基于网络结构的区域旅游合作与竞争研究
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摘要
旅游业已成为当今世界规模最大的产业，是许多国家和地区实现经济腾飞和持续发展的有效切入点和重要突破口。无论在学术界还是实践领域，对区域旅游合作与竞争的关注达到前所未有的高度。虽然运用社会网络分析方法和复杂网络理论研究各种合作与竞争系统已成为近年来学术界讨论和关注的热点，但是鲜有学者从网络结构视角去研究区域旅游合作与竞争系统。
笔者通过融合复杂网络理论、社会网络分析、新经济社会学、新经济地理学及新制度经济学理论与方法，借鉴合作与竞争经济关系网络的最新研究成果，从网络图模型的构建、结构性特性指标的测量和分析、关联性分析、演化模型、合作与竞争均衡等5个角度系统地研究区域旅游合作与竞争，并以中国入境旅游系统为例进行了实证研究。
笔者将各地区视为一类节点，旅游者视为另一类节点，地区和旅游者之间的联系视为边，构建了一个比较有特色的区域旅游合作与竞争二分图模型。地区的项目度可以代表地区的市场份额或者竞争实力。将二分图向地区节点按照几何方法直观投影，得到一个区域旅游合作单模式网络。在投影图中，从一个地区到另一个地区的旅游者投影成一条边，表示两个地区之间存在合作关系。该网络不仅是加权网络，而且是有向的、有重边和自环的网络。使用DEMATEL法测算出的地区中心度可以代表该地区的合作实力。
笔者结合2005年的中国入境旅游者流向抽样数据和各地区接待旅游者人数数据，对该网络的拓扑特性主要统计属性—聚集系数、最短路径长度、点的项目度分布、边权分布、核心—边缘结构、结构洞等进行了详细深入的统计分析。分析结果表明：①聚类系数大，特征路径长度小，该网络具有典型的小世界特征；②边权分布和项目度规模名次分布均呈现幂律特征，表明该网络是一个无标度网络；③存在典型的核心-边缘结构，核心阶层成员之间的密度远高于边缘阶层成员之间的密度，也高于核心阶层与边缘阶层之间的密度；④地区拥有的结构洞优势与该地区项目度和中心度大小并不一致。
笔者应用改进的DEMATEL法，从影响度、被影响度、中心度和原因度四个方面对区域旅游合作与竞争网络的关联性进行深入分析。与以往社会网络分析中所提出的关联性指标相比较，中心度和原因度更全面地体现各节点在关联网络中的地位和作用。中心度综合考虑了影响度和被影响度，可以全面地反映各节点的合作实力。原因度反映了各节点在关联网络中的作用结果。笔者根据中心度和原因度绘制出该网络的因果关系图，划分出潜力充足区、急待改进区、锦上添花区和优势保持区四大板块，并分别给出合理的优化升级策略。
基于地区和旅游者二类节点的区域旅游合作与竞争网络二分图，笔者构建了一个基于吸引优先连接的区域旅游合作与竞争网络演化模型，用于描述各地区累积接待旅游者人数增长演化过程。笔者以中国入境旅游系统2005年－2009年数据为例，采用SPSS软件对演化模型的仿真理论值和实际值进行回归分析，检验该模型的正确性。由于F=1.005×104> F1,29(0.05)=4.18，因而从统计意义上验证吸引优先连接是该网络的关键演化机制。
最后，笔者通过定性分析私产和财政分权制度及其聚合行为，构建了一个基于私产和财政分权制度的区域旅游合作与竞争均衡模型。该理论模型表明：在私产制度和财政分权制度下，由于有吸引优先连接演化机制导致的“富者越富”结构特性的约束，政府和私产投资商必然是将资本更多地投向初始优势强的地区，从而形成旅游产业聚集现象，并且这种产业集聚是有效率的。中国入境旅游产业集聚现象也验证了私产制度、财政分权制度及“富者越富”结构特性是形成旅游产业集聚的重要原因。
The tourism industry, which is the world's largest industry today, has become aneffective entry point and an important breakthrough for the economic boom andsustainable development in many countries and regions. The regional tourismcooperation and competition has been paid great attention to an unprecedented heightby both academic scholars and industrial practitioners. Although it has been a hottopic to adopt social network analysis method and complex network theory to studythe system of cooperation-competition in the academic field recently, few scholarsconduct researches on regional tourism cooperation-competition system from theperspective of network structure.
Combining the complex network theory, social network analysis approach, neweconomic sociology theory, new economic geography theory, new institutionaleconomics theory and borrowing ideas from the latest research findings in the field ofcooperation-competition economic relation networks, the present study proposed ananalysis method of regional tourism cooperation-competition network from fiveaspects of network graph model construction, structural feature index measurementand analysis, relevance analysis, evolution model and cooperation-competitionequilibrium mathematic model. An empirical study on Chinese inbound tourismsystem was conducted to test the validity of the analysis method and models proposed.
The present study built a binary graph model of regional tourism cooperation-competition system with special features, in which regions and tourists were taken asnodes, relations between regions and tourists as edges. The project degree of regionsrepresented the regions’ market share or competition competence. The geometricmethod was adopted to project the bipartite graph to regional nodes, which led to asingle-model network of regional tourism cooperation. In the project graph, thenumber of tourists from one region to another made an edge representing thecooperation relationship between two regions. This network was not only a weightednetwork, but also a directed multiple network with self-loop. The central degree ofregions calculated by the method of DEMATEL represented the regions’ cooperationstrength.
Using the data of Chinese inbound tourist flows and the number of inboundtourists in every regions in2005, the present study analyzed the main statistical properties of the network in terms of clustering coefficient, the length of the shortestpath, the distribution of project degree, the distribution of edge weight, core-periphery structure, structural hole, etc. The main findings showed that:①thenetwork had a big clustering coefficient and a short characteristic path length whichindicated the characteristics of a small world；②both the edge weight distribution andthe project degree size ranking distribution emerged as a power law which suggested ascale-free network；③the network has a typical core-periphery structure and thedensity between the core members was much higher than both the density between theperiphery members and the density between the core members and the peripherymembers；④the regions’ advantages of structure hole was not consistent with theirproject degree and central degree.
The present study adopted the improved DEMATEL method to analyze therelevance of the Chinese inbound tourism network from four aspects of influencedegree, influenced degree, central degree and reason degree. Compared with therelevance indexes used in the previous social network analysis study, the centraldegree and reason degree were the more effective indicators for the position and roleof nodes in the network. The central degree presented the cooperation strength of eachnode by taking the influence degree, influenced degree into consideration. The reasondegree reported the role of each node in the relevance network. Based on the data ofcentral degree and reason degree, a cause and effect diagram of the network wasdrawn. According to this diagram, the author divided the whole network into fourareas named potential, improvement, advancement and strengths. It was followed byoptimization and upgrading strategies for each area.
Based on the regional tourism collaboration-competition network bipartite graphwith two kinds of nodes of region and tourist, the author constructed an evolutionmodel of regional tourism collaboration-competition networks which was based onattract preferential attachment to describe how the accumulative number of tourists ina region grew and evolved. Then the author used SPSS software to run a regressionanalysis on the simulation data and the actual data by adopting the data of Chineseinbound tourism system from the year of2005to2009to verify the correctness of themodel. The result was F=1.005×104> F1,29(0.05)=4.18, which implicated that theattract preferential attachment was the key evolution mechanism of tourismcollaboration-competition networks.
In the end, through a qualitative study on private property and fiscaldecentralization system and polymerization behavior, the author built up a regional tourism cooperation-competition equilibrium mathematic model based on PrivateProperty Rights and Fiscal Decentralization institution. The model showed that withthe precondition of private property system and fiscal decentralization system, due tothe structural constraint of “the rich get richer” resulting from the evolutionmechanism of attracting preferential attachment, the government and privateinvestors would surely invest more money in the areas with original preferenceleading to the effective tourism industrial clustering. Chinese inbound tourismindustrial cluster was a good example to indicate that private property system, fiscaldecentralization system and the structural constraint of “the rich get richer” were theimportant reasons for tourism industrial clustering.
笔者通过融合复杂网络理论、社会网络分析、新经济社会学、新经济地理学及新制度经济学理论与方法，借鉴合作与竞争经济关系网络的最新研究成果，从网络图模型的构建、结构性特性指标的测量和分析、关联性分析、演化模型、合作与竞争均衡等5个角度系统地研究区域旅游合作与竞争，并以中国入境旅游系统为例进行了实证研究。
笔者将各地区视为一类节点，旅游者视为另一类节点，地区和旅游者之间的联系视为边，构建了一个比较有特色的区域旅游合作与竞争二分图模型。地区的项目度可以代表地区的市场份额或者竞争实力。将二分图向地区节点按照几何方法直观投影，得到一个区域旅游合作单模式网络。在投影图中，从一个地区到另一个地区的旅游者投影成一条边，表示两个地区之间存在合作关系。该网络不仅是加权网络，而且是有向的、有重边和自环的网络。使用DEMATEL法测算出的地区中心度可以代表该地区的合作实力。
笔者结合2005年的中国入境旅游者流向抽样数据和各地区接待旅游者人数数据，对该网络的拓扑特性主要统计属性—聚集系数、最短路径长度、点的项目度分布、边权分布、核心—边缘结构、结构洞等进行了详细深入的统计分析。分析结果表明：①聚类系数大，特征路径长度小，该网络具有典型的小世界特征；②边权分布和项目度规模名次分布均呈现幂律特征，表明该网络是一个无标度网络；③存在典型的核心-边缘结构，核心阶层成员之间的密度远高于边缘阶层成员之间的密度，也高于核心阶层与边缘阶层之间的密度；④地区拥有的结构洞优势与该地区项目度和中心度大小并不一致。
笔者应用改进的DEMATEL法，从影响度、被影响度、中心度和原因度四个方面对区域旅游合作与竞争网络的关联性进行深入分析。与以往社会网络分析中所提出的关联性指标相比较，中心度和原因度更全面地体现各节点在关联网络中的地位和作用。中心度综合考虑了影响度和被影响度，可以全面地反映各节点的合作实力。原因度反映了各节点在关联网络中的作用结果。笔者根据中心度和原因度绘制出该网络的因果关系图，划分出潜力充足区、急待改进区、锦上添花区和优势保持区四大板块，并分别给出合理的优化升级策略。
基于地区和旅游者二类节点的区域旅游合作与竞争网络二分图，笔者构建了一个基于吸引优先连接的区域旅游合作与竞争网络演化模型，用于描述各地区累积接待旅游者人数增长演化过程。笔者以中国入境旅游系统2005年－2009年数据为例，采用SPSS软件对演化模型的仿真理论值和实际值进行回归分析，检验该模型的正确性。由于F=1.005×104> F1,29(0.05)=4.18，因而从统计意义上验证吸引优先连接是该网络的关键演化机制。
最后，笔者通过定性分析私产和财政分权制度及其聚合行为，构建了一个基于私产和财政分权制度的区域旅游合作与竞争均衡模型。该理论模型表明：在私产制度和财政分权制度下，由于有吸引优先连接演化机制导致的“富者越富”结构特性的约束，政府和私产投资商必然是将资本更多地投向初始优势强的地区，从而形成旅游产业聚集现象，并且这种产业集聚是有效率的。中国入境旅游产业集聚现象也验证了私产制度、财政分权制度及“富者越富”结构特性是形成旅游产业集聚的重要原因。
The tourism industry, which is the world's largest industry today, has become aneffective entry point and an important breakthrough for the economic boom andsustainable development in many countries and regions. The regional tourismcooperation and competition has been paid great attention to an unprecedented heightby both academic scholars and industrial practitioners. Although it has been a hottopic to adopt social network analysis method and complex network theory to studythe system of cooperation-competition in the academic field recently, few scholarsconduct researches on regional tourism cooperation-competition system from theperspective of network structure.
Combining the complex network theory, social network analysis approach, neweconomic sociology theory, new economic geography theory, new institutionaleconomics theory and borrowing ideas from the latest research findings in the field ofcooperation-competition economic relation networks, the present study proposed ananalysis method of regional tourism cooperation-competition network from fiveaspects of network graph model construction, structural feature index measurementand analysis, relevance analysis, evolution model and cooperation-competitionequilibrium mathematic model. An empirical study on Chinese inbound tourismsystem was conducted to test the validity of the analysis method and models proposed.
The present study built a binary graph model of regional tourism cooperation-competition system with special features, in which regions and tourists were taken asnodes, relations between regions and tourists as edges. The project degree of regionsrepresented the regions’ market share or competition competence. The geometricmethod was adopted to project the bipartite graph to regional nodes, which led to asingle-model network of regional tourism cooperation. In the project graph, thenumber of tourists from one region to another made an edge representing thecooperation relationship between two regions. This network was not only a weightednetwork, but also a directed multiple network with self-loop. The central degree ofregions calculated by the method of DEMATEL represented the regions’ cooperationstrength.
Using the data of Chinese inbound tourist flows and the number of inboundtourists in every regions in2005, the present study analyzed the main statistical properties of the network in terms of clustering coefficient, the length of the shortestpath, the distribution of project degree, the distribution of edge weight, core-periphery structure, structural hole, etc. The main findings showed that:①thenetwork had a big clustering coefficient and a short characteristic path length whichindicated the characteristics of a small world；②both the edge weight distribution andthe project degree size ranking distribution emerged as a power law which suggested ascale-free network；③the network has a typical core-periphery structure and thedensity between the core members was much higher than both the density between theperiphery members and the density between the core members and the peripherymembers；④the regions’ advantages of structure hole was not consistent with theirproject degree and central degree.
The present study adopted the improved DEMATEL method to analyze therelevance of the Chinese inbound tourism network from four aspects of influencedegree, influenced degree, central degree and reason degree. Compared with therelevance indexes used in the previous social network analysis study, the centraldegree and reason degree were the more effective indicators for the position and roleof nodes in the network. The central degree presented the cooperation strength of eachnode by taking the influence degree, influenced degree into consideration. The reasondegree reported the role of each node in the relevance network. Based on the data ofcentral degree and reason degree, a cause and effect diagram of the network wasdrawn. According to this diagram, the author divided the whole network into fourareas named potential, improvement, advancement and strengths. It was followed byoptimization and upgrading strategies for each area.
Based on the regional tourism collaboration-competition network bipartite graphwith two kinds of nodes of region and tourist, the author constructed an evolutionmodel of regional tourism collaboration-competition networks which was based onattract preferential attachment to describe how the accumulative number of tourists ina region grew and evolved. Then the author used SPSS software to run a regressionanalysis on the simulation data and the actual data by adopting the data of Chineseinbound tourism system from the year of2005to2009to verify the correctness of themodel. The result was F=1.005×104> F1,29(0.05)=4.18, which implicated that theattract preferential attachment was the key evolution mechanism of tourismcollaboration-competition networks.
In the end, through a qualitative study on private property and fiscaldecentralization system and polymerization behavior, the author built up a regional tourism cooperation-competition equilibrium mathematic model based on PrivateProperty Rights and Fiscal Decentralization institution. The model showed that withthe precondition of private property system and fiscal decentralization system, due tothe structural constraint of “the rich get richer” resulting from the evolutionmechanism of attracting preferential attachment, the government and privateinvestors would surely invest more money in the areas with original preferenceleading to the effective tourism industrial clustering. Chinese inbound tourismindustrial cluster was a good example to indicate that private property system, fiscaldecentralization system and the structural constraint of “the rich get richer” were theimportant reasons for tourism industrial clustering.
引文
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