This viewpoint article argues that the impacts of the novel coronavirus COVID-19 call for transformative e-Tourism research. We are at a crossroads where one road takes us to e-Tourism as it was before the crisis, whereas the other holds the potential to transform e-Tourism. To realize this potential, e-Tourism research needs to challenge existing paradigms and critically evaluate its ontological and epistemological foundations. In light of the paramount importance to rethink contemporary science, growth, and technology paradigms, we present six pillars to guide scholars in their efforts to transform e-Tourism through their research, including historicity, reflexivity, equity, transparency, plurality, and creativity. We conclude the paper with a call to the e-Tourism research community to embrace transformative research.

Purpose-This study examines the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research. Design/methodology/approach-The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; data reporting and visualization. Findings-Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of Business Intelligence and Big Data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big datadriven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research. Research limitations/implications-This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed. Originality/value-This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on Business Intelligence and Big Data. To the best of our knowledge, it is the first systematic literature review within hospitality and tourism research dealing with Business Intelligence and Big Data.

Tourism destinations behave as dynamic evolving complex systems, encompassing numerous factors and activities which are interdependent and whose relationships might be highly nonlinear. Traditional research in this field has looked after a linear approach:variables and relationships are monitored in order to forecast future outcomes with simplified models and to derive implications for management organisations. The limitations of this approach have become apparent in many cases, and several authors claim for a new and different attitude.While complex systems ideas are amongst the most promising interdisciplinary research themes emerged in the last few decades, very little has been done so far in the field of tourism. This paper presents a brief overview of the complexity framework as a means to understand structures, characteristics, relationships, and explores the implications and contributions of the complexity literature on tourism systems. The objective is to allow the reader to gain a deeper appreciation of this point of view.

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Tourism destinations have a necessity to innovate to remain competitive in an increasingly global environment. A pre-requisite for innovation is the understanding of how destinations source, share and use knowledge. This conceptual paper examines the nature of networks and how their analysis can shed light upon the processes of knowledge sharing in destinations as they strive to innovate. The paper conceptualizes destinations as networks of connected organizations, both public and private, each of which can be considered as a destination stakeholder. In network theory they represent the nodes within the system. The paper shows how epidemic diffusion models can act as an analogy for knowledge communication and transfer within a destination network. These models can be combined with other approaches to network analysis to shed light on how destination networks operate, and how they can be optimized with policy intervention to deliver innovative and competitive destinations. The paper closes with a practical tourism example taken from the Italian destination of Elba. Using numerical simulations the case demonstrates how the Elba network can be optimized. Overall this paper demonstrates the considerable utility of network analysis for tourism in delivering destination competitiveness.

Purpose-The growing interest in complexity science as a framework for understanding social and economic systems has had, in recent times, an influence on the study of tourism destinations. This paper aims to describe this approach and discuss its theoretical and methodological implications in terms of destination governance. Design/methodology/approach-Traditional research has adopted a reductionist approach to modelling tourist destinations: variables and relationships are embedded in simplified linear models that explain observed phenomena and allow implications for management or forecasting of future behaviours. In comparison, this paper adopts an adaptive management approach. Rather than imposing lines of action to force the evolutionary path of a system, the effect of different management actions are modelled, producing experimental results that provide information about the system that is being managed, and used to refine strategies and governance styles. Complex systems provide a theoretical framework in which this adaptive philosophy is naturally embedded. After a brief overview of the complexity framework, the paper discusses its validity and applicability to the study of tourism systems by using a set of network analysis methods and numerical simulations. Findings-This paper discusses a new perspective useful for the study of tourism destination governance, providing insights into its organisational structure and dynamic behaviour. Originality/value-The paper proposes a philosophy and practical toolset to analyse and understand a tourism destination and the relationships between its stakeholders. It discusses the implications of this new approach with regard to the governance methods.

Tourism systems have been considered more and more in the light of complexity and chaos theories. Most of the work done in this area has highlighted the reasons and the issues for this approach. A steadily growing strand of the recent literature uses it to overcome the problems of a reductionist and mechanicistic view considered unable to provide a full understanding of the structural and dynamic characteristics of tourism systems and specifically of tourism destinations. This paper continues this approach and provides a series of quantitative methods to assess the dynamics of nonlinear complex tourism systems. Design/methodology/Approach: The time series used in the paper contains data collected from a sample of 23 large (four star) hotels located in Milan, Italy. For each structure daily data of occupancy, average room rate and RevPAR (Revenue Per Available Room) were recorded for the period 2006-2009. The daily distributions of these observations are highly skewed, therefore the median of the daily values were considered. This results in three series of 1461 points per type (occupancy, room rate and RevPAR). Findings: The data confirm the complex nature of the destination system and its tendency towards a chaotic state. Additionally, high stability and long memory effects are detected. The outcomes and the implications of this analysis are examined. Research implications: A comparison of the values obtained leads to the conclusion that the series under study has a detectable level of nonlinearity, even if it does not reach the pure chaoticity of the Lorenz attractor. A first conclusion is that, as qualitatively assessed in many similar studies, the tourism destination is a complex system with a tendency to become chaotic. Originality/value: The picture obtained with the analyses conducted can be summarized by saying that the system under study exhibits an unequivocal complex nature. It tends towards a chaotic stage but does so at a slow pace. The stability of the system is quite high: it might be able to resist well transient shocks, but once led into one direction, its long memory characteristics tend to keep it on the resulting path.

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