Pathways for traditional villages' transition from "internet-famous" to "long-term popularity" in the flow economy: A case study of Xunpu village, Quanzhou

With the vigorous development of the Internet, data have become an important means of production, penetrating all areas of the tourism industry and giving rise to various "Internet-famous" tourist destinations. The "explosion of popularity" of traditional villages is not only a concentrated reflection of the transformation of rural tourism development mode promoted by digital intelligence technology, but also the basic characteristics and operating logic of the traffic economy in the New Era. Taking Xunpu village in Quanzhou city as a case study, this paper applies the Tourism Area Life Cycle (TALC) theory, grounded theory and the fsQCA method to reveal the influencing factors behind traditional villages becoming "Internet-famous" and to explore pathways for sustaining "long-term popularity". The research shows that: (1) Xunpu village has become an Internet-famous village with the help of network traffic, and its life cycle curve has deformed due to time compression, showing an extruded S shape. (2) The "explosion of popularity" of Xunpu village results from the interaction of five factors: symbolic value, virtual and real culture, spatial reconstruction, optimized management, and traffic dissemination, which jointly promote the mobility of village stock resources and production factors. (3) The three main paths for Xunpu village to achieve long-term success are "symbolic value-driven type by traffic dissemination", "symbolist value-optimized management boosted type" and "spatial reconstruction-optimized management driven type". Under the coordination of virtual and real culture and traffic dissemination, symbolic value plays a core leading role. The flow economy, as an important form of tourism productivity, has brought new opportunities and challenges to the sustainable development of traditional villages. Future related research can further expand the sample range to cover more types of Internet-famous villages, to enhance the explanatory power of the lifecycle model of Internet-famous tourist destinations. At the same time, further in-depth exploration is needed on the path and impact mechanism of transforming the information flow of Internet-famous villages into tourist flow, as well as determining the conversion rate of different types of tourist destinations.