Journal articles: 'Social network'

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Trevillion, S. "Social work, social networks and network knowledge." British Journal of Social Work 30, no. 4 (August 1, 2000): 505–17. http://dx.doi.org/10.1093/bjsw/30.4.505.

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Sharma, Anita, and Prashant Salwan. "Network Matters! Revisiting Social Networks." Academy of Management Proceedings 2017, no. 1 (August 2017): 17317. http://dx.doi.org/10.5465/ambpp.2017.17317abstract.

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Gilles, Robert, Tabitha James, Reza Barkhi, and Dimitrios Diamantaras. "Simulating Social Network Formation." International Journal of Virtual Communities and Social Networking 1, no. 4 (October 2009): 1–20. http://dx.doi.org/10.4018/jvcsn.2009092201.

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Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

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Jadhav, Pranavati, and Dr Burra Vijaya Babu. "Detection of Community within Social Networks with Diverse Features of Network Analysis." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (December 31, 2019): 366–71. http://dx.doi.org/10.5373/jardcs/v11sp12/20193232.

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Treese, Win. "Social network?" netWorker 12, no. 1 (March 2008): 13–15. http://dx.doi.org/10.1145/1346942.1346946.

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Bongomin, George Okello Candiya, John C. Munene, Joseph Mpeera Ntayi, and Charles Akol Malinga. "Social network." African Journal of Economic and Management Studies 9, no. 3 (September 3, 2018): 388–406. http://dx.doi.org/10.1108/ajems-07-2017-0157.

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Purpose The purpose of this paper is to test for the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda. Design/methodology/approach The study employed a cross-sectional research design and data were collected from a total of 400 poor households located in Northern, Eastern, Central and Western Uganda. The authors adopted ordinary least square hierarchical regression analysis to test for the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda. The effects were determined by calculating the significant change in coefficient of determination (R2) between the dimensions of social network in explaining financial inclusion. In addition, analysis of variance was also used to test for variation in perceptions of the poor about being financially included. Findings The findings revealed that the dimensions of ties and interaction significantly explain financial inclusion of the poor in rural Uganda. Contrary to previous studies, the results indicated that interdependence as a dimension of social network is not a significant predictor of financial inclusion of the poor in rural Uganda. Combined together, the dimensions of social network explains about 16.6 percent of the variation in financial inclusion of the poor in rural Uganda. Research limitations/implications The study was purely cross-sectional, thus, ignoring longitudinal survey design, which could have investigated certain characteristics of the variable over time. Additionally, although a total sample amounting to 400 poor households was used in the study, the results cannot be generalized since other equally marginalized groups such as the disabled persons, refugees, and immigrants were not included in this study. Furthermore, the study used only the questionnaire to elicit responses from the respondents. The use of interview was ignored during data collection. Practical implications Policy makers, managers of financial institutions, and financial inclusion advocates should consider social network dimensions of ties and interaction as conduits for information flow and sharing among the poor including the women and youth about scarce financial resources like loans. Advocacy towards creation of societal network that brings the poor together in strong and weak ties is very important in scaling up access to and use of scarce financial services for improving economic and social well-being. Originality/value Contrary to previous studies, this particular study test the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda. Thus, it methodologically isolates the individual contribution of each of the dimensions of social network in explaining financial inclusion of the poor. The authors found that only ties and interaction are significant predictors of financial inclusion of the poor in rural Uganda. Therefore, the findings suggest that not all dimensions of social network are significant predictors of financial inclusion as opposed to previous empirical findings.

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Bolster, Mary. "Social Network." Brain & Life 16, no. 5 (October 2020): 38. http://dx.doi.org/10.1097/01.nnn.0000719040.69363.cd.

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Williams, Ruth, and Andrea Shaw. "Social network." Nursing Management 22, no. 6 (September 30, 2015): 17. http://dx.doi.org/10.7748/nm.22.6.17.s24.

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Kutz, Giorgio. "Social network." Giornale della L 12, no. 6 (June 20, 2013): 47–50. http://dx.doi.org/10.1472/ser-art-ve.

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Usui, Shohei, Fujio Toriumi, Masato Matsuo, Takatsugu Hirayama, and Kenji Mase. "Greedy Network Growth Model of Social Network Service." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 4 (July 20, 2014): 590–97. http://dx.doi.org/10.20965/jaciii.2014.p0590.

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As new network communication tools are developed, social network services (SNS) such as Facebook and Twitter are becoming part of a social phenomenon globally impacting on society. Many researchers are therefore studying the structure of relationship networks among users. We propose a greedy network growth model that appropriately increases nodes and links while automatically reproducing the target network. We handle a wide range of networks with high expressive ability. Results of experiments showed that we accurately reproduced 92.4% of 189 target networks from real services. The model also enabled us to reproduce 30 networks built up by existing network models. We thus show that the proposed model represents the expressiveness of many existing network models.

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Quinn, Darren, Liming Chen, and Maurice Mulvenna. "Social Network Analysis." International Journal of Ambient Computing and Intelligence 4, no. 3 (July 2012): 46–58. http://dx.doi.org/10.4018/jaci.2012070104.

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Social Network Analysis is attracting growing attention as social networking sites and their enabled applications transform and impact society. This paper aims to provide a comprehensive review of social network analysis state of the art research and practice. In the paper the authors’ first examine social networking and the core concepts and ingredients of social network analysis. Secondly, they review the trend of social networking and related research. The authors’ then consider modelling motivations, discussing models in line with tie formation approaches, where connections between nodes are taken into account. The authors’ outline data collection approaches along with the common structural properties observed in related literature. They then discuss future directions and the emerging approaches in social network analysis research, notably semantic social networks and social interaction analysis.

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Lakon, Cynthia M., Yu Zheng, and Cornelia Pechmann. "Social network tie functions of social support and social influence and adult smoking abstinence." PLOS ONE 19, no. 3 (March 7, 2024): e0296458. http://dx.doi.org/10.1371/journal.pone.0296458.

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Adults’ social network ties serve multiple functions and play prominently in quitting smoking. We examined three types of adults’ egocentric social networks, including family, friends, and friends online to investigate how two network characteristics with major relevance to health behavior, network size and tie closeness, related to the emotional and confidant support and to pro- and anti-smoking social influence these ties may transmit. We also examine whether the social support and social influence constructs related to smoking abstinence. We utilized baseline and 7-day abstinence survey data from 123 adult current smokers attempting to quit prior to the start of a randomized controlled quit-smoking trial of a social support intervention for quitting smoking on Twitter. To examine study relationships, we estimated Negative Binomial Regression models and Logistic Regression models. For all networks, network size and tie closeness related positively to most of the social support and social influence constructs, with tie closeness related most strongly, especially for online friends. Family pro-smoking social influence related negatively to smoking abstinence, and there were marginally negative relationships for family emotional support and family confidant support. Online friend emotional support had a marginally positive relationship with smoking abstinence. Overall, our findings indicated the importance of the social support and social influence functions of each type of network tie, with larger networks and closer ties related to higher levels of social support and social influence. Moreover, family network pro-smoking social influence may compromise abstinence while emotional support from online friend network ties may reinforce it.

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Roy, Arjun, Hongyi Zeng, Jasmeet Bagga, George Porter, and Alex C. Snoeren. "Inside the Social Network's (Datacenter) Network." ACM SIGCOMM Computer Communication Review 45, no. 4 (September 22, 2015): 123–37. http://dx.doi.org/10.1145/2829988.2787472.

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Del Fresno García, Miguel. "Connecting the Disconnected: Social Work and Social Network Analysis. A Methodological Approach to Identifying Network Peer Leaders." Arbor 191, no. 771 (February 28, 2015): a209. http://dx.doi.org/10.3989/arbor.2015.771n1011.

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Abdul Rauf, Siti Hajar, Siti Hajar Abu Bakar Ah, and Adi Fahrudin. "Social Change PostCOVID-19 in Malaysia: The Density of Social Network." Asian Social Work Journal 5, no. 2 (July 27, 2020): 1–5. http://dx.doi.org/10.47405/aswj.v5i2.136.

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The COVID-19 pandemic is a global health problem that has given the greatest challenge for humanity today. This paper discusses the social changes that have taken place on social network density after COVID-19 hit the world. The social network density discussed is based on Social Network Theory according to the current situation that has hit Malaysia. The methods used are based on document analysis and case analysis from official documents issued by the government. The analysis revealed that the COVID-19 pandemic has had a devastating impact on human health, social and economic. However, seen from the standpoint of social networks, the COVID-19 pandemic has led to the emergence of densities in social networks due to increased informal sector involvement in the formation of social networks. This means that, as the number of social networks is formed, the density of social network will increased as defined by Social Network Theory.

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van Ijzendoorn, Marinus H. "Attachment in Social Networks: Toward an Evolutionary Social Network Model." Human Development 48, no. 1-2 (2005): 85–88. http://dx.doi.org/10.1159/000083218.

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Nasution, Mahyuddin K. M., Rahmad Syah, and Marischa Elveny. "Social Network Analysis: Towards Complexity Problem." Webology 18, no. 2 (December 23, 2021): 449–61. http://dx.doi.org/10.14704/web/v18i2/web18332.

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Social network analysis is a advances from field of social networks. The structuring of social actors, with data models and involving intelligence abstracted in mathematics, and without analysis it will not present the function of social networks. However, graph theory inherits process and computational procedures for social network analysis, and it proves that social network analysis is mathematical and computational dependent on the degree of nodes in the graph or the degree of social actors in social networks. Of course, the process of acquiring social networks bequeathed the same complexity toward the social network analysis, where the approach has used the social network extraction and formulated its consequences in computing.

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Cadima, Rita, Carlos Ferreira, Josep Monguet, Jordi Ojeda, and Joaquin Fernandez. "Promoting social network awareness: A social network monitoring system." Computers & Education 54, no. 4 (May 2010): 1233–40. http://dx.doi.org/10.1016/j.compedu.2009.11.009.

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Machuco Rosa, António. "Mimesis, network theory and digital social networks." Xiphias Gladius Revista interdisciplinar de Teoría Mimética, no. 1 (December 19, 2018): 93–111. http://dx.doi.org/10.32466/eufv-xg.2018.1.387.93-111.

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We analyse the formal theory of networks and the new digital media from the perspective of mimetic theory. It is emphasised that the spatial form of the new media is characterised by distribution functions that are power laws. We show that this spatial form can be derived from the presence of mimetic desire in digital social networks. Finally, we show that network theory allows the representation of the scapegoat mechanism as it was analysed by René Girard.

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Davel, Ronel, Adeline S. A. Du Toit, and Martie M. Mearns. "Understanding Knowledge Networks Through Social Network Analysis." International Journal of Knowledge Management 13, no. 2 (April 2017): 1–17. http://dx.doi.org/10.4018/ijkm.2017040101.

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Social network analysis (SNA) is being increasingly deployed as an instrument to plot knowledge and expertise as well as to confirm the character of connections in informal networks within organisations. This study investigated how the integration of networking into KM can produce significant advantages for organisations. The aim of the research was to examine how the interactions between SNA, Communities of Practice (CoPs) and knowledge maps could potentially influence knowledge networks. The researchers endeavour to illustrate via this question that cultivating synergies between SNA, CoPs and knowledge maps will enable organisations to produce stronger knowledge networks and ultimately increase their social capital. This article intends to present a process map that can be useful when an organisation wants to positively increase its social capital by examining influencing interactions between SNA, CoPs and knowledge maps, thereby enhancing the manner in which they share and create knowledge.

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Đuričanin, Jasminka, Marko Gašić, Jelena Veličković, and Nebojša Pavlović. "Advertising on Facebook social network." Bizinfo Blace 12, no. 2 (2021): 171–81. http://dx.doi.org/10.5937/bizinfo2102171d.

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The exponential growth of users on social networks around the world has led companies to explore effective ways of their presence on social networks. Accordingly, the trend of advertising as one of the most important forms of communication mix has changed and now companies are mainly focused on advertising on social networks. There are many social networks that companies can use for advertising, however, this paper points to the importance of advertising through the social network Facebook, given the fact that Facebook is the largest and most popular social network in the world and is the perfect marketing tool with a built-in advertising system, allows businesses to use each user's information for targeted advertising. Hence, Facebook is the most dominant social network for advertising, which every company should take into account when creating marketing strategies, in order to gain and maintain a competitive advantage and maximize business success. The paper presents secondary data that clearly indicate that Facebook is the social network that has the most potential for advertising, reaching and engaging consumers, which is supported by a discussion of the results of empirical research.

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Levin, Ilya, Mark Korenblit, and Vadim Talis. "STUDY OF SOCIAL NETWORKS’ DYNAMICS BY SIMULATION WITHIN THE NODEXL-EXCEL ENVIRONMENT." Problems of Education in the 21st Century 54, no. 1 (June 20, 2013): 125–37. http://dx.doi.org/10.33225/pec/13.54.125.

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The present study is an analysis of the learning activity, which constitutes simulation of networks and studying their functioning and dynamics. The study is based on using network-like learning environments. Such environments allow building computer models of the network graphs. According to the suggested approach, the students construct dynamic computer models of the networks' graphs, thus implementing various algorithms of such networks’ dynamics. The suggested tool for building the models is the software environment comprising network analysis software NodeXL and a standard spreadsheet Excel. The proposed approach enables the students to visualize the network's dynamics. The paper presents specific examples of network models and various algorithms of the network's dynamics, which were developed based on the proposed approach. Key words: learning environments, modelling, social networks.

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HARA, Shinji. "Social Network and Social Space." Geographical Review of Japa,. Ser. A, Chirigaku Hyoron 67, no. 10 (1994): 701–22. http://dx.doi.org/10.4157/grj1984a.67.10_701.

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Claybaugh, Craig C., and Peter Haried. "Professional Social Network Site Participation." International Journal of Virtual Communities and Social Networking 4, no. 4 (October 2012): 19–32. http://dx.doi.org/10.4018/jvcsn.2012100102.

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Online professional social networks have become a noteworthy tool to help professionals create, strengthen, and maintain valuable business connections. However, the question remains as to who is actually using online professional social networks and how the diffusion of the social network has occurred. Looking at diffusion and usage through innovation diffusion theory, critical mass and a network effects lens, this paper seeks to examine the diffusion of an online professional social network (LinkedIn) for a specific population of university business schools. Using longitudinal analysis (2008 compared to 2011) our findings advocate network dynamics of homogeneous populations display consistent patterns of participation and non-participation. The authors’ findings suggest LinkedIn diffusion is not consistent across all business schools examined. A greater critical mass and network effect appears to have been achieved across larger research universities when compared to smaller university populations. An analysis of the results and future research directions are presented.

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Gemici, Kurtuluş, and Anthony Vashevko. "Visualizing Hierarchical Social Networks." Socius: Sociological Research for a Dynamic World 4 (January 1, 2018): 237802311877298. http://dx.doi.org/10.1177/2378023118772982.

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The authors propose a novel technique for the visualization of networks that contain a hierarchical structure: networks in which certain nodes and groups of nodes can be classified through a relation of precedence. Networks with a hierarchical structure frequently arise in sociology and various other disciplines, but the existing methods for visualizing such networks leave much to be desired. The method developed in this work builds on the tradition of visualization in social network analysis; it aims to simultaneously represent the positions of different nodes and the relationships between groups containing the nodes in the network. As such, the proposed visualization method facilitates theoretical and empirical analysis of social structures by algorithmically combining information from the underlying network with the information from the hierarchical structure of the network. The authors illustrate the proposed method with social networks examined through cohesive blocking and k-core decomposition.

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AYDIN, Nursen. "Social Network Analysis: Literature Review." AJIT-e Online Academic Journal of Information Technology 9, no. 34 (November 1, 2018): 73–80. http://dx.doi.org/10.5824/1309-1581.2018.4.005.x.

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In this article, social network analysis SNA is defined and historical development process is explained. A comprehensive literature search has been conducted for this purpose. SAA is a powerful method that centralizes individuals and their relations, in that the effect of the individual on the social network can be uncovered and the network of individual groups can be evaluated holistically. SNA shows the structural gaps and social capital in institutions, and focuses managers' attention on critical informal networks. Evaluating strategically important networks within an organization, make "invisible" groups visible in the interaction and allows them to work with key groups to facilitate effective collaboration.

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Liu, Guanfeng, Yan Wang, and Mehmet Orgun. "Social Context-Aware Trust Network Discovery in Complex Contextual Social Networks." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 101–7. http://dx.doi.org/10.1609/aaai.v26i1.8114.

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Trust is one of the most important factors for participants' decision-making in Online Social Networks (OSNs). The trust network from a source to a target without any prior interaction contains some important intermediate participants, the trust relations between the participants, and the social context, each of which has an important influence on trust evaluation. Thus, before performing any trust evaluation, the contextual trust network from a given source to a target needs to be extracted first, where constraints on the social context should also be considered to guarantee the quality of extracted networks. However, this problem has been proved to be NP-Complete. Towards solving this challenging problem, we first propose a complex contextual social network structure which considers social contextual impact factors. These factors have significant influences on both social interaction between participants and trust evaluation. Then, we propose a new concept called QoTN (Quality of Trust Network) and a social context-aware trust network discovery model. Finally, we propose a Social Context-Aware trust Network discovery algorithm (SCAN) by adopting the Monte Carlo method and our proposed optimization strategies. The experimental results illustrate that our proposed model and algorithm outperform the existing methods in both algorithm efficiency and the quality of the extracted trust network.

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Roth, Adam R. "Informal Caregiving and Social Capital: A Social Network Perspective." Research on Aging 42, no. 9-10 (April 28, 2020): 272–80. http://dx.doi.org/10.1177/0164027520912659.

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A large literature emphasizes the importance of social relationships during the caregiving process. Yet these issues are seldom presented in a social network framework that examines the structure of caregivers’ personal networks. In this study, I examine how older caregivers experience changes in personal network structure. Using two waves from the National Social Life, Health, and Aging Project, I investigate whether caregivers are more or less likely to exhibit bridging or bonding capital potential compared to noncaregivers. I find that older adults transitioning into caregiving are more likely to develop the ability to bridge social ties within their personal networks than noncaregivers despite potential constraints in their personal freedom. Caregivers in the latter stages, meanwhile, do not differ from noncaregivers in terms of network change. These findings have implications for older adults’ potential to pool resources across social domains as well as negotiate stress and well-being during the caregiving process.

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Schmälzle, Ralf, Matthew Brook O’Donnell, Javier O. Garcia, Christopher N. Cascio, Joseph Bayer, Danielle S. Bassett, Jean M. Vettel, and Emily B. Falk. "Brain connectivity dynamics during social interaction reflect social network structure." Proceedings of the National Academy of Sciences 114, no. 20 (May 2, 2017): 5153–58. http://dx.doi.org/10.1073/pnas.1616130114.

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Social ties are crucial for humans. Disruption of ties through social exclusion has a marked effect on our thoughts and feelings; however, such effects can be tempered by broader social network resources. Here, we use fMRI data acquired from 80 male adolescents to investigate how social exclusion modulates functional connectivity within and across brain networks involved in social pain and understanding the mental states of others (i.e., mentalizing). Furthermore, using objectively logged friendship network data, we examine how individual variability in brain reactivity to social exclusion relates to the density of participants’ friendship networks, an important aspect of social network structure. We find increased connectivity within a set of regions previously identified as a mentalizing system during exclusion relative to inclusion. These results are consistent across the regions of interest as well as a whole-brain analysis. Next, examining how social network characteristics are associated with task-based connectivity dynamics, we find that participants who showed greater changes in connectivity within the mentalizing system when socially excluded by peers had less dense friendship networks. This work provides insight to understand how distributed brain systems respond to social and emotional challenges and how such brain dynamics might vary based on broader social network characteristics.

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Yang, Hong Mei, Chun Ying Zhang, Rui Tao Liang, and Fang Tian. "Set Pair Social Network Analysis Model." Applied Mechanics and Materials 50-51 (February 2011): 63–67. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.63.

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Through the study on social network information, this paper explore that there exists the certain and uncertain phenomena in the process of finding the relationship between individuals by using social networks, and the social networks are constantly changing. In light of there are some uncertainty and dynamic problems for the network, this paper put forward the set pair social network analysis model and set pair social network analysis model and its properties.

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Jalali, Zeinab S., Alireza Rezvanian, and Mohammad Reza Meybodi. "Social network sampling using spanning trees." International Journal of Modern Physics C 27, no. 05 (May 2016): 1650052. http://dx.doi.org/10.1142/s0129183116500522.

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Due to the large scales and limitations in accessing most online social networks, it is hard or infeasible to directly access them in a reasonable amount of time for studying and analysis. Hence, network sampling has emerged as a suitable technique to study and analyze real networks. The main goal of sampling online social networks is constructing a small scale sampled network which preserves the most important properties of the original network. In this paper, we propose two sampling algorithms for sampling online social networks using spanning trees. The first proposed sampling algorithm finds several spanning trees from randomly chosen starting nodes; then the edges in these spanning trees are ranked according to the number of times that each edge has appeared in the set of found spanning trees in the given network. The sampled network is then constructed as a sub-graph of the original network which contains a fraction of nodes that are incident on highly ranked edges. In order to avoid traversing the entire network, the second sampling algorithm is proposed using partial spanning trees. The second sampling algorithm is similar to the first algorithm except that it uses partial spanning trees. Several experiments are conducted to examine the performance of the proposed sampling algorithms on well-known real networks. The obtained results in comparison with other popular sampling methods demonstrate the efficiency of the proposed sampling algorithms in terms of Kolmogorov–Smirnov distance (KSD), skew divergence distance (SDD) and normalized distance (ND).

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BURT, RONALD S. "Social network and temporal discounting." Network Science 5, no. 4 (July 25, 2017): 411–40. http://dx.doi.org/10.1017/nws.2017.23.

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AbstractFor reasons of social influence and social logistics, people in closed networks are expected to experience time compression: The more closed a person's network, the steeper the person's discount function, and the more narrow the expected time horizon within which the person deliberates events and behavior. Consistent with the hypothesis, data on managers at the top of three organizations show network closure associated with a social life compressed into daily contact with colleagues. Further, language in closed networks is predominantly about current activities, ignoring the future. Further still, discount functions employed by executive MBA students show more severe discounting by students in more closed networks. Inattention to the future can be argued to impair achievement, however, I find no evidence across the managers of daily contact diminishing the achievement associated with network advantage. I close with comments on replication and extrapolation to language more generally, within-person variation, and select cognitive patterns (closure bias, end of history, and felt status loss).

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Niese, Bethany, and Sharath Sasidharan. "Getting Social." International Journal of Knowledge Management 18, no. 1 (January 1, 2022): 1–23. http://dx.doi.org/10.4018/ijkm.313956.

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Knowledge acquired by end users through their social networks facilitates optimal use of a newly implemented enterprise system. Existing research has conceptualized end users as being the only actors within such networks. Knowledge ties between actors have been treated as unidimensional. The actor-network theory emphasizes the role of all actors in influencing networking outcomes; hence, this study proposes an expanded multimodal social network that includes four institutionally mandated knowledge actors: the technology champions, the help desk, the service desk, and the shared inbox. Knowledge ties are treated as bidimensional through incorporating both technical and business process knowledge. Data collected from an enterprise resource planning system implementation validated this approach; end users sourced knowledge from other end users and the institutionally mandated network actors based on contextual requirements. End user performance outcomes were significantly associated with knowledge source and knowledge dimension.

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Almahmoud, Essam, and Hemanta Kumar Doloi. "Assessment of Social Sustainability in Construction Projects Using Social Network Analysis." JOURNAL OF INTERNATIONAL BUSINESS RESEARCH AND MARKETING 3, no. 6 (2018): 35–46. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.36.3003.

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This paper aims to propose a framework that puts the stakeholders at the forefront of achieving sustainability in the social context. This research, thus, argues that the social sustainability outcomes in construction are best achieved by taking into account the satisfaction of the stakeholders. Based on sustainability and equity theories, a dynamic assessment model has been developed to evaluate the contributions of projects in a social context. Multiple stakeholders and their differing interests associated with the construction projects have been integrated using social network analysis. The mapping of the relationships between the project stakeholders, with respect to their relative stakes and seven social core functions, have been integrated into the assessment model. The findings of this research suggest that the degree of satisfying the needs of diverse stakeholders is highly significant in achieving social sustainability performance of projects. Using a case study from Saudi Arabia, the applicability and significance of the assessment model has been demonstrated. The application of the model provides the opportunity to identify any problems and to enhance the overall performance of projects in the social context. The functionality and efficacy of the model need to be further tested outside the Saudi Arabian region. The research is original in the sense that for the first time, a novel approach has been developed, putting the stakeholders at the forefront of achieving sustainability outcomes in construction projects

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Westaby, James D., and Adam K. Parr. "Network Goal Analysis of Social and Organizational Systems: Testing Dynamic Network Theory in Complex Social Networks." Journal of Applied Behavioral Science 56, no. 1 (October 24, 2019): 107–29. http://dx.doi.org/10.1177/0021886319881496.

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Grounded in dynamic network theory, this study examined network goal analysis (NGA) to understand complex systems. NGA provides new insights by inserting goal nodes into social networks. Goal nodes can also represent missions, objectives, or desires, thus having wide applicability. The theory ties social networks to goal nodes through a parsimonious set of social network role linkages, such as independent goal striving, system supporting, feedback, goal preventing, supportive resisting, and system negating (i.e., those who are upset with others in the pursuit). Moreover, we extend the theory’s system reactance role linkage to better account for constructive conflicts. Two complex systems were examined: a team’s mission and an individual’s work project. In support of dynamic network theory, using the Quadratic Assignment Procedure, results demonstrated significant shared goal striving, system supporting, and shared connections between goal striving and system supporting. These findings manifest what we coin as multipendence: Systems having some actions independently involved with goals, while others are dependently involved in the associated network. NGA also demonstrated that the goal nodes manifested strong betweenness centrality, indicating that goal striving and feedback links were connecting entities across the wider system. Strategies to plan network goal interventions are illustrated with implications for practice.

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KC, Birendra, Duarte B. Morais, M. Nils Peterson, Erin Seekamp, and Jordan W. Smith. "Social network analysis of wildlife tourism microentrepreneurial network." Tourism and Hospitality Research 19, no. 2 (June 30, 2017): 158–69. http://dx.doi.org/10.1177/1467358417715679.

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Social networks are an important element of entrepreneurship. Entrepreneurs rely on social networks to access ideas, information, and resources to facilitate their entrepreneurial process. Strong and weak ties influence the entrepreneurial process in unique ways. This study utilized social network analysis approach to examine wildlife tourism microentrepreneurship through in-person structured interviews with 37 microentrepreneurs from North Carolina’s Pamlico Sound Region. Specifically, this study examined the extent of network ties, the type of support received from those network ties, and the process of creating and maintaining the business network ties. Weak ties were more prevalent than strong ties. Support was received in terms of marketing and advertising, information sharing, and product sponsorship. Weak ties were established through professional workshops and seminars or while working in the same territory, whereas reciprocity, togetherness, communication, and trust were identified as major factors to maintain weak ties. This study suggests that cognitive social capital factors (e.g. reciprocity, togetherness, and trust) can be highly important toward effective use of social networks, as well as to ensure entrepreneurial success.

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Cortez, Paola, Serguei Levachkine, and Carlos de la Cruz Cruz. "Tourist Information Evaluation Using a Social Network." Journal of Advances in Computer Networks 2, no. 3 (2014): 202–6. http://dx.doi.org/10.7763/jacn.2014.v2.112.

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Yin, Zong-Xian, and He-Yi Tsai. "Create Living Context Map from Social Network." International Journal of Future Computer and Communication 7, no. 4 (December 2018): 103–6. http://dx.doi.org/10.18178/ijfcc.2018.7.4.529.

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Rowley, Timothy J. "Social Network Analysis." Proceedings of the International Association for Business and Society 7 (1996): 999–1009. http://dx.doi.org/10.5840/iabsproc1996794.

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Bohlin, Erik, Aijaz A. Shaikh, and Payam Hanafizadeh. "Social Network Banking." International Journal of E-Business Research 14, no. 2 (April 2018): 1–13. http://dx.doi.org/10.4018/ijebr.2018040101.

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Social media is widely recognized as a challenging new communication technology in both economic and social contexts. The present article explores how banks have exploited this technology in the range of consumer retail banking services offered by 100 leading global banks on the three major social networking sites (SNS): Facebook, Twitter, and YouTube. Viewing social network (SN) banking as a separate delivery channel and offering a working definition of SN banking, the article shows that banks have been more cautious than other businesses in using SNS. The available services are classified on nine main dimensions: marketing, financial education and advice, information support, customer support, sales representativeness, customer engagement, online recruitment, survey and polling, and other services. The scope of these SN banking services is for the most part non-cash-based. Conclusions, implications, and recommendations are discussed and future research priorities are identified.

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Azizifard, Narges. "Social Network Clustering." International Journal of Information Technology and Computer Science 6, no. 1 (December 1, 2013): 76–81. http://dx.doi.org/10.5815/ijitcs.2014.01.09.

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S., Sukharev O., and Kurmanov N.V. "Social Network Analysis." Advances in Economics and Business 2, no. 3 (March 2014): 121–26. http://dx.doi.org/10.13189/aeb.2014.020301.

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Jain, Susha, Mahaveer Jain, and Balasubramani R. "Social Network Analysis." IJARCCE 8, no. 5 (May 30, 2019): 236–40. http://dx.doi.org/10.17148/ijarcce.2019.8543.

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Pugacheva, E. "Social Network Modelling." Мiждисциплiнарнi дослiдження складних систем, no. 6 (2015): 49–62. http://dx.doi.org/10.31392/2307-4515/2015-6.2.

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Zammar, Nisrine. "Social Network Sites." International Journal of Actor-Network Theory and Technological Innovation 2, no. 2 (April 2010): 54–62. http://dx.doi.org/10.4018/jantti.2010040104.

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This article examines the role of actors in a Social Network Sites and also the triggers and challenges they represent to social networking between today’s communities and businesses. A Social Network Sites is the product of the evolution of social liaisons and the emergence of online communities of people who are interested in exploring the concerns and activities of others. A social network is the assembly of direct or indirect contacts; a network is the product of interactions with the actors (individuals, families, enterprises, etc.) enabled by means of the structural design of web 2.0. Social Network Sites bring people together to interact through chat rooms, and share personal information and ideas around any topics via personal homepage publishing tools. This article is intended to be a trigger to deeply and more intensely explore potential roles of actor-network theory in the Social Network Sites context, in today’s and tomorrow’s world.

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Madani, Youness, Mohammed Erritali, Jamaa Bengourram, and Francoise Sailhan. "Social Network Analysis." Journal of Information Technology Research 13, no. 3 (July 2020): 142–55. http://dx.doi.org/10.4018/jitr.2020070109.

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Sentiment analysis has become an important field in scientific research in recent years. The goal is to extract opinions and sentiments from written text using artificial intelligence algorithms. In this article, we propose a new approach for classifying Twitter data into classes (positive, negative, and neutral). The proposed method is based on two approaches, a dictionary-based approach using the sentimental dictionary SentiWordNet, and an approach based on the fuzzy logic system (fuzzification, rule inference, and defuzzification). Experimental results show that our approach outperforms some other approaches in the literature and that by using the fuzzy logic we improve the quality of the classification.

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Marx, Chenee, Katie Infante, and Rachel Murphy. "The Social Network." Journal of Feminist Family Therapy 24, no. 2 (April 2012): 179–82. http://dx.doi.org/10.1080/08952833.2012.648131.

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Scott, John. "Social Network Analysis." Sociology 22, no. 1 (February 1988): 109–27. http://dx.doi.org/10.1177/0038038588022001007.

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Amati, Viviana, Alessandro Lomi, and Antonietta Mira. "Social Network Modeling." Annual Review of Statistics and Its Application 5, no. 1 (March 7, 2018): 343–69. http://dx.doi.org/10.1146/annurev-statistics-031017-100746.

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Scott, John. "Social Network Packages." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 36, no. 1 (September 1992): 100–109. http://dx.doi.org/10.1177/075910639203600109.

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Journal articles on the topic 'Social network'

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