Segmenting the market of West Australian senior tourists using an artificial neural network

Kim, Jae-Soo; Wei, Sherrie; Ruys, Hein

doi:10.1016/s0261-5177(02)00050-x

Cited by 125 publications

(62 citation statements)

References 19 publications

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“…This supports researchers who have adopted a combined segmentation approach involving two (e.g. Bansal & Eiselt, 2004;Bonn et al, 2005;Kim et al, 2003;Sarigollu & Huang, 2005) to four (e.g. Becken et al, 2003;Beh & Bruyere, 2007;Chang, 2006;Frochot, 2005) segmentation bases.…”

Section: Discussionsupporting

confidence: 80%

Segmentation: A tourism stakeholder view

Tkaczynski¹,

Rundle‐Thiele²,

Beaumont³

2009

Tourism Management

152

102

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Tourism segmentation research has focused on (1) developing tourist segment profiles using primary and secondary tourist data and (2) understanding which segmentation bases can accurately predict future tourist behaviour. Researchers have not considered how tourism stakeholders are segmenting their tourist markets. This paper presents evidence to suggest that the use of combined segmentation variables to develop tourism profiles is warranted and that a 'one size fits all' approach is not suitable because different tourism stakeholders within a single destination attract different tourists.Furthermore, this research identified that the segments used by the destination marketing organisation failed to holistically describe the tourist groups using the different services provided by tourism stakeholders within a single destination.Destination marketing is complex involving many stakeholders each likely to be attracting different tourist segments and future research endeavours must acknowledge this complexity.

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Section: Discussionsupporting

confidence: 80%

Segmentation: A tourism stakeholder view

Tkaczynski¹,

Rundle‐Thiele²,

Beaumont³

2009

Tourism Management

152

102

View full text Add to dashboard Cite

show abstract

“…Dolnicar (1997) used the SOM to identify the characteristics of summer tourists visiting Austria. The latter method was also used, for example: to identify strategic groups of UK hotels (Curry et al, 2001); to segment senior travelers in Western Australia (Kim et al, 2003); to segment the international tourist market in Cape Town, South Africa (Bloom, 2005); to segment the visitors of a particular cultural attraction in the Northern Italy, the Christmas Markets .…”

Section: Introductionmentioning

confidence: 99%

Bagged Clustering and its application to tourism market segmentation

D’Urso

Giovanni

Disegna

et al. 2013

Expert Systems with Applications

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Aim of the paper is to propose a segmentation technique based on the Bagged Clustering (BC) method. In the partitioning step of the BC method, B bootstrap samples with replacement are generated by drawing from the original sample. The Fuzzy C-Medoids Clustering (FCMdC) method is run on each bootstrap sample, obtaining (B × C) medoids and the membership degrees of each unit to the different clusters. The second step consists in running a hierarchical clustering algorithm on the (B × C) medoids. The best partition of the medoids is obtained investigating properly the dendrogram. Then each unit is assigned to each cluster based on the membership degrees observed in the partitioning step. The effectiveness of the suggested procedure has been shown analyzing a suggestive tourism segmentation problem. We analyze two sample of tourists, each one attending a different cultural attraction, enlightening differences among clusters in socio-economic characteristics and in the motivational reasons behind visit behavior.

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“…About one in five hotels failed to thank the guest sults reduces this subjectivity and profits from ANN's flexible data assumptions (Alon et al, for their interest. Even though the query included the guest's name (e.g., Susanne Forbes), just two 2001; Kim et al, 2003;Kohonen, 2001). This study used SPSS for the multivariate clustering replies out of three began with "Dear Susanne Forbes."…”

Section: Introductionmentioning

confidence: 99%

“…The multivariate Ward hierarchical technique (Everitt, 1993) Kim, Wei, & Ruys, 2003). ANN training resembles statistical learning.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Evolution of Hotel Internet Adoption

Murphy

Schegg²,

Olaru³

2006

Information Technology & Tourism

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This article draws upon Diffusion of Innovations and Configurational theories to investigate how website features and email responses by 200 Swiss hotels reflect evolving Internet adoption. Complementary multivariate and artificial neural network (ANN) techniques support classifying the hotels into three clusters based on their website features. These clusters and the results of a structural equation model confirm that Internet adoption evolves from static to dynamic use, as organizations add website features and provide quality responses to customer emails. Practically, differences among these clusters suggest caution in adopting some website features. Academically, the study extends diffusion research and introduces metrics, particularly domain name age and quality email responses, for future research of organizational Internet adoption. Finally, the study illustrates how ANNs complement and help overcome limitations of multivariate techniques.

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Segmenting the market of West Australian senior tourists using an artificial neural network

Cited by 125 publications

References 19 publications

Segmentation: A tourism stakeholder view

Segmentation: A tourism stakeholder view

Bagged Clustering and its application to tourism market segmentation

Investigating the Evolution of Hotel Internet Adoption

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