Open AccessEstablishment of data-driven statistical tourism science and demonstration of its effectiveness
Author(s) -
Hiroshi Tsuda
Publication year - 2021
Publication title -
impact
Language(s) - English
Resource type - Journals
eISSN - 2398-7081
pISSN - 2398-7073
DOI - 10.21820/23987073.2021.3.49
Subject(s) - tourism , government (linguistics) , sustainability , big data , process (computing) , work (physics) , business , order (exchange) , marketing , computer science , political science , engineering , mechanical engineering , ecology , philosophy , linguistics , finance , law , biology , operating system
Tourism is an invaluable industry for many countries for many reasons, including the jobs and incomes it provides and the benefits to the economy. Tourism is of particular importance in Japan where the country is working towards a national growth strategy. Measures implemented by the Japanese Government to promote tourism have seen success in this area but the Covid-19 pandemic has presented challenges. Professor Hiroshi Tsuda, Department of Mathematical Sciences, Doshisha University, Japan, is working on research surrounding the establishment of data-driven statistical tourism science and the demonstration of its effectiveness in order to overcome these challenges. The goal of his current work is to help tourist destinations, tourist facilities and lodging facilities to achieve management sustainability, ultimately sustainably revitalising Japan's tourism industry. The innovative methodologies that Tsuda and the team are using in their work are big data analytical methods combined with explainable artificial intelligence (XAI) technology. XAI technology is an AI programmed to explain its decision-making process in a way that can be understood by humans, thereby ensuring the decision-making process is fair. Using XAI technology, the team will develop a tourist support system that can make valuable predictions. The researchers are also facilitating the creation of a new field called 'human and design social science', which could prove beneficial in policy and management science.