TOURISM COMPETITIVENESS AND MOBILE DATA: A GREY RELATIONAL ANALYSIS FOR EUROPEAN ENTREPRENEURSHIP PRE & DURING PANDEMIC

Manuela  Rozalia Gabor; Petru  Alexandru Curta; Flavia  Dana Oltean

doi:10.2478/eoik-2024-0015

Authors

Manuela Rozalia Gabor ED1 – Economic Science Department, Faculty of Economics and Law, ”G.E. Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, Târgu Mureș, Romania; Department of Economic Research, Centre for Law, Economics and Business Studies, ”G.E. Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, Târgu Mureș, Romania; IOSUD – Doctoral School, ”G.E. Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, , Târgu Mureș, Romania
Petru Alexandru Curta Department of Economic Research, Centre for Law, Economics and Business Studies, ”G.E. Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, Târgu Mureș, Romania
Flavia Dana Oltean IOSUD – Doctoral School, ”G.E. Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, , Târgu Mureș, Romania

DOI:

https://doi.org/10.2478/eoik-2024-0015

Keywords:

competitiveness, tourism, GRA – Grey Relational Analysis, Google mobility data, Apple Mobility Data, General Linear Model, ANOVA with interaction effects

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 com-
munities, local administrations, governments of competitiveness des-
tinations. 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 (statisti-
cally significant for p-value< 0.05) for European tourism competitive-
ness (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 – Gen-
eral 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 Goo-
gle and Apple Mobility indices proved by reflection of overnights on
TTCI 2021 – during pandemic.

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