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Tourist market segmentation with linear and non-linear techniques [An article from: Tourism Management]
Book Details
Author(s)J.Z. Bloom
PublisherElsevier
ISBN / ASINB000RR1KZ0
ISBN-13978B000RR1KZ9
AvailabilityAvailable for download now
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Tourism Management, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
The need for in-depth knowledge of tourist market segments and the need to overcome the limitations of using linear techniques to analyse non-linear relationships requires a re-assessment of generally used approaches such as cluster analysis and multiple linear regression. The objectives of the research are (1) to consider the use of self-organising (SOM) neural networks for segmenting tourist markets and (2) to analyse the predictive ability of backpropagation (BP) neural networks for classifying tourists from follow-up surveys by using the output provided by a SOM neural network. The findings of the SOM neural network modelling indicate three natural clusters. In addition, the predictive ability of the BP neural network model appears to be superior to that of MLR static filter and logistic regression models. The BP neural network model developed for this application appears suitable for deployment (i.e. classification of tourists from follow-up surveys).
Description:
The need for in-depth knowledge of tourist market segments and the need to overcome the limitations of using linear techniques to analyse non-linear relationships requires a re-assessment of generally used approaches such as cluster analysis and multiple linear regression. The objectives of the research are (1) to consider the use of self-organising (SOM) neural networks for segmenting tourist markets and (2) to analyse the predictive ability of backpropagation (BP) neural networks for classifying tourists from follow-up surveys by using the output provided by a SOM neural network. The findings of the SOM neural network modelling indicate three natural clusters. In addition, the predictive ability of the BP neural network model appears to be superior to that of MLR static filter and logistic regression models. The BP neural network model developed for this application appears suitable for deployment (i.e. classification of tourists from follow-up surveys).
