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In this study, the authors sought to investigate how the implicit social ties of both project owners and potential backers are associated with crowdfunding project success.

Drawing on social ties theory and factors that affect crowdfunding success, in this research, the authors developed a model to study how project owners' and potential backers' implicit social ties are associated with crowdfunding projects' degrees of success. The proposed model was empirically tested with crowdfunding data collected from Kickstarter and social media data collected from Twitter. The authors performed the test using an ordinary least squares (OLS) regression model with fixed effects.

The authors found that project owners' implicit social ties (specifically, their social media activities, degree centrality and betweenness centrality) are significantly and positively associated with crowdfunding projects' degrees of success. Meanwhile, potential project backers' implicit social ties (their social media activities and degree centrality) are negatively associated with crowdfunding projects' degrees of success. The authors also found that project size moderates the effects of project owners' social media activities on projects' degrees of success.

This work contributes to the literature on crowdfunding by investigating how the implicit social ties of both potential backers and project owners on social media are associated with crowdfunding project success. This study extends the previous research on social ties' roles in explaining crowdfunding project success by including implicit social ties, while the literature explored only explicit social ties.

This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.

Using Social Network Analysis (SNA), this paper examines the inter-provincial logistics relationships in China. Based on the annual data of inter-provincial railway logistics quantity during the period 1998-2009, the degree of interconnection between regions could imply intensified trends of regional economic integration.

The main results of the logistic relationships in China are as follows: the regional logistic interconnection, especially between western and eastern China has increased continuously, which would imply a rising national economic integration. However, the increased centralization index and the average Degree Centrality level imply that a logistics bottleneck has intensified in several hub provinces.

Secondly, logistic center provinces evaluated by the Degree Centrality have changed. In 2009, Hebei, Liaoning, Jiangsu, Shandong, Henan and Sichuan provinces revealed the highest inward Degree Centrality. Sichuan Province is the region that most surprisingly increased its centrality.

Thirdly, the number of logistic hub provinces, evaluated by the Degree Betweenness Centrality, has increased. In 2008, Henan province was only a focal hub but in 2009, Shandong, Hubei, Sichuan provinces became logistic hubs.

Lastly, the Community Modularity which analyzed grouping structures shows that there are three time-consistent communities. This means that even though there is enhanced between-region integration, the inter-regional inter-connection is more important in explaining the regional logistic relationship.

Using Social Network Analysis (SNA), this paper examines the inter-country airline logistics relationships in East Asia. Based on the flight schedule data, which has been gathered by the authors, the overall features of airline logistics structure and the specific roles of each airport could be more clearly examined.

The main results of this paper are as follows: Beijing has the highest Degree Centrality, but excluding the domestic lines, Incheon has the highest Degree Centrality, which would imply that a relatively high Centrality of logistics for China’s airports is due to the greater number of domestic lines. The focal hub status of Incheon airport is also supported by the fact that Incheon contains the highest Betweenness Centrality.

Secondly, evaluated by the types of brokerage role, Incheon has a remarkably strong role as a liaison, which means Incheon airport functions as a transfer hub between two different regions outside of Korea. However, considering only the to/from China airline links, Hong Kong has the highest score as a liaison. These conflicting results imply that as China’s airline transportation continuously increases, Hong Kong will become a strong rival to Incheon.

Thirdly, in the analysis of Structural Hole which functions the broker and acts as a hub by linking unconnected airports, Incheon has the highest possibilities as a hub airport. However, only considering to/from China airlines, Hong Kong would dominate Incheon.

This paper examines the network dynamics of the cross-border trades utilizing Social Network Analysis (SNA) based on data obtained from the WTO-OECD Trade in Value Added database from 2000-2011. The main results of this paper are as follows: regarding the top 10 in-degree centrality industries, industries in China, Germany, and the U.S. have emerged as the largest importers of foreign value added, implying that the global production network is dominated by two different types of industries. The first type includes processing and assembling functions in China and Germany. The other type of industry involves foreign value added largely for domestic final demand in the U.S. Secondly, there are also two types of brokerage roles. U.S. industries are operating in a liaison role, while Chinese and German industries are mostly operating as coordinator or gatekeeper. Thirdly, manufacturing industries in China and Germany which have emerged as higher in-degree centrality incur a large portion of their value added from the logistics industry. This suggests that those leading industries with the highest characteristics of hubness in the global production network cannot sustain their network status without efficient utilization of the logistics industry.

This study aims to evaluate the network robustness of major Asian airlines and to explore which airport types have the greatest impact on robustness. We also analyze airports’ specific brokerage roles and their impacts on the robustness of the entire air route network. We select 10 major Asian full-service airlines that operate the main passenger terminals at the top-ranked hub airports in Asia. Data is collected from the Official Airline Guide passenger route dataset for 2017. The results of the network robustness analysis show that Air China and China Eastern Airlines have relatively high network robustness. In contrast, airlines with broader international coverage, such as Japan Airlines, Korean Air, and Singapore Airlines have higher network vulnerability. The measure of betweenness centrality has a greater impact on the robustness of air route networks than other centrality measures have. Furthermore, the brokerage role analysis shows that Chinese airports are more influential within China and Asia but are less influential globally when compared to other major hub airports in Asia. Incheon International Airport, Singapore Changi Airport, Hong Kong International Airport, and Narita International Airport play strong “liaison” roles. Among the brokerage roles, the liaison role has a greater impact on the robustness of air route networks.

This study aims to evaluate the network robustness of major Asian airlines and to explore which airport types have the greatest impact on robustness. We also analyze airports’ specific brokerage roles and their impacts on the robustness of the entire air route network. We select 10 major Asian full-service airlines that operate the main passenger terminals at the top-ranked hub airports in Asia. Data is collected from the Official Airline Guide passenger route dataset for 2017. The results of the network robustness analysis show that Air China and China Eastern Airlines have relatively high network robustness. In contrast, airlines with broader international coverage, such as Japan Airlines, Korean Air, and Singapore Airlines have higher network vulnerability. The measure of betweenness centrality has a greater impact on the robustness of air route networks than other centrality measures have. Furthermore, the brokerage role analysis shows that Chinese airports are more influential within China and Asia but are less influential globally when compared to other major hub airports in Asia. Incheon International Airport, Singapore Changi Airport, Hong Kong International Airport, and Narita International Airport play strong “liaison” roles. Among the brokerage roles, the liaison role has a greater impact on the robustness of air route networks.

This paper presents a critical review of the economic network analysis methods and their applications to maritime transport. A network can be presented in terms of its structure, topology, characteristics as well as the connectivity with different measures such as density, degree distribution, centrality (degree, betweenness, closeness, eigenvector and strength), clustering coefficient, average shortest path length and assortative. Various models such as the random graph model, block model, and ERGM can be used to analyse and explore the formation of a network and interaction between nodes. The review of the existing theories and models has found that, while these models are rather computationally intensive, they are based on some rather restrictive assumption on network formation and relationship between ports in the network at the local and global levels that require further investigation. Based on the review, a conceptual framework for maritime transport network research is developed, and the applications for future research are also discussed.

This paper presents a critical review of the economic network analysis methods and their applications to maritime transport. A network can be presented in terms of its structure, topology, characteristics as well as the connectivity with different measures such as density, degree distribution, centrality (degree, betweenness, closeness, eigenvector and strength), clustering coefficient, average shortest path length and assortative. Various models such as the random graph model, block model, and ERGM can be used to analyse and explore the formation of a network and interaction between nodes. The review of the existing theories and models has found that, while these models are rather computationally intensive, they are based on some rather restrictive assumption on network formation and relationship between ports in the network at the local and global levels that require further investigation. Based on the review, a conceptual framework for maritime transport network research is developed, and the applications for future research are also discussed.

Using overlapped portfolio data on public equity funds in Korea, the authors construct several types of fund-stock weighted bipartite networks and measure fund network centrality. The authors also examine the relationship between network centrality and fund investment performance. The authors' results are three-fold. First, the authors find that the fund centrality of the network in which funds and stocks are connected based on the most active investing behavior positively affects the fund performance. Second, the funds with a high centrality level based on the same network generate higher returns by holding stocks with high value uncertainty. Third, the authors find that fund centrality is not associated with herd behavior. Based on these results, the authors argue that fund centrality is a proxy of information advantage and skill of fund managers. The authors' paper shows that network analysis could be a new way to identify funds with better performance and measure the skill and information advantage to construct an optimal portfolio.