Next Article in Journal
Modeling of Biofuel’s Emissivity for Fuel Choice Management
Previous Article in Journal
An Evaluation of Historical Trends in New Mexico Beef Cattle Production in Relation to Climate and Energy
Previous Article in Special Issue
Penyagolosa Trails: From Ancestral Roads to Sustainable Ultra-Trail Race, between Spirituality, Nature, and Sports. A Case of Study

Sustainability 2019, 11(23), 6841; https://doi.org/10.3390/su11236841

Article
Residents’ Negative Perceptions towards Tourism, Loyalty and Happiness: The Case of Fuengirola, Spain
1
Department of Economics and Business Administration, Campus de El Ejido 6, University of Malaga, 29071 Malaga, Spain
2
Department of Applied Economics (Statistics and Econometrics), Facultad de Comercio y Gestión, University of Malaga, Andalucía-Tech, Av. Francisco Trujillo Villanueva, 1, 29071 Málaga, Spain
*
Author to whom correspondence should be addressed.
Received: 16 September 2019 / Accepted: 25 November 2019 / Published: 2 December 2019

Abstract

:
Current studies suggest focusing on the study of residents’ negative perceptions towards tourism. This study estimates a confirmatory model of structural equations based on variance. The negative perceptions of residents living in Fuengirola (Spain), divided into three dimensions (negative economic impact, negative social impact, and negative environmental impact), according to social exchange theory, and, also the residents’ loyalty to their city are considered to be predictors of residents’ happiness. We have proposed a construct of residents’ loyalty to their tourism destination residence as a novelty, being composed of two indicators. The model has been computed based on partial least squares-structural equation modeling. The following hypotheses have been tested: (a) negative perceptions have positive effects on each other; (b) negative perceptions have a negative effect on happiness; and, (c) residents’ loyalty has a positive effect on happiness. The hypotheses were not rejected. Moreover, the loyalty of the residents has the highest direct positive impact, while the negative impacts have weak negative effects, direct and indirect, on the happiness of the residents. The result of the positive relationship between place loyalty and happiness referred by those persons who perceive their living place (neighborhood) as prestigious is consistent with our findings, which is, an increase of loyalty was found to be associated with an increase in happiness.
Keywords:
Tourism; residents’ perceptions; residents’ loyalty; place loyalty; happiness; Fuengirola; partial least squares structural equation modeling (PLS-SEM); SmartPLS; social exchange theory (SET)

1. Introduction

Tourism is a very important sector in the economy of many countries, regions, cities, or places. In relation to Spain, in 2018, the tourism and travel sector contributed EUR178 billion to the economy of Spain (EUR 1 in every EUR 7) with a share of 14.6% of the gross domestic product and 14.7% of all jobs. The Spanish tourism sector is split 55% vs. 45% in terms of international and domestic visitors [1]. In the first half of 2019, more than 38 million people visited Spain, according to the survey of Border Tourism Movements (known as FRONTUR); and, as of June, more than EUR 40,000 million were spent, according to the Survey of Tourist Expenditure (known as EGATUR). The National Statistics Institute (INE) in Spain publishes both of the surveys.
According to the official estimates data of the Andalusian Tourism Situation Survey (Statistical and Mapping Institute of Andalusia), this region received 30.6 millions of tourists in 2018, comprised 49.6% of foreigners, with the remainder domestic tourists. The main motivations are holidays, which account for 83.8% and hotels accommodation, representing 63.2%. Ports and nautical activities along with landscapes and natural parks were the aspects best evaluated in Andalusia by tourists. The Province of Malaga (Andalusia, southern Spain, and southern Europe) accounts for 31.4% of the total number of tourists received. This region is a consolidated international tourism destination. The provinces Malaga and Cordoba are the best evaluated with a score of 8.6 from 10 [2]. In spite of the relevance of studying the Spanish destination of western Costa del Sol (Malaga), due to the importance of tourism in its economy, it might also be worthwhile to analyze its municipalities, such as the mass tourism areas of Torremolinos, Benalmadena, and Fuengirola, among others. We have chosen Fuengirola (Figure 1) in this study, a municipality belonging to Malaga, with an area of 3.86 squares miles and five miles of sandy beaches. As of 2018, this Mediterranean coastal city reached a population of 75,396 inhabitants [3]. It is located at 24.3 km (15.53 miles) from the International Malaga Airport and 29.9 km from the capital of the Province of Malaga, which is, the Malaga municipality. Fuengirola is one of the popular destinations of the Costa del Sol, in which tourism constitutes an outstanding component of this town´s economy [4,5].
In addition to its economic benefits, the tourism industry also affects the host country’s society as well as its physical and cultural environment [10]. The economic aspect will not only determine residents’ perceptions of tourism, but also the social, cultural, and environmental components that will affect the destination receiving the tourism [11,12]. Additionally, a correlation between sustainable urban development and residents’ happiness has been identified as being a key component for the sustainability of the community [13]. The authors [13] consider the residents’ happiness as a factor that allows for the creation of a sensation of tourist hospitality and, in this way, contribute to the sustainability of the city. Other studies conclude that local residents are vital to tourism development, and they play a critical role in achieving sustainable tourism [14,15,16]. Research indicates that residents’ perceptions of their local tourism development can have a significant effect on their level of happiness and quality of life [17,18].
Studies of the local residents of host countries outline the relevance of the host resident as a component of tourism development and its sustainability [14,19,20]. Thus, policy makers must consider the quality of life and the subjective well-being of the local residents [21]. Residents who are in favor of tourism development are hospitable to tourists, which determines a better experience for them and a greater chance of their return [22]. Therefore, it is essential that a destination actively pursues the well-being of its residents and, therefore, increases their happiness.
In relation to well-being, the work by [23], as cited by [24], could be the first attempt to construct an evidence-base framework for analyzing the social well-being and quality of life. Regarding subjective well-being, two components are fundamentally mentioned: satisfaction and happiness with life [21,25]. Some examples of subjective well-being studies are those that are based on citizens’ opinions of overall satisfaction [10,17,26] and happiness with satisfaction [18], or where subjective well-being is used interchangeably with happiness, as in [27].
On the other hand, we have considered the indicator “residents’ loyalty of living in a tourist destination”. In reviewing literature, as far as we know, only one reference related to residents’ and tourism [28] and a second reference concerning “place loyalty” [29] were found. Community loyalty is operationally defined as “residents’ willingness to attract more tourists into the community” by [28]. Besides, we have utilized the definitions of “tourists’ loyalty” or “destination loyalty” (for example, [30,31,32,33,34] by translating the concepts of destination loyalty or tourists’ loyalty to residents’ loyalty. Thus, we consider “the willingness to recommend to other people to visit the destination” a suitable indicator to measure the residents’ loyalty provided, as provided by [28]. Independently, our contribution (as far as we know) is another indicator of the residents’ loyalty, being defined as “the recommendation to live in the tourist destination from the point of view of the resident”.
Our aim is to analyze the relationship between negative (economic, social, and environmental) residents’ perceptions [10,17,35], with residents’ happiness, specifically, residents of a mature, coastal, and mass tourism destination, as is the case of Fuengirola (Spain), applying social exchange theory (SET). It also considers the residents’ loyalty to the place where they live as a predictor of residents’ happiness. Although the residents have been studied for more than 30 years, as provided by [36], we focus our attention on the gaps or suggestions highlighted in the most recent research [10,17,35,37]. These arguments encourage the study of the negative aspects of the perception towards tourism, in order to evaluate its influence and inform policy makers or entrepreneurs, among others, since the positive aspects of tourism development for residents have been widely studied, either singly [17,38] or in combination with negative perceptions [10,39].
The structure of the article consists of four sections, in addition to the introduction. Section 2 reviews the literature on residents’ perceptions towards tourism, social exchange theory (SET), tourism and subjective well-being, and residents’ loyalty. Section 3 includes the description of the sample and how the survey was carried out among the residents of Fuengirola, the hypotheses to test, and the applied methodology. Section 5 shows the results obtained and, to conclude, Section 5 offers discussion of the results.

2. Literature Review

2.1. Review of Residents’ Perception

Three reviews regarding residents’ have been published by [35,36,40]. The first review dates from 2015 and it was conducted by [36], and its authors indicate that “residents’ attitude regarding the impacts of tourism have been studied for more than 30 years”. It is cited articles discuss the negative perceptions from 1985 to 2007. Among others negative aspects of tourism, we have selected those aspects according to our survey and the authors that were identified in the review [36]: it increases the cost of living [41,42]; it raises the price of goods and services [43]; it increases property values and causes house prices to go up [42,44,45]; the seasonality [46]; traffic congestion [46,47]; pollution [43,48]; and, collapse public services [46].
The second review consists of a meta-analysis dated 2019, of articles between 2000 and 2016, mainly concerning the application of SET to explain resident´s opinions and attitudes to supporting the development of tourism. The meta-analysis, among others, is focused on a total of 28 articles that were selected from four outstanding academic databases [35]. The reviewed studies were classified based on tourism impacts while using SET as the theoretical framework. As is described by these authors, several researchers adopt a two-dimensional approach, cataloging the perceived tourism impacts into benefits and costs. However, other researchers have adopted a three-dimensional approach, dividing local´s perceptions into three aspects: economical aspects, socio-cultural issues, and environmental aspects, and then dividing these into costs and benefits (positive and negative residents’ perceptions) that are related to their attitudes to supporting tourism. Moreover, an integrated model has been expanded, and, other studies have adopted an overall perception of resident toward tourism impacts.
On the other hand, the review that was conducted by [40], which was also published in 2019, refers to articles on residents´ attitudes selected in the highest ranked journals in terms of quality and impact factor from 2011 to 2017: Annals of Tourism Research, Journal of Sustainable Tourism, Journal of Travel Research, and Tourism Management. This review, in turn, complements the review that was performed by [49] based on resident´s attitudes selected from 1984 to 2010 in the journals: Annals of Tourism Research, Tourism Management and Journal of Travel Research. Content analysis was conducted while using the Endnote software in the current 2019 review. The SET framework accounted for 44.6% of the 90 articles that were reviewed during 2011–2017, which confirmed the increase in the application of this theory. This finding is shared with previous literature [49,50]. Moreover, quantitative approaches for data collection are adopted in most studies, representing 83% of articles. Regarding their geographical setting, studies were mainly conducted in USA and China. Five articles specifically refer to Spain, which shares third ranking with Japan and Malaysia. However, the majority of the studies have applied a theoretical SET framework to study resident´s attitudes from 2011 to 2017. Among other recommendations, tourism scholars are encouraged to study feelings, and emotions in research on local´s attitudes, in order to draw on theoretical advances in foundation disciplines for this particular area of research, such as anthropology, ethnography, psychology, and sociology. In addition, other research has addressed the overall impact [10] and others have distinguished between the positive and negative impacts) [37,50,51].
As mentioned earlier, SET has been criticized for assuming that the human-decision making procedure is rational and systematic, while not attending to the cognitive aspects of this process and the affective opinions of locals. In reality, residents’ opinions play a key role in tourism development [40]. Moreover, this is the reason why we focus on happiness.

2.2. Social Exchange Theory

“Interaction” is considered to be synonymous with “social behavior” in SET [52]: “when an action (or sentiment) emitted by one man is rewarded (or punished) by the action issued by another man, then, regardless of the type of emitted behavior, we say that these two people interact”. SET proposes social interactions as a process of exchange of activities or resources between individuals or groups based on the expected benefits of this process [52]. When a person understands that the future benefits will exceed the costs that are associated with the exchange, this person will participate in an exchange process. People seek to maximize the expected benefits in exchanges with others.
In the field of tourism, [53] used social exchange theory to predict the attitudes of local residents regarding tourism. According to this theory, in the context of tourism, if the residents perceive that the expected benefits exceed the expected costs, they are more likely to support tourism development [54]. Other equally important aspects can be taken into account, such as socio-cultural and environmental factors [55]. Recent research relates the load capacity of tourism with SET, thus linking sustainability and social exchange [56].
There is a necessity to identify the variables that can better predict residents’ perceptions of negative tourism impacts; the researchers in [35] did not obtain significant results from their study. Thus, one of the objectives of this study is to test the relationship between residents’ perceptions of negative tourism impacts and residents’ happiness.

2.3. Tourism and Subjective Well-Being

As mentioned above, satisfaction and happiness with life are considered to be basic measures of well-being, and both measures are usually indistinctly used based on the positive correlation between them [21]. However, “life satisfaction is the evaluative/cognitive component of subjective well-being and happiness is its hedonic/affective component. Life satisfaction draws on how people remember things and think about life, while happiness draws on how people experience life”, as described by [25].
According to [18], 50% of the subjective well-being of an individual is provided by inheritance, which is called a reference point [57]. A further 10% is provided by the different events of life, and 40% by intentional activity [58]; within this 40%, in which tourism is included.
Happiness is a very important feeling in life, because the happier people are, the more likely they are to carry out behaviors and tasks that benefit the society in which they live [59]. Happiness is closely related to how satisfied the individual is with the life they live [37]. In addition, personal, demographic and contextual factors (such as the physical, social, economic, and political environment) can affect happiness [37].
A high level of happiness indicates positive feelings regarding life. At the cognitive level, it is a generalized sense of satisfaction with life, both personal and work. At the emotional level, an individual with a high level of happiness has positive feelings and it is satisfied with the life that he or she leads [60]. Subjective happiness is a personal perception and experience of the positive/negative that is used to evaluate the quality of life. Subjective happiness refers to one’s own perception of the happiness experienced [17].

2.4. Residents’ Happiness towards Tourism Impacts

Recent literature suggests that the perceived value of tourism is an important determinant of the subjective well-being of residents [10,21,26,37]. Thus, many destinations consciously use tourism as a way of increasing the well-being of citizens through improvements in physical space infrastructure [37]. Fostering well-being and happiness in time should be an objective of any public manager. Therefore, determining the nature of the way tourism development affects the well-being and happiness of residents is an important issue.
The lives of community residents are affected when a destination focuses on attracting tourists [61], and the participation of these residents is needed for a real transformation [54]. Thus, residents who are in favor of tourism development are hospitable to tourists, which determines a better experience for them and a greater chance of their return [22]. Literature also refers to the importance of residents supporting tourism development [12,18,62,63]. Therefore, it is essential that a destination actively pursues the well-being of its residents increases their happiness. Residents tend to consider themselves happy if they perceive positive tourism development [37].
However, tourism development does not always bring positive effects and improve the lives of residents. Tourism can break the traditional local culture of the hosting countries [64] and increase the incidence of traffic jams [37]. Several studies have indicated that residents think that tourism generally increases pollution from the point of view of environmental impacts [64].
The meta-analysis of [35] concludes that the perceived benefits produce important effects on the support of tourism development, while, for costs, a more detailed examination of the measurement elements used to evaluate the perceptions of such impacts is required, which makes this idea the starting point of our study. However, negative and positive impacts are shown in more detail in the review that was conducted by [36].
There have been several published studies in the context of Andalusia (the region to which the municipality of Fuengirola belongs) and its residents. Several authors can be cited from the most recent research. A study that was carried out of Huelva analyzed the relationship between satisfaction and attitudes of residents. It concludes that it is not possible to try to explain the attitude of residents towards tourism by means of the variable "satisfaction with their community". Moreover, it is impossible to classify satisfaction according to its nature, because the residents whose the social aspects are more satisfied will perceive the advantageous, as well as the negative effects, of tourism with more intensity. Thus, as the authors [65] indicate, prevents us from knowing the overall perception.
The other study regarding residents’ attitudes set in Benalmadena, a municipality next to Fuengirola [66]. This study highlights three aspects of negative attitudes: having low educational attainment, having been born in Benalmadena, and having lived in Benalmadena for more than 10 years.

2.5. Literature Review of Residents’ Loyalty

Based on our review of the literature, two references were quoted in the introduction: one related to residents’ loyalty (defined as community’s loyalty) by [28] and the other about place loyalty [29]. The authors of the latter observe that “studies about place loyalty are very limited” [29]. They also point out that the pioneering research on city loyalty was conducted by [67], who highlights the developing and fostering of place loyalty from the perspective of town and city residents. Additionally, [29] state that “place loyalty can be defined as residents’ choice to continue living in a place over other places”, transferring the definition of brand loyalty [68] from a marketing context to the place context; that is, place loyalty reflects people’s subjective decision to live in a particular place.
In relation to loyalty, the studies mainly deal with tourist loyalty [69,70]. Other revised studies on residents’ loyalty relates this variable to the support that residents give to tourism development [28]. Based on the SET framework, the resident perceptions were divided into positive and negative perceptions while using three dimensions: environmental, economic, and sociocultural. However, only the three negative dimensions of residents’ perceptions and residents’ loyalty are predictors of residents’ happiness. We study whether the negative perceived impacts of tourism are associated with happiness and, in addition, consider residents’ loyalty towards the tourist destination as an explanatory variable with a positive effect on happiness.

3. Methodology

A structural equation modeling (SEM) simultaneously allows for examining the interrelated dependence relationships between various constructs and, in turn, between the constructs and observed indicators. Description and explanations of PLS-SEM is provided by [71,72,73]. Following its description in the survey, a brief description of the methodology and the obtained results is provided.

3.1. Data

We used primary data from a survey where a Fuengirola residents is the unit of analysis. The complete survey included a total of 31 questions. The data preparation has been done in Microsoft Excel version 2016. This work only presents the issues that are related to negative impacts. In addition, items that are related to loyalty and happiness are included. The surveys were partially carried out through Google forms, but, mainly, in person using a tablet, where it was quickly performed with total anonymity. The places that were chosen to carry out the survey were the Miramar Shopping Center and the Fuengirola waterfront during different hours during four weekends due to the concurrence of different types of people. The first question of the questionnaire that was related to the place of residence to ensure the subject was a resident of Fuengirola. Although almost 200 surveys were obtained, in total, from both sources, only 156 corresponded to residents of Fuengirola. Data were missing in the 156 valid surveys. The earliest response age was 13 years, as compared to the oldest respondent, who was 71 years old. The proportion of respondents who were women was 65%, the remainder were men. Only age and sex were asked, in addition, to the questions that were related to perceptions, loyalty, and happiness. No more demographic questions were required to simplify the survey.
Table 1 shows the items that were included in the survey (negative perception, loyalty, and happiness). A Likert scale was used to evaluate the degree of agreement or disagreement with the statement on a five-point scale (1 strongly disagree, 2 disagree, 3 neutral, 4 agree, and 5 strongly agree). The questionnaire consisted of five parts. The first, second, and third parts evaluate negative perceptions, economic (ECO), socio-cultural (SOCUL), and environment (ENVR) factors, respectively. The fourth section measured the residents’ loyalty to their residence (LOYAL) and the last section evaluated the residents’ happiness (HAPPY). Missing data were reported for 10 values that represented 6.5% of the total data. The missing data were replaced by the correspondent mean.

3.2. Statistical Technique

Among structural equation modeling (SEM) techniques, there are two approaches: (a) the covariance-based methods implemented by software, such as AMOS, LISREL, or EQS, and (b) the variance-based SEM methods, including the technique known as partial least squares (PLS-SEM) [74]. PLS-SEM focuses on the prediction and explanation of the variance of key target constructs (happiness) by different explanatory latent variables (loyalty and negative perceptions). Non-normal data, small sample sizes, formatively measured constructs, and prediction are some justifications for using PLS-SEM [71,73,75]. In this case, the SmartPLS version 3.2.8 software [76] was used to execute all of the PLS-SEM analyses.
Our main purpose was to predict residents’ happiness from living in Fuengirola based on the SET framework. As a novelty, residents’ loyalty was included in the SEM, in conjunction with happiness regarding the place that they live (Fuengirola). Our model has a construct (residents’ loyalty), part of which is derived from our literature review, but includes our own contribution. Thus, the model has aspects of both confirmatory and exploratory studies. We consider it is a confirmatory model that is related to the negative impact based on the SET framework and our own review of literature related to happiness (subjective well-being), but exploratory in relation to the construct of residents’ loyalty.
Specifically, the following hypotheses attempt to assess the residents’ happiness:
Hypothesis 1 (H1)2 (H2), 3 (H3), 4 (H4), and 5 (H5).
The negative perceptions are positively related by pairs of constructs as they have a relationship in the same direction.
Hypothesis 6 (H6).
The negative perceptions construct negatively affect residents’ happiness.
According to [55] and based on social exchange theory, the perception of cost and benefits on economic, social, and environmental impacts (taking into account negative and positive impacts on residents’ perception) and related to tourism development is considered. Besides, the tourism development is a second-order construct in a confirmatory CB-SEM model, this second-order is reflectively related to several first-order constructs (social impact, macroeconomic impact, socioeconomic impact, cultural impact, and environmental impacts), in turn the second-order construct positively affects the subjective happiness construct provided by [37]. Thus, we proposed a parsimony confirmatory model that relates the different impacts (residents’ perceptions of costs) directly and negatively affect to the happiness to achieve our proposed goal.
Hypothesis 7 (H7).
Construct residents’ loyalty affects happiness.
As we mentioned above, residents’ loyalty has been related to explain the support that residents give to tourism development according to [28]; hence, our new perspective introduces a different point of view, as happiness is the dependent variable in our study. There are authors [18] who point out that the perceived impacts of tourism are associated with satisfaction with life, which is, the cognitive component (satisfaction), and not with the hedonic level of affection, that is, the affective component (happiness). Similar to the mentioned authors findings [29] and, independently, we have defined a suitable measure of residents’ loyalty as the recommendation to live in the tourist destination from the point of view of the resident (a novelty as far as we know), which is derived from definitions of “tourists’ loyalty” or “destination loyalty”. Thus, we incorporated our proposed definition conforming to a construct of residents’ loyalty to their tourism destination residence into the measurement provided by [28]. Following [29], the city loyalty positively affects happiness.
The PLS-SEM path model of Figure 2 visually displays the described hypotheses and variable relationships. Table 1 shows the items and constructs.

4. Results

Traditionally, the evaluation of the measurement model and the assessment of the structural model are the two main analysis steps to be developed. At present, the squared root mean residual (SRMR) evaluates the overall model; for the estimated model, this is 0.078 and for the saturated model it is 0.073. These values are near the established threshold of 0.08, among other measurements. Next, evaluation is described. Figure 3 shows the estimated PLS_SEM model.

4.1. Measurement or Outer Model

We follow the recommendations that are based on the functionality of the software SmartPLS (version 3.2.8). Mode A was selected, as the constructs are factor composite. Reflective measurement model evaluation must follow several steps [78,79,80]: item reliability, internal consistency reliability, convergent validity, and discriminant validity.

4.1.1. Indicator Reliability, Internal Consistency Reliability, and Convergent Validity

Reliability is a condition for validity, so, the item reliability is first checked to guarantee the associated items to a construct have much in common, which is requires loadings values above 0.7 that is captured by the latent variable [71]. The square of standardized item’s outer loading represent the item’s variance explained by the latent variable, so, at least is required to explain 50% (in percentage). The squared root of 0.5 is 0.708 and 0.70 is near enough to be considered the threshold. Moreover, internal consistency reliability is evaluated by means of the composite reliability (CR) [81], Cronbach´s alpha [82], and the measure Dijkstra–Henseler rho (ρA), which should be between 0.7 and 0.95. Nunnally & Bernstein states that a modest reliability (e.g., value of 0.7 might be a satisfactory value of the Cronbach alpha in early stages of validation or predictive research [83]. According to Hair et al. values of the coefficient alpha 0.60 to 0.70 are acceptable in exploratory research; meanwhile, it is considered to be satisfactory between 0.70 and 0.90 and, finally, values above 0.90 and definitely above 0.95 indicate that items are measured the same and are not then a valid measurement if the construct [71]. Convergent validity is measured by the average variance extracted (AVE) and it is established by the criterion of an AVE value of 0.50 or higher that indicates that, on average, the latent variable explains 50% (in percentage) or more of the variance of its indicators [71]. Table 2 displays computed values.

4.1.2. Discriminant Validity

Items of different constructs should load on separate constructs. Discriminant validity measures the extent to which the constructs are different from each another; in other words, the constructs should discriminate amongst them. Traditionally, the Fornell-Larcker criterion and the cross loadings criterion assess discriminant validity. Currently, researchers are recommended to apply the Heterotrait-Monotrait ratio (HTMT). No discriminant validity problems are found according to the HTMT0.85 and HTMT0.90 criteria [78]. If the HTMT value is below 0.90, discriminant validity has been established between two reflective constructs. Table 3 and Table 4 report the discriminant validity results.

4.2. Structural or Inner Model

Several steps are recommended to evaluate the structural model. The issues to evaluate the inner model are, mainly: (a) collinearity among constructs; (b) statics significance and relevance of the path coefficients); (c) discriminant validity; (d) model´s predictive power (R2); and, (e) evaluation of effect size, f2, following [70,71,72,73,84].

4.2.1. Collinearity Assessment

We computed the reciprocal of the tolerance (TOL), i.e., the variance inflation factor (VIF=1/TOL), to assess the level of collinearity to the antecedent constructs of the endogenous construct. In PLS-SEM context, TOL values below or equal to 0.20 equivalent to VIF values above or equal to 5 mean a potential problem of collinearity [73]. However, VIF values above 3.3 [85] and the threshold above 3 [86] have also been considered. The results indicate no potential problem of collinearity in computing the VIF (Table 5).

4.2.2. Statistical significance and relevance of path coefficients

The significance standardized path coefficients were obtained via a bootstrap re-sampling procedure of 15,000 samples; using at least 5000 samples is recommended and 10,000 samples is considered to be ideal [87,88]. Standardized path coefficients are reported in the path diagram (Figure 3); all are significant at the 0.001 level. The relevance of the standardized path coefficients is obtained from the comparison between them, while bearing in mind that the higher the value of these coefficients, the higher the relevance. Hence, loyalty has the most substantial impact, positively. From the negative effects, only seasonality negatively and directly affects the happiness, although with a weak impact (−0.137). Table 6 presents the total effects computed for residents’ happiness.

4.2.3. Effect size

The change in the R2 value in case an exogenous construct is removed from the structural model can be used to indicate whether the omitted construct has substantive impact on the endogenous construct; this change is measured by the effect sizes. The construct’s effect size measures the impact of the exogenous construct on the structural model’s predictive power. Values of effect size (f2) of 0.02, 0.15, and 0.35 indicate weak, moderate, and strong effects, respectively, following [89]. In our case, all f2 oscillate between 0.02 and 0.15. The effect size for residents’ loyalty to happy (0.473) and for negative environment to negative sociocultural are strong (0.451) (Table 7).

4.2.4. Explained Variance

The coefficient of determination (R2 value) is the measure of the model’s predictive power. One criterion is R2 ≥ 0.10 [71]. In marketing research studies, 0.75 is described as indicating substantial predictive power, 0.5 as moderate, and 0.25 as weak; meanwhile, while following [74], the same thresholds are achieved with other values: 0.67 (substantial), 0.33 (moderate) y 0.1 (weak).
The construct negative_ECO2 shows a low R2 of 0.124. The remaining determination coefficients oscillate between weak and moderate, as shown in Table 8 (0.311–0.341). Nevertheless, it is possible to calculate the part of explained variance of the endogenous construct that corresponds to each exogenous construct; the decomposition of variance that is included in Table 8. The residents’ loyalty is considered as the source that explained the greatest portion of the variance of happiness (31.590%).

4.2.5. Predictive relevance Q2

The Stone-Geisser Q2 value [90,91] is an indicator of predictive relevance. The Q2 values are all larger than zero in our study (Table 9).
To summarize, in standardized data terms, from the total effects it can be seen that the latent loyalty of the Fuengirola residents with a coefficient of standardized values of 0.577 has the greatest direct effect on the residents’ happiness and explains 31.59% of the variance in residents’ happiness. Logically, negative perceptions of any kind are positively related to each other, because they are items that maintain the same direction in their relationship (H1 to H5). In addition, most of the negative perceptions constructs have an indirect effect on happiness through one of the negative economic constructs, (ECO2), which, in return, is the only direct negative effect on the happiness of the residents. The remaining effects of negative perceptions are indirect effects and weaker. Thus, the highest value of total effect is −0.137, which corresponds to the second negative economic perceptions construct (NP_ECO2 equal to item ECO 9, which is the consideration that the improvement in the economy as a result of tourism is very seasonal), followed by the negative environment perception, with a total effect of −0.043.

5. Conclusions and Discussion

In tourism research, the need to analyze at a more disaggregated level, that is, to research at the local level (regions, communities, cities) [59] is highlighted. Therefore, following these recommendations to analyze the smallest geographical level possible, at the local, city, or municipality level, we analyze a mature, coastal, and mass tourism destination, Fuengirola, belonging to the Costa del Sol (Malaga, Spain).
As noted in the reviewing of the literature, as far as we know, only one reference related to residents’ loyalty and tourism [28], definitions of “tourists’ loyalty” or “destination loyalty” (e.g., [30,69,70,83]) have been adapted to the context of residents’ loyalty, being defined as “the willingness to recommend to other people to visit the destination”. This is adopted measuring the residents’ loyalty as a suitable indicator [25]. Independently, our contribution, as far as we know, is another indicator of the residents’ loyalty, being defined as “the recommendation to live in the tourist destination from the point of view of the resident”. To conclude the composition of the construct, we have taken these two cited indicators, which are pioneering in the tourism context.
In relation to loyalty and tourism, researches mainly deal with tourist loyalty (e.g. [90,91]), and consider tourists’ loyalty as an endogenous construct. This is the second time that residents’ loyalty has been related to happiness in the context of living places; the first, as far as we are aware, was conducted by [29] in the context of the neighborhood. However, [29] state that “place loyalty can be defined as residents’ choice to continue living in a place over other places” and measure place loyalty while using five indicators that were adapted from [68] concerning customer loyalty adapted to the context of living place. From these five items, only one is similar to our indicator specifically proposed for a tourism destination (“I will recommend that others live in this neighborhood” in a seven-point Likert scale, while ours is “the recommendation to live in the tourist destination from the point of view of the resident” in a five-point Likert scale). Thus, there are differences between the study of [26] and ours: first, the scale (seven-point versus five-point Likert scale); second, the number of items (five versus two); and third, the place (neighborhood versus tourist destination).
The significance of the path of the direct positive relationship between residents’ loyalty and residents’ happiness from living in a tourism destination supports the hypothesis, as this hypothesis has not been rejected. The other revised study on residents’ loyalty amends this variable with the support that residents give to tourism development [28], so we believe that our new perspective introduces a different point of view and is thus pioneering.
Recent research suggests complementing studies of residents’ positive perceptions of tourism development with those that analyze the negative issues of tourism for the hosts of the tourism destination [10,17,35,37]. The purpose is to solve or improve these negative aspects by either policy makers or/and entrepreneurs, and to understand how residents’ loyalty affects residents’ happiness. Therefore, in we have focused on subjective well-being related to the affective aspect (happiness) our study [55], obtaining significant but very weak effects on the perceived costs of tourism on happiness.
The results regarding the non-significant relationship between environmental impacts and support for tourism obtained by [25] are not comparable with these results, as residents’ happiness is our dependent variable. As we mentioned earlier, most research uses constructs: the support for tourism, or the residents’ well-being or satisfaction being as the dependent variable in the model, although, well-being and happiness are used interchangeable by some scholars (e.g., [27]). Moreover, as a novelty, happiness in relation to residents living in a tourism destination has been considered as the dependent variable in this model, and residents’ loyalty to a tourist destination as an exogenous construct.
In our case, none of the hypothesis have been rejected.
  • H1 to H5 There is a positive relationship between pairs of negative perceptions constructs.
    Among pairs of negative perceptions there is a positive relationship.
  • Hypothesis 6 (H6): The negative perceptions latent variables negatively affect residents’ happiness.
    The negative perceptions are mostly negative indirect effects through seasonality. The only direct negative relationship with happiness is the one regarding seasonality. However, the total effects of negative perceptions are weak or very weak: NP_ECO1 (−0.026); NP_ECO2 (−0.137); NP_ENVR (−0.043); and, NP_SOCU (−0.009).
  • Hypothesis 7 (H7). Construct residents’ loyalty affects the construct residents’ happiness.
    The result of the positive relationship between place loyalty and happiness referring to those persons who perceive their living place (neighborhood) as prestigious is consistent with our findings, that is, an increment of loyalty is associated with an increment in happiness [29], although they are differently defined.
The number of respondents in this study could be considered to be limitation (156 residents), and it was very similar to the number of questionnaires in the research of [29], which surveyed 150 respondents. Further research is required in relation to residents’ loyalty to investigate the issues or indicators that have a greater effect more on this construct based on its direct positive effect on happiness. Moreover, we agree with [29] that further investigation of the effects of residents’ loyalty on happiness is required globally. To conclude, the perception of place status should be taken into account and tested to determine whether the positive direct relationship between residents’ loyalty and happiness is maintained for those who perceive their living place as ordinary when compared to those who perceive their residence as prestigious, as the scholars in [29] have done.
The end purpose was to solve or improve these negative aspects either by policy makers or/and entrepreneurs, but only the seasonality has a direct and represents a small amount of the explained variance of happiness. It is a problem that concerns both residents and the government authorities. Additionally, the environmental construct only explained nearly half of the explained variance of seasonality (31.6%). The cost of environment does represent a weak effect, thus, in the context of sustainability, will suggest a better relation with the reception of tourist, which, in turn, is positive for the performance of tourist in the Costa del Sol. These results are interesting for policy makers, as well as companies whose objective is the development of sustainable tourism according to the implemented 2030 Agenda for Sustainable Development (2030 Agenda) and its Sustainable Development Goals (SDGs) by the European Union [92].
In this case, Fuengirola Town Hall will also be interested in finding the variables that affect the residents’ happiness, but also to deeply research more possible negative impacts. These findings could be transferred to the surrounding cities of Costa del Sol. A complementary open question might be asked to determine important negative impacts that are not included in the survey.
Concerning the loyalty, research in the surrounding tourism cities that belong to Costa del Sol are also required to compare these findings to understand how residents’ loyalty affects residents’ happiness. The key is to investigate the variables that affect the residents’ loyalty more than the loyalty itself, as it increases the happiness, which is an affective measure of the well-being. As we mentioned before, residents who are in favor of tourism development are hospitable to tourists [22]; also, the importance of residents supporting tourism development is remarkable, which will be supported if they are happier [12,18,62,63]. Residents tend to consider themselves happy if they perceive positive tourism development [37], in this case, is similar to the idea of not perceiving the negative impact, even more in destination where the tourism is the base of the local economy.

Author Contributions

Design the research framework E.M.S.-T. & M.D.B.-M.; data collection T.R.-N. methodology, E.M.S.-T. & M.D.B.-M.; software, validation & formal analysis, M.D.B.-M.; investigation, E.M.S.-T. & M.D.B.-M.; writing-original draft preparation, E.M.S.-T. & M.D.B.-M.; writing-review, E.M.S.-T. & M.D.B.-M.; editing, M.D.B.-M.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. wttc.org. 2019. Available online: https://www.wttc.org/about/media-centre/press-releases/press-releases/2019/spanish-eir/ (accessed on 28 July 2019).
  2. Instituto de Estadística y Cartografía de Andalucía. [Encuesta de Coyuntura Turística de Andalucía, known as ECTA in Spanish] Andalusian Tourism Situation Survey Survey (Statistical and Mapping Institute of Andalusia). 2018. Available online: https://www.juntadeandalucia.es/institutodeestadisticaycartografia/badea/informe/anual?CodOper=b3_271&idNode=818 (accessed on 16 July 2019).
  3. Instituto Nacional de Estadística. 2019. Available online: http://www.ine.es/jaxiT3/Datos.htm?t=2882 (accessed on 1 August 2019).
  4. Instituto-de-Estadística-y-Cartografía-de-Andalucía. 2018. Available online: https://www.juntadeandalucia.es/institutodeestadisticaycartografia/sima/ficha.htm?mun=29054 (accessed on 16 July 2019).
  5. Web of Town Hall of Fuengirola. 2019. Available online: turismo.fuengirola.es/ (accessed on 1 August 2019).
  6. Own creation based on location of Spain within Europe by NuclearVacuum [CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0)]. Available online: https://upload.wikimedia.org/wikipedia/commons/2/21/EU-Spain.svg (accessed on 24 October 2019).
  7. Own Creation Based on the Map of Web. Available online: http://andalucia.com/ (accessed on 24 October 2019).
  8. Own creation based on Costa del Sol Occidental—Junta de Andalucia.PNG by ZephyrusCom [Public domain]. Available online: https://commons.wikimedia.org/wiki/File:Costa_del_Sol_Occidental_-_Junta_de_Andalucia.PNG (accessed on 24 October 2019).
  9. Playa de Carvajal, Fuengirola by Tyk [CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0)]. Available online: https://commons.wikimedia.org/wiki/File:Playa_de_Carvajal,_Fuengirola_11.jpg (accessed on 24 October 2019).
  10. Ozturk, A.B.; Ozer, O.; Çaliskan, U. The relationship between local residents’ perceptions of tourism and their happiness: A case of Kusadasi, Turkey. Tour. Rev. 2015, 70, 232–242. [Google Scholar] [CrossRef]
  11. Deery, M.; Jago, L.; Fredline, L. Rethinking social impacts of tourism research: A new research agenda. Tour. Manag. 2012, 33, 64–73. [Google Scholar] [CrossRef]
  12. Kim, K.; Uysal, M.; Sirgy, J. How does tourism in a community impact the quality of life of community residents? Tour. Manag. 2013, 36, 527–540. [Google Scholar] [CrossRef]
  13. Cloutier, S.; Larson, L.; Jambeck, J. Are sustainable cities “happy” cities? Associations between sustainable development and human well-being in urban areas of the United States. Environ. Dev. Sustain. 2014, 16, 633–647. [Google Scholar] [CrossRef]
  14. Harrill, R.; Uysal, M.; Cardon, P.W.; Vong, F.; Dioko, L. Resident attitudes towards gaming and tourism development in Macao: Growth machine theory as a context for identifying supporters and opponents. Int. J. Tour. Res. 2011, 13, 41–53. [Google Scholar] [CrossRef]
  15. Lee, S.; Jeon, S.; Kim, D. The impact of tour quality and tourist satisfaction on tourist loyalty: The case of Chinese tourists in Korea. Tour. Manag. 2011, 32, 1115–1124. [Google Scholar] [CrossRef]
  16. Nunkoo, R.; Ramkissoon, H. Developing a community support model for tourism. Ann. Tour. Res. 2011, 38, 964–988. [Google Scholar] [CrossRef]
  17. Kafashpor, A.; Ghasempour Ganji, S.F.; Sadeghian, S.; Johnson, L.W. Perception of tourism development and subjective happiness of residents in Mashhad, Iran. Asia Pac. J. Tour. Res. 2018, 23, 521–531. [Google Scholar] [CrossRef]
  18. Nawijn, J.; Mitas, O. Resident attitudes to tourism and their effect on subjective well-being. J. Travel Res. 2012, 51, 531–541. [Google Scholar] [CrossRef]
  19. Nunkoo, R.; Ramkissoon, H. Modeling community support for a proposed integrated resort project. J. Sustain. Tour. 2010, 18, 257–277. [Google Scholar] [CrossRef]
  20. Lee, H. Influence analysis of community resident support for sustainable tourism development. Tour. Manag. 2013, 34, 37–46. [Google Scholar] [CrossRef]
  21. Ivlevs, A. Happy hosts? International tourist arrivals and residents’ subjective well-being in Europe. J. Travel Res. 2017, 56, 599–612. [Google Scholar] [CrossRef]
  22. Carmichael, B.A. Linking quality tourism experiences, residents’ quality of life, and quality experiences for tourists. Qual. Tour. Exp. 2006, 115–135. [Google Scholar] [CrossRef]
  23. Smith, D. The Geography of Social Well-Being; McGraw-Hill: New York, NY, USA, 1973. [Google Scholar]
  24. Ballas, D. What makes a ‘happy city’? Cities 2013, 32, S39–S50. [Google Scholar] [CrossRef]
  25. Kahneman, D.; Riis, J. Living and Thinking about It: Two Perspectives on Life. In The Science of Wellbeing: Integrating Neurobiology, Psychology, and Social Science; Huppert, F., Baylis, N., Kaverne, B., Eds.; Oxford University Press: Oxford, UK, 2005. [Google Scholar] [CrossRef]
  26. Woo, E.; Kim, H.; Uysal, M. Life satisfaction and support for tourism development. Ann. Tour. Res. 2015, 50, 84–97. [Google Scholar] [CrossRef]
  27. Otis, N. Subjective well-being in China: Associations with absolute, relative, and perceived economic circumstances. Soc. Indic. Res. 2017, 132, 885–905. [Google Scholar] [CrossRef]
  28. Chen, J. An investigation of urban residents’ loyalty to tourism. J. Hosp. Tour. Res. 2000, 24, 5–19. [Google Scholar] [CrossRef]
  29. Gilboa, S.; Herstein, R. Place status, place loyalty and well-being: An exploratory investigation of Israeli residents. J. Place Manag. Dev. 2012, 5, 141–157. [Google Scholar] [CrossRef]
  30. Chen, J.; Gursoy, D. An investigation of tourists’ destination loyalty and preferences. Int. J. Contemp. Hosp. Manag. 2001, 13, 79–85. [Google Scholar] [CrossRef]
  31. Do Valle, P.O.; Silva, J.A.; Mendes, J.; Guerreiro, M. Tourist satisfaction and destination loyalty intention: A structural and categorical analysis. Int. J. Bus. Sci. Appl. Manag. 2006, 1, 25–44. [Google Scholar]
  32. Oppermann, M. Tourism destination loyalty. J. Travel Res. 2000, 39, 78–84. [Google Scholar] [CrossRef]
  33. Chen, C.F.; Tsai, D. How destination image and evaluative factors affect behavioural intentions? Tour. Manag. 2007, 28, 1115–1122. [Google Scholar] [CrossRef]
  34. Ramseook-Munhurrun, P.; Seebaluck, V.N.; Naidoo, P. Examining the structural relationships of destination image, perceived value, tourist satisfaction and loyalty: Case of Mauritius. Procedia Soc. Behav. Sci. 2015, 175, 252–259. [Google Scholar] [CrossRef]
  35. Gursoy, D.; Ouyang, Z.; Nunkoo, R.; Wei, W. Residents’ impact perceptions of and attitudes towards tourism development: A meta-analysis. J. Hosp. Market. Manag. 2019, 28, 306–333. [Google Scholar] [CrossRef]
  36. Almeida García, F.; Balbuena Vázquez, A.; Cortés Macías, R. Residents’ attitudes towards the impacts of tourism. Tour. Manag. Perspect. 2015, 13, 33–40. [Google Scholar] [CrossRef]
  37. Rivera, M.; Croes, R.; Lee, S.H. Tourism development and happiness: A residents’ perspective. J. Destin. Mark. Manag. 2015, 5, 5–15. [Google Scholar] [CrossRef]
  38. Stylidis, D.; Terzidou, M. Tourism and the economic crisis in Kavala, Greece. Ann. Tour. Res. 2014, 44, 210–226. [Google Scholar] [CrossRef]
  39. Ko, D.W.; Stewart, W.P. A structural equation model of residents’ attitudes for tourism development. Tour. Manag. 2002, 23, 521–530. [Google Scholar] [CrossRef]
  40. Hadinejad, A.; Moyle, B.D.; Scott, N.; Kralj, A.; Nunkoo, R. Residents’ attitudes to tourism: A review. Tour. Rev. 2019, 74, 150–165. [Google Scholar] [CrossRef]
  41. Liu, J.; Var, T. Resident attitudes toward tourism impacts in Hawaii. Ann. Tour. Res. 1986, 13, 193–214. [Google Scholar] [CrossRef]
  42. Saveriades, A. Establishing the social tourism carrying capacity for the tourist resorts of the east coast of the Republic of Cyprus. Tour. Manag. 2000, 21, 147–156. [Google Scholar] [CrossRef]
  43. Johnson, J.D.; Snepenger, D.J.; Akis, S. Residents’ perceptions of tourism development. Ann. Tour. Res. 1994, 21, 629–642. [Google Scholar] [CrossRef]
  44. Korca, P. Resident attitudes toward tourism impacts. Ann. Tour. Res. 1996, 23, 695–726. [Google Scholar] [CrossRef]
  45. Var, T.; Kendall, K.W.; Tarakcioglu, E. Resident attitudes towards tourists in a Turkish resort town. Ann. Tour. Res. 1985, 12, 652–658. [Google Scholar] [CrossRef]
  46. Bujosa, A.; Rosselló, J. Modelling environmental attitudes toward tourism. Tour. Manag. 2007, 28, 688–695. [Google Scholar] [CrossRef]
  47. Liu, J.; Sheldon, P.J.; Var, T. Resident perception of the environmental impacts of tourism. Ann. Tour. Res. 1987, 14, 17–37. [Google Scholar] [CrossRef]
  48. Yoon, Y.; Gursoy, D.; Chen, J.S. Validating a tourism development theory with structural equation modeling. Tour. Manag. 2001, 22, 363–372. [Google Scholar] [CrossRef]
  49. Nunkoo, R.; Smith, S.L.; Ramkissoon, H. Resident attitudes to tourism: A longitudinal study of 140 articles from 1984 to 2010. J. Sustain. Tour. 2013, 21, 5–25. [Google Scholar] [CrossRef]
  50. Stylidis, D.; Biran, A.; Sit, J.; Szivas, E.M. Residents’ support for tourism development: The role of residents’ place image and perceived tourism impacts. Tour. Manag. 2014, 45, 260–274. [Google Scholar] [CrossRef]
  51. Abdollahzadeh, G.H.; Sharifzadeh, A. Rural residents’ perceptions toward tourism development: A study from Iran. Int. J. Tour. Res. 2014, 16, 126–136. [Google Scholar] [CrossRef]
  52. Homans, G. Social Behaviour: Its Elementary Forms; Routledge and Kegan Paul: London, UK, 1961. [Google Scholar]
  53. Ap, J. Residents’ perceptions research on the social impacts of tourism. Ann. Tour. Res. 1990, 19, 665–690. [Google Scholar] [CrossRef]
  54. Jurowski, C. Distance effects on residents’ attitudes toward tourism. Ann. Tour. Res. 1994, 31, 296–312. [Google Scholar] [CrossRef]
  55. Gursoy, D.; Jurowski, C.; Uysal, M. Resident attitudes: A structural modeling approach. Ann. Tour. Res. 2002, 29, 79–105. [Google Scholar] [CrossRef]
  56. Muler Gonzalez, V.; Coromina, L.; Gali, N. Overtourism: Residents’ perceptions of tourism impact as an indicator of resident social carrying capacity-case study of a Spanish heritage town. Tour. Rev. 2018, 73, 277–296. [Google Scholar] [CrossRef]
  57. Larsen, R.J.; Buss, D.V. Personality Psychology, Domains of Knowledge about Human Nature; McGraw-Hill: New York, NY, USA, 2002. [Google Scholar]
  58. Lyubomirsky, S.; Sheldon, K.M.; Schkade, D. Pursuing happiness: The architecture of sustainable change. Rev. Gen. Psychol. 2005, 9, 111–131. [Google Scholar] [CrossRef]
  59. Diener, E.; Lucas, E.R. Subjective Emotional Well-Being. In Handbook of Emotions; Lewis, M., Haviland, J.M., Eds.; Guilford: New York, NY, USA, 2000; pp. 325–337. [Google Scholar]
  60. Myers, D.G.; Diener, E. Who Is Happy? Am. Psychol. Soc. 1995, 6, 10–19. [Google Scholar] [CrossRef]
  61. Kim, K. The Effects of Tourism Impacts upon Quality of Life of Residents in the Community. Ph.D. Thesis, Faculty of the Virginia Polytechnic Institute and State University, Blacksburg, VA, USA, 2002. [Google Scholar]
  62. Andereck, K.L.; Valentine, K.; Vogt, C.; Knopf, R. Across-cultural analysis of tourism and quality of life perceptions. J. Sustain. Tour. 2007, 15, 483–502. [Google Scholar] [CrossRef]
  63. Andereck, K.L.; Nyaupane, G.P. Exploring the nature of tourism and quality of life perceptions among residents. J. Travel Res. 2011, 50, 248–260. [Google Scholar] [CrossRef]
  64. Ahmed, Z.U.; Krohn, F.B. Understanding the unique consumer behavior of Japanese tourists. J. Travel Tour. Mark. 1993, 1, 73–86. [Google Scholar] [CrossRef]
  65. Porras-Bueno, N.; Plaza-Mejía, M.; de los, Á; Vargas-Sánchez, A. Quality of Life and Perception of the Effects of Tourism: A Contingent Approach. In Best Practices in Hospitality and Tourism Marketing and Management: A Quality of Life Perspective; Campón-Cerro, A.M., Hernández-Mogollón, J.M., Folgado-Fernández, J.A., Eds.; Springer Nature: Cham, Switzerland, 2019. [Google Scholar] [CrossRef]
  66. Almeida-García, F.; Peláez-Fernández, M.Á.; Balbuena-Vazquez, A.; Cortes-Macias, R. Residents’ perceptions of tourism development in Benalmádena (Spain). Tour. Manag. 2016, 54, 259–274. [Google Scholar] [CrossRef]
  67. Florek, M. No place like home: Perspectives on place attachment and impacts on city management. J. Town City Manag. 2011, 1, 346–354. [Google Scholar]
  68. You, X.; Donthu, N. Developing and validating a multidimensional consumer-based brand equity scale. J. Bus. Res. 2001, 52, 1–14. [Google Scholar] [CrossRef]
  69. Baloglu, S. Image variations of Turkey by familiarity index: Informational and experiential dimensions. Tour. Manag. 2001, 22, 127–133. [Google Scholar] [CrossRef]
  70. Chaulagain, S.; Wiitala, J.; Fu, X. The impact of country image and destination image on US tourists’ travel intention. J. Destin. Mark. Manag. 2019, 12, 1–11. [Google Scholar] [CrossRef]
  71. Hair, J.F.J.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Equation Modeling (PLS SEM), 1st ed.; Sage Publication, Inc.: Thousand Oaks, CA, USA, 2014. [Google Scholar]
  72. Hair, J.F.J.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Equation Modeling (PLS-SEM), 2nd ed.; Sage Publication, Inc.: Thousand Oaks, CA, USA, 2014. [Google Scholar]
  73. Hair, J.F.; Hult, G.T.; Ringle, C.M.; Sarstedt, M.; Castillo-Apraiz, J.; Cepeda Carrion, G.; Roldán, J. Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM); OmniaScience (Omnia Publisher SL): Barcelona, Spain, 2019. [Google Scholar]
  74. Chin, W.W. The Partial Least Squares Approach to Structural Equation Modeling. In Modern Methods for Business Research; Marcoulides, G.A., Ed.; Lawrence Erlbaum Associates: Mahwah, NJ, USA; London, UK, 1998; pp. 295–336. [Google Scholar]
  75. Hair, J.F., Jr.; Sarstedt, J.; Hopkins, L.; Kuppelwieser, V.G. Partial least squares structural equation modeling (PLS-SEM). Eur. Bus. Rev. 2014, 26, 106–121. [Google Scholar] [CrossRef]
  76. Ringle, C.M.; Wende, S.; Becker, J.M. Software “SmartPLS 3” Version 3.2.8 (2018 Nov 22). Boenningstedt. 2015. Available online: http://www.smartpls.com (accessed on 18 November 2019).
  77. Ozturk, A.B.; Qu, H. The impact of destination images on tourists’ perceived value, expectations, and loyalty. J. Qual. Assur. Hosp. Tour. 2008, 9, 275–297. [Google Scholar] [CrossRef]
  78. Cepeda-Carrion, G.; Cegarra-Navarro, J.; Cillo, V. Tips to use Partial Least Squares Structural Equation Modelling (PLS-SEM) in knowledge management. J. Knowl. Manag. 2019, 23, 67–89. [Google Scholar] [CrossRef]
  79. Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
  80. Henseler, J.; Ringle, C.R.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef]
  81. Werts, C.E.; Linn, R.L.; Jöreskog, K.G. Intraclass reliability estimates: Testing structural assumptions. Educ. Psychol. Meas. 1974, 34, 25–33. [Google Scholar] [CrossRef]
  82. Cronbach, L.J. Coefficient alpha and internal structure of test. Psychometrika 1951, 16, 297–334. [Google Scholar] [CrossRef]
  83. Nunnally, J.C.; Bernstein, I.H. Psychometric Theory, 3rd ed.; McGraw-Hill: New York, NY, USA, 1994; pp. 264–265. [Google Scholar]
  84. Marin-Garcia, J.; Alfalla-Luque, R. Key issues on Partial Least Squares (PLS) in operations management research: A guide to submissions. J. Ind. Eng. Manag. 2019, 12, 219–240. [Google Scholar] [CrossRef]
  85. Diamantopoulos, A.; Siguaw, J.A. Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. Br. J. Manag. 2006, 17, 263–282. [Google Scholar] [CrossRef]
  86. Hair, J.F.; Black, W.C.; Anderson, R.E.; Babin, B.J. Multivariate Data Analysis, 8th ed.; Cengage Learning (EMEA) Ltd.: Andover, MA, USA, 2018. [Google Scholar]
  87. Ringle, C.M.; Sarstedt, M.; Mitchell, R.; Gudergan, S.P. Partial least squares structural equation modeling in HRM research. Int. J. Hum. Resour. Manag. 2018, 1–27. [Google Scholar] [CrossRef]
  88. Streukens, S.; Leroi-Werelds, S. Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. Eur. Manag. J. 2016, 34, 618–632. [Google Scholar] [CrossRef]
  89. Chin, W.W. How to write and report PLS analyses. In Handbook of Partial Least Squares: Concepts, Methods and Applications; Esposito Vinzi, V., Chin, W.W., Henseler, J., Wang, H., Eds.; Springer Handbooks of Computational Statistics Series; Springer: Heidelberg, Germany, 2010; Volume II, pp. 655–690. [Google Scholar]
  90. Stone, M. Cross-Validatory Choice and Assessment of Statistical Predictions. J. R. Stat. Soc. Ser. B-Stat. Methodol. 1974, 36, 111–147. [Google Scholar] [CrossRef]
  91. Geisser, S. The Predictive Sample Reuse Method with Applications. J. Am. Stat. Assoc. 1975, 70, 320–328. [Google Scholar] [CrossRef]
  92. ec.europa.eu. The 2030 Agenda for Sustainable Development and the SDGs. 25 September 2015. Available online: https://ec.europa.eu/environment/sustainable-development/SDGs/index_en.htm (accessed on 18 November 2019).
Figure 1. Locations and photo of Fuengirola. (a) Location of Spain within Europe. (b) Location of Costa del Sol Occidental (Malaga) within Andalusia. (c) Location of Fuengirola within Costa del Sol Occidental. (d) Photo of Fuengirola. Source: (a): own creation based on [6]; (b): own creation based on [7]; (c): own creation based on [8]; and, (d): [9].
Figure 1. Locations and photo of Fuengirola. (a) Location of Spain within Europe. (b) Location of Costa del Sol Occidental (Malaga) within Andalusia. (c) Location of Fuengirola within Costa del Sol Occidental. (d) Photo of Fuengirola. Source: (a): own creation based on [6]; (b): own creation based on [7]; (c): own creation based on [8]; and, (d): [9].
Sustainability 11 06841 g001
Figure 2. Partial least squares-structural equation modeling (PLS-SEM) path model. Source: Own creation; output of nomogram by version 3.2.8 of PLS-SEM software “SmartPLS Professional”.
Figure 2. Partial least squares-structural equation modeling (PLS-SEM) path model. Source: Own creation; output of nomogram by version 3.2.8 of PLS-SEM software “SmartPLS Professional”.
Sustainability 11 06841 g002
Figure 3. Estimated path model. Source: Own creation; output of data by version 3.2.8 of PLS-SEM software “SmartPLS Professional”. Note: p-values in brackets.
Figure 3. Estimated path model. Source: Own creation; output of data by version 3.2.8 of PLS-SEM software “SmartPLS Professional”. Note: p-values in brackets.
Sustainability 11 06841 g003
Table 1. Negative perceptions (NP), loyalty and happiness of Fuengirola residents.
Table 1. Negative perceptions (NP), loyalty and happiness of Fuengirola residents.
LabelsSurvey Items
NP_ECO1Latent 1 negative economic perception [37,77]
ECO2Tourism increases rental prices due to the effect of holiday homes
ECO4Tourism increases prices of goods and services in the city
Increase in the cost of living
ECO7Tourism benefits mainly favor foreign investors
NP_ECO2Latent 2 of negative economic perception [46]
ECO9The improvement in the economy as a result of tourism is very seasonal
HAPPINESSConstruct of overall happiness (own elaboration with one item)
HAPPYHow happy do you feel living in Fuengirola?
LOYALConstruct of residents’ loyalty (own contribution)
LOYALWould you recommend the destination Fuengirola for a holiday being a resident? [28]
LOYALWould you recommend Fuengirola to live as a resident?(own contribution)
NP_ENVRConstruct ofenvironment [10,21,37,39]
ENV1 *Tourism affects negatively traffic congestion in the city
ENV3Fuengirola tourism causes noise and visual pollution
ENV4Tourism causes litter in the natural environment and landscape
ENV5 *Tourism affects negatively the water resources that supply the city
ENV6Tourism affects negatively the state of the beaches in Fuengirola
NP_SOCLatent Socio-cultural [36]
SOC3I think Fuengirola has reached a state of saturation in terms of tourism
SOC8 *Tourists collapses public services such as health, citizen security, to the detriment of the resident.
Source: Own creation. Notes: * Items in cursive were excluded.
Table 2. Item reliability, internal consistency reliability (CR) and convergent validity (AVE).
Table 2. Item reliability, internal consistency reliability (CR) and convergent validity (AVE).
LabelsSurvey itemsLoadingCRAVE
NP_ECO1Latent 1 of negative economic perception 0.8330.714
ECO2Tourism increases rental prices due to holiday homes0.830
ECO4Tourism increases prices of goods and services in the city 0.860
ECO7Tourism benefits mainly favor foreign investors 0.7950.565
NP_ECO2Latent 2 of negative economic perception0.719
ECO9The economy as a result of tourism is very seasonal0.740
HAPPYConstruct of happiness0.794
HAPPYHow happy do you feel living in Fuengirola? 1.0001.000
LOYALTYConstruct of loyalty1.000
LOYAL1Would you recommend the destination Fuengirola for a holiday? 0.9080.768
LOYAL2Would you recommend Fuengirola to live as a resident?0.894
NP_ENVRConstruct ofenvironment0.919
ENVR3Fuengirola tourism causes noise and visual pollution0.812
ENVR4Tourism causes litter in the natural environment and landscape 1.0001.000
ENVR6Tourism negatively affects the cleanliness of Fuengirola’s beaches1.000
NP_SOCConstruct of socio-cultural 1.0001.000
SOC3I think Fuengirola has reached saturation in terms of tourism 0.8330.714
Source: Output of data by version 3.2.8 of PLS-SEM software “SmartPLS Professional”.
Table 3. Discriminant validity (1st part).
Table 3. Discriminant validity (1st part).
Fornell-LarckerLOYALNP_ECO1NP_ECO2NP_ENVRNP_SOCUHAPPY
LOYAL0.845-----
NP_ECO1−0.1310.752----
NP_ECO2−0.0860.2941.000---
NP_ENVR−0.1290.4800.3110.876--
NP_SOCU−0.1790.5100.2220.5571.000-
HAPPY 0.571−0.128−0.185−0.116−0.1821.000
Source: Output of data by version 3.2.8 of PLS-SEM software “SmartPLS Professional”.
Table 4. Discriminant validity (2nd part).
Table 4. Discriminant validity (2nd part).
Cross LoadingLOYALNP_ECO1NP_ECO2NP_ENVNP_SOCUHAPPY
HAPPY 0.571−0.128−0.185−0.116−0.182 1.000
ECO2−0.124 0.719 0.216 0.348 0.369−0.124
ECO4−0.100 0.740 0.172 0.350 0.423−0.127
ECO7−0.073 0.794 0.273 0.384 0.360−0.042
ECO9−0.086 0.294 1.000 0.311 0.222−0.185
LOYAL1 0.830−0.156−0.061−0.074−0.197 0.461
LOYAL2 0.860−0.069−0.083−0.141−0.109 0.502
ENVR3−0.046 0.457 0.285 0.894 0.509−0.064
ENVR4−0.198 0.481 0.292 0.919 0.533−0.171
ENVR6−0.085 0.299 0.235 0.812 0.411−0.059
SOC3−0.179 0.510 0.222 0.557 1.000−0.182
HTMT RatioLOYALNP_ECO1NP_ECO2NP_ENVNP_SOCUHAPPY
LOYAL------
NP_ECO10.221-----
NP_ECO20.1100.374----
NP_ENVR0.1760.6510.335---
NP_SOCUL0.2340.6530.2220.599--
HAPPY0.7350.1660.1850.1210.182-
Source: Output of data by version 3.2.8 of PLS-SEM software “SmartPLS Professional”.
Table 5. Variance Inflation Factor (VIF).
Table 5. Variance Inflation Factor (VIF).
VIFLOYALTYNP_ECO1NP_ECO2NP_ENVRNP_SOCULHAPPY
RESLOY-----1.007
NP_ECO1--1.300---
NP_ECO2-----1.007
NP_ENVR-1.4511.300-1.000-
NP_SOCUL-1.451----
HAPPY------
Source: Output of data by version 3.2.8 of PLS-SEM software “SmartPLS Professional”.
Table 6. Total effects.
Table 6. Total effects.
Total EffectsRESLOYALNP_ECO1NP_ECO2NP_ENVRNP_SOCULHAPPY
RESLOYAL-----0.559
NP_ECO1--0.188--−0.026
NP_ECO2-----−0.137
NP_ENVR-0.4800.311-0.557−0.043
NP_SOCUL-0.3520.066--−0.009
Source: Output of data by version 3.2.8 of PLS-SEM software “SmartPLS Professional”. Significant path coefficients al 1% level.
Table 7. Effect size.
Table 7. Effect size.
Total Effects
LOYAL→HAPPYNP_ECO1→
NP_ECO2
NP_ECO2→
HAPPY
NP_ENVR→
NP_ECO1
NP_ENVR→
NP_ECO2
NP_ENVR→
NP_SOCUL
NP_SOCUL→
NP_ECO1
f20.4730.0310.0280.0810.0430.4510.125
Source: Output of data by version 3.2.8 of PLS-SEM software “SmartPLS Professional”.
Table 8. Decomposition of variance.
Table 8. Decomposition of variance.
Dependent ConstructR2AntecedentPath CoefficientCorrelation with AntecedentExplained Variance% Explained Variance
NP_SOCUL0.311 31.025%
NP_ENVR 0.557 0.5570.31031.025%
NP_ECO10.316 31.600%
NP_ENVR 0.284 0.4800.13613.632%
NP_SOCUL 0.352 0.5100.18017.952%
NP_ECO20.124 12.401%
NP_ENVR 0.221 0.3110.0696.873%
NP_ECO1 0.188 0.2940.0555.528%
HAPPY0.341 34.140%
LOYAL 0.571 0.5680.32431.594%
NP_ECO1−0.116−0.1850.021 2.549%
Source: Own creation and output of data by version 3.2.8 of PLS-SEM software “SmartPLS Professional”.
Table 9. Q2 Stone-Geisser.
Table 9. Q2 Stone-Geisser.
LOYALNP_ECO1NP_ECO2NP_ENVRNP_SOCULHAPPY
Q20.1920.1741.0000.4921.0001.000
Source: Output of data by version 3.2.8 of PLS-SEM software “SmartPLS Professional”.
Back to TopTop