Contributions to the design of regional tourism innovation policies: Evaluation of determinants in Latin America - José Dias Lopes; Jo\~ao Estêv\~ao (Journal of Innovation & Knowledge (JIK), 2024)
In recent years, the transnational and regional dimensions of tourism have strengthened. Unlike in the past, we can now consider regional tourism policies, such as innovation policies. The recent creation of the UN Tourism Office for the Americas is a step in this direction. Within this framework, this study analyzes the competitive factors of tourism and innovation in Latin America. This study uses cluster analysis, multidimensional scaling, and FsQCA to answer two questions: Are there similarities in the tourism development profiles of different Latin American countries that would allow the design of common and specific tourism policies aimed at innovation and growth in each profile? If common profiles (regional clusters) exist, what are the determining factors that have driven the recent evolution of tourism performance? To answer these questions, this study uses data from the Travel and Tourism Competitiveness Index and the Global Innovation Index. The study shows that it is possible to identify two clusters of countries in Latin America with similar levels of tourism competitiveness. The first cluster includes Brazil, Mexico, Colombia, and Peru, which have higher but more heterogeneous levels of competitiveness. Additionally, there is a second cluster of more homogeneous countries with lower levels of competitiveness. The study also made it possible to identify a set of factors related to the enabling environment (business environment, safety and security, health and hygiene, human resources and labor market, qualification of the labor force, labor market, and ICT readiness) as pivotal for affirming the sector's competitiveness. Therefore, this study makes important contributions to the design of innovation policies in this sector.
Keywords: L38; O00; O54; Z30; Z38; 650; Tourism; Innovation; Competitiveness; Clusters analysis; Multidimensional scaling; FsQCA