Tourism competitiveness of Italian municipalities

Abstract

The purpose of the paper is the study of tourism competitiveness and its determinants for the almost 8000 Italian municipalities. The partial least squares method is used on secondary data. Competitiveness is a latent factor measured by a number of reflective indicators, and influenced by some latent determinants derived within a formative scheme. The main findings show the importance of competitive advantages as key drivers of tourism competitiveness. Local specialized areas in tourism are identified through the destination competitiveness scores. The work contributes to the study of local competitiveness, also giving evidence on how it is possible to cover the complex feature of tourism competitiveness with secondary data. Moreover, as the municipality is generally the basic territorial unit for identifying specialized local areas, future research can apply these results in drawing a map of Italy with sounder areas for tourism analysis. Main limitations are concerned with the imputation of missing values, as data on tourist flows of smaller municipalities are not released for confidentiality issues. Moreover, the analysis at local level limits the use of some indicators, which are recognized as important determinants of competitiveness (prices of tourist services, transportation costs).

Formative-reflective scheme for the assessment of tourism destination competitiveness: an analysis of Italian municipalities

“…( 2014 ), Magrini and Grassini ( 2019 ), Conti et al. ( 2020 ). In this case, the formative-reflective scheme may represent the cause-effect relationships hidden in the definition of TDC.…”

“…Although the data is a bit outdated, they allow a comparison with recent empirical analysis on Italian municipalities (Conti et al. 2020 ). Our dataset consists of 1575 municipalities out of 7903, which are those for which ISTAT released data on tourist flows in 2015.…”

“…At the best of our knowledge, only Alves and Nogueira ( 2015 ) and Conti et al. ( 2020 ) have analysed TDC on municipality data using structural equation models. Alves and Nogueira ( 2015 ) applied PLS-PM for studying the competitiveness of Brazilian municipalities and found the predominance role of the ‘Tourism Infrastructure’ construct (i.e., sources of competitive advantages), followed by ‘Heritage and Culture’ (i.e., sources of comparative advantages).…”

“… 2014 ; Magrini and Grassini 2019 ; Conti et al. 2020 ). In the operationalization of TDC, PLS-PM appears more appropriate than composite indicators, because it allows the representation of potential cause-effects relationships hidden in the complex definition of TDC (Mazanec et al.…”

Big data for corporate social responsibility: blockchain use in Gioia del Colle DOP

“…In the new competitive challenge of globalization, it is the latter that must integrate the concerns of customers, employees, and environmental criteria for a higher quality of the final product with the ultimate aim of improving their reputation and staying on the market (Conti et al 2019 ). CSR has the function of bringing out hidden costs linked to waste and management inefficiencies, both from the point of view of the use of resources and the inadequate enhancement of the company profile and therefore sets itself the goal of systematizing investments and promoting added value created for the various business stakeholders.…”