaviour.

Patrícia Oom do Valle, João Albino Silva, Júlio Mendes and Manuela Guerreiro

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