scholarly journals A large-scale location-based social network to understanding the impact of human geo-social interaction patterns on vaccination strategies in an urbanized area

2018 ◽  
Vol 72 ◽  
pp. 78-87 ◽  
Author(s):  
Wei Luo ◽  
Peng Gao ◽  
Susan Cassels
Keyword(s):  
Social Network ◽  
Social Interaction ◽  
Large Scale ◽  
Interaction Patterns ◽  
Vaccination Strategies ◽  
Urbanized Area ◽  
Location Based Social Network ◽  
The Impact

Entrepreneurial advice sources and their antecedents

2017 ◽  
Vol 11 (2) ◽  
pp. 214-236 ◽  
Author(s):  
Bettina Lynda Bastian ◽  
Christopher L. Tucci
Keyword(s):  
Social Interaction ◽  
Social Relations ◽  
Large Scale ◽  
Organizational Capabilities ◽  
Future Research ◽  
Interaction Patterns ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Content Type ◽  
The Impact

Purpose Entrepreneurs interact with others and, through this, benefit from access to knowledge, resources and skills that enhance their own entrepreneurial and organizational capabilities. This paper aims to contribute to the literature interested in identifying and analyzing important antecedents of entrepreneurs’ choices regarding social relations. The study shows how the venture stage, innovativeness and internationalization of the firm potentially influence entrepreneurial choices regarding their social sources of advice. Design/methodology/approach The analysis is based on cross-sectional survey data for the years 2009 and 2010, involving 13 Middle East and North African (MENA) countries. Respondents include future prospective entrepreneurs, start-ups and owner-managers of operating businesses, a total of 13,251 respondents across all countries for the entire period. Findings Entrepreneurs with innovative ventures draw more on advice sources that are able to give information useful for the commercialization of innovative products, and entrepreneurs of internationally exposed ventures rely on a broad base of advice sources that can connect them with a foreign market. However, the outcomes regarding the impact of “different venture stages” point to social interaction patterns that are strongly influenced by local culture and that do not support the assumption of universal entrepreneurship behavior. This study shows that social interactions decline in quantity the more as the venture progresses in age. However, the type of social interaction (e.g. private or professional sources) that entrepreneurs engage throughout the different venture stages remains essentially the same and does not change across different entrepreneurial phases. In the MENA sample, private relations remain the most important source of advice throughout all phases, and they are not replaced by other contacts. Research limitations/implications Limitations of this paper refer to the use of a large-scale database that cannot address certain issues without more direct observation, such as the quality of different social relations. Future research could address this issue by offering more fine-grained items for the different advice sources. Originality/value The paper contributes to the debate on whether entrepreneurship is universal in nature. It focuses on data from emerging and developing countries in the Arab world, which is has not been studied very much in the entrepreneurship literature.

scholarly journals Causal networks of biosphere–atmosphere interactions

2019 ◽  
Author(s):  
Christopher Krich ◽  
Jakob Runge ◽  
Diego G. Miralles ◽  
Mirco Migliavacca ◽  
Oscar Perez-Priego ◽  
...  
Keyword(s):  
Time Series ◽  
Observational Data ◽  
Large Scale ◽  
Terrestrial Ecosystem ◽  
Causal Discovery ◽  
Mediterranean Ecosystem ◽  
Interaction Patterns ◽  
Climate Data ◽  
Detection Rates ◽  
The Impact

Abstract. Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models.

Social Interaction and Academic Performance of Construction Management Students

2021 ◽  
Vol 2 (3) ◽  
pp. 377-387
Author(s):  
Bao Ngoc Nguyen
Keyword(s):  
Academic Performance ◽  
Social Network ◽  
Social Interaction ◽  
Learning Outcomes ◽  
Large Scale ◽  
Learning Performance ◽  
Policy Makers ◽  
Management Students ◽  
Case Basis ◽  
Initial Results

Social interaction between students is a crucial but under-researched part of the education realm. Understanding how connections form in university classes and their effects on learning outcomes may provide extraordinary knowledge for researchers, educators, and policy-makers. This paper collected data from the questionnaire survey and then processed them with Gephi software to produce visualization and measurement. Initial results seem to indicate a significant correlation between students' connectedness and academic performance in one class. However, in another class, the results show a contrasting situation as there is no evidence that social network attributes impact learning performance. Taken together, these results would seem to suggest that the characteristics of the network should be judged on a case-by-case basis, and large-scale SNA analyses have been rarely reported. This present study provides a springboard for a new way to shed some light on classmates' interconnection. Using a similar approach to this article, it is believed that there is ample opportunity to study the association between classmate connectedness and career success. Research techniques and approaches around Social Network Analysis are expected to evolve further in the foreseeable future.

scholarly journals Social networks influence farming practices and agrarian sustainability

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244619
Author(s):  
Amaia Albizua ◽  
Elena M. Bennett ◽  
Guillaume Larocque ◽  
Robert W. Krause ◽  
Unai Pascual
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Land Management ◽  
Agricultural Intensification ◽  
Large Scale ◽  
Management Practices ◽  
Farming Community ◽  
Social Ecological ◽  
The Social ◽  
The Impact

The social-ecological effects of agricultural intensification are complex. We explore farmers’ perceptions about the impacts of their land management and the impact of social information flows on their management through a case study in a farming community in Navarra, Spain, that is undergoing agricultural intensification due to adoption of large scale irrigation. We found that modern technology adopters are aware that their management practices often have negative social-ecological implications; by contrast, more traditional farmers tend to recognize their positive impacts on non-material benefits such as those linked with traditions and traditional knowledge, and climate regulation. We found that farmers’ awareness about nature contributions to people co-production and their land management decisions determine, in part, the structure of the social networks among the farming community. Since modern farmers are at the core of the social network, they are better able to control the information flow within the community. This has important implications, such as the fact that the traditional farmers, who are more aware of their impacts on the environment, rely on information controlled by more intensive modern farmers, potentially jeopardizing sustainable practices in this region. We suggest that this might be counteracted by helping traditional farmers obtain information tailored to their practices from outside the social network.

scholarly journals Effects of worldwide interventions and vaccination on COVID-19 between waves and countries

2021 ◽  
Author(s):  
Yong Ge ◽  
Wenbin Zhang ◽  
Haiyan Liu ◽  
Corrine W Ruktanonchai ◽  
Maogui Hu ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Large Scale ◽  
International Travel ◽  
Spatiotemporal Variations ◽  
School Closures ◽  
Vaccination Strategies ◽  
Travel Restrictions ◽  
The Impact ◽  
Second Wave ◽  
Pharmaceutical Interventions

Abstract Worldwide governments have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic, together with the large-scale rollout of vaccines since late 2020. However, the effect of these individual NPI and vaccination measures across space and time has not been sufficiently explored. By the decay ratio in the suppression of COVID-19 infections, we investigated the performance of different NPIs across waves in 133 countries, and their integration with vaccine rollouts in 63 countries as of 25 March 2021. The most effective NPIs were gathering restrictions (contributing 27.83% in the infection rate reductions), facial coverings (16.79%) and school closures (10.08%) in the first wave, and changed to facial coverings (30.04%), gathering restrictions (17.51%) and international travel restrictions (9.22%) in the second wave. The impact of NPIs had obvious spatiotemporal variations across countries by waves before vaccine rollouts, with facial coverings being one of the most effective measures consistently. Vaccinations had gradually contributed to the suppression of COVID-19 transmission, from 0.71% and 0.86% within 15 days and 30 days since Day 12 after vaccination, to 1.23% as of 25 March 2021, while NPIs still dominated the pandemic mitigation. Our findings have important implications for continued tailoring of integrated NPI or NPI-vaccination strategies against future COVID-19 waves or similar infectious diseases.

scholarly journals Association of Social Network Analysis with Mental Models in Emergency Management Teams

2019 ◽  
Author(s):  
Bjørn Sætrevik ◽  
Line Solheim Kvamme
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Emergency Management ◽  
Mental Models ◽  
Shared Mental Models ◽  
Management Teams ◽  
Interaction Patterns ◽  
Team Members ◽  
The Impact

Social network analysis is a preferred approach to examine the impact of social processes and mechanisms on team performance, but it can be challenging to measure these dynamics in applied settings. Our aim was to test whether the understanding of the task at hand was more accurate and more shared for teams with more evenly distributed interaction patterns. We pre-registered a novel approach for measuring social networks from sparse reporting of ranked interactions. Our sample was eleven emergency management teams that performed a scenario training exercise, where we asked factual questions about the ongoing task during performance, and retrospective questions about who were the most important communication and collaboration partners. We quantified shared mental models as the extent to which a team member showed the same understanding as the rest of their team, and quantified situation awareness as the extent to which team members showed the same knowledge as their team leader. We calculated which team members where most central to the network, and which networks had more evenly distributed networks. Our findings support the pre-registered hypotheses that more interconnected teams are associated with more accurate and more shared mental models, while the individual’s position in the network was not associated with MM.

scholarly journals DYNAMIC TRIP ATTRACTION ESTIMATION WITH LOCATION BASED SOCIAL NETWORK DATA BALANCING BETWEEN TIME OF DAY VARIATIONS AND ZONAL DIFFERENCES

2015 ◽  
Vol II-4/W2 ◽  
pp. 193-198 ◽  
Author(s):  
N. W. Hu ◽  
P. J. Jin
Keyword(s):  
Social Network ◽  
Large Scale ◽  
Ground Truth ◽  
Time Of Day ◽  
Mobility Patterns ◽  
Sensitive Data ◽  
Ground Truth Data ◽  
Fine Grained ◽  
Social Network Data ◽  
Location Based Social Network

The emergence of location based social network (LBSN) services make it accessible and affordable to study individuals’ mobility patterns in a fine-grained level. Via mobile devices, LBSN enables the availability of large-scale location-sensitive data with spatial and temporal context dimensions, which is capable of the potential to provide traffic patterns with significantly higher spatial and temporal resolution at a much lower cost than can be achieved by traditional methods. In this paper, the Foursquare LBSN data was applied to analyze the trip attraction for the urban area in Austin, Texas, USA. We explore one time-dependent function to validate the LBSN’s data with the origin-destination matrix regarded as the ground truth data. The objective of this paper is to investigate one new validation method for trip distribution. The results illustrate the promising potential of studying the dynamic trip attraction estimation with LBSN data for urban trip pattern analysis and monitoring.

POLA INTERAKSI SOSIAL SISWA PENGGUNA GADGET DI SEKOLAH DASAR KOTA YOGYAKARTA

2021 ◽  
Vol 12 (2) ◽  
pp. 305-319
Author(s):  
Ivan Stevanus ◽  
Lusila Parida
Keyword(s):  
Elementary School ◽  
Social Interaction ◽  
Elementary School Students ◽  
Survey Method ◽  
Interaction Patterns ◽  
School Students ◽  
Purposive Sampling ◽  
Technological Advances ◽  
The Impact ◽  
The City

ABSTRAKPenelitian ini bertujuan memberikan bukti empiris terkait pola interaksi sosial antar siswa, dan memaparkan gambaran terkait dampak pemanfaatan gadget dikalangan siswa sekolah dasar kota Yogyakarta. Penelitian ini menggunakan metode survei, dengan teknik pengumpulan data melalui angket. Populasi dan sampel penelitian diambil secara purposive sampling yaitu siswa kelas II dan Kelas V dari empat sekolah dasar swasta dan negeri di kota Yogyakarta dengan tingkat perbedaan predikat akreditasi sekolah. Hasil penelitian menunjukan bahwa ada dua gambaran yang dominan dari pola interaksi sosial siswa sekolah dasar dalam memanfaatkan gadget yakni pola asosiatif dan disasosiatif. Kehadiran dan berkembangnya gadget sangat positif membantu siswa dalam kegiatan belajar, dan dipihak lain gadget juga mereduksi pola interaksi sosial dan personal dikalangan siswa. Untuk menghidupkan pola interaksi sosial siswa dengan adanya gadget maka sekolah perlu membuat pendekatan pembelajaran yang edukatif adaptif dalam memanfaatkan kemajuan teknologi sebagai instrumen kunci keberhasilan pembelajaran abad 21.Kata Kunci: gadget,  interaksi sosial, pola asosiatif dan disasosiatif ABSTRACTThis study aims to provide empirical evidence regarding patterns of social interaction between students, and to describe the impact of using gadgets among elementary school students in the city of Yogyakarta. This study uses a survey method, with data collection techniques through questionnaires. The population and sample of the study were taken by purposive sampling, namely class II and class V students from four private and public elementary schools in the city of Yogyakarta with different levels of school accreditation predicate. The results showed that there were two dominant images of the social interaction patterns of elementary school students in using gadgets, namely associative and dissociative patterns. The presence and development of gadgets is very positive in helping students in learning activities, and on the other hand gadgets also reduce social and personal interaction patterns among students. To turn on students' social interaction patterns with gadgets, schools need to make an adaptive educative learning approach in utilizing technological advances as a key instrument for the success of 21st century learning.Keywords: gadgets, social interaction, associative and dissociative patterns

Sign in / Sign up

Export Citation Format

Share Document