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Applications of social network analysis (SNA) are evidently popular amongst scholars for mapping stakeholder and other relational networks in improving the sustainability of construction activities and the resulting built environment. Nevertheless, the literature reveals a lack of thorough understanding of optimal SNA applications in this field. Therefore, this paper aims to convey a comprehensive critical review of past applications of SNA in this field.

95 relevant journal papers were initially identified from the “Web of Science” database and a bibliometric analysis was carried out using the “VOS Viewer” software. The subsequent in-depth review of the SNA methods, focussed on 24 specifically relevant papers selected from these aforesaid 95 papers.

A significant growth of publications in this field was identified after 2014, especially related to topics on stakeholder management. “Journal of Cleaner Production”, “International Journal of Project Management” and “Sustainability” were identified as the most productive sources in this field, with the majority of publications from China. Interviews and questionnaires were the popular data collection methods while SNA “Centrality” measures were utilised in over 70% of the studies. Furthermore, potential areas were noted, to improve the mapping and thereby provide useful information to managers who could influence relevant networks and consequentially better sustainability outcomes, including those enhanced by collaborative networks.

Closer collaboration has been found to help enhance sustainability in construction and built environment, hence attracting research interest amongst scholars on how best to enable this. SNA is established as a significant methodological approach to analysing interrelationships and collaborative potential in general. In a pioneering application here, this paper initiates the drawing together of findings from relevant literature to provide useful insights for future researchers to comprehensively identify, compare and contrast the applications of SNA techniques in construction and built environment management from a sustainability viewpoint.

To exploit collaboration-driven innovation, in recent years, many government-sponsored innovation programs and mentor services have emerged. These services support an effective exchange of knowledge among innovation actors, including innovation mentors and enable mentor connectedness as an important factor to develop and sustain effective innovation mentors’ community of practice (CoP). The purpose of this paper is to examine the degree of connectedness in an innovation mentor CoP.

In this study, the innovation mentors CoP as part of a national innovation program is considered a network. The connectedness and assortative mixing of this CoP and the effects of these two on each other were examined by using social network measures, including component analysis, the giant component (GC) and assortativity.

The authors provide the analytical interconnectedness results for both the GC and the whole network with network analysis and assortativity measurements of three attributes of mentors (institution, title and degrees). The degree of correlation of community for the GC shows preferential attachment between high-ranking and low-ranking mentors, while preferential attachment was not observed for the whole network. The correlation coefficient for the institution attribute has the highest value for GC, while the title has the highest value for the whole network.

The study is one of the early attempts to apply social network analysis for an innovation mentor CoP. This study reveals the criticality of evaluating the GC and the whole network separately and provides a number of research and practical directions that will contribute to the development of the innovation mentor CoP.

The purpose of this article is to focus on the role of networks in organizations as a critical aspect of knowledge management and learning processes.

The article has built on an established technique, namely SNA, by shifting from individuals to identities and then to abstractions.

By making the shifts identified above, a solid and tool‐rich body of research is used as a base on which to build, but in effect the focus is on using the network intelligence rather than assemblies of atomistic individual intelligences: the whole is greater than the sum of the parts, but only if one understands it as a whole.

The article has focused on problem resolution, but the technique also shows potential as a generic innovation tool, and as a possible model to create an alternative mechanism for the distribution of funding within organizations including governments.

The present research is trying to construct the network of relationships between different department of an organization during the design and development of car engine. Analyzing the structure of the network, finding the patterns of collaborations, and determining the important departments are the main purposes of the present research.

Improving relationships during a project life is an effective way to enhance employee performance in project-oriented organizations. This paper examines the collaborative relationships between internal project stakeholders through social network analysis (SNA) in a project for the design and development of car engine. In the first step of the research, the network of internal stakeholders was studied based on collaboration in the common activities performed by the resources. Then, the network of correspondences between internal stakeholders was studied. Finally, the two networks were integrated into a single network.

In the integrated network, the “fuel and combustion department” had the largest degree centrality (i.e. highest collaboration with others). The “integration department” was found to have the highest closeness centrality (i.e. more rapid access to other nodes). Furthermore, the “procurement department” had the highest betweenness centrality (i.e. the most strategic department). Our results revealed the potential capabilities of SNA method for the project management in the vehicle industry.

The obtained results of the present research show us the value of applying SNA methods and concepts to analyze the inter-organizational network of the Project Stakeholders relationship.

With the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown.

This study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them.

Based on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering.

At the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.

Little is known, about the role played by start-ups in open innovation networks. Start-ups – due to their nature of new and emerging companies – can largely benefit from the knowledge that can flow intentionally or unintentionally from external partners during open innovation practices. When open innovation networks are not set among peers on both sides the authors expect to have more unintended knowledge flows. Such knowledge “leaks” – as the authors named them – in open innovation networks are totally unexplored in literature. Hence, the purpose of this paper is to focus “whether and how knowledge leaks occur in open innovation networks with start-ups”.

The research design of this study relies on social network analysis methods and techniques to disentangle the role of start-ups in open innovation networks – in a major Italian aerospace cluster – vis-à-vis the three types of knowledge considered in this study. Then the authors confirmed knowledge leaks to occur through a multiplexity analysis. In the second stage of the research, the authors decided to strengthen the results, making them more vivid and thorough, relying on four case studies.

The paper sheds light on a totally unexplored phenomenon, theorizing on the role of start-ups in open innovation networks and suggesting intriguing implications both for theory and managers on whether and how knowledge leaks occur.

The main limitations arise from the specific research context, in fact the study has been conducted in an aerospace cluster. So future studies might consider to explore knowledge leaks in non-cluster settings and in low tech industries.

The results have practical implications both for policy makers and for managers. First of all, the research confirms how open innovation often originates from a combination of different knowledge types acquired through the collaboration with heterogeneous players, start-ups included. Hence, managers may design open innovation strategies balancing their portfolio of collaborations to maximize the absorption of relevant knowledge and start-uppers may consider to engage in open innovation practices to accelerate knowledge absorption. Nevertheless, the study warns managers against the risk of knowledge leaks, especially in cases like start-ups where the eagerness to participate or the prestige associated with participating in open innovation networks with key players may hamper the control over knowledge leaks.

This opens up for possible interventions for policy makers too. First of all, policy makers may consider incorporating the concept of knowledge leaks in their campaign in favour of open innovation. Second, the study may help policy makers in designing programmes for knowledge transfer partnerships amongst the various players of a cluster in a more conscious way, especially warning new to business companies, like start-ups, about possible leaks. Finally, there is also the need of developing professional figures like consultants capable of supporting start-ups in their open innovation practices.

Findings reported in the paper confirm multiplexity and heteromorphism in knowledge exchanges and shed the light on a completely unexplored field (i.e. open innovation and start-ups), focussing on knowledge leaks. Relevant implications for policy makers and managers are included in the study.

This paper aims at investigating the multifaceted nature of innovation networks by focusing on two research questions: Do cluster actors exchange only one type of innovation-related knowledge? Do cluster actors play different roles in innovation-related knowledge exchange?

This paper builds on data collected at the firm level in an Italian aerospace cluster, that is a technology-intensive industry where innovation is at the base of local competitiveness. A questionnaire was used to collect both attribute data and relational data concerning collaboration and the flows of knowledge in innovation networks. The authors distinguished among three types of knowledge (technological, managerial and market knowledge) and five types of brokerage roles (coordinator, gatekeeper, liaison, representative and consultant). Data analysis relied on social network analysis techniques and software.

Concerning the first research question, the findings show that different types of knowledge flow in different ways in innovation networks. The different types of knowledge are unevenly exchanged. The exchange of technological knowledge is open to everyone in the cluster. The exchange of market and managerial knowledge is selective. Concerning the second research question, the authors suggest that different types of cluster actors (large firms, small- and medium-sized enterprises, research centers and universities and institutions for collaboration) do play different roles in innovation networks, especially with reference to the three types of knowledge considered in this study.

The present paper has some limitations. First of all, the analysis focuses on just one cluster (one industry in one specific location), cross- and comparative analyses with other clusters may illuminate the findings better, eliminating industry and geographical biases. Second, the paper focuses only on innovation-related knowledge exchanges within the cluster and not across it.

The results have practical implications both for policy makers and for managers. First, this research stresses how innovation often originates from a combination of different knowledge types acquired through the collaboration with heterogeneous cluster actors. Further, the analysis of brokerage roles in innovation-driven collaborations may help policy makers in designing programs for knowledge-transfer partnerships among the various actors of a cluster.

The paper suggests a clear need of developing professional figures capable of operating at the interface of different knowledge domains.

The data illuminate several aspects of how innovation takes place in a cluster opening up intriguing aspects that have been overlooked by extant literature. The authors believe that this may trigger several lines of further research on the topic.

This paper aims to contribute to the debate about creating shared value (CSV) and clusters, by shedding light on how clusters might generate shared value, i.e. cause social and business benefits, hence focusing on the following research question “do clusters create shared value?”

The study relied on social network analysis methods and techniques. Data have been collected from both primary and secondary sources, in the empirical context of the Motor Valley cluster in Emilia-Romagna. The authors computed three independent and four dependent variables to operationalize the concept of cluster development and shared value creation. A multiple regression quadratic assignment procedure and, more specifically, the most accurate model of that procedure, that is the double semi-partialling method, has been carried out to answer the research question. Finally, empirical evidence has been complemented with other cluster-level data recently collected by the Italian Cluster Mapping project.

The findings confirm how the development of the Motor Valley cluster in Emilia-Romagna contributed to the creation of economic and social growth opportunities for all the actors. The study shows that clusters do create shared value and the chosen cluster development variables do explain much of the business and social impact variables at a very high statistical significance level.

The paper contributes to the under-explored research on clusters and CSV with a very first attempt in providing quantitative evidence of the phenomenon.

The study aims to explore the digital fashion trend within the Metaverse, characterized by non-fungible tokens (NFTs), across Twitter networks. Integrating theories of diffusion of innovation, two-step flow of communication and self-efficacy, the authors aimed to uncover the diffusion structure and the influencer's social roles undertaken by social entities in fostering communication and collaboration for the advancement of Metaverse fashion.

Social network analysis examined the critical graph metrics to profile, visualize, and cluster the unstructured network data. The authors used the NodeXL program to analyze two hashtag keyword networks, “#metaverse fashion” and “#metawear,” using Twitter API data. Cluster, semantic, and time series analyses were performed to visualize the contents and contexts of communication and collaboration in the diffusion of Metaverse fashion.

The results unraveled the “broadcast network” structure and the influencers' social roles of opinion leaders and market mavens within Twitter's “#metaverse fashion” diffusion. The roles of innovators and early adopters among influencers were comparable in collaborating within the competition venues, promoting awareness and participation in digital fashion diffusion during specific “fad” periods, particularly when digital fashion NFTs and cryptocurrencies became intertwined with the competition in the Metaverse.

The study contributed to theory building by integrating three theories, emphasizing effective communication and collaboration among influencers, organizations, and competition venues in broadcasting digital fashion within shared networks. The validation of multi-faceted Social Network Analysis was crucial for timely insights, highlighting the critical digital fashion equity in capturing consumers' attention and driving engagement and ownership of Metaverse fashion.

Despite the undeniable need for innovation in hospital building design, the literature highlights the disconnect between research and practice as the primary knowledge gap hindering such innovation. This study shows this focus to be an oversimplification, for the complex processes that trigger design innovations and impact their ecosystems need to be examined from a systemic perspective. This paper aims to conceptualise the evolution of hospital building design and identify and explain the main factors triggering design and construction innovations over the past 100 years.

A novel hybrid research design to mixed grounded theory (MGT) methodology, with Charmaz constructivist paradigm, is developed as a new systematic way of constructing and interpreting the concepts and interconnections among them that triggered design innovation.

This study represents a taxonomy of concepts and an explanatory innovation framework, containing 617 interconnections between 146 factors classified across 14 categories. The complex innovation ecosystem comprises multi-faceted processes between heterogenous factors with both individual and collective impacts on design innovations.

This research highlights the main components of the innovation ecosystem and its overall behaviour in this field, and the most influential and interrelated contextual factors, as well as representing and mapping generative interactions that support innovation processes. This knowledge can help hospital researchers, designers, policymakers and stakeholders adopt a multidimensional outlook to analyse the strength of all influential factors, introduce potential novel ways of collaborating, conceptualise an organisational approach, re-formulate research questions through transdisciplinary methods and introduce interdisciplinary courses and programs in architecture schools, thereby contributing to timely design innovation.