Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

An online niche-market tour identification system for the travel and tourism industry

Wu, C. H., Ho, G. T. S., Lam, C. H. Y., Ip, W. H., Choy, K. L. and Tse, Y. K. ORCID: https://orcid.org/0000-0001-6174-0326 2016. An online niche-market tour identification system for the travel and tourism industry. Internet Research 26 (1) , pp. 167-185. 10.1108/IntR-08-2014-0204

Full text not available from this repository.

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.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Business (Including Economics)
Publisher: Emerald
ISSN: 1066-2243
Last Modified: 07 Nov 2022 09:59
URI: https://orca.cardiff.ac.uk/id/eprint/130792

Citation Data

Cited 15 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item