A Sociocybernetics Data Analysis Using Causality in Tourism Networks

The aim of this paper is to propose a mathematical model to determine invariant sets, set covering, orbits and, in particular, attractors in the set of tourism variables. Analysis was carried out based on a pre-designed algorithm and applying our interpretation of chaos theory developed in the context of General Systems Theory. This article sets out the causal relationships associated with tourist flows in order to enable the formulation of appropriate strategies. Our results can be applied to numerous cases. For example, in the analysis of tourist flows, these findings can be used to determine whether the behaviour of certain groups affects that of other groups and to analyse tourist behaviour in terms of the most relevant variables. Unlike statistical analyses that merely provide information on current data, our method uses orbit analysis to forecast, if attractors are found, the behaviour of tourist variables in the immediate future.

Linking attitudes and demographics in a tourist segmentation model : a two-stage approach

Segmentation has been widely studied in tourism research e.g. Dolnicar (2004). Dawley (2006) points that commonly used segmentation variables such as demographics lead to identifiable segments which are not actionable while other useful approaches e.g. psychographics, are actionable but not identifiable. The objective of this paper is to develop a two-stage linkage approach to segmentation whereby cluster analysis using psychographic variables is conducted within demographic group. Demographic groups are selected based on propensity to travel. This research utilizes data generated from a cross-sectional self-completed survey of 49,105 Australian respondents on travel and tourism. The managerial usefulness of this segmentation is assessed. Clearly segments can be directly linked both demographically and psychographically.

Tourist benefits research : old and new issues and uses in a cultural tourism context

Benefits sought and gained by tourists are relevant to developing tourist markets for cultural attractions and experiences as this paper will demonstrate. The main objectives of this paper are to identify benefit variables commonly used in tourism marketing, their purpose of use, and associated issues by reviewing existing empirical research of tourism benefits and specifically examining the applications to date in cultural tourism contexts. For the benefits most commonly found in tourist benefit research and of relevance to tourists’ cultural experiences, a benefits typology from the allied area of leisure (Driver & Bruns, 1999) is applied to categorise these benefits and develop a conceptual typology for consideration. Research propositions for future research of tourists’ cultural experiences are also proposed including new uses for benefit variable dimensions.

Measuring customer satisfaction in tourist service encounters

This paper sets out to model quality and satisfaction judgements of college students within four distinct tourist encounters. A novel approach is used where respondents rated an entire service setting by proxy when evaluating a picture in which certain quality variables had been manipulated. Each picture contained separate ôobjectsö of the service that we claim would be evaluated on a cognitive plane along different dimensions for the relevant attributes of each encounter. The scores were then used to develop regression models to estimate the influence of distinct quality factors on overall satisfaction

Tourist See Tourist Do: The Influence of Hollywood Movies and Television on Tourism Motivation and Activity Behavior

The purpose of this study was to investigate the influence of Hollywood movies and television (movies/TV) on US viewer’s motivation to travel to and participate in activities featured in Hollywood movies/TV productions. A survey was administered in an online format to a convenience sample of 433 respondents via Qualtrics. Factor analysis, correlation, and regression was employed to explore relationships between the variables. Findings identified a profile of Hollywood movies/TV viewers, sources of information used to determine destination choice, and level of involvement among viewers of Hollywood movies/TV productions. Additionally, this study explored the relationships between Hollywood movies/TV productions, tourist motivations, and the propensity to participate in activities featured. Findings indicate that Hollywood movies/TV productions have a positive impact on viewer involvement and that movie/TV related tourism is likely to be affected by movie and TV viewing preference and destination image. The results identify that the predictor “TV viewing behavior” is the strongest predictor of entertainmentmotivated tourism, followed by “destination image” and “movie viewing behavior.” Findings also indicate that “destination image” is the strongest predictor of movie-related activities and that the image portrayed in a movie does influence the viewer’s inclination to visit and participate in activities featured in a movie.

Efecto determinante de la motivación de viaje sobre la imagen de destino en turistas de ocio a un destino urbano: El caso de Monterrey, México. Una aproximación por medio de mínimos cuadrados parciales (PLS) = Determinant effect of travel motivation on the destination image of leisure tourist in an urban destination: the case of Monterrey, México. An approach through partial least squares (PLS)

Los turistas urbanos se caracterizan por ser uno de los segmentos de mayor crecimiento en los mercados turísticos actuales. Monterrey (México), uno de los principales destinos urbanos del país, ha apostado en la actualidad por mejorar su competitividad. Esta investigación se propuso encontrar evidencia acerca de la relación causal de la motivación de viaje sobre la imagen percibida del destino, dos variables importantes por su influencia en la satisfacción de los visitantes. Una revisión de la literatura permitió proponer constructos teóricos integrados en un instrumento para la recogida de datos vía encuesta a una muestra representativa. Por medio del método de regresión y ecuaciones estructurales por mínimos cuadrados parciales (PLS), se identificaron los componentes principales de ambas variables y se obtuvo un modelo explicativo de la imagen percibida del destino en función de la motivación de viaje. Finalmente, se emiten recomendaciones para la gestión del destino urbano en función de los resultados obtenidos. ABSTRACT: Abstract Urban tourists are recognized as one of the fastest growing segments in today’s tourism markets. Monterrey, Mexico, one of the main urban destinations in the country aims at improving its competitiveness. This research work had the purpose of finding evidence on the causal relationship between travel motivation and destination image, two important variables because of their influence on visitors’ satisfaction. A literature review enabled the proposal of a research instrument with theoretically based constructs to gather data through survey from a representative sample. Using regression and structural equations modelling by partial least squares (pls) a set of main components of both variables were identified thus enabling the obtention of a explanatory model of destination image in terms of travel motivations. Finally based on the results some recommendations of tourism management are given.

Cross-correlations between weather variables in Australia

The information on climate variations is essential for the research of many subjects, such as the performance of buildings and agricultural production. However, recorded meteorological data are often incomplete. There may be a limited number of locations recorded, while the number of recorded climatic variables and the time intervals can also be inadequate. Therefore, the hourly data of key weather parameters as required by many building simulation programmes are typically not readily available. To overcome this gap in measured information, several empirical methods and weather data generators have been developed. They generally employ statistical analysis techniques to model the variations of individual climatic variables, while the possible interactions between different weather parameters are largely ignored. Based on a statistical analysis of 10 years historical hourly climatic data over all capital cities in Australia, this paper reports on the finding of strong correlations between several specific weather variables. It is found that there are strong linear correlations between the hourly variations of global solar irradiation (GSI) and dry bulb temperature (DBT), and between the hourly variations of DBT and relative humidity (RH). With an increase in GSI, DBT would generally increase, while the RH tends to decrease. However, no such a clear correlation can be found between the DBT and atmospheric pressure (P), and between the DBT and wind speed. These findings will be useful for the research and practice in building performance simulation.

Best after rain : waterfall discharges and the tourist experience

Waterfalls attract tourists because they are aesthetically appealing landscape features that are not part of everyday experience. It is generally understood that falls are usually seen at their best when there is a copious flow of water, especially after heavy rain. Guidebooks often contain this observation when referring to waterfalls, sometimes warning readers that the flow may be severely reduced during dry periods. Indeed, many visitors are disappointed when they see falls at such times. Some are saddened when the discharge of a waterfall has been depleted by the abstraction of water upstream for power generation or other purposes. While, for those in search of the Sublime or merely the superlative, size is often important, small waterfalls can give great pleasure to lovers of landscape beauty. According to guidebooks, however, even these falls are usually best seen after rain. Drawing on tourist and travel literature and personal journals from the eighteenth century to the present, and with reference to examples from different parts of the world, this paper discusses the importance of discharge in the tourist experience of waterfalls.

Identification of critical input variables for risk-based cost estimates for road maintenance and rehabilitation

An estimation of costs for maintenance and rehabilitation is subject to variation due to the uncertainties of input parameters. This paper presents the results of an analysis to identify input parameters that affect the prediction of variation in road deterioration. Road data obtained from 1688 km of a national highway located in the tropical northeast of Queensland in Australia were used in the analysis. Data were analysed using a probability-based method, the Monte Carlo simulation technique and HDM-4’s roughness prediction model. The results of the analysis indicated that among the input parameters the variability of pavement strength, rut depth, annual equivalent axle load and initial roughness affected the variability of the predicted roughness. The second part of the paper presents an analysis to assess the variation in cost estimates due to the variability of the overall identified critical input parameters.

Using Trauma Injury Severity Score (TRISS) variables to predict length of hospital stay following trauma in New Zealand

Aim – To develop and assess the predictive capabilities of a statistical model that relates routinely collected Trauma Injury Severity Score (TRISS) variables to length of hospital stay (LOS) in survivors of traumatic injury. Method – Retrospective cohort study of adults who sustained a serious traumatic injury, and who survived until discharge from Auckland City, Middlemore, Waikato, or North Shore Hospitals between 2002 and 2006. Cubic-root transformed LOS was analysed using two-level mixed-effects regression models. Results – 1498 eligible patients were identified, 1446 (97%) injured from a blunt mechanism and 52 (3%) from a penetrating mechanism. For blunt mechanism trauma, 1096 (76%) were male, average age was 37 years (range: 15-94 years), and LOS and TRISS score information was available for 1362 patients. Spearman’s correlation and the median absolute prediction error between LOS and the original TRISS model was ρ=0.31 and 10.8 days, respectively, and between LOS and the final multivariable two-level mixed-effects regression model was ρ=0.38 and 6.0 days, respectively. Insufficient data were available for the analysis of penetrating mechanism models. Conclusions – Neither the original TRISS model nor the refined model has sufficient ability to accurately or reliably predict LOS. Additional predictor variables for LOS and other indicators for morbidity need to be considered.