The influence of market heterogeneity on the relationship between a destination's image and tourists’ future behaviour

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Tourism Management 28 (2007) 175–187 www.elsevier.com/locate/tourman

Research Article

The influence of market heterogeneity on the relationship between a destination’s image and tourists’ future behaviour Carmen Barroso Castro, Enrique Martı´ n Armario, David Martı´ n Ruiz Departmento de Administracio´n de Empresas y Marketing, Facultad de Ciencias Econo´micas y Empresariales, Avda. Ramo´n y Cajal, s/n 41011, Sevilla, Spain Received 11 April 2005; accepted 18 November 2005

Abstract In the past decade, companies and academics have become aware of the great benefits of maintaining a solid base of loyal customers. Such customer loyalty is also important to other entities—such as tourist destinations. Building on the services-marketing literature, the present study develops an innovative model for evaluation of the effect of a destination’s image on the loyalty intentions of tourists. The study then explores whether market heterogeneity affects this relationship by performing a latent cluster analysis. Four major clusters of tourists emerge—according to the tourists’ need for variety. The results show that there are significant differences among these segments in terms of the effects of a destination’s image on tourists’ intentions to return to a destination and their intentions to recommend it to friends and relatives. r 2006 Elsevier Ltd. All rights reserved. Keywords: Destination image; Loyalty; Market heterogeneity; Latent cluster segmentation; Need for variety

1. Introduction One of the main challenges for tourist managers is understanding the patterns of behaviour of tourists. If repetitive patterns of behaviour can be established, this can help to ensure future sources of income and can also create informal channels of relationships whereby potential tourists can be attracted to specific destinations (Petrick, 2004; Reid & Reid, 1993). The recent tourism literature reflects this increasing interest in the behaviour of tourists (Baker & Crompton, 2000; O’Leary & Deegan, 2005; Petrick, 2004; Sirakaya & Woodside, 2005). Indeed, tourism literature offers substantial conceptual and empirical work to describe tourists’ destination choice process, although there are yet knowledge gaps to address (Sirakaya & Woodside, 2005). In words of Sirakaya and Woodside (2005, p. 816), ‘‘given the centrality of the selection decision process, a clear understanding of the complexities and interrelationships of these variables is an important research agenda’’. Corresponding author. Tel.: +34 954 557 521; fax: +34 954 556 989.

E-mail addresses: [email protected] (C.B. Castro), [email protected] (E. Martı´ n Armario), [email protected] (D. Martı´ n Ruiz). 0261-5177/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tourman.2005.11.013

In general, these studies are sustained by premises developed within the literature on services in general (Anderson & Sulliwan, 1993; Boulding, Kalra, Staelin, & Zeithaml, 1993; Cronin & Taylor, 1992; Hallowell, 1996; Rust & Oliver, 1994; Taylor & Baker, 1994; Ruyter, Wetzels, & Bloomer, 1996; Zeithaml, Berry, & Parasuraman, 1996), as well as from the literature on consumer decision-making behaviour in tourism (Mathieson & Wall, 1982; Um & Crompton, 1990; Woodside & Dubelaar, 2002; Woodside & Lysonski, 1989). One argument of the first stream of research (Anderson & Mittal, 2000; Storbacka, Strandvik, & Gro¨nroos, 1994) is that identifying the antecedents of tourists’ loyalty (perceived service quality, satisfaction, value, and so on) enables managers to develop strategies that will increase loyalty. The relationship between the image of a destination and the loyalty of tourists is also important—because it has been shown that the image of a destination is a critical factor in influencing tourist satisfaction (Abdullah, Alnasser, Aamjad, & Husain, 2000; Cai, Wu, & Bai, 2003; Kandampully & Suharatanto, 2000; O’Leary & Deegan, 2005). On the other hand, consumer decision-making literature acknowledges that understanding travel decisions require an analysis of effects of social and psychological

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factors (Mayo & Jarvis, 1981), where choice of destinations is one of many travel-related decisions the tourist has to make (Woodside & MacDonald, 1994). Despite the abundance of studies in this area, there has been little consideration of how such relationships are affected by the individual characteristics of tourists (Kim, Forsythe, Gu, & Moon, 2002; Gwinner et al., 1998; Zins, 2001) and market heterogeneity (Mittal & Kamakura, 2001; Sun, Wilcox, & Zhu, 2004). This is despite the fact that the tourism literature has emphasised the importance of segmentation of the market if effective marketing strategies are to be employed (Decrop & Snelders, 2005). According to Sirakaya and Woodside (2005, p. 828), ‘‘developing a model that fits all decision makers may not be realistic. Priori segmentation of travel markets y is an approach useful for future models.’’ In view of the relative paucity of studies in this field, the present study therefore aims to analyze the influence of market heterogeneity— based on a psychological characteristic of the individual— on the relationship between the destination’s image and tourist’s evaluations and future behaviour. The study makes three significant contributions in this area. First, it explores the influence of a psychographic feature of tourists—the need for variety—as a latent base for segmentation. Although the concept of ‘latent segmentation’ has received attention in the psychology literature and the marketing literature (Lebrun, 2002; Sivakumaran & Kannam, 2002), there is little evidence of its having been extensively studied in the tourism literature (Bello & Etzel, 1985; Niininen, Szivas, & Riley, 2004; Riley, Niininen, Szivas, & Willis, 2001). Secondly, it conducts a latent cluster segmentation that identifies market heterogeneity and its possible effects on the relationships under consideration. Finally, it evaluates the effect of the image of a destination—especially with respect to service quality and satisfaction—on the future behaviour of tourists. The rest of the paper is arranged as follows. Following this introduction, the paper presents a literature review and the conceptual model that underlies the research. This is followed by a presentation of the methodology of the study—with particular emphasis on latent segmentation. Finally, the results of the study are discussed, along with future lines of research on the topic.

According to the disconfirmation paradigm (Oliver, 1980), loyalty depends on the level of consumer satisfaction. There is plenty of evidence to support a cause-andeffect relationship between loyalty and satisfaction (Rust & Oliver, 1994; Tam, 2004; Taylor, 1997; Taylor & Baker, 1994), as well as significant evidence identifying service quality as an antecedent of customer satisfaction (Bitner & Hubbert, 1994; Caruana, 2002; Cronin & Taylor, 1992; Spreng & Mackoy, 1996; Sureshchandar, Rajendran, & Anantharaman, 2002; Tam, 2004; Yi & La, 2004; Zeithaml et al., 1996). Similarly, Woodside and Dubelaar (2002) link micro- and macro-evaluations of the destination with the future behaviour of the tourist, such as its willingness to recommend and revisit the destination. The other main stream of research in this area—the cognitive psychology stream—has focused on analyzing the relevance of cognitive schemes in buyer decision processes (Andreassen & Lindestad, 1998). On this regard, a recent contribution by Sirakaya and Woodside (2005) provides an extensive qualitative review of the tourism decision-making literature. Their ‘‘meta-theory’’ article identifies four major areas for prior and future research on this broad topic— consumer decision making in tourism, information-processing theory, foundation travel decision models, and behavioural and choice-set approaches to decision making in tourism. The present research explores the influence exerted by the image of a destination—a highly cognitive construct— on the future behaviour of tourists, using service quality and satisfaction as mediating variables (Andreassen & Lindestad, 1998; Bigne´, Sa´nchez, & Sa´nchez, 2001). The study is based on two premises: the first is that market heterogeneity plays an important role in the relationships among image, satisfaction, and loyalty—because it has been demonstrated that various market segments display substantially different behaviours (Kamakura & Russell, 1989; Mittal & Kamakura, 2001). The second premise is that image plays an important role in services that are complex to evaluate—such as tourist destinations. In these cases, the image of the service can be a significant factor in conditioning customers’ perceptions of quality and satisfaction—and their consequent future behaviour. 2.2. Market heterogeneity

2. Literature review and conceptual model 2.1. Two major streams of research Most of the literature on tourist loyalty comes from studies of consumer behaviour in service settings (Riley et al., 2001). This has been a growing area of interest in recent years (Andreassen & Lindestad, 1998; Zins, 2001), with two major contributions of note having been made: (i) the disconfirmation paradigm (Oliver, 1980; Oliver & Desarbo, 1988); and (ii) proposals based on cognitive psychology (Folkes, 1988). Both of these approaches have focused on predicting consumer behaviour.

There is a strong link between customer loyalty and an organization’s profitability (Buzzell & Gale, 1987; Gupta, Lehmann, & Stuart, 2004; Ho, Park, & Zhou, 2004; Loveman, 1998; Rust & Zahorik, 1993). Indeed, this relationship might explain the relative success (or lack of success) of many companies in various industries (Reichheld, 1993). The tourism industry is no exception (Baker & Crompton, 2000). Nevertheless, it has been difficult to demonstrate the exact nature of the relationship between the perceptions of customers and their future behaviour (Mittal & Kamakura, 2001; Sun et al., 2004). Apparently contradictory findings have been obtained—many of which

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might be explained by the fact that the individual characteristics of customers have been neglected. In this context, Mittal and Kamakura (2001) have contended that customers who display different personal characteristics also display differences in their future behaviour—despite having similar levels of satisfaction with their providers. Ignoring the effect of customer heterogeneity might therefore mislead organizations into believing that there are ambiguous conclusions regarding the relationship between customer loyalty and their profitability (Sun et al., 2004). In this regard, the marketing concept of segmentation is important. Segmentation presumes the existence of heterogeneity among customers in the market, and has received considerable support within the tourism literature (Decrop & Snelders, 2005; Yuksel & Yuksel, 2002). Segmentation is usually based on demographic, socioeconomic, and psychographic characteristics—all of which have been shown to be associated with differing consumer needs and preferences (Gonza´lez & Santos, 2003). Although other contributions have identified certain segmenting variables that can be linked to customer satisfaction and future behaviour (Ho et al., 2004; Senguder, 2003; Sun et al., 2004), such studies have been relatively rare. Similarly, there is limited evidence in the literature supporting the notion that customer heterogeneity affects the nature of relationships between providers and customers (Bolton, 1998; Danaher, 1998; DeSarbo, Jedidi, & Sinha, 2001; Garbarino & Johnson, 1999). 2.3. Need for variety An important psychological concept in the marketing literature that is relevant to consumer behaviour is the concept of the ‘need for variety’ (Chen & Paliwoda, 2004). Some people seem to require more stimuli and variety than others, and this affects their future behaviour (Wahlers & Etzel, 1985; Hanna & Wagle, 1988; Lebrun, 2002). Individuals obtain various dynamic stimuli from their environments—such as ambiguity, novelty, and complexity. For all of these, there is an ‘‘optimum level of stimuli’’ (Zuckerman, Kolin, Price, & Zoob, 1964). Although there are few studies that have addressed this variable within the scope of services (Homburg & Giering, 2001; Wahlers & Etzel, 1985), there seems to be a certain consensus that the need for variety is a specific feature of certain individuals (Parker & Tavassoli, 2000), and that this has a direct effect on the exploratory behaviour of certain consumers (Chen & Paliwoda, 2004; Lebrun, 2002; Sivakumaran & Kannam, 2002). This variable ‘need for variety’ can provide a means of segmenting markets in terms of ‘high-variety seekers’ and ‘low-variety seekers’. It can be presumed that highvariety seekers will be less loyal than low-variety seekers— given that loyalty constrains their psychological need to try new things (Va´zquez Carrasco, 2003). If the concept of ‘high-variety seekers’ is combined with the concept of

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‘optimum levels of stimuli’, it can be presumed that loyalty should be associated with low levels of stimuli (Riley et al., 2001). The tourism industry is an appropriate industry in which to analyze the need for variety (Niininen et al., 2004). This is because it is a voluntary activity, detached from the daily routine, limited in space and time, and surrounded by a certain degree of mystery (Godbey & Graefe, 1991). The need for variety might lead certain tourists to repeat the same type of holidays, but not in the same destination (Bello & Etzel, 1985; Opperman, 1997). Alternatively, the complexity of a tourism offer might allow certain consumers to repeat their stay at a particular destination and still receive new stimuli. However, in general, it is likely that high levels of need for variety reduce the probability of tourists returning to the same destination (Bello & Etzel, 1985; Niininen et al., 2004). Taking the above arguments into account, we will continue under the following working premise: ‘‘different levels of ‘need for variety’ and/or an individual’s optimum level of stimuli determine the existence of different latent segments in the market’’. 2.4. Perceived image as an antecedent of service quality and customer satisfaction Image can be understood as the general impression that a tourist has about a destination (Rynes, 1991). Image has been identified as a relevant factor in a customer’s final evaluation of a service (Bitner, 1995; Gro¨nroos, 1984). However, ‘image’ is a tricky concept that is difficult to define. In practice, image is composed of several elements that go beyond the perception of any given individual (Flavian, Tores, & Guilaniu, 2004). In this regard, some authors point out that image is the outcome of interactions among various experiences, impressions, beliefs, feelings, and fragments of knowledge that customers have about a particular organization (Worcester, 1997). Image is thus characterized by a high level of subjectivity—including both cognitive aspects (beliefs) and affective aspects (feelings) of such subjectivity (Baloglu & Brinberg, 1997; Beerli, Diza, & Pe´rez, 2002; Bigne´ et al., 2001). The combination of these cognitive and affective aspects provides a ‘global image’ of the provider—reflecting an overall positive or negative assessment on the part the customer (Baloglu & McCleary, 1999). Although there is evidence that the cognitive component precedes the affective component (Baloglu & McCleary, 1999; O’Neil & Jasper, 1992; Stern & Krakover, 1993), the affective component has a stronger influence on the customer’s overall global image (Beerli et al., 2002). There is wide agreement among scholars concerning the influence that the destination’s image exercises on the future behaviour of tourists (Ashworth & Goodall, 1998; Bigne´ et al., 2001; Chen & Gursoy, 2001; Mansfeld, 1992). This image is created through communications and the past experiences of the customer.

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In addition, the service literature has established a definite relationship between image and perceptions of service quality (Flavian et al., 2004). Service quality can be defined in terms of a comparison between a customer’s initial expectations and that customer’s perception of the actual result of the service (Parasuraman, Zeithaml, & Berry, 1988). Service quality is thus closely related to satisfaction, but not equivalent to it. The literature supports the view that service quality and customer satisfaction are different variables, although closely related (Spreng & Mackoy, 1996; Taylor & Baker, 1994). Giese and Cote (2000) emphasised the need to specify satisfaction according to the context in which it is evaluated. They identified three basic elements of customer satisfaction. According to their conception, satisfaction is: (i) a response to an emotional judgment; (ii) related to a specific aspect of a service (such as a service encounter); and (iii) linked to a specific moment in time (for example, immediately after the service has been experienced). The relationship between customer satisfaction and image has not received much attention from researchers. This is because they are typically analyzed with other constructs—such as perceived value, perceived quality, and customer loyalty (Abdullah et al., 2000; Kandampully & Suharatanto, 2000). However, this relationship has been considered in some earlier works in this field (Gro¨nroos, 1984) and, more recently, it has been explored in several service industries (Bigne´ et al., 2001; Selnes, 1993; Zins, 2001). Andreassen and Lindestad (1998) have concluded that this relationship is indirect and is mediated by service quality. Moreover, they found that its influence is larger when the tangibility of the service offering decreases. Bloemer, De Ruyter, and Peeters (1998) contended that the link between image and future behaviour is still a matter of debate, although they identified service quality and customer satisfaction as possible mediators between the two constructs. These authors considered that image influences customer’s expectations, and that these play a decisive role in service quality and customer satisfaction. There have also been some contradictory results. Bloemer et al. (1998) could not validate their hypothesis regarding the indirect effect of image on customer loyalty mediated by customer satisfaction, whereas other studies were able to confirm this link (Andreassen & Lindestad, 1998; Beerli et al., 2002; Bigne´ et al., 2001). In contrast to these somewhat equivocal results, the service literature has demonstrated the critical role played by perceived quality and customer satisfaction when it comes to influencing the future behaviour of customers (Rust & Oliver, 1994; Taylor & Baker, 1994). There is plenty of evidence to support the contention that high levels of customer satisfaction imply positive future behaviour towards the organization supplying the service (Bolton & Drew, 1991; Fornell, 1992; Taylor, 1997). Customer loyalty has been addressed from two academic perspectives within the services literature (Hallowell, 1996): services management and services marketing. The first of

these, services management, states that loyalty requires the maintenance of a durable and intense relationship with the organization. Such a relationship results from the customer’s perception of the organization’s superior value compared to its competitors (Chen & Gursoy, 2001). The second of these, services marketing, analyzes loyalty as an attitude and as behaviour (Berne´, Mu´gica, & Yague, 1996). Loyalty as an attitude implies that customers must have positive attitudes towards the organization, and that behavioural loyalty is reflected in the frequency of repurchasing and the size of each purchase. It can therefore be concluded that customer loyalty has a subjective dimension and an objective dimension (Huete, 1997). The subjective dimension is focused on building emotional bonds with the customer, whereas the objective dimension is behaviour based (with objective indicators of the relationship between the customer and the organization). Customers demonstrate their bonds with the organization through various signals—such as increased purchasing volume, neglect of competitors, positive word-of-mouth recommendation, and so on. Zeithaml et al. (1996) attempted to integrate all the empirical results of preceding research by suggesting that customers’ future intentions can be collected into four major categories: (i) referrals; (ii) price sensitivity; (iii) repurchase; (iv) and complaining behaviour. Some studies that have analyzed the relationships among service quality, customer satisfaction, and future behaviour have limited the concept of loyalty to repurchase alone (Anderson & Sulliwan, 1993; Cronin & Taylor, 1992), whereas other studies have included the provision of recommendations or referrals (Boulding et al., 1993; Ruyter et al., 1996). In the tourism industry, various studies have used these same variables to explain tourist’s loyalty (Baker & Crompton, 2000; ; Beerli, 2002; Bigne´ et al., 2001; Cai et al., 2003; O’Leary & Deegan, 2005; Petrick, 2004). As a result of the above discussion, the following working hypotheses are presented: H1. The intensity of the relationship between the destination’s image and service quality is moderated by the tourist’s need for variety. H2. The intensity of the relationship between the destination’s image and satisfaction is moderated by the tourist’s need for variety. H3. The intensity of the relationship between service quality and satisfaction is moderated by the tourist’s need for variety. H4. The intensity of the relationship between service quality and tourist’s future behaviour is moderated by the tourist’s need for variety. H5. The intensity of the relationship between satisfaction and tourist’s future behaviour is moderated by the tourist’s need for variety. H6. The intensity of the relationship between the destination’s image and tourist’s future behaviour is moderated by the tourist’s need for variety.

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3. Research methodology

Table 1 Sample

3.1. Scope of the study Data were collected by means of personal interviews performed by trained interviewers during spring’s years 2004 and 2005.1 The questionnaire is presented in the appendix. The population was defined as the tourists who visited a large city in the south of Spain. From this population, a random sample was selected. The sample was representative of the whole population in terms of country of origin and month of visit. A total of 1526 valid questionnaires were obtained (see Table 1). 3.2. Measurement tools There are many different proposals for a measurement tool for the ‘need for variety’ construct. These include: (i) the scale of Steenkamp and Baumgartner (1995); (ii) the ‘‘sensations search scale’’ developed by Zuckerman (1964); (iii) the ‘‘tendency to excitement search scale’’ developed by Mehrabian and Russell (1974); (iv) the ‘‘index of change search’’ by Garlington and Shimota (1964); and (v) the scale of ‘‘novelty experimentation’’ developed by Pearson (1970). After examining these various tools, the present study concluded that the scale proposed by Steenkamp and Baumgartner (1995) was a useful tool with a reasonable number of items. As with the ‘need for variety’ construct, there are several suggested measurement tools for ‘image’ (Beerli, 2002). There are two basic options: (i) asking individuals to evaluate the image according to several attributes; and/or (ii) asking individuals to provide a relative weighting for each of these attributes. Most studies have followed a multi-attribute approach in which the ‘global image’ is assessed as being the average of all attributes (Echtner & Ritchie, 1993). One of the limitations of this alternative is that some relevant attributes of a destination might be omitted. Nevertheless, the present study chose a multiattribute battery of 18 items whereby cognitive aspects were evaluated on a Likert-type scale of 1–5. The measurement tool was developed from proposals provided by Hanyu (1993), Walmsley and Jenkins (1993), and Beerli (2002). There are also various approaches to the measurement of ‘service quality’. Although there is consensus regarding the multi-dimensionality of this construct, there is no such consensus with respect to the definition of these dimensions (Llosa, Chandon, & Orsingher, 1998). The best-known scale is the SERVQUAL tool proposed by Parasuraman et al. (1988). However, following Carman (1990), the present study estimated service quality directly using a single item that compared customers’ perceptions against their expectations. 1 There were no significant differences among the variables between the two samples.

179

2004

2005

Total

Spaniards Foreigners

270 340

473 443

743 783

Total

610

916

1526

‘Customer satisfaction’ can be measured by disconfirmation between expectations and results, or, in specific situations, through perceptions of outcome alone (Bigne´ et al., 2001; Oliver, 1980). In addition, the degree of customer satisfaction can be assessed directly through specific service attributes or as a global measure. The present study estimated customer satisfaction with a single general item, in accordance with previous research in the field (Bigne´ et al., 2001; Fornell, 1992; Spreng & Mackoy, 1996). The construct of ‘future behavioural intention’ was estimated with two items—(i) intention of repurchase; and (ii) intention to provide positive recommendations (Cronin & Taylor, 1992; Homburg & Giering, 2001; Parasuraman, Berry, & Zeithaml, 1991). In tourism, the intention to return to a destination and to recommend the visit to others are both indicators of loyalty (Bigne´ et al., 2001; Cai et al., 2003; Chen & Gursoy, 2001; Niininen et al., 2004; Petrick, 2004). 3.3. Data analysis The proposed model was tested by path analysis after measurement tools that included more than a single item had been evaluated. Market segmentation according to customers’ needs for variety was performed with a latent cluster analysis (Desarbo, Wedel, Vriens, & Ramaswamy, 1992; Wedel & Kamakura, 2000). There is little research segmenting the market according to psychographic attributes (attitudes, motivation, personality) because this requires latent variables (Pico´n & Varela, 2004a, b). Nevertheless, decisions-making process and behaviours of tourists is influenced by psychological variables, for example, attitudes, motivations and beliefs (Woodside & Dubelaar, 2002). Therefore, developing a model that fits all decision makers may not be realistic. Different segments might have dissimilar methods of approaching problem solving and the decision making (Sirakaya & Woodside, 2005). Latent cluster analysis, also known as finite mixture regression, is a multi-variate model whose purpose is to find subgroups of cases from a certain number of variables, such that underlying segments from the general population can be identified. This methodology assumes that all of the dataset cannot be explained with a single distribution of probabilities; rather, it requires a mixture of them. Thus, each cluster is formed by the cases that belong to a specific distribution. This method assumes that individual preferences constitute

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a population that is a mixture of several segments of unknown size. It follows that it is impossible to know, a priori, which individual belongs to a particular segment (Pico´n, 2004). It is necessary to separate the samples by identifying the number of segments and estimating the parameters that define each of them. This is considered an optimum segmentation because the number of market segments is not set by the researcher. The technique provides the optimum number of clusters that should divide the market. The estimation method begins with a hierarchical cluster and continues with the iterative algorithm expectation-maximization (EM), until the combination of model and number of clusters is found that enables the collection of more information. Then, the number of clusters in the sample is identified by looking at which alternative displays the smallest Bayesian Information Criteria (BIC). Three software packages were used to apply this technique: SPSS 12.0, AMOS 4.0, and Latent Gold 3.0. 4. Analysis of results 4.1. Measurement and structural models Before conducting the path analysis, the validity and reliability of the scales that included more than a single indicator were evaluated. An exploratory factor analysis of the image scale allowed the information to be reduced to five dimensions (Beerli, 2002; Beerli, Martı´ n, & Quintana, 2004; Zins, 2001). These five dimensions accounted for 57.35% of the explained variance (Kaiser–Meyer–Olkin: 0.883). According to Cronbach’s a, items 5, 7, and 14 should be discarded due to low consistency. This left five dimensions: (i) development of the destination (items 9, 16, and 18); (ii) popular destination (items 8 and 15); (iii) fun of the destination (items 6, 10, 11, 12, and 13); (iv) attractiveness of the destination (items 1, 2 and 3); and (v) tourist aspects of the destination (items 4 and 17). A confirmatory factor analysis was conducted in order to verify the scale’s validity (results are displayed in Table 2). From these results, it was Table 2 Model adjustment Structural model Overall model fit

X 2 ¼ 37:746 (p ¼ 0:000); df ¼ 4; MDN ¼ 0.98 GFI ¼ 0.989; RMR ¼ 0.012; RMSEA ¼ 0.08; CFI ¼ 0.983

Destination’s image

St. weights

CR

R2

Development of the destination Popular destination Fun of the destination Attractiveness of the destination Tourist aspect of the destination

0.486 0.723 0.799 0.628 0.658

* 17.084 15.428 14.309 14.611

0.436 0.622 0.738 0.535 0.585

Parameter fixed to 1.

0.504 (22.8)

Destination’s image

0.311 (13.9)

0.205 (8.2)

Service quality 0.143 (5.0)

0.442 (19.7) 0.154 (5.0)

0.263 (9.1)

Intention to recommend

Tourist’s Satisfaction 0.17 (5.5)

Intention to revisit

Fig. 1. Path analysis (whole sample): w2 with one degree of freedom ¼ 33,969; MDN ¼ 0.989, GFI ¼ 0.991; AGFI ¼ 0.900; RMSR ¼ 0.009. Standardized loading (critical coefficient). Critical coefficient o1.96 indicates non-significant relationships.

possible to develop an index of a destination’s global image taking the factor loadings into account. The results of the path analysis without considering the market heterogeneity (including the whole sample) are presented in Fig. 1. As can be observed in Fig. 1, a destination’s perceived image influences both service quality and tourist satisfaction. However, service quality is an antecedent of tourist satisfaction. In addition, service quality and tourist satisfaction are mediators and significant determinants of the intention to revisit the destination or to recommend it to friends and relatives. This evidence is consistent with previous results in the tourism literature (Bigne´ et al., 2001) and in the services literature (Andreassen & Lindestad, 1998; Zins, 2001). Our results confirm that there is a strong indirect relationship between a destination’s image and the future behaviour of tourists, moderated by service quality and tourist satisfaction. In general, the model adjustment is satisfactory. Given the sample size, the MDN is a more appropriate adjustment indicator than the w2 /degrees of freedom (Hair, Anderson, Tatham, & Black, 1999). 4.2. Market heterogeneity results The present study speculated that significant differences in the path analysis could be explained by the personal characteristics of tourists (Homburg & Giering, 2001). As noted above, a latent cluster analysis was performed in which, initially, the number of segments was unknown. The basis for segmenting the market was the ‘need for variety’—a psychographic feature of tourists. Following Mittal and Kamakura (2001), the present study began by performing an analysis of the variance (ANOVA) to test whether the indicators of the latent variable were significantly different from the ‘intention to revisit’ and the ‘intention to recommend’ variables. This test was positive and significant (sig: 0.000). Latent cluster segmentation requires the identification of the number of segments by a statistical criterion (Gonza´lez & Santos,

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2003). One of the more commonly used indicators is the BIC, which is displayed in Table 3. Table 3 suggests that there were four latent segments of tourists in this market (according to their need for variety). Table 4 shows those clusters in terms of their need-forvariety items. Table 5 shows the size of every cluster and its profile. According to the present results, the market could be divided into four latent segments (depending on their need for variety). The first cluster was the largest, collecting those tourists who had a ‘medium’ need for variety (43.61%). The second cluster was smaller than the first (26.91%), and captured those tourists who needed continuous changes and new experiences (and who would be consequently less willing to repeat their visit to a destination). The third cluster was formed by tourists who had no need for variety at all (23.17%), whereas the fourth grouped those who were willing to experiment with change, but with certain time gaps between such changes (6.31%) (Fig. 2). Once the clusters had been identified, a multi-group analysis was performed to investigate whether there were significant differences among them in the proposed model. As can be seen in Fig. 3, in Cluster 1 a destination’s image increased its influence through service quality and diminished it through tourist satisfaction. Service quality was a stronger antecedent to come back than was satisfaction, but not to the intention to recommend the destination. Service quality was thus the key variable for this segment, since—unlike the general model—this variable exerts a larger influence over the intention to revisit the destination than does satisfaction. Finally, there are no differences between the general model and this cluster regarding the direct relationship between a destination’s image and the intention to recommend it (Fig. 4).

The second cluster was formed by individuals with a high need for variety. The results for this group were quite different from those for the first cluster, since none of the relationships determining the intention to revisit the destination were significant. In addition, a destination’s image influenced the intention to recommend the destination only through tourist satisfaction and not through service quality. Thus, customer satisfaction emerged as the key mediator in the model. Besides, the loading towards the intention to recommend the destination were weaker than the two models analyzed previously.

Table 3 Bayesian information criteria (BIC) Model

Likelihood

# of parameters

BIC

1 2 3 4 5 6


6489.51
6292.09
6212.64
6181.22
6182.41
6193.34

22 27 32 37 42 47

13140.29 12782.11 12659.86 12633.66 12672.71 12692.21

segment segments segments segments segments segments

Table 5 Clusters’ size and profiles Cluster 1

Cluster 2

Cluster 3

Cluster 4

0.4361

0.2691

0.2317

0.0631

I am a person who enjoys to do new things 1 0.0000 0.0000 2 0.0000 0.0000 3 0.0426 0.0000 4 0.6790 0.0215 5 0.2784 0.9785

0.0594 0.1555 0.7344 0.0506 0.0001

0.0000 0.0000 0.0000 0.0041 0.9959

Mean

4.9785

2.7765

4.9959

I like to experience changes 1 0.0005 2 0.0086 3 0.2181 4 0.6292 5 0.1435

0.0000 0.0000 0.0000 0.0087 0.9913

0.0867 0.1620 0.5284 0.2157 0.0072

0.0000 0.0000 0.0000 0.0043 0.9957

Mean

4.9913

2.8946

4.9957

I like to change activities constantly 1 0.0197 0.0000 2 0.0615 0.0010 3 0.3681 0.0045 4 0.4375 0.1401 5 0.1133 0.8553

0.2138 0.2013 0.4004 0.1684 0.0161

0.1278 0.1861 0.4631 0.2052 0.0179

Mean

2.5716

2.7992

Size

4.2357

3.9066

3.5632

4.8508

When things start to be boring, I look for new experiences 1 0.0020 0.0000 0.0754 2 0.0143 0.0000 0.1202 3 0.1866 0.0001 0.4151 4 0.5400 0.0211 0.3420 5 0.2571 0.9789 0.0473

0.0000 0.0000 0.0003 0.0423 0.9574

Mean

4.9571

4.0359

4.9788

3.1656

Bold numbers represent the cluster profile.

Table 4 Need for variety latent clusters Cluster 1

Cluster 2

Cluster 3

Cluster 4

Wald

p value

R2

I am a person who enjoys to do new things I like to experience changes l like to change activities constantly When things start to be boring, I look for new experiences

0.9673 2.8463 0.0704 1.7943

2.6527 2.4080 2.5060 2.0521

5.9509 3.7025 0.6896 2.0680

4.2655 4.1409 1.7460 1.8103

65.9616 39.4080 258.45 56.3191

3.1e-14 1.4e-8 9.7e-56 3.6e-12

0.7587 0.6395 0.5157 0.5042

Intercept

0.7621

0.2792

0.1295

1.1707

124.7125

7.2e-27

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182

1.0 0.480 (10.4)

0.8

Destination’s image

0.416 (9.2)

0.15 (2.9)

0.6

Cluster1 Cluster2 Cluster3 Cluster4

0.4

Service quality

0.355 (7.9) 0.400 (7.0)

0.2

Tourist’s Satisfaction 0.275 (4.6)

P31.4 0-1 Mean

P31.3 0-1 Mean

P31.1 0-1 Mean

P31.2 0-1 Mean

0.0

Fig. 2. Latent clusters.

0.556 (16.8)

Destination’s image

Service quality

0.526 (5.4)

Tourist’s Satisfaction

0.465 (12.9) 0.226 (4.7)

0.212 (4.7)

Fig. 5. Path analysis of Cluster 3 (no need for variety): w2 with one degree of freedom ¼ 22.557; MDN ¼ 0.991, GFI ¼ 0.994; AGFI ¼ 0.913; RMSR ¼ 0.005. Standardized loading (critical coefficient). Critical coefficient o1.96 indicates non-significant relationships.

0.271 (7.5)

0.205 (5.2)

0.232 (5.3)

Intention to revisit

Intention to recommend

Destination’s image

0.229 (2.4)

0.266 (3.0)

Service quality

0.562 (5.9)

Tourist’s Satisfaction

0.360 (2.5)

Intention to recommend

Intention to revisit

Intention to recommend

Fig. 3. Path analysis of Cluster 1 (medium need for variety): w2 with one degree of freedom ¼ 22.557; MDN ¼ 0.991, GFI ¼ 0.994; AGFI ¼ 0.913; RMSR ¼ 0.005. Standardized loading (critical coefficient). Critical coefficient o1.96 indicates non-significant relationships.

0.402 (9.2)

Destination’s image

0.258 (6.3)

0.200 (4.1)

Service quality

0.471 (11.6)

Tourist’s Satisfaction 0.144 (2.5)

Intention to recommend

Intention to revisit

Fig. 4. Path analysis of Cluster 2 (high need for variety): w2 with one degree of freedom ¼ 22.557; MDN ¼ 0.991, GFI ¼ 0.994; AGFI ¼ 0.913; RMSR ¼ 0.005. Standardized loading (critical coefficient). Critical coefficient o1.96 indicates non-significant relationships.

Curiously, for individuals belonging to cluster 3 (showing no need for variety), satisfaction was also the key mediator in the relationship between a destination’s image and the future behaviour of tourists. However, this group

Intention to revisit

Fig. 6. Path analysis of Cluster 4 (high need for variety but separate in time): w2 with one degree of freedom ¼ 22.557; MDN ¼ 0.991, GFI ¼ 0.994; AGFI ¼ 0.913; RMSR ¼ 0.005. Standardized loading (critical coefficient). Critical coefficient o1.96 indicates non-significant relationships.

showed the largest loading in linking satisfaction and intention to come back to the destination and to recommend it to friends and relatives. The low need for variety among the members of this group of tourists might have inclined them not to change their behaviour (Fig. 5). Finally, in cluster 4 (high need for variety but after certain amount of time), service quality was the only determinant of future behaviour. Again, the relationships explaining the intention to revisit the destination are not significant—like cluster 2—but for these tourists, service quality is the driver of their intention to recommend the destination (Fig. 6). Table 6 shows the results of a test comparing the loadings of the groups and testing for any significant differences among them. The results show that there were general significant differences between loadings in the path model of the four clusters identified. On this regard, the significant differences in terms of loadings reveal the distinct behaviour of the mediating variables—service quality and

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Table 6 Critical differences among groups Cluster1–Cluster2

Cluster1–Cluster3

Cluster1–Cluster4

Cluster2–Cluster3

Cluster2–Cluster4

Cluster3–Cluster4

Destination’s imageService quality


2.489

n.s

n.s

n.s

n.s

n.s

Destination’s imageSatisfaction

n.s.

3.627

n.s

3.627

n.s

n.s.

Service qualitySatisfaction

n.s.

n.s

n.s

n.s

n.s

2.184

Service quality
2.77 Intention to recommend

n.s

n.s.

n.s

2.021

n.s

Service qualityIntention to revisit

n.s

n.s.

n.s

n.s.

n.s

Satisfaction
2.250 Intention to recommend

2.611

3.397

4.518


2.456

4.612

SatisfactionIntention to revisit

2.22

n.s.

3.104

n.s

n.s.

n.s

n.s

n.s

n.s.

n.s.


2.831

n.s

Destination’s imagen.s Intention to recommend

satisfaction—in their influence on the tourist’s future behaviour. Thus, service quality emerges as the key mediating variable for those tourists with a medium, and high—but not continuous—need for variety. On the other hand, satisfaction is the key mediator for those tourists with a high need for variety, as well as for those tourists with a low need for variety, but with some nuances. Hence, when the tourist’s need for stimuli is high, satisfaction only moderates its intention to recommend the destination. On the contrary, when the need for variety is low, high levels of satisfaction heavily influence both intentions to revisit and recommend the destination. According to this finding, the research hypotheses are confirmed. 5. Discussion of findings Tourism literature has examined extensively what are the reasons that influence tourists in their destination choices, as well as the stages in the process of tourist behaviour (O’leary & Deegan, 2005; Petrick, 2004; Reid & Reid, 1993). Recent studies contend that the traveller’s decisionmaking process is influenced by a number of psychological and non-psychological or external variables (Sirakaya & Woodside, 2005). According to Woodside and Dubelaar (2002, p. 120), ‘‘tourist’s decisions and behaviours represent a rich mosaic of relationships among multiple set of variables’’ (demographic, psychographic, micro- and macro-evaluations, and future intentions), which complexity justifies the need for further research on the topic. Following Sirakaya and Woodside’s (2005) guidance for further research regarding traveller’s decision making, our research aims to test two specific propositions (first and sixth) raised in their article. The first considers that choices

of destinations are affected by a number of psychological or internal variables, provided that decision-making styles are individualistic (Sirakaya, Mclellan, & Uysal, 1996). Continuing on this stream of research—initiated by Chen (2003)—the major contribution of this article is confirming that the market is not only affected, but also heterogeneous in terms of tourist’s psychological variables (specifically need for variety). The second proposes that different segments might have dissimilar methods of approaching problem solving and the decision making (Sirakaya & Woodside, 2005). Our results focus attention on the potentially critical importance of a psychological variable and provide credence to backward segmentation theory in tourism research. To date, there have been few studies of the influence that the personal characteristics of consumers might have on future behaviour. The present research makes a significant contribution to the literature on the topic because it explores the influence of a psychographic feature of consumers that has not been examined before— need for variety. Results have confirmed its relevance. Path analysis results illustrate the influence that a destination’s image has on the future behaviour of tourists—a relationship that is mediated by service quality and/or tourist satisfaction. In addition, a major finding of the present study is that such relationships are conditioned by market heterogeneity. Thus, the initial model, which did not consider differences among tourists, is significantly different from the heterogeneous model that identifies four latent clusters in the market. According to the latent cluster analysis, there were four segments of tourists—depending on their need for variety. The largest segment was formed by individuals with a ‘medium’ need for variety (43.61% of the sample). The influence of a destination’s image on the future behaviour

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of this group occurred through service quality and satisfaction. For these tourists, the intention to recommend and the intention to revisit the destination have both the same relevance in their global future behaviour. The second largest segment was formed by individuals who had a strong desire for change (26.91% of the sample). For these tourists, a destination’s image channelled its influence on future behaviour through their satisfaction. This is consistent with the services literature—which indicates that customer satisfaction is an emotional response linked to a specific moment in time. Given the personal characteristics of these individuals, the intention to provide positive references about the destination was the salient dimension of loyalty, whereas the intention to revisit the destination was not significant. In short, those tourists with a high need for variety do not have the intention to revisit the destination. They are only likely to provide good references in case they are satisfied with their visit. The third segment consisted of individuals who had no need for variety at all (23.17%). Again, satisfaction is the key variable for these tourists’ intentions to revisit and recommend the destination. It is remarkable the strong relationship between satisfaction and recommendation in this cluster. The fourth segment is the smallest one (6.31%). Its major difference with the second cluster—also high need for variety—relies on the influence of service quality on their intention to recommend the destination. This result is coherent with the service marketing literature, since this segment is comprised of individuals who have a need for changes and new experiences, but not so frequently as the second cluster. Further research is required in this area. However, it could be reasonably surmised that tourists with no need for variety (cluster 3) would be more willing to revisit and recommend a destination than members of other clusters, rooting on the satisfaction with the services received in the destination. In contrast, tourists with a high continuous need for variety (cluster 2) and those with a need for changes and new experiences, but not so often as the second segment (cluster 4) are likely to provide good references for the destination, rather than coming back to revisit it, although through different mediating variables.

In terms of implications for city tourism managers, the existence of different segments of tourists implies a need for different actions—if the objective is to maximize the number of returning tourists to a destination by increasing the unofficial sales force spreading positive word-of-mouth recommendation about the destination, thus attracting new visitors.

6. Limitations of research

References

The limitations of the present research provide opportunities for further investigation. It would be desirable to include an affective component of a destination’s image to achieve a complete picture of this construct. It can be speculated that the image’s loadings on the loyalty chain might increase significantly if affective components were present. Similarly, increasing the size of the sample would allow confirmation of the distribution of the latent clusters and would provide more individuals to allow generalization of the present results.

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Appendix A Destination’s image scale 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.

Pleasant weather. Hospitality and courtesy. Cultural attractiveness. Historical destination. Good infrastructures. Gastronomic offering. Lifestyle and particular customs. Wealth and economic development. Cleanliness of destination. Welfare and quality of life. Entertainment and leisure opportunities. Nightlife. Shopping and malls. Popular place. Famous destination. Safety and security. Hotel infrastructure. Exotic destination.

Need for variety 1. 2. 3. 4.

I am a person who enjoys doing new things. I like to experience changes. I like to change activities frequently. When things become boring, I look for new experiences.

Future behaviour 1. Will you recommend that others visit this destination and its surroundings? 2. Will you return to visit this destination again?

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