Examining a Supply-Side Predictive Model in Tourism using Partial Least Squares Path Modeling

Abstract

This study examines the predictive relationships between tourism supply factors and tourism demand. Based on data from 154 countries, partial least squares path modeling (PLSPM) was used to test a predictive model that examines causal relationships among the environment, economy, infrastructure, and tourism demand at the aggregate country level. The results suggest that the latent economy variable (operationalized in this study as a formative, rather than a reflective, construct) has a positive, indirect effect on tourism demand. This relationship is mediated by the infrastructure and the environment, which in turn have a positive, direct effect on tourism demand, respectively. Results from this study support the operationlization of the "economy" construct as a formative construct where consumer price index (CPI), purchasing power parity (PPP), foreign direct investment (FDI), trade (TRA), and industry value added (IVA) all "form" the latent economy variable. Tourism demand, however, is a "reflective" latent construct represented by international tourist arrivals (TA) and international tourist receipts (TEXP). Thus, this predictive model presents a number of theoretical and practical contributions. First, this research expands existing theories on tourism demand by presenting a more accurate predictive model that examines the casual relationships among the economy, infrastructure, environment, and tourism. Second, understanding these complex relationships provides destination managers with an analytical framework on how certain factors can strengthen tourism demand for the destination.