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In recent years, centrality measures have been extensively used to analyze real-world complex networks. Water distribution networks (WDNs), as a good example of complex networks, exhibit properties not shared by other networks. This raises concerns about the effectiveness of applying the classical centrality measures to these networks. The purpose of this paper is to generate a new centrality measure in order to stick more closely to WDNs features.

This work refines the traditional betweenness centrality by adding a hydraulic-based weighting factor in order to improve its fit with the WDNs features. Rather than an exclusive focus on the network topology, as does the betweenness centrality, the new centrality measure reflects the importance of each node by taking into account its topological location, its demand value and the demand distribution of other nodes in the network.

Comparative analysis proves that the new centrality measure yields information that cannot be captured by closeness, betweenness and eigenvector centrality and is more accurate at ranking the importance of the nodes in WDNs.

The following practical implications emerge from the centrality analysis proposed in this work. First, the maintenance strategy driven by the new centrality analysis enables practitioners to prioritize the components in the network based on the priority ranking attributed to each node. This allows for least cost decisions to be made for implementing the preventive maintenance strategies. Second, the output of the centrality analysis proposed herein assists water utilities in identifying the effects of components failure on the network performance, which in turn can support the design and deployment of an effective risk management strategy.

The new centrality measure, proposed herein, is distinct from the conventional centrality measures. In contrast to the classical centrality metrics in which the importance of components is assessed based on a pure topological viewpoint, the proposed centrality measure integrates both topological and hydraulic attributes of WDNs and therefore is more accurate at ranking the importance of the nodes.

This paper takes a unique approach to provide additional insight into the agency view of tax avoidance. We directly investigate the association between the presence of agency conflicts and corporate tax avoidance. Using a measure of CEO centrality, developed by Bebchuk, Cremers, and Peyer (2011), we identify settings in which agency conflicts are likely to be high. In contrast to prior literature, our primary tests do not rely on the inferences of market participants regarding tax avoidance. We find that CEO centrality is positively and significantly associated with tax avoidance. Additionally, we analyze the mediating role of monitoring by institutional investors in our setting. We find that the relation between tax avoidance and the existence of agency conflicts is strongest for firms with low levels of CEO monitoring. We also add to prior literature by investigating the implications of our setting on future accounting performance and future firm value.

While a global supply network can provide stability to address localized interruptions, however, the recent global pandemic materialized many concerns and risks associated with the global supply network. Considering the short-term and long-term effects of changes in the global supply chain, this research explores how the location characteristics of the firms across the supply chain affect their performance.

Using the mined data from five tiers of the backward supply chain of medical equipment, the authors constructed a large supply chain network consisting of close to 160,000 dyadic connections. The authors used various network centrality and clustering algorithms to measure the influence of each firm across the supply chain structure. Furthermore, the authors ran a scenario to simulate the elimination of Chinese firms from the global supply chain and recalculated all centralities. Regression analysis was used to measure the effect of supply chain network centralities on firms' performance across the supply chain with and without Chinese firms.

The complexity of global purchasing across global tiers of supply networks had been recognized as a source of uncertainty before the COVID-19 pandemic. This pandemic was the black swan that the medical supply chain professionals had noted its threat in recent years. While a global supply network can provide stability to address localized interruptions, however, the recent global pandemic materialized many concerns and risks associated with the global supply network. Considering the short-term and long-term effects of changes in the global supply chain, this research explores how the location characteristics of the firms across the supply chain affect their performance.

This research has three main implications. First, it provides a benchmark for manufacturing firms and distributors around the world operating in the post-COVID-19 business environment to better understand the relationship of their supply chain strategy and firms' financial performance. Second, investors and asset managers can evaluate their portfolios in light of the changing relationship as a result of possible protectionism initiatives. Finally, policymakers can apply the research methodology of this work in various industries while reevaluating post-COVID-19 international relations and trades policies at the firm, industry and country levels.

Policymakers working on global connection can utilize the outcome of this research to explore the consequences of local and global policies on trade patterns, organizational performance as well as individuals' movements. Another implication of this study for policymakers is that it provides a powerful simulation and analytical tool to launch or combat the global ruptures, including trade wars and natural disasters stemming from natural events (e.g. climate change) and human-made events (e.g. wars, supply-chain interruptions, sanctions).

To the best of our knowledge, this is the first large-scale empirical study that measures the effect of supply chain structure across multiple (five) tiers of the global supply chain on firms' performance. The present study uses the original supply chain network data mined by the authors from financial publications.

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.

This study aims to examine publications of supply chain management (SCM) researchers from across the world and maps the leadership role of authors and institutions based on how prolific they are in publishing and on network measures of centrality while accounting for the quality of the outlets that they publish in. It aims to inform stakeholders on who the leading SCM scholars are, their primary areas of SCM research, their publication profiles and the nature of their networks. It also identifies and informs on the leading SCM research institutions of the world and where leadership in specific areas of SCM research is emerging from.

Based on SCM papers appearing in a set of seven leading journals over the 15-year period of 2001-2015, publication scores and social network analysis measures of total degree centrality and Bonacich power centrality are used to identify the highest ranked agents in SCM research overall, as well as in some specific areas of SCM research. Social network analysis is also used to examine the nature and scope of the networks of the ranked agents and where leadership in SCM research is emerging from.

Authors and institutions from the USA and UK are found to dominate much of the rankings in SCM research both by publication score and social network analysis measures of centrality. In examining the networks of the very top authors and institutions of the world, their networks are found to be more inward-looking (country-centric) than outward-looking (globally dispersed). Further, researchers in Europe and Asia alike are found to exhibit significant continental inclinations in their network formations with researchers in Europe displaying greater propensity to collaborate with their European-based counterparts and researchers in Asia with their Asian-based counterparts. Also, from among the journals, Supply Chain Management: An International Journal is found to exhibit a far more expansive global reach than any of the other journals.

The journal set used in this study, though representative of high-quality SCM research outlets, is not exhaustive of all potential outlets that publish SCM research. Further, the measure of quality that this study assigns to the various publications is based solely on a publication score that accounts for the quality of the journals, as rated by Association of Business Schools that the papers appear in and nothing else.

By informing the community of stakeholders of SCM research about the top-ranked SCM authors, institutions and countries of the world, the nature of their networks, as well as what the primary areas of SCM research of the leading authors in the world are, this research provides stakeholders, including managers, researchers and students, information that is helpful to them not only because of the insights it provides but also for the gauging of potential for embedding themselves in specific networks, engaging in collaborative research with the leading agents or pursuing educational opportunities with them.

This research is the first of its kind to identify and rank the top SCM authors and institutions from across the world using a representative set of seven leading SCM and primary OM journals based on publication scores and social network measures of centrality. The research is also the first of its kind to identify and rank the top authors and institutions within specific areas of SCM research and to identify future research opportunities relating to aspects of collaboration and networking in research endeavors.

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.

This study aims to examine the impact of a chief executive officer (CEO) social network centrality on corporate social responsibility (CSR) performance.

This study carries out a multivariate linear regression analysis on a panel data sample of 11,507 firm-year observations (representing 1,386 unique US firms) from 2004 to 2014.

This paper finds a significant negative relation between CEO network centrality and irresponsible CSR performance (measured as CSR concerns). The findings suggest that better-connected CEOs can better mitigate CSR concerns or weaknesses, leading to improved overall CSR performance of a firm.

This is the first study that directly examines the empirical link between CEO centrality and CSR performance.

The increased complexity of water distribution networks (WDNs) emphasizes the importance of studying the relationship between topology and vulnerability of these networks. However, the few existing studies on this subject measure the vulnerability at a specific location and ignore to quantify the vulnerability as a whole. The purpose of this paper is to fill this gap by extending the topological vulnerability analysis further to the global level.

This paper introduces a two-step procedure. In the first step, this work evaluates the degree of influence of a node by employing graph theory quantities. In the second step, information entropy is used as a tool to quantify the global vulnerability of WDNs.

The vulnerability analysis results showed that a network with uniformly distributed centrality values exhibits a lower drop in performance in the case of partial failure of its components and therefore is less vulnerable. In other words, the failure of a highly central node leads to a significant loss of performance in the network.

The vulnerability analysis method, developed in this work, provides a decision support tool to implement a cost-effective maintenance strategy, which relies on identifying and prioritizing the vulnerabilities, thereby reducing expenditures on maintenance activities.

By situating the research in the entropy theory context, for the first time, this paper demonstrates how heterogeneity and homogeneity of centrality values measured by the information entropy can be interpreted in terms of the network vulnerability.

The understanding of leaders’ network centrality in social networks has been acknowledged as a major topic that can advance the social network field; most studies in this area have either taken firms as the subject by which the network centrality of firms was measured or/and have been conducted for the functional project context. Very little research has been done in the pure project context. This paper aims to revisit the centrality–performance link in the singular specialized project context.

The proposed relationships using panel data on 48 movie directors who lead pure projects has been studied. Freeman’s (1979) and Wasserman and Faust’s (1994) procedures have been adopted to compute our three centrality measures and their effects have been examined on box-office and artistic performance. A random effect and a mixed-effects Poisson model have been fit to examine the significance of the centrality–performance relationship.

The findings provide empirical evidence to support three out of the six hypotheses. The findings suggested that degree and closeness centrality are positively related to commercial performance and betweenness centrality is negatively related to commercial performance. However, it was found that only the degree centrality is related to artistic performance.

This study has two features that distinguish it from prior studies that link centrality to performance. First, the focus is on centrality attached to the leaders instead of the centrality attached to functional project teams or firms, as previously investigated. Second, this study is the first attempt of its kind to analyse the proposed relationship for an Asian market.

The paper models the financial interconnectedness and systemic risk of shadow banks using Granger-causal network-based measures and takes the Indian shadow bank crisis of 2018–2019 as a systemic event.

The paper employs pairwise linear Granger-causality tests adjusted for heteroskedasticity and return autocorrelation on a rolling window of weekly returns data of 52 financial institutions from 2016 to 2019 to construct network-based measures and calculate network centrality. The Granger-causal network-based measure ranking of financial institutions in the pre-crisis period (explanatory variable) is rank-regressed with the ranking of financial institutions based on maximum percentage loss suffered by them during the crises period (dependent variable).

The empirical result demonstrated that the shadow bank complex network during the crisis is denser, more interconnected and more correlated than the tranquil period. The closeness, eigenvector, and PageRank centrality established the systemic risk transmitter and receiver roles of institutions. The financial institutions that are more central and hold prestigious positions due to their incoming links suffered maximum loss. The shadow bank network also showed small-world phenomena similar to social networks. Granger-causal network-based measures have out-of-sample predictive properties and can predict the systemic risk of financial institutions.

The study considers only the publicly listed financial institutions. Also, the proposed measures are susceptible to the size of the rolling window, frequency of return and significance level of Granger-causality tests.

Supervisors and financial regulators can use the proposed measures to monitor the development of systemic risk and swiftly identify and isolate contagious financial institutions in the event of a crisis. Also, it is helpful to policymakers and researchers of an emerging economy where bilateral exposures' data between financial institutions are often not present in the public domain, plus there is a gap or delay in financial reporting.

The paper is one of the first to study systemic risk of shadow banks using a financial network comprising of commercial banks and mutual funds. It is also the first one to study systemic risk of Indian shadow banks.