Identification of Tourism Clusters in the Russian Far East

Abstract

Around the world, tourism is one of the most dynamically developing spheres in international trade in services, and the Russian Federation is no exception. But in Russia tourism industry potential is still used very poor. To a large extent, the realization of the development potential of regional tourism complexes will be promoted by the use of the cluster approach. Foreign experience shows the high efficiency of introducing a cluster approach in the tourism industry. However, in the Russian economy, there is a problem of identifying industries with the greatest potential for clustering. This problem is especially acute with the aim of developing the necessary measures for state support of clustering industries. In this article, using the example of the Far Eastern Federal District, a method is proposed that makes it possible to identify tourism clusters. In contrast to the existing ones, the method developed by the authors is characterized by an expanded set of analyzed indicators (number of employees, the volume of investments in fixed assets, the share of sectoral gross value added, labor productivity), and a system for evaluating the criteria characterizing the processes of clustering in time (the number of time periods in which : the localization coefficient and the regional factor takes admissible values and has a positive dynamics of development). As a result of identification, the authors identified regions of the Russian Far East that have the greatest potential for the formation of specialized territorial clusters in tourism industry.

Cluster Identification of Agrotourism in Banten Province Indonesia

“…The regions in Banten Province consisting of four municipals and four regencies were grouped via cluster analysis based on the three variables. The regions clustering [7][8][9] was intended to identify potential regions w.r.t agrotourism. A distance-based clustering was applied in the cluster analysis where the k-medoids method was opted for due to its distance variation [17].…”

“…Clustering has been conducted for regions [7][8][9] and tourist destination sites [10][11]. The efficient indicators, important attributes, inefficient behaviour, and popular destinations can be obtained by this analysis so that cooperation among regions can increase tourism competitiveness.…”