Symptoms of complexity in a tourism system

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.Comment: 32 pages, 3 figures, 1 table; accepted in Tourism Analysi

Computational modelling and simulations in tourism: A primer

Abstract The aim of this contribution is to briefly sketch and discuss the main issues that concern the activities of modelling and simulating complex phenomena and systems. The focus is on numerical and computational techniques. We discuss the validity of these methods and examine the different steps to be taken for ensuring a correct, accurate and reliable implementation. The approach is essentially of general methodological nature, regardless of specific techniques or tools

Tourism networks and computer networks

The body of knowledge accumulated in recent years on the structure and the dynamics of complex networks has offered useful insights on the behaviour of many natural and artificial complex systems. The analysis of some of these, namely those formed by companies and institutions, however, has proved problematical mainly for the difficulties in collecting a reasonable amount of data. This contribution argues that the World Wide Web can provide an efficient and effective way to gather significant samples of networked socio-economic systems to be used for network analyses and simulations. The case discussed refers to a tourism destination, the fundamental subsystem of an industry which can be considered one of the most important in today's World economy.Comment: 10 pages, 3 figures, 2 table

The relevance of mixed methods for network analysis in tourism and hospitality research

Purpose Taking stock of extant hospitality and tourism research using social network analysis approaches, this study highlights why using either quantitative or qualitative approaches to examine social networks can be misleading and generate potentially biased findings. Indeed, purely qualitative and purely quantitative studies display limitations. The purpose of this study is to provide methodological insights by suggesting that mixed methods can be suitably used, depending on the specific research questions. Design/methodology/approach The study consists of an analysis and critical discussion of the methods used in a number of papers leveraging social network approaches to study social networks in tourism and hospitality. The authors describe the benefits and limitations of each method studies considered are examined based on a number of aspects. Findings More than half of the studies classified as network studies adopt quantitative designs and quantitative methods including statistical analyses and observational data. Mixed methods study is a minority and they are almost never labeled as mixed methods. A relevant portion of qualitative studies increasingly embeds a number of rudimentary statistical analyses. With an example, the authors also discuss that purely quantitative or purely qualitative methods can lead to discrepant results, and thus, the authors encourage scholars to embrace mixed method research designs such as explanatory or exploratory sequential designs. Advanced researchers might attempt in the future to embrace transformative, embedded or multiphase mixed methods. Research limitations/implications This study is based on academic papers and research published before 2019. A rich research agenda is designed. Originality/value This study contributes to explore the way social networks have been dealt with in tourism and hospitality research so far, by advancing a proposal to adopt mixed methods in the form of explanatory or exploratory sequential designs. To the best of the knowledge, it is the first study addressing methodological pitfalls in extant network-based research within the tourism and hospitality domain

Looking into the future of complex dynamic systems

The desire to know and foresee the future is naturally bound to human nature. Traditional forecasting methods have looked after reductionist linear approaches: variables and relationships are monitored in order to foresee future outcomes with simplified models and to derive theoretical and practical implications. The limitations of this attitude have become apparent in many cases, mainly when dealing with dynamic evolving complex systems, that encompass numerous factors and activities which are interdependent and whose relationships might be highly nonlinear, resulting in an inherent unpredictability of their long-term behavior. Complexity science ideas are important interdisciplinary research themes emerged in the last few decades that allow to tackle the issue, at least partially. This paper presents a brief overview of the complexity framework as a means to understand structures, characteristics, relationships, and explores the most important implications and contributions of the literature on the predictability of a complex system. The objective is to allow the reader to gain a deeper appreciation of this approach

Looking into the future of complex dynamic systems

The desire to know and foresee the future is naturally bound to human nature. Traditional forecasting methods have looked after reductionist linear approaches: variables and relationships are monitored in order to foresee future outcomes with simplified models and to derive theoretical and practical implications. The limitations of this attitude have become apparent in many cases, mainly when dealing with dynamic evolving complex systems, that encompass numerous factors and activities which are interdependent and whose relationships might be highly nonlinear, resulting in an inherent unpredictability of their long-term behavior. Complexity science ideas are important interdisciplinary research themes emerged in the last few decades that allow to tackle the issue, at least partially. This paper presents a brief overview of the complexity framework as a means to understand structures, characteristics, relationships, and explores the most important implications and contributions of the literature on the predictability of a complex system. The objective is to allow the reader to gain a deeper appreciation of this approach