The perception of destination competitiveness by tourists

Abstract

Based on a review of the academic literature this study identifies some of the most frequently mentioned factors and indicators in the field of a tourist destination competitiveness to design a survey subsequently conducted among tourists in Cancun, México. An exploratory factor analysis was performed with the collected data; the result was the reduction from twelve competitiveness factors most commonly mentioned in academic literature to five: Destination marketing and attractions, Destination management and security, Cultural heritage, ICT adoption and Transportation. The study confirms the contributions of several works on the subject while some others common assumptions found in the literature could not be corroborated.

Full text

Revista Investigaciones Turísticas, nº 14, pp. 1-20
ISSN: 2174-5609
DOI. http://dx.doi.org/10.14461/INTURI2017.14.01
Fecha de recepción: 24-02-2017 Fecha de aceptación: 17-07-2017 1
Este trabajo se publica bajo una licencia Creative Commons Attribution License Reconocimiento 4.0
Cita bibliográfica: Amaya Molinar, C.M., Sosa Ferreira, A.P, Ochoa-Llamas, I. y Moncada Jiménez, P. (2017). The
perception of destination competitiveness by tourists. Investigaciones Turísticas (14), pp. 1-20.
https://dx.doi.org/10.14461/INTURI2017.14.01
The perception of destination competitiveness by tourists
La percepción del turista sobre la competitividad de destinos turísticos
Carlos Mario Amaya-Molinar. Universidad de Colima, México. cmamaya@ucol.mx
Ana Pricila Sosa-Ferreira. Universidad del Caribe, México. psosa@ucaribe.edu.mx
Ileana Ochoa-Llamas. Universidad de Colima, México. ileana8a@ucol.mx
Pedro Moncada-Jiménez. Universidad del Caribe, México. pmoncada@ucaribe.edu.mx
ABSTRACT
Based on a review of the academic literature this study identifies some of the most
frequently mentioned factors and indicators in the field of a tourist destination
competitiveness to design a survey subsequently conducted among tourists in Cancun,
México. An exploratory factor analysis was performed with the collected data; the result was
the reduction from twelve competitiveness factors most commonly mentioned in academic
literature to five: Destination marketing and attractions, Destination management and
security, Cultural heritage, ICT adoption and Transportation. The study confirms the
contributions of several works on the subject while some others common assumptions
found in the literature could not be corroborated.
Keywords: Destination, competitiveness, tourist, image, exploratory factor
RESUMEN
A partir de una revisión de la literatura académica, el trabajo identifica algunos de los
factores e indicadores más citados en el campo de estudio de la competitividad de destinos
turísticos para diseñar una encuesta realizada entre turistas en Cancún, México. Se realizó
un análisis factorial exploratorio con los datos recogidos, obteniendo como resultado la
reducción de doce factores de competitividad mencionados con mayor frecuencia en la
literatura académica a cinco: Marketing de destino y atracciones, Gestión y seguridad de los
destinos, Patrimonio cultural, Adopción de las TIC y Transporte. El estudio confirma las
contribuciones de varias obras sobre el tema, aunque algunos de los supuestos más
comunes en la literatura del tema no fueron corroborados.
Palabras clave: destino, competitividad, turismo, imagen, análisis exploratorio, factor.
I. INTRODUCTION
Academic, political, technological and business discourses frequently include the
term competitiveness applied in studies on countries, regions, industries, and companies.
128 institutional and academic publications on the subject were initially gathered; after a
preliminary review, 37 works were retained to be included in the study. Most of the

Amaya-Molinar, C. M.; Sosa-Ferreira, A.P., I.; Ochoa-Llamas, I.; Moncada-Jiménez, P.
Investigaciones Turísticas 2
N° 14, julio-diciembre 2017, pp. 1-20
theoretical tourist competitiveness models analyzed provide little academic attention
toward the main actor in the tourism system: the tourist. Important parts of the examined
studies stand on statistics supplied by national and international agencies or in surveys and
interviews with industry officials, executives, graduate students, and scholars. This study is
an exploratory research performed to advance the understanding of the direct perception of
tourism destination's competitiveness by visitors. Utilizing an exploratory factor analysis, the
work has sought to determine if domestic tourists visiting a destination can perceive some of
the main factors of tourism competitiveness mentioned in academic literature. A sample of
Mexican tourists answered a survey, as crucial stakeholders in the tourism system in Cancun,
a major national sun and sea destination.
The study can be replicated in other destinations and with international tourists to
confirm its results; it is advisable to investigate the perception of competitiveness by other
stakeholders not yet considered in studies in this field, like workers in the tourism industry
and host population.
The study confirms the importa7nce of policies and regulations, air transport
infrastructure, cultural resources and coverage of information and communications
technologies as factors influencing the competitiveness of tourist destinations.
II. LITERATURE REVIEW
2.1. Concepts and models of tourism competitiveness
Historically, initial approaches to competitiveness in academia go back to the XVII
century (Cho and Moon, 2013). More recently, the World Economic Forum (1994: 18)
defines competitiveness as “the degree to which a nation can, under free trade and fair
market conditions, produce goods and services which meet the test of international
markets, while simultaneously maintaining and expanding the real income of its people over
the long-term." Definitions of tourism competitiveness found in the academic literature are
very diverse; some authors use very simplistic definitions, others don’t even define it; quotes
to the works of Porter (1993, 1998) and Ritchie and Crouch (1993, 1999, 2005) are very
common.
Enright and Newton (2005:340) propose a simple concept of destination
competitiveness: "A tourist destination is competitive if it can attract and satisfy potential
tourists". Cracolici & Nijkamp (2008) define competitiveness as the qualitative and
quantitative superiority of a unit, company or territory above all competitors. To Croes &
Rivera (2010), competitiveness influences the quality of life of residents at the destination,
assuming that GDP income per capita is a proxy of quality of life; Zhang & Jensen (2007) and
Li & Huang (2010) examine competitiveness analyzing temporary fluctuations in the flows of
tourists. Claver-Cortés et al. (2007) assume that the competitiveness' level of a destination
depends on the efficiency of the companies operating there. The Travel & Tourism
Competitiveness Report 2013 (Blanke & Chiesa, 2013) measures the factors and policies that
make tourism development an attractive industry in different countries, but does not define
a concept; the index is composed of 3 subindexes and 14 pillars.
The strength of the theoretical model of Ritchie and Crouch (2005) makes it the most
cited in the academic literature of tourism; their definition emphasizes the environmental,
economic and social sustainability of tourist operation, as well as the satisfaction of visitors.

The perception of destination competitiveness by tourists
Investigaciones Turísticas
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The model incorporates Porter's comparative and competitive advantages (1998), places the
destination in its macroenvironment, relating it to their microenvironment and considers
five basic factors, composed, in turn, by a set of 36 sub-factors influencing the
competitiveness of a destination. While this model has a sound theoretical basis, it is very
difficult to measure the 436 quantitative and qualitative dimensions of the phenomenon
proposed to determine destination competitiveness.
2.2. Types of studies of tourism competitiveness
A significant proportion of the works on competitiveness consulted uses an economic
approach (Mangion et al., 2012; Vu & Turner, 2011; Zhang & Jensen; 2007). Several studies
(Barros et al., 2011; Claver-Cortes et al., 2007; Cracolici et al., 2008; Croes & Rivera, 2010;
Molina-Azorin et al., 2010) analyze the efficiency in the operation of tourist destinations, an
approach intrinsically linked with the economic aspect. Another important group of works
establishes theoretical models of tourism competitiveness (Blanke & Chiesa, 2013; Dwyer &
Kim, 2003; Ritchie & Crouch, 2005). Some of these models frequently are the basis for other
works; for example, the studies of Mazanec et al. (2007); Webster & Ivanov (2014); Wu et al.
(2012) use the tourism competitiveness paradigm developed by Blanke & Chiesa (2013) for
the World Economic Forum.
Some academic works search an appropriate method to study tourism
competitiveness (Botti & Peypoch, 2013; Huang & Peng, 2012; Medina-Muñoz et al., 2013;
Zhang et al., 2011). Another set of papers focus on the management and marketing of
tourist destinations (Andrades-Caldito et al., 2013; Buhalis, 2000; Go & Govers, 2010; Pike &
Mason, 2010). For Faulkner et al. (1999) and Enright & Newton (2004, 2005) the attractions
are the center of attention, while only two works focus on the relationship between tourism
competitiveness and sustainability: Hassan (2000) and Huybers & Bennett (2003).
2.3. Methodologies employed in the study of competitiveness
The methodologies employed in the academic works on competitiveness reviewed
are even more diverse than the types of approaches. An important part of the analyzed
studies (42%) derives from statistics supplied by national and international agencies,
especially those focused on economics subjects and in the World Economic Forum’s index.
10% of works do not provide statistical or empirical data, including some of the theoretical
models most quoted by other researchers, like Ritchie & Crouch (2005) and Dwyer & Kim
(2003). Regarding the 48% of works with empirical studies, 38% utilize surveys; the rest use
qualitative methods. Only eight studies (17%) consider perceptions of consumers and
tourists; the rest of works with empirical study take into account perceptions of
stakeholders, industry executives or people with relatively high educational levels: travel
agents, hotel managers, executives and officials from the industry, academics, postgraduate
students and administrators of destination management offices, among others.
While some of the theoretical models of competitiveness most quoted in the
literature mention the well-being of the community and stakeholders, virtually none of the
consulted works considers the point of view of the residents. Therefore, the majority of the
consulted works provides little academic attention towards tourists, host population and the
large base of workers of the tourism industry; that is, to the vast majority of the
stakeholders involved in the operation of the industry. Regarding the unit of study, 52% of

Amaya-Molinar, C. M.; Sosa-Ferreira, A.P., I.; Ochoa-Llamas, I.; Moncada-Jiménez, P.
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the works examine a region or city, 46% analyze countries and 2% do not specify; in this
study, we analyze the destination as a city.
2.4. Measures of tourism competitiveness
The great diversity of approaches between the revised works makes it difficult to
define what the dependent and independent variables of theoretical models are.
Nevertheless, we identified a set of measurements to consider as independent variables or
results of competitiveness in tourist destinations. The indicators of tourism competitiveness
results usually are dynamically registered tracking its temporary evolution and comparing
the outcomes with competing destinations, by the very nature of the subject. Several of
these indicators refer to the number of visitors and data relating to the hotel industry
operation and its efficiency, such as the occupation rate, length of stay and revenue per
available room (Barros et al., 2011; Claver-Cortes et al., 2007; Cracolici et al., 2008).
Exploring beyond the number of visitors and their stay, the analysis of the
expenditure of tourists is considered relevant in this field, as well as its economic impact on
the destination and the population’s well-being (Croes, 2010; Ritchie & Crouch, 2005;
Webster & Ivanov; 2014). Some standard marketing measures are also important, such as
the evolving of market share or, with more depth in the field of consumer behavior,
information about quality, satisfaction and behavioral intentions of tourists (Li & Huang,
2010; Pike & Mason, 2010; Mazanec et al., 2007).
2.5. Factors of tourism competitiveness
The literature review presents a large number of factors that the competitiveness of
tourist destinations. Table 1 presents the 12 most frequently mentioned factors in 27
academic and institutional papers.
The study of competitiveness of tourist destinations is similar to the investigation of
the destination image. Pike (2002) and Gallarza et al. (2002) reviewed in depth 142 and 65
works on this topic, respectively. They highlighted the variety of approaches, objects,
subjects, and methods used to identify the most frequently analyzed attributes of the
destination image. Some of the works on competitiveness reviewed include dimensions of
destination image's field. Gallarza et al. (2002) underline that researchers classified the
destinations according to the aims of the study.

The perception of destination competitiveness by tourists
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Table 1. Factors determining the competitiveness of tourist destinations
N°
AUTHORS
FACTORS
1
2
3
4
5
6
7
8
9
10
11
12
Nature, environmental
sustainability
Destination Marketing
Cultural Heritage
Price, Value
Attractions
Humans Resources
Security
Resident's quality of
life
e - readiness
Infrastructure
Transport
Destination
Management
1
Andrades-Caldito et al. (2012)
X
X
X
X
X
X
2
Barros et al. (2011)
X
X
X
X
3
Blanke & Chiesa (2013)
X
X
X
X
X
X
X
X
4
Bornhorst et al. (2010)
X
X
X
X
5
Caber et al. (2012)
X
X
X
X
X
X
X
6
Carmichael (2002)
X
X
X
7
Claver et al. (2007)
X
X
X
X
X
8
Cracolici & Nijkamp (2008)
X
X
X
9
Cracolici et al. (2008)
X
X
10
Crouch (2010)
X
X
X
X
X
X
11
Dupeyras & McCallum, 2013
X
X
X
X
X
12
Dwyer & Kim (2003)
X
X
X
X
X
X
13
Dwyer et al. (2012)
X
X
X
X
14
Enright & Newton (2004, 2005)
X
X
X
X
X
X
X
X
X
15
Faulkner et al. (1999)
X
X
16
Go & Govers (2010)
X
X
X
X
X
17
Gomezelj & Mihalic (2008)
X
X
X
X
X
X
18
Gooroochurn & Sugiyarto (2005
X
X
X
X
X
19
Hassan (2000)
X
X
X
X
X
20
Huang & Peng (2012)
X
X
X
X
X
X
X
21
Huybers & Bennett (2003)
X
X
X
22
Mazanec et al. (2007)
X
X
X
X
X
X
23
Pike & Mason (2010)
X
24
Rodrigues & Carrasqueira (2011)
X
X
X
X
X
X
25
Webster & Ivanov (2014)
X
X
26
Wu et al. (2012)
X
X
X
X
X
X
X
27
Zhang et al. (2011)
X
X
X
TOTAL
20
15
14
13
13
11
10
8
7
7
6
6
Own elaboration.

Amaya-Molinar, C. M.; Sosa-Ferreira, A.P., I.; Ochoa-Llamas, I.; Moncada-Jiménez, P.
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In this study, the academic literature was reviewed to identify the factors of
competitiveness of tourist destinations most frequently mentioned, like Pike (2002) and
Gallarza et al. (2002) did on destination image. Likewise, these components consider
different approaches, methods, objects, and subjects; therefore, the factors used in this
work are a generalization. In addition, this research includes an empirical study, while the
papers on destination image previously mentioned a focus on literature review.
Draws attention the factor "Nature, environmental sustainability", the most
frequently quoted in the mentioned works and surprisingly the least studied and more
complex to research. At the opposite extreme, we have less mentioned factors, like human
resources, adoption of information and communication technologies and destination
management, although the studies from Enright & Newton (2004, 2005) and Wu et al.
(2012) demonstrate their influence on competitiveness.
III. METHODOLOGY AND EMPIRICAL STUDY
Beyond the studies based on indexes and indirect measurements, this work utilizes
an exploratory factor analysis to find out if some of the main factors of tourism
competitiveness mentioned in academic literature can be perceived by tourists visiting a
destination. It is not the purpose of this study to identify the degree of influence of identified
components in the levels of competitiveness, but only find out if a crucial stakeholder of the
tourism system, the domestic tourist, is able to perceive them.
Among the few studies on tourist competitiveness based on surveys answered
directly by visitors are the works of Pike & Mason (2010) and Caber et al. (2012), which have
in common that both utilize Importance-Performance Analysis methodology. Carmichael
(2002) interviewed visitors directly in a cultural exhibition at an art gallery while Botti &
Peypoch (2013), Cracolici & Nijkamp (2008) and Andrades-Caldito et al. (2012) employed
data generated in surveys provided by official institutions; none of these works considers an
exploratory factor analysis.
3.1. Survey design
Since there is no consensus among researchers to define a univocal concept of
tourism competitiveness and it is not likely that common visitors have knowledge of any of
them, it was not considered appropriate to ask directly for tourists surveyed about their
perception of the issue; instead, they were told the questionnaire was designed to obtain
their views on some aspects of the destination. The survey does not refer directly to tourism
competitiveness, but retrieves information on the factors most frequently mentioned in the
academic literature (table 2); a set of items was designed to represent each of them.
Table 2: Subjects corresponding with competitiveness factors
FACTORS
SUBJECT
Nature, environmental
sustainability
Nature's beauty and conservation, absence of pollution.
Destination Marketing
Considers consumer behavior, destination image, branding.
Cultural Heritage
History, museums, archeology, traditions, public art, etc.
Price, Value
Benefits received in exchange for money and travel efforts.

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Attractions
Quality, diversity, and interest of tourist attractions and
recreation offer.
Humans Resources
Qualification and training of staff in contact with visitors.
Security
Feeling of security in the destination.
Resident's quality of life
Perception of welfare and prosperity of host population.
E - readiness
Coverage and adoption of information and communication
technologies in the destination.
Infrastructure
Public and urban services, institutions, facilities,
transportation terminals, etc.
Transportation
Transportation services to and into the destination: bus,
taxis, ferryboats, air carriers, etc.
Destination Management
The organization, cooperation, priority, and stability in
tourist operations at the destination.
The questionnaire has two parts: a first one based on the respondents’ profile
gathers information on socio-demographic data; the second section presents items on the
factors of competitiveness, using Likert scales of 5 points.
3.2. The tourist destination case study
The empirical study was conducted in Cancun, Quintana Roo, the main Sun and
beach destination of Mexico, with the country's most competitive tourist industry; it is
located on the Caribbean Sea, northeast of the Yucatan peninsula. It is a typical third
generation's tourist destination (Claver-Cortes et al., 2007), developed by the Bank of
Mexico to generate foreign currency. Since the decade of the 1970’s, this destination has
been the powerhouse of economic development for the Yucatan peninsula. Table 3 shows
how Cancun can be considered competitive in some indicators proposed in the academic
literature, in absolute terms, in trends and socio-demographic indexes; its outcomes in a
number of visitors and occupation rate show positive evolution, with outstanding results in
terms of length of stay, tourism receipts and lodging offer.
Table 3: Cancun’s basic tourist data
Visitors
Δ%2012-2013
Occupation Rate %
Δ % 2012-2013
Length
of Stay (Days)
4,093,942
12.4
76.8
4.3
5.1
Tourism Receipts US
Millions
Δ % 2012-2013
Average
Expenditure per
tourist (U$D)
Hotels
Rooms
4,348.78
16.1
1,062
145
30,608
Source: DATATUR (2014), Gobierno de Quintana Roo (2014).

Amaya-Molinar, C. M.; Sosa-Ferreira, A.P., I.; Ochoa-Llamas, I.; Moncada-Jiménez, P.
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3.3. Sample and profile of respondents
250 questionnaires were applied to domestic tourists with a convenience sampling
procedure; students and professors from the University of the Caribbean applied the survey
in August of 2014. Table 4 presents the demographic information of the polled population;
the sample tends to concentrate in young and middle-aged individuals, with significant
proportions of employees and self-employed, higher education in undergraduate and
graduate levels and with income higher than the national average. The profile of
respondents might be explained considering that Cancun is not an affordable destination for
Mexicans, since most visitors must fly to get there.
Table 4: Socio-demographic characteristics of the sample
Gender
%
Age
%
Occupation
%
Male
51.4
18 – 24
20.4
Housewife
2.8
Female
48.6
25 -34
24.1
Employee
46.6
100
35 – 44
31.1
Self Employed
27.9
Education
%
45 – 54
16.6
Student
14.6
No schooling
2.1
55 - 64
6.2
Retired
6.3
Basic Studies
3.0
100
Others
1.8
High School
20.6
Income
%
100
Technical
Studies
17.3
Below national average
21.4
Bachelor
45.1
On the range of national
average
52.2
Postgraduate
11.9
Above the national
average
26.4
100
100
Own elaboration.
Regarding the sample, in Exploratory Factor Analysis it is generally recommended a
minimum ratio of 10 surveys per item; in this study, there are 250 for 44 items, a ratio of
5.68 for the item; nevertheless, studies on the methodology show that sample size might be
determined by the nature of data: if data is strong, a smaller sample can be used; strong
means high commonalities, no cross-loadings and several variables loading firmly on each
factor (MacCallum et al., 1999).
In social sciences, commonalities between 0.40 and 0.70 are considered acceptable
(Velicer & Jackson, 1990); the smallest dimension of commonality obtained in the data was
0.449, for item TRA3, “Quality of local transport system”. Tabachnick & Fidell (2012)
consider 0.32 is the lowest load factor to accept a variable; in the present survey, the lesser
load (0.428) was registered in item DM3, “Importance of tourism in the destination”, also
with the second smaller magnitude for a commonality, 0.494. Regarding the number of
variables, a factor comprising less than three items is considered weak (Osborne & Costello,
2005); all the factors retained in this study have at least three items, being the case of
Information and Communications Technology (ICT) and Transport (TRA) (Table 6).

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3.4. Exploratory Factor Analysis
The literature review provided a large number of factors and indicators of tourism
competitiveness, varying according to the approaches adopted by different authors;
considering this, it was decided to adopt the Exploratory Factor Analysis method to reduce
data and find the underlying structure within the broad set of variables. Hair et al. (2006)
define factors as homogeneous groups formed with variables solidly correlated to each
other and independent from the rest; this procedure can identify a minimum of dimensions
able to explain much material contained in their data, simplifies and exposes the internal
structure, delivering the same information with fewer aspects.
There are several methods for exploratory factor analysis; Principal Component
Analysis and Varimax rotation are some of the techniques most commonly used in this type
of methodology; however, they cannot be considered an obligatory standard for all the
studies in its kind; some authors claim they are not the best option available, considering
that the exploratory factor analysis is precisely that, a probing tool, not designed to test
hypotheses or draw statistical inferences. Considering rotations, its essential purpose is to
visualize clearly and simply the load factors and their grouping (Finch, 2006; Osborne, 2015);
regarding this particular work, the Promax rotation is the procedure that functioned the best
to visualize the factors included in the research.
To perform the exploratory factor analysis in this study, the data collected through
surveys were processed with the software SPSS 22, applying the method of Maximum
Likelihood with Promax rotation and extracting the Cronbach's alpha coefficient as an
indicator of reliability with a minimum threshold of 0.7 (De Vellis, 2011); with a couple of
exceptions, the items with a minimal load of 0.50 were kept, as well as factors with the
lowest Eigenvalue of 1 and explanation of the variance with minimum level of 60% (table 6).
Also included were the sphericity test of Bartlett with significance p < .05 and a Kayser-
Meyer-Olkin value of 0.6 or more to confirm the adequacy of the sample (Tabachnick &
Fidell, 2012).
From twelve factors related to tourist destination's competitiveness proposed
originally, five were deleted along with the Exploratory Factor Analysis process. The
perception of competitiveness by tourists presented a substantial reduction: only 5 were
retained (table 5). Although it must be remarked that 2 out of those factors apparently
vanished actually persisted regrouped with others; such is the case of Attractions and
Security, which reassembled with Destination Marketing and Destination Management,
respectively; factors marked with * regroup with others, while aspects labeled with **
disappear. Table 7 shows the adequacy of sampling with the Kaiser-Meyer-Olkin measures,
as well as the significance test of Bartlett and the appropriate explanation of variance, with
63.90 %. In chart 1, the scree plot shows the 5 factors with eigenvalues above 1.

Amaya-Molinar, C. M.; Sosa-Ferreira, A.P., I.; Ochoa-Llamas, I.; Moncada-Jiménez, P.
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Table 5: Reduction of tourism competitiveness factors perceived by tourists
Initial
Final
Factors proposed in literature
Items
New factors
Items
1
Nature, sustainability *
3
1
Destination marketing and attractions
11
2
Destination marketing *
7
2
Destination management and security
7
3
Cultural heritage
4
3
Cultural heritage
4
4
Price, value **
3
4
ICT adoption
3
5
Attractions *
4
5
Transportation
3
6
Human Resources **
3
TOTAL
28
7
Security *
3
8
Residents quality of life **
3
9
ICT adoption
3
10
Infrastructure **
3
11
Transportation
3
12
Destination management *
4
TOTAL
44
Table 6: Factors and items on perception of tourism competitiveness preserved
ITEMS
COMPONENT
Cronbach’s
Alpha
Commonalities
1
2
3
4
5
.954
IMA3
Positive destination image
.874
.940
0.738
IMA4
Expectations prior to the visit met.
.811
0.785
IMA2
Quality of visit experience.
.787
0.694
NAT1
Region’s natural beauty
.779
0.589
IMA1
Destination brand awareness
.775
0.571
IMA7
Positive word of mouth
.771
0.632
IMA5
Preference for the destination
.736
0.623
IMA6
Intention to return
.726
0.612
ATR3
Variety of tours
.654
0.548
ATR1
Quality of attractions
.617
0.522
ATR4
Variety of recreation offer
.520
0.592
Eigenvalue: 12.781
% of Variance Explained: 45.65
% Cumulative variance: 45.65
SEC2
Trustworthiness of local population
.866
.922
0.679
DM4
Political stability in the destination.
.822
0.760
SEC3
Positive conditions of hygiene
.808
0.713
DM2
Respect for laws and regulations
.758
0.605
SEC1
Feeling of safety
.753
0.679
DM1
Cooperation among organizations to
foster tourism
.731
0.677
DM3
Importance of tourism in the
destination
.428
0.494
Eigenvalue: 2.253
% of Variance Explained: 8.045
% Cumulative variance: 53.69
HER1
Value of destination’s cultural
heritage
.820
.866
0.655
HER4
Interesting traditions
.778
0.695
HER3
Artistic heritage
.761
0.595
HER2
Pleasant urban landscape
.720
0.592
Eigenvalue: 1.801
% of Variance Explained: 6.431
% Cumulative variance: 60.123
ICT1
Coverage of ICT’s
.870
.848
0.691
ICT2
Adoption of ICT’s in local
.841
0.725

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Investigaciones Turísticas
N° 14, julio-diciembre 2017, pp. 1-20 11
organizations
ICT3
Applications of ICT’s for tourists
.614
0.600
Eigenvalue: 1.538
% of Variance Explained: 5.493
% Cumulative variance: 65.62
TRA1
The diversity of transportation
offers to access the destination.
.822
.796
0.689
TRA2
A network of transportation services
to access the destination.
.718
0.685
TRA3
Quality of local transport system
.480
0.449
Eigenvalue: 1.271
% of Variance Explained: 4.538
% Cumulative variance: 70.15
Table 7: Adequacy of the analysis of preserved factors and items
Measures of adequacy of analysis
Tourists
Kaiser-Meyer-Olkin measure of adequacy of sampling
0.920
Bartlett's Sphericity Test
Chi-square Approximated
5416.97
Degrees of freedom
378
Significance
0.000
Minimum Eigenvalue
1.271
Variance Explanation
63.90 %
IV. DESCRIPTIVE ANALYSIS
In table 8 we observe that the survey’s higher scores are for Destination Marketing &
Attractions with an average evaluation of 4.53 on the Likert's scale of 5 points, which can be
considered a very assertive outcome. The lowest grades go to Destination Management &
Security, with a mean of 4.02, not an extremely negative result. On the affirmative side the
best results go to brand awareness (IMA1), natural beauty (NAT 1), positive word of mouth
(IMA7), quality of attractions (ATR1) and value of cultural heritage (HER1); this make much
sense: Cancun is a very well-known world-class resort, with its mixed offer of Mayan legacy
and beautiful beaches.
In the down side, the results on the Destination Management & Security's factor
reflect the serious national problem of crime, insecurity and impunity, especially in the items
referred to trustworthiness of local population (SEC2), political stability (DM4), respect for
laws and regulations (DM 2) and feeling of safety (SEC 1). Structural problems in quality of
local transportation systems are reflected in the item TRA3; the similarity of results in the
ICT’s, and Transport variables draw the attention. Considering the standard deviation, with
the exception of the Destination's Management & Security factor, the values are smaller
than 1, with averages between 0.90 and 0.93, confirming the data’s reliability. The ratings on
the retained variables reflect a destination's positive perception by the surveyed sample.

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Table 8: Descriptive results
Destination Image and Attractions
Mean
SD
Heritage
Mean
SD
IMA3
Positive destination image
4.52
0.98
HER1
Value of destination’s
cultural heritage
4.54
0.77
IMA4
Expectations prior to the visit
met.
4.38
1.05
HER4
Interesting traditions
4.30
0.97
IMA2
Quality of visit experience.
4.51
0.89
HER3
Artistic heritage
4.20
1.01
NAT1
Region’s natural beauty
4.72
0.72
HER2
Pleasant urban landscape
4.36
0.90
IMA1
Destination brand awareness
4.79
0.73
Average
4.35
0.91
IMA7
Positive word of mouth
4.62
0.87
Information and Communications
Technologies
Mean
SD
IMA5
Preference for the destination
4.26
1.07
ICT1
Coverage of ICT’s
4.29
0.92
IMA6
Intention to return
4.53
0.90
ICT2
Adoption of ICT’s in local
organizations
4.37
0.84
ATR3
Variety of tours
4.47
0.87
ICT3
Applications of ICT’s for
tourists
4.21
0.99
ATR1
Quality of attractions
4.58
0.81
Average
4.29
0.92
ATR4
Variety of recreation offer
4.47
0.93
Average
4.53
0.90
Destination Management and Security
Mean
SD
Transport
Mean
SD
SEC2
Trustworthiness of local
population
3.92
1.14
TRA1
The diversity of
transportation offers to
access the destination.
4.47
0.82
DM4
Political stability in the
destination.
4.04
1.17
TRA2
A network of
transportation services to
access the destination.
4.25
1.00
SEC3
Positive conditions of hygiene
4.13
1.04
TRA3
Quality of local transport
system
4.12
0.97
DM2
Respect for laws and
regulations
3.96
1.12
Average
4.28
0.93
SEC1
Feeling of safety
4.02
1.11
DM1
Cooperation among
organizations to foster tourism
4.14
1.01
DM3
Importance of tourism in the
destination
4.38
0.94
Average
4.08
1.07

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Investigaciones Turísticas
N° 14, julio-diciembre 2017, pp. 1-20 13
Image 1: Competitiveness factors perceived by tourists
V. DISCUSSION
Therefore, from twelve factors related to tourist destination's competitiveness
proposed originally, five were deleted along with the Exploratory Factor Analysis process:
Price & Value, Residents Quality of Life, Human Resources and Infrastructure. Another two,
Attractions and Security, didn't disappear but were merged with other factors, plus one
isolated item from the Nature-Sustainability aspect that was blended with the largest
emergent variable. This leaves five remaining components: Destination Marketing &
Attractions, Destination Management & Security, Cultural Heritage, ICT Adoption and
Transportation (image 1). Remarkably, the most frequently mentioned destination
competitiveness factor quoted in the academic literature, Nature & Sustainability, (table 1)
almost completely vanished.
The most important factor of destination competitiveness turned out to be
Destination Marketing & Attractions, on the meanings considered in the fields of consumer
behavior, destination image, and branding (table 2), in a mixture with Attractions and
natural beauty. This “super factor” concentrates eleven items from three original factors, an
Eigenvalue of 12.78 and 44.30 % of explained variance. The items with stronger load factor
(0.874) were Positive Destination Image, in the sense mentioned by Crouch (2010) Huang &
Peng (2012) and Pike & Mason (2010); expectations prior to the visit met (load factor 0.811)
refer to satisfaction with the visit experience (Go & Govers, 2010; Dupeyras & MacCallum,
2013; Barros et al., 2011) and Quality of visit experience, as proposed by Claver et al. (2007),
Dwyer & Kim (2003) and Go & Govers (2010).

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The item Destination brand awareness (load factor 0.775) belongs to the field of
branding, as has been considered its influence in tourist competitiveness by Crouch (2010),
Gomezelj & Mihalic (2008) and Pike & Mason (2010). The aspects Positive word of mouth,
Preference for the destination and Intention to return, with load factors 0.771, 0736 and
0.726, respectively, have to do with the loyalty of visitors to the place. It is interesting how
the items related to the conative intentions of visitors lined neatly into the factor; these
issues are analyzed by Caber et al. (2012), Hassan (2000) and Pike & Mason (2010) in their
works on competitiveness.
It draws attention the way the items belonging to the original Attraction aspect are
tidily grouped at the bottom of the issues corresponding to the new factor, like if they were
about to generate a different one (table 6); it must be said that Attractions are a subject
mentioned more often in the tourism competitiveness literature than in marketing works. As
its name implies, importance of attractions in destinations derives of their capacity to pull
the visitors, confirmed in the number of authors that quote them as component of
competitiveness: Andrades-Caldito et al. (2012), Cracolici & Nijkamp (2008), Crouch (2010),
Barros et al. (2011), Enright & Newton (2004, 2005), Huang & Peng (2012), Caber et al.
(2012), Gomezelj & Mihalic (2008) and Zhang et al. (2011).
The second new factor is a mixture of destination management and security, issues
that relate directly to tourism policy; Destination management & Security gathers seven
items, four are referred to the first part of the binomial while three connect with the second
portion. It has been mentioned that nowadays security is a major issue in Mexico, which
might help to explain its presence in the remaining factor. Security at the tourist destination
is a basic condition, a prerequisite to the existence of tourism activity in any place, maybe as
essential as the presence of attractions. Likewise, is mentioned by many authors studying
competitiveness (Andrades-Caldito et al., 2012; Blanke & Chiesa, 2013; Caber et al., 2012;
Crouch, 2010; Huang & Peng, 2012; Rodrigues & Carrasqueira, 2011 and Wu et al., 2012).
Destination management, in its relations with strategy, public policies and planning is
also analyzed in an ample amount of competitiveness studies (Dupeyras & MacCallum, 2013;
Dwyer & Kim, 2003; Gooroochurn & Sugiyarto, 2005; Go & Govers, 2010; Gomezelj &
Mihalic, 2008; Enright & Newton, 2005; Huang & Peng, 2012; Zhang et al., 2011 and Wu et
al., 2012). What is different in the results of this study is the association of management with
security, creating a new factor perceived by visitors. From a theoretical point of view, its
emergence confirms the approaches of Presenza et al. (2005) and Ritchie & Crouch (2005):
they differentiate and separate the functions of administration and marketing in
destinations.
Cultural heritage is another factor consistently quoted in the academic literature of
destination competitiveness (Caber et al., 2012; Huang & Peng, 2012; Andrades-Caldito et
al., 2012; Dupeyras & MacCallum, 2013; Dwyer & Kim, 2003; Mazanec et al., 2007; Blanke &
Chiesa, 2013; Barros et al., 2011; Rodrigues & Carrasqueira, 2011 and Carmichael, 2002).
Actually, it has been demonstrated that heritage is more important than natural resources in
terms of tourist attraction and competitiveness: Europe, the continent that received 51 % of
international tourists and 41 % of its receipts in 2014 is mainly a cultural destination (World
Tourism Organization, 2015).
The results of this study confirm the importance of cultural heritage in destination
competitiveness. The component is located in third place among the five remaining and the

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Investigaciones Turísticas
N° 14, julio-diciembre 2017, pp. 1-20 15
four items related to the issue were retained, with load factors going from 0.720 to 0.820;
the results for the variable in the descriptive scale were 4.35, the second highest in the
survey after Destination Image & Attractions (tables 6 and 8). Although Cancun is basically a
Sun and sea destination, the main factor of its international success is the mixture of quality
beaches with the Mayan heritage of Yucatan; there are archaeological ruins in Cancun´s
hotel zone and is decorated with models of ancient sculptures. The town is located nearby
antique Mayan cities like Chichén Itza, Tulum, Xcaret, and Cobá; many autochthonous
traditions persist in the region.
Information and communications technology infrastructure is not one of the most
quoted factors of destination competitiveness in academic literature; in a comparison with
aspects like security, attractions, and cultural heritage, only a few consider it (Blanke &
Chiesa, 2013; Claver et al., 2007; Enright & Newton, 2004, 2005; Gooroochurn & Sugiyarto,
2005; Mazanec et al., 2007 and Wu et al., 2012). In the present survey, the three items
proposed originally for this element persisted, with load factors going from 0.614 to 0.870
(table 6). The Travel & Tourism Competitiveness Index 2009 treats all the pillars and sub-
indices equally and flatly. Processing its data through Bayesian networks, Wu et al. (2012)
identified causal relations among variables, finding that policy and regulations, air transport
infrastructure, human resources, ICT's infrastructure, cultural resources, health, and hygiene
are factors that influence the results presented in the ranking. The results of this study
coincide with the findings of Wu et al. (2012) in that policy and regulations, ICT coverage, air
transport infrastructure, and heritage are factors of destination competitiveness.
Transportation, the fifth factor remaining in the study, is not one of the components
most frequently mentioned in the field's literature; besides, there are different approaches:
some of the works revised refer to air transport infrastructure. Another one analyzes
international transport and some others focus on public transportation (Blanke & Chiesa,
2013; Caber et al., 2012; Huang & Peng, 2012, Wu et al., 2012). Our study considered
transportation in general; the three items proposed were retained, although Quality of local
transport system shows one of the smallest load factors in the survey (0.480, table 6).
At the national level, it can be said that Cancun’s transportation infrastructure is
advanced: the city has the second most important airport in the country, second only to
Mexico City, but receiving more international passengers, with connections to The Americas
and Europe; it has several motorways communicating with the main cities in the peninsula,
substantial presence of rental car agencies, ferryboats, a large fleet of taxis and 24 hours bus
service; most of the visitors, national and international, arrive flying.
VI. CONCLUSION
The review of contemporary academic literature in the field of competitiveness of
tourist destinations showed a wide dispersion among authors in terms of objects and
subjects of study, methodologies, indicators, and factors. Trying to reduce the range, twelve
competitiveness components were chosen among the most frequently mentioned in
academic literature to perform an Exploratory Factor Analysis. The application of the
method reduced the number of factors from twelve to five, although seven of them did not
disappear, but two regrouped.

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The elimination of Nature, sustainability, Price - Value, Human Resources, Residents
quality of life and Infrastructure through an Exploratory Factor Analysis process does not
necessarily mean that these factors do not affect tourist destinations’ competitiveness; it
might also mean that they are not considered or perceived by tourists.
The identification of five tightly integrated factors represents an important advance
in understanding the perception of tourism competitiveness. Among the main findings of
this study are the elimination of the environmental sustainability component and the
perception by visitors of differences between destination's management and marketing, as
proposed by Presenza et al. (2005) and Ritchie & Crouch (2005). The study as well confirms
the findings of Wu et al. (2012) on the importance of policies and regulations, air transport
infrastructure, cultural resources and coverage of ICT as factors influencing competitiveness.
The study was applied to a sample of Mexican tourists in a national sun and sea
destination. It can be replicated in other types of destinations and tourists of different
nationalities to confirm its results. It is as well advisable to investigate the perceptions of
other types of stakeholders not included yet in studies on the subject, like workers in the
hospitality industry and host population. The results and conclusions, by now, are referred
to Mexican visitors; it would be interesting and valuable for the study of competitiveness to
identify the factors noticed by several market segments or travelers of different nationalities
over the same destination. Likewise, another opportunity for research refers to a deeper
analysis of the descriptive results, which we are not performing in this paper due to
extension limitations. The next logical step to advance the understanding of the subject
would be the performance of a confirmatory factor analysis based on the five aspects of
tourist destination's competitiveness identified in this study.
As managerial implications, tourism entrepreneurs and executives of destination
management offices could use the study’s results to act on the factors of competitiveness
perceived directly by tourists in order to improve their performance. Studying different
tourists segments or nationalities will provide them with the knowledge to improve the
competitiveness according to their preferences and characteristics.
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