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Research on the Spatial Differentiation and Driving Factors of Tourism Enterprises’ Efficiency: Chinese Scenic Spots, Travel Agencies, and Hotels

Bing Xia (), Suocheng Dong (), Duoxun Ba (), Yu Li (), Fujia Li (), Haimeng Liu (), Zehong Li () and Minyan Zhao ()
Additional contact information
Bing Xia: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Suocheng Dong: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Duoxun Ba: Tourism College, Northwest Normal University, Lanzhou 730070, China
Yu Li: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Fujia Li: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Haimeng Liu: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Zehong Li: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Minyan Zhao: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Sustainability, 2018, vol. 10, issue 4, 1-22

Abstract: Tourism is an important sustainable industry in the economy that optimizes the industrial structure. Thus, as a core part of this market, tourism enterprises perform a key role in the effective operation of this industry. This paper applies data envelopment analysis (DEA) and Malmquist index (MI) models to calculate the efficiency of Chinese tourism enterprises between 2005 and 2014. Results showed that: (1) The efficiency and the total factor productivity change index (TFPC) of tourism enterprises remained low, and both have decreased. (2) The efficiency of regional tourism enterprises across China cloud be characterized as high in the east region, low in the central region, and high in both northeast and western regions. (3) The efficiency levels of the cities of Beijing and Shanghai were ahead of the country over the period of this study, while Chongqing, Tibet, Qinghai, and Ningxia all possess a number of obvious advantages in the western region. (4) Centers of overall tourism enterprise efficiency mainly moved in a southeast-to-northwest direction over the period of this research. (5) The spatial autocorrelation of tourism enterprise efficiencies is also assessed in this study, and the results show that the comprehensive efficiency (CE) of tourism enterprises in southeastern coastal regions of China tended to a certain spatial agglomeration effect, while the correlation between the central region and northern China was not significant. (6) The Geodetector model is applied to analyze the key factors driving the spatial differentiation of tourism enterprise efficiencies, and the results show that the degree of opening to the outside world, potential human capital, and traffic conditions were the most important factors driving spatial differentiation in the efficiency of tourism enterprises.

Keywords: efficiency of tourism enterprises; spatial differentiation; DEA-Malmquist; spatial autocorrelation; Geodetector (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2018
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