The use of social network analysis in social support and care: a systematic scoping review protocol

Abstract Background In recent decades, the literature on Social Network Analysis and health has experienced a significant increase. Disease transmission, health behavior, organizational networks, social capital, and social support are among the different health areas where Social Network Analysis has been applied. The current epidemiological trend is characterized by a progressive increase in the population’s ageing and the incidence of long-term conditions. Thus, it seems relevant to highlight the importance of social support and care systems to guarantee the coverage of health and social needs within the context of acute illness, chronic disease, and disability for patients and their carers. Thus, the main aim is to identify, categorize, summarize, synthesize, and map existing knowledge, literature, and evidence about the use of Social Network Analysis to study social support and care in the context of illness and disability. Methods This scoping review will be conducted following Arksey and O'Malley's framework with adaptations from Levac et al. and Joanna Briggs Institute’s methodological guidance for conducting scoping reviews. We will search the following databases (from January 2000 onwards): PubMed, MEDLINE, Web of Science Core Collection, SCOPUS, CINAHL, PsycINFO, Cochrane Database of Systematic Reviews, PROSPERO, and DARE. Complementary searches will be conducted in selected relevant journals. Only articles related to social support or care in patients or caregivers in the context of acute illnesses, disabilities or long-term conditions will be considered eligible for inclusion. Two reviewers will screen all the citations, full-text articles, and abstract the data independently. A narrative synthesis will be provided with information presented in the main text and tables. Discussion The knowledge about the scientific evidence available in the literature, the methodological characteristics of the studies identified based on Social Network Analysis, and its main contributions will highlight the importance of health-related research's social and relational dimensions. These results will shed light on the importance of the structure and composition of social networks to provide social support and care and their impact on other health outcomes. It is anticipated that results may guide future research on network-based interventions that might be considered drivers to provide further knowledge in social support and care from a relational approach at the individual and community levels. Trial registration Open Science Framework https://osf.io/dqkb5.

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Can videoconferencing affect older people's engagement and perception of their social support in long-term conditions management: a social network analysis from the Telehealth Literacy Project

Health & Social Care in the Community ◽

10.1111/hsc.12382 ◽

2016 ◽

Vol 25 (3) ◽

pp. 938-950 ◽

Cited By ~ 8

Author(s):

Annie Banbury ◽

Daniel Chamberlain ◽

Susan Nancarrow ◽

Jared Dart ◽

Len Gray ◽

...

Keyword(s):

Social Support ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Long Term Conditions

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Value of social network analysis for developing and evaluating complex healthcare interventions: a scoping review

BMJ Open ◽

10.1136/bmjopen-2020-039681 ◽

2020 ◽

Vol 10 (11) ◽

pp. e039681

Author(s):

Linda C Smit ◽

Jeroen Dikken ◽

Marieke J Schuurmans ◽

Niek J de Wit ◽

Nienke Bleijenberg

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Complex Intervention ◽

Scoping Review ◽

Complex Interventions ◽

Intervention Research ◽

Healthcare Intervention ◽

Scoping Reviews ◽

Healthcare Interventions

ObjectivesMost complex healthcare interventions target a network of healthcare professionals. Social network analysis (SNA) is a powerful technique to study how social relationships within a network are established and evolve. We identified in which phases of complex healthcare intervention research SNA is used and the value of SNA for developing and evaluating complex healthcare interventions.MethodsA scoping review was conducted using the Arksey and O’Malley methodological framework. We included complex healthcare intervention studies using SNA to identify the study characteristics, level of complexity of the healthcare interventions, reported strengths and limitations, and reported implications of SNA. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews 2018 was used to guide the reporting.ResultsAmong 2466 identified studies, 40 studies were selected for analysis. At first, the results showed that SNA seems underused in evaluating complex intervention research. Second, SNA was not used in the development phase of the included studies. Third, the reported implications in the evaluation and implementation phase reflect the value of SNA in addressing the implementation and population complexity. Fourth, pathway complexity and contextual complexity of the included interventions were unclear or unable to access. Fifth, the use of a mixed methods approach was reported as a strength, as the combination and integration of a quantitative and qualitative method clearly establishes the results.ConclusionSNA is a widely applicable method that can be used in different phases of complex intervention research. SNA can be of value to disentangle and address the level of complexity of complex healthcare interventions. Furthermore, the routine use of SNA within a mixed method approach could yield actionable insights that would be useful in the transactional context of complex interventions.

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Identifying Project Corporate Behavioral Risks to Support Long-Term Sustainable Cooperative Partnerships

Sustainability ◽

10.3390/su13116347 ◽

2021 ◽

Vol 13 (11) ◽

pp. 6347

Author(s):

Marco Nunes ◽

António Abreu ◽

Célia Saraiva

Keyword(s):

Social Network ◽

Project Management ◽

Social Network Analysis ◽

Network Analysis ◽

Building Blocks ◽

Research Literature ◽

Management Tools ◽

Dynamic Interactions ◽

Major Factors

Projects are considered crucial building blocks whereby organizations execute and implement their short-, mid-, and long-term strategic visions. Projects are thought, developed, and implemented to solve problems, drive change, satisfy unique needs, add value, and exploit opportunities, just to name a few objectives. Although existing project management tools and techniques aim to deliver projects with success, according to the latest reviewed literature, projects still keep failing at an impressive pace. Among the extensive list of factors that may threaten project success, several articles from the research literature place particular importance on a still underexplored factor that may strongly lead to unsuccessful project delivery. This factor—usually known as corporate behavioral risks—usually emerges and evolves as organizations work together to deliver projects across a bounded period of time, and is characterized by the mix of formal and informal dynamic interactions between the different stakeholders that constitute the different organizations. Furthermore, several articles from the research literature also point out the lack of proper models to efficiently manage corporate behavioral risks as one of the major factors that may lead to projects failing. To efficiently identify and measure how such corporate behaviors may contribute to a project’s outcomes (success or failure), a heuristic model is proposed in this work, developed based on four fundamental fields ((1) project management, (2) risk management, (3) corporate behavior, and (4) social network analysis), to quantitatively analyze four critical project social networks ((1) communication, (2) problem-solving, (3) advice, and (4) trust), by applying the theory of social network analysis (SNA). The proposed model in this work is supported with a case study to illustrate its implementation and application across a project lifecycle, and how organizations can benefit from its application.

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Nursing Students’ Subjective Happiness: A Social Network Analysis

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182111612 ◽

2021 ◽

Vol 18 (21) ◽

pp. 11612

Author(s):

Eun-Joo Kim ◽

Ji-Young Lim ◽

Geun-Myun Kim ◽

Seong-Kwang Kim

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Nursing Students ◽

Network Analysis ◽

Group Dynamics ◽

Future Research ◽

Subjective Happiness ◽

Cross Sectional Survey ◽

Cross Sectional ◽

High Level

Improving nursing students’ subjective happiness is germane for efficiency in the nursing profession. This study examined the subjective happiness of nursing students by applying social network analysis (SNA) and developing a strategy to improve the subjective happiness of nursing. The study adopted a cross sectional survey to measure subjective happiness and social network of 222 nursing students. The results revealed that the centralization index, which is a measure of intragroup interactions from the perspective of an entire network, was higher in the senior year compared with the junior year. Additionally, the indegree, outdegree, and centrality of the social network of students with a high level of subjective happiness were all found to be high. This result suggests that subjective happiness is not just an individual’s psychological perception, but can also be expressed more deeply depending on the subject’s social relationships. Based on the study’s results, to strengthen self-efficacy and resilience, it is necessary to utilize strategies that activate group dynamics, such as team activities, to improve subjective happiness. The findings can serve as basic data for future research focused on improving nursing students’ subjective happiness by consolidating team-learning social networks through a standardized program approach within a curriculum or extracurricular programs.

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Social Network Analysis Visualization

Advances in Marketing, Customer Relationship Management, and E-Services - Handbook of Research on Integrating Social Media into Strategic Marketing ◽

10.4018/978-1-4666-8353-2.ch015 ◽

2015 ◽

pp. 257-270

Author(s):

Mohana Shanmugam ◽

Yusmadi Yah Jusoh ◽

Rozi Nor Haizan Nor ◽

Marzanah A. Jabar

Keyword(s):

Social Networks ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Online Social Network ◽

Future Research ◽

Research Gaps ◽

The Social ◽

Network Studies ◽

Academic Study

The social network surge has become a mainstream subject of academic study in a myriad of disciplines. This chapter posits the social network literature by highlighting the terminologies of social networks and details the types of tools and methodologies used in prior studies. The list is supplemented by identifying the research gaps for future research of interest to both academics and practitioners. Additionally, the case of Facebook is used to study the elements of a social network analysis. This chapter also highlights past validated models with regards to social networks which are deemed significant for online social network studies. Furthermore, this chapter seeks to enlighten our knowledge on social network analysis and tap into the social network capabilities.

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Identify project corporate behavioral risks to support long-term sustainable cooperative partnerships.

10.36227/techrxiv.14069048 ◽

2021 ◽

Author(s):

marco nunes ◽

Antônio José de Abreu Pina

Keyword(s):

Social Network ◽

Project Management ◽

Social Network Analysis ◽

Network Analysis ◽

Building Blocks ◽

Two Dimensions ◽

Corporate Behavior ◽

Proposed Model ◽

Project Lifecycle

Projects can be seen as the crucial building blocks whereby organizations execute and implement their short, and long-term strategic vision. Projects are thought to solve problems, drive change, satisfy unique needs, add value, or exploit opportunities, just to name a few. In order to successful deliver projects, project management tools and techniques are applied throughout a project´s lifecycle, essentially to efficiently and in a timely manner, identify and manage project risks. However, according to latest reviewed literature, projects keep failing at an impressive rate. Although research in the project management field argues that such failure rate is due to a huge variety of reasons, it highlights particular importance to a still underexplored and not quite well understood (regarding how it emerges and evolves) risk type, that may lead projects to failure. This risk type, called as corporate behavioral risks, usually emerge, and evolve as organizations work together across a finite period of time (for example, across a project lifecycle) to deliver projects, and is characterized by the mix of countless formal and informal dynamic interactions between the different elements that constitute the different organizations. Understanding the extent to which such corporate behavior influences project´s outcomes, is a breakthrough of high importance that positively impacts two dimensions; first, enables organizations that deliver projects (but not only), to increase the chances of project success, which in turn is a driver of sustainable business, because it allows the development and implementation of effective, and timely corrective measures to project´s tasks and activities, and second, it contributes to the scientific community (on the organizations field), to generate valuable and actionable new knowledge regarding the emergence and evolution of such cooperative risks, which can lead to the development of new theories and approaches on how to manage them. In this work, we propose a heuristic model to efficiently identify and analyze how corporate behavioral risks may influence project´s outcomes. The proposed model in this work, lays its foundations on four fundamental fields ((1) project management, (2) risk management, (3) corporate behavior, and (4) social network analysis), and will quantitatively measure four critical project social networks ((1) communication, (2) problem-solving, (3) advice, and (4) trust) that usually emerge as projects are being delivered, by applying the theory of social network analysis (SNA), more concretely, SNA centrality metrics. The proposed model in this work is supported with a case study to illustrate its implementation across a project lifecycle, and how organizations can benefit from its application.

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Social network analysis in international business research: An assessment of the current state of play and future research directions

International Business Review ◽

10.1016/j.ibusrev.2019.101633 ◽

2020 ◽

Vol 29 (2) ◽

pp. 101633 ◽

Cited By ~ 7

Author(s):

Yusuf Kurt ◽

Mustafa Kurt

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

International Business ◽

Future Research ◽

Business Research ◽

Research Directions ◽

Current State ◽

International Business Research ◽

Future Research Directions

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Social network analysis: organizational implications in tourism management

International Journal of Organizational Analysis ◽

10.1108/ijoa-12-2019-1971 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Cited By ~ 10

Author(s):

Marco Valeri ◽

Rodolfo Baggio

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Single Case ◽

Effective Interaction ◽

Organizational Structures ◽

Review Literature ◽

Future Research ◽

Economic Sectors ◽

Content Type

Purpose The purpose of this paper is to provide an overview of how quantitative analysis methods have been and can be used to improve the competitiveness of tourism destination. The focus of the study is social network analysis (SNA). Design/methodology/approach The research methodology is qualitative and consists of the review literature relevant to this thesis. This methodology is necessary to give an account of the methods and the techniques adopted for the data collection used in other economic sectors. Findings SNA is needed to analyze the creation and configuration of communities of practice within destination and to identify possible barriers to effective interaction. Essentially, it is a complex adaptive socio-economic system. It shares many (if not all) of the characteristics usually associated with such entities, namely, non-linear relationships among the components, self-organization and emergence of organizational structures, robustness to external shocks. Research limitations/implications The most important limit of this paper is that all the results presented here do not concern a single case study. Future research studies will provide a larger number of cases and examples to give the necessary validation to the findings presented here. Practical implications This paper provides a view into the network of relationships that may give tourism organization managers a strong leverage to improve the flow of information and to target opportunities where this flow may have the most impact on regulatory or business activities. Originality/value SNA can help to detect actual expertise and consequently project the potential losses deriving from an inefficient flow of knowledge. In addition, the authors will be able to define roles in the organizational networks and make an evaluation of informal organizational structures over the formal ones. Traditional organizational theories lack a concrete correspondence with mathematical studies and in this respect the authors sought to identify a correspondence.

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