• Application of machine learning to predict visitors’ green behaviours in marine protected areas: evidence from Cyprus

      Rezapouraghdam, 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.
    • Distributed leadership in DMOs: a review of literature and directions for future research

      Hristov, Dean; Ramkissoon, Haywantee; Naumov, Nick; University of Northampton; University of Derby; The Arctic University of Norway, Tromsø, Norway; University of Johannesburg, South Africa; Nexford University, Washington DC, USA (Taylor & Francis, 2020-07-27)
      Amidst key emergent challenges for Destination Management Organisations (DMOs) and destinations triggered by changes in the funding and governance landscape for tourism on a global scale, Distributed Leadership (DL) has emerged as a promising concept to provide a collaborative framework for channelling resources and leadership to cope with such changes. Current evidence from academic literature discussing the importance of embedding shared forms of leadership is scarce and few studies discuss the application of DL in the context of DMOs. The key purpose of the following conceptual study is to provide a critical overview of key DL contributions in the mainstream and DMO academic literature. The study seeks to examine the relevance of DL in the context DMOs with the purpose to stimulate future empirical investigations in the application of DL in DMO organisations.
    • Online tourism information and tourist behavior: a structural equation modeling analysis based on a self-administered survey

      Majeed, Salman; Zhou, Zhimin; Lu, Changbao; Ramkissoon, Haywantee; Shenzhen University, Shenzhen, China; Fuzhou University, Fuzhou, China; University of Derby; Monash University, Melbourne, VIC, Australia; The Arctic University of Norway, Tromsø, Norway; University of Johannesburg, Johannesburg, South Africa (Frontiers in Psychology, 2020-04-21)
      This study presents the interacting phenomena of perceptions of tourist destination online content (TDOC) and tourists’ behavioral intentions with a mediating role of tourists’ satisfaction, which is as yet under-explored in hospitality and tourism research. A model based on three main constructs, namely TDOC (with sub-constructs of online information quality and user-friendly accessibility), satisfaction, and tourists’ behavioral intentions [with sub-constructs of intentions to visit a tourist destination and electronic word-of-mouth (eWOM)], is presented to determine the growth of tourism business with the internet. Data were collected via a questionnaire-based survey from 413 tourists staying at hotels in Lahore city in Pakistan. Partial least square structural equation modeling was used to statistically analyze the gathered data. The findings indicate that tourists’ perceptions of TDOC directly influence their behavioral intentions, while tourists’ satisfaction exerts a mediating influence between tourists’ perceptions of TDOC and their behavioral intentions. Taking advantage of an economical and widespread online environment, destination marketing organizations could attract more tourists by fostering confidence in TDOC and positive eWOM to remain competitive in the long run. Important theoretical and practical implications are discussed.