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Article

Are Tourists Really Willing to Pay More for Sustainable Destinations?

Department of Economics, University of Jaén, Campus de Las Lagunillas, s/n, 23071 Jaén, Spain
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Author to whom correspondence should be addressed.
Academic Editor: Giuseppe Ioppolo
Sustainability 2016, 8(12), 1240; https://doi.org/10.3390/su8121240
Received: 17 October 2016 / Revised: 15 November 2016 / Accepted: 22 November 2016 / Published: 30 November 2016
(This article belongs to the Section Economic and Business Aspects of Sustainability)

Abstract

The understanding of pro-sustainable behavior and its true economic implications is an important subject for tourism destination marketers and policymakers, especially given that limited research has focused on the economic implications of tourist preferences for more sustainable destinations. Following the identification of three different demand segments using the concept of “sustainable intelligence” (level of commitment, attitude, knowledge and/or behavior with regard to sustainability), this study hypothesizes that the tourist segment with high level of “sustainable intelligence” (called “pro-sustainable tourist”) is willing to pay more to visit a more sustainable destination. The main aim of this paper is to use the logistic regression model to estimate the premium price that each segment is willing to pay to visit a sustainable destination. This paper reports the result of a willingness to pay study using data from 1118 respondents visiting the Western Costa del Sol (Andalusia, Spain), a mature sun-and-sand destination that is currently facing several developmental challenges supposedly associated with sustainability. The results obtained from this research study indicate that the tourist segment with high levels of “sustainable intelligence” is willing to pay more to visit a more sustainable tourism destination. However, there is little willingness to pay if the destination’s commitment to sustainability increases the price of the tourism product (26.6% of respondents).
Keywords: willingness to pay; sustainable intelligence; pro-sustainable tourist; sustainability; destination management; Western Costa del Sol (Spain) willingness to pay; sustainable intelligence; pro-sustainable tourist; sustainability; destination management; Western Costa del Sol (Spain)

1. Introduction

Different issues related to the sustainability field, including sustainable consumption, have gained significant institutional, scientific, academic, corporate and social momentum over the past two decades. However, even though the idea of sustainable development has been discussed in tourism research for almost a quarter of a century, the inclusion of the tourist in this debate has been delayed and partial. In that sense, Sharpley [1] (p. 37) declares “the exclusion of the tourist from the majority of the research”. However, for over a decade, tourist behaviour has been fundamental issue in tourism studies [2].
The subject areas in sustainable tourism research have significantly changed over the 25-year period of analysis and, in particular, sustainable tourist behavior is currently a thriving field of study [3,4], experiencing significant growth since 2008 [5]. Nevertheless, in academic discussions, the conceptual basis of the sustainable tourist has been controversial and somewhat bewildering, thus resulting in different conceptualizations of “sustainable or pro-sustainable tourists”.
The role of tourists is crucial to research on the challenge of sustainability. As the World Tourism Organization already noted over a decade ago, “sustainable tourism should also maintain a high level of tourist satisfaction and ensure a meaningful experience to the tourist, raising there awareness about sustainability issues and promoting sustainable tourism practices amongst them” [6] (p. 7).
It is undeniable that tourists’ profile has changed significantly in recent years. Numerous studies on tourist consumer behavior indicate that there is a growing awareness of the environmental, social and cultural impact that tourism activity can generate. Tourists who promote and consume sustainable tourism are sensitive to the impacts that this activity can generate and therefore try to protect the destination [7,8,9].
However, the scientific debate regarding consumer behavior in sustainability issues is intense. Hence, many contemporary academics, e.g., [10,11,12,13] suggest that most consumers pay little attention to ethical considerations in their final purchase decisions; therefore, there is a gap between the expressed attitudes and actual purchase behavior (attitude–behavior or word–deed gap).
From a management and research perspective, it is important to obtain a greater understanding of consumer attitudes and economic implications of the current tourist regarding sustainability, with the aim of achieving a new tourism model whose central value is sustainability. In that sense, an important challenge facing policymakers and marketing managers is to identify which tourists are willing to pay more for sustainable destinations and if pro-sustainable tourists spend more than their less-sustainable counterparts [14].
This paper’s aim is to contribute an understanding of the role played by tourists to a destination as a strategic element of differentiation. Drawing on contemporary research on sustainable behavior and the “pro-sustainable tourist”, this article begins by outlining the ways in which the “pro-sustainable tourist” has been understood and the importance of determining the economic value of sustainability from the tourist perspective.
This research extends previous work regarding the concept of “sustainable intelligence” as a component of tourism demand, to which a latent class analysis can be applied. Using this method, the resulting three segments (“reflective tourist”, “unconcerned tourist” and “pro-sustainable tourist”) were compared, showing their different levels of commitment, attitude, knowledge and behavior with regard to sustainability (“sustainable intelligence”) [15].
To further expand and refine the theoretical understanding of “sustainable intelligence”, this work estimates the premium price that each segment is willing to pay to visit a sustainable destination.
The paper is structured as follows. The paper’s introduction gives an overview of the approach. The theoretical framework presents a review and update of key concepts on which this study is conducted. Then, prior to the empirical study, a brief analysis of the context in which the research is conducted is presented, an emblematic coastal tourism destination, the Western Costa del Sol (Andalusia, Spain), which is recognized as mature by the scientific literature that has carried out an expensive strategy of repositioning in the market through investment in sustainable tourism development (Plan Qualifica (Contained in the Requalification Program for Mature Tourism Destinations (Spanish Tourism Plan Horizon 2020)) of Western Costa del Sol) [16].
In the following section, the methodology adopted in the study is described and the accepted methods in the literature to derive WTP (Willingness to Pay) are compared. Then, the study findings are presented. The results enrich the understanding pro-sustainable tourist background and portray the practical and theoretical contributions of the suggested conceptual model, as well as its limitations. The article concludes by exposing the future research themes in the field of pro-sustainable tourism.

2. Literature Review

2.1. What Does It Mean to Be a “Sustainable Tourist”?

As stated by Gössling et al. [17], there is academic, political and industry consensus that tourism should become more sustainable, a process that requires the participation of all stakeholders. In this regard, the role of the tourist, the main actor of the tourism system, is crucial in advancing towards sustainable tourism. However, in the sustainable tourism debate, it is common to relegate, even overlook, the role of tourists, even though they should be the focus of discussion by exploring what it means to be a “sustainable tourist”—that is, a tourist with sustainable or pro-sustainable attitudes and behaviors [1,18].
It should be stressed that tourists’ sustainability must be understood as a continuous process rather than a static state; it must be considered a permanent process of adaptation and balance to adjust their relationship with ecological, economic and social systems.
Many studies have looked at tourists who identify themselves as being sustainable in some way. In fact, an overview of the state of sustainable tourism in the scientific literature indicates that sustainable tourist behavior is currently a thriving field of study [3]. In the literature, there are many definitions that have been developed around tourists’ sustainability according to different criteria and using different methodological approaches. A problem in the study and analysis of pro-sustainability consumerism is the variety of variables that can be considered when analyzing sustainable behavior, which vary according to how the researcher understands sustainability. For example, some authors relate sustainable tourism behavior only to environmental issues [19,20,21], but in the search of the conditions under which tourism development, improvements in environmental quality and economic growth can simultaneously occur [22], it is important to analyze the effect and the importance of tourist in the sustainable tourism. In that sense, other authors (although there are few contributions) study the relationship between tourists and sustainability from a more holistic and inclusive perspective [8].
In the past two decades, there have been a significant number of conceptual approaches to the idea of “sustainable tourist”. Not all of these, of course, have been accepted. Worthy of special mention is the green, environmental or ecological tourist [2,9,23,24], ethical tourist [3,25,26,27] or sustainable tourist [8,28]. Additionally, other conceptualizations related to different aspects of sustainability are the nature-based tourist [29,30,31], slow tourist [32,33], responsible tourist [34,35], community-based tourist [36,37], geotourist [38,39,40], people-first tourist [41], pro-poor tourist [42], and just tourist [43].
Progress has not been made solely in the conceptualization of tourists’ sustainability; there is also an extensive body of literature that analyzes the relationship between individuals’ values, attitudes and behavior. Indeed, previous research has shown an existing relationship between individuals’ values, attitudes and behavior [23,44,45,46,47]. Moreover, numerous studies on tourist consumer behavior indicate that there is a growing awareness of the environmental, social and cultural impact that tourism activity can generate [3,48,49,50,51].
As Peeters [52] and Weaver [13] argue, although sustainable development is now a well-established term, its implementation has not been particularly successful. In fact, several authors submit that this proposed mass awareness of the environmental, social and cultural impact that tourism activity can generate in destinations is not directly reflected in actual consumer behavior, particularly regarding tourism-related decisions [13,53]. Additionally, other studies argue that there is an important difference between the declared values and the actual tourist behavior [13,53,54,55,56]. Aside from this, other authors argue that most consumers pay little attention to ethical considerations in their final purchase decisions, so there is a gap between the expressed attitudes and actual purchase behavior (attitude–behavior or word–deed gap) [10,11,12,57,58].
Several authors [53,59,60] support the idea that consumers make their purchasing decisions on the basis of the environmental, social and economic quality of products and are willing to transfer these habits to the purchase of tourism products. As a matter of fact, consumer behavior shows trends towards the consumption of green brands and sustainability, as these factors are important when booking their holiday destinations [20,61,62,63,64]. For this reason, as Edgel [65] recognizes, destinations try to respond to these new demands and show a greater concern to develop more sustainable tourism. Furthermore, as noted by Darnall et al. [66], sustainable consumption is often perceived as a business opportunity.
This implies a willingness to buy beyond the incentive of price comparison, in search of the quality and opportunities that are consistent with environmental and social concerns. Miller [53] supports the idea that consumers make their purchasing decisions on the basis of the environmental, social and economic quality of products and are willing to transfer these habits to the purchase of tourism products. In fact, recent studies demonstrate the real potential of “sustainable” consumers in different types of companies directly linked to tourism (hotels, restaurants, transportation companies, etc.), showing the profitability of tourists with sustainable behavior [67,68,69]. In short, the academic debate regarding the sustainable behavior of tourism consumers and real economic implications on sustainability issues is intense and even controversial.

2.2. Should Tourists Be Willing to Pay More for More Sustainable Destinations?

Another highly pertinent issue being widely discussed in the scientific sphere is whether investment in sustainability involves higher costs for the destination and, therefore, an increase in price. If, on the other hand, this commitment to sustainability leads to the improvement of efficiency and the price rises, this process is put forward solely as a differentiation strategy. In this regard, recent studies contribute to breaking with one of the most widespread myths about sustainability—which tourism sustainability can be obtained only at the expense of economic progress [70]. It is important to note that a commitment to sustainability does not necessarily mean that the destination should be more expensive, but rather, it can be a price strategy. As León [71] (p. 8) states, “sustainability plays a determining role in product differentiation, which allows to offer a price above the level of perfect competition, exercising a market power that would increase the profitability of tourism for the destination society”. As stated by Hedlund [63] (p. 279), “many ecologically sustainable tourism alternatives are more expensive than their less sustainable counterparts” and, in that case, tourists need to be willing to pay more in order to be more sustainable. An interesting question is: are pro-sustainable tourists a higher spending market? Furthermore, is it true that, as stated by Nickerson et al. [14] (p. 176), “sustainable tourists have significantly higher spending patterns than less-sustainable tourists?”
In the field of tourism, numerous studies have analyzed the WTP of tourists on issues related to the conservation and protection of natural areas [72,73,74], ecotourism services [75], sustainable products [8], sustainable destinations [76], climate change [54], natured-based tourism [29], etc. Other studies support the relevance of the WTP, showing the existence of tourists willing to pay more for sustainability-related issues from different perspectives (hotels, transport, products, etc.), [62,77,78].
Accordingly, it is interesting to examine the willingness to pay as an economic instrument for sustainable tourism management, especially in destinations that try to restructure their business model towards sustainability as a differentiating element in the market [79] and demonstrate that in regard to tourism, it pays to be sustainable.

3. Case Study

This analysis has been conducted on the Western Costa del Sol (Andalusia, Spain) (Figure 1), which stands out as a typical mature and obsolete sun-and-sand tourism destination with recognized problems of unsustainability [80,81,82] in need of renewal of its tourism development patterns. It is a destination that has carried out an expensive repositioning strategy in the market through investment in sustainable tourism development (Plan Qualifica).
In truth, as the Plan Qualifica itself recognizes, “the new model we advocate is based on rehabilitation facing the expansion; defends an economic and not territorial expansion. Breaking the dynamics of tourism growth, investing in sustainable tourism development” [16] (p. 65). Therefore, the debate is particularly interesting with regard to the attitude and behavior of tourists about the sustainability of the destination, in a territory that is facing the challenge of achieving true transformation towards a new model of tourism through the implementation of tourism policies whose core value is sustainability.
It is particularly striking that all strategies proposed in the Plan Qualifica are based exclusively on supply-side measures (mainly infrastructure projects such as construction, remodeling, rehabilitation, etc.). However, there are no strategies aimed at tourists, nor has any specific analysis of the demand for this destination been made. This crucial issue is indispensable to adjust strategies and actions, considering the actual or desired tourist demand (redirecting to new segments) for that destination.
Since tourism began to develop in Malaga in the mid-1950s, it has become a powerful driving force for the transformation and modernization of Spain’s entire coastline [83]. However, this process has generated notable pressure on the land, which, in turn, has radically changed the coast’s character. The historical imposition of unsustainable tourism has caused a serious impact on the landscape that has resulted in a new reality that is more complex from a territorial and sustainable point of view. This has led to a chaotic tourism development model [84] that requires the implementation of new tourism policies supported by planned action based on sustainability criteria (To gain an understanding of the transformation and impacts of the tourism sector in Western Costa del Sol, it is recommended to read, among others, [82,85,86,87,88]). This is precisely the aim of the Plan Qualifica, whose ultimate goal is to achieve a profound change in the Western Costa del Sol traditional tourism model from a development model based on growth to a new one based on quality, innovation and sustainability. This fact underlies the final decision to select this destination for the methodological application of this research.

4. Methodology

4.1. Questionnaire and Survey

To verify our hypothesis, a survey was conducted in municipalities that are part of the tourism destination under study (Table 1).
Given the impossibility of identifying the target population (tourists visiting the Western Costa del Sol), due to the lack of adequate statistical information, a simple random sampling method was conducted, carrying out 1188 valid surveys with domestic and foreign tourists who stayed overnight in any of the cities that comprise this destination.
During the month of June (2013), the survey was validated by five specialists in this paper’s area of analysis, with the intention of improving, supplementing and corroborating the final content of the survey.
The survey was divided into three parts: (i) characterization of tourism demand; (ii) characterization of tourism demand in terms of sustainability; and (iii) economic implications associated with improving the sustainability of the destination.

4.2. Segmenting the Tourism Market: A Latent Class Analysis Based on Tourists’ “Sustainable Intelligence”

In an attempt to delve further than the current literature’s partial consideration of several attitudes and values in order to segment the tourism demand concerning sustainability, López-Sánchez and Pulido-Fernández [15] (p. 61) introduce the concept of “sustainable intelligence” as a latent variable considered as “an inherent capacity to a certain type of tourist, whose possession conditions their motivations, expectations and behaviors”. According to the authors, a tourist with high “sustainable intelligence” is one who shows empathy towards sustainable tourism development in the territory where he enjoys holidays. Consequently, he has an intellectual understanding of sustainability and takes measures to facilitate the incorporation of sustainability in the processes of production and tourism consumption. Therefore, the development of “sustainable intelligence” is essential for tourists to adapt their motivations, expectations and behaviors towards a much more sensible and responsible stance regarding the destination.
López-Sánchez and Pulido-Fernández [15] identify and compare three segments—the “reflective tourist”, “unconcerned tourist” and “pro-sustainable tourist”—showing their different levels of commitment, attitude, knowledge and behavior with regard to sustainability segmentation. The aim of this previous study [15] was to demonstrate that, in these mature sun-and-sand tourism destinations, it is possible to identify one or more segments of demand that show a high level of commitment, attitude, knowledge and/or behavior towards sustainability. Details of this methodology and findings have been published; for that reason, this paper includes only those results that are absolutely necessary for the reader to understand the origin of the investigation. Readers are invited to read the previous work for a supplemental background and methodological details that enhance the credibility of this paper. This segmentation has been used in this article to analyze to what extent demand segments, whose motivations, expectations and behaviors are different when visiting the destination in which this research was carried out, show a different willingness to pay to visit a more sustainable destination.

4.3. Most Common Methodologies to Study the WTP: Reasons to Choose This Concrete Method

Within the direct methods group/declared preferences, the Contingent Valuation Method allows calculating the Willingness to Pay (WTP) through different models. The final model choice depends on the sample size or on the question format. Some of these formats are referendum, multiple, auction and open format. However, there is still no standard about which is the best format, under which circumstances it should be applied, or which is the appropriate statistical model to estimate the WTP trustworthily.
Contingent valuation (CV) is a stated preferences method widely used to measure, in monetary units, people’s preferences concerning environmental assets and public goods. It consists of creating a hypothetical market by asking people how much they would be willing to pay for a certain nonmarket good as “sustainability”.
Parametric approaches (such as logit and probit models) are most used, but non-parametric methods have some advantages with respect the parametric approaches since the WTP estimators are easily calculated and are more robust to wrong specifications of the probability distribution function. The non-parametric approach presented by Kriström [89] is based on a theorem developed by Ayer et al. [90], but it can be considered a particular case of the Turnbull’s algorithm [91], which faces the problem posed by Ayer et al. [90] as an extreme case of double censoring. Later, Kriström [92] developed a parametric estimation based on a specific likelihood function that allows for zero responses in the WTP: the spike model.
To summarize, in Table 2, the main methodologies used to estimate the WTP are revised and summarized, even quoting as a reference some general studies and others within the tourism area that define and/or use these models.
The next step is to estimate, using a logistic regression model, the probability that any of the identified segments would be willing to pay for Western Costa del Sol to become a more sustainable tourist destination. The extent to which any segment is willing to pay more than the rest is also included.
Using referendum question requires a large sample size. In this regard, Hanemann and Kanninen [93] recommend a sample size between 500 and 1000 (in this case, the initial sample size was 1188 surveys).
The statistical analysis differentiated real zeros from protest zeros, and protest zeros were eliminated. Real zeros correspond to tourists who participate in the hypothetical market but do not recognize the worth of it, whilst protest zeros correspond to tourists showing a clear discomfort with the suggestion of paying an additional amount. This type of tourist, corresponding to protest zeros, is not included in the hypothetical market; therefore, it should be excluded from the analysis.
In order to identify protest zeros, tourists who were not willing to pay more for their stay in Western Costa del Sol were asked to give a reason for their negative response. Thus, protest zeros are considered tourists whose negative response is one of the following:
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I am not interested in this type of issue.
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I do not believe in this type of contribution; at the end of the day, nothing will change.
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I do not think a sustainable destination has to be more expensive, but rather, it should use its resources more efficiently.
Consequently, the initial sample comprised 1188 tourists; however, records with missing data and “protest zeros” were dismissed, so, finally, there were 666 valid surveys, with a sampling error of 3.797%, for a confidence level of 95% (p = q = 0.50).
Moreover, a first descriptive analysis through contingency tables is carried out using descriptive statistics tools, through the statistical software SPSS version 22.0 (International Business Machines Corp., Armonk, NY, USA, 2013), in regard to categorical variables and analysis of variance (ANOVA) to analyze interval or quantitative variables. The purpose is to identify which of the identified segments can explain WTP and to what extent.

4.4. Logistic Regression Model

Removing the “zeros” as a complaint has significantly reduced the number of valid questionnaires to undertake this research. As a consequence, the available information is much more limited, and the application of traditional methodologies in order to estimate the WTP was completely fruitless, as the results obtained in all cases were not statistically significant. Therefore, it has been necessary to look for a different methodology that allowed to the intended analysis to be achieved and that included statistically significant results.
Models of logistic regression are commonly used to model, through a linear predictor, the probability of the occurrence of an event [87,108,109,110]. Our research applies this type of model to predict the probability of positive WTP.
The dependent variable was defined for Yi = 1 when WTP is positive and Yi = 0 when WTP is negative. Logistic regression analyzes data of binomial distribution Yi ~ B (Pi, ni), were, for i = 1, …, n, where the numbers of Bernoulli trial ni are known and the probabilities of success Pi are unknown. The applicable model follows the Logit function:
Logit   P   ( Y i =   1 | β , X i ) = β 0 + β 1 X i 1 + β 2 X i 2 + ... + β k X ik ,    i = 1 ,    ... , n
where
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Logit   P   ( Y i =   1 | β , X )   =    ln   [ P   ( Y i   =   1 | β , X ) / ( 1 P   ( Y i   =   1 | β , X ) ) ] ;
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βj is the parameter associated with the variable j, j = 1, …, k, that has been estimated through the maximum likelihood method; and
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Xj are dummy variables; in other words, Xij (value of variable j on the individual i) takes values 0 and 1.
An algebraic transformation provides the corresponding logistic model that indicates the probability of predicted occurrence by the predictive linear function that subject i presents a positive WTP:
P   ( Y i =   1 | β , X )   =   [ 1 + exp ( β 0 + β 1 X i 1 + β 2 X i 2 + ... + β k X i k , i = 1 , ... , n ) ] 1
where Odds refer to the following ratio:
Odds   =   P   ( Y i   =   1 | β , X ) / ( 1 P   ( Y i   =   1 | β , X )
The calculation of two Odds concerning a unitary increase in one of the explicative variables of the model leads to the concept of the Odds-ratio as the ratio of the two Odds:
Odds-ratio = Odds 2 / odds 1 = exp   ( β j )
Software SPSS, version 22.0 was used. The reference category to analyze the adjusted model is the last one (pro-sustainable tourists).

5. Results

5.1. Segmented Analysis Regarding Willingness to Pay

As shown in Table 3, only 26.6% of the survey (n = 666) is willing to pay more for Western Costa del Sol to become a more sustainable destination than it is at present. The vast majority of this type of tourist willing to pay more money for destination sustainability is part of the segment called “pro-sustainable tourists”. In fact, 97.8% of this segment shows their availability to pay more, versus a small number of tourists from the other two segments who have shown willingness to pay an additional charge.
This lack of commitment shown by survey respondents in terms of accepting a higher cost of travel to a destination that is making relevant efforts to improve its sustainability is confirmed, as seen in Table 3, by the notion that even the people willing to pay would only pay a very reduced additional amount, which is limited, in general, to 10% of the total amount of the cost of travel.
Again, “pro-sustainable tourists” are the ones who are willing to pay more money, even identifying a small group who is willing to pay up to 50% of the current amount paid, although this group should be considered a merely testimonial case because the vast majority (85.4%) of people estimate their additional payment to not exceed 10%.
Table 4 shows the tourists willing to pay over 100 euros more than the amount they are paying now in order to enjoy a more sustainable tourist destination (in this case, Western Costa del Sol). In order to choose this value, an estimation of the central tendency on WTP per tourist has been carried out through the median, whose value was 100 euros, which indicates that half of people are willing to pay less than 100 euros more to enjoy a sustainable tourist destination, while the other half shows a WTP higher than 100 euros.
The mean is the most used average measure, as all opinions are taken into account, and offers a more exact value; however, it may be affected by the so-called “asymmetry” that represents the distribution of observations in relation to a central value and may be caused by the existence of extreme values that distort its meaning. Therefore, the mean is used to prevent the arithmetic mean from becoming excessively distorted by the existence of extreme values.
In general, Table 4 shows that, amongst the people willing to pay more, the group of people who would pay over 100 euros is greater than the group of people willing to pay less than 100 euros. It is also noticeable that the difference between both groups (in favor of the ones willing to pay over 100 euros) is more significant in “reflective tourists” and “unconcerned tourists” than in “pro-sustainable tourists”.
Table 5 summarizes the descriptive statistical principles. In general, the people included in the analyzed survey are willing to pay (WTP) 6.29% (62.89 euros) more than the amount actually paid (cost of travel (CT)) to visit a more sustainable tourist destination. However, when the survey is segmented into the three previously identified groups, it is shown that “reflective tourists” are willing to increase the amount paid by only 2.31% (17.64 euros), whereas the “unconcerned tourists” would overpay 2.41% (30 euros). “Pro-sustainable tourists” have expressed their intention to pay the highest amount in relation to the cost of travel. The tourists in this segment express their intention to pay 15.35% (226.42 euros).
The following aspect to be compared is the two dependent variables (cost of travel and WTP) between the three segments. Table 6 includes descriptive statistics. The previously mentioned statement is confirmed: “pro-sustainable tourists” are willing to pay an amount 7.5 times higher than the amount “unconcerned tourists” would pay; and “unconcerned tourists”, at the same time, are willing to pay 70% more than “reflective tourists”.
Since variances are not homogeneous, the robust statistical testing under the violation of homogeneity assumption has been estimated. The overall F test indicates that there are significant differences among the three segments within the two analyzed variables: Cost of travel (F(2288.8) = 20.558; p < 0.001)) and WTP (F(2152.5) = 22.291; p < 0.001)). The overall F test also indicates that there are significant differences among the three segments; however, it does not specify the segments.
Multiple comparisons carried out by the use of Tukey’s HSD test reveal (Table 7) that there are differences regarding the cost of travel (CT) among the three groups, being highest in “pro-sustainable tourists” with respect to “reflective tourists” and similar to “unconcerned tourists”, as well as in “unconcerned tourists” with respect to “reflective tourists”. The increase in cost of travel (WTP) is higher among “pro-sustainable tourists” in relation to “unconcerned tourists” and “reflective tourists”, with the last two groups having the same value.
Table 8 shows that “pro-sustainable tourists” would mostly be willing to pay more for their stay in Western Costa del Sol, in case projects to improve the sustainability of this destination would imply an increase in prices. For its part, the independence hypothesis test, carried out through Pearson’s chi-squared test, demonstrates that there is a relationship between both variables (identified segments and willingness to pay).
Finally, having broken down the value WTP = yes based on the median of WTP (Table 9), “pro-sustainable tourists” are divided equally between both values.
The results shown in Table 9 reveal two relevant issues. First, the issue is focused on not paying or paying over 100 euros of the actual cost of travel because, within the three segments, the percentage of people willing to pay over 100 euros is higher than the percentage of people willing to pay less than 100 euros. Second, although “pro-sustainable tourists” are the ones showing higher WTP for a sustainable destination, when responses regarding the median of WTP are analyzed, this group shows a smaller difference between the people willing to pay more than 100 euros and the group willing to pay less than 100 euros. Finally, it is worth highlighting that the chi-squared test shows a relationship between the segment and the willingness to pay with a threshold of 100 euros.
It is possible to identify a segment willing to pay more than the rest of tourists due to extra costs to improve the sustainability of the destination of the Western Costa del Sol.

5.2. Are Pro-Sustainable Tourists Willing to Pay More to Visit a More Sustainable Destination?

Results regarding the adjustment of logistic regression are shown in Table 10, including the estimation of the maximum likelihood of the model parameters for the variables that are statistically significant (it should be noted that, in this case, segments are used as explicative variables of WTP).
Beta coefficients of such variables are significantly different from zero, with a significance level of 5% (Wald test). This indicates that the variables explain the risk ratio of the model; in other words, they have the ability to describe the probability of one segment to have WTP for the destination to become more sustainable in relation to the probability of not having WTP. As it can be demonstrated, the beta estimation is a negative number, which implies that an increase in the respective variables will produce a decrease in the probability of willingness to pay.
The signs of parameter estimations correspond to the expected results. The analysis in Table 10 supports statements made so far. In fact, it has been verified that “reflective tourists” have a lower probability to contribute with an additional payment for the destination subject to this research to improve sustainability than “pro-sustainable tourists” (reference category).
The same situation can be found with “unconcerned tourists” to a lesser extent, which also shows less probability to pay an additional amount to visit a sustainable destination than “pro-sustainable tourists”.
Accordingly, it has been stated that pro-sustainable tourists are the group of tourists showing more WTP due to the efforts made by Western Costa del Sol to become a more sustainable tourist destination.
Finally, it should be mentioned that the omnibus test on the model coefficients indicates that this model includes an appropriate adjustment, in which not all coefficients are null (Chi-squared test (2) = 450.178; p = 0.000).
The coefficient of Cox and Snell R2 (0.491) and the coefficient of Nagelkerke R2 (0.716) indicate that the variation in the dependent variable explained by the model is very high. The chi-squared coefficient of the Hosmer and Llemeshow test, evaluating the goodness of the overall adjustment of the model, shows that there are no significant differences between the observed values and the values predicted by the model (Chi-squared test (1) = 0.000; p = 1.000).

6. Discussion and Conclusions

The results obtained from this research support the hypothesis raised at the beginning of this paper: the tourist segment with high levels of “sustainable intelligence” (commitment, attitude, knowledge and/or behavior with regard to sustainability) is willing to pay more to visit a more sustainable tourism destination. The combination of pro-sustainable behavior and increased spending provides evidence of a market segment in which destinations can benefit [14].
It is undeniable that not all tourists visiting the Western Costa del Sol have the same degree of awareness with regard to what sustainability means and implies; therefore, their behavior is very different, demonstrating that “pro-sustainable tourists” have a greater WTP and are also willing to pay a higher amount.
On the other hand, there is little willingness to pay if the destinations’ commitment to sustainability would increase the price of the tourism product (26.6% of respondents). These data are significant, especially in view of the fact that a large majority of those who are willing to pay more would not pay more than 10% above the cost of travel. As Budeanu [28] (p. 499) states, “the low support from customers is one of the main barriers for progress towards sustainable tourism. One reason can be that existing initiatives are missing customers' attention, discouraging industry and governments to continue promoting sustainable tourism”.
The results obtained in this study corroborate what has been advocated by recent literature [13]. Beyond grandiloquent statements about their interest in sustainability, tourist behavior indicates little concern for the impacts generated by their activity in the destination visited, especially in relation to their economic implications. It seems incongruous to be obsessed with the creation of a tourism offer for which, a priori, there seems to be no demand. However, although customers are not willing to pay a significant premium, the supply of sustainable tourism products can be a successful differentiation strategy to gain market share.
The solution to be raised could be twofold: either by trying to change the behavior patterns of the present tourists (especially “unconcerned tourists”), improving their knowledge on sustainability, their environmental awareness and, consequently, their empathy with the destination, to the point of increasing their WTP and the maximum amount that they are willing to pay or through repositioning itself in the market, trying to find other demand segments that value the new tourism offer that will emerge after this process (pro-sustainable tourists). Both cases must be undertaken with strategic actions aimed at supply and demand. Thus, public authorities, the receiving community, private companies and, finally, tourists should cooperate and have a share in the commitment to achieve comprehensive management based on sustainability criteria.
Not only is it important to change the character of the destination, to which most of the restructuring plans of mature coastal destinations have been devoted, at least in Spain, or even its tourism model, but it is also necessary to inform the tourism demand, both real and potential, for its new philosophy and the need to be accompanied by a change in the behavior. However, for this to occur, tourists need to obtain relevant information regarding the destinations’ commitment to sustainability. However, achieving comprehensive information about destination sustainability could be difficult because, ultimately, it will depend on the transparency and credibility of destination. In short, the destination must first offer products and services that are really based on the philosophy of sustainability, as this can help becoming revalued as a unique, differentiated and responsible destination. Finally, it should communicate the change to current and potential tourists. Another important first step in fostering a productive public debate on the transformation is a better understanding of its benefits and costs.
To summarize, market segmentation in tourism is useful for the sake of planning and managing demand-oriented policies. Hence, a segmentation based on tourists’ “sustainable intelligence” could be a crucial tool for improving sustainable destination development. To identify “pro-sustainable” tourists would be extremely useful, especially in destinations whose public policies are committed to sustainability, such as Western Costa del Sol, because within the extensive body of literature on sustainable tourism, its successful implementation is an emerging and important theme [111].
The results obtained suggest, in fact, the need for complementing supply policies with demand policies in order to overcome the current actions exclusively from the supply perspective, which has resulted in little success. As Pulido-Fernández [112] (p. 260) highlights, “sustainability cannot be covered exclusively from a supply perspective. On the contrary, tourism demand—i.e., tourists—plays a key role in achieving sustainable tourism”.
In other words, it is not sufficient to guide tourism supply towards more sustainable practices; it is also necessary that tourists appreciate these efforts and are willing to make their own efforts by improving their behavior, of course, and by paying a higher price to enjoy a destination that is making an effort to be more sustainable. In this way, the demand would support the actions undertaken on the supply side. The long-run sustainability of tourism is not a utopian idea, but it requires a careful management and strategic planning [113].
The main methodological problems and limitations should be addressed. Clearly, one limitation of our approach is that, due to the reduced sample size after removing the “zeros” as a complaint, the traditional WTP models could not be used, as the final results had no statistical significance. This has led to an alternative methodology that ensured that the intended analysis was more reliable. In addition, for example, [113] discussed how the WTP in a tourism context might depend on several factors, including environmental quality and congestion factors, and it should be noted that these factors is likely to affect and distort the results. Marsiglio [113] and Farr et al. [114] dealt with the issue from a theoretical point of view and from an empirical one, respectively.
Finally, with an eye to future research directions, it would also be interesting to discover whether there is a significant difference between the attitudes (and motives) of each segment identified regarding the establishment of a tax for projects to improving the sustainability of the destination [115,116,117].
Another interesting route to extend this research might be comparing the different levels of satisfaction with the destination and the social carrying capacity of each segment identified.
In addition, another challenge will be to develop a strategic agenda highlighting the priorities that policymakers and managers of destinations must face to attract, keep or increase “pro-sustainable” tourists.
Finally, going a step further in the analysis of the WTP due to the sustainability of the destinations, it is proposed, once the sample has been broadened, to carry out a comparative analysis of the WTP to check the sustainability of the segments identified through different models (logit, probit, spike, etc.) [105,118] (Del Saz-Salazar and García-Menéndez, 2001; Bengochea-Morancho et al., 2005). In conclusion, an interesting question could be, “which factors affect the willingness of tourists to pay for sustainable destinations?”

Author Contributions

Juan Ignacio Pulido-Fernández and Yaiza López-Sánchez introduced the concept of tourists’ “sustainable intelligence” (level of commitment, attitude, knowledge and/or behavior with regard to sustainability) to provide a richer understanding of tourists’ heterogeneous preferences for sustainable destinations. Following the identification of three different demand segments using the concept of “sustainable intelligence”, their studies allow ascertaining the current tourist profile with respect to sustainability and to identify tourists’ willingnes to pay more for a more sustainable destination and the explanatory variables that influence tourists’ willingness to pay to enjoy a more sustainable destination.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Territorial situation of the Western Costa del Sol (Malaga Province). Source: Authors’ own elaboration.
Figure 1. Territorial situation of the Western Costa del Sol (Malaga Province). Source: Authors’ own elaboration.
Sustainability 08 01240 g001
Table 1. Survey data sheet.
Table 1. Survey data sheet.
UniverseTourists (domestic and foreign) staying overnight at one of the cities of the destination
ScopeBenalmádena, Casares, Estepona, Fuengirola, Manilva, Marbella, Mijas and Torremolinos (Plan Qualifica municipalities)
Type of surveyStructured questionnaire conducted by personal interview
Sample size1188 valid surveys
Sampling error±2.843%
Confidence level95% (p = q = 0.50)
Fieldwork periodJuly and August 2013
Source: Authors’ own elaboration.
Table 2. Willing to pay approaches.
Table 2. Willing to pay approaches.
MODELLITERATURE
PARAMETRICS approaches
PARAMETRICS approaches (Logit, Provit and their different variations)GENERAL LITERATURE: [94,95,96]
TOURISM LITERATURE: [97,98,99,100,101]
NON PARAMETRICS approaches
Turnbull Model l: This technique only needs as information the negative answers proportion to each of the prices offered in the referendum type questions. It provides a simple estimation way. The disadvantage it has compared to the regression model is that the WTP is only explained through the proportion of negative answers to the prices offered and in no case through other variations or individual features.
Kriström Model: Contrary to the Turnbull technique, the amount that leads to the probability of zero acceptation must be specified by the analyst, if the survey does not provide this information. The average in Kriström depends partly on this arbitrary value.
GENERAL LITERATURE: [83,91,102]
TOURISM LITERATURE: [103,104]
OTHER APPROACHES
SPIKE: This model, contrary to Logit and Probit (which only represents WTP > 0) allows to include the WTP = 0, detected on the first rating question that identifies the individuals that would be willing to pay, in contrast with those who would not be willing to pay in an hypothetic market. This model, therefore, allows the estimation of the WTP distribution with a “spike”, i.e., with a discontinuity on the 0 value, due to the fact that a positive probability is assigned to WTP = 0.GENERAL LITERATURE: [86,105]
TOURISM LITERATURE: [106,107]
Source: Authors’ own elaboration.
Table 3. Willingness to pay more for a more sustainable tourist destination.
Table 3. Willingness to pay more for a more sustainable tourist destination.
Are You Willing to Pay More?TotalReflective TouristUnconcerned TouristPro-Sustainable Tourist
N%N%N%N%
No48973.437993.610786.332.2
Yes17726.6266.41713.713497.8
10%14621.9164.01310.511785.4
20%111.720.532.464.4
30%30.520.5--10.7
40%20.310.2--10.7
50%50.820.5--32.2
75%10.2----10.7
Double101.541.010.853.6
Total666100.0405100.0124100.0137100.0
Source: Authors’ own elaboration.
Table 4. Tourists willing to pay over 100 euros more than at present.
Table 4. Tourists willing to pay over 100 euros more than at present.
TotalReflective TouristUnconcerned TouristPro-Sustainable Tourist
N%N%N%N%
048873.337893.310786.332.2
<100 euros7711.661.564.86547.4
>100 euros10115.2215.2118.96950.4
Total666100.0405100.0124100.0137100.0
Source: Authors’ own elaboration.
Table 5. Descriptive statistics.
Table 5. Descriptive statistics.
TotalReflective TouristUnconcerned TouristPro-Sustainable Tourist
Cost of Travel (CT)Δ CT (WTP)Cost of Travel (CT)Δ CT (WTP)Cost of Travel (CT)Δ CT (WTP)Cost of Travel (CT)Δ CT (WTP)
NValid666666405405124124137137
Lost00000000
Mean999.129162.8874762.963017.63701245.250030.00811474.5182226.4161
Median700.00000.0000600.0000.0000900.00000.00001100.0000100.0000
Standard Dev.1037.97092254.76028707.9848496.777631161.0383099.533101452.91697496.53998
Minimum0.000.000.000.000.000.0044.000.00
Maximum7150.003500.006000.001200.006500.00700.007150.003500.00
Percentiles5100.00000.0000100.00000.0000105.00000.0000211.200011.8000
10200.00000.0000150.00000.0000300.00000.0000440.000024.0000
25400.00000.0000300.00000.0000600.00000.0000605.000050.0000
50700.00000.0000600.00000.0000900.00000.00001100.0000100.0000
751200.000020.00001000.00000.00001500.00000.00001650.0000200.0000
902030.0000140.00001600.00000.00002405.000090.00003300.0000500.0000
953000.0000300.00002100.0000100.00003500.0000232.50005050.0000710.0000
Source: Authors’ own elaboration.
Table 6. ANOVA to analyze the differences of WTP between the three segments.
Table 6. ANOVA to analyze the differences of WTP between the three segments.
NMeanStandard DeviationCL 95%MinimumMaximum
Lower LimitHigher Limit
Cost of travel (CT)reflective tourist405762.9630707.98484693.8041832.12180.006000.00
unconcerned tourist1241245.25001161.038301038.86521451.63480.006500.00
pro-sustainable tourist1371474.51821452.916971229.04151719.995044.007150.00
Total666999.12911037.97092920.15461078.10370.007150.00
Δ CT (WTP)reflective tourist40517.637096.777638.183427.09070.001200.00
unconcerned tourist12430.008199.5331012.315247.70100.00700.00
pro-sustainable tourist137226.4161496.53998142.5234310.30870.003500.00
Total66662.8874254.7602843.503882.27100.003500.00
Source: Authors’ own elaboration.
Table 7. Multiple comparisons (Tukey’s HSD).
Table 7. Multiple comparisons (Tukey’s HSD).
(I) Class(J) ClassMean Difference (I−J)Standard ErrorSig.CL 95%
Lower LimitHigher Limit
Cost of travel (CT)reflective touristunconcerned tourist−482.28704 *102.043550.000−721.9858−242.5883
pro-sustainable tourist−711.55529 *98.267050.000−942.3831−480.7275
unconcerned touristreflective tourist482.28704 *102.043550.000242.5883721.9858
pro-sustainable tourist−229.26825123.238160.151−518.752860.2163
pro-sustainable touristreflective tourist711.55529 *98.267050.000480.7275942.3831
unconcerned tourist229.26825123.238160.151−60.2163518.7528
Δ CT (WTP)reflective touristunconcerned tourist−12.3710324.742960.871−70.491945.7498
pro-sustainable tourist−208.77902 *23.827250.000−264.7489−152.8092
unconcerned touristreflective tourist12.3710324.742960.871−45.749870.4919
pro-sustainable tourist−196.40799 *29.882110.000−266.6006−126.2154
pro-sustainable touristreflective tourist208.77902 *23.827250.000152.8092264.7489
unconcerned tourist196.40799 *29.882110.000126.2154266.6006
* The mean difference is significant at level 0.05. Source: Authors’ own elaboration.
Table 8. Contingency table—chi-squared tests.
Table 8. Contingency table—chi-squared tests.
In Case Sustainability for This Destination Implies an Increase in Prices; Would You Be Willing to Pay More to Stay in This Destination?Total
NoYes
Segmentsreflective touristCount37926405
Percent within the segment93.6%6.4%100.0%
unconcerned touristCount10717124
percent within the segment86.3%13.7%100.0%
pro-sustainable touristCount3134137
percent within the segment2.2%97.8%100.0%
TotalCount489177666
percent within the segment73.4%26.6%100.0%
ValueGlAsymptotic sig. (bilateral)
Pearson’s Chi-squared test451.099 20.000
Source: Authors’ own elaboration.
Table 9. Contingency table—Chi-squared tests.
Table 9. Contingency table—Chi-squared tests.
WTP CategoryTotal
0<100 Euros>100 Euros
Segmentsreflective touristCount378621405
percent within the segment933%1.5%5.2%100.0%
unconcerned touristCount107611124
percent within the segment86.3%4.8%8.9%100.0%
pro-sustainable touristCount36569137
percent within the segment2.2%47.4%50.4%100.0%
TotalCount48877101666
percent within the segment73.3%11.6%15.2%100.0%
ValueGlAsymptotic sig. (bilateral)
Pearson’s Chi-squared test454.375 40.000
Source: Authors’ own elaboration.
Table 10. Model adjustment of logistic regression.
Table 10. Model adjustment of logistic regression.
BSig.ORI.C. 95% for OR
LowerHigher
reflective tourist−6.4790.0000.0020.0000.005
unconcerned tourist−5.6390.0000.0040.0010.012
Constant3.7990.00044.667
Source: Authors’ own elaboration.
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