Title: Using data mining for service satisfaction performance analysis for mainland tourists in Taiwan
Authors: Wen-Tsao Pan
Addresses: Department of Business Administration, Hwa Hsia Institute of Technology, No. 111, Gongzhuan Rd., Zhonghe Dist., New Taipei City 235, Taiwan
Abstract: Since Taiwan and mainland China signed the Economic Cooperation Framework Agreement (ECFA) across the Taiwan Strait, the number of mainland tourists visiting Taiwan has grown significantly. To cope with the needs of mainland tourists, Taiwan must reinforce the software and hardware facilities and service quality of its entire tourism industry. This will attract more tourists to Taiwan and create more opportunities for the Taiwanese tourism industry. In this article, tourists visiting Taiwan are asked to complete a questionnaire survey; we then use the satisfaction information gathered to perform grey relational analysis so as to understand the best and worst scoring questions related to satisfaction. From the analysis results, it can be seen that in the assessment of satisfaction question performance, Taiwanese cuisine scores the highest, the cleanliness of Taiwan's streets scores the lowest, and of people interviewed between 30 and 40 years old, more rated satisfaction performance and characteristics negatively.
Keywords: Economic Cooperation Framework Agreement; ECFA; grey relational analysis; GRA; self-organising feature maps; SOM; artificial fish swarm algorithm; AFSA; general regression neural networks; GRNN; Taiwan; data mining; service satisfaction; performance evaluation; mainland tourists; mainland China; tourism industry; Taiwanese cuisine; street cleanliness; tourist services.
International Journal of Technology Management, 2014 Vol.64 No.1, pp.31 - 44
Published online: 10 May 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article