Causal Modelling Methodology in Tourism: An Empirical Analysis

Article excerpt

This study captures the relationship between three constructs, i.e. the economy, the infrastructure, and tourism to explain the direction and magnitude of the relationship between these constructs. To do so, an apriori multidimensional causal model was hypothesised and tested using Structural Equations Modelling methodology. The economy was hypothesised to have a positive significant relationship with the infrastructure construct and a negative relationship with the tourism construct, while the infrastructure construct was hypothesised to have a positive significant relationship with the tourism construct. The sample comprised of 189 countries, which was extracted from the World Bank (1995) data set. Results supported the structural relationship between economy and infrastructure and infrastructure and tourism. Contrary to the hypothesised relationship, the direct relationship between the economy and tourism was found to be insignificant. The significance of this study is in terms of verifying causal relationships between constructs at the aggregate level using countries as the unit of analysis.


The importance of tourism development and its contribution to the growth of countries has been well documented through prior research. "From the most optimistic perspective, tourism may be seen to foster economic growth, improve human living standards, promote intercultural understanding, and nurture world peace" (BeIk and Costa, 1995). These contributions have been even more pronounced to the growth of developing countries encouraged by international organisations such as UNESCO, The United Nations, the International Monetary Fund, The World Tourism Organisation, and the World Bank (Belk and Costa, 1995). The contributions of tourism to a given region or country is well researched in terms of the economic and social perspective vis-à-vis effects on GDP, multiplier effects, culture and so forth. The direction of research initiatives has been to study direct and indirect effects of tourism on the economy and society in order to assess its present and/or future contributions.

Approaches to modelling in the tourism industry have revolved around time-series forecast models and regression models with the primary intent to predict and/or explain tourism-related issues. These models developed in tourism studies are predominantly single equation models used to explain demand at the aggregate or cross-country level of analysis (Sinclair, 1998). This study is a step taken towards verifying the theoretical underpinnings (structure) pertaining to the variables that affect tourism. Although it is apparent that the relationship between variables may be self-explanatory based on past research, significant contributions have not been made to develop models in tourism research to confirm the theoretically hypothesised structural relationships between the constructs/variables at the aggregate level. Moreover, a majority of the research efforts have focussed on the effects of tourism on the economy and society; and not vice versa, which would help explain the effects of the state of the economy on tourism.

With the aforementioned points as the precursor, the overall objective of this paper is to confirm the relationship between the variables that have been used in prior research to explain the impact on tourism. By identifying the underlying structural relationship between variables used in tourism based studies, the intent is to develop a causal model in order to explicate the relationship between constructs. To accomplish this, Structural Equation Modelling was used to confirm a hypothesised model derived from theoretical underpinnings. The model will help integrate the issues dealt with by other researchers in isolated studies. Since this approach is at the aggregate level with countries taken as the unit of analysis, the outcome of this study will help understand and verify the relationship between constructs and/or variables; that is, whether the theory established through prior research really holds good at the aggregate level. …


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