The modern service industry agglomeration and tourism efficiency in China: regional difference and influencing mechanism

Tang, Rui; Xiu, Pishi

doi:10.1080/13547860.2021.1955529

Cited by 12 publications

(8 citation statements)

References 23 publications

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“…Some studies calculated tourism efficiency within a study area and analyzed its spatial distribution characteristics [15]. Some studies analyzed the relationship between tourism efficiency and transportation accessibility, service industry concentration, and the ecological environment [16,17]. Choi, et al [18] explored festival tourism efficiency and its influencing factors in Korea using parametric and non-parametric methods.…”

Section: Introductionmentioning

confidence: 99%

The Spatial Network Structure of Tourism Efficiency and Its Influencing Factors in China: A Social Network Analysis

et al. 2022

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Although tourism has gradually become a popular form of leisure and entertainment in China, the quality of China’s tourism development remains unclear. Through the panel data of 30 provinces in China, an SBM-DEA model and a social network analysis are used to explore the quality of tourism development, and a spatial econometric regression is used to identify the relevant factors affecting tourism efficiency. The study found that the level of tourism efficiency in Southwest China is high and stable. The northwest region has a low level of tourism efficiency, but a slow growth trend. The rest of the regions show fluctuating trends of tourism efficiencies. The spatial correlation network of provincial tourism efficiency is gradually complicated. Regarding influencing factors, the number of patents granted, traffic levels, financial development, and government macro-control all have positive effects on tourism efficiency. The study uncovered some useful management insights and implications for the travel industry.

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Section: Introductionmentioning

confidence: 99%

The Spatial Network Structure of Tourism Efficiency and Its Influencing Factors in China: A Social Network Analysis

et al. 2022

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“…The traffic passenger volume, the upgrading of industrial structure, and the government consumption were selected as control variables. The traffic passenger volume reflects the tourism traffic accessibility [ 43 ], which needs to be guaranteed by transportation infrastructure. The upgrading of industrial structure was measured by the industrial structure supererogation.…”

Section: Methodsmentioning

confidence: 99%

How does the integration of cultural and tourism industries impact the value added to tourism value chain: Evidences from Jiangsu Province of China

Zeng

Shen

2023

PLoS ONE

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China has been fully implementing the policy of the cultural and tourism industrial integration since 2018. However, the value-added benefits of this policy are not prominent, and the relationship between industrial integration and the value added to the tourism value chain was seldom addressed by researchers. In the context of China’s high-quality development, it is necessary to conduct the impact of the integration of cultural and tourism industries on the value added to tourism value chain. This paper proposed four theoretical hypotheses and the corresponding econometric models based on the panel data from 2013 to 2020 in China’s Jiangsu Province. According to empirical results, the integration of cultural and tourism industries is spatially unbalanced, with notable imbalances between the south and the north. This paper identified a new connection between cultural and tourism integration and the tourism value chain. It is found that the integration of cultural and tourism industries can enhance the value added to tourism value chain either directly or indirectly through the information technology, with the direct effect being positively moderated by tourism agglomeration. Moreover, this paper may overturn how people generally think about the integration between cultural and tourism industries. It reveals a single-threshold effect that only when the integration of cultural and tourism industries reached a high level will it exert a positive effect. To be more specific, not all Chinese cities are suitable for implementing cultural and tourism integration, because the integration is likely to be ineffective in regions where the cultural industry is substantially less developed than the tourism industry.

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“…Zhang et al examined the quantity and types of land tourism resources in Hainan from a spatial perspective and further analyzed the spatio-temporal changes and differences in the number of tourists and tourism revenue in Hainan from 2010 to 2019 (Zhang & Ju, 2021). Based on tourism data from 2012 to 2017, Tang et al found that there was a positive relationship between modern service industry agglomeration and tourism efficiency in China, and modern service industry agglomeration in eastern and western regions promoted the improvement of tourism efficiency (Tang & Xiu, 2021). Liu et al tested the changes and differences in inbound tourism sources using inbound tourism statistics for Thailand from 1996 to 2015.…”

Section: Literature Reviewmentioning

confidence: 99%

Changes in the size of domestic tourists in mainland China under the impact of COVID-19

Zhong

Dong

2023

IJTC

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Purpose The purpose of this paper is to explore the changes of the scale of urban tourists in mainland China under the impact of COVID-19 and, specifically, the following questions: how did the scale of domestic tourists change nationwide and in the seven geographic regions? What are the differences in the changes among the seven geographic regions? What are the changes in the hot spot areas and spatial clustering of domestic tourists across the country? Design/methodology/approach Using the data of domestic tourist arrivals in 337 cities in mainland China from 2018 to 2021, this research analyzes the absolute differences and relative differences in the scale of domestic tourists nationwide and in seven geographic divisions with the help of indicators such as range analysis, standard deviation, coefficient of variation and Herfindahl–Hirschman Index and explores the changes in the hot spot areas and spatial concentration degree of the spatial scale of domestic tourists nationwide under the influence of the epidemic using kernel density analysis and spatial auto-correlation analysis. Findings The absolute differences in all seven geographical divisions continue to increase during 2018–2021. The domestic tourism in southwest China is extremely uneven. Absolute differences in the northwest and northeast regions are relatively small, and the development in attracting domestic tourists is more balanced. Relative differences in southwest China are comparatively large, with the trend of uneven development being obvious. The northeast, northwest and eastern regions of China are small, and the development is more balanced. The popularity of domestic tourism in the Beijing–Tianjin–Hebei region, as well as the Yangtze River Delta region, continues to decline and then pick up in 2021. The inland southwest region became a new domestic tourism hot spot in 2021. The size of domestic tourists from 2018 to 2021 in mainland China cities shows a significant positive spatial correlation, and there is a spatial agglomeration phenomenon, but some regional agglomeration types change from 2018 to 2021. Research limitations/implications The impact of the epidemic on the number and spatial scale of domestic tourism in China has been clarified, which makes up for the comparison of domestic tourism changes before and after the epidemic. A clear understanding of the changes in the number and spatial scale of domestic tourists in different regions after the epidemic is conducive to the development of domestic tourism revitalization strategies in accordance with the actual situation of each province and promotes the internal circulation of Chinese tourism. Practical implications This paper tries to clarify the quantitative scale of domestic tourism in different regions after the epidemic, which is conducive to the development of domestic tourism revitalization strategies in cities in different regions according to regional characteristics and the actual situation of each province and to promote the healthy operation of the internal circulation of tourism in China. This paper also tries to show the changes of domestic tourism market hot spots, agglomeration conditions changes before and after the outbreak and the clarity of tourists’ preference space changes. Originality/value Scale of domestic tourists; Absolute difference; Relative difference; Spatial hot spot distribution; Spatial agglomeration change

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The modern service industry agglomeration and tourism efficiency in China: regional difference and influencing mechanism

Cited by 12 publications

References 23 publications

The Spatial Network Structure of Tourism Efficiency and Its Influencing Factors in China: A Social Network Analysis

The Spatial Network Structure of Tourism Efficiency and Its Influencing Factors in China: A Social Network Analysis

How does the integration of cultural and tourism industries impact the value added to tourism value chain: Evidences from Jiangsu Province of China

Changes in the size of domestic tourists in mainland China under the impact of COVID-19

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