The use of strategic visioning to enhance local tourism planning in periphery communities

Tourism has had a profound impact upon destinations worldwide, and although this impact has been positive for many destinations, there are numerous examples where tourism has adversely impacted upon the environment and social fabric of the destination community (Coccossis 1996; Murphy 1985). The negative impacts of tourism have been attributed, among other things, to inadequate or non-existent planning for development (Gunn 1994; Hall2000). This has led to increased calls for tourism planning to offset some of the negative impacts that tourism can have on the destination community. While a number of approaches have been advocated, a collaborative philosophy, based on the principles of sustainability, is more likely to result in acceptable and successful policies and programmes for tourism destinations (Farrell1986; Jamal & Getz 1995; Maitland 2002; Minca & Getz 1995). Such an approach focuses on cooperation and broader based participation in tourism planning and decision-making between stakeholders to lead to agreement on planning directions and goals, with one of the primary objectives of collaborative arrangements being to develop a strategic vision for a destination (Bramwell & Lane 2000). [Extract from introduction]

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.