Tourist Tax to Improve Sustainability and the Experience in Mass Tourism Destinations: The Case of Andalusia (Spain)

Abstract

The maturity of the tourism destinations, along with the sector’s growing competitiveness and evolving tourist habits, demands the implementation of a series of strategies to increase the sustainability of these destinations while improving the tourism experience. Therefore, the imposition of taxes and/or fees on distinct tourism activities has become a viable option for the financing of these policies. The objective of this study is to determine the amounts of taxes and/or public fees that tourists appear to be more willing to pay in order to improve the sustainability and experience of the mature tourism destination. It also attempts to identify the factors that determine tourists’ willingness to pay. The study was carried out in Andalusia, a prominently touristic region of southern Spain, which received 32.4 million tourists in 2019. To do so, a survey was conducted on 1068 tourists at the main tourism arrival points of this region. First, the results identify the dimensions of taxes and/or public fees that tourists are more willing to pay, linked to environmental factors and tourism services. Second, the following factors were found to influence the tourists’ willingness to pay these taxes: the purpose of the trip, income, budget and place of origin.

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sustainability
Article
Tourist Tax to Improve Sustainability and the Experience in
Mass Tourism Destinations: The Case of Andalusia (Spain)
JoséLuis Durán-Román1, * , Pablo Juan Cárdenas-García2and Juan Ignacio Pulido-Fernández 2


Citation: Durán-Román, J.L.;
Cárdenas-García, P.J.; Pulido-Fernández,
J.I. TouristTax to Improve Sustainabil-
ity and the Experience in Mass Tourism
Destinations: The Case of Andalusia
(Spain). Sustainability 2021,13, 42.
https://dx.doi.org/10.3390/su13010042
Received: 29 November 2020
Accepted: 21 December 2020
Published: 23 December 2020
Publisher’s Note: MDPI stays neu-
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Creative CommonsAttribution (CCBY)
license(https://creativecommons.org/
licenses/by/4.0/).
1Department of Business Organization, Marketing & Sociology, University of Jaén, 23071 Jaén, Spain
2Department of Economics, University of Jaén, 23071 Jaén, Spain; pcgarcia@ujaen.es (P.J.C.-G.);
jipulido@ujaen.es (J.I.P.-F.)
*Correspondence: jduran@ujaen.es; Tel.: +34-6785-312-44
Abstract:
The maturity of the tourism destinations, along with the sector’s growing competitiveness
and evolving tourist habits, demands the implementation of a series of strategies to increase the
sustainability of these destinations while improving the tourism experience. Therefore, the imposition
of taxes and/or fees on distinct tourism activities has become a viable option for the financing of
these policies. The objective of this study is to determine the amounts of taxes and/or public fees
that tourists appear to be more willing to pay in order to improve the sustainability and experience
of the mature tourism destination. It also attempts to identify the factors that determine tourists’
willingness to pay. The study was carried out in Andalusia, a prominently touristic region of southern
Spain, which received 32.4 million tourists in 2019. To do so, a survey was conducted on 1068 tourists
at the main tourism arrival points of this region. First, the results identify the dimensions of taxes
and/or public fees that tourists are more willing to pay, linked to environmental factors and tourism
services. Second, the following factors were found to influence the tourists’ willingness to pay these
taxes: the purpose of the trip, income, budget and place of origin.
Keywords:
tourist tax; willingness to pay; sustainability; sociodemographic profile; trip characteris-
tics; tourism destination; tourist experience
1. Introduction
The touristic specialization of many destinations focuses on mass tourism has led to
quite negative impacts on the territory where it is carried out since the economic benefits
generated by the tourism activity have been obtained at the cost of the environmental
and sociocultural balance of the tourism destination [
1
]. This situation has resulted in the
deterioration of fragile environmental resources, causing an alarming situation [
2
] that
has led to a decline in attractiveness and competitiveness [
3
] and potentially hindering its
middle and long-term competition with other tourism destinations.
Moreover, for decades now, international tourism has been experiencing a period of
transformation, increasing over recent years, and requiring that measures be adopted by
destination managers to improve the destinations’ competitiveness and sustainability.
On the supply side, the maturing tourism market has slowed growth rates and increased
competition in the sector [
4
] due to the appearance of new destinations that may satisfy
similar tourist motivations [
5
,
6
]. Furthermore, new problems can also be considered in many
destinations resulting from a disproportionately high influx of tourists—overtourism [
7
],
leading to overcrowding, environmental and cultural degradation and dissatisfaction, of both
the tourist and the resident population [8].
On the demand side, it is clear that the tourist profile has changed significantly over
recent years. Today’s tourists are more complex and experienced, and therefore, they
demand more personalization, authenticity, and memorable experiences [
9
]. However,
there is also a growing concern among tourists with regard to potential environmental,
social and cultural impacts caused by the tourism activity [10].
Sustainability 2021,13, 42. https://dx.doi.org/10.3390/su13010042 https://www.mdpi.com/journal/sustainability

Sustainability 2021,13, 42 2 of 20
Hence, the maturity of the tourism destinations requires that challenges be met, given
the changes taking place in travel habits and behavior patterns, as warned even back in
the 1990s [
11
]. This new scenario demands a more dynamic role on behalf of the public
sector, which should attempt to promote new and more sustainable growth strategies that
are based on gradual differential competitive repositioning.
In the case of Spain, competencies in the area of tourism are structured in three levels,
distinct from the government, according to the country’s administrative division: central,
regional and municipal. The public administrations of the territories, mainly regional,
in which tourism activity has a major weight in the Spanish economy, have highlighted
certain financing problems [12].
In this sense, OECD [
13
] defined tourism taxation as “the indirect taxes, taxes and tributes
that mainly affect the activities related to tourism”; and it is considered to be one of the main
elements by which it is possible to “contribute to the obtaining of taxation income, financing the
protection of the environment and public investment and the development of infrastructures to im-
prove the management of the tourism impact in sensitive areas”. Moreover, Tourism taxation has
been configured, on numerous occasions, as an instrument for dealing with the problems
arising from tourism development [
14
]; it is an activity taxed since its inception to increase
revenues, offset the costs derived from the provision of goods and services of a public na-
ture and correct market failures or negative externalities caused by tourism activity [
15
,
16
].
In addition, it can also be used to achieve other purposes, such as job creation, economic
development promotion, environmental protection, destination promotion, etc. [13].
Therefore, tourism taxation becomes a tool through which to distribute the costs
associated with tourism activity, through the establishment of specific taxes [
17
] destined
directly to the activity [
18
], which pursue an extra-fiscal purpose, and whose objective is to
improve the product [17] and hence the tourist experience.
Therefore, the objective of this article is to determine the tourists’ willingness to pay
(WTP) taxes and/or public fees that permit the financing of policies to offer increased
sustainability in mature destinations and to simultaneously improve the tourism experience.
To achieve this objective, first, a multiple correspondence analysis was performed to
identify the taxes and/or public fees imposed on the tourism activity and that tourists
were more willing to pay. In addition, second, a regression analysis using conditioned
inference decision trees was performed to identify the sociodemographic variables and trip
characteristics influencing tourists’ WTP these taxes.
The study was conducted in Andalusia, a predominantly touristic region of southern
Spain that follows mainly a mature mass tourist model and which received 32.4 million
tourists in 2019. For this, a survey was given to 1068 tourists at the main tourism departure
points of the region.
In accordance with the proposed objectives, two hypotheses were established: (i) there
are certain taxes and/or public fees that tourists visiting Andalusia are more willing to
pay and, (ii) it is possible to identify certain factors—sociodemographic factors and trip
characteristics—that condition the willingness of tourists to pay taxes and/or public fees
when visiting this tourism destination.
The results of this study may assist policymakers and consolidated tourism destination
managers in facing some of the problems related to sustainability, competitiveness and
financing of tourism activities in these destinations, determining the appropriateness of
establishing specific taxes that are related to tourism activity from the demand perspective.
In addition, these results may be used for decision-making purposes in other destinations
where tourism activity has a significant effect on the local economy.
The rest of the paper is organized as follows: Section 2presents the concept of WTP,
contexts of WTP and factors influencing the WTP, followed by Section 3, in which the
research methodology is introduced. Section 4presents the empirical results of factors
influencing the WTP of the dimensions identified, and Section 5concludes the paper with
a discussion of the results, its limitations and future research themes.

Sustainability 2021,13, 42 3 of 20
2. Theoretical Framework
In the distinct works found in the scientific literature regarding tourism activity, WTP
has often been used as a means of estimating the value of non-market goods according
to the assumptions of rational choice and maximization of usefulness [
19
]. Furthermore,
tourists’ WTP can be used as compensation for the damage caused to the public welfare
through the negative externalities caused by the activity, permitting them to obtain certain
marginal benefits [20].
Along these lines, tourists’ WTP has been used in many studies related to tourism
activity, analyzing the tourist’s WTP with regard to the preservation and/or protection
of natural areas, environmental protection, the sustainability of the tourism destination,
or improvement of the tourism experience. Table 1reveals details from studies that have
analyzed the WTP of tourists based on distinct tourism contexts.
Table 1. Contexts of willingness to pay (WTP) (literature review).
Contexts Literature
Preservation/protection of natural areas and
biodiversity
Casey et al. (2010); García-Llorente et al. (2011); Lindsey et al. (2005); Piriyapada
and Wang (2015); Reynisdottir et al. (2008); Wilson and Tisdell (2003) [19,21–25]
Environmental protection Birdir et al. (2013); Dodds et al. (2010); Hedlund (2011) [26–28]
Outdoor recreational activities Asafu-Adjaye and Tapsuwan (2008); Chun-Hung et al. (2019) [29,30]
Ecological tourism Chaminuka et al. (2012); Cheung and Jim (2014); Hinnen et al. (2017);
Hultman et al. (2015) [31–34]
Sustainability of the destination Baddeley (2004); Cheung and Jim (2014); Lee (1997); López-Sánchez and
Pulido-Fernández (2014) [2,32,35,36]
Climate change Araña et al. (2013); Bujosa and Riera (2019); Rodríguez and Bujosa (2020) [37–39]
Improve the quality of the product and/or
the experience
Bignéet al. (2008); Choong-Ki et al. (2019); Laarman and Gregersen (1996);
Mgxekwa et al. (2018); Miller (2003); Veréb and Azevedo (2019) [40–45]
Cultural preservation Bertacchini and Sultan (2019); Meilan et al. (2019); Scarpa et al. (2011); Seongseop
et al. (2007) [46–49]
Final opportunity Groulx et al. (2019); Vander Naald (2019) [50,51]
Source: author’s own creation.
In addition, in many of the studies on tourists’ WTP, the main determinants of this
WTP have been identified since this helps offer a greater understanding of tourist demand in
the face of the increased tourism product. Therefore, WTP is considered to be a dependent
variable that is explained, to a greater or lesser extent, by a set of sociodemographic factors,
psychographic factors [
19
,
20
] and trip characteristics [
52
] related to tourist demand as
shown in Table 2.
Thus, it is evident that tourists’ WTP has been widely analyzed in distinct contexts
related to the estimation of non-marketable goods. In addition, the sociodemographic and
psychographic factors and trip characteristics that may explain this WTP have also been
analyzed. However, these studies have tended to consider the individual WTP of tourists
in diverse contexts related to tourism activity.
This work analyzes the tourists’ WTP, explained using distinct factors—in this case,
sociodemographic factors and trip characteristics—with regard to distinct dimensions that
are made up by some of the fifteen proposed taxes and/or public fees that are related to the
tourism activity. Hence, this study is novel, as compared to past works, in its consideration
of tourists’ WTP. The taxes and/or public fees that are considered in this work include
almost all of the previously studied dimensions, so a global analysis can be performed for
the WTP of the tourist demand in distinct touristic contexts, which, until now, have only
been examined in an individual manner.

Sustainability 2021,13, 42 4 of 20
Table 2. Factors influencing the WTP (literature review).
Sociodemographic Factors Literature
Income level Garrod and Fyall (2000); More and Stevens (2000); Reynisdottir et al. (2008) [19,53,54]
Nationality Bignéet al. (2008); Davis and Tisdell (1998); Reynisdottir et al. (2008); Schroeder and
Louviere (1999) [19,40,55,56]
Age
Daniere and Takahashi (1999); Kostakis and Sardianou (2011); Van Liere and Dunlap (1980)
[57–59]
Education level Alves et al. (2014); Bowker et al. (1999); Halkos and Matsiori (2012); Reynisdottir et al.
(2008) [19,60–62]
Sex Arcury et al. (1987); Kostakis and Sardianou (2011); Liu et al. (2019) [58,63,64]
Profession Rose et al. (1995) [65]
Professional category López-Sánchez and Pulido-Fernández (2017) [52]
Trip Characteristics Literature
Purpose of the trip
López-Sánchez and Pulido-Fernández (2017); Westerberg et al. (2013). Witt (2019) [
52
,
66
,
67
]
Length of stay López-Sánchez and Pulido-Fernández (2017); Liu et al. (2019); Schuhmann et al. (2019)
[52,64,68]
Previous visitation López-Sánchez and Pulido-Fernández (2017); Schuhmann et al. (2019). [52,68]
Group size López-Sánchez and Pulido-Fernández (2017) [52]
Type of accommodation used López-Sánchez and Pulido-Fernández (2017); Westerberg et al. (2013) [52,66]
Psychographic Factors Literature
Environmental awareness Carlsson and Johansson-Stenman (2000); Reynisdottir et al. (2008) [19,69]
Moral responsibility Choi and Ritchie (2014) [70]
Transparency and public credibility Juvan and Dolnicar (2014); Polonsky et al. (2010) [71,72]
Source: author’s own creation.
3. Materials and Methods
The objective of this study is to determine tourists’ WTP taxes and/or public fees
that would permit the financing of policies to improve the competitiveness and sustain-
ability of the destination and thereby improve the tourism experience. The study was
conducted in Andalusia, a mass tourism destination—which, in 2018, received a total of
32,476,854 tourists, of which 12,633,644 were foreigners [73].
To achieve this study objective, which on one hand, is to identify the existence of
public taxes and/or fees with a higher WTP by the tourist demand, and on the other
hand, to determine the sociodemographic variables and trip characteristics that influence
the WTP of the tourists when establishing these fiscal instruments, below is a list of the
analyzed tourist variables, how the data were obtained, and a specification of the statistical
models that have been applied.
3.1. Composition of the Tourist Characteristics
Upon initially considering the analyzed sample, a detailed description of the char-
acteristics of the tourists participating in this study is carried out. This initial descriptive
approach makes it possible to identify the average characteristics of the tourists visiting this
destination: without distinction by gender, between 25 and 65 years of age, with secondary-
level education or higher, traveling in family, without a defined stay duration, with sun
and beach motivations, using hotel lodging, being the first time visiting the destination,
coming from another location other than Andalusia, employed in the service or industry
sectors, having an annual net income of over 25,000 euros, and with a mean daily budget
of 87.61 euros. Details of the analyzed sample are presented in Table 3. The data from the
said table are provided by the descriptive statistical treatment of the surveys carried out in
this study.

Sustainability 2021,13, 42 5 of 20
Table 3. Sociodemographic variables and trip characteristics.
Variables Count % N Variables Count % N Variables Count % N
Gender N = 1068 100% Purpose of the Trip N = 1068 100% Professional Activity N = 1068 100%
Male 524 49.1% Coastal—sun and
beach 301 28.2% Non-tourism services 270 25.3%
Female 544 50.9% Cultural 256 24.0% Industry 220 20.6%
Age N = 1068 100% Interior/rural 175 16.4% Tourism services 101 9.5%
<18 26 2.4% Family 118 11.0% Retired 100 9.4%
18 to 24 119 11.1% Health-well-being 54 5.1% Public administration 99 9.3%
25 to 34 179 16.8% Golf 51 4.8% Construction 89 8.3%
35 to 44 228 21.3% Nature 35 3.3% Student 82 7.7%
45 to 54 213 19.9% Nautical/sports
marina 26 2.4%
Agriculture/livestock/fish
39 3.7%
55 to 64 201 18.8% Meetings/Congresses 18 1.7% Household work 35 3.3%
>65 102 9.6% Languages 12 1.1% Sales 21 2.0%
Education
Level N = 1068 100% Food and wine 12 1.1% Unemployed 12 1.1%
No
education 9 0.8% Cruise 6 0.6% Occupational Group N = 1068 100%
Primary
school 56 5.2% Do not know/do not
answer 4 0.4% Employee—middle
level 304 28.5%
Secondary
school 523 49.0% Lodging Type N = 1068 100% Others 238 22.3%
Higher
education 480 44.9% Hotel lodging 497 46.5% Employee 238 22.3%
Companions N = 1068 100% Hostel 197 18.4% Employer 148 13.8%
Family 645 60.4% Camping 145 13.6% Employee—upper
executive 117 10.9%
Friends 374 35.0% Others 112 10.5% Do not know/do not
answer 23 2.1%
Alone 40 3.7% Tourist apartment 65 6.1% Annual Net Income N = 1068 100%
Do not
know/Do
not answer
9 0.8% Do not know/do not
answer 52 4.9% <12,000 €124 11.6%
Duration
of the Stay N = 1068 100% Frequency of the Trip N = 1068 100% 12,001 €to 15,000 €66 6.2%
1 to 3 days 267 25.0% First time 500 46.8% 15,001 €to 20,000 €84 7.9%
4 to 6 days 401 37.5% Second time 359 33.6% 20,001 €to 25,000 €130 12.2%
7 or more
days 400 37.5% Three or more times 188 17.6% 25,001 €to 30,000 €157 14.7%
Budget
(ongoing) N = 1068 —Do not know/do not
answer 21 2.0% 30,001 €to 35,000 €152 14.2%
Mean 87.61 — Place of Origin N = 1068 100% 35,001 €to 40,000 €143 13.4%
Standard
deviation 30.77 — Andalusia 109 10.2% 40,001 €to 50,000 €122 11.4%
Minimum 10.00 — Spain 430 40.3% Over 50,000 €84 7.9%
Median 90.00 — Foreigner—European
Union 371 34.7% Do not know/do not
answer 6 0.6%
Maximum 250.0 — Foreigner—Rest of the
world 158 14.8%
Source: author’s own creation.
3.2. Data Collection
Given the impossibility of identifying the study subjects (all tourists visiting Andalu-
sia), a probability sample was carried out, in which the sole selection criteria is having
spent at least one night in any of the Andalusian destinations.
The sampling process was approached through a time location sampling (TLS) design,
as in [
74
]. TLS attempts to recruit respondents in places and times where they would
be reasonably expected to gather. The sampling framework consists of venue–day–time

Sustainability 2021,13, 42 6 of 20
units (VDT)—also known as time-location units—which represent the potential universe
of venues, days and times. The units of interest were represented by tourists leaving
Andalusia, where we collected information related to the entire period of time spent
in Andalusia. As for the TLS design, we have selected all airports and the three high-
speed train stations in Andalusia. The period covered by the survey was from July to
October, during which a large percentage of tourists visiting Andalusia are concentrated.
The specific TLS implementation was treated as a two-stage stratified sampling design
with unequal selection probabilities for the first-stage units and with constant selection
probabilities for the second-stage units. Finally, first-stage units included a combination
of places, days and hours, and the second-stage units were made up of tourists who were
selected within the first stage units through a systematic selection procedure.
This sample consists of a total of 1068 surveys (sampling error: 3.1%; confidence
level 95%; p= q = 0.50). As seen in Table 4, the total distribution of interviews conducted
was based on the tourist’s point of exit to Andalusia criteria (Andalusian airports and
Andalusian high-speed train stations), maintaining the proportionality in the number of
surveys with respect to the total number of passengers received in both transport means.
Table 4. Distribution of interviews by the port of departure.
Total Travelers Proportion Interviews
Total plane and train passengers 38,259,350 1.00 1068
Traffic of Andalusian plane passengers 28,693,606 0.75 801
Malaga airport 19,021,704 0.66 531
Seville airport 6,380,465 0.22 178
Almeria airport 992,043 0.03 28
Jerez airport 1,133,621 0.04 32
Granada-Jaen airport 1,126,389 0.04 31
Algeciras heliport 31,129 0.00 1
Cordoba airport 8255 0.00 0
Total number of (high-speed) train travelers in Andalusia 9,565,744 0.25 267
Seville 4,384,100 0.46 122
Malaga 3,191,800 0.33 89
Cordoba 2,833,000 0.21 56
Source: author’s own creation based on [
75
] data and information provided by Renfe, upon request, on the 28 March 2019 (JCA file-0331-2019).
Interviews were conducted between the months of July and October 2019 and con-
sisted of two blocks of questions:
•
An initial block, classifying the tourist based on sociodemographic variables and trip
characteristics (detailed in Table 3);
•
A second block, related to the tourism experience in Andalusia (possibility of improv-
ing the tourism experience and WTP of the tourists with regard to fifteen taxes and/or
public fees, both to improve the experience as well as to contribute to offering greater
sustainability to the destination).
3.3. Multiple Correspondence Analysis
In accordance with the objectives established in this work, first, an attempt is made to
identify those taxes and/or public fees having a higher WTP by the tourists. Along these
lines, this work attempts to analyze fifteen taxes and public fees, making it necessary to
reduce the sample into homogenous groups of taxes and/or public fees.
In order to reduce the dimension, numerous statistical techniques have been frequently
used in other studies: the grouping of the original variables to define underlying constructs.
Of these statistical techniques, two of the most commonly used are exploratory factor
analysis (EFA) and multiple correspondence analysis (MCA). It is widely recognized that
EFA is more appropriate for use with continuous variables, and, on the other hand, MCA
is more suitable for categorical variables [
76
]. However, EFA may be used at a descriptive
level with dichotomous categories.

Sustainability 2021,13, 42 7 of 20
In this work, which considers fifteen dichotomous categorical variables, the MCA
technique was used, given that the homogeneity of the variables makes this the ideal
analysis to explain the phenomenon of interest: WTP by the tourist demand.
Finally, from a purely exploratory perspective, the results of MCA were compared
with those of EFA with Varimax rotation, given that the objective of this comparison was
carried out with a confirmatory purpose.
3.4. Decision Trees
Upon identifying the groups or dimensions of taxes and/or public fees imposed on
tourism activity with a higher WTP by the tourist demand, the second proposed objective
of this work is to identify which sociodemographic variables and trip characteristics of the
tourists determine this WTP. Decision trees were used to achieve this objective.
A decision tree is a type of supervised learning algorithm that is used for classification
and regression tasks based on complex databases. It may be applied to categorical or
continuous variables, which are easy to understand, interpret, and visualize [
77
]. With
decision trees, it is possible to extract and analyze which variables in this study explain the
WTP of the tourist demand with respect to the natural grouping of the data in the previ-
ously mentioned underlying dimensions. For this, the decision tree executes a recursive
algorithm, minimizing a cost function—prediction cost.
The advantages of the decision trees are the clarification of the results, the understand-
ing of the interaction between the variables and the application of this technique to massive
data. In this work, conditional inference decision trees are used [
77
], presenting advantages
as compared to the classic decision trees.
Conditional inference decision trees estimate the relationship between variables
through a recursive partition in an area of conditional inference. The algorithm func-
tions as follows [78]:
1.
It tests the null hypothesis of independence between the explanatory variables and
the explained variable through a permutation test for each explanatory variable. The
partitioning process ends if this hypothesis cannot be rejected. Alternatively, the
variable having the greatest association is selected, and this association is measured
using the p-value of a partial test between each explanatory variable and the explained
variable. The one with the lowest p-value is selected;
2. A binary partition is made for the selected variable;
3. Steps (1) and (2) are recursively repeated.
The implementation used for step 1 is based on the permutation test developed by [
79
].
The stop criterion in step 1 is based on the p-value adjusted by the Bonferroni method [
80
].
4. Results
Of a total of 1068 tourists interviewed, 904 (84.7%) declared that there is a margin for
improvement in the tourism experience that they are enjoying in Andalusia, while the remain-
ing 15.3% believe that this experience cannot be improved. As seen in Table 5observing the
correlations (Spearman’s Rho) between the margin of improvement of the tourism experience
and the options by which it could be improved (the tourism experience), it may be concluded
that the improvement of the infrastructures, in general, is the option that most conditions the
opinion that it is necessary to improve the tourism experience—having the highest correlation,
coefficient 0.405. That is, it is the most relevant factor of the four proposed ones, followed by
tourism services and the wellbeing of the population.
As for the WTP, an additional amount, both to improve their tourism experience
(more and better infrastructures, public and tourism services) and to minimize the negative
impacts of the tourism activity (waste generation, pollution, overcrowding in sites of touris-
tic interest, saturation of certain services, environmental, patrimonial and architectural
degradation, etc.), as well as to expand upon the cultural and artistic offerings, 75.3% of the
interviewed (803 tourists) are willing to pay an additional amount. The remaining 24.7%
believes that the additional payment would only serve to increase payments to the public

Sustainability 2021,13, 42 8 of 20
sector, which will spend this money as it deems more useful and will not, in fact, make any
changes with regard to the tourism destination.
Table 5. Options for improving the tourism experience.
Valid N Correlations Sig
How could the tourism experience that you are enjoying be improved? 1068
General infrastructure (public transport, safety, cleanliness, crowding, traffic, etc.) 904 0.405
0.000
Touristic infrastructure (preservation and maintenance of tourism attractions, emblematic
buildings, the environment, etc.) 904 0.218
0.000
Tourism services (cultural and leisure offering, tourism lodging, tourist information services,
Internet connection, etc.) 904 0.259
0.000
Wellbeing of the population (safety, cleanliness, waste collection, public service provision, etc.) 904 0.235
0.000
Source: author’s own creation using calculations from the R Commander Library of R software.
4.1. WTP Taxes and/or Public Fees
The next step consists of identifying the tourist demand that declares its WTP, the
acceptance or not, of a series of taxes and/or public fees on tourism activity, whose objective
is to improve the tourism experience and sustainability of the destination. These taxes
and/or public fees were previously identified by a panel of experts [81].
As Figure 1reveals, there are large differences in WTP depending on which of the
fifteen analyzed taxes and fees are considered, ranging from a broad consensus to pay a
public fee to access public tourism resources (71.6%) to a very small WTP a betting tax
(3.6%). This difference in acceptance of the taxes and fees that have higher or lower WTP
by tourists is motivated by distinct perceptions of the surveyed tourist.
Sustainability 2021, 13, x FOR PEER REVIEW 8 of 19
Table 5. Options for improving the tourism experience.
Valid
N
Correla-
tions
Sig
How could the tourism experience that you are enjoying be improved?
1068
General infrastructure (public transport, safety, cleanliness, crowding, traffic, etc.)
904
0.405
0.000
Touristic infrastructure (preservation and maintenance of tourism attractions, emblematic buildings, the
environment, etc.)
904
0.218
0.000
Tourism services (cultural and leisure offering, tourism lodging, tourist information services, Internet con-
nection, etc.)
904
0.259
0.000
Wellbeing of the population (safety, cleanliness, waste collection, public service provision, etc.)
904
0.235
0.000
Source: author’s own creation using calculations from the R Commander Library of R software.
As for the WTP, an additional amount, both to improve their tourism experience
(more and better infrastructures, public and tourism services) and to minimize the nega-
tive impacts of the tourism activity (waste generation, pollution, overcrowding in sites of
touristic interest, saturation of certain services, environmental, patrimonial and architec-
tural degradation, etc.), as well as to expand upon the cultural and artistic offerings, 75.3%
of the interviewed (803 tourists) are willing to pay an additional amount. The remaining
24.7% believes that the additional payment would only serve to increase payments to the
public sector, which will spend this money as it deems more useful and will not, in fact,
make any changes with regard to the tourism destination.
4.1. WTP Taxes and/or Public Fees
The next step consists of identifying the tourist demand that declares its WTP, the
acceptance or not, of a series of taxes and/or public fees on tourism activity, whose objec-
tive is to improve the tourism experience and sustainability of the destination. These taxes
and/or public fees were previously identified by a panel of experts [81].
As Figure 1 reveals, there are large differences in WTP depending on which of the
fifteen analyzed taxes and fees are considered, ranging from a broad consensus to pay a
public fee to access public tourism resources (71.6%) to a very small WTP a betting tax
(3.6%). This difference in acceptance of the taxes and fees that have higher or lower WTP
by tourists is motivated by distinct perceptions of the surveyed tourist.
Figure 1. WTP taxes and/or public fees. Source: author’s own creation.
In addition, it should be noted that this question was only answered by those tourists
who had previously declared their WTP (75.3%). For example, in the case of the betting
tax, this WTP only represents 2.7% of the overall population (75.3% × 3.6%).
However, based on this initial descriptive analysis, it may be deduced that there are
certain taxes/fees having a high percentage of WTP by the tourist demand, associated with
71.6%
62.2%
52.5%
52.5%
50.1%
44.5%
32.5%
23.6%
20.5%
19.5%
18.0%
14.2%
10.2%
8.5%
3.6%
Public fee to access public tourism resources (f15)
Tax on tourism stays (f1)
Tax on tourist attractions (f3)
Public museums entrance fee (f8
Environmental conservation tax in municipalities… (f14)
Public theaters and shows entrance fee (f12)
Public natural/national parks entrance fee (f10)
Tax on entry to municipality classified as touristic (f13)
Tax on hiking and mountain climbing (f2)
Entrance fee at monuments and national parks (f7)
Taxes for overnight stays at peer to peer (P2P)…
Visitor tickets at the main tourist attractions (f6)
Gambling tax (f4)
Vehicle rental fee (f11)
Betting tax (f5)
Figure 1. WTP taxes and/or public fees. Source: author’s own creation.
In addition, it should be noted that this question was only answered by those tourists
who had previously declared their WTP (75.3%). For example, in the case of the betting tax,
this WTP only represents 2.7% of the overall population (75.3% ×3.6%).
However, based on this initial descriptive analysis, it may be deduced that there are
certain taxes/fees having a high percentage of WTP by the tourist demand, associated with
paying to enjoy tourism resources, as well as tourism, stays, confirming the results from
Table 5with respect to the improvement options for the Andalusian tourism experience.
The issue, in this point, lies in reducing these taxes and/or public fees into homoge-
nous taxes/public fees groups and identifying the sociodemographic features and trip
characteristics that determine this WTP by the tourist, with respect to these groups of taxes
and/or public fees.

Sustainability 2021,13, 42 9 of 20
4.2. Dimensions of the Taxes and/or Public Fees
4.2.1. Creation of Taxes and/or Public Fees Dimensions
Below, the fifteen taxes and/or public fees analyzed in this work have been homoge-
neously grouped. To do so, a multiple correspondence analysis (MCA) was used, applying
this model as follows:
1.
It is based on a complete model, including the fifteen proposed taxes/public fees. The
MCA indicates the existence of five underlying dimensions that explain 68.03% of the
variability—complete inertia. In this initial analysis, it is concluded that the “public
fee to access public tourism resources” has a weight in the two dimensions with the
greatest variability and, therefore, is quite transversal. This result is not surprising,
considering that this tax fee receives the highest WTP. In addition, the “vehicle rental”
fee determines one single dimension, given that the WTP for this fee does not appear
to be related to the other fees/taxes, which is logical given that it is the fee with the
second to lowest WTP. Finally, it appears that the “visitor tickets at the main tourist
attractions” fee has no identification with any dimension and is quite transversal,
perhaps because its imposition is similar to other fees (public fee to access public
tourism resources, public museums entrance fee or national parks entrance fee).
2.
Based on this initial analysis, a new MCA was created, suppressing the previously
mentioned fees (public fee to access public tourism resources, vehicle rental fee and
visitor tickets at the main tourist attractions fee). Table 6shows the results from the
MCA of the contributions by categories, revealing the four dimensions of greatest
explained inertia. Having extracted the previously indicated fees, the five resulting
dimensions explain 69.39% of the total inertia.
Table 6. Multiple correspondence analysis (MCA) contributions by tax/public fee category.
Dimension 1 Dimension 2 Dimension 3 Dimension 4
Cord Corr Cord Corr Cord Corr Cord Corr
f 1.0 0.134 0.115 0.276 0.494 −0.034 0.008 0.095 0.058
f 1.1 −0.081 0.115 −0.168 0.494 0.021 0.008 −0.058 0.058
f 2.0 0.114 0.513 −0.037 0.053 −0.037 0.055 0.012 0.006
f 2.1 −0.441 0.514 0.142 0.053 0.144 0.055 −0.047 0.006
f 3.0 0.003 0.000 0.203 0.413 0.001 0.000 −0.073 0.053
f 3.1 −0.003 0.000 −0.184 0.412 −0.001 0.000 0.066 0.053
f 4.0 −0.021 0.045 0.029 0.084 −0.053 0.278 0.054 0.287
f 4.1 0.186 0.044 −0.258 0.084 0.470 0.279 −0.475 0.285
f 5.0 −0.009 0.027 0.001 0.000 −0.043 0.565 0.007 0.016
f 5.1 0.255 0.027 −0.034 0.000 1.160 0.564 −0.196 0.016
f 7.0 0.000 0.000 −0.015 0.010 −0.064 0.194 −0.088 0.373
f 7.1 0.001 0.000 0.060 0.010 0.263 0.194 0.364 0.372
f 8.0 −0.063 0.040 0.140 0.197 0.051 0.027 −0.168 0.287
f 8.1 0.057 0.040 −0.126 0.197 −0.047 0.027 0.152 0.286
f 9.0 0.001 0.000 0.021 0.023 −0.097 0.481 −0.047 0.114
f 9.1 −0.002 0.000 −0.096 0.023 0.440 0.480 0.214 0.114
f 10.0 0.149 0.482 −0.058 0.073 −0.003 0.000 −0.022 0.010
f 10.1 −0.310 0.484 0.121 0.074 0.007 0.000 0.046 0.011
f 12.0 −0.037 0.018 0.180 0.439 0.056 0.042 0.009 0.001
f 12.1 0.046 0.018 −0.224 0.439 −0.069 0.042 −0.011 0.001
f 13.0 0.127 0.538 0.021 0.015 −0.002 0.000 −0.028 0.026
f 13.1 −0.409 0.538 −0.068 0.015 0.007 0.000 0.089 0.025
f 14.0 0.276 0.718 0.089 0.074 0.021 0.004 0.029 0.008
f 14.1 −0.274 0.718 −0.088 0.074 −0.021 0.004 −0.029 0008
Source: author’s own creation using calculations from the R Commander Library of R software.
Table 6details the correlation of each tax/public fee category with the dimensions
and the main coordinates of these categories in each dimension. After removing the
confusion from the “access public tourism resources” and “visitor tickets at the main tourist

Sustainability 2021,13, 42 10 of 20
attractions” fees, different taxes and/or public fees dimensions are detected
(Table 7)
. These
dimensions should be considered underlying constructs formed by groups of taxes/public
fees having a certain level of association.
Table 7. Dimensions of the resulting taxes/public fees.
Dimension 1: Environmental
Tax on hiking and mountain climbing
Public natural/national parks entrance fee
Tax on entry to municipality classified as touristic
Environmental conservation tax in municipalities whose main activity is ecotourism
Dimension 2: Tourism Services
Tax on tourism stays
Tax on tourist attractions
Public museums entrance fee
Public theaters and shows entrance fee
Dimension 3: Recreational
Gambling tax
Betting tax
Dimension 4: Tourism Infrastructure
Entrance fee at monuments and national parks
Taxes for overnight stays at P2P accommodations
Dimension 5: Mobility
Vehicle rental fee
Source: author’s own creation.
The environmental and tourism service dimensions have an explained inertia of 22.11%
and 15.58%, respectively; they are not fully independent, given that the “tax on tourism
stays”, although with a lower weight, is also slightly correlated with the first dimension.
Moreover, the mainstreaming of the “public fee to access public tourism resources” reveals
a positive association with both dimensions.
The third dimension, recreational, having 9.88% of the total explained inertia, and
the fourth dimension, tourism infrastructure, with 13.24% of the total explained inertia,
associate each one to two taxes/public fees. Finally, the “vehicle rental” fee determines one
sole dimension, referred to as mobility.
In addition, it should be indicated that the environmental and tourism services di-
mensions, as they are configured, are not independent, as will be discussed later; likewise,
in this sense, there is also a certain association between the tourism services and tourism
infrastructure dimensions, through the “public museums entrance fee”.
Even though, as indicated in the methodology section, EFA is more useful for con-
tinuous variables, the weights of the Varimax rotation—seeking the highest degree of
non-correlation between the factors—ratifies and clarifies the associations detected by the
MCA, as shown in Table 8.
The analysis clearly detects a third (gambling tax and betting tax) and fourth (entrance
fee at monuments and national parks and taxes for overnight stays in P2P accommodations)
factors. This reinforces the independent study of these dimensions, as seen in Table 7.
Hence, like the MCA, a certain dependence is evident between these dimensions, through
the “public museums entrance fee” and “betting tax”.
Therefore, it should be noted that both reduction studies, MCA and EFA, clearly
detect an environmental construct such as a latent variable, which represents the increased
explained variability; likewise, a tourism services construct is detected, not as cohesive
as the previous one, but with a clear association between the taxes/public fees. Finally,
both analyses indicate an association between the variables making up the recreational and
tourism infrastructure dimensions.

Sustainability 2021,13, 42 11 of 20
Table 8. Exploratory factor analysis with Varimax rotation.
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5
f1 0.175349 0.678529 0.227355 −0.135906 0.0852854
f2 0.661436 −0.164022 0.152084 0.0474974 −0.105763
f3 −0.170348 0.755308 −0.190222 0.203942 −0.00417628
f4 −0.0896535 0.0799295 0.774946 −0.0929837 0.0378306
f5 −0.0232468 −0.0882468 0.619965 0.343841 −0.043734
f7 0.046499 −0.0821208 −0.08313 0.688841 0.0425991
f8 −0.063181 −0.0358739 −0.17025 0.230757 0.783037
f9 0.0248082 0.165737 0.243613 0.653641 −0.0402075
f10 0.618462 −0.195363 −0.109837 0.0492367 −0.0365936
f12 −0.0227465 0.116324 0.179255 −0.219363 0.763556
f13 0.565879 0.215787 −0.143833 0.0903886 0.0182755
f14 0.673197 0.355245 −0.0248802 −0.163798 0.0396767
Source: author’s own creation using calculations from the R Commander Library of R software.
4.2.2. Description of Taxes/or Public Fees Dimensions
In order to analyze the tourists’ sociodemographic variables and the characteristics of
the trip with respect to the WTP of the distinct dimensions of the taxes and/or public fees,
the latter is transformed into artificial variables that indicate a higher or lower intensity
within the dimension, by adding together the amounts of its components. For example, a
tourist receives a score of 0 to 4 on the first dimension (environmental), suggesting that they
are in agreement with the payment of one, two, three, or four taxes/public fees making up
said dimension.
Therefore, as shown in Table 9, which includes the response frequencies, the first two
dimensions will have a range of values from 0 to 4 (as they are made up of 4 taxes/public
fees), while the third and fourth dimensions will have a range of values from 0 to 2 (since
they consist of two taxes/public fees) and the last dimension will have a range of 0 to 1
(since it is made up of one single fee).
Table 9. Response frequencies by dimensions of taxes/public fees.
Dimension 1 Dimension 2 Dimension 3 Dimension 4 Dimension 5
Value N % N % N % N % N %
0 260
0.3242
70
0.0872
703
0.8755
544
0.6775
735
0.9153
1 229
0.2855
180
0.2242
89
0.1108
216
0.2690
68
0.0846
2 182
0.2269
233
0.2902
11
0.0137
43
0.0535
3 98
0.1222
226
0.2814
4 33
0.0423
94
0.1171
Source: author’s own creation.
The second dimension (tourism services) clearly has the highest percentages of tourists
that are willing to pay at least one of the taxes/public fees making up this dimension;
only 8.72% do not agree with the payment of any taxes/public fees in this dimension. The
first dimension (environmental) also has a high rate of acceptance, although 32.42% of the
tourists are unwilling to pay any of the taxes/public fees in this dimension. As expected,
for the third (recreational) and fourth (tourism infrastructure) dimensions, tourists are
unlikely to be willing to pay since only 12.45% and 32.25% of the tourists are willing to pay
for at least one of the taxes/public fees in these dimensions. Finally, tourists reject the fifth
dimension (mobility) by over 90%.
Once again, these results confirm what is shown in Table 5, since tourists revealed a
greater WTP, mainly an additional amount, in those dimensions containing taxes/public
fees that permit an improvement of both sustainability of the destination through envi-
ronmental protection and an improved tourism experience through the payment of taxes
and/or public fees related to tourism services (culture, shows or lodging).

Sustainability 2021,13, 42 12 of 20
4.3. Determinant Factors of WTP for Taxes and/or Public Fees
Below, the decision tree technique is applied to predict the five identified dimensions
(as well as the two transversal fees: visitor tickets at the main tourist attractions and public
fee to access public tourism resources) based on the tourists’ sociodemographic variables
and the trip characteristics as described in Table 3.
Thus, the analysis is carried out only to detect those variables that explain variability
in the dimensions and not for predictive purposes, such that all of the data are considered
as training data. The database consists of the 803 tourists with positive WTP out of the
1068 total tourists surveyed.
4.3.1. Environmental Dimension
Figure 2reveals the decision tree for the first dimension (environmental), based on the
variables having the most significant association, from a statistical perspective: purpose of
the trip, income, budget and place of origin. Table 10, on the other hand, reveals the coding
of the relevant variables.
Sustainability 2021, 13, x FOR PEER REVIEW 12 of 19
tourists are unwilling to pay any of the taxes/public fees in this dimension. As expected,
for the third (recreational) and fourth (tourism infrastructure) dimensions, tourists are
unlikely to be willing to pay since only 12.45% and 32.25% of the tourists are willing to
pay for at least one of the taxes/public fees in these dimensions. Finally, tourists reject the
fifth dimension (mobility) by over 90%.
Once again, these results confirm what is shown in Table 5, since tourists revealed a
greater WTP, mainly an additional amount, in those dimensions containing taxes/public
fees that permit an improvement of both sustainability of the destination through envi-
ronmental protection and an improved tourism experience through the payment of taxes
and/or public fees related to tourism services (culture, shows or lodging).
4.3. Determinant Factors of WTP for Taxes and/or Public Fees
Below, the decision tree technique is applied to predict the five identified dimensions
(as well as the two transversal fees: visitor tickets at the main tourist attractions and public
fee to access public tourism resources) based on the tourists’ sociodemographic variables
and the trip characteristics as described in Table 3.
Thus, the analysis is carried out only to detect those variables that explain variability
in the dimensions and not for predictive purposes, such that all of the data are considered
as training data. The database consists of the 803 tourists with positive WTP out of the
1068 total tourists surveyed.
4.3.1. Environmental Dimension
Figure 2 reveals the decision tree for the first dimension (environmental), based on
the variables having the most significant association, from a statistical perspective: pur-
pose of the trip, income, budget and place of origin. Table 10, on the other hand, reveals
the coding of the relevant variables.
Figure 2. Environmental dimension vs. sociodemographic factors and trip characteristics. Source: author’s own creation
using calculations from the R Commander Library of R software.
Table 10. Coding of the relevant variables in the environmental dimension decision tree.
Variable
Coding
Purpose of the trip
1 (coastal sun/beach), 2 (interior), 3 (cultural), 4 (family), 5 (golf), 6 (health and well-being), 7 (meetings and congresses), 8
(nautical/sports marina), 9 (cruise), 10 (nature), 11 (food and wine), 12 (languages), 13 (snow)
Income (thou-
sands of euros)
1 (under 12), 2 (between 12 and 15), 3 (between 15 and 20), 4 (between 20 and 25), 5 (between 25 and 30), 6 (between 30 and
35) 7 (between 35 and 40), 8 (between 40 and 50), 9 (over 50)
Budget (euros)
*ongoing
Place of origin
1 (Andalusia), 2 (Spain), 3 (European Union), 4 (Rest of the world)
Source: author’s own creation.
Figure 2.
Environmental dimension vs. sociodemographic factors and trip characteristics. Source: author’s own creation
using calculations from the R Commander Library of R software.
Table 10. Coding of the relevant variables in the environmental dimension decision tree.
Variable Coding
Purpose of the trip
1 (coastal sun/beach), 2 (interior), 3 (cultural), 4 (family), 5 (golf), 6 (health and well-being), 7 (meetings and
congresses), 8 (nautical/sports marina), 9 (cruise), 10 (nature), 11 (food and wine), 12 (languages), 13 (snow)
Income (thousands
of euros)
1 (under 12), 2 (between 12 and 15), 3 (between 15 and 20), 4 (between 20 and 25), 5 (between 25 and 30),
6 (between 30 and 35) 7 (between 35 and 40), 8 (between 40 and 50), 9 (over 50)
Budget (euros) *ongoing
Place of origin 1 (Andalusia), 2 (Spain), 3 (European Union), 4 (Rest of the world)
Source: author’s own creation.
The purpose of the trip variable is the root node, the variable that best groups in
terms of association (dependence); and it is evident that the greater WTP is concentrated
in the tourists whose purpose of the trip is interior/rural tourism, health/wellbeing,
nautical/sporting marina and nature; in line with the “environmental” labeling of this
dimension.
In addition, the budget and income variables are always positively associated with
WTP. In addition, unlike those mentioned previously, Spanish tourists in the subgroup of
other purposes have a lower WTP in the environmental dimension as compared to foreign

Sustainability 2021,13, 42 13 of 20
tourists; this may be due to the fact that foreign tourists travel considerably further to
reach the tourism destination (Andalusia) and therefore are willing to pay to enjoy this
novel experience in the vacation destination, and because Spanish residents may be less
environmentally conscientious than foreign tourists.
4.3.2. Tourism Services Dimension
Figure 3reveals the decision tree for the second dimension (Tourism services), based on
the variables with the most significant association, statistically speaking: income, purpose
of the trip, budget and place of origin. Table 11 reveals the coding of the relevant variables.
Sustainability 2021, 13, x FOR PEER REVIEW 13 of 19
The purpose of the trip variable is the root node, the variable that best groups in
terms of association (dependence); and it is evident that the greater WTP is concentrated
in the tourists whose purpose of the trip is interior/rural tourism, health/wellbeing, nau-
tical/sporting marina and nature; in line with the “environmental” labeling of this dimen-
sion.
In addition, the budget and income variables are always positively associated with
WTP. In addition, unlike those mentioned previously, Spanish tourists in the subgroup of
other purposes have a lower WTP in the environmental dimension as compared to foreign
tourists; this may be due to the fact that foreign tourists travel considerably further to
reach the tourism destination (Andalusia) and therefore are willing to pay to enjoy this
novel experience in the vacation destination, and because Spanish residents may be less
environmentally conscientious than foreign tourists.
4.3.2. Tourism Services Dimension
Figure 3 reveals the decision tree for the second dimension (Tourism services), based
on the variables with the most significant association, statistically speaking: income, pur-
pose of the trip, budget and place of origin. Table 11 reveals the coding of the relevant
variables.
Figure 3. Tourism services dimension vs. sociodemographic factors and trip characteristics. Source: author’s own creation
using calculations from the R Commander Library of R software.
Table 11. Coding of relevant variables in the tourism services dimension decision tree.
Variable
Coding
Income (thou-
sands of euros)
1 (under 12), 2 (between 12 and 15), 3 (between 15 and 20), 4 (between 20 and 25), 5 (between 25 and 30), 6
(between 30 and 35) 7 (between 35 and 40), 8 (between 40 and 50), 9 (over 50)
Purpose of the
trip
1 (coastal sun/beach), 2 (interior), 3 (cultural), 4 (family), 5 (golf), 6 (health and well-being), 7 (meetings and
congresses), 8 (nautical/sports marina), 9 (cruise), 10 (nature), 11 (food and wine), 12 (languages), 13 (snow)
Budget (euros)
*ongoing
Place of origin
1 (Andalusia), 2 (Spain), 3 (European Union), 4 (Rest of the world)
Source: author’s own creation.
It is seen that the root node lies in the income variable, being the one that best groups
in terms of association (dependence); so, the greatest WTP is concentrated in the tourists
with high budgets, in line with the economic capacity required by the taxes/public fees
that make up this dimension—tax on tourism stays, taxes on tourist attractions or public
theatres and shows entrance fees.
Similarly, a lower WTP was identified for Spanish tourists. In addition, as expected,
a lower WTP was observed in those tourists having a low budget. There is also a segment
Figure 3.
Tourism services dimension vs. sociodemographic factors and trip characteristics. Source: author’s own creation
using calculations from the R Commander Library of R software.
Table 11. Coding of relevant variables in the tourism services dimension decision tree.
Variable Coding
Income (thousands
of euros)
1 (under 12), 2 (between 12 and 15), 3 (between 15 and 20), 4 (between 20 and 25), 5 (between 25 and 30),
6 (between 30 and 35) 7 (between 35 and 40), 8 (between 40 and 50), 9 (over 50)
Purpose of the trip
1 (coastal sun/beach), 2 (interior), 3 (cultural), 4 (family), 5 (golf), 6 (health and well-being), 7 (meetings and
congresses), 8 (nautical/sports marina), 9 (cruise), 10 (nature), 11 (food and wine), 12 (languages), 13 (snow)
Budget (euros) *ongoing
Place of origin 1 (Andalusia), 2 (Spain), 3 (European Union), 4 (Rest of the world)
Source: author’s own creation.
It is seen that the root node lies in the income variable, being the one that best groups
in terms of association (dependence); so, the greatest WTP is concentrated in the tourists
with high budgets, in line with the economic capacity required by the taxes/public fees
that make up this dimension—tax on tourism stays, taxes on tourist attractions or public
theatres and shows entrance fees.
Similarly, a lower WTP was identified for Spanish tourists. In addition, as expected, a
lower WTP was observed in those tourists having a low budget. There is also a segment of
tourists having high incomes (over 30,000 Euros net annual income) and travel motivations
linked to nature, who, although not very representative, have a behavior that is contrary
to the usual one for this dimension; this is clearly due to the fact that their main travel
purpose is not cultural.

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4.3.3. Recreational Dimension
Figure 4reveals the decision tree for the third dimension (recreational), based on
variables in which there is a more significant association, from a statistical perspective:
purpose of the trip, income and budget. Table 12 reveals the coding of the relevant variables.
Sustainability 2021, 13, x FOR PEER REVIEW 14 of 19
of tourists having high incomes (over 30,000 Euros net annual income) and travel motiva-
tions linked to nature, who, although not very representative, have a behavior that is con-
trary to the usual one for this dimension; this is clearly due to the fact that their main
travel purpose is not cultural.
4.3.3. Recreational Dimension
Figure 4 reveals the decision tree for the third dimension (recreational), based on var-
iables in which there is a more significant association, from a statistical perspective: pur-
pose of the trip, income and budget. Table 12 reveals the coding of the relevant variables.
Figure 4. Recreational dimension vs. sociodemographic factors and trip characteristics. Source: author’s own creation us-
ing calculations from the R Commander Library of R software.
Table 12. Coding of relevant variables in the recreational dimension decision tree.
Variable
Coding
Purpose of the
trip
1 (coastal sun/beach), 2 (interior), 3 (cultural), 4 (family), 5 (golf), 6 (health and well-being), 7 (meetings and
congresses), 8 (nautical/sports marina), 9 (cruise), 10 (nature), 11 (food and wine), 12 (languages), 13 (snow)
Income (thou-
sands of euros)
1 (under 12), 2 (between 12 and 15), 3 (between 15 and 20), 4 (between 20 and 25), 5 (between 25 and 30), 6
(between 30 and 35) 7 (between 35 and 40), 8 (between 40 and 50), 9 (over 50)
Budget (euros)
*ongoing
Source: author’s own creation.
The third dimension reveals a very low WTP, as evidenced in Table 9. However, the
analysis via decision trees shows that the population subgroup with the greatest WTP is
the group of tourists whose travel purpose is diverse, although mainly linked to the sun
and beach (coastal, golf, nautical, cruise, food and wine and languages) and having a large
budget (>95 euros). The remainder of the tourists is not very willing to pay either of the
two taxes making up this dimension.
4.3.4. Tourism Infrastructure Dimension
The fourth dimension (tourism infrastructure) reveals a very low WTP, as seen in
Table 9. In addition, the study carried out via decision trees does not find any sociodem-
ographic variable of the tourists or trip characteristics making up this dimension. There-
fore, the WTP for this dimension is not subject to any specific profile, with its perception
being transversal across the population.
Figure 4.
Recreational dimension vs. sociodemographic factors and trip characteristics. Source: author’s own creation using
calculations from the R Commander Library of R software.
Table 12. Coding of relevant variables in the recreational dimension decision tree.
Variable Coding
Purpose of the trip
1 (coastal sun/beach), 2 (interior), 3 (cultural), 4 (family), 5 (golf), 6 (health and well-being), 7 (meetings and
congresses), 8 (nautical/sports marina), 9 (cruise), 10 (nature), 11 (food and wine), 12 (languages), 13 (snow)
Income (thousands
of euros)
1 (under 12), 2 (between 12 and 15), 3 (between 15 and 20), 4 (between 20 and 25), 5 (between 25 and 30),
6 (between 30 and 35) 7 (between 35 and 40), 8 (between 40 and 50), 9 (over 50)
Budget (euros) *ongoing
Source: author’s own creation.
The third dimension reveals a very low WTP, as evidenced in Table 9. However, the
analysis via decision trees shows that the population subgroup with the greatest WTP is
the group of tourists whose travel purpose is diverse, although mainly linked to the sun
and beach (coastal, golf, nautical, cruise, food and wine and languages) and having a large
budget (>95 euros). The remainder of the tourists is not very willing to pay either of the
two taxes making up this dimension.
4.3.4. Tourism Infrastructure Dimension
The fourth dimension (tourism infrastructure) reveals a very low WTP, as seen in
Table 9
. In addition, the study carried out via decision trees does not find any sociodemo-
graphic variable of the tourists or trip characteristics making up this dimension. Therefore,
the WTP for this dimension is not subject to any specific profile, with its perception being
transversal across the population.
4.3.5. Mobility Dimension
The fifth dimension (mobility) reveals a very low WTP, as evidenced in Table 9. In
addition, the decision tree study did not reveal any sociodemographic variables of the
tourists or trip characteristics making up this dimension. Therefore, the WTP for this
dimension is not subject to any specific profile, with its perception being transversal across
the population.

Sustainability 2021,13, 42 15 of 20
4.3.6. Transversal Taxes/Public Fees (Not Linked to Any Specific Dimension)
As seen previously, specifically in the section devoted to the creation of dimensions
(see Section 4.2.1), there are two taxes/public fees: visitor tickets at the main tourist
attractions and public fee to access public tourism resources, which are quite transversal
and hence, cannot be associated with any specific dimension; therefore, they are analyzed
independently.
As for the former, in addition to having a very low WTP, the visitor tickets at the
main tourist attractions fee revealed no sociodemographic variables of the tourists or trip
characteristics in the decision trees making up this dimension. Therefore, the WTP this
public fee is not subject to any specific profile, with its perception being transversal across
the population. As for the second fee that was independently analyzed, the public fee to
access public tourism resources, it should be mentioned that tourists have the greatest WTP
for this of all fifteen taxes/public fees that were analyzed. However, in the case of tourists
with average and low income, there was a large unwillingness to pay it, as observed in
Figure 5. Table 13 reveals the coding of the relevant variables.
Sustainability 2021, 13, x FOR PEER REVIEW 15 of 19
4.3.5. Mobility Dimension
The fifth dimension (mobility) reveals a very low WTP, as evidenced in Table 9. In
addition, the decision tree study did not reveal any sociodemographic variables of the
tourists or trip characteristics making up this dimension. Therefore, the WTP for this di-
mension is not subject to any specific profile, with its perception being transversal across
the population.
4.3.6. Transversal Taxes/Public Fees (Not Linked to Any Specific Dimension)
As seen previously, specifically in the section devoted to the creation of dimensions
(see Section 4.2.1), there are two taxes/public fees: visitor tickets at the main tourist attrac-
tions and public fee to access public tourism resources, which are quite transversal and
hence, cannot be associated with any specific dimension; therefore, they are analyzed in-
dependently.
As for the former, in addition to having a very low WTP, the visitor tickets at the
main tourist attractions fee revealed no sociodemographic variables of the tourists or trip
characteristics in the decision trees making up this dimension. Therefore, the WTP this
public fee is not subject to any specific profile, with its perception being transversal across
the population. As for the second fee that was independently analyzed, the public fee to
access public tourism resources, it should be mentioned that tourists have the greatest
WTP for this of all fifteen taxes/public fees that were analyzed. However, in the case of
tourists with average and low income, there was a large unwillingness to pay it, as ob-
served in Figure 5. Table 13 reveals the coding of the relevant variables.
Figure 5. Public fee to access public tourism resources vs. sociodemographic factors and trip characteristics. Source: au-
thor’s own creation using calculations from the R Commander Library of R software.
Table 13. Coding of relevant variables in the public fee to access public tourism resources decision tree.
Variable
Coding
Income (thou-
sands of euros)
1 (under 12), 2 (between 12 and 15), 3 (between 15 and 20), 4 (between 20 and 25), 5 (between 25 and 30), 6
(between 30 and 35) 7 (between 35 and 40), 8 (between 40 and 50), 9 (over 50)
Purpose of the
trip
1 (coastal sun/beach), 2 (interior), 3 (cultural), 4 (family), 5 (golf), 6 (health and well-being), 7 (meetings and
congresses), 8 (nautical/sports marina), 9 (cruise), 10 (nature), 11 (food and wine), 12 (languages), 13 (snow)
Education level
1 (no education), 2 (primary school), 3 (secondary school), 4 (higher education)
Length of the
stay (days)
1 (1 to 3 days), 2 (4 to 6 days), 3 (7 days or more)
Source: author’s own creation.
However, no sociodemographic features of the tourist or trip characteristics were sig-
nificant; there was only one subgroup of tourists, with average-high incomes (33 tourists),
Figure 5.
Public fee to access public tourism resources vs. sociodemographic factors and trip characteristics. Source:
author’s own creation using calculations from the R Commander Library of R software.
Table 13. Coding of relevant variables in the public fee to access public tourism resources decision tree.
Variable Coding
Income (thousands
of euros)
1 (under 12), 2 (between 12 and 15), 3 (between 15 and 20), 4 (between 20 and 25), 5 (between 25 and 30),
6 (between 30 and 35) 7 (between 35 and 40), 8 (between 40 and 50), 9 (over 50)
Purpose of the trip
1 (coastal sun/beach), 2 (interior), 3 (cultural), 4 (family), 5 (golf), 6 (health and well-being), 7 (meetings and
congresses), 8 (nautical/sports marina), 9 (cruise), 10 (nature), 11 (food and wine), 12 (languages), 13 (snow)
Education level 1 (no education), 2 (primary school), 3 (secondary school), 4 (higher education)
Length of the stay
(days) 1 (1 to 3 days), 2 (4 to 6 days), 3 (7 days or more)
Source: author’s own creation.
However, no sociodemographic features of the tourist or trip characteristics were
significant; there was only one subgroup of tourists, with average-high incomes (33 tourists),
not very representative, whose purpose of the trip—family tourism and languages—with
average stays that had a lower WTP, as compared to the general population.

Sustainability 2021,13, 42 16 of 20
5. Discussion
This article explores the WTP of tourists in a mature destination that specialized in
mass tourism. The study carried out and the results obtained confirm the two hypotheses
proposed at the onset of the work, contributing to the scientific literature with regard to the
WTP of tourists in various aspects.
It has been verified that a large majority of tourists visiting Andalusia would be willing
to pay an additional amount to improve their tourism experience and to improve increased
sustainability in the destination. Along these lines, a series of taxes and/or public fees have
been identified, which tourists have been shown to be more willing to pay, mainly linked
to taxation related to environmental factors (public natural/national parks entrance fee or
the environmental conservation tax in municipalities whose main activity is ecotourism) or
linked to the taxation of certain tourism services (taxes on tourism stays, taxes on tourist
attractions or public theaters and shows entrance fees).
Tourists’ WTP for taxes/public fees in the dimension linked to environmental factors is
coherent with the highly recognized environmental awareness of 21st-century tourists [
82
]
and with the generally unsustainable nature of mass tourism.
The dimension that taxes tourism services is seen to be the category of taxes/public
fees that tourists are the most willing to pay for, confirming, in accordance with [
9
], that
tourists who travel long distances demand authenticity and memorable experiences and
therefore, will be willing to pay for them.
Thus, given that the tourists’ WTP is a voluntary issue, it may be expected that the
implementation of financial instruments by policymakers to obtain the financing needed
to implement policies aimed at improving sustainability and the tourism destination
experience will be in line with the tourist preferences. Otherwise, if tourists disagreed with
the implementation of a specific tax, the imposition of the same may ultimately reduce the
destination’s competitiveness and attractiveness.
Hence, these results confirm the first of the proposed hypotheses, since it has been
verified that there are certain taxes/public fees (linked to environmental and tourism
services factors) that tourists visiting Andalusia are more willing to pay; this is quite
relevant information for policymakers and managers of mature tourism destinations since
it may permit the implementation of taxes and/or public fees through which they can obtain
a source of income to permit the financing of policies that will increase the destination’s
competitiveness.
Second, we have examined how the WTP certain taxes/public fees may depend on
distinct factors—sociodemographic aspects of tourists and trip characteristics. Hence,
almost all of the factors, both sociodemographic and trip characteristics, are common in
explaining the WTP for the distinct dimensions, mainly: the purpose of the trip, income,
budget and place of origin.
The analysis of the factors that may condition the willingness to pay for to improve
sustainability and the experience corroborates what has been advocated by recent literature.
More specifically, the purpose of the trip and income are the variables that best groups in
terms of association to the willingness to pay variable, as found by other studies [
52
,
66
,
67
]
and [
19
,
53
,
54
], respectively. Furthermore, these variables have interaction with others that
have been found to be determinant, place of origin and daily budget. The influence of place
of origin on the willingness to pay for to improve sustainability and the experience verified
empirically in this work was aligned with the results achieved in other studies [
19
,
40
,
55
,
56
].
The daily budget—linked, obviously, to the income variable—is a new explanatory variable
of tourist behavior in terms of paying an additional amount.
Although these factors do not manifest themselves in the same way in all dimensions,
for the purposes of this study, it is relevant to identify the degree of explanation of these
factors for the two dimensions in which tourists were more willing to pay: environmental
dimension and tourism services dimensions. Hence, the variables with the greatest expla-
nation capability are the purpose of the trip (rural, health/wellbeing, nautical/sporting

Sustainability 2021,13, 42 17 of 20
marina and nature motivations) with respect to the first dimension and income level (as of
30,000 euros net annual income) with respect to the second dimension.
To ensure the long-term existence of tourism destinations that may be considered “ma-
ture”, especially the coastal or sun and beach destinations, it is necessary to find a balance
between economic advantages and sociocultural and environmental sustainability [
83
],
while also redesigning the mass tourism model.
Therefore, these results confirm the second proposed hypothesis, since distinct factors
have been identified (sociodemographic factors and trip characteristics) that condition
tourists’ WTP certain taxes/public fees, in this case, with special relevance being placed on
environmental and tourism services taxes/public fees.
However, this work has certain limitations, including the fact that no interviews
were conducted in other departure points, distinct from those analyzed, such as highway
transport. In addition, the time limitations of the study should be noted, given that
the tourists were administered the surveys over a period of less than one calendar year.
Moreover, no questions addressed the identification of certain psychographic factors, such
as environmental awareness or moral responsibility.
Finally, as future lines of research, it may be interesting to explore the WTP of tourist
clusters, that is, groups of individuals having similar characteristics.
Author Contributions:
Conceptualization, J.I.P.-F. and P.J.C.-G.; methodology, J.L.D.-R.; software,
J.L.D.-R.; validation, J.I.P.-F.; formal analysis, P.J.C.-G.; investigation, P.J.C.-G.; resources,
P.J.C.-G.;
data curation, J.L.D.-R.; writing—original draft preparation, P.J.C.-G.; writing—review and edit-
ing, P.J.C.-G.; visualization, J.I.P.-F.; supervision, J.I.P.-F.; project administration, J.L.D.-R.; funding
acquisition, P.J.C.-G. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
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