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<p>Segmentation is crucial for developing sustainability strategies, and tourists’ perceptions of destinations offer important segmentation criteria. This paper aims to gain insight into the tourist segments with similar perceived sustainability and satisfaction levels in three different Spanish destination types: urban, rural, and sun and sea. Perceived sustainability is based on tourist perceptions and is measured as a multidimensional construct. Using a sample of 1406 Spanish tourists, we use machine learning algorithms for clustering. The results show four tourist segments: <i>Enthusiastic</i> (Segment 1), <i>Moderately enthusiastic</i> (Segment 3), <i>Moderate</i> (Segment 2), and <i>Critic</i> (Segment 4). These segments differ considerably in terms of the dimensions of perceived sustainability (environmental, socio-cultural, and economic) and satisfaction. They also differ regarding socio-demographic characteristics: age and education, and especially destination type. <i>Enthusiastic</i> and <i>Moderately enthusiastic</i> display the highest perceived sustainability and satisfaction, and mostly visit rural destinations. While <i>Critic</i> and <i>Moderate</i> show the lowest perceived sustainability and satisfaction, and mainly visit urban destinations. Moreover, <i>Critic</i> are the most educated and on average the youngest. The research showcases the potential of machine learning in creating more nuanced and dynamic tourist segments, thereby improving destination management and marketing strategies regarding sustainability, mainly, in rural destinations.</p>

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Segmenting tourists based on perceived sustainability and satisfaction using machine learning

  • 期刊论文

  • 发表日期：

    2025年1月23日

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  • Lidia Vidal-Meliá,
  • Miguel Ángel Moliner,
  • Diego Monferrer,
  • Marta Estrada,
  • Keith Quille

摘要

Segmentation is crucial for developing sustainability strategies, and tourists’ perceptions of destinations offer important segmentation criteria. This paper aims to gain insight into the tourist segments with similar perceived sustainability and satisfaction levels in three different Spanish destination types: urban, rural, and sun and sea. Perceived sustainability is based on tourist perceptions and is measured as a multidimensional construct. Using a sample of 1406 Spanish tourists, we use machine learning algorithms for clustering. The results show four tourist segments: Enthusiastic (Segment 1), Moderately enthusiastic (Segment 3), Moderate (Segment 2), and Critic (Segment 4). These segments differ considerably in terms of the dimensions of perceived sustainability (environmental, socio-cultural, and economic) and satisfaction. They also differ regarding socio-demographic characteristics: age and education, and especially destination type. Enthusiastic and Moderately enthusiastic display the highest perceived sustainability and satisfaction, and mostly visit rural destinations. While Critic and Moderate show the lowest perceived sustainability and satisfaction, and mainly visit urban destinations. Moreover, Critic are the most educated and on average the youngest. The research showcases the potential of machine learning in creating more nuanced and dynamic tourist segments, thereby improving destination management and marketing strategies regarding sustainability, mainly, in rural destinations.