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

Yang, Guangming; Yunrui, Yang; Gong, Guofang; Gui, Qingqing

doi:10.3390/su14169921

Cited by 22 publications

(17 citation statements)

References 44 publications

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“…Other inputs. We use the number of A-class scenic spots, star-rated hotels and travel agencies as other input indicators to reflect the level of service input and infrastructure construction of tourism industry and to a certain extent to make up for the lack of land input elements [32]. The main sports industry unit is a vital carrier for the development of sports industry and a key indicator to reflect the development scale of the industry.…”

Section: • Input Indexmentioning

confidence: 99%

Efficiency Evaluation and Influencing Factors of Sports Industry and Tourism Industry Convergence Based on China’s Provincial Data

Yang

Feng

et al. 2023

Sustainability

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As sunrise industries, green industries and happiness industries, the sports industry and the tourism industry have natural convergence, and the convergence of the sports and tourism industries is not only a new strategic support point for the sustainable development of the two industries but also an important way to drive healthy and sustainable development of related industries. We measure the efficiency of sports–tourism convergence in 31 provinces (municipalities and autonomous regions) in China from 2013 to 2019 using the benevolent cross-efficiency DEA model and the qualitative comparative analysis (fsQCA) method and conduct a configuration analysis of the influencing factors. The research indicates: (1) the efficiency of the development of sports–tourism convergence is low and its role in the sustainable development of the tourism and sports industries needs to be improved. (2) The efficiency of sports–tourism convergence shows spatial distribution characteristics of decreasing efficiency in the eastern region–central region–western region. (3) Industrial structure, transportation conditions, openness to the outside world, political support and their combination are the core driving factors affecting integration of sports and tourism.

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Section: • Input Indexmentioning

confidence: 99%

Efficiency Evaluation and Influencing Factors of Sports Industry and Tourism Industry Convergence Based on China’s Provincial Data

Yang

Feng

et al. 2023

Sustainability

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“…Previous studies on urban tourism efficiency have mostly used data envelopment models (DEA). However, the traditional DEA model can only measure efficiency values up to 1, making it difficult to effectively rank urban points and neglecting the impact of slack variables on efficiency measures [40]. In contrast, the Super-SBM model considers slack variables and fully measures the previously unmeasurable efficiency value of spillover effects of urban points.…”

Section: Super-sbm Modelmentioning

confidence: 99%

“…Following the studies of existing scholars [40][41][42], this research considers the cross-attributes of the tourism industry and data availability, and selects the number of A-class scenic spots, the number of employees in accommodation and catering above the quota, and the number of travel agencies in each city as resource input factors (i.e., tourism resource endowment), labor input factors (i.e., tourism labor advantage), and capital input factors (i.e., tourism capital advantage), respectively. The total tourism revenue (i.e., tourism revenue capacity) and the total number of tourist arrivals (i.e., tourism market scale) of each city are chosen as output evaluation indicators.…”

Section: Super-sbm Modelmentioning

confidence: 99%

Analysis on the Spatial Structure and Interaction of Aviation Network and Tourism Efficiency Network in Major Cities in China

Cai¹,

Gong²,

Han³

2023

AJMSS

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Tourism efficiency is crucial for measuring sustainable tourism development. Examining the relationship between aviation and tourism efficiency networks is key to promoting their synergistic development in China's urban areas. This study employs various methods, such as complex network analysis method, entropy-weighted TOPSIS, tourism efficiency gravity model, and quadratic assignment procedure, to analyze the networks' spatial structure evolution characteristics and interaction effects. Results show that (1) China's major cities' aviation network has improved its organizational efficiency and formed a "double rhombus-single axis" spatial evolution pattern of the axis-spoke network. The number of intermediary networks and hub cities in the central and western regions has increased. (2) The tourism efficiency network adopts a "honeycomb" structure pattern with the simultaneous layout of "point-to-point" and "star" networks. The network's tourism efficiency follows "Pareto's Law," and tourism cities above the second level form a club group development. The tourism efficiency development potential area is shifting to the southwest. (3) The aviation and tourism efficiency networks exhibit a clear trend of synergistic evolution with a "path locking" phenomenon between them. Differences in tourism resource endowment, labor advantage, and capital advantage positively impact the aviation network's structure. Conversely, differences in revenue capacity and market scale negatively impact the structure. The aviation scale advantage, openness, intimacy, and influence exhibit decreasing positive effects on the tourism efficiency network's structure.

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“…Using a spatial economic regression, Yang et al (2022) set out to identify the elements that are likely to influence tourism efficiency using the panel data of 30 provinces in China. The results suggested that different parts of the selected provinces offer a varying degree of efficiency based on a number of factors such as patents held, traffic congestion levels, financial structures, and governmental issues.…”

Section: Literature Reviewmentioning

confidence: 99%

Tourism Location Choice of Local and Foreign Tourist: A Perspective through Spatial Analysis

Çetin¹,

Aksoy²,

Arslantürk³

2022

WJAE

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The economic outreach of tourism businesses has undoubtedly had a significant contribution to the economic growth of countries and regions. Attracting tourists to the tourist provinces is an important regional growth and development issue. The main aim of this study is to present the factors influencing the tourism location choice of both foreign and domestic tourists. Cross-sectional spatial analysis is applied to Turkish province-level data for 2002-2019, and the time and spatial effects of regional tourism demands are considered. Lisa and Geary’s cluster maps provide the regional clusters. Accordingly, Bitlis, Siirt and Tunceli are the common low-tourist number provinces surrounded by low-ranking provinces (low-low cluster), whereas Burdur is the common low province surrounded by high-ranking provinces (low-high cluster). Both domestic and foreign tourist location choices were strictly influenced by their choice in 2002, i.e., time consistency in location choice is valid. The location choices do not depend on whether the province is on the seaside or inland, which is contrary to our expectations. Foreign tourists’ location choices are influenced by domestic tourists’ location choices three times more than that domestic tourists. Most importantly, according to the spatial autocorrelation results, location choice made by foreign tourists is spatially dependent, but this is not true for domestic tourists.

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

Cited by 22 publications

References 44 publications

Efficiency Evaluation and Influencing Factors of Sports Industry and Tourism Industry Convergence Based on China’s Provincial Data

Efficiency Evaluation and Influencing Factors of Sports Industry and Tourism Industry Convergence Based on China’s Provincial Data

Analysis on the Spatial Structure and Interaction of Aviation Network and Tourism Efficiency Network in Major Cities in China

Tourism Location Choice of Local and Foreign Tourist: A Perspective through Spatial Analysis

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