| In recent years,with the rapid economic-social development and the continuous improvement of information technology,the development of e-commerce has entered a new stage.As an important aspect of e-commerce,online shopping becomes a new channel for consumers to shop.As shown by data in 2017,the scale of online-consumers reached 713 million in 2017,the transaction amount is about 7.18 trillion yuan,accounting for about 19.6% of total retail scales of consumer goods.As an important means of measuring the development of modern service industry,e-commerce business is an important way to promote the optimization of industrial structure and improvement the level of information technology,and is also important manifestation of the comprehensive economic strength of the region.Under the flourishing situation of the e-commerce industry,online shopping mall,especially the C2 C online shopping mall platform,as an important sales platform for the e-commerce economy,its sale level and competitive ability directly affects the regional modernization process.Therefore,it is very valuable to study the urban C2 C online shopping competitiveness.From the research perspective of C2 C online shopping,the competitiveness model was constructed for evaluating the urban C2 C online competitiveness in this paper.By the competitiveness index of quantity,credit,sale and comprehensive level,using the natural breaking point method,this paper firstly gave an in-depth spatial analysis for high-low pattern and spatial distribution trend.Secondly,according to the comprehensive index,by the methods of gravity model,the potential model and the complex network mode,this paper analyzed the urban C2 C online comprehensive potential value,spatial interactive strength and subgroup characteristics from point-line-polygon perspective.Finally,the multiple linear regression model was used to explore factors influencing the spatial difference pattern of urban C2 C online comprehensive competitiveness.The main results were shown as follows.(1)The competitiveness pattern of urban C2 C online store showed obvious characteristics of spatial agglomeration in Central Plains Urban Agglomeration.In detail,the number competitiveness of different kind online store showed spatial distribution pattern of high value agglomeration in the middle,east and north and low value in the northwest except for clothing.The spatial distribution pattern of sale competitiveness and credit competitiveness were roughly the same in classification competitiveness,which showed the spatial distribution pattern of high value agglomeration in the middle,east and north and low value in the northwest,except for footwear.Footwear online stores showed an obvious gradient centerperiphery descending feature around the center Zhengzhou.From the comprehensive competitiveness index,the sale,credit competitiveness pattern was similar to the overall pattern,which showed one-core,three poles and multi-polygon with low score.However,the number competitiveness pattern was different from the overall patter,which showed one-core,three polygons with relative higher score and multi-polygons with low score.(2)From the point spatial interaction pattern of comprehensive competitiveness of urban C2 C online stores,the overall spatial pattern was similar to the comprehensive competitiveness pattern,which was basically similar by different kind online stores.The footwear,electric,daily necessities and luggage,food,cosmetic online stores showed an obvious gradient center-periphery descending feature around the center Zhengzhou,the clothing online stores was different by the three-core city Heze,Handan and Xingtai.From the line spatial interaction pattern of comprehensive competitiveness of urban C2 C online stores,the overall spatial pattern showed a close contact area with the core city Zhengzhou and formed a main contact area in Zheng-Bian-Luo-Xin-Jiao-Ji-Xu-Pin-Luo region.From the polygon spatial interaction pattern of comprehensive competitiveness of urban C2 C online stores,the pattern can be divided into four interactive contact areas including Middle-Northeast combined group,Middle-Northwest combined group,MiddleSouthwest combined group and Southeast combined group.It was worth talking about the concentration of cities within the same combined group was relatively high.(3)From the analysis of the influencing factors by regression model,the urban C2 C comprehensive competitiveness level was mainly influenced by urban economic strength,urbanization level,cultural and educational level,transportation and logistics index,the informatization level.In these factors,the urban economic strength played the leading role,the urbanization level played the boosting role,the level of culture and education played the promotion role,the transportation logistics index played the basic role,and the information level played the supporting role.The above influencing factors together shaped the overall pattern of urban competitiveness by intertwined interactive.High comprehensive competitive level region of the C2 C online stores ranked the top among the central cities in many aspects,such as its economic power,urbanization level,culture and education level,traffic and logistics index.Impacted by these factors,its competitiveness was also ahead of other cities.High comprehensive and competitive level region of the C2 C online stores were mainly promoted by the high level of economic strength and information level.The urbanization level,culture and education level,and its informatization of the medium level regions was relatively high,but its traffic and logistics was relatively low,which controlled the competitiveness development of the C2 C online stores.The low level regions had the relatively low levels in its urbanization,culture and education,and its informatization,which also controlled the competitiveness development of the C2 C stores.What`s worse,the economic power,urbanization level,culture and education level,informatization,and traffic and logistics index were all relatively low,so the restricting effect were more obvious. |
PDF Full Text Request