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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.

The health equity and prosperity of communities is closely linked to the effectiveness and success of local health coalitions. Social network analysis (SNA) is one mechanism to quantify and understand the factors leading to collaboration and effectiveness within these coalitions. This study aims to investigate network characteristics associated with perceived success and satisfaction in a health coalition and determine significant factors related to organizational collaborations.

This study examined the Olympic Peninsula Healthy Community Coalition (OPHCC) which aims to prevent chronic disease in rural Clallam County, Washington. Representatives (n = 21) from member organizations (n = 18) were asked to report on organization characteristics, perceived satisfaction in coalition activities, perceived success toward coalition's mission, and collaborations with other organizations in the coalition. Multilevel modeling used to analyze whether an organization's position within the coalition network was associated with their perceived satisfaction and perceived success. Exponential random graph modeling was used to examine what factors may impact collaboration ties between coalition members.

Organization representatives reported a total of 252 collaboration ties. In multilevel models, organization characteristics and network centrality scores accounted for between 61 and 68% of variance displayed in satisfaction scores and 45–61% of variance in perceived success scores. Exponential random graph modeling revealed activity level, for-profit status, and transitivity as significant factors in collaborative tie presence.

Encouraging consistent active participation, a balance of organizational type, and projects which require more than two collaborators may provide an environment for collaborative ties between organizations.

Interlocking directorates are a common phenomenon across several markets around the world. Yet, the institutional environment and the role of corporate elites in forming board networks promote some developing markets as a unique setting to understand the corporate boards network structure. This study aims to first explore the board directors’ network of all publicly listed companies in Kuwait. This paper then evaluates the effects of exogenous factors and endogenous network structural processes on the likelihood of board interlock.

This study analyzes the interlocks network structures of 167 listed companies in 13 different market sectors in Kuwait relying on hand-collected directors’ data and using four measures of network centrality: betweenness, degree, closeness and eigenvector. The authors predict board interlocks using exponential random graph models (ERGM) and firm-level information from the Bloomberg database.

This study observes that both the firms and directors’ networks consist of 55 components, with the largest component containing about half of the total number of firms/directors. The firm’s network consists of one giant component of 85 firms, including all but one bank. This study shows the importance of endogenous network variables, such as the number of edges, centralization and triangles on the estimation of the factors that promote the board interlocks. Highly centralized firms are less likely to interlock with other firms, while two firms that are interlocked with a common third firm are more likely to interlock.

This paper is the first to analyze in-depth the structure of the directors network of companies in Kuwait. This study illustrates the complex map of interfirm and directors social networks in Kuwait. To the best of the authors’ knowledge, this study is among the first to exploit ERGM in the context of board interlocks to account for potential cross-dependencies and emergent network structures. Managers can identify the director interlock with other firms in the network and take advantage of the connection as a source of external knowledge and influence.

The social and cultural duality perspective suggests dual ordering of interpersonal ties and cultural similarities. Studies to date primarily focus on cultural similarities in interpersonal dyads driven by principles such as homophily and contagion. We aim to extend these principles for sociocultural networks and investigate potentially competing micro-principles that generate these networks, taking into account not only direct dyadic overlap between interpersonal ties and cultural structures, but also the indirect interplay between the social and the cultural.

The empirical analysis utilizes social and semantic network data gathered through ethnographic studies of five creative organizations around Europe. We apply exponential random graph models (ERGMs) for multiplex networks to model the simultaneous operation of several generative principles of sociocultural structuring yielding multiplex dyads and triads that combine interpersonal ties with meaning sharing links.

The results suggest that in addition to the direct overlap of shared meanings and interpersonal ties, sociocultural structure formation is also affected by extra-dyadic links. Namely, expressive interpersonal ties with common third persons condition meaning sharing between individuals, while meaning sharing with common alters leads to interpersonal collaborations. Beyond dyads, the dual ordering of the social and the cultural thus operates as asymmetrical with regard to different types of interpersonal ties.

The paper shows that in addition to direct dyadic overlap, network ties with third parties play an important role for the co-constitution of the social and the cultural. Moreover, we highlight that the concept of network multiplexity can be extended beyond social networks to investigate competing micro-principles guiding the interplay of social and cultural structures.

To analyze networks of social interactions between the residents of a therapeutic community (TC) for women and the way, in which such interactions predict the discussion of issues that arise in treatment.

In total, 50 residents of a corrections-based TC for women were surveyed on the peers with whom they socialized informally, shared meals, shared letters from home and discussed issues that arose in treatment over a 12 h period. The data were analyzed using exponential random graph models (ERGM).

Reciprocity occurred in all networks while transitivity (a tendency of two residents who are connected to both connect to a third peer) occurred in all networks measuring informal social interactions. When controlling for reciprocity and transitivity, residents avoided spending social time or sharing meals with the same peers. There was no evidence of homophily by race, age or years of education. Homophily by entrance time and case manager occurred in social time. Case manager homophily occurred in the discussion of treatment issues but disappeared when controlling for social time and sharing letters from home.

Social networks in this TC arise from factors endogenous to the TC itself. It should be possible to determine the characteristics of optimal social networks in TCs. External validity is limited.

It should be possible to intervene to optimize the social networks of TC residents.

This is the first ERGM analysis of both informal and formal interactions in a TC.

The purpose of this study is to analyze the dynamic changes of the arms trade network not only from the network structure but also the influence mechanism from the aspects of the economy, politics, security, strategy and transaction costs.

The study employs the Temporal Exponential Random Graph Model and the Separable Temporal Exponential Random Graph Model to analyze the endogenous network structure effect, the attribute effect and the exogenous network effect of 47 major arms trading countries from 2015 to 2020.

The results show that the international arms trade market is unevenly distributed, and there are great differences in military technology. There is a fixed hierarchical structure in the arms trade, but the rise of emerging countries is expected to break this situation. In international arms trade relations, economic forces dominate, followed by political, security and strategic factors.

Economic and political factors play an important role in the arms trade. Therefore, countries should strive to improve their economic strength and military technology. Also, countries should increase political mutual trust and gain a foothold in the industrial chain of arms production to enhance their military power.

The contribution of this paper is to analyze the special trade area of arms trade from a dynamic network perspective by incorporating economic, political, security, strategic and transaction cost factors together into the TERGM and STERGM models.

We study organizational vocabularies as complex social structures emerging from the association between organizational participants and words they use to describe and make sense of their experiences at work. Using data that we have collected on the association between managers in a multi-unit international company and words they use to describe their organizational units and the overall company, we examine the relational micro-mechanisms underlying the observed network structure of organizational vocabularies. We find that members of the same subsidiary tend to become more similar in terms of the words they use to describe their units. Members of the same subsidiary, however, do not use the same words to describe the corporate group. Consequently, the structure of organizational vocabularies tends to support consistent local interpretations, but reveals the presence of divergent meanings that organizational participants associate with the superordinate corporate group.

The purpose of this study is to show that social relations in a corporate governance platform between members of supervisory boards and between members of supervisory and executive board tiers can serve as an alternative viewpoint for understanding mechanisms of social selection in corporate governance networks. The study shows that through the lenses of social network analysis, it is possible to identify and understand how the process of corporate governance member selection unfolds within companies and how that selection process may have been potentially influenced by the cross-board relations, such as interlocking directorships.

To estimate network parameters and attribute effects of network tie emergence, this study has used exponential random graph models (ERGMs) on corporate governance data of Danish publicly listed companies. Econometric models are applied to estimate parameter statistics which serve further to explain tendencies of tie emergence.

The results of this study reveal that the process of selection of both supervisory boards and executive directors is interdependent. Also, the study showed that board members are more likely to select popular supervisory board members and top managers who have their expertise gained through multiple companies affiliated with multiple industries. However, these conditions for CEO selection apply only to the extent to which they have their experience gained from multiple companies but not multiple industries.

On one hand, this study demonstrates that being a dynamic practitioner who is exposed to diverse corporate environments by being affiliated with different companies belonging to different industries generally increases practitioners visibility in the corporate governance network, and therefore their attractiveness to boards of directors. On the other hand, the results show that the research on board assemblage, nowadays, should be rather observed through the methodology of social network analysis as the method gives an opportunity to understand structures through relations, from which the executive tier should not be exempted as well.

The objective of this research is to methodically categorize the various types of Open Government Data (OGD) stakeholders, and to elucidate the intricate network relationships among OGD stakeholders, along with the underlying mechanisms that shape their formation.

To comprehend the collaboration mechanism of stakeholders in the OGD ecosystem, the authors constructed an OGD multi-stakeholder relationship network by using data from the Shandong Province Data Application Innovation and Entrepreneurship Competition. Based on the structural social capital theory and exponential random graph model (ERGM), an analytical framework was established to explore the formation mechanism of the collaborative network of OGD multi-stakeholder.

The results indicate that multi-stakeholder collaboration among government, enterprises and the public is crucial for achieving OGD goals. Organizing OGD competitions serves as an effective mechanism for solidifying and maintaining relationships among OGD stakeholder groups. Degree centrality and structural parameters reveal a Matthew effect within the connection process of the OGD ecosystem's collaborative network. Additionally, there is evidence of agglomeration and transferability within the network's structure.

This study contributes to the understanding regarding the formation mechanism of OGD stakeholders. The findings have implications for developing multi-stakeholder relationship networks of OGD and driving OGD initiatives.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2023-0284