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Title: An online niche-market tour identification system for the travel and tourism industry
Authors: Wu, CH
Lam, CHY
Ip, WH 
Choy, KL 
Tse, YK
Keywords: Customer characteristics
Customer requirements
Database marketing
Information processing
Market segmentation
Niche tourism
Issue Date: 2016
Publisher: Emerald Group Publishing Limited
Source: Internet research, 2016, v. 26, no. 1, p. 167-185 How to cite?
Journal: Internet research 
Abstract: Purpose – The purpose of this paper is to present a novel approach for niche-market tour identification, with the objective to obtain a better segmentation of target tourists and support the design of tourism products. A proposed system, namely the Niche Tourism Identification System (NTIS) was implemented based on the proposed scheme and its functionality was showcased in a case study undertaken with a local travel agency. Design/methodology/approach – The proposed system implements automated customer market segmentation, based on similar characteristics that can be collected from potential customers. After that, special-interest tourism-based market strategies and products can be designed for the potential customers. The market segmentation is conducted using a GA-based k-means clustering engine (GACE), while the parameter setting is controlled by the travel agents. Findings – The proposed NTIS was deployed in a real-world case study which helps a local travel agency to determine the various types of niche tourism found in the existing market in Hong Kong. Its output was reviewed by experience tour planners. It was found that with the niche characteristics can be successfully revealed by summarizing the possible factors within the potential clusters in the existing database. The system performed consistently compared to human planners. Originality/value – To the best of the authors’ knowledge, although some alternative methods for segmenting travel markets have been proposed, few have provided any effective approaches for identifying existing niche markets to support online inquiry. Also, GACE has been proposed to compensate for the limitations that challenge k-means clustering in binding to a local optimum and for its weakness in dealing with multi-dimensional space.
ISSN: 1066-2243
DOI: 10.1108/IntR-08-2014-0204
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