scholarly journals The impact of social capital on the market access of coffee farmer house holds in Lam Dong province

2020 ◽  
Vol 4 (3) ◽  
pp. First
Author(s):  
Duong The Duy ◽  
Nguyen Thai Dung
Keyword(s):  
Social Capital ◽  
Social Networks ◽  
Market Access ◽  
Agricultural Extension ◽  
Research Model ◽  
Poor Households ◽  
Informal Social Networks ◽  
Coffee Farmers ◽  
The Difference ◽  
The Impact

The purpose of this study is to analyze the impact of social capital and other factors that affect the market access of coffee farmer households. The study synthesized theories, as well as previous studies, to provide criteria for measuring social capital of coffee farmers' households: including formal social networks, informal social networks, and trust. Besides, the study uses descriptive statistics, logistic regression, two t-average tests to analyze and assess the difference in social capital between poor and non-poor households of 235 coffee farmer households in Lam Dong Province. The results show that Agricultural Extension Associations, Association Organizations (proxy for official social networks), Agents at all levels, Colleagues - Friends (informal social networks), age, years of living in the locality, and labor have an impact on the market access of coffee farmer households. Some recommendations to improve market access of coffee farmers in the province have been proposed, based on the previous theories as well as the results of the research model The research, however, is yet to clarify the impact of social capital on each market of finance, land, labor, extension services, material, and output markets.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals PERANAN MODAL SOSIAL MELALUI PENGEMBANGAN KELEMBAGAAN DAN PEMBERDAYAAN RUMAH TANGGA MISKIN DI DAERAH PESISIR (Studi Kasus: Rumah Tangga Di Kecamatan Koto IV Tarusan Kabupaten Pesisir Selatan)

2012 ◽  
Vol 1 (1) ◽  
pp. 77
Author(s):  
Neng Kamarni
Keyword(s):  
Social Capital ◽  
Social Networks ◽  
Human Capital ◽  
Social Welfare ◽  
Institutional Development ◽  
Coastal Areas ◽  
Poor Households ◽  
The Social ◽  
Household Members

The purpose of research include studying the characteristics of social capital in the communities especially poor fishermen fishing in the coastal areas, Kabupaten Pesisir Selatan, studying characteristic of social networks and institutional owned by poor households, which is the primary vehicle for empowerment, and to analyze the contribution and role of social capital to welfare of fishermen households in Kecamatan Koto IV Tarusan Kabupaten Pesisir Selatan, and to formulate and create a model for institutional development for the empowerment of poor fishermen.The regression results obtained influence SC (social capital) on the level of social welfare is positive, the higher the social capital of the area, the higher the welfare seaboard district. Kec. Koto XI Tarusan. So also with the HC (human capital) suggests that increased education will improve the welfare of the community. Effect of land will provide opportunities to increase the welfare of society. Variable Z (number of household members) suggests that the increase in the number of family members will reduce the level of social welfare.

scholarly journals Karakterisasi parameter model prediksi untuk eleviasi dari perangkap kemiskinan melalui intervensi kebijakan fiskal

2014 ◽  
Vol 27 (2) ◽  
pp. 65
Author(s):  
Agus Setiawan ◽  
S Bakri ◽  
A Effendi ◽  
I Nurhaida
Keyword(s):  
Social Capital ◽  
Probability Model ◽  
Buffer Zone ◽  
Qualitative Probability ◽  
Fuel Prices ◽  
Family Head ◽  
Cash Assistance ◽  
Poor Households ◽  
Productive Assets ◽  
The Impact

The purpose of this study was to develop a model of poverty eleviation in the buffer zone of National Park through the optimization of the impact of the fuel subsidiary reallocation (BLT), statrted from April to October 2013 in three villages which representent ethnic culture in Lampung, Java, and the mixture of both, selecting 50 poor households (RTM) who received BLT (direct cash assistance) in 2005. In-depth interview was conducted to capture the economy performance of each household. Qualitative probability model was applied to the response variable (Yi) that were scored 1 for those have been exited from the poverty and scored 0 if not yet. The predictor variables used (Xij) including: fuel prices (Rp/liter), total of BLT (Rp), demographics (age, sex, family head education, as well as the number of dependents), the value of productive assets (Rp million/household), revenue from damar resin, and other sources (Rp million/household), access to public services, the distance to the border of BBSNP, the availlability of PNPM (national project of community empowermnent that scored 1 if any and 0 if not), the performance of social capital and the extention program conveyed. The Minitab Version 16 was employed for exzamining the goodness to the model and the significancy of the parameters pursued at the level of 5% and 10%. The conclusions were the exit capability against poverty were significantly affected by: fuel prices, the ammount of the BLT, the exisistency of PNPM, gender & education of household, number of dependents, the distance to central districts, the presence of electricity, the power of social capital, land ownership of both damar agroforest and paddy field acreages, the owner of productive assets such as ruminants. Accordingly, there were no culturetribe significantly affected by the existing from of poverty, which means that the poverty in this region was more structural cause than that of the cultural one.

scholarly journals Impact of Social Networks on Diffusion of Products

2014 ◽  
Vol 5 (1) ◽  
pp. 35-50
Author(s):  
Yudai Arai ◽  
Tomoko Kajiyama ◽  
Noritomo Ouchi
Keyword(s):  
Social Networks ◽  
Consumer Behaviour ◽  
Advertising Budget ◽  
The World ◽  
Agent Simulation ◽  
Multi Agent ◽  
Simulation Results ◽  
The Difference ◽  
Using Data ◽  
The Impact

In light of the rapid growth of social networks around the world, this study analyses the impact of social networks on the diffusion of products and demonstrates the effective way to diffuse products in the society where social networks play an important role. We construct a consumer behaviour model by multi-agent simulation taking the movie market as an example. After validating it by using data from 13 US movies, we conduct simulations. Our simulation results show that the impact of social networks on the diffusion differs according to the customers’ expectations and evaluation for a movie. We also demonstrate the effective weekly advertising budget allocations corresponding to the types of movies. We find that the difference of weekly advertising budget allocations gives greater impact on the diffusion with the growth of social networks. This paper provides firm’s managers with important suggestions for diffusion strategy considering the impact of social networks.

scholarly journals In-class social networks and academic performance: how good connections can improve grades

2019 ◽  
Author(s):  
Luiz Gomes Jr.
Keyword(s):  
Social Capital ◽  
Social Networks ◽  
Academic Performance ◽  
Student Performance ◽  
Significant Role ◽  
Social Factors ◽  
Social Indicators ◽  
Academic Context ◽  
The Social ◽  
The Impact

Understanding how different variables affect student performance is an important requirement for improving educational practices. Since humans are highly social beings, social factors should play a significant role in the academic context. This paper analyzes the impact on academic performance of social indicators such as students friendship circle and in-class clustering. The analysis is based on data from six different classes of the topic Databases taken by students of computing-related majors. We assessed students’ friendship circle in terms of density (sociability) and also quality (grades) of their friends. The paper shows results with strong, statistically relevant relationships between the social factors and student performance. Among other results, the analysis indicates that (i) students with higher social capital tend to perform better, and (ii) students with friends with higher grades have better chances of recovering from a low exam grade.

scholarly journals DINAMIKA MODAL SOSIAL DALAM PEMBERDAYAAN MASYARAKAT PADA DESA WISATA HALAL SETANGGOR: KEPERCAYAAN, JARINGAN SOSIAL DAN NORMA

REFORMASI ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 34-44
Author(s):  
Vidya Yanti Utami
Keyword(s):  
Social Capital ◽  
Social Networks ◽  
Economic Activity ◽  
Dry Season ◽  
Community Empowerment ◽  
Male Population ◽  
Economic Problems ◽  
Village Community ◽  
The Social ◽  
The Impact

Penelitian ini dilatari konteks ekonomi yang terjadi pada masyarakat Desa Setanggor di Kabupaten Lombok Tengah. Hasil panen yang biasa mengalami penurunan tiap kali terjadi musim kemarau karena sawah-sawah tidak bisa ditanami menyebabkan sebagian besar penduduk laki-laki yang memiliki mata pencaharian sebagai petani hanya menganggur. Melihat potensi besar yang dimiliki oleh Desa Setanggor, Ida Wahyuni seorang anggota masyarakat Desa Setanggor, mempelopori dengan memanfaatkan modal sosial yang ada pada masyarakat Desa Setanggor. Penelitian ini bertujuan untuk mengetahui dinamika modal sosial dalam pemberdayaan masyarakat dan dampak yang dirasakan bagi masyarakat Desa Setanggor. Penelitian ini menggunakan metode penelitian kualitatif dengan pendekatan studi kasus. Hasil penelitian menunjukkan bahwa modal sosial yang kuat yang dimiliki oleh masyarakat Desa Setanggor mampu mengatasi permasalahan yang terjadi. Berhasilnya pemberdayaan yang terjadi di Desa Setanggor menciptakan aktivitas ekonomi baru yang dikemas menjadi Desa Wisata Halal. Keberhasilan Setanggor dalam memberdayakan masyarakatnya, melalui sejumlah dinamika modal sosial. Modal sosial yang ditemui tersebut berupa kepercayaan, jaringan sosial dan norma. This research is motivated by economic problems that occur in community of Setanggor Village, Central Lombok Regency. Yields that are usually reduced during the dry season because the fields cannot be planted, so that most of the male population who have a livelihood as farmers are just unemployed. Seeing the great potential possessed by Setanggor Village, Ida Wahyuni as a Setanggor Village community pioneered by utilizing the existing social capital in the Setanggor Village community. This study aims to determine the dynamics of social capital in community empowerment and the impact for the community of Setanggor Village. The results showed that the social capital owned by the community of Setanggor Village was able to solve the problems that occurred. The successful empowerment that occurred in Setanggor Village created a new economic activity that was packaged into a Halal Tourism Village. The success of Setanggor Village in empowering the community, through some dynamics of social capital. The found social capital in the form of trust, social networks and norms.

scholarly journals The Impact of Social Capital on Mental Health: Evidence from the China Family Panel Survey

2021 ◽  
Vol 19 (1) ◽  
pp. 190
Author(s):  
Xianhua Dai ◽  
Nian Gu
Keyword(s):  
Mental Health ◽  
Social Capital ◽  
Social Networks ◽  
Social Trust ◽  
Panel Survey ◽  
Least Squares Regression ◽  
Mutual Help ◽  
Using Data ◽  
Cognitive Social Capital ◽  
The Impact

The influence of social capital on mental health is a controversial topic. As some studies have pointed out, cognitive social capital significantly affects mental health but structural social capital does not. Using data from the China Family Panel Survey, this study measured social capital from social help, social trust, social networks, and social participation as the instrumental variables (regional average level of social capital), and applied a two-stage least squares regression. We found that the mental health of residents who trust and help each other is significantly higher than that of residents without trust and mutual help. When residents’ efforts to maintain social networks increase, their mental health significantly improves. These results are robust. Furthermore, the impact of social capital on mental health was heterogeneous in terms of urbanicity, gender, age, and area. These results are helpful for making policies for promoting residents’ mental health.

“Who should I grant access to my post?”: Identifying the most suitable privacy decisions on online social networks

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jose Alemany ◽  
Elena Del Val ◽  
Ana María García-Fornes
Keyword(s):  
Social Capital ◽  
Social Networks ◽  
Online Social Networks ◽  
Personal Information ◽  
Online Communication ◽  
Cost Benefit ◽  
Content Type ◽  
Capital Building ◽  
The Cost ◽  
The Impact

PurposeOnline social networks (OSNs) provide users with mechanisms such as social circles and individual selection to define the audiences (i.e., privacy policy) of the shared information. This privacy decision-making process is a hard and tedious task for users because they have to assess the cost-benefit in a complex environment. Moreover, little is known about how users assess the cost-benefit of matching the elements of online communication and their interests. Therefore, the purpose of this paper is to develop and test a research model to understand the impact that the types of receivers and the sensitivity of messages have on privacy decisions.Design/methodology/approachA study was conducted to understand how users evaluate the cost-benefit of the disclosure action in online social networks for the different types of receivers identified and the sensitivity of the message. Data from 400 respondents was collected and analyzed using partial least squares modeling.FindingsThe findings of this study demonstrated a trade-off variance between the perceived cost-benefit and the disclosure of sensitive information with different receiver types. Disclosing personal information with trusted receivers, influencer receivers and receivers from the circle of coworkers had a positive significant effect on social capital building. Conversely, disclosing personal information with receivers from the circle of family or unknown receivers had a significant negative effect on social capital building and even a significant positive effect on privacy concerns.Originality/valueRecent literature has documented the increasing interest of the research community in understanding users' concerns and interests in making the most suitable privacy decisions. However, most researchers have worked on understanding the disclosure action from a user-centered perspective and have not considered all of the elements of online communication. This study puts the focus on all of the elements of communication during disclosure actions, taking into account the properties of the message and receivers and the impact on users' cost benefit value.

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