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dc.contributor.authorRezapouraghdam, Hamed
dc.contributor.authorAkshiq, Arash
dc.contributor.authorRamkissoon, Haywantee
dc.date.accessioned2021-03-12T10:02:08Z
dc.date.available2021-03-12T10:02:08Z
dc.date.issued2021-03-10
dc.identifier.citationRezapouraghdam, H., Akhshik, A., & Ramkissoon, H. (2021). 'Application of machine learning to predict visitors’ green behaviours in marine protected areas: evidence from Cyprus'. Journal of Sustainable Tourism, pp. 1-28.en_US
dc.identifier.doi10.1080/09669582.2021.1887878
dc.identifier.urihttp://hdl.handle.net/10545/625652
dc.description.abstractInterpretive 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.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.relation.urlhttps://www.tandfonline.com/toc/rsus20/currenten_US
dc.relation.urlhttps://www.tandfonline.com/doi/full/10.1080/09669582.2021.1887878en_US
dc.subjectMachine learningen_US
dc.subjectFuzzy set Qualitative Comparative Analysisen_US
dc.subjectAdaptive Neuro-fuzzy Inference Systemen_US
dc.subjectPro-environmental Behaviouren_US
dc.subjectMemorable Tourism Experienceen_US
dc.subjectEnvironmental Passionen_US
dc.titleApplication of machine learning to predict visitors’ green behaviours in marine protected areas: evidence from Cyprusen_US
dc.typeArticleen_US
dc.identifier.eissn1747-7646
dc.contributor.departmentCyprus University, Lefkosa, Turkeyen_US
dc.contributor.departmentJagiellonian University, Gronostajowa 7, Krakow, Polanden_US
dc.contributor.departmentThe Arctic University of Norway, Tromsø, Norwayen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentUniversity of Johannesburg, Johannesburg, South Africaen_US
dc.identifier.journalJournal of Sustainable Tourismen_US
dcterms.dateAccepted2021-02-02
dc.author.detail786764en_US


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