Measuring tourism coopetition networks of scenic villages : case study of Dapeng Peninsula

Abstract

Rural tourism has gained increasing popularity in China in the past two decades, due to enormous market demand. Especially, scenic villages, that is, those rural villages located within the boundary of scenic districts, gradually become desirable tourism destinations for urbanites longing for different lifestyles from fast-paced and stressful urban routines, thanks to their high accessibility to scenic districts. Scenic villages are an integral part of the entire scenic districts, because tourists are able to enjoy traditional rural landscape in these villages and conveniently visit the surrounding designated scenic spots. As a result, tourism industry prospers, with growing monetary benefits and employment opportunities for local villagers. A notable side effect of the unregulated rural tourism market, however, is the overheated competition between different scenic villages and scenic spots. For instance, in order to maximize individual benefits, scenic villages tend to replicate a few successful models, which ends up with rising tourism management cost and oversupply of homogeneous tourism products. This, in turn, harms the overall competitiveness of scenic districts. Given this background, this research targets at tourism coopetition networks (hereafter TCN) of scenic villages, with the hope of breaking the vicious competition cycle between scenic villages and scenic spots. The core statement of this study is that TCNs have the potential to become an ideal option to rebuild a well-connected relationship between scenic villages and scenic spots in a specific scenic district. This study develops the model with a combination of methodology to measure the TCNs. Planning strategies of scenic villages are suggested based on TCNs. The above statement is examined through the empirical case study of Dapeng Peninsula in Shenzhen City, Guangdong Province. The main content of this research consists of the following four aspects. First of all, the theoretical construct of this dissertation is established through conducting a comprehensive literature review and systematic analysis. Coopetition theory and network theory together contribute to the solid theoretical basis for measuring TCNs. On one hand, coopetition theory reveals the value proposition of this study by advocating the statement that TCNs are the best choice to construct an integrated interest-convergent system between scenic villages and scenic spots, from an overall perspective of the whole scenic district. Network theory, on the other hand, illustrates the core research methodology by serving as main approach of measuring TCNs. Built upon aforementioned theories, two structural elements of TCNs are derived, namely, nodal competitiveness (hereafter NC) and cooperation networks (hereafter CN). The dissertation also justifies why TCNs can guide scenic villages planning. Second, a model is built to measure the value of NC quantitatively. Specifically speaking, this model relies on a myriad of techniques, including the process of data mining, cleaning and analyzing under the framework of Machine Learning, on the basis of analytics of big data from social media and questionnaire survey. The model is applied to Dapeng Peninsula, which generates the value of NCs for its 43 scenic villages and 34 scenic spots. In addition, the factors which exert influence on NC of scenic villages are evaluated from the dimensions of situational conditions, tourism resources, sustainable development and tourism management, in order to explore the potential reasons behind the different results of NC. The third part attempts to build a model to quantify CN, which is made up with cooperation linkages (hereafter CL). On one hand, the connotation and evaluation system of cooperation between tourism destinations is explored in depth. On the other hand, this study designs a model to measure the strength of CL, reliant again on the analysis of big data from social media and questionnaire survey results. This model is also verified through the empirical study of scenic villages in Dapeng Peninsula in which the CL strength between 43 scenic villages and 34 scenic spots is measured. In a similar fashion with the exploration of NC, the factors of CL strength between scenic villages and scenic spots are evaluated from the dimensions of spatial interaction, tourism resource interaction and administrative affiliation interaction, in order to explain the potential reasons behind the CL strength measured by the model. Fourth, TCN presents a new approach to scenic village planning (hereafter SVP) by integrating NC and CN. This new paradigm of SVP is respectively elaborated based on the measurement result of both NC and CN, including village group plan, individual village plan and plan of transport, cultural and administrative connections. SVP not only enriches China's urban and rural planning system, but also helps shape healthy relationship between scenic villages and scenic spots located in the same scenic district. To sum up, the novelty of this study is to contribute a methodology and models to measure TCNs of scenic villages quantitatively. Along the way, this study also confirms a set of factors influencing NC and CL strength. What's more, SVP are proposed based on TCNs in order to ensure the sustainable development of scenic villages, which add the practical value of this research.