Description
The effects of tourism on residents’ perceptions are a relevant topic in the literature. Residents can perceive tourism effects positively or negatively in different dimensions, such as economic, sociocultural, and environmental (e.g., Nunkoo, Smith & Ramkissoon, 2013, Hadinejad et al., 2019, Scalabrini & Remoaldo, 2022). Several studies have attempted to understand these perceptions in different contexts around the world, including cross-border areas (Scalabrini et al., 2023). These studies often examine how residents’ attitudes towards tourism development are influenced by demographic, social, and economic factors. Cluster analysis is a useful technique to explore the diversity of residents’ perceptions towards tourism and has been applied in various settings (e.g., Choi, Couto & Imon, 2020, Pavlić, Portolan & Puh, 2020).
In this context, this research aimed to analyse residents’ perceptions towards cross-border tourism, applying a cluster analysis. A survey was conducted among residents from the cross-border area between the Northern region of Portugal and Spain. The data collection took place between March and June of 2023, and a final sample of 622 valid questionnaires was obtained. A descriptive analysis of the participants’ sociodemographic and professional profiles revealed that most of them (64%) were born in the municipalities from the border of Portugal and Spain and with an average age of 31 years old (±24.215), indicating a strong connection to the local. The sample had a slightly higher proportion of women (52.7%), with an average age of 48 years (±18.261). Furthermore, a considerable percentage of the sample (67.3%) had attained secondary or higher education, suggesting a relatively educated population. Most of them were also married (53.5%). Regarding the economic aspects, the participants’ average monthly income ranged from €760.00 to €2,160.00 for 53.1% of the respondents.
To achieve the research aim, cluster analysis was performed. The first step was to use hierarchical analysis to determine the optimal number of clusters, which was three. Then, the non-hierarchical clustering method, known as K-means, was applied, which assigns each individual to the cluster whose centroid is the closest (Hair et al., 2014).
Following previous studies, this research also clustered the residents based on their perceptions of the economic, sociocultural, and environmental tourism effects. Thirty benefits and cost effects were considered and measured on a 5-point Likert scale.
The results revealed three clusters, which are described as follows. Cluster 1 (Optimist): this was the largest cluster, comprising 45.2% of the sample. It consisted mainly of women (51.1%), with a mean age of 49 years old (±19.446). This cluster was labelled as an optimist because the mean values of the effects were intermediate among the three clusters and the benefits and costs were similar, with slightly higher benefits.
Cluster 2 (Sceptics): this was the smallest cluster, involving 23.6% of the sample. It had a higher percentage of women (58.9%), with a mean age of 43 years old (±16.293). This cluster had the highest mean values for the economic, sociocultural, and environmental costs, indicating a high sensitivity to these aspects. This cluster also had a higher percentage of non-natives (42.5%) and the lowest average length of residency (26 years ±21.621). Another notable point was the perceived actual number of visitors, which was higher or too high for a larger proportion of this cluster compared to the others.
Cluster 3 (Enthusiasts): this cluster comprised 31.2% of the sample. It had a majority of women (50.8%), with a mean age of 50 years old (±17.225). This cluster had the highest mean values for the economic, sociocultural and environmental benefits, indicating a positive perception of the tourism effects. This cluster also had the highest percentage of natives (68.4%), which could reflect a stronger sense of belonging to the destination. Regarding the perceived actual number of visitors, 60.7% of the respondents considered it low or moderate.
The results corroborated previous findings in other contexts that analysed residents’ perceptions, such as women being more negative in some situations (e.g., Alrwajfah, Almeida-Gacía & Cortés-Macías, 2019, Scalabrini & Remoaldo, 2020), and the sense of belonging enhancing the positive perception of the tourism effects (e.g., Stylidis, 2018 Alrwajfah, Almeida-Gacía & Cortés-Macías, 2019, Gannon, Rasoolimanesh & Taheri, 2020). The cluster analysis is important to understand how tourism affects different groups of residents and can support a more sustainable planning of the activity, considering the opinion of this key stakeholder.
Keywords: Residents’ perceptions; Tourism effects; Cross-border tourism; Clustering.
References
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