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Application of machine learning to predict visitors’ green behaviours in marine protected areas: evidence from CyprusRezapouraghdam, Hamed; Akshiq, Arash; Ramkissoon, Haywantee; Cyprus University, Lefkosa, Turkey; Jagiellonian University, Gronostajowa 7, Krakow, Poland; The Arctic University of Norway, Tromsø, Norway; University of Derby; University of Johannesburg, Johannesburg, South Africa (Taylor & Francis, 2021-03-10)Interpretive marine turtle tours in Cyprus yields an alluring ground to unfold the complex nature of pro-environmental behavior among travelers in nature-based destinations. Framing on Collins (2004) interaction ritual concept and the complexity theory, the current study proposes a configurational model and probes the interactional effect of visitors’ memorable experiences with environmental passion and their demographics to identify the causal recipes leading to travelers’ sustainable behaviors. Data was collected from tourists in the marine protected areas located in Cyprus. Such destinations are highly valuable not only for their function as an economic source for locals but also as a significant habitat for biodiversity preservation. Using fuzzy-set Qualitative Comparative Analysis (fsQCA), this empirical study revealed that three recipes predict the high score level of visitors’ environmentally friendly behavior. Additionally, an adaptive neuro-fuzzy inference system (ANFIS) method was applied to train and test the patterns of visitors’ pro-environmental behavior in a machine learning environment to come up with a model which can best predict the outcome variable. The unprecedented implications on the use of technology to simulate and encourage pro-environmental behaviors in sensitive protected areas are discussed accordingly.