Tourism Competitiveness and Mobile Data: A Grey Relational Analysis for European Entrepreneurship Pre & During Pandemic

Abstract

The tourism country’s competitiveness is important especially when countries strive for bigger market shares as European countries are. The COVID-19 pandemic has had devastating effects on tourism and the entire tourism industry must be rethinking and reshaping given some opportunities and challenges to the entrepreneurs, local communities, local administrations, governments of competitiveness destinations. In this paper we measured if the Google and Apple mobility indices cand predict (or not) the movement of TTCI during pandemic compared with pre-pandemic TTCI ranks by using grey system theory and statistical methods. The aim of the paper is to validate the Grey Relational Analysis (GRA) as identifier of the good predictor (statistically significant for p-value< 0.05) for European tourism competitiveness (measured by TTCI) in particular conditions, especially before and during COVID-19 pandemic time by take into consideration the Google Mobility and Apple Mobility data and their relationship with overnight stays for 11 European countries. To validate the GRA as method for good and accurate predictor for tourism competitiveness for TTCI 2021 (during COVID-19 pandemic time) a GLM – General Linear Model ANOVA with interaction effects and Tukey HSD Post Hoc Multiple Comparisons was applied. Our results validated the powerful of GRA for tourism competitiveness, statistically significant according to GLM with interaction effects, and emphasis (especially for entrepreneurs) that the overnight stays will give the right rank on top of tourism competitiveness, prior to other ITC support as Google and Apple Mobility indices proved by reflection of overnights on TTCI 2021 – during pandemic.

Keywords

• competitiveness
• tourism
• gra – grey relational analysis
• google mobility data
• apple mobility data
• general linear model
• anova with interaction effects
• z32
• q01
• o35
• l83