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This research study focuses on the positive impact of eWOM in determining the selection of eco-tourism destinations. A positive review of tourism destinations has significant images, content and experience. Its natural beauty, environment-friendly practices, sustainability, and other factors determine an eco-tourism destination. The paper's findings suggest that tourists research before selecting a destination. An honest review of a previous consumer has more impact on a potential customer. Travellers opt for online and offline media to judge the authenticity of the destination. This research paper also examines how electronic word of mouth (e-WOM) impacts the tourists' decision to select and visit eco-friendly destinations?. [ABSTRACT FROM AUTHOR]

This paper aims to develop a behavioral intention theoretical model using destination image, tourist motivation, tourist satisfaction, tourist experience, and re-visit behavioral intention constructs. The research analyzes and measures the direct impact of destination image, tourist motivation, and tourist satisfaction to re-visit visitors and the role of tourist experience as a moderator variable. These relationships will be investigated for a sample of international tourists visiting Hue city in Vietnam. A total of 421 questionnaires were returned, and the data were analyzed using Structural Equation Modeling (SEM). A path analysis results indicated that destination image and tourist satisfaction get affected to re-visit tourist sites, and tourist experience has significantly affected behavioral intention to re-visit visitors. The implications and limitations of the study would be discussed and presented in conclusion. The outcomes of the study have a significant impact on destination managers. [ABSTRACT FROM AUTHOR]

The study utilizes robust neural network mechanisms to analyse and predict Filipinos' sharing tendencies of capacity-constrained resources on sharing economy platforms. Specifically, it underscores insights that supplement marketers' efforts in the peer-to-peer accommodation sector by clustering various groups with their preferred choice of shared resources. The literature answers market related gaps in the implementation of sharing economy enterprises in the tourism industry by advising possible avenues of consolidation. The investigation leads to further discourse by experimenting with demographic level data to split the market into groups for service design and delivery. The paper attempts to contribute to the existing corpus of knowledge by introducing machine learning algorithms in the sharing economy perspective. [ABSTRACT FROM AUTHOR]