Author Bios (50 Words for each Author)

Tetsuo Shimizu is a professor at Tokyo Metropolitan University. He is currently one of the National Committee members on Tourism Statistics Development organised by the Japan Tourism Agency. His research interest includes data science in tourism, sustainable tourist destination development, behavioural travel analysis, and transport policy for tourism promotion. (https://orcid.org/0000-0002-3592-1139)

Nguyen Van Truong is a lecturer at the University of Transport and Communication and researcher at Tokyo Metropolitan University. His research interest includes theoretical, qualitative, and quantitative studies of tourism impacts, travel behaviour, and transport policy to promote tourism and economics. (https://orcid.org/0000-0002-2095-7462)

Abstract (150 Words)

Development accommodation statistics for small areas have been largely neglected in tourism literature. The challenges are high variance and/or bias of estimates. Therefore, tourism management policies and business strategies for regional tourism areas have not been well supported by good statistics. This study utilizes accommodation data of Japanese DMOs as a case study. Regression is used to compare to a traditional linear estimator. Each estimator is tested with two sampling schemes, namely simple random sampling and clustering sampling. The results demonstrate that the traditional estimation method, which has been utilized widely in tourism literature, failed in developing tourism accommodation statistics for small-scale regions. Integration of regression and clustered sampling scheme made it possible to provide a more accurate estimate, i.e., less bias and variability. The results make the policy formulation process possible for small tourism areas.

Download

Share

COinS
 

Development of Accommodation Statistics for Small Regions: Case of Japanese Destination Management Organizations (DMOs)

Development accommodation statistics for small areas have been largely neglected in tourism literature. The challenges are high variance and/or bias of estimates. Therefore, tourism management policies and business strategies for regional tourism areas have not been well supported by good statistics. This study utilizes accommodation data of Japanese DMOs as a case study. Regression is used to compare to a traditional linear estimator. Each estimator is tested with two sampling schemes, namely simple random sampling and clustering sampling. The results demonstrate that the traditional estimation method, which has been utilized widely in tourism literature, failed in developing tourism accommodation statistics for small-scale regions. Integration of regression and clustered sampling scheme made it possible to provide a more accurate estimate, i.e., less bias and variability. The results make the policy formulation process possible for small tourism areas.