Abstract:Tourism is one of the pillar industries of modern service industry, which is “innovation-driven to promote industrial transformation and upgrade”. It plays an important role in promoting regional industrial upgrade. Through the Analytic Hierarchy Process (AHP), questionnaire surveys, professional discussions and other methods, taking the data of 147 tourism enterprises in Guangdong-Hong Kong-Marco Greater Bay Area as samples, from the perspective of “management-product-marketing-service”, we can construct a tourism enterprise innovation ability evaluation index system which includes 4 first-level indexes and 16 second-level indexes. Besides, the industry coefficient of corresponding second-level indexes obtained from “scenic spots, hotels, travel agencies, e-commerce tourism enterprises, tourism products and equipment supply enterprises”, together with the second-level index weights, we use the linear weighted synthesis method to construct an evaluation model to test the innovation ability of tourism enterprises. The study shows that the level of innovation ability of tourism enterprises in Guangdong-Hong Kong-Macao Greater Bay Area is generally higher, and the innovation ability of tourism enterprises in Hong Kong, Macao, Guangzhou and Shenzhen is better, especially in Guangzhou and Shenzhen. The sub-industries ranked in order of innovation ability are “tourism products and equipment supply enterprises, scenic spots, travel agencies, hotels, and e-commerce tourism enterprises”. The five sub-industries of tourism enterprises have big differences in tourism innovation ability. Scenic spots, tourism products and equipment suppliers are the main force of tourism innovation. Hong Kong is the most prominent in scenic spots, Macao is the most prominent in hotels, and Zhongshan is the best in tourism products and equipment suppliers.
吴开军. 粤港澳大湾区旅游企业创新评价体系与模型检验[J]. 《深圳大学学报》(人文社科版), 2019, 36(4): 71-79.
WU Kai-jun. Innovation Evaluation System and Model Test for Tourism Enterprises in Guangdong-Hong Kong-Marco Greater Bay Area. , 2019, 36(4): 71-79.
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