Social network analysis: Exploring connections to advance military nursing science

Pierce, Penny Pierce; Kabo, Felichism; Killian, Jacqueline; Stucky, Christopher H.; Huffman, Sarah; Migliore, Laurie; Braun, Lisa

doi:10.1016/j.outlook.2020.12.013

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…Social network analysis (SNA), an interdisciplinary research method, aims to describe the relationship among members in a network and the influence of different relationship patterns on the characteristics regarding network structure based on graph theory and algebra [1,28]. Due to the advantages of intuitive graphics and accurate characterization, the application of SNA has been gradually expanded from sociology to economics, management, psychology, geography and other disciplines [27,29,30]. This study mainly adopted the SNA to explore the overall and individual characteristics regarding spatial network structure of tourism eco-efficiency in China and to reveal the actual or potential relationship between provinces.…”

Section: Social Network Analysismentioning

confidence: 99%

Spatial Network Structure of China’s Provincial-Scale Tourism Eco-Efficiency: A Social Network Analysis

et al. 2022

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While tourism eco-efficiency has been analyzed actively within tourism research, there is an extant dearth of research on the spatial network structure of provincial-scale tourism eco-efficiency. The Super-SBM was used to evaluate the tourism eco-efficiency of 30 provinces (excluding Tibet, Hong Kong, Macao and Taiwan). Then, social network analysis was employed to examine the evolution characteristics regarding the spatial network structure of tourism eco-efficiency. The main results are show as follows. Firstly, tourism eco-efficiency of more than two thirds’ provinces witnessed an increasing trend. Secondly, the spatial network structure of tourism efficiency was still loose and unstable during the sample period. Thirdly, there existed the multidimensional nested and fused spatial factions and condensed subsets in the spatial network structure of tourism eco-efficiency. However, there was still a lack of low-carbon tourism cooperation among second or third sub-groups. These conclusions can provide references for policymakers who expect to reduce carbon emissions from the tourism industry and to achieve sustainable tourism development.

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Section: Social Network Analysismentioning

confidence: 99%

Spatial Network Structure of China’s Provincial-Scale Tourism Eco-Efficiency: A Social Network Analysis

et al. 2022

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“…It is an empirical tool to describe social structure on the basis of relations between social entities. The key indicators of SNA include centrality measures such as degree, betweenness, closeness, position, the strength of ties, cohesion (like groups and cliques), division (like structural holes) and partition (Pierce et al, 2021).…”

Section: Challenges Of Cem In Social Commercementioning

confidence: 99%

Challenges of customer experience management in social commerce: an application of social network analysis

Pour¹,

Hosseinzadeh

Mansouri³

2021

INTR

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PurposeAs social media applications have turned into popular platforms for interacting with customers, creation of a consistent customer experience in social commerce has attracted the attention of many practitioners and academics. The migration to create and manage customer experience in social commerce has become an essential issue that will bring new challenges for companies. Despite the increasing investment in this area, few studies have been conducted on the challenges of managing customer experience in social commerce. To fill this theoretical gap, the current study aims at comprehensively exploring the main challenges of customer experience management (CEM) in social commerce and investigating their importance and possible effects in relation to each other.Design/methodology/approachUsing the mixed method, first, the main challenges regarding CEM in social commerce were identified by reviewing the related literature. Then the challenges were enriched and categorized by expert opinions. Next, the challenges and the categorizations were confirmed by conducting a survey analysis applying the t-test and the factor analysis method. Afterwards, the main challenges were identified and weighted. Finally, the Social Network Analysis (SNA) approach was applied to investigate the causal relationship network among the challenges.FindingsThe results indicated that the main challenges of CEM in social commerce can be categorized into eight groups. Their weights and causal effects were calculated to identify the high priority challenges. By calculating the main SNA metrics such as degree and betweenness centralities, the high priority challenges of CEM in social commerce were identified. It was revealed that challenges with high out-degree centrality can create many other challenges and those with high betweenness centrality act as intermediary points, through which cause challenges may create effect challenges.Research limitations/implicationsThe research results can help marketers to get a big picture of the challenges to successfully implement CEM in social commerce and select the appropriate migration strategies more effectively. They are further recommended to pay due attention to customers' issues as well as the organizational challenges of CEM in social commerce.Originality/valueSocial media has become a priority for businesses to create and improve the customer experience; yet there is no tool to identify the challenges of CEM in this context. This study addresses the overlooked but critically important area of social commerce. The most important contribution of this research is an attempt to provide a comprehensive and integrated framework of the challenges in implementing CEM in social commerce and explore the causal effects they may have on creation of other challenges using SNA.

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“…Social network analysis (SNA) is a normative approach used to reveal the social relationships, structure, and resource fluidity between different actors within a network (Scott, 1988). It is used extensively in the social and behavioral sciences, including in the virtual community (Can & Alatas, 2019; Watanabe et al, 2021), the biological field (Jowett & Amory, 2021; Suyo et al, 2020), the medical field (De Rosis et al, 2021; Pierce et al, 2021), tourism areas (Gan et al, 2021; Restrepo et al, 2021), economic areas (Yuan et al, 2021; Zhong et al, 2021), and the ecological field (Chen et al, 2020; Ghafoori Kharanagh et al, 2020). SNA has been applied in the field of industrial ecology (Domenech & Davies, 2011; Song et al, 2018) because it can facilitate the operation of industrial symbiosis networks by combining the impact of both economic and environmental aspects (Chertow, 2007; Doménech & Davies, 2009; Li et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Measuring structural characteristics and evolutionary patterns of an industrial carbon footprint network: A social network analysis approach

Yuan

Sun

Liu³

2022

Regional Science Policy & Practice

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The formation of an industrial network is inevitable because an industrial structure evolves to a higher level. The continuous material exchange among sectors creates a path for the embodied carbon in products to flow freely among industries. With the gradual complication of industrial networks, however, the emission reduction behavior of sectors not only becomes rooted in individual conduct, but also originates from the interaction forces between individuals. Therefore, carbon management in industries in the real economy needs to be explored from the perspective of these complex networks. On the basis of these considerations, this study utilized a hybrid model, the Economic Input–Output Life Cycle Assessment model, to construct the carbon footprint network of China's industries in 2002, 2007, 2012, and 2017. We also applied social network analysis to quantify the structural characteristics and evolutionary patterns of the network. Results showed that the transmission efficiency of resources and the subgroup‐integrated degree of the actors within the carbon footprint network were excellent, which was conducive to coordinated carbon emission reduction of these sectors from a network perspective. Agriculture and services were the engines driving sector‐wide carbon emission reduction through links within the network. Furthermore, some sectors, such as transportation and the warehousing industry, basic chemicals and chemical products manufacturing, and metal smelting and rolling processing industry, act as bridges and brokers. These sectors are keys to ensuring network cohesion and reducing the risk of network fragmentation. Finally, the industrial block network presented a hierarchical structure of a core–edge network. The network followed a trend of structural changes in which the network core became increasingly clear, and the network distribution pattern gradually concentrated on a single block. This finding revealed a new pathway for collaborative carbon mitigation in the industry from the perspective of the industrial community.

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Social network analysis: Exploring connections to advance military nursing science

Cited by 5 publications

References 35 publications

Spatial Network Structure of China’s Provincial-Scale Tourism Eco-Efficiency: A Social Network Analysis

Spatial Network Structure of China’s Provincial-Scale Tourism Eco-Efficiency: A Social Network Analysis

Challenges of customer experience management in social commerce: an application of social network analysis

Measuring structural characteristics and evolutionary patterns of an industrial carbon footprint network: A social network analysis approach

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