Social Network Analysis Metrics Research Articles

Longitudinal analysis of interorganizational collaborative networks of cross-laminated timber (CLT) construction

Purpose Cross-laminated timber (CLT) is an innovative construction material that provides a balanced mix of structural stiffness, fabrication flexibility and sustainability. CLT development and innovation diffusion require close collaborations between its supply chain architectural, engineering, construction and manufacturing (AECM) stakeholders. As such, the purpose of this study is to provide a preliminary understanding of the knowledge diffusion and innovation process of CLT construction. Design/methodology/approach The study implemented a longitudinal social network analysis of the AECM companies involved in 100 CLT projects in the UK. The project data were acquired from an industry publication and decoded in the form of a multimode project-company network, which was projected into a single-mode company collaborative network. This complete network was filtered into a four-phase network to allow the longitudinal analysis of the CLT collaborations over time. A set of network and node social network analysis metrics was used to characterize the topology patters of the network and the centrality of the companies. Findings The study highlighted the scale-free structure of the CLT collaborative network that depends on the influential hubs of timber manufacturers, engineers and contractors to accelerate the innovation diffusion. However, such CLT supply collaborative network structure is more vulnerable to disruptions due to its dependence on these few prominent hubs. Also, the industry collaborative network’s decreased modularity confirms the maturity of the CLT technology and the formation of cohesive clusters of innovation partners. The macro analysis approach of the study highlighted the critical role of supply chain upstream stakeholders due to their higher centralities in the collaborative network. Stronger collaborations were found between the supply chain upstream stakeholders (timber manufacturers) and downstream stakeholders (architects and main contractors). Originality/value The study contributes to the field of industrialized and CLT construction by characterizing the collaborative networks between CLT supply chain stakeholders that are critical to propose governmental policies and industry initiatives to advance this sustainable construction material.

Representation of the hierarchical and functional structure of an ambulatory network of medical consultations through Social Network Analysis, with an emphasis on the role of medical specialties.

Ambulatory Health Care Networks (Amb-HCN) are circuits of patient referral and counter-referral that emerge, explicitly or spontaneously, between doctors who provide care in their offices. Finding a meaningful analytical representation for the organic and hierarchical functioning of an Amb-HCN may have managerial and health policymaking implications. We aimed to characterize the structural and functional topology of an Amb-HCN of a private health insurance provider (PHIP) using objective metrics from graph theory. This is a cross-sectional quantitative study with a secondary data analysis study design. A Social Network Analysis (SNA) was conducted using office visits performed between April 1, 2021 and May 15, 2022, retrieved from secondary administrative claim databases from a PHIP in Belo Horizonte, Southeastern Brazil. Included were beneficiaries of a healthcare plan not restricting the location or physician caring for the patient. A directional and weighted network was constructed, where doctors were the vertices and patient referrals between doctors, within 7-45 days, were the network edges. Vertex-level SNA measures were calculated and grouped into three theoretical constructs: patient follow-up (aimed at assessing the doctor's pattern of patient follow-up); relationship with authorities (which assessed whether the doctor is an authority or contributes to his or her colleague's authority status); and centrality (aimed at positioning the doctor relative to the network graph). To characterize physician profiles within each dimension based on SNA metrics results, a K-means cluster analysis was conducted. The resulting physician clusters were assigned labels that sought to be representative of the observed values of the vertex metrics within the clusters. Overall, 666,263 individuals performed 3,863,222 office visits with 4,554 physicians. A total of 577 physicians (12.7%) had very low consultation productivity and contributed very little to the network (i.e., about 1.1% of all referrals made or received), being excluded from subsequent doctor profiles analysis. Cluster analysis found 951 (23.9%) doctors to be central in the graph and 1,258 (31.6%) to be peripheral; 883 (22.2%) to be authorities and 266 (6.7%) as seeking authorities; 3,684 (92.6%) mostly shared patients with colleagues, with patient follow-up intensities ranging from weak to strong. Wide profile dispersion was observed among specialties and, more interestingly, within specialties. Non-primary-care medical specialties (e.g., cardiology, endocrinology etc.) were associated with central profile in the graph, while surgical specialties predominated in the periphery, along with pediatrics. Only pediatrics was associated with strong and prevalent (i.e., low patient sharing pattern) follow-up. Many doctors from internal medicine and family medicine had unexpectedly weak and shared patient follow-up profiles. Doctor profiles exhibited pairwise relationships with each other and with the number of chronic comorbidities of the patients they treated. For example, physicians identified as authorities were frequently central and treated patients with more comorbidities. Ten medical communities were identified with clear territorial and specialty segregation. Viewing the Amb-HCN as a social network provided a topological and functional representation with potentially meaningful and actionable emerging insights into the most influential actors and specialties, functional hierarchies, factors that lead to self-constituted medical communities, and dispersion from expected patterns within medical specialties.

Navigating the asthma network on Twitter: Insights from social network and sentiment analysis.

Asthma is a condition in which the airways become inflamed and constricted, causing breathing difficulties, wheezing, coughing, and chest tightness. Social networks can have a substantial effect on asthma management and results. However, no studies of social networks addressing asthma have been undertaken. The aim of this research was to identify the significant social network structures, key influencers, top topics, and sentiments of asthma-related Twitter conversations. All the tweets collected for this study included the keyword "asthma" or were mentioned in or in replies to tweets that were performed. For this study, a random sample of Twitter data was collected using NodeXL Pro software between December 1, 2022, and January 29, 2023. The data collected includes the user's display name, Twitter handle, tweet text, and the tweet's publishing date and time. After being imported into the Gephi application, the NodeXL data were then shown using the Fruchterman-Reingold layout method. In our study, SNA (Social Network Analysis) metrics were utilized to identify the most popular subject using hashtags, sentiment-related phrases (positive, negative, or neutral), and top influencer by centrality measures (degree, betweenness). The study collected 48,122 tweets containing the keyword "asthma" or mentioned in replies. News reporters and journalists emerged as top influencers based on centrality measures in Twitter conversations about asthma, followed by government and healthcare institutions. Education, trigger factors (e.g., cat exposure, diet), and associated conditions were highly discussed topics on asthma-related social media posts (e.g., sarscov2, copd). Our study's sentiment analysis revealed that there were 8427 phrases associated neutral comments (18%), 12,582 words reflecting positive viewpoints (26%), and 27,111 words reflecting negative opinions (56%). This study investigates the relevance of social media influencers, news reporters, health experts, health organizations, and the government in the dissemination and promotion of asthma-related education and awareness during public health information.

Effect of behavioural sampling methods on local and global social network metrics: a case-study of three macaque species.

Social network analysis (SNA) is a powerful, quantitative tool to measure animals' direct and indirect social connectedness in the context of social groups. However, the extent to which behavioural sampling methods influence SNA metrics remains unclear. To fill this gap, here we compare network indices of grooming, huddling, and aggression calculated from data collected from three macaque species through two sampling methods: focal animal sampling (FAS) and all-occurrences behaviour sampling (ABS). We found that measures of direct connectedness (degree centrality, and network density) were correlated between FAS and ABS for all social behaviours. Eigenvector and betweenness centralities were correlated for grooming and aggression networks across all species. By contrast, for huddling, we found a correlation only for betweenness centrality while eigenvector centralities were correlated only for the tolerant bonnet macaque but not so for the despotic rhesus macaque. Grooming and huddling network modularity and centralization were correlated between FAS and ABS for all but three of the eight groups. By contrast, for aggression network, we found a correlation for network centralization but not modularity between the sampling methodologies. We discuss how our findings provide researchers with new guidelines regarding choosing the appropriate sampling method to estimate social network metrics.

Analyzing the Spread of Misinformation on Social Networks: A Process and Software Architecture for Detection and Analysis

The rapid dissemination of misinformation on social networks, particularly during public health crises like the COVID-19 pandemic, has become a significant concern. This study investigates the spread of misinformation on social network data using social network analysis (SNA) metrics, and more generally by using well known network science metrics. Moreover, we propose a process design that utilizes social network data from Twitter, to analyze the involvement of non-trusted accounts in spreading misinformation supported by a proof-of-concept prototype. The proposed prototype includes modules for data collection, data preprocessing, network creation, centrality calculation, community detection, and misinformation spreading analysis. We conducted an experimental study on a COVID-19-related Twitter dataset using the modules. The results demonstrate the effectiveness of our approach and process steps, and provides valuable insight into the application of network science metrics on social network data for analysing various influence-parameters in misinformation spreading.

Evaluating energy harvesting from water distribution networks using combined stakeholder and social network analysis

The chance of success in implementing a new project in a water distribution network (WDN) significantly depends on the behaviour of the involved stakeholders. In this paper, the feasibility of installing the micro-turbines in WDNs for generating hydro-power energy is studied from the stakeholders' perspective. Stakeholders’ analysis (SA) and social network analysis (SNA) are performed to methodically recognise the environment and the relationships among stakeholders. 18 stakeholders at the local, regional, and national levels were identified and their representatives were interviewed. In SA, the power, interest, access to information, and satisfaction from the current condition of the system were evaluated for each stakeholder. The Water and Wastewater Company and Ministry of Energy were found as the stakeholders with the highest power and interest. Unexpectedly, the Regional Electricity Company was discovered with medium power and low access to information. In SNA, cooperative and non-cooperative relationships between stakeholders were analysed and an assessment was made for the role of each stakeholder in their social network, through four centrality metrics. The correlation between SA and SNA results suggests that SA factors could be estimated using the SNA metrics.

Mapping analytical hierarchy process research to sustainable development goals: Bibliometric and social network analysis

The drive to achieve the Sustainable Development Goals (SDGs) becomes more urgent as the 2030 deadline draws near, increasing research in various sectors. Nevertheless, studies that systematically map Analytical Hierarchy Process (AHP) publications with the SDGs need to be more conspicuously lacking. Our study adds a new perspective to the field by creatively bridging this knowledge gap using the Elsevier SDG Mapping Initiative. To find research clusters, trends, and themes linked to SDGs and their connection to environmental sustainability, we thoroughly analyzed 29,897 publications from 2012 to 2022. The analysis showed that SDG 15, SDG 7, SDG 12, SDG 13, and SDG 11 were the top five SDGs, with an environmental focus among the 17 SDGs. These top SDGs had many clusters connected to them, illustrating various sustainability-related problems. The study also looked at connections between SDGs, the nations with the highest rates of productivity, the top contributors, and the journals with the highest citation counts. We discovered three separate SDG clusters using co-occurrence network analysis, each representing a different SDG. We discovered relevant SDGs using Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis and centrality indicators like eigenvector and betweenness. This novel method for publishing analysis combines an AHP focus that aligns with the SDGs with social network analysis and centrality metrics. Our research advances knowledge of how the AHP technique can assess initiatives supporting the SDGs. We offer essential insights into prioritizing sustainable development measures by identifying research clusters, trends, and issues related to environmental sustainability. This study highlights the subject's most important SDGs, productive nations, helpful organizations, and significant journals.

Network analysis of innovation mentor community of practice

PurposePositive effect of knowledge sharing (KS) on innovation has come to the fore and government-supported innovation and mentoring communities or mentor networks have become widespread. This article aims to examine the community connectedness and mentors' preferences for professional competency-based KS of such innovation community of practice networks (CoPNs).Design/methodology/approachThe paper constructs a directed weighted CoPN model with a node-attribute-based novel fingerprint edge weights. Based on the CoPN, Social Network Analysis (SNA) metrics and measures including Giant Component (GC) were proposed and analyzed to identify mentors' connectedness preferences. The fingerprint was proposed as a novel binarized node attribute of competence. Jaccard similarity of fingerprints was proposed as edge weights to reveal correlations between competences and preferences for KS.FindingsThe work opted to conduct a survey of 28 innovation mentors to measure a CoPN. Both a name generator question and a second set of questions were employed to invite respondents to name their collaborators and indicate their professional competence. SNA metrics result in differing values for GC and the rest, which lead us to focus on GC to reveal salient metrics of connectedness. Jaccard similarity analysis results on GC demonstrate that mentors collaborate in an interdisciplinary manner.Originality/valueBased on the CoPN, the methods proposed may be effective in predicting preferred relationships for interdisciplinary collaborations, providing the managers with an analytical decision support tool for KS in practice.

Supporting the Task-driven Skill Identification in Open Source Project Issue Tracking Systems

[Background] Selecting an appropriate task is challenging for contributors to Open Source Software (OSS), mainly for those who are contributing for the first time. Therefore, researchers and OSS projects have proposed various strategies to aid newcomers, including labeling tasks. [Aims] In this research, we investigate the automatic labeling of open issues strategy to help the contributors to pick a task to contribute. We label the issues with APIdomains- categories of APIs parsed from the source code used to solve the issues. We plan to add social network analysis metrics gathered from the issues conversations as new predictors. By identifying the skills, we claim the contributor candidates should pick a task more suitable to their skill. [Method] We are employing mixed methods. We qualitatively analyzed interview transcripts and the survey's open-ended questions to comprehend the strategies communities use to assist in onboarding contributors and contributors used to pick up an issue. We applied quantitative studies to analyze the relevance of the API-domain labels in a user experiment and compare the strategies' relative importance for diverse contributor roles. We also mined project and issue data from OSS repositories to build the ground truth and predictors able to infer the API-domain labels with comparable precision, recall, and F-measure with the state-of-art. We also plan to use a skill ontology to assist the matching process between contributors and tasks. By quantitatively analyzing the confidence level of the matching instances in ontologies describing contributors' skills and tasks, we might recommend issues for contribution. In addition, we will measure the effectiveness of the API-domain labels by evaluating the issues solving time and the rate among the labeled and unlabelled ones. [Results] So far, the results showed that organizing the issues?which includes assigning labels is seen as an essential strategy for diverse roles in OSS communities. The API-domain labels are relevant, mainly for experienced practitioners. The predicted labels have an average precision of 75.5%. [Conclusions] Labeling the issues with the API-domain labels indicates the skills involved in an issue. The labels represent possible libraries (aggregated into domains) used in the source code related to an issue. By investigating this research topic, we expect to assist the new contributors in finding a task, helping OSS communities to attract and retain more contributors.

A social network approach to critical discourse studies

Abstract Viewing discourse as a social practice, one important task of critical discourse studies (CDS) is to unveil inequality, power, and ideologies through linguistic and discoursal analysis of social events, social identities, and social relations represented in oral or written texts. As such, it lends itself to social network analysis (SNA) which aims to discover relation patterns and structures among such social actors as people, organizations, and political entities. This article proposes an integration of SNA techniques into CDS and explores this possibility via an example in diachronic study on international relations represented in news reporting on China in The New York Times between 1980 and 2020 by examining subject–direct object pairs to approach the target relations between national actors. It is shown that the coupling of these two approaches effectively reveals international relation networks surrounding key national actors, based on which detailed linguistic and discoursal analysis further sheds light on the underlying ideological and socio-political factors in the news reports. Considering the fact that SNA consists of a great variety of network statistics, which can be broadly categorized into those related to the relative position of the actor in the network and those concerned with the structure of the network itself, there is vast space for the utility of SNA metrics in aiding CDS in further research.

How Output Outweighs Input and Interlocutors Matter for Study‐Abroad SLA: Computational Social Network Analysis of Learner Interactions

AbstractThis data‐driven study framed in the interactionist approach investigates the influence of social graph topology and peer interaction dynamics among foreign exchange students enrolled in an intensive German language course on second language acquisition (SLA) outcomes. Applying the algorithms and metrics of computational social network analysis (SNA), we find that (a) the best predictor of target language (TL) performance is reciprocal interactions in the language being acquired, (b) the proportion of output in the TL is a stronger predictor than input (Principle of Proportional Output), (c) there is a negative relationship between performance and interactions with same‐first‐language speakers, (d) a significantly underperforming English native‐speaker dominated cluster is present, and (e) there are more intense interactions taking place between students of different proficiency levels. Unlike previous study abroad social network research concentrating on the microlevel of individual learners’ egocentric networks and presenting an emic view only, this study constitutes the first application of computational SNA to a complete learner network (sociogram). It provides new insights into the link between social relations and SLA with an etic perspective, showing how social network configuration and peer learner interaction are stronger predictors of TL performance than individual factors such as attitude or motivation, and offering a rigorous methodology for investigating the phenomenon.

The Who in VR/AR for Education: A Scoping Review from IEEE Publications

HEIs have continuously been the vanguard of novel technologies, pushing progress and establishing the afterward generation of scientists, businesspersons, and engineers. Virtual and augmented reality technologies are on the borderline of enlargement nowadays. To better understand who are essential actors in this new research area, we have extracted metadata of all published papers by IEEE, dating from 1990 to 2021, using keywords VR, AR, and Education. For each publication, we have extracted the list of all the authors, creating a co-authorship dataset. This dataset is then further processed using social network analysis metrics. The results are discussed to understand research publications' main actors driving VR/AR/Education trends.

Network Approach to the Digital Innovation Hub as an Ecosystem Supporting the Digital Transformation of Enterprises in a Region

The study aims to examine the two-mode network of digital innovation hubs (DIH) operating in Poland, understood as ecosystems and competence centers, to support the digital transformation of enterprises in a region. Digital Innovation Hub is also one of the S3 Smart Specialization Platform mapping tools and identifying areas of economic specialization of regions, aimed at facilitating interregional cooperation and creating partnerships between different actors across Europe. While the functions and goals of DIH are more and more often presented in national and regional development documents, little is known about the network structure of DIHs operating in a given country (region) and in the European Union. The study used structured and secondary Smart Specialization Platform data, which allows the creation of two-mode relationship networks and shows how interrelated the studied DIHs and digital technologies they use are. Based on the social network analysis and network metrics (centrality, density, and network projection) integrated into programs such as UCINET and ORA-PRO, the visualization and measurement of the network structure of two-mode networks (actor x technology ATij) as well as its projection into actor x actor (AAij) and technology x technology (TTij) networks were created. The results show 15 DIHs or actors (A) and 29 technologies (T), each of which a given DIH offers to show primarily influential DIHs and digital technologies as well as the areas of cooperation. DIHs are a promising and still little explored area of interest for researchers worldwide. The article is the first attempt to investigate the network structure of DIHs operating in Poland and their relations to technologies.

Identifying the risk factors and their interactions of human–robot collaboration implementation during engineering project construction: evidence from China

PurposeHuman–robot collaboration (HRC) is an emerging research field for the construction industry along with construction robot adoption, but its implementation remains limited in construction sites. This paper aims to identify critical risk factors and their interactions of HRC implementation during engineering project construction.Design/methodology/approachLiterature research, expert interviews, a questionnaire survey and a social network analysis (SNA) method were used. First, literature research and expert interviews were employed to identify risk factors of HRC implementation and preliminarily understand factor interactions. Second, a questionnaire survey was conducted to determine the degree of interactions between risk factors. Third, based on the data collected from the questionnaire survey, SNA metrics were used to find critical risk factors and critical interactions.FindingsThe critical risk factors consist of robot technology reliability, robot-perceived level, conflict between designers and users of construction robots, organisational culture, organisational strength, project cost requirements, changeability of project construction, project quality requirements and project safety requirements. The interactions between risk factors are strong and complex. Robot technology risk factors were relatively fundamental risk factors, and project risk factors had a direct influence on the risk of HRC implementation. The implementation cost of HRC was not identified as a critical risk factor. Individual risk factors could be mitigated by improving technical and organisational factors.Originality/valueThis paper contributes to the body of knowledge in the field of both HRC behaviours and its risk management in construction project management. Identifying the critical risk factors and their interactions of HRC implementation in the construction industry and introducing social network theory to the research on critical risk factors are the innovations of this paper. The findings and proposed suggestions could help construction professionals to better understand the HRC risk factors and to manage the risk of HRC implementation more effectively.

Relational capital in referrer networks of medical specialists in office settings

PurposeThe purpose of this article is to explore the association of the Relational Capital (RC) embedded in a medical specialist’s social–professional network with the specialist’s economic performance based on social network analysis (SNA).Design/methodology/approachUsing health insurance claims data regarding ∼108,000 physicians treating ∼72,000,000 patients, social–professional networks (patient-sharing-networks (PSNs)) of ∼26,000 medical specialists were simulated. To explore the correlation of the network's characteristics (degree centrality, density, relative betweenness centrality and referrer concentration) with economic performance, ordinary-least-squares (OLS)-regression models were estimated for ten common specialties (gynecology, internal medicine, orthopedics, ophthalmology, otolaryngology, dermatology, urology, neurology, radiology and rehabilitative medicine).FindingsThe study confirms the applicability and strong explanatory power of SNA metrics for RC measurement in ambulatory healthcare. Degree centrality and relative betweenness centrality correlate positively with economic performance, whereas density and referrer concentration exhibit negative coefficients. These results confirm the argument that RC has a strong association with the economic performance of medical specialists.Originality/valueThe study pioneers SNA for RC measurement in healthcare. It is among the first publications on specialists' PSNs. Questions for future research are proposed.

Application of Social Network Analysis to Visualization and Description of Industrial Clusters: A Case of the Textile Industry

This paper discusses the issues of industrial cluster analysis. Initially, the authors explore theoretical approaches to understanding the clusters phenomenon and their identification and analysis. Looking at industrial clusters as network structures connected by various forms of interaction between members, such as ownership linkages, transactions, the presence of common counterparts, and participation in arbitration processes, the authors propose visualizing clusters using social network analysis metrics. This approach helps to address one of the main difficulties when contacting the members of industrial clusters for a subsequent survey or in-depth interviewing. The analysis concludes with a discussion of the proposed method as a way to identify cluster members and determine the most significant ones that are the primary nodes of the network. These key members usually possess enough relevant information about the structure, coordination mechanisms, general strategy, and cluster management system. Therefore, it is possible to limit the list of interviewed respondents without a substantial loss in empirical data quality. The case of the textile industry cluster presented in this paper confirms the applicability of social network analysis to the visualization and description of industrial clusters.

Spatializing Social Networking Analysis to Capture Local Innovation Flows towards Inclusive Transition

The location of the local network of firms impacts, positively or negatively, their economic performance. The interactions between different sectors in a territory are still not easily observable. We test the complexity of the economic structure at a local level, given the availability of data at a very granular scale. This could greatly assist in observing sectors or/and locations that play a dominant role in the regional economy. Thus, in order to interpret the economic structure of a territory, we used cluster-based analysis. The analysis helps in evaluating the interconnections among sectors that constitute a cluster. A novel method of describing the territorial economic structure is presented by applying Social Network Analysis (SNA) within cluster-based analysis to characterize the importance of both location and economic interconnections. In this study, we focus on the industrial agglomerations in Calabria, Italy, to underpin the potential of the region’s industries by using social networking analysis metrics. This research put forward new interpretations of SNA metrics that describe regional economic compositions. Our findings reveal that territorial social networks are a potential instrument for understanding interactions in regional systems and economic clusters and might help in highlighting local industrial potentials. We believe that this study’s results could be considered as the initial steps for a pioneer data-driven place-based structural analysis model.

Hybrid Onion Layered System for the Analysis of Collective Subjectivity in Social Networks

This research aims to analyze the Digital Social Networks (DSN) behavior, constructed from the network’s relationships, interactions, and expressions of users’ private states through collective subjectivity. For this purpose, an onion-ring system called COSSOL has been built in a case study for Twitter, following a hybrid approach to integrate Machine Learning classifiers and structural metrics from Computational Linguistics and Computational Sociology disciplines, respectively. The paper designs two experimentation scenarios divided into cases of collective subjectivity analysis for Colombia under different levels of communities’ granularity. The first case validates the system by performing a cointegration test on the metrics of each construct for the onion rings’ communities. The results show that some communities better propagate their subjective expressions against the disclosed topic when they have a higher network density and a common polarity. Moreover, the most stable communities in polarity towards a topic are those whose members are highly connected. Conversely, communities with a higher centrality index in a subset of members do not exhibit stability in collective subjectivity towards a topic disclosed in that community. The second case validates the model with a series of Social Network Analysis (SNA) metrics with a polarity layer to describe the second onion ring subcommunities and their temporal variation through community recalculation. The results show no polar distributions similar to the bimodal ones representing consensus in the values of the common Thinking Acting and Feeling (TAF) forms. In addition, general negative sentiment is identified for the ten most representative nodes of the subcommunities analyzed.

Evaluating Supply Chain Network Designs: An Approach Based on SNA Metrics and Random Forest Feature Selection

Supply chain network design is an important decision-making problem affecting the long-term profitability of firms. Evaluating the performance of supply chain network designs can help decision-makers to select the network configuration that meets the business specifications while operating at a reasonable cost. In this study, Social Network Analysis (SNA) metrics are used to evaluate the performance of closed-loop supply chain (CLSC) Network designs in terms of resilience when exposed to disruptions and the balance of flows. CLSC Networks accommodate the flow of returned products from the customers for recycling, remanufacturing, or disposal, increasing the design complexity compared to traditional supply chain networks. The proposed approach involves custom-designed network-level SNA metrics and random forest (RF) feature selection which are computationally low-cost approaches. The proposed metrics are implemented in an R package titled NetworkSNA and shared on GitHub, and RF feature selection method is performed in python. The optimal and near-optimal network designs from a CLSC Network based on real data are used as a case study. The metric values are interpreted into practical recommendations to compare the alternative CLSC Networks.

Managing stakeholder-associated risks and their interactions in the life cycle of prefabricated building projects: A social network analysis approach

Sustainability has recently become popular in the prefabricated construction industry worldwide, particularly in terms of developing cleaner production methods to improve energy savings, emissions reduction, and environmental protection. Prefabricated building projects (PBPs) introduce not only positive changes to the construction process but also complexity in project implementation. Such complexity results in potential risks that pose new challenges to the success of PBPs. Previous studies on risks in PBPs primarily concentrated on what types of risks exist and how they affect project performance while considering the risks in isolation from each other. In practice, these risks embedded across the life cycle of PBPs are associated with various stakeholders and interact with each other. Therefore, using China as an example, this study employed social network analysis (SNA) to establish a risk network throughout the PBP life cycle to prioritize stakeholder-associated risks and risk interactions. Stakeholder-associated risks in the life cycle of PBPs were investigated based on a systematic literature review and interviews with critical stakeholders. A focus group meeting with experts was conducted to quantify the interrelations between the risks. Key risks and important risk interactions were deciphered by calculating multiple SNA metrics, and they were classified into ten major challenges that must be addressed. A network simulation revealed the significant effects of key risks and interactions on network complexity. Based on the network analysis results, the major challenges in PBPs were discussed, and responding strategies to address these challenges were proposed accordingly. This is the first study that considered dynamic risk interactions and related stakeholders to manage risks within the life cycle of PBPs. The proposed SNA-based risk management approach can provide a structured framework for researchers and practitioners to effectively and efficiently track and control these interactive stakeholder-associated risks through the life cycle of PBPs.