ABSTRACT
The current global health crisis is a consequence of the pandemic caused by COVID-19. It has impacted the lives of people from all factions of society. The re-emergence of new variants is threatening the world, which urges the development of new methods to prevent rapid spread. Places with more extensive social dealings, such as offices, organizations, and educational institutes, have a greater tendency to escalate the viral spread. This research focuses on developing a strategy to find out the key transmitters of the virus, particularly at educational institutes. The reason for considering educational institutions is the severity of the educational needs and the high risk of rapid spread. Educational institutions offer an environment where students come from different regions and communicate with each other at close distances. To slow down the virus's spread rate, a method is proposed in this paper that differs from vaccinating the entire population or complete lockdown. In the present research, we identified a few key spreaders, which can be isolated and can slow down the transmission rate of the contagion. The present study creates a student communication network, and virus transmission is modeled over the predicted network. Using student-to-student communication data, three distinct networks are generated to analyze the roles of nodes responsible for the spread of this contagion. Intra-class and inter-class networks are generated, and the contagion spread was observed on them. Using social network strategies, we can decrease the maximum number of infections from 200 to 70 individuals, with contagion lasting in the network for 60 days.
ABSTRACT
Social distancing policies and other restrictive measures have demonstrated efficacy in curbing the spread of the COVID-19 pandemic. However, these interventions have concurrently led to short- and long-term alterations in social connectedness. Comprehending the transformation in intracity social interactions is imperative for facilitating post-pandemic recovery and development. In this research, we employ social network analysis (SNA) to delve into the nuances of urban resilience. Specifically, we constructed intricate networks utilizing human mobility data to represent the impact of social interactions on the structural attributes of social networks while assessing urban resilience by examining the stability features of social connectedness. Our findings disclose a diverse array of responses to social distancing policies regarding social connectedness and varied social reactions across U.S. Metropolitan Statistical Areas (MSAs). Social networks generally exhibited a shift from dense to sparse configurations during restrictive orders, followed by a transition from sparse to dense arrangements upon relaxation of said orders. Furthermore, we analyzed the alterations in social connectedness as demonstrated by network centrality, which can presumably be attributed to the rigidity of policies and the inherent qualities of the examined MSAs. Our findings contribute valuable scientific insights to support informed decision-making for post-pandemic recovery and development initiatives.
ABSTRACT
This study examines the coping capacities of families during the Covid-19 lockdown in relation to their material and social resources. Specifically, we examine (1) how familial modes of living changed during the lockdown;(2) how the affected families coped with these changes;and (3) how families' coping strategies were influenced by their social and material capital. We analyze problem centered interviews with 30 families and the ego-centered networks of these families, conducted in spring 2020 and autumn 2021. Our typology shows that inequalities increased in the pandemic. In families that had both strong networks and ample material security, the crisis had only a minor impact on how they lived their everyday lives. By contrast, families that had neither supportive networks nor sufficient material resources felt vulnerable during the crisis, and threatened to break down. Families that had either support from their networks or greater material security were able to cope only with great effort. © 2023 Nomos Verlagsgesellschaft mbH und Co. All rights reserved.
ABSTRACT
COVID-19 has driven the formation of regional supply chains. In addition, cities became the basic units of intra-regional supply chain organization under urban administrative economies. Based on the data mining of the buyer-supplier relationship of listed manufacturing firms, this study explores the spatial characteristics of city supply networks within Shandong by the indexes of degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and a community detection algorithm using the social network analysis (SNA) method and ArcGIS software. It investigates the influencing factors of city supply networks by the correlation and regression of the quadratic assignment procedure (QAP). The results show the following: 1) Shandong has formed a multi-center city supply network with Jinan, Qingdao, Yantai-Weihai, and the distribution pattern of city centrality measured by different centrality indicators shows differences. 2) Cities belonging to the same network community show a coexistence of spatial proximity and "enclave” distribution. 3) Geographic proximity, convenient transportation links, administrative district economy, similarity of business environments represented by development zones, export-oriented or domestic market-oriented division of labor between cities, value chain division of labor between cities, and land price differences between cities promote the formation of regional city supply networks. Conversely, differences in local market size and wage levels between cities hinder the formation of city supply networks. This study attempts to apply the analysis results to regional planning from the perspective of regional industrial synergy development. Additionally, as it is based on typical Chinese provinces, it can provide policy references for national administrative regions and countries/regions at similar spatial scales for manufacturing supply chains, as well as for regional spatial layout decisions of manufacturing enterprises. Copyright © 2023 Yan, Wang, Zhao and Zhang.
ABSTRACT
Interest in healthcare has grown significantly worldwide, especially since the Covid-19 outbreak. Digitalisation has allowed users to interact on social networks through platforms like Twitter, collecting user interactions over time, resulting in the proliferation of fake news. This research aims to analyse, evaluate and classify the predictive potential of Twitter analytics in healthcare, identifying the latent knowledge insights and distinguishing them from related rumours and fake news. Thus, a systematic literature review (SLR) is carried out to identify and analyse the existing academic research and applications in Twitter in predicting healthcare. The most important predictive applications are detecting mental health issues and public health emergencies. Covid-19 has been the main topic of most of the studies linked to fake news and misinformation, and this research provides a practical contribution to the use of unstructured data from Twitter and raises awareness of the importance of this content applied to healthcare. Therefore, it is pertinent to focus on the advances offered by these data as a predictive tool in healthcare since it is essential, to this end, to evaluate the veracity of the information shared on Twitter.
ABSTRACT
This two-year study followed a professional learning community (PLC) of STEM Teachers Leaders, referred to as L-PLC. The onset of the COVID-19 pandemic accelerated changes in the focus of many professional development frameworks from face-to-face to online communication. We sought for new ways and tools to follow the professional development and the dynamics in our L-PLC. In particular, we explored professional knowledge development and social interactions, as derived from its WhatsApp group (43-48 participants) discourse, before and during the COVID-19 pandemic. Data were extracted from 6599 WhatsApp messages issued during four consecutive semesters (March 2019-March 2021), as well as from participant background questionnaires. The analysis incorporated both structure and content examination of the L-PLC WhatsApp discourse, using social network analysis (SNA), and a distinctive coding scheme followed by statistical analysis, heat map, and bar graph visualizations. These provided insights into whole group (macro), subgroups (meso), and individual (micro) profiles. The results indicated that over time, the participants gradually began to use the WhatsApp platform for professional purposes on top of its initial administrative intention. Moreover, the pandemic seemed to lead to a unique adjustment process, denoted by enhanced professional interactions, regarding content knowledge, professional content knowledge, and technological knowledge, and also accelerated the development of productive community behaviors, such as sharing and social support. The research approach enabled us to detect changes in key PLC characteristics, follow their dynamics under the influence of chaotic changes and navigate the community accordingly. Taken together, WhatsApp exchanges can serve as a rich source of data for a noninvasive continuous evaluation of group processes and progress. Supplementary Information: The online version contains supplementary material available at 10.1007/s40593-022-00320-3.
ABSTRACT
Emergency cooperative social networks (ECSNs) play a very important role in emergency management for magnitude emergencies in China recently. Based on the data set of cooperative fight against COVID-19 of the Beijing-Tianjin-Hebei region in China, using social network analysis (SNA) and asymmetric evolutionary game model, this study finds that the asymmetry between regions is comprehensively determined by resource endowment, administrative level, geographical distance, regional vulnerability, political pressure and other factors;vertical control is still the main operating mechanism of ECSNs;network derivation is caused by the superposition of multiple factors, of which political factors are very important, and asymmetry may become an obstacle.
ABSTRACT
One of the effective ways of preventing the spread of the COVID-19 outbreak is dependent on non-pharmaceutical interventions due to the absence of a vaccine against the virus. This study aims to assess organizational emergency responses to COVID-19 using a social network analysis (SNA) method via Gephi. The data were obtained from a weekly Indonesian magazine, TEMPO, which reported on the Indonesian government's response to COVID-19 from early March to early April 2020. One hundred and fifty actors represent the nodes and 180 connections denoted as edges are explored and analyzed using the SNA tool. The study found that the emergency response to the pandemic consisted of less solid, non-traditional structural interactions, and that the head of the task force played a lesser role in the response to this outbreak. The content analysis revealed that the team members' issues of concern included coordination, hoaxes and fake issues, instruction/consultation, social distancing, and the lack of testing equipment. The results of the study are expected to contribute to the literature on research on emergency responses to pandemics. The findings also contribute to the development of the design of organizational emergency policies in the contexts of large-scale pandemics.
ABSTRACT
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.
ABSTRACT
The factors that lead to coronavirus disease 2019 (COVID-19) vaccine hesitancy among health-care workers (HCWs) are unclear. We aimed to identify the factors that influence HCWs' hesitancy, especially the influence of their social network. Using an online platform, we surveyed HCWs in Chongqing, China, in January 2021 to understand the factors that influence the COVID-19 vaccine hesitancy among HCWs. Proportional allocation stratified sampling method was used to recruit respondents. Multivariable logistic regression and social network analysis (SNA) were used to analyze the influence factors. A total of 5247 HCWs were included and 23.3% of them were vaccine-hesitant. Participants were more hesitant if they had chronic diseases (OR = 1.411, 95% CI: 1.146-1.738), worked in tertiary hospitals (OR = 1.546, 95% CI: 1.231-1.942), and reported a history of vaccine hesitancy (OR = 1.637, 95% CI: 1.395-1.920) and refusal toward other vaccines (OR = 2.433, 95% CI: 2.067-2.863). The participants with a social network to communicate COVID-19 immunization were less hesitant (OR = 0.850, 95% CI: 0.728-0.993). Several influential members with social networks were found in SNA. Most of these influential members in the networks were department leaders who were willing to get COVID-19 vaccines (P < .05). Hesitant subgroups among Chinese HCWs were linked to the lack of a social network to communicate COVID-19 immunization. Our findings may lead to tailored interventions to enhance COVID-19 vaccine uptake among HCWs by targeting key members in social network.