Integrating Big Data and economic indicators: enhancing tourism growth through destination competitiveness theory

This study investigates the impact of Big Data innovation intensity, inflation, and national income on Tourism Growth (TG) in European and BRICS tourist destinations from 2010 to 2022, framed within Destination Competitiveness Theory. By integrating technological advancements and economic factors, we aim to address gaps in existing literature that typically isolate economic impacts from technological tools. Using Artificial Neural Networks (ANN) for sensitivity analysis and various econometric models, including OLS regression, PCSEs, 2-step SYS-GMM, and MMQR, we examine how these factors contribute to tourism growth. The findings reveal that National Income and Big Data innovation intensity are the most influential drivers of tourism growth, with Inflation showing a significant negative impact. Additionally, Inflation hampers tourism by raising costs. The study underscores the importance of combining technological advancements with sound economic policies to foster sustainable tourism growth. These findings provide practical implications for policymakers and tourism stakeholders, suggesting that country-level investments in data analytics ecosystems, controlling inflation, and promoting economic growth are essential for enhancing tourism competitiveness and ensuring long-term growth.