Social Network Analysis

The measurement of meaning

Statistical Analysis with Missing Data

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

https://coromina.webs.com/Yoon_2005_EffectsMotivationSatisfaction_SEM.pdf

An examination of the effects of motivation and satisfaction on destination loyalty: a structural model

Consumer decision making.

This article contributes to the recent literature on tourism destination competitiveness including the gargantuan compilations of competitiveness factors by Ritchie and Crouch (2003), or Dwyer and Kim (2003), and, particularly, the widely known prototype of a Competitiveness Monitor (CM) initiated by the World Travel and Tourism Council (WTTC) The central question underlying this article is whether an arrangement of data such as the CM can be transformed from a purely definitional system into an explanatory model A number of criticisms regarding the way of constructing the CM, its epistemological nature, and the absence of any accessibility factors lead to a moderately revised system that is explored by latent variable modeling The empirical findings support this type of model, which tends to better explain the levels of tourism activity already achieved than sustained tourism growth A discussion of the detailed results produces several recommendations on how to adjust the future strategy of research 

Tourism Destination Competitiveness: From Definition to Explanation?

Marker segments in travel and tourism like elsewhere are defined either predetermined criteria (a priori segmentation) or result from a clustering process (a posteriori segmentation). Each method requires searching for additional descriptive attributes in order to streamline selective market operation. These descriptors should be much easier to measure than those the segments originate from. For a posteriori segments, in particular, it is desirable to predict segment membership from a set of easily determined consumer attributes without having to process a larger number of psychographics. Prediction of segment membership often employs discriminate analysis. Neural network models are also capable of classifying tourists. Having been trained on a set of input variables (descriptors) and output data (segment membership), they may excel discriminate analysis in determining the correct segment affiliation.

Classifying tourists into market segments: a neural network approach.

Marketing management and tourism.

Positioning analysis examines the consumers' evaluative processes involving perceptions and preferences. Consumer behavior theory provides the hypotheses linking the perceptual and preferential positions of product brands or businesses to the expected brand choice decisions. For better visualization, various techniques of multivariate analysis assist in locating these positions in a space of low dimensionality. This data-processing step is necessary for bridging the gap between the fuzzy and redundant language of the consumer and the more abstract vocabulary of managers and service engineers. Hospitality services tend to be described with a rather large number of emotional and functional attributes. Condensing them into a compact spatial representation requires a significant data reduction effort. But the analyst faces still other challenges. Measuring perceptions and preferences turns out to be a segment-specific exercise. Strategic decisions on product positioning and market segmentation are interrelated. Contemporary decision-support models run the risk of becoming too complex to meet with managerial acceptance. Therefore, a simple method such as self-organizing maps (SOMs) may be attractive for exploratory purposes. SOMs are a variant of neural network models capable of detecting symptomatic brand positions. Managers may learn about typical combinations of product attributes, how they are assigned to brands, and how many and which consumers share similar views about brand profiles. A sample application of image and satisfaction measurement for luxury hotels demonstrates the technique. Strengths and weaknesses of this new neurocomputing approach to joint positioning/segmentation analysis are cautiously assessed. More experience from real-world applications is needed, and the industry is encouraged to experiment with SOMs on a larger scale.

Josef A. Mazanec

Papers

Consumer decision making.

Tourism Destination Competitiveness: From Definition to Explanation?

Classifying tourists into market segments: a neural network approach.

Marketing management and tourism.

Positioning analysis with self-organizing maps: An exploratory study on luxury hotels