Search results

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

Given the scarce research on the intention to use the metaverse by higher education students in the marketing field and since the introduction of the metaverse brings a new dimension to consumer interaction, the purpose of this study aims to understand what psychological and contextual factors such as the feeling of loneliness or perceived social risk of young people affect the incorporation of immersive reality tools in the teaching-learning process.

Currently, the use of the metaverse in university education has captured the interest of teachers and researchers. The adoption of immersive virtual platforms where students experience different forms of interaction through customized avatars appears as a promising opportunity in the field of marketing, while concerns arise about the social and psychological risks that may affect its main adopters, youth. The proposed relationships were evaluated using PLS-SEM (symmetric) and QCA (asymmetric) approaches.

The results revealed that the feeling of loneliness, as well as personality traits of openness to experience and conscientiousness, in relation to hedonic motivations, are significant for the intention to use the metaverse.

This research provides useful implications from an educational perspective, as well as applications in the commercial management of immersive platforms, taking into account their social impact, allowing for effective exploration and development of the metaverse’s potential in educational environments.

This research analyzes the relationship between the experience of feelings of loneliness and perceived social risk in the educational environment, along with the psychological and motivational factors affecting the intention to use the metaverse by higher education students which have not been investigated in the scientific literature so far.