Understanding tourism destination competitiveness complexity

Purpose The topic of tourism destination competitiveness has resonated in academic research for decades. This paper aims to enrich the current research stream by introducing a system-based conceptualisation that links the complexity of destination competitiveness with managerial approaches required to address it. Design/methodology/approach The study uses a qualitative research design that combines thematic analysis of 20 conceptual models with principles of system dynamics modelling. It conceptualises destination competitiveness by developing an innovative model following system dynamics methodology. Findings The model enables a thorough analysis of destination competitiveness. Its interpretation through three spinal loops introduces a new lens that translates the model’s complexity into three managerial perspectives: resource-based, sustainability-based and systems-based. These perspectives reveal how destination management should evolve in response to growing competitiveness complexity. Research limitations/implications The model captures a limited set of determinants as the essentials of destination competitiveness. Its interpretation is qualitative, based on the purposive generalisation of three management perspectives, although it also encompasses relations beyond management. Future research may validate the model and transform it into a quantitative form required for simulation. Practical implications This paper provides destination managers with a conceptual tool for recognising interdependencies among competitiveness determinants and adopting more adaptive and collaborative management styles in a dynamic tourism environment. Originality/value This paper presents an original system-based synthesis that integrates thematic analysis with system dynamics modelling. It contributes to understanding destination competitiveness as a complex system. It translates this insight into an interpretive managerial framework, overcoming the static and fragmented nature of previous models.