It’s so hot: predicting climate change effects on urban tourists’ time–space experience

Abstract

Progressive changes in mean annual temperatures are arguably the strongest evidence of ongoing climate change. In destinations with a Mediterranean climate, in contrast to the colder months, during summer, rising air temperatures are believed to inhibit tourist movements and activities, and consequently affect tourists' evaluation of and satisfaction with their experiences. To the best of our knowledge, no previous study has investigated the potential impact of climate change on tourists' time-space activity using actual behavioural tracking-based information. Data collected via GPS technology and a post-visit survey of tourists (n = 404) visiting Lisbon during the summer were analysed via structural equation modelling (PLS-SEM). The results report empirical evidence of the present impact of (summer) weather on urban tourists' time-space activity and on their intra-destination experience evaluation. Specifically, maximum air temperature is found to have a significant negative effect on overall satisfaction, while the meteorological conditions of the entire day reveal a significant impact on tourists' activities and movements. The results are particularly useful for the sustainable adaptive management of urban attractions and destinations that are especially vulnerable to climate change, as well as in managing its adverse impact on tourists' experiences.

Home and away: cross-contextual consistency in tourists’ pro-environmental behavior

“…The data was input into SPSS v.23 and transformed and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with Smart PLS3 software (Caldeira & Kastenholz, 2018). PLS-SEM estimates partial model structures by combining principal components analysis with ordinary least squares regressions, it is usually used as an alternative to Covariance Based SEM (CB-SEM).…”

“…Hair et al (2014) suggested a two-step process to ensure the validity and reliability of PLS-SEM. First, evaluate the measurement (outer) model, and the relationship between the constructs and the associated indicators; then evaluate the structural (inner) model and analyze the hypothetical relationship between the constructs in the theoretical model (Caldeira & Kastenholz, 2018). Each of the various hypothetical relationships is associated with a corresponding causal path associated with each pair of structures in the structural model (Henseler et al, 2009).…”

“…Each of the various hypothetical relationships is associated with a corresponding causal path associated with each pair of structures in the structural model (Henseler et al, 2009). Standardized path coefficients and significance levels provide evidence of the quality of the inner model, where t values are obtained through a bootstrap procedure (5000 samples) (Caldeira & Kastenholz, 2018).…”

Tourists’ perceptions of climate: Application of machine learning to climate and weather data from Chinese social media

“…The climate and weather quality of the destination perceived by tourists is the key factor affecting the overall travel satisfaction and loyalty of many tourists. During summer in Lisbon, Portugal, the maximum air temperature is found to have a significant negative effect on overall satisfaction (Caldeira and Kastenholz 2018). Perceived weather quality directly and indirectly affects tourist satisfaction and revisit intention and is correlated with the perceived quality of physical attributes and services (Kim et al 2017).…”

“…Changes in weather patterns as a result of climate change will have widespread consequences for tourism demand (Hübner and Gössling 2012). Climate and weather affect tourists' satisfaction and intentions to revisit (Scott et al 2008;Kim et al 2017;Jeuring 2017;Caldeira and Kastenholz 2018). Curnock et al (2019) also analyzed the relationship among tourists' emotions, threat perception, and values related to their attitude toward climate change.…”