A systemic and cybernetic perspective on causality, big data and social networks in tourism

Abstract

Purpose

The purpose of this paper is to propose a mathematical model to determine invariant sets, set covering, orbits and, in particular, attractors in the set of tourism variables. Analysis was carried out based on an algorithm and applying an interpretation of chaos theory developed in the context of General Systems Theory and Big Data.

Design/methodology/approach

Tourism is one of the most digitalized sectors of the economy, and social networks are an important source of data for information gathering. However, the high levels of redundant information on the Web and the appearance of contradictory opinions and facts produce undesirable effects that must be cross-checked against real data. This paper sets out the causal relationships associated with tourist flows to enable the formulation of appropriate strategies.

Findings

The results can be applied to numerous cases, for example, in the analysis of tourist flows, these findings can be used to determine whether the behaviour of certain groups affects that of other groups, as well as analysing tourist behaviour in terms of the most relevant variables.

Originality/value

The technique presented here breaks with the usual treatment of the tourism topics. Unlike statistical analyses that merely provide information on current data, the authors use orbit analysis to forecast, if attractors are found, the behaviour of tourist variables in the immediate future.

Citation

Lloret-Climent, M., Montoyo, A., Gutierrez, Y., Muñoz Guillena, R. and Alonso, K. (2019), "A systemic and cybernetic perspective on causality, big data and social networks in tourism", Kybernetes, Vol. 48 No. 2, pp. 287-297. https://doi.org/10.1108/K-02-2018-0084