基于GWR模型的中国区域旅行社业效率空间分异及动力机制分析

基于DEA模型对中国31个省域的旅行社业效率空间分异特征进行了分析,首次运用GWR模型探索交通、资本、人才、信息化和经济动力对区域旅行社业效率影响的空间差异。结果表明：旅行社业效率在空间上具有正相关性和集聚特征,空间格局从“川”字型向“山”字型转变。各动力因子的系数均存在空间非平稳性。资本和人才动力的回归系数在空间分布上从南向北依次递减;经济动力的分布趋势为从北向南依次递减;交通动力对中西部地区旅行社效率提升的促进作用显著于东部地区;信息化动力则在东部地区表现出较强的促进作用。

Spatial Differentiation and Dynamic Mechanism of Regional Travel Agency Efficiency in China Based on GWR Model

Although abundant studies on tourism efficiency have been made both at home and abroad, few of them have explored and analyzed the dynamic mechanism of tourism efficiency from the perspective of spatial nonstationarity. Based on DEA model, this article analyzes the features of travel agency efficiency spatial differentiation of the 31 provincial-level regions in China. And by using GWR model initially, the spatial differentiation of regional travel agency efficiency influenced by the five driving forces, i.e. transportation, capital, human resource, informatization, and economy has been explored in this article. Compared with the ordinary least square(OLS), GWR model extends the traditional regression framework by allowing the estimation of local rather than global parameters. The results show that: Firstly, the distribution of China travel agency efficiency shows evident positive correlation and spatial dependence; and as time goes on, this dependence increases. Secondly, in space differentiation, the difference between east and west China is increasing, the difference between north and south China is narrowing, and the role of central and south region becomes more significant to some extent. Due to the influence of these changes, the spatial pattern oftravel agency efficiency transforms from a three-vertical-line type to a three-vertical-and-one-horizontal-line type. Thirdly, the test result shows that the GWR model is more suitable than the ordinary OLS model in terms of seeking the driving forces of the regional travel agency industry efficiency since the coefficient of each driving force has spatial nonstationary property. What’s more, the spatial distribution of the regression coefficients of different driving forces shows some complexity. Although capital and human resource driving forces have negative and positive impact on travel agency industry efficiency respectively, the spatial distributions of regression coefficients of these two factors exert much more influences in the south than that of the north. On the contrary, the regression coefficient of the economy driving force decreases from north to south. The regression coefficients of transportation and informatization driving forces show the features of two belt distributionsin the east and in the west respectively, and their influences are just the opposite. Transportation force plays a more important role to boost the travel agency efficiency in the central and west regions than that in the east; On the contrary, informatization force is more significant to enhance the travel agency efficiency in the east region, which indicates that the extent of informatization exerts more influences on the travel industry in the east region. In conclusion, priority should be given to choose and perfect the more powerful factors according to the different influences exerted on the local travel agency industry efficiency by different dynamic factors so as to boost the efficiency of the travel agency industry.