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The purpose of this paper is to attempt to shift away from an exclusive probabilistic viewpoint or a pure network theory-based perspective for vulnerability assessment of infrastructure networks (INs), toward an integrated framework that accounts for joint considerations of the consequences of component failure as well as the component reliability.

This work introduces a fuzzy inference system (FIS) model that deals with the problem of vulnerability analysis by mapping reliability and centrality to vulnerability. In the presented model, reliability and centrality are first fuzzified, then 16 different rules are defined and finally, a defuzzification process is conducted to obtain the model output, termed the vulnerability score. The FIS model developed herein attempts to explain the linkage between reliability and centrality so as to evaluate the degree of vulnerability for INs elements.

This paper compared the effectiveness of the vulnerability score in criticality ranking of the components against the conventional vulnerability analysis methods. Comparison of the output of the proposed FIS model with the conventional vulnerability indices reveals the effectiveness of the vulnerability score in identifying the criticality of components. The model result showed the vulnerability score decreases by increasing reliability and decreasing centrality.

Two key practical implications for vulnerability analysis of INs can be drawn from the suggested FIS model in this research. First, the maintenance strategy based on the vulnerability analysis proposed herein will provide an expert facilitator that helps infrastructure utilities to identify and prioritize the vulnerabilities. The second practical implication is especially valuable for designing an effective risk management framework, which allows for least cost decisions to be made for the protection of INs.

As part of the first contribution, we propose a novel fuzzy-based vulnerability assessment model in building a qualitative and quantitative picture of the vulnerability of INs. The second contribution is especially valuable for vulnerability analysis of INs by virtue of offering a key to understanding the component vulnerability principle as being constituted by the component likely behavior as well as the component importance in the network.

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.

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.

Networks linking tourist attractions or organizations are a major focus of tourism research. Despite extensive research on tourism networks, academic research on the spatial structure and formation of wine tourism networks is limited. This study aims to investigate the spatial structure and factors influencing the development of a network among Ningxia wineries, an emerging wine tourism destination in China.

This study uses social network analysis to uncover “what” the spatial structure of wine tourism networks looks like. Sixteen in-depth interviews were conducted among key stakeholders to explain the “why” of such structural characteristics.

The results show that in an emerging wine tourism destination, popular tourist attractions enjoy high centrality and hold key positions in the wine tourism network. Small wineries exhibit high closeness centrality, and only one winery serves as a network broker. According to the stakeholders, the importance of network actors will increase as their economic and political importance increase, while small wineries that lack differentiation in the network may perish.

Local governments can implement the suggested measures for improving network connections, and wineries are advised to find suitable positions to improve the experiences of tourists.

This study pioneers the identification of the distinct structure and factors influencing the network of an emerging wine tourism destination, thus enriching the understanding of the interplay and roles of different actors.

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 methods presently used for project stakeholder analysis have typically followed two distinct patterns: (1) a project-centric approach that places a project at the center, and subsequently, concentrates on dyadic relationships between the project and its stakeholders; (2) a network theory-based approach that emphasizes on the interconnections within the network of project stakeholders. The main contention of this study is built upon the premise that neither the sole analysis of dyadic relationships between a project and its stakeholders nor the stand-alone use of the network theory methods is adequate for reliable analysis of stakeholders.

This article proposes a model that bases the salience of stakeholders on their relationships with the project as well as on their interdependencies in the project. In doing so, this work explores the potential of a Fuzzy Inference System (FIS) to provide a comprehensive picture of stakeholder analysis. Using a real-world biodiversity project, this paper analyses the project stakeholders based on their possession of various attributes as well as the extent to which each individual stakeholder influences the entire connected network of all stakeholders.

A salient feature of the proposed FIS model is its ability to provide a high capacity for analyzing the results. The model is able to generate the input–output relationship surface view for stakeholder analysis. Further, unlike the traditional project stakeholder analysis methods that are linear, the proposed model is strongly nonlinear. This implies that change in the input variables of the fuzzy-based model is not expected to lead to a proportional change in the model output.

Two practical implications can be drawn from the presented stakeholder analysis model. First, confronted with mounting pressure to understand the stakeholder environment and to effectively manage stakeholders, project managers need to establish a sound stakeholder management strategy. The stakeholder analysis model developed herein casts a wider net for the critical ranking of stakeholders in a project, thereby providing a more accurate prioritization of the stakeholders. Second, while stakeholders independently require managerial attention, understanding the effect of competing and cooperative stakeholder interactions are unarguably of great importance. The presented model prompts the project managers to recognize not only the influence of key stakeholders on the project but also the interactions of multiple stakeholders within the stakeholder network.

The proposed stakeholder analysis model possesses several desirable features. First, it is not constrained to capturing only stakeholder attributes discussed in the example project provided in this study. The model is flexible and adaptable to all business and management contexts. Second, the stakeholder mapping in the model is not a function of a sole attribute but rather a cumulative effect of multiple stakeholder attributes. In fact, the power of the suggested model lies in its ability to incorporate the three aspects of stakeholder theory into a single model. Third, the presented model builds a quantitative and qualitative picture of the stakeholder salience. The suggested FIS model is capable of processing both qualitative perception of stakeholder attributes and quantitative analysis of the network of stakeholder interactions. This allows for a more comprehensive and synergistic utilization of model inputs.

This study aims to find technologically important patent identification methods and indicators early and efficiently to grasp the technical qualitative level of patents, which are output indicators of research and development (R&D) results.

This paper reports on two methods for distinguishing important patents and the indicators obtained from those methods. One of the discrimination methods is Heckman's two-step estimation procedure. The second method is to find the centrality of each patent by network analysis of the citation relationship between publications and to find the importance from the magnitude of the centrality value.

In Heckman's analysis, the number of citations within three years after publication and the applicant's right acquisition/maintenance motivation index had positive effects on patent importance. The discriminative indicators of important patents by network analysis were degree centrality, mediation centrality, proximity centrality and transit values in the aggregated subnetworks. These two analytical methods are in a relationship that can complement each other's shortcomings. To efficiently evaluate the qualitative importance of patents, it is recommended to use these two methods together.

The indicators of important technical patents might change depending on the technical field. Future studies can apply this research to multiple technical fields to improve robustness and to construct an algorithm that can efficiently evaluate the quality of patents.

This study's results can be useful for grasping the patent position of the company or competitors numerically and for quantitatively evaluating the quality of R&D activities. Furthermore, it is possible to streamline the routine for an exploratory search of a huge number of patents. For example, it could be useful for detecting changes in the paradigm of specific technical knowledge, evolving the genealogy of technical knowledge and creating patent maps for new R&D. These methods greatly increase the effectiveness of technical knowledge information, which is the basis of R&D. In addition, the results of this study can help in evaluating patented assets.

This study confirmed the development process of technical knowledge. It is a fact that sharing, sympathy and mutual trust for technical issues and technical values are created among professional engineers and researchers inside and outside the organization, and their preferences and interactions develop and expand technical knowledge. Understanding the process of development and the evolution of this technical knowledge gives hints, such as expanding the discretionary power of engineers and researchers regarding corporate secrets, or reviewing the balance between control and independence, to solve Japanese management problems, which are often closed and monetized in R&D activities.

This study presents a scoring of the technical significance of patents by combining the two analytical methods. In addition, there are proposals as a method for detecting changes in the genealogy and paradigm of technical knowledge. As an analysis method, it is a new proposal that has never existed before.

In recent years, the proliferation of free trade agreements (FTA) has led to rapid economic boons and political security among nations. This study investigates whether the percentage of expats or permanent foreign residents plays a role in deciding which countries to partner with in bi-lateral FTAs. Previously, the reasons for FTA formation were believed to be primarily based upon economic, geographic, and political reasons. However, through the use of social network analysis, a larger picture can be mapped which allows for a more comprehensive understanding of key players in the current state of FTA formation in relation to the immigrant network that they share between them. More specifically, through computerized social network analysis, centrality can be calculated to determine the key players and the most central immigrant populations. When analyzing both the immigrant centrality in relation to FTA centrality, it becomes evident that there is a high correlation between the two factors. Thus, the findings highlight that immigration trends can be used as a predictor of FTA formation. As a result, it emphasizes the weight of immigration policy on the formation of bi-lateral FTAs.

The purpose of this paper is to explore the dynamics of an online community by examining its participants’ centrality measures: degree, closeness, and the betweenness centrality. Each centrality measure shows the different roles and positions of an individual participant within an online community. To be specific, this research examines how an individual participant’s role and position affects her/his information sharing activities within an online community over time. Additionally, it investigates the differences between two different online communities (a personal interest focussed community and a social interest focussed community), in terms of the interaction patterns of participants.

For this research, the authors collected log files from Korean online discussion communities (café.naver.com) using a crawler program. A social network analysis was used to explore the interaction patterns of participants and calculate the centrality measures of individual participants. Time series cross-sectional analysis was used to analyze the effects of the roles and the positions on their information sharing activities in a longitudinal setting.

The results of this research showed that all three centrality measures of an individual participant in previous time periods positively influenced his/her information sharing activity in the current periods. In addition, this research found that, depending on the nature of the discussion issues, the participants showed different interaction patterns. Throughout this research, the authors explored the interaction patterns of individual participants by using a network variable, the centrality, within a large online community, and found that the interaction patterns provided strong impact on their information sharing activities in the following months.

To investigate the changes of participant’s behaviors, this study simply relies on the numbers of comments received and posted without considering the contents of the comments. Future studies might need to analyze the contents of the comments exchanged between participants, as well as the social network among participants.

Online communities have developed to take a more active role in inviting public opinions and promoting discussion about various socio-economic issues. Governments and companies need to understand the dynamics which are created by the interactions among many participants. This study offers them a framework for analyzing the dynamics of large online communities. Furthermore, it helps them to respond to online communities in the right way and in the right time.

Online communities do not merely function as a platform for the free exchange and sharing of personal information and knowledge, but also as a social network that exerts massive influence in various parts of society including politics, economy, and culture. Now online communities become playing an important role in our society. By examining communication or interaction behaviors of individual participants, this study tries to understand how the online communities are evolved over time.

In the area of online communities, many previous studies have relied on the subjective data, like participant’s perception data, in a particular time by using survey or interview. However, this study explores the dynamics of online communities by analyzing the vast amount of data accumulated in online communities.

This study aims to conduct a literature review on factors that influence the implementation and design of performance measurement systems in nonprofit organizations.

The research strategy is conceived through a literature review focused on the analysis of authorship, supported by bibliometric techniques such as citation, co-citation and co-authorship social networks.

Models and theories proposed for measuring performance in non-profit organizations are being researched, starting to form an intellectual structure related to performance measurement systems and nonprofits. Three main research topics have been given more attention: strategic performance and public service performance, health-care performance and nonprofit operations strategy and performance measurement.

The study is limited to scientific journal papers and could benefit from the addition of new sources of information such as conference papers, books and standards. The body of knowledge of this topic could also benefit from an in-depth investigation through a comprehensive review of models and theories, as a proposal for a future research agenda.

As practical applications are identified, groups of researchers in different countries and subjects that can generate research agendas, scientific communities used to investigate issues related to performance in nonprofit activities.

Performance measurement in nonprofit organizations is a topic of study that has been receiving considerable attention in recent years, to the point that the literature is revealing specific models for measuring performance in this type of organizations. Particular models and theories are being conceived, specializing existing models and theories related to performance measurement and management.