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Revista Encontros Científicos - Tourism & Management Studies

versão impressa ISSN 1646-2408

Encontros Científicos  n.6 Faro  2010

 

Residential Tourism in the South of Spain: An Approach towards Consumption

 

Pilar Alarcón, Eva M. González and Javier R. Pérez

1PhD, Catedrático de E.U. en Comercialización e Investigación de Mercados, Universidad de Málaga. palarconu@uma.es

2PhD, Profesor Contratado Doctor, Universidad de Málaga.

3PhD, Profesor Asociado en Organización y Dirección de Empresas, Universidad de Málaga

 

ABSTRACT

This paper contributes to the residential tourism literature by analyzing possible influences on the choice of length of stay, since this factor is the main variable used in conceptualization studies to differentiate the tourism classification of the activity. Based on a bibliographical review and on the World Tourism Organization definition of tourism, this paper establishes a primary starting point. Then, a structural equation model is adopted to measure the influence of product features and destination image on the length of stay decision. The scope of this study is the British and Nordic generating markets, and the Andalusia region of southern Spain as destination market. The findings suggest that, while perception of product features directly affects the length of stay decision, this election appears to be less influenced by destination image; however, this decision is nevertheless indirectly influenced by destination image.

Keywords: Second Home, Residential Tourism, Andalusia, Consumer Behavior

 

Turismo Residencial no sul de Espanha: uma abordagem referente ao consumo

RESUMO

Este artigo dá um contributo à literatura sobre o turismo residencial ao analisar possíveis influências na escolha da duração da estadia, já que este factor é a principal variável usada em estudos de conceptualização para diferenciar a classificação turística da actividade. Baseado numa revisão bibliográfica e na definição de turismo da Organização Mundial de Turismo, este artigo estabelece um ponto de partida. Seguidamente é adoptado um modelo de equação para medir a influência das características do produto e da imagem do destino na decisão da duração da estadia. Os alvos deste estudo são os mercados emissores britânico e nórdico e, como mercado receptor, a região da Andaluzia e o sul de Espanha. Os resultados sugerem que, enquanto a percepção das características do produto influencia directamente a decisão da duração da estadia, esta escolha parece ser menos influenciada pela imagem do destino. Contudo, esta decisão é, apesar disso, indirectamente influenciada pela imagem do destino.

Palavras-Chave:  Segunda Residência, Turismo Residencial, Andaluzia, Comportamento do Consumidor  

 

1. Introduction

According to the World Tourism Organization (WTO), "tourism covers the activities of people during their travels and stays in places other than their usual environment for a period of time less than a consecutive year, for leisure, business or other purposes" (WTO, 1995: 1). This definition has lead to debate among those studying residential tourism, the conceptualization, for example, that there is no unique criterion to distinguish residential tourism. Within the residential tourism industry, what in some forums (academic or institutional) is considered a tourism activity is not considered so in others; yet in some sectors residential tourism is still not understood as a form of tourism accommodation. This article studies residential tourism from a consumer behavior point of view. Such an approach provides a very important perspective on residential tourism: that of the consumer behavior.

As a starting point, we develop a definition of residential tourism that reflects published work, depending on whether residential tourism is regarded as a tourist activity. This study distinguishes between several distinct groups based on annual duration of stay at the chosen destination and level of engagement. Our approach is rounded out with a classification of residential tourism as a tourist activity depending on the time spent at the destination and the level of consumption. The residential tourism conceptualization debate tends to hinge on its classification as a tourist activity or a non-tourist activity. The former is classified as second-home tourism, that is, less than six months per year in one destination, and the latter is classified as residential tourism, that is, more than six consecutive months per year in the same destination. These temporal limitations, based on the WTO definition, do not distinguish among consumption profiles. The aim of this study is to provide sufficient evidence to add to the conceptual definition of residential tourism from the perspective of consumption.

 

2. Literature Review

The first appearance of the term residential tourism in the literature occurs in Regionale Aspekte eines skandinavischen Problems, (Taubmann, 1973), in which an approach is set forth to the phenomenon in the Scandinavian countries. There have since been many and varied approaches to residential tourism, for example, as the marginal tourist (Cohen, 1974), as ‘other tourist’ typologies (Coppock, 1977), as an activity with tourist aspects (Jaakson, 1986), as an activity closely associated with real estate (Mazón, 1987), as artificial municipal revitalization (Bertolín, 1983), and as an example of a model of irrational, wasteful, and contradictory dynamics in urban politics (García, 1983). Another tendency in the literature stresses the owner of the second home; this is the tourist who forms an important segment of domestic tourism anywhere in the world (Waters, 1990). These early considerations were critical with respect to policy toward the new phenomenon, clearly influencing the lack of planning or political measures to regulate its growth. More recent studies focus on analysis of its characteristics. Furthermore, during the past thirty years, two schools of thought have emerged, one that considers residential tourism partly a tourist activity (García, 2005; Raya, 1999; Tress, 2002), and another that does not (García, 1983; Mazón, 1987; Tonda, 2003; Torres, 2003).

For their conceptual delimitations, both schools of thought use the definition of residential tourism proposed by the Statistical Commission of the United Nations and the WTO; this definition serves as a basis for the configuration of tourism statistics in each member country. According to the WTO, tourism comprises the activities of persons traveling to and staying in places outside their usual environment for not more than one consecutive year, for leisure, business, and other purposes not related to the exercise of an activity remunerated from within the place visited. Closely examining both schools of thought, we see, on the one hand, some approaches that do not consider residential tourism as a tourist activity. In such cases, the arguments relate to the time spent at the destination and the type of accommodation used; but the arguments differ from one author to another.

For example, one type of hypothetical tourist is not included in the universe defined by WTO: one who changes residence to a tourist destination (Torres, 2003: 49). This author defines residential tourism as one characterized by people normally grouped into family units, who for given periods of time move their residences to certain areas, usually traditional tourist destinations, for long periods of time through any form of property connection, realizing longer stays than traditional tourists and even setting their habitual residence in the destination. This point of view is shared by Tonda (2003: 71), who maintains that from the real estate point of view, residential tourism is radically different from conventional tourism in many respects. That is, the term “tourism” implies connotations of “temporary stay” and “mobility” (outside the residence phenomenon) which have nothing to do with the term "residential."

However, there are opposing tendencies in the literature that regard this phenomenon as fundamentally a tourist activity. For Tress (2002: 110), residential tourism is defined as the recreational use of second homes by owners, friends, or tourists who rent them. This notion covers all types of relationships, behaviors, and actions that result from the trip and the stay in the accommodation. Furthermore, within this school of thought, other authors assume that the permanent use of second homes is not considered a tourist activity, but that all groups of users of second homes who travel from their main homes to second homes are tourists (Gartner and Girard, 1993; Jaakson, 1986; Taubmann, 1973; Tress, 2000, 2002). Following this tendency, and based on the WTO definition, Raya (2002: 70) sees residential tourism as a form of tourism, arguing that the residential tourist moves to a different place from his usual environment, usually spending less than 12 months at the destination, and the main reason for the visit is not engagement in a paid activity; leisure, fun, and quality of life are the generally given reasons for such visits. This idea is shared by García (2005), in which the conceptualization of the phenomenon is explored exhaustively and it is concluded that a stay at a second home for less than six consecutive months is to be understood as tourism, regardless of recidivism throughout most of the year will be considered a tourist activity, when not repeating the destination.

These schools of thought in the literature show that a consensus on the definition of residential tourism is still distant. Conceptual debate is the reality. The authors who have conceptualized residential tourism differ from those associated with the idea of the use of housing as a tourist activity (García, 2005; Gartner and Girard, 1993; Jaakson, 1986; Raya, 1999; Taubmann, 1973; Tress, 2002) and from those who see it as a form of tourist accommodation (García, 1983; Mazón, 1996; Salvá, 1990; Tonda, 2003; Torres, 2003). In this study, following this latter tendency in the literature (García, 2005; Raya, 1999; Tress, 2002), residential tourism is characterized by the use of housing as accommodation, either owned or rented, and is considered a tourist activity if and only if the stay at the destination is less than six consecutive months per year. In this particular case, this activity is also known as second-home tourism.

 

3. Consumer Decisions on Time Spent at Destination

Conceptualization studies are based primarily on temporal considerations, not been assiduously collected studies of other tourist behavior characteristics. As discussed above, residential tourism differs in its characteristics from other tourist activities, because it is not homogeneous. The defining characteristics of residential tourism stem from consumer behavior. The principal characteristic is the strong link to the destination (Raya, 1994; Torres, 2003). The second-home tourist’s life becomes integrated in the everyday life of the destination, due to the high frequency of visits (Müller, 2002: 444). Such linking to a destination, usually a tourist destination, will differ for a residential tourist compared to a second-home tourist (Torres, 2003: 54). Thus, the principal difference between one activity and the other is the degree of subordination to destination, which is stronger in the residential tourism case. The considerations outlined by Raya propose a distinction between residential tourism and second-home tourism, based on the length of stay at the destination (Raya, 1994), which is consistent with the classification of Torres. Shorter stays are characterized as visiting a second home or residence, while longer stays are defined as residential tourism (Torres, 2003).

This study addresses several key characteristics of residential tourists. With respect to residential tourist income received from outside the destination, an important distinction is between those who keep their pensions and do not lose their resident status at the place of origin (second home tourists), and those who lose that status by moving away for more than six months, becoming in-residents (residential tourists). On the availability of free time: free time arises from various factors, mostly associated with work/study status, for example, being retired, self-employed, or a student. Several studies on the phenomenon of free time focus on professional status (Haldrup, 2004; Raya, 1994, 1996). The availability of free time and the strength of the link to a destination are directly related, as the more time available, the greater the tendency toward being a residential tourist. On the high average age: most people with the option of changing their place of residence to a tourist area or its environs have an average age of approximately 50. The residential tourist is typically between 46 and 56 years (Raya, 1996). Furthermore, among individuals who spend more than six months in a destination and those who do not, the first group (residential tourists) tends to have a higher average age than the second (second-home tourists).

 

4. Data Source and Methodology

Our analysis of the literature leads us to study the amount of time spent by tourists at a destination, whatever the type and use (tourist or not) of the housing. To do this through a descriptive and causal analysis, we separately profile each of the two visitor groups, second home tourists and residential tourists. In this way, we seek to describe and understand the behavior of tourists, in order to define residential patterns of choice, and then to look for differences in such choices. Further, the influence of destination image and product features on time spent at the accommodation is analyzed, specifically with respect to how weightings of product features and the characteristics of the destination influence visitor choice. We propose two hypotheses that attempt to identify the proposed relations:

H1: There is a relation between destination image and the decision on the amount of time to be spent at the destination.

H2: There is a relation between the perception of product features and the decision on the amount of time to be spent at the destination.

Data Collection in the Studied Region

The British and Nordic generating markets and the Andalusia region of southern Spain, the destination market, form the scope of this study. These markets were chosen because Britain was the first significant generating market in Andalusia, amounting to 11 percent of all tourists and 30 percent of foreign tourists (non-Spanish) in 2007 (Instituto Nacional de Estadística, 2008). Together the Nordic countries (Sweden, Norway, Finland, and Denmark) form the fourth largest market source; 14.60 percent of Nordic visitors to Spain in 2006 chose Andalusia as their destination. Furthermore, in recent years the Nordic market has shown growing interest in Andalusia. This situation enables a comparison between a consolidated market (Britain) and an emerging market (the Nordic countries); various consumer behavior characteristics can thus be analyzed. Andalusia, specifically the coastline municipalities, is an example of the phenomenon of residential tourism. This has been occurring internationally, but as a template of destination characteristics, the region is regarded as sufficiently representative to test general models. To collect the most representative data possible, the data were collected over an entire year, from June 2006 to July 2007, in many different geographic points of the region. Mijas, Torremolinos, Marbella, and Malaga airport were the most significant information collection points; the airport serves as a gateway for the visitor nationalities surveyed, accounting for 73.3 percent of all entries to the region in 2006. Mijas, Torremolinos, and Marbella are the main municipalities attracting residential tourism in the region. The collection methods employed included personal interviews and self-administered questionnaires by hand.

The final sample size consists of a total of 342 family surveys (estimation error ± 5.29 percent) conducted on 241 British tourists (70 percent of the total sample) and 101 Nordic tourists,(30 percent of the total sample). The sampling technique is proportional stratified random analysis. The proportionality is explained by the behavior of the source markets analyzed. Taking the sample analyzed to be 100 percent of the population and studying the monthly private accommodation demanded during 2007, a proportional sample is obtained. While the Nordic visitors totaled close to 30 percent (27.60 percent) of the nights demanded in Spain, the British totaled close to 70 percent (72.40 percent). Specifically, according to the Spanish National Institute of Statistics (Instituto Nacional de Estadística, 2008) 28,487,372 nights were demanded by British and Nordic visitors to Spain in 2007, 20,623,504 by the British (72.40 percent) and 7,863,504 by the Nordics (27.60 percent). Considering that the population is divided into two groups of nationalities, we opted for the stratified sampling method. Stratified sampling is one type of random sampling method (Sánchez-Crespo, 1976). Random sampling includes in the sample single elements of the population with equal chances for each element, while stratified sampling is used when the population is divided into several strata. It is indisputable that the latter method has definite advantages both from the point of view of the accuracy of results and of the ease of performing the sampling (Neyman, 1934).

Descriptive Analysis

First, a descriptive analysis of the sample is developed; this highlights any differences among the profiles described. The tourist profile: A person (regardless of gender), married in most cases, irrespective of having dependent children or not, with an average age between 56 and 65 years, with secondary studies, self-employed or retired, who spends less than six months in the destination, with a single family or semi-detached house at the origin, and strongly linked to the destination. The main differences between residential tourists and second-home tourists relate to age and its associated characteristics. While in the case of the second-home tourist there is a tendency toward a younger average age, implying a greater number of individuals with dependent children and professional employment status, the residential tourist shows higher levels of non-dependent children and self-employed or retired status. House characteristics: More differences between the two profiles are observed with respect to house characteristics. When second-home tourists were asked for their preference of stay, most answered at the beach or as close as possible to the seaside, whereas residential tourists sought other locations, highlighting the countryside over other places. With regard to the typology of housing, second-home tourists seek apartments, while the residential tourist, who spends more time at the location, prefers the house typology, regarding this housing type as more comfortable for long stays. The type of housing also shows statistically significant results. Thus, while the second-home tourist is not characterized by ownership over other forms of property tenure (rented houses or houses of friends and relatives), residential tourists are clearly associated with home ownership. Finally, it is interesting to compare these conclusions with the literature review findings, matching them with the differentiation among housing types observed. These findings reinforce the idea that second-home tourists gravitate toward forms of tourism, while the residential tourist tends to become a resident. Once the literature review was performed and the descriptive analysis obtained, we opted for multivariate techniques of data analysis for developing the different contrasts, particularly structural equation models. The tools used to develop the contrasts include the SPSS version 15.0 and the Analysis of Moment Structures version 16.0 for Windows.

Study Methods

Using differentiation in time spent at a destination as the starting point, this study contributes to the literature by providing a theoretical enhancement of the current level of knowledge on residential tourism, allowing a better understanding of the behavior of the two studied groups. Since it is becoming widely used in travel behavior research (Golob, 2001: 495), for this analysis we selected a structural equation model. A structural equation framework allows the simultaneous consideration of dependencies over time, between individual indicators (e.g., numbers of trips and tours, trip durations, and distances, etc. (endogenous variables)), and the effects of spatial, sociodemographic and cost characteristics (exogenous variables) (Weis and Axhausen, 2009: 3).

The model tested in this study is adapted from Lee (2009: 205). It proposes that destination image, attitude, motivation, and satisfaction affect future tourist behavior, since these variables frequently indicate future behavior and tourist loyalty (Baker and Crompton, 2000; Bigné et al., 2001; Cai, Wu, and Bai, 2004; Lee, Yoon, and Lee, 2007). In this case, the model is formed by two exogenous variables, destination image and product features, with the decision on time spent at destination as the future tourist decision. Destination image variables include weather, people, culture, price, entertainment, transport, supply quality, beach, landscapes, natural spaces, urban spaces, security, quality–price, and product features. Product features variables include environmental impact, security and cleaning, Spain’s level of economic development, own country level of economic evolution, municipality size, housing typology, location, and price. The items designed to measure destination image are based on published studies on this issue (Bigné, 2001; Beerli, 2004; Castro, 2007; Court, 1997; Lee, 2009). Due to the impossibility of obtaining previous models of second‑home product features, these items are based on various studies of housing demand (Barrios, 2008; Brigham, 1965; Colom, 2003; Gelfand, 2004; Gili, 2003; Jaen, 1994).

A structural equation model is designed to evaluate how well a proposed conceptual model that contains observed indicators and hypothetical constructs explains or fits the collected data (Bollen, 1989a, 1989b; Hoyle, 1995; Yoon, 2002). It also provides the ability to measure or specify the structural relationships among sets of unobserved (latent) variables, while describing the amount of unexplained variance (Byrne, 1998; Davies, Goode, Mazanec and Moutinho, 1999; Hoyle, 1995; Tuner and Reisinger, 2001). Since the observed values for all variables follow a nearly normal distribution with respect to skewness and kurtosis, the sample size is over the recommended limits of 100 to 200 individuals and there is an even greater number of observations per parameter than recommended (five comments recommended setting). Furthermore, the maximum likelihood method of estimation is used to analyze the data (Anderson and Gerbing, 1988; Fraley, 2002), yielding an unbiased and efficient estimator, and determining the statistical significance of the coefficients.

A construct, usually called a latent variable, is a hypothesized and unobserved concept that can be measured only by observable or measurable variables (Bollen, 1989a; Hair, Anderson, Tatham, and Grablowsky, 1998; Yoon, 2002). The measurement variables or scales are collections of items intended to reveal levels of theoretical variables to measure the construct. Concretely, our model is formed by two constructs, one for product features (internal and external features) and the other for destination image. Accordingly, the measurement scales, included in the constructs, are developed to measure phenomena that are believed to exist because of a theoretical underpinning or observations, but that cannot be accessed directly. As a result, this measurement enables assignment of numerals to objects, events, or observable phenomena with different degrees of a quality or properties (Duncan, 1984). The measurement scales for this study were developed based on the relevant literature and theories, previous empirical studies and observations, and experiences of the given phenomena. Thus, the empirical results of the structural model examine the impacts of product features and destination image (latent exogenous variables) that play a role in forming the time spent at destination decision (endogenous variable). These factors are formed by eight observed variables or indicators; six are arithmetic means of others. Next, the measurement model is analyzed; this involves examination of the validity and reliability of the scales, which is done by examining the skewness and kurtosis of the variables (Byrne, 1998; Kline, 1998). Once validity and reliability is assured and the number of parameters to estimate is obtained, the proposed model is evaluated.

 

Figure 1. Path model

 

Measurement Scales and Instruments

The fit of the measurement scales and of the structural model are determined by examining chi‑square statistics (X2), the Normed-Fit Index (Yoon 2002), the Comparative Fit Index (Bentler, 1990; Gursoy, 2003), the Root Mean Square Error of Approximation (Yoon, 2002), and the Incremental Fix Index (Gursoy, 2003; Mulaik, Alstine, Bennett, Lind and Stilwell, 1989). The values of Root Mean Square Error of Approximation, Incremental Fix Index, Comparative Fix Index, and Normed-Fit Index range from 0 to 1.00, with a value close to 1.00 indicating a good fit (Gursoy, 2003; Mulaik et al., 1989). Normality is tested by analyzing skewness and kurtosis, skewness values up to 2.00 and kurtosis values up to 7.00 indicate a non-normal distribution (Curran, West and Finch, 1996). As another general model evaluation fit index, Hoelter (1983), proposes a cutoff of 200 or greater as an indication of an adequate model fit for the critical N statistic (Gursoy, 2003; Hoelter, 1983; Yooshik, 2002). In this case, the results of skewness and kurtosis for each scale of measurement are presented in the table below (Table 1).

 

Table 1. Skewness and kurtosis values for the components

  First Component

Minimum

Maximum

Mean

Std. Dev.

Skewness

Kurtosis

Weather

1

5

4.22

1.203

-1.670

1.780

People

1

5

3.44

1.233

-.553

-.610

Culture

1

5

3.38

1.192

-.462

-.507

Price

1

5

3.57

1.287

-.681

-.543

Entertainment

1

5

3.11

1.281

-.174

-.984

Transport

1

5

2.81

1.275

.120

-1.016

Supply Quality

1

5

2.86

1.214

.013

-.923

Beach

1

5

3.30

1.359

-.349

-1.022

Landscapes

1

5

3.36

1.277

-.478

-.719

Natural Spaces

1

5

3.23

1.259

-.265

-.894

Urban Spaces

1

5

2.88

1.183

.015

-.718

Security

1

5

3.44

1.162

-.482

-.495

Quality–Price

1

5

3.65

1.117

-.663

-.140

 

             Second Component

Minimum

Maximum

Mean

Std. Dev.

Skewness

Kurtosis

Environmental Impact

1

5

3.11

1.128

-0.146

-0.626

Security and Cleaning

1

5

3.28

1.252

-.339

-.918

Spain Economic Evolution

1

5

3.34

1.116

-.311

-.554

Own Country

Economic Evolution

1

5

3.23

1.190

-.266

-.766

Municipality Size

1

5

2.96

1.170

.002

-.813

Housing Typology

1

5

3.68

1.104

-.559

-.387

Location

1

5

4.11

1.039

-1.427

1.919

Price

1

5

4.06

1.112

-1.216

.886

 

Both ordinal scales show that all variable distributions are observed to be close to a normal distribution, not finding skewness values up to 2.00 and kurtosis up to 7.00. These results suggest that the variables used in our analysis do not diverge significantly from the normal distribution. Therefore, the variables are considered to be moderately or almost normally distributed. Other indicators studied include the reliability and validity of scales. Reliability is essential in any measurement scale and it is usually measured via internal consistency. It is recommended to use the Cronbach’s Alpha, which ranges between 0 and 1.00, with a value close to 1.00 indicating a greater internal consistency of the items that compose the scale. However, when interpreted as a correlation coefficient, there is widespread agreement on the value at which a scale can be regarded as reliable. In this case, we follow the assumptions of George and Mallery (1995): if Alpha is greater than 0.9, the measurement instrument is excellent, if it is in the 0.9–0.8 range, it is good, if between 0.8 and 0.7, the instrument is acceptable, in the range of 0.7–0.6, it is weak, between 0.6 and 0.5, it is poor, and if it is less than 0.5 it is not acceptable.

In the first scale, a Cronbach’s Alpha coefficient of 0.866 is obtained, indicating a quite acceptable, reliable, and suitable scale for analysis. In the second scale, reliability is also quite acceptable (0.709), adequate, and appropriate for the analysis. There is no item whose removal increases the internal consistency of the scales, although, as shown by item-scale correlations, the item quality–price is shown as the least reliable item of the scale. Both scales show good indices of reliability, and no item shows a correlation coefficient–item scale below 0.35. Furthermore, the elements with a low level of item–scale correlation do not affect the internal consistency of the scale. Ultimately, it adds a component related to medical treatment, consisting of two observed variables. Since there are only two observed variables, it is not possible to perform any validation process, principal components analysis, or confirmatory factor analysis, leaving verification of the structural assessment and measurement to the study of the structural model overall adjustment.

Validity of the Scales

There are two types of validity measurements discriminant validity and convergent validity. Content validity reflects a measurable degree of the different aspects of the phenomenon to be measured. In general, it is accepted if the scale was developed from the existing theories in the literature about the phenomenon analyzed (Davila and Romero, 2007: 2826). In this study, face/content validity is addressed by acquiring information from tourists and statistical experts who are familiar with the concepts and content of residential tourism. For criterion-related validity, concurrent validity is assessed by examining the Pearson relationship and multiple regressions between the measurement scale and the criterion variable. In order to test the concurrent validity of each measurement scale, the variables are included and measured in the survey questionnaire, using a Likert scale.

 

Table 2. Principal components analysis

    First Component

1

2

3

Culture

.754

 

 

People

.740

 

 

Weather

.695

 

 

Beach

.540

 

 

Urban Spaces

 

.744

 

Landscapes

 

.624

 

Natural Spaces

 

.622

 

Transport

 

.599

 

Security

 

 

,500

Supply Quality

 

 

,465

Entertainment

 

 

,701

Quality–Price

 

 

,695

Price

 

 

,524

 

    Second Component

1

2

Localization

.657

 

Price

.640

 

House Typology

.623

 

Environmental Impact

.610

 

Security and Cleaning

.574

 

Municipality Size

.499

 

Own Country Economic Evolution

 

.710

Spain Economic Evolution

 

.603

Extraction method: principal component analysis.

Rotation method: varimax with Kaiser normalization.

 

The principal components analysis of the first component (destination image) shows the presence of three factors. The first, qualities of the destination, explains 38.50 percent of the variance; the second, attitudes toward the environment, 10.10 percent of the variance; and the third, tourism attractions, 8.30 percent. The structure of these three factors and the factors of the second component (Table 4) are discussed below. In the second component (product features) case, the principal components analysis supports the existence of two dimensions responsible for 48.77 percent of the variance; the first specifically explains 33.19 percent of the variance and the second 15.57 percent of the variance, with a Cronbach’s Alpha of 0.706; so in this first approach, any element of the scale has been eliminated. This analysis provides a measure of internal consistency for the scales, as it does not show a low significant correlation of items within them. Thus, any item of the scales is dispensed for the rest of the analysis. Furthermore, on analyzing the reliability of the three components of the first scale, it is found that in all cases the measures are above 0.7; specifically, the first component is 0.738, the second component is 0.742, and the third component is 0.725. Hence, the resulting scales are reliable. The reliability analysis of the second scale gives us a measure of the internal consistency of the scale, showing a low significant correlation for all items. Furthermore, analyzing the reliability of both components, internal features and economic influences, of the second scale reveals that the measures are above or near 0.60; namely, the first component is 0.690 and the second is 0.597. Thus, the resulting scales meet the criteria (George and Mallery, 1995).

 

Table 3. Measurement model properties (n = 342)

Constructs and Indicators

Completely Standardized Loadings

 

 

 

Critical Ratio for Regression Weight

 

Indicator Reliability

 

 

 

Destination Image

Destination Qualities

Environmental Perceptions

Tourism Attractions

Medical Treatment

Product Features

Internal Factors

External Factors

Decision

Before Visits

This Visit

 

0.765

0.642

0.76

0.48

 

0.803

0.402

 

0.568

0.444

 

a

10.676

12.168

7.57

 

a

4.374

 

a

2.016

0.75b

0.585

0.413

0.578

0.62b

0.69

0.323

0.486

0.64b

0.603

0.497

a Value set.

b Composite reliability of each construct.

 

Table 4. Structural model discriminant validity

 

r2

 

 

D

PF

DC

EV

PF

0.128

1

 

0.454

DC

0.0081

0.196

1

0.521

 

In summary, there are three components in the destination image scale. The first is associated with qualities of the destination; the second is formed by items related to attitudes toward the environment; while the third shows the tourism attractions. With respect to the product features scale, two components are obtained, the first collects internal features and the second collects the economic influences. Construct validity is assessed through the structural equation modeling process. Specifically, the convergent validity is assessed in the measurement model by confirmatory factor analysis, by estimating t-tests of factor loadings, as well as the corresponding significance (Anderson and Gerbing, 1988; Bagozzi and Philips, 1982; Yooshik, 2002).

Structural Model

Due to the large number of indicators of the model, each dimension is measured using an indication obtained from the average parameters in each scale with which it is associated (Mackenzie, Scott, Podsakoff and Ahearne, 1998; Martín, 2004), by using a smaller number of variables and improving the parsimony of the results (Babin and Boles, 1998). A series of nested structural models was tested to identify the best model for the study. After this assessment, sequential chi-square difference tests are conducted to provide successive fit information (Anderson and Gerbing, 1988). The results indicate that the constrained model is better. As a result, the proposed theoretical model was rejected and the constrained model was accepted as the best fit. In the constrained model, four of the 25 proposed paths (parameters) are fixed at zero (eliminated).

The results indicate that the proposed measurement model has both discriminant and convergent validity. The overall fit of this final measurement model is chi-square 37.724, 18 free degrees, Root Mean Square Error of Approximation = 0.056, Non-Central Parameter = 19.274, Normal-Fit Index = 0.931, Comparative Fit Index = 0.962. To assess discriminant validity, the relation between the reliability correlation square and the variance extracted is studied. In this process it is found that a correlation square of the reliability is less than the variance extracted. This technique is widespread in sociology studies (Martín, 2001; Martín, 2004). In our scale, in all cases, it is verified that Extract Variance > Squared Correlation, thereby fulfilling the criterion of discriminant validity.

After this process, the measurement model that describes the predefined relationships of the variables is finally obtained. The relations between the observed and latent variables are assessed using confirmatory factor analysis, and the model is presented as general in nature. The most prominent effects are those produced by product features with respect to the decision (t-value 12.05), which is the most direct connection and which enables support for the proposed model partially in line with the hypothesis. Regarding the destination image effects, the influence on product features is t-value 0.56 and over decision making, -06.49.

 

5. Results and Discussion

The findings discussed above render the hypotheses as follows:

For H1, the result of structural equation model analysis indicates that the paths from the construct of the product feature perceptions and the construct of time spent at the destination are significant and positive (t-value = 12.05, p < 0.01). This result supports the notion that if a residential tourist perceives the product features positively, the residential tourist would like to spend more time at the destination. Specifically, these results confirm that factors from the internal point of view, such as product price, location, housing typology, security and cleaning, and municipality size; and from the external point of view, such as the economic evolution of the destination country and the country of origin affects decision making regarding time spent at the destination. In fact, this finding is consistent with findings in the literature (Raya, 1994, 2003). Researchers have demonstrated that if people perceive better product features such as location or house typology, they tend to become residents.

H2 addresses the connection between destination image and preferences about time spent at the destination. However, the result of structural equation model analysis does not support H2; t-value is -06.49, which is not statistically significant at the 0.01 level. This finding suggests that destination image does not affect the amount of time spent by residential tourists at the destination. More specifically, this study indicates that destination image does not influence preferences or time spent at the destination. However, the result of the structural equation model analysis supports an indirect relation between the construct’s destination image and product features having a positive relation (t-value = 0.56, p < .05). Accordingly, this finding suggests that if residential tourists have a positive perspective on destination image, such as environmental attitudes, attraction, destination quality, and medical treatment, they would have a more positive perception of residential tourism product features. Additionally, since the destination image construct is indirectly related to time spent at the destination, it can be argued that the decision on time spent at the destination of residential tourists is indirectly influenced by destination image.

 

6. Conclusion

First, throughout this study, a large discrepancy in the definition of the phenomenon of residential tourism is evident. What for some authors is considered residential tourism is not so for others, with some not acknowledging residential tourism as a tourism activity at all. As a consequence, there are diverse studies defining and describing the phenomenon and differentiating between residential tourism and second-home tourism. Moreover, the absence of overall schools of thought and theoretical frameworks only worsens this situation. In response to this state of affairs, this article provides a definition for the concept and establishes a starting point for the study of residential tourism. Residential tourism is characterized by the use of housing as a form of accommodation, whether owned or not, and is regarded as a tourist activity if, and only if, the time spent at the destination is less than six consecutive months per year. In this particular case, this activity is known as second-home tourism.

The greatest differences in tourist profiles are found with respect to age and the factors associated with age. While in the first case (second-home tourism) there is a tendency toward young age, implying a greater number of individuals with dependent children and a career as an employee, the second (residential tourism) is associated with higher levels of non-dependent children and a self‑employed or retired professional category. Throughout the study of this sample and its distribution, it is found that different product characteristics are demanded by each typology. Since it is an activity linked to the use of housing, it seems interesting to intensify scrutiny of the time spent at destination decision. We must emphasize the different results obtained when comparing the product characteristics demanded for a residential tourist compared to a second-home tourist. In this sense, for the variables analyzed in relation to the housing demanded (type, typology, and location), the greatest differentiation between the residential tourist and the second-home tourist is the coastal accommodation preference of the latter (most responses were “at the beach” or “as close to the seaside as possible”), while the residential tourist prefers other residential locations, highlighting the interior and evenly divided among the remaining location types. With regard to the typology of housing demanded, the second-home tourist prefers the apartment typology (non-single family), while the residential tourist, who spends more time at the destination, demands the villa typology (single family), which is more comfortable for longer stays. The type of housing tenure (owned or rented) also shows statistically significant results, and while the second-home tourist is not predominantly a home-owner compared to other possibilities (rented accommodation, friends and relatives, etc.), in the case of the residential tourist, there is a clear preference for home ownership. These findings allow us to note how the differentiating characteristics match with the literature review; this comparison reinforces the idea that second-home tourist activity meets the definition of tourism, while the residential tourist tends to become a resident. This evidence and its relevance for urban planning should be considered by planning authorities in destination regions. Local authorities could distinguish between products (house typologies) and their users (tourists or residents), and as a result, improve the efficiency of resource utilization.

With respect to the positive influence of product features, such as price, typology of housing, location, environmental impact, security and cleaning, municipality size, own country and Spanish economic evolution, in making a decision on time spent at the destination, numerous questions about the perception of these product values when making a decision were posited to the survey respondents. These questions relate to aspects of the product and are intrinsic to the destination, that is, inseparable features of the location. The results show, in relation to the sample distribution, that there is a positive relationship between product features and the time spent at destination decision. This conclusion is not unexpected, since it relates to an essential product that is basic during the holiday period, so its attributes acquire great importance in an election. The consumer’s perception of the destination image is also examined. On this issue, the findings do not show a positive connection between perceptions of the issues addressed, such as quality of the destination, environmental attitude, attraction, medical treatment, and the decision on time spent at destination. However, an indirect connection between destination image and the time spent at destination, through the perception of product features, was established, indicating less pronounced levels of influence among the two constructs. Therefore, the absence of influence between destination image and time spent at the destination are evident; however, these characteristics affect the perception of product features directly related to that decision. In this manner, this approach to the time spent at destination decision highlights the importance of product features in consumer decision making. This study allows us to differentiate, from a consumer perspective, between two different realities in residential tourism, a residential consumer and a second-home consumer, who each demand different products depending on the frequency of visits and the time spent at destination.

 

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Submitted: 25.07.2010

Accepted: 28.10.2010