In the era of social media, online reviews have become a crucial factor influencing the exposure of tourist destinations and the decision-making of potential tourists, exerting a profound impact on the sustainable development of these destinations. However, the influence of review valence on visit intention, especially the role of affective commitment and reputation (ability vs. responsibility), remains unclear. Drawing on emotion as a social information theory, this paper aims to elucidate the direct impact of different review valences on tourists’ visit intentions, as well as mediating mechanisms and boundary conditions. Three experiments indicate that positive (vs. negative) reviews can activate stronger affective commitment and visit intention, with affective commitment also playing a mediating role. Additionally, destination reputation significantly moderates the after-effects of review valences. More specifically, a responsibility reputation (compared with an ability reputation) weakens the effect of negative valence on affective commitment and visit intention. This study provides valuable theoretical insights into how emotional elements in online reviews influence the emotions and attitudes of potential tourists. Particularly for tourism managers, review valence and responsibility reputation hold practical significance in destination marketing.
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This paper presents four Destination Stewardship scenarios based on different levels of engagement from the public and private sector. The scenarios serve to support destination stakeholders in assessing their current context and the pathway towards greater stewardship. A Destination Stewardship Governance Diagnostic framework is built on the scenarios to support its stakeholders in considering how to move along that pathway, identifying the key aspects of governance that are either facilitating or frustrating a destination stewardship approach, and the required actions and resources to achieve an improved scenario. Moreover, the scenarios and diagnostic framework support stakeholders to come together to debate and scrutinise how tourism is managed in a way that meets the needs of the destination, casting new light on the barriers and opportunities for greater destination stewardship.
This paper presents a Decision Support System (DSS) that helps companies with corporate reputation (CR) estimates of their respective brands by collecting provided feedbacks on their products and services and deriving state-of-the-art key performance indicators. A Sentiment Analysis Engine (SAE) is at the core of the proposed DSS that enables to monitor, estimate, and classify clients’ sentiments in terms of polarity, as expressed in public comments on social media (SM) company channels. The SAE is built on machine learning (ML) text classification models that are cross-source trained and validated with real data streams from a platform like Trustpilot that specializes in user reviews and tested on unseen comments gathered from a collection of public company pages and channels on a social networking platform like Facebook. Such crosssource opinion analysis remains a challenge and is highly relevant in the disciplines of research and engineering in which a sentiment classifier for an unlabeled destination domain is assisted by a tagged source task (Singh and Jaiswal, 2022). The best performance in terms of F1 score was obtained with a multinomial naive Bayes model: 0,87 for validation and 0,74 for testing.
In the Glasgow declaration (2021), the tourism sector promised to reduce its CO2 emissions by 50% and reduce them to zero by 2050. The urgency is felt in the sector, and small steps are made at company level, but there is a lack of insight and overview of effective measures at global level.This study focuses on the development of a necessary mix of actions and interventions that the tourism sector can undertake to achieve the goal of a 50% reduction in greenhouse gases by 2030 towards zero emissions by 2050. The study contributes to a better understanding of the paths that the tourism sector can take to achieve this and their implications for the sector. The aim of the report is to spark discussion, ideas and, above all, action.The study provides a tool that positively engages the sector in the near and more distant future, inspires discussion, generates ideas, and drives action. In addition, there will be a guide that shows the big picture and where the responsibilities lie for the reduction targets. Finally, the researchers come up with recommendations for policymakers, companies, and lobbyists at an international and European level.In part 1 of the study, desk research is used to lay the foundation for the study. Here, the contribution of tourism to global greenhouse gas emissions is mapped out, as well as the image and reputation of the sector on climate change. In addition, this section describes which initiatives in terms of, among other things, coalitions and declarations have already been taken on a global scale to form a united front against climate change.In part 2, 40 policies and measures to reduce greenhouse gas emissions in the sector are evaluated in a simulation. For this simulation, the GTTMdyn simulation model, developed by Paul Peeters from BUAS, is used which works on a global scale and shows the effect of measures on emissions, tourism, transport, economy, and behaviour. In this simulation, the researchers can 'test' measures and learn from mistakes. In the end one or more scenarios will; be developed that reach the goals of 50% reduction in 2030 and zero emissions in 2050. In part 3, the various actions that should lead to the reduction targets are tested against the impacts on the consequences for the global tourism economy, its role in providing leisure and business opportunities and the consequences for certain destinations and groups of industry stakeholders. This part will be concluded with two workshops with industry experts to reflect on the results of the simulation.Part 4 reports the results of the study including an outline of the consequences of possibly not achieving the goal. With this, the researchers want to send a warning signal to stakeholders who may be resistant to participating in the transition.
