Methodological and theoretical advancements in social impacts of tourism research

This lead article introduces the double special issue dedicated to methodological and theoretical advancements in social impacts of tourism research. We begin by providing an overview of five key developmental stages of research within this area: Definitions, typologies, and conceptual model development;the advent of case study-based, atheoretical empirical inquiry;scale design, development, and testing;further scale development/refinement and theoretical application;and theoretical model development and testing. Brief evolutionary histories of the methodological and theoretical advancements of research dedicated to social impacts of tourism are then discussed. This includes a review of the most pertinent predictor variables (along with a visual display of each and key studies) in explaining residents' perceptions of social impacts of tourism and a thorough review of most frequently used theoretical frameworks. Following this, brief synopses of the articles are provided along with key themes (e.g. resident-tourist relationships, social impacts and residents' attitudes, residents' empowerment, overtourism, and methodologies) and salient points of each work. In closing, we suggest numerous lines of inquiry that will continue to advance research into social impacts of tourism. Though these studies were undertaken prior to the COVID-19 outbreak, we emphasize that future work should be designed with the pandemic in mind.Supplemental data for this article is available online at https://doi.org/10.1080/09669582.2022.2046011

Spatiotemporal evolution of COVID-19 in Portugal's Mainland with self-organizing maps.

BACKGROUND: Self-Organizing Maps (SOM) are an unsupervised learning clustering and dimensionality reduction algorithm capable of mapping an initial complex high-dimensional data set into a low-dimensional domain, such as a two-dimensional grid of neurons. In the reduced space, the original complex patterns and their interactions can be better visualized, interpreted and understood. METHODS: We use SOM to simultaneously couple the spatial and temporal domains of the COVID-19 evolution in the 278 municipalities of mainland Portugal during the first year of the pandemic. Temporal 14-days cumulative incidence time series along with socio-economic and demographic indicators per municipality were analyzed with SOM to identify regions of the country with similar behavior and infer the possible common origins of the incidence evolution. RESULTS: The results show how neighbor municipalities tend to share a similar behavior of the disease, revealing the strong spatiotemporal relationship of the COVID-19 spreading beyond the administrative borders of each municipality. Additionally, we demonstrate how local socio-economic and demographic characteristics evolved as determinants of COVID-19 transmission, during the 1st wave school density per municipality was more relevant, where during 2nd wave jobs in the secondary sector and the deprivation score were more relevant. CONCLUSIONS: The results show that SOM can be an effective tool to analysing the spatiotemporal behavior of COVID-19 and synthetize the history of the disease in mainland Portugal during the period in analysis. While SOM have been applied to diverse scientific fields, the application of SOM to study the spatiotemporal evolution of COVID-19 is still limited. This work illustrates how SOM can be used to describe the spatiotemporal behavior of epidemic events. While the example shown herein uses 14-days cumulative incidence curves, the same analysis can be performed using other relevant data such as mortality data, vaccination rates or even infection rates of other disease of infectious nature.

Informing the Design of Data Visualization Tools to Monitor the COVID-19 Pandemic in Portugal: A Web-Delphi Participatory Approach.

Due to the large amount of data generated by new technologies and information systems in the health arena, health dashboards have become increasingly popular as data visualization tools which stimulate visual perception capabilities. Although the importance of involving users is recognized in dashboard design, a limited number of studies have combined participatory methods with visualization options. This study proposes a novel approach to inform the design of data visualization tools in the COVID-19 context. With the objective of understanding which visualization formats should be incorporated within dashboards for the COVID-19 pandemic, a specifically designed Web-Delphi process was developed to understand the preferences and views of the public in general regarding distinct data visualization formats. The design of the Delphi process aimed at considering not only the theory-based evidence regarding input data and visualization formats but also the perception of final users. The developed approach was implemented to select appropriate data visualization formats to present information commonly used in public web-based COVID-19 dashboards. Forty-seven individuals completed a two-round Web-Delphi process that was launched through a snowball approach. Most respondents were young and highly educated and expressed to prefer distinct visualisation formats for different types of indicators. The preferred visualization formats from the participants were used to build a redesigned version of the official DGS COVID-19 dashboard used in Portugal. This study provides insights into data visualization selection literature, as well as shows how a Delphi process can be implemented to assist the design of public health dashboards.

The Influence of Place Attachment on Social Distance: Examining Mediating Effects of Emotional Solidarity and the Moderating Role of Interaction

Building on common identity theory and intergroup contact theory, this study sought to further understanding of people–place relationships by developing a holistic theoretical model to scrutinize place attachment as an antecedent of social distance, mediated by emotional solidarity and moderated by frequency of contact between tourists and residents. Visitor data analyzed with SEM revealed that place dependence is a significant predictor of social distance given it affects affinity positively and avoidance negatively, both of which are mediated by the three dimensions of emotional solidarity. Furthermore, the mediated relationships (via emotional solidarity) between place attachment and social distance vary by level of visitors' frequency of interaction with residents. This study expands current theorization by examining the merits of emotional solidarity as an affective link in a tourist cognitive-behavioral model. From a practical standpoint, DMOs need to understand these construct linkages and include residents in their marketing strategies to increase repeat visitation. [ABSTRACT FROM AUTHOR] Copyright of Journal of Travel Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Customer Acceptance of Autonomous Vehicles in Travel and Tourism

Since the COVID-19 pandemic has significantly increased the use of personal vehicles in travel, adoption of self-driving autonomous vehicles can radically transform the travel industry. Thus, this study develops and tests a conceptual autonomous vehicle acceptance model that identifies hedonic motivation, trust in autonomous vehicles and social influence as critical determinants of performance expectancy, perceived risk and emotions, which determine travelers? intentions to use autonomous vehicles (AVs) utilizing the Cognitive Appraisal Theory and the Artificially Intelligent Device Use Acceptance model as conceptual frameworks. Findings indicate that trust is the most powerful determinant of performance expectancy and essential to decrease risk perceptions. Furthermore, performance expectancy and hedonic motivation are critical determinants of travelers? positive emotions, which in turn determines the acceptance of AVs. Contribution to theoretical knowledge and implications for practice are provided, and limitations and recommendations for future studies are discussed.

Geostatistical COVID-19 infection risk maps for Portugal.

The rapid spread of the SARS-CoV-2 epidemic has simultaneous time and space dynamics. This behaviour results from a complex combination of factors, including social ones, which lead to significant differences in the evolution of the spatiotemporal pattern between and within countries. Usually, spatial smoothing techniques are used to map health outcomes, and rarely uncertainty of the spatial predictions are assessed. As an alternative, we propose to apply direct block sequential simulation to model the spatial distribution of the COVID-19 infection risk in mainland Portugal. Given the daily number of infection data provided by the Portuguese Directorate-General for Health, the daily updates of infection rates are calculated by municipality and used as experimental data in the geostatistical simulation. The model considers the uncertainty/error associated with the size of each municipality's population. The calculation of daily updates of the infection risk maps results from the median model of one ensemble of 100 geostatistical realizations of daily updates of the infection risk. The ensemble of geostatistical realizations is also used to calculate the associated spatial uncertainty of the spatial prediction using the interquartile distance. The risk maps are updated daily and show the regions with greater risks of infection and the critical dynamics related to its development over time.