Tourism Economics, 2015, 21 (4), 741–759 doi: 10.5367/te.2014.0381
Economic valuation of tourism destination image MARÍA M. CARBALLO, JORGE E. ARAÑA, CARMELO J. LEÓN AND SERGIO MORENO-GIL
Institute of Tourism and Sustainable Economic Development (TiDES), Universidad de Las Palmas de Gran Canaria. (Corresponding author: Jorge E. Araña, Facultad de Economía, Turismo y Empresa, Campus Universitario de Tafira S/N, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas, Spain. E-mail:
[email protected].) This paper develops a novel methodology for estimating the value of destination image, which incorporates two principal advantages over the methods used to date. First, it allows tourism destination image to be assessed in economic terms, so a formal cost–benefit analysis can be executed to ascertain whether or not a specific marketing action should be implemented. Second, it enables a disentangling of the economic assessment of tourist destination image in terms of destination attributes. This can be used to design marketing actions aimed at optimizing marketing efforts to enhance a destination’s image. Keywords: economic assessment; destination; image; economic valuation
The difficult situation in which many destinations are currently arising from factors such as the unfavourable economic climate, more discerning tourists, the rise of new, competing destinations and the resulting increase in promotional activities by both established and new destinations and has caused major concerns among destination marketing organizations (DMOs).1 DMOs have concentrated on projecting a favourable image as a distinguishing factor for destinations so that potential visitors will view visiting the destination as an attractive option and decide to go there. Destination image differentiates one destination from another and constitutes a key determinant of a successful destination (Cai, 2002; Qu et al, 2011). Previous evidence shows that destination image is useful in assisting DMOs to measure achievements and is a significant factor in determining visitor choice (Ekinci and Hosany, 2006; Qu et al, 2011), However, creating a successful
The authors fully acknowledge financial support for this work from the European FEDER Fund through Project PI2007/040 from Agencia Canaria de Investigación, Innovación y Sociedad de la Información in 2009 and under the declaration of Canary Islands as “Objetivo de Progreso”.
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image of a destination in an effective manner requires thorough market research (Middleton, 1994). In spite of this, destination image is often promoted with little knowledge either of the value of the image or of the effort it required to develop it. Tourism destination image is very important in both academic and professional spheres due to its high power of persuasion. It plays a key role in tourists’ destination decision process (Hunt, 1975; Goodrich, 1978b; Woodside and Lysonsky, 1989; Um and Crompton, 1992) and in subsequent tourist behaviour at the destination (Pearce, 1982; Chon, 1990, 1991; Ross, 1993; Son and Pearce, 2005). As a result, it can be said that image fulfils an important function and contributes positively to the development of a destination (Hunt, 1975), Destination image is a mixture of positive and negative perceptions representing a setup context within which individuals make up their minds about what destination to select from among potential alternatives. Thus, destination image plays a role in destination choice: potential tourists will decide to choose the destination only when the image components that produce a positive effect outweigh those that produce a negative effect (Milman and Pizan, 1995; Chen and Kerstetter, 1999), Therefore, destinations with stronger, more positive images are more likely to be considered and chosen (Hunt, 1975; Goodrich, 1978b; Pearce, 1982; Woodside and Lysonsky, 1989; Ross, 1993; Bigné et al, 2001; Prayag, 2009), However, the intangible nature of tourist products means it is impossible for tourists to make this decision without some degree of uncertainty (Goossens, 1994). As the tourist product is a personal experience at the destination, first-time visitors have a very fuzzy image of the destination, and although returning visitors have a clearer image, some uncertainty is also present in their assessments. As a result, destinations must develop images that position them appropriately in relation to competitors and help them both to capture clients and to create subsequent customer satisfaction and tourist loyalty. However, there are prerequisites for any destination wishing to optimize management of the image-formation process as well as the resources used and the return on the investment. First, they must conduct an economic assessment of the destination image, including tourists’ willingness to pay for a particular destination over others, and, second, they must assess the influence of improvements in the components or attributes of the image on tourists’ willingness to pay.
Literature review Image is the second most popular topic researched in recent tourist academic literature (Law et al, 2009). Following Chon (1990) and Baloglu and McCleary (1999a) the main lines of research on tourist destination image have focused on: (a) proposing alternative methodologies for assessing a perceived image; (b) analysing the relationship between destination image and various aspects associated with the consumer’s behavioural process in the context of tourism, such as preference, intentionality of the visit and satisfaction; (c) explaining the differences between the image projected by the destination and the image
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perceived by individuals; (d) analysing how image affects the tourism development of a location; (e) assessing change in the image of a destination over time; (f) determining the influence on destination image of a tourist’s internal variables associated with socio-demographic characteristics, cultural factors, familiarity with destinations and motivations; and (g) discovering how certain sources of information influence tourist destination image. The present study comes under the first of these lines of research; that is, the proposal of an alternative methodology for assessing destination image. The present review section is structured in three parts. In the first we discuss the destination image concept, dimensions and its formation process. The second part reviews the different methods applied in the literature to measure destination image. Finally, the last part presents the alternative of discrete choice models as tools for measuring destination image.
Destination image concept Although tourist destination image has received much attention in academic and professional literature, this concept has been only vaguely defined and lacks a solid conceptual structure (Mazanec and Schwieger, 1981; Fakeye and Crompton, 1991, Baloglu and Assante, 1999; Martínez and Alvarez, 2010), These initial premises merely hinder any measurement or assessment of tourism destination image. According to the World Tourism Organization (1998), destination image comprises the ideas or concepts of a destination held individually or collectively, and following Buhalis (2000), it constitutes a set of expectations and perceptions a potential tourist has of the destination. As Kotler et al (1993) described it, destination image is the information, beliefs, impressions, attitudes and emotional thoughts an individual has in relation to a place. From a more disjointed perspective, image can be defined as an overall mental picture of an object (Dichter, 1985; Fridgen, 1987; Stabler, 1995; Baloglu, 2001, Huang and Gross, 2010), where objects are broken down into the seven levels of image as distinguished by Riel (1997): product category image, brand image, company image, sector image, sales outlet image, geographical location image and user image. Following previous research (Beerli and Martin, 2004), in the present study we use Riel’s dimensions of image applied to a group of tourism destinations, to assess destination image using a methodology designed to distinguish between a subjective component of the image perceived by the visitor and the component of the objective image referring to the specific features of the holiday experience. Echtner and Ritchie (1991) incorporated a three-dimensional framework of analysing mutually overlapping continuums of destination image: (a) attributeholistic, considering the image of individual attributes of a destination and the overall perception; (b) functional-psychological, considering tangible and rational or cognitive features, and more affective, abstract and emotional feelings; and (c) common-unique, as image can range from common features that are comparable among destinations to distinctive or even unique features of a destination. Image formation theory as proposed by Gunn (1972) uses three main constructs related to the modification of images: (a) organic; (b) induced; and (c) modified induced, where organic image is formed by unbiased independent information sources, induced image is formed by exposure to a destination’s marketing programme
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(from the promotional efforts made by the industry to attract tourists) whose role is to build on or modify the extant organic images (O’Leary and Deegan, 2005). Finally, although organic and induced images will be formed prior to travel to a destination, induced images may be changed by actual vacation experience, thus producing a ‘modified induced’ image (Phau et al, 2010). Destination image formation is an ongoing process. Although it is difficult to change, a review of the literature on tourism image reveals a number of factors that affect the imageformation process, such as sources of information, including information acquired at the destination on previous visits (Gartner, 1993; Baloglu, 1997; Baloglu and McCleary, 1999b; Stepchenkova and Eales, 2011; León and Araña, 2014) and other personal characteristics of the tourist and internal factors, for example, tourist motivation (Gartner, 1993; Stern and Krakover, 1993; Stabler, 1995; Dann, 1996; Baloglu, 1997; Boo and Busser, 2005; Gertner, 2010). Tourists perceive destination as an overall impression that includes a variety of products and services (Chon, 1990, 1991; Baloglu and Brinberg, 1997; Wang and Hsu, 2010). During their holidays, tourists consume the destination as a global, integral experience made up of many individual experiences in which a number of external elements come into play. As a result, the image-formation process can be seen as a combination of independent agents that act both independently and in conjunction with other agents. Nonetheless, tourists perceive an overall impression or general assessment of place. The cognitive and affective dimensions of image therefore make up an overall or composite image that refers to the possible positive or negative assessment of the destination (Gil and Ritchie, 2009). However, the main problem for managing the imageformation process lies in the initial need to make an economic assessment of the dimensions of image, so that suitable resources for improving image can be allocated and the necessary investments and action plans can be undertaken.
Measuring perceived destination image Destination image must be continually analysed; guests must be interviewed and the results obtained need to be confirmed regularly through rigorous studies (Croizé, 1989). However, a review of the methods used by researchers to measure image reveals a high level of heterogeneity. Images are difficult to express; they are highly subjective and on occasion subconscious, which makes them difficult to quantify. Most studies undertaken in this area are descriptive rather than methodological, analysing destination image without assessing it in any depth (Tasci et al, 2007). Gallarza et al (2002) highlight two very different approaches in the literature to measure destination image: (a) empirical studies that, without actually developing theoretic bodies, apply statistical instruments; and (b) empirical studies, less common, that deal with the problems of the measurement of discussion on multidimensionality of image and the problems related to its assessment and measurement. The methods used to date to assess tourism destination image have several limitations in terms of their focus and the measuring scales used. Echtner and Richie (1991) and Chen et al (2010) state that, owing to the complex, diverse nature of the tourism product, it may be necessary to develop more specific conceptual frameworks and methodologies to measure tourism destination image.
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Previous studies acknowledge that more research is necessary to improve the way we measure destination image (Riel, 1997; Qu et al, 2011). Researchers have developed a variety of methodological techniques that enable them to measure image using the responses to measuring instruments: multidimensional and semantic differential scales, free elicitation of descriptive adjectives through open-ended questions and the repertory grid technique. All have advantages and limitations, as analysed below. The use of multidimensional scales to assess perceived image is where survey subjects rate the dimensions separating destinations by judging similarities and dissimilarities in a set of stimuli or attributes (Goodrich, 1978a; Gartner, 1989). The simplest instrument for measuring image is the technique of ordering tourism destinations by preference using a Likert scale or others. This type of methodology makes it possible to determine the relative perceived image of a series of tourism destinations and transform the opinions of individuals about similarities or preferences into distances represented in a multidimensional space. The utility of multidimensional analysis lies in the opportunity to determine (a) the basic dimensions underlying the rating of tourism destinations by survey subjects, (b) the relative importance of each dimension and (c) the relations at the perceptual level between the tourism destinations considered (Beerli and Martin, 2004). Several studies on tourism destination image have applied this methodology to rate the image of a number of tourism destinations: for example, Goodrich (1977, 1978a, 1078b). Gartner (1989) and Baloglu and Brinberg (1997). The main limitations or disadvantages of this method are: (a) the difficulty in collecting data, given the large number of judgements survey subjects have to respond to and (b) the limited reliability of the results when respondents are not familiar with the destinations. As a result, this methodology is recommended only when individuals have ample knowledge of the tourism destination analysed and the destinations are similar in terms of tourism profile. The methodology based on semantic differential scales has been applied intensively in the tourism sector through the use of widely heterogeneous attributes (Schroeder, 1996). This study method is based on the perception respondents have of the target destination, using subjective or bipolar scales, which also means that several destinations can be studied at the same time by repeating the scale for each destination. The main limitations of this methodology, according to Goodrich (1977), Crompton (1979b) and Albaum and Golden (1991), are (a) the difficulties in obtaining information when the study includes a large number of attributes to be rated and (b) the possibility that the destination image is rated on a series of attributes of little significance for respondents, which do not define the destination image for them. The methodology based on rating perceived image by free elicitation of descriptive adjectives from individuals in response to open-ended questions enables respondents to highlight the attributes or adjectives that best describe the destination under study. Researchers then group the answers into categories. Key studies on measuring destination image have been conducted by Reilly (1990), who analysed destination by free elicitation of three words from respondents, and Tapachai and Waryszak (2000), who used an unstructured methodology through open-ended questions and classified the answers as func-
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tional, social, emotional, epistemic and conditional, following the design proposed by Sheth et al (1991). The main advantages of this methodology are (a) greater ease of data collection and (b) the fact that individuals rate the dimensions that characterize the image of a destination rather than rating dimensions suggested by researchers (Beerli and Martin, 2004). Some of the principal disadvantages are the possible bias in the interpretation and encoding of answers by researchers, particularly when dealing with tourists who speak different languages, and the possibility that some of the answers obtained may be ambiguous or unclear. Finally, the repertory grid method, although less frequently applied, is based on Kelly’s Personal Construction Theory, and has been used in studies by Potter and Coshall (1988). Embacher and Buttle (1989), Walmsley and Jenkins (1993) and Coshall (2000), and has the advantage of measuring the comparative image of several tourism destinations. This methodology is structured in four stages: (a) spontaneous, open suggestions by survey subjects about the tourism destinations considered for their latest or next trip (elicited set); (b) combination into triads of the destinations evoked by the respondents, so that each destination appears at least once in each triad; (c) free suggestion of constructs for each destination, in such a way that respondents find two of the destinations elicited similar and distinguish them from a third destination elicited in each triad established; and (d) scale rating of each destination elicited in relation to the constructs proposed by respondents. The repertory grid format allows the process to be simplified when several destinations are analysed, so respondents do not have to repeat the ratings for each destination (Driscoll et al, 1994). The attributes to be rated are placed on the vertical axis of the grid and the destinations to be rated are placed on the horizontal axis. All destinations are rated every time a new attribute is introduced. Jaffe and Nebenzahl (1984) compared this method with the semantic differential scale method and obtained differences in the perceived image depending on the format used. A further advantage of this methodology is that it is possible to show the real dimensions individuals consider for distinguishing and choosing between several destinations. However, it has its limitations, given that it forces respondents to establish differences between tourism destinations whether they perceive them or not, which can give rise to forced rather than real images (Driscoll et al, 1994). In addition, the length of time tourists take to fill in the questionnaires makes obtaining information difficult. Other techniques exist for measuring tourism destination image, although they have been little used to date in the literature. Noteworthy among these are qualitative methods such as in-depth interviews and projective techniques such as word association and photo interpretation (Mazanec, 1989). Many studies have used a combination of methodologies to assess tourism destination image, such as the Likert, semantic differential, and descriptive scales, although their common goal is for individuals to provide a numerical value for destination image on a continuum ranging from very favourable to very unfavourable (Hunt, 1975; Goodrich, 1978a; Crompton, 1979a; Walmsley and Jenkins, 1993). The variety of approaches, definitions and measuring scales for destination image makes it difficult to compare destinations and generalize studies. In
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particular, it raises the need for a methodological tool to enable destination image to be assessed in economic terms, so that comparative and market analyses between different destinations can be undertaken and each individual destination may study the changes in the value of its image, thus facilitating the management of investments to be made in the destination and the return on these investments. The assessment method proposed in this study is therefore based on the methodology of Gensch (1978), in conjunction with a discrete choice model, as this emerges as the most appropriate method for measuring image assessment in economic terms, thus making it possible to identify the impact of changes in image on tourist perceptions (Araña and León, 2008, 2012; León et al, 2003). Tourists with different understandings of experiences found different contents of images inspired them to visit a destination (Ye and Tussyadiah, 2011). Some authors as Ryan and Ninov (2011) propose a deeper analysis differentiating destination perception, destination attributes, and destination and experience evaluation. The method therefore distinguishes between the ‘measurement perception space’, ‘image perception space’ and the ‘measurement influenced by image perception space’. The ‘measurement perception space’ (the tourist’s perceived image of the experience at a destination) and the rating the tourist gives to it. The holiday experience is recreated for tourists through text and/ or photo prompts so they can rate the experience provided by the holidays. The ‘image perception space’ is obtained when tourists rate a destination simply from its name or label, with all the implicit evocations this involves in their minds. Lastly, the ‘measurement influenced by image perception space’ is obtained when individuals rate the image of the experience at a destination objectively through a description (text and/or photos), although they know the name of the destination beforehand. This method makes it possible to isolate the influence of destination image and distinguish it from its objective and functional rating measure, and also enables an economic assessment to be made of these dimensions.
Discrete choice models as tools for rating destination image: theoretical model Discrete choice experiments (DCE) have been widely used to study consumer decisions and preferences in fields such as transport, health and the environment. However, few studies in the academic literature on tourism destinations have used these methods, although DCEs are attracting growing interest as they provide a more sound, rigorous and flexible theoretical tool (Crouch and Louviere, 2001). This technique has been applied to tourism by Jeng and Fesenmaier (1998), Morey et al (2002), Crouch and Louviere (2004), Apostolakis and Jaffry (2005, 2006), Araña and León (2013a, 2013b), Araña et al (2013) and León and Araña (2014). In the literature review of destination image papers during the period 1973–2000, there were only 2 papers out of 142 dealing with image value (Murphy and Pritchard, 1997; Murphy et al, 2000) and only one paper using DCE to destination image (van Limburg, 1998). The present study uses this methodology to obtain a monetary value for destination image. The impact of image on tourist destination preferences can be obtained using DCE responses. This method also allows the impact of image to be assessed in economic terms. That is, DCE enables researchers to estimate
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the value of the image of a specific destination by calculating the amount an average visitor would be willing to pay to change his or her mind and take the same holiday package at a different destination. In order to rate an image, it is necessary first of all to rate the importance that destination attributes (accommodation services, environment quality, theme parks and so forth) have in tourist choices. Thus a formal model is needed that allows a connection to be made between the way individuals choose between alternatives, grouped into holiday packages, and the role of these factors in the choice process. The random utility model (RUM), first proposed by Thurstone (1927) and later considerably improved by McFadden (1974), was used for this purpose. For the sake of simplicity, purely discrete choice modelling is normally used, in line with McFadden (1974), rather than a combination of discrete/ continuous choices, as proposed by Hanemann (1984). Suppose tourist q in the sample (q = 1,…,Q) is faced with choice i (i = 1,…,I) among holiday package alternatives in each choice situation t (t = 1,…,T). The RUM assumes that tourist q considers the complete set of holiday packages Q offered and chooses the one that provides the greatest utility. For an increasing, continuous and concave utility function, once a package i has been chosen, the overall satisfaction of individual q can be represented by the following general conditional indirect utility function: Uqit = βXqit + eqit,
(1)
where Xqit is a non-stochastic vector of explanatory variables observed by the researchers through any source, which includes the attributes of the alternatives (such as package price, beach area available, quality of the urban environment, leisure activities and theme parks, accommodation services, natural landscape, etc), the socio-economic characteristics of the respondent, and the situation of the decision context, as well as the choice task itself, that is, moment t. β is the parameter vector representing the conditional marginal utility of each explanatory variable; that is, the variations in total utility in relation to changes occurring in the explanatory variables. eqit is the stochastic component and includes all the factors affecting individual choice not observed by the researcher. The RUM structure assumes that if an individual faces a multiattribute discrete choice problem, the researcher notes that individual q chooses alternative i* at moment t in the choice situation if and only if: Uqi*t = βqXqi*t + eqi*t > Uqit = βqXqit + eqit ∀i ≠ i*.
(2)
McFadden (1974) proposed a conditional logit model, assuming the condition that the components eqit are independently and identically distributed (iid) extreme values type I. Therefore, the probability of a random individual q, choosing i package from a choice set of J holiday packages becomes: e βqXqit . Pqit = ——— J Σ e βqXqit j=1
(3)
The conditional logit model has been criticized for its rigidity in representing preference heterogeneity and the independence of irrelevant alternatives, termed IA hypotheses. Several options have been suggested recently to overcome these
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problems (for example, nested logit, mixed logit, hierarchical Bayes, G-mnl and scale-adjusted latent class model). The models used in the present study were conditional logit, hierarchical Bayes and mixed logit. Given that the main results were not sensitive to the econometric approach, the discussion is presented based on the conditional logit model for simplicity. Results from all sensitivity analysis are available from authors under request.
Fieldwork The study followed DCE methodology to analyse tourist preferences in relation to tourism destinations in accordance with the definition of specific attributes. Most empirical studies poll tourists at the destination, either during their holiday or when it is almost over. However, conducting surveys at these stages of the holidays can lead to what is termed ‘self-selection bias’. Obviously nothing is known of these potential tourists, or whether they would have changed their final destination if changes had been suggested in aspects of their holiday package. To avoid this bias, a survey was conducted on a representative sample of the population in Germany, Europe’s top source of international tourism. The sample method was a random method with fixed quotas for the main socio-demographic variables, that is, age, gender and province. Thus, this sample is representative of the German national population. Thus potential tourists were polled at home before deciding on their holidays. The field work was conducted in 2007. The interviews were conducted in person, with professional interviewers hired by a specialized survey firm. The interview lasted for 30 minutes on average. Fieldwork was carried out on a sample of 1,200 potential tourists (this sample size guarantees an error below 3%), through personal interviews conducted by a professional polling company whose staff had received training in this technique. The potential candidates for the study were screened by whether they had spent their holidays in the last 12 months at a ‘sun and beach’ tourism destination. The field of study used was sun and beach destinations because they are very popular (Melián-González, et al, 2011; Mendes, et al, 2011) and they are typically purchased as package holidays, and can be easily compared by tourists (Thrane, 2005). Moreover, package holidays to sun and beach destination is the most popular product in the German tourism market. Three pre-test studies and two focus groups enabled the specific wording of the questionnaire to be prepared. The full potential list of attributes presented in Echtner and Ritchie (1991) was scaled down to the final list of attributes presented in Table 1 by intensively using several qualitative and quantitative techniques (for example, focus groups, face-to-face interviews with tourism officials and tourists, and a pilot survey). The attributes finally chosen were: (a) holiday package price; (b) services available at the accommodation resort; (c) location and management of the urban environment of the tourism area; (d) state of the beach and area available per person; (e) tourist attractions in the vicinity and average travel time to shopping centres and entertainment; (f) leisure activities in the vicinity and types of theme parks; and (g) preservation of the natural landscape. These attributes were selected as they represent a tourism destination image (Echtner and Ritchie, 1991), and tourism officers
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identified them as key dimensions that they need to choose from in order to plan future destination image policies. After the attributes had been defined, the choice set of sun and beach destinations to be included was considered. The set was determined by consulting the sales popularity of the destinations of this type in the Mediterranean, as well as the comments obtained in personal interviews with experts in the industry and in the focus groups with tourists. It was also considered necessary to limit the number of destinations in the choice set so respondents could focus on the task and concentrate better. The destinations finally considered were the Canary Islands (Spain), the Balearic Islands (Spain), the Greek Islands, Turkey, Cyprus and Tunisia. Each objective tourism experience was then carefully described in terms of the attributes chosen, with the aid of verbal descriptions and image and photo prompts. The choice scenario for the destination was presented to potential tourists as follows: ‘The following questions present you with two alternative holiday packages, in which these attributes vary. Each profile defines a holiday package with specific features, including the price per person. This price includes only the accommodation services, flights and transport to your hotel or apartment. Considering the possibility of a two-week holiday, and assuming that these are the alternatives available, please tick the package you would choose from those presented in each option.’ The total number of possible combinations or profiles, resulting from the definition of the attributes and their levels, was too large for a single individual to be able to complete the entire questionnaire, which meant that the number of combinations had to be reduced using appropriate techniques. The card profiles were prepared using the D-optimal design method proposed by Huber and Zwerina (1996). Given the total number of possible combinations resulting from the attributes and their levels, the D-optimal design was governed by maximization of the information matrix (that is, the inverse of D-efficiency). D-optimal design of the principal effects led to 10 combinations that were randomly paired and distributed in two subsamples. After the attributes and their levels had been presented, the survey subjects were shown a succession of five cards with pairs of alternatives. Each alternative involved a particular definition of the attributes in the detection programme. The potential tourists were given a series of proposals with eight discrete choice questions. Each alternative was labelled with an attribute indicating the particular destination the profile was attached to. For each card, subjects were asked to choose one of the possible alternatives or neither (that is, reject both alternatives on the card).
Results The image utility function of the discrete choice model depends on a set of explanatory variables, or attributes, of the tourism product. These variables can explain the choice between the alternative tourism destinations in terms of how
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Table 1. Definition of holiday package attributes. Attribute
Description
PRICE Price per person, in euros (450, 630, 750, 900) BEACH Beach area per person (4, 25, 100 m2) ACCOMMODATION SERVICES Services available at the accommodation resort (full or standard services) NATURAL LANDSCAPE 1 if the package proposed involves preservation of natural landscapes; 0 otherwise LEISURE 1 if the package proposed includes availability of attractive leisure activities and theme parks; 0 otherwise TRAVEL TIME Travel time to leisure and shopping centres (5, 15, 45 minutes) URBAN ENVIRONMENT Quality or preservation of the urban environment (excellent or standard) CANARY ISLANDS 1 if the package proposed is in the Canary Islands; 0 otherwise BALEARIC ISLANDS 1 if the package proposed is in the Balearic Islands; 0 otherwise TURKEY 1 if the package proposed is in Turkey; 0 otherwise GREEK ISLANDS 1 if the package proposed is in the Greek Islands; 0 otherwise CYPRUS 1 if the package proposed is in Cyprus; 0 otherwise TUNISIA 1 if the package proposed is in Tunisia; 0 otherwise
they are perceived by tourists. Table 1 shows the descriptions and specifications of the variables the tourists themselves noted as important for explaining the choice of tourism product profile. Dummy variables were added for the alternative tourism destinations so that the profiles of the specific product features could be defined. When added to the utility function, these variables indicate the contribution of the measure of the dimension of destination image to the measure of perceived experience and utility provided to the individual. The multinomial logit, discrete choice model was estimated using maximum likelihood methods. Table 2 shows the results of the models. The image attributes of the holiday package definition have a significance level of 95%. As expected, holiday package price contributes negatively to utility. Similarly, greater crowding at beaches and increased travel time from the accommodation for shopping and enjoying leisure areas both have a negative impact on the level of tourist utility. Attributes referring to accommodation resort facilities, the number of leisure areas and theme parks in the vicinity and their attractiveness, preservation of the natural landscape, and the state of the urban environment have a positive effect on individual utility. It is noteworthy that the attribute with the highest positive contribution to utility was preservation of the natural landscape, followed by accommodation resort services, travel time required and availability of leisure activities. These results confirm the expectations in qualitative terms obtained from the focus groups. The dummy variables indicating the name of the destination for which the specific holiday package profile is defined show the impact and effects of the image and the associations and connotations implicit in the image. All the
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Table 2. Conditional logit model results. Covariates
Coefficients
Standard error
PRICE BEACH ACCOMMODATION SERVICES LEISURE NATURAL LANDSCAPES TRAVEL TIME URBAN ENVIRONMENT CANARY ISLANDS BALEARIC ISLANDS TURKEY GREEK ISLANDS CYPRUS Log L N
–0.0358 0.1837 0.7278 0.6233 0.8606 –0.6283 0.4822 0.7937 0.8406 0.3229 0.5588 0.3623 –1,861.52 2,960
0.0077 0.0437 0.1034 0.1346 0.1480 0.1410 0.1026 0.1483 0.2214 0.2239 0.1590 0.0574
destinations considered in the set were valued higher than Tunisia, which was therefore modelled as the control destination. The image of both the Balearic and the Canary Islands contributed most to individual utility, with the Greek Islands occupying an intermediate position and Turkey situated at the lowest level of the profiles, with Tunisia. Lastly, the impact of the utility of the image attributes, in addition to the overall image of the destination (with all its connotations), can be expressed in monetary terms by rescaling the parameters estimated for each attribute of the estimation for the holiday package price parameter. The estimation of the impact on the welfare of each attribute of the perceived experience of the travel package (influence of improvements in destination image attributes on marginal willingness to pay) and the welfare of the destination image (value of the destination image measured in mean daily marginal willingness to pay) are shown in Tables 3 and 4, respectively. Confidence intervals were calculated using Monte Carlo simulation. Significant differences are also seen for all the attributes considered, as the confidence intervals of the means of willingness to pay for each attribute do not overlap. For example, the mean daily marginal willingness to pay for improvements Table 3. Influence of improvements in destination image attributes on marginal WTP (euros) Attribute
Marginal WTP
BEACH ACCOMMODATION SERVICES LEISURE NATURAL LANDSCAPES TRAVEL TIME URBAN ENVIRONMENT
5.13 [4.43, 5.81] 20.33 [19.51, 21.14] 17.41 [16.47, 18.34] 24.04 [22.17, 25.90] –17.55 [–16.99, –18.10] 13.47 [12.23, 14.70]
Note: 95% confidence intervals in brackets.
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Table 4. Value of the destination image measured in mean daily marginal WTP, in euros (95% confidence intervals in brackets) Attribute
Marginal WTP
CANARY ISLANDS BALEARIC ISLANDS TURKEY GREEK ISLANDS CYPRUS
22.17 [21.31, 23.02] 23.48 [21.57, 25.38] 9.02 [7.19, 10.84] 15.61 [13.87, 17.34] 10.12 [9.71, 10.52]
Note: 95% confidence intervals in brackets.
in destination image attributes in terms of the alternatives offered ranges substantially. From €24.04 a day for improvements in the natural landscape at the destination, €20.33 a day for improvements in the accommodation, €17.55 a day for improvements in travel time to leisure and shopping offer, €17.41 a day for improvements in leisure offer and theme parks, €13.47 a day for improvements in urban environment, to €5.13 a day for the chance to enjoy an extra square metre of beach area per person. The results reveal that all the destinations proposed have higher values than Tunisia (taken as the control destination), which shows the willingness to pay (WTP) for the extra contribution made by the destination image to enjoy the same perceived experience. WTP ranges from €23.48 a day for the Balearic Islands, €22.17 a day for the Canary Islands, €15.61 a day for the Greek islands, €10.12 a day for Cyprus, to just €9.02 a day for Turkey. The calculation of the differences between destinations shows the contribution of the image of each destination in relation to the others (Tunisia as the control destination).
Conclusion Tourist destination image is a concept of enormous significance that fulfils an important function, as it has a decisive influence on the decision of potential tourists to visit a destination and their subsequent behaviour and spending at the destination. Moreover, destination image has been widely studied in the literature, where one of the priorities is to propose alternative methodologies for assessing perceived image (Chon, 1990; Baloglu and McCleary, 1999a). This task includes the difficulty of integrating the diversity of approaches, definitions and measurement scales associated with destination image. Nonetheless, an obvious need exists to establish a measure by which destinations can be compared and studies can be generalized. In particular, a methodological tool is needed so that destination image can be assessed in economic terms, with the dual purpose of enabling comparative studies to be made between destinations and making it possible to determine the value of destination image and WTP for improved destination image attributes. An assessment of this type will make it possible to manage investments better, estimate returns on investments and improve the efficiency of promotional activities. However, despite the proven importance of image and the major financial investment destinations make in promotion initiatives to change and improve
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image, no methodology has been put forward to enable destination image to be assessed in economic terms or to help manage the destination image formation process. The principal contribution of this study is in establishing a new methodology for assessing destination image which incorporates two main advantages over methods used to date: (a) it enables the image of a tourism destination to be assessed economically in relation to its competitors and tourist willingness to pay; and (b) it allows the economic assessment to be made independently of the improvements proposed at the destination and tourist willingness to pay, with the impact of these attributes on destination image constituting a fundamental aspect for improving image (Bigné et al, 2009). In an approach to this challenge, the present study distinguishes between destination image and the objective perception of the holiday experience at a destination, thus separating the measure of the dimension of perceived image, the measure of perceived experience, and the measure of perceived experience influenced by the dimension of destination image. The assessment method developed is based on the methodology of Gensch (1978), in conjunction with a discrete choice model, as this emerges as the most appropriate method for measuring image assessment in economic terms, as it enables the changes image produces in tourist perceptions to be identified. This method distinguishes between ‘the measure of the perceived experience’ (the experience of the tourist at a destination and how it is rated by the tourist) and ‘the measure of the dimension of perceived image’ (the destination rating made by tourists simply from the name of the destination, with all the implicit evocations this generates in their minds). The discrete choice method was used to assess destination image in terms of the value tourists gave to each of the alternatives presented to them, structured in holiday packages: (a) holiday package price; (b) state of the beach and area available per person; (c) services available at the accommodation resort; (d) preservation of the natural landscape; (e) leisure activities in the vicinity and types of theme parks; (f) average travel time to shopping centres and entertainment; and (g) location and management of the urban environment of the tourism area. Each alternative contains specific attributes and is located in competitive sun and beach destinations in the Canary Islands and the Mediterranean (Balearic Islands, Greek Islands, Turkey, Tunisia and Cyprus). The study was conducted in Germany, Europe’s top source of tourism, on a sample of 1,200 potential tourists. The results demonstrate the validity and utility of this methodology for assessing destination image. In particular, it has been shown that destination image significantly affects tourist utility when deciding on travel plans for a set of destinations in the Mediterranean and the Canary Islands in relation to their willingness to pay. It was also found that the value of destination image differs considerably between these destinations. The value of destination image for locations such as the Balearic and Canary Islands is seen in tourist willingness to pay in excess of €20 a day more in comparison with other destinations, such as Tunisia. Moreover, the study is not limited to this assessment, but focuses on the impact that improvements and corrective measures in certain components of the destination image can have on greater tourist willingness to pay, such as natural landscapes or the services available at the accommodation
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resort, where willingness to pay for improvements is more than €20 a day. The study identifies the economic impact of changes in image on tourist perceptions. This study highlights aspects for future lines of research. First, it presents the possibility of contrasting or refuting the validity of the analysis for other destinations and contexts, and of conducting subsequent meta-analyses. Comparative and market image analyses between different destinations and segments can be undertaken. Second, it poses the challenge of integrating the economic assessment of the affective dimension of image and its various attributes. Third, it shows that economic assessment studies need to be approached with a greater understanding of the destination image formation process, by including the phases of the holiday process and the variables that come into play, such as sources of information and motivation and their economic assessment. It would also be worthwhile incorporating an analysis of the impact and economic assessment of image on tourist satisfaction during their visit, as the impact of image on satisfaction has been widely noted in the literature (Chon, 1990; Pearce, 1997; Im and Chon, 2008). Moreover, it would be interesting studying how the introduction of new products or events can influence destination image in economic terms. Additionally, there is a need to test how tourists from different countries show differences in their preferences and economic valuation of destination image. Lastly, an analysis could be made of the effects of the economic assessment of destination image from a long-term perspective that evaluates the dynamic adjustment process of image over time. The results of this study could also have important implications for the management of destination image by DMOs. In an increasingly competitive market, tourism destinations have to use image as a basis for both measuring and working. Brand equity will represent a key role, as it integrates the total value of the attributes implicit in the brand that convince a tourist to visit a destination over competing offerings. This methodology reveals points (attributes) of possible leverage for increasing destination image. Further research is therefore needed for more in-depth study of the causes and consequences of the impact of communication and product improvement initiatives that project tourist destination image. To this end, the methods used in this study can help to design studies to gauge tourist reactions to these campaigns (Daye, 2010). Similarly, the results have implications in assessing the economic resources invested by destinations to be more competitive in the market, and help to improve planning of the marketing efforts to enhance destination image. Endnote 1. DMOs are a critical component of tourism industry. There are different types of DMOs depending on the jurisdictions they cover (multinational, national, regional, local, etc). This study will specifically examine regional DMOs (Consejer’a de Turismo of the Canary Islands), whose major purpose is to market their destination to potential visitors, both individuals and groups, to provide economic benefit to the community and its members. DMO members may include hospitality-related companies (hotels, restaurants, tour operators, governmental bodies, and any company which directly or indirectly supports tourism).
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