Hisashi MASUDA, Graduate School of Management, Kyoto University, Japan, email@example.com
HOW TO CITE:
<insert-authors> (2019). <insert-abstract-title>. AIRTH 2019 Conference: Innovation and Entrepreneurship for Sustainable Success; 2019 Sep 12 - 14; Innsbruck, Austria. Retrieved: <insert-date>, from http://www.airth.global
Today tourists who have a wide variety of travel interests can effectively pursue their expected destination experiences on their smartphone. This requires tourism service providers to adapt to use such Information and Communication Technology (ICT) on the basis of tourist perspectives. Although ICT is in an environment where various personalization/customization of services can be performed, it is not clear how it is effective for customer evaluation in a series of Customer/User Experience (CX/UX). There is no established method to systematically evaluate tourist satisfaction based on the difference between criteria to the majority of tourists and ones from tourist to tourist(Peterson, Wilson, 1992; Vittersø, Vorkinn, Vistad and Vaagland, 2000). Also, how to conduct such a customer satisfaction assessment will differ in work, health, marital status, life processes and so on (Westbrook and Oliver, 1981). Therefore, the aim of this research is how to classify the standard and nonstandard criteria in the customer satisfaction evaluation. In this study, based on the customer satisfaction data collection of a series of tourist spots, we analyze the distribution of their evaluation data by cluster analysis to determine the parameters of the Gaussian function. By focusing on the distribution function, it is possible to make a numerical judgment as to what balance the UX standard strategy and customization strategy should be used in the tourism industry. For example, spots that are often introduced in a guidebook are that can be accepted by many people. For that reason, the distribution function of customer evaluation might be bell-shaped, and spots that are less introduced in a guidebook might be vice versa. In order to verify such a relationship, the data was collected by conducting a questionnaire as a university of the tourist area at a university adjacent to the tourist resort. As the result, cluster analysis was performed on the customer satisfaction rating for each tourist spot collected. And then, based on the results of the cluster analysis, we performed parameter fitting using a Gaussian function for customer satisfaction rating of each tourist spot, and searched for the value of the parameter for separating bell-shaped and non-bell-shaped distribution. Bell-shaped distributions are considered to represent context-free tourist spots that are more standardized by evaluation criteria. On the other hand, non-bell-shaped distributions are considered to represent context-dependent tourist spots where tourists' evaluation criteria diversify. Knowing both characteristics and distribution shape of tourism attractions, we can design tourism experiences with a balance between context-free and context-dependent attractions more systematically and automatically. We are currently working on additional surveys to clarify and validate this concept.
Peterson, R. A. and Wilson, W. R. (1992). Measuring Customer Satisfaction: Fact and Artifact, Journal of the Academy of Marketing Science, 20(I), 61-71.
Vittersø, J., Vorkinn, M., Vistad, O. I. and Vaagland, J. (2000). Tourist experiences and attractions. Annals of Tourism Research ,27(2), 432-450.
Westbrook, R. A. and Oliver, R. L. (1981). Developing Better Measures of Consumer Satisfaction: Some Preliminary Results. In: Kent B. Monroe (Ed.). Advances in Consumer Research (8). Ann Arbor, MI: Association for Consumer Research, 94-99.