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Segmentation and Classification of Indian Domestic Tourists : A Tourism Stakeholder Perspective

Journal of Management and Training for Industries

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Title Segmentation and Classification of Indian Domestic Tourists : A Tourism Stakeholder Perspective
 
Creator Dutta, pp.1-24. Saibal; Senior Data Scientist, Usha Martin Group
and PhD scholar IIT Kharagpur india.
Bhattacharya, Sujoy; Indian Institute of Technology
Guin, Kalyan Kumar; Indian Institute of Technology
 
Subject Travel and Tourism
 
Description The rapid growth of the domestic tourism sector has open immense opportunity that has not been marketed properly so far by the Indian government. In this regard, tourism stakeholders are the primary responsible for the implementation of successful destination marketing. Till date, Tourism research has been focused on stakeholder involvement and their complex relationship in destination marketing, but never appreciates their view point and direct involvement in the market segmentation process. In this paper, the concept of stakeholder theory has been successfully utilized the problem of market segmentation and classification of the Indian domestic tourism sector. The research approach followed in this work in qualitative and quantitative in nature where in-depth interviews of three key stakeholders are conducted for assessment of variable selection and in the consecutive step, analysis was performed on 459 complete primary data collected through a self-administered survey of potential tourist to the study area using factor-cluster segmentation approach. The result suggests three, five and six cluster solution possible for accommodation service provider, tour operator, and tourism policy maker respectively. The result of the segmentation was then further used to train and develop various efficient classifier models, namely, Recursive Partitioning Trees, Pruning Recursive Partitioning Tree, Conditional Inference Tree, K-Nearest Neighbor Classifier, Naive Bayes Classifier, Neural Network (Radial basis function network and Multilayer Perceptron), Support Vector Machine, Linear Discriminant Analysis and Random Forest. The study indicated that the SVM classifier model achieved the highest accuracy of 90.71%, 86.43 % and 89.29 % for accommodation service providers, tour operators and tourism policy makers respectively compared to those given by the above mentioned methods. The results hold substantial implications to the stakeholders’ perspective of successful destination marketing and positively contributed to formulating useful policy, planning and strategy buildup.
 
Publisher The Institute of Industrial Applications Engineers, Japan
 
Contributor —
 
Date 2017-04-01
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
—
 
Format application/pdf
 
Identifier https://www2.ia-engineers.org/JMTI/index.php/jmti/article/view/411
 
Source Journal of Management and Training for Industries; Vol 4, No 1
Journal of Management and Training for Industries; Vol 4, No 1
2188-2274
2188-8728
 
Language eng
 
Relation https://www2.ia-engineers.org/JMTI/index.php/jmti/article/view/411/38
 
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