Measuring Tourists’ Preferences – Which Road to Choose?

Abstract

EditorialThe continuous growth in the tourism industry leads that destinations today must compete more than ever to attract tourists. Understanding destination competitiveness and attractiveness as well as modeling and forecasting tourism demand are critical to decision makers and destination managers, since it is known that tourism affects their regional and national economy through direct and indirect tourism revenues. For example, Song and Witt [1] refer that estimates of future tourism demand constitute a very important element in all planning activities of tourism-related business and, consequently, have a key role as a determinant of their profitability. Furthermore, Dwyer and Kim [2] mention that destination competitiveness would appear to be linked to the ability of a destination to deliver goods and services that perform better than other destinations on those aspects of the tourism experience considered to be important by tourists, because tourists would be expected to choose the destination that generates the highest level of utility.To assess the performance of a destination at aggregate level (e.g. at country or region levels), researchers have developed several different approaches to measure destination competitiveness using a large set of indicators and a wide range of methodologies. Some have used survey data of tourists' perceptions to measure competiveness [2][3][4][5]. Others have used published data (about the economy, environment, infrastructure, etc) to assess and compare the competitiveness of tourist destinations across a range of countries [6]. However, as far as we know, none of these researches have included an indicator of consumers' preferences, which was considered by Dwyer et al. [7], one of the three main elements of tourism demand as determinant of tourism competitiveness.At less aggregate levels, destination attractiveness and market share have been studied using stated preferences methods. Discrete choice modeling in economic theory complies with lancaster's new approach to the individual utility maximization problem in consumer theory [13] and with the random utility theory [14,15]. Discrete choice-based approaches use the random utility function where the stochastic component includes all unidentified factors that affect choices. Under the random utility theory, individual preference can be elicited by asking respondents to rank a set of alternative options from most to least preferred (contingent ranking data) or to choose their most preferred option (first-choice data). The measurement scale for the dependent variable determines the model to be estimated.With first-choice data, the choice experiment determines the multinomial logit, which is the most commonly used discrete choice model [14]. The appeal of the multinomial logit arose from the fact that it is simple to estimate. However, the problem with the multinomial logit is that it makes very strong assumptions about consumer behavior. The assumption that has received the most attention is the independence of irrelevant...