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Guo, Zenghua, and Boyu Zhu. "Does Mobile Internet Use Affect the Loneliness of Older Chinese Adults? An Instrumental Variable Quantile Analysis." International Journal of Environmental Research and Public Health 19, no. 9 (May 4, 2022): 5575. http://dx.doi.org/10.3390/ijerph19095575.
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Based on the 2018 China Family Panel Studies (CFPS) data, we empirically analyze the effect, heterogeneity, quantile differences and influencing mechanisms of mobile Internet use on loneliness in the elderly. The study found that the loneliness of the elderly who used mobile Internet was 33.1% lower than that of the elderly who did not use the mobile Internet The study also passed the robustness test. There is heterogeneity in the effect of mobile Internet use on loneliness among the elderly of different ages, educational levels and marital status. Among them, the use of mobile Internet has a significant alleviating effect on the loneliness of the 60–70-year-old elderly, those of junior high school education level and below, and the elderly with a partner. The quantile regression analysis showed that the elderly group with a high level of loneliness benefited more from the use of mobile Internet. Mediation analysis further showed that mobile Internet use can improve parent–child relationship, increase offline interactions with children, and reduce children’s tangible support, which we interpret as a potential mechanism for mobile Internet use to alleviate loneliness in the elderly.
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Zhan, Yunqiu, and Shuwen Yang. "Does internet use improve employment?——Empirical evidence from China." PLOS ONE 19, no. 4 (April 16, 2024): e0301465. http://dx.doi.org/10.1371/journal.pone.0301465.
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Achieving comprehensive and high-quality employment is essential to achieving new levels of people’s well-being. The advancement of Internet technology not only affect the massiveness of employment, but also the quality of that. On the basis of constructing an employment quality evaluation index system, this article uses CLDS (China Labor-force Dynamics Survey) data to explore the impact of Internet use on the employment quality of workers and its underlying mechanisms. The results reveal that Internet use has a significant positive impact on improving the employment quality of workers. As the quantile of employment quality increases, internet use has a greater impact on workers with a lower employment quality quantile. In addition, the use of Internet has a more significant promoting effect on the employment quality of rural and female workers. From the perspective of mechanism, Internet use can increase workers’ social capital and influence their employment quality through the accumulation of social capital. Based on this, countermeasures and suggestions are put forward from the aspects of increasing investment and construction of Internet infrastructure, further perfecting the reform of household registration system, promoting human capital investment and social capital construction.
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Choi, Baek-Young, Sue Moon, Rene Cruz, Zhi-Li Zhang, and Christophe Diot. "Quantile sampling for practical delay monitoring in Internet backbone networks." Computer Networks 51, no. 10 (July 2007): 2701–16. http://dx.doi.org/10.1016/j.comnet.2006.11.023.
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Parwodiwiyono, Suparna. "Kaitan Akses Internet dan Penyelesaian Tugas Sekolah Berdasarkan Regresi Kuantil Menuju Masyarakat 5.0." JARTIKA Jurnal Riset Teknologi dan Inovasi Pendidikan 3, no. 2 (July 10, 2020): 342–50. http://dx.doi.org/10.36765/jartika.v3i2.283.
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Abstrak: Bagi generasi pasca milenial penggunaan internet sangat akrab tetapi dengan berbagai tujuan penggunaan. Penelitian ini ingin melihat keterkaitan penggunaan internet oleh penduduk yang sedang sekolah untuk kepentingan penyelesaian tugas sekolah di Indonesia untuk mendapatkan hasil belajar yang baik. Analisis berdasarkan data sekunder dari Survei Sosial Ekonomi Nasional tahun 2018. Hanya saja data yang didapatkan tidak simetris dengan adanya pencilan. Regresi kuantil digunakan untuk meminimumkan pengaruh dari pencilan yang ada. Penelitian mendapatkan hasil bahwa terdapat kaitan yang erat antara akses internet dari penduduk yang sedang sekolah dengan penyelesaian tugas sekolah. Hasil regresi kuantil menunjukkan bahwa proporsi akses internet untuk penyelesaian tugas sekolah berbeda antar golongan proporsi penggunaan internet. Proporsi penggunaan internet yang tinggi akan digunakan untuk penyelesaian tugas sekolah yang lebih tinggi pula.
Abstract: For the post millennial generation the use of the internet is very familiar but with various purposes of use. This study wants to look at the relationship between the use of the internet by residents who are currently in school for the sake of completing school work in Indonesia to get good learning outcomes. Analysis based on secondary data from the 2018 National Socio-Economic Survey. It's just that the data obtained is not symmetrical with outliers. Quantile regression is used to minimize the effect of outliers. The study found that there was a close relationship between internet access from residents who were in school and completion of school work. The quantile regression results show that the proportion of internet access for completing school work differs between groups of proportions of internet use. A high proportion of internet use will be used for completing higher school work.
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Long, Cuihong, Jiajun Han, and Chengzhi Yi. "Does the Effect of Internet Use on Chinese Citizens’ Psychological Well-Being Differ Based on Their Hukou Category?" International Journal of Environmental Research and Public Health 17, no. 18 (September 14, 2020): 6680. http://dx.doi.org/10.3390/ijerph17186680.
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This paper draws support from the 2018 wave of the China Family Panel Studies (CFPS 2018) and uses unconditional quantile regression, re-centered influence function (RIF) decomposition, linear structural equation modelling, extended regression modelling and censored regression to explore the heterogeneity of the impact of Internet use on the psychological well-being of Chinese non-agricultural and agricultural hukou holders. We find that Internet use better improves the psychological well-being of non-agricultural hukou holders, thereby widening the gap in psychological well-being between urban and rural residents in China. Through RIF decomposition, we observe that, except for the 10th quantile, the expansion effect of Internet use on the inequality in psychological well-being between agricultural and non-agricultural hukou holders is mainly reflected in the structure effect, which shows that compared to non-agricultural hukou holders, the return rate of Internet use on the psychological well-being of agricultural hukou holders is lower. Further mechanism analysis shows that using the Internet to socialize, obtain information and understand politics is more beneficial for the psychological well-being of non-agricultural hukou holders; moreover, Internet use can further exert different effects on the psychological well-being of the two groups by differently influencing their job satisfaction, government evaluation, and sleep quality. This study also confirms that relying only on external scientific and technological progress has a limited corrective effect on existing inequalities.
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Rolland, G. "Etude des variations de rendement quantique interne d'un détecteur CCD en fonction de la temperature." Revue de Physique Appliquée 20, no. 9 (1985): 651–59. http://dx.doi.org/10.1051/rphysap:01985002009065100.
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Gao, Jiacheng, and Yue Liu. "Internet technology usage and female employment: Evidence from a Chinese general social survey." Economics and Finance Letters 10, no. 1 (February 13, 2023): 57–68. http://dx.doi.org/10.18488/29.v10i1.3287.
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This study empirically analyzes the effect of internet technology usage on female employment and their wage income by constructing logit and quantile regression models to examine the moderating effect of years of education. The results show that internet technology usage significantly suppresses the likelihood of female employment and reduces their wage income before their years of education reach the target requirement but has a differential effect on the wage income of females at different levels. After females have reached their years of education target, internet technology usage significantly increases their likelihood of obtaining employment and improving their wage income. The positive effect of internet technology usage on female employment and their wage income is more obvious as the years of education increases. Therefore, this study proposes that females should be encouraged to strengthen their internet technology usage skills and improve their education level.
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Lohr, Luanne, and Timothy Park. "Demand for Private Marketing Expertise by Organic Farmers: A Quantile Analysis Based on Counts." Journal of Agricultural and Applied Economics 44, no. 2 (May 2012): 157–71. http://dx.doi.org/10.1017/s1074070800000249.
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We study the demand by organic farmers for technical advice using a quantile regression for the demand of organic farmers for consultations with private information providers. There is substantial heterogeneity in the impact of critical explanatory variables on consultations of organic farmer. Larger farm size has a positive effect on contacts, but the effect is absent for the highest number of consultations. Internet use has a positive marginal effect on visits to private information providers across each quantile, suggesting that expanded efforts to deliver programs through web-based resources are a useful investment for information providers.
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Antoine, Jean-Pierre. "Symétries en physique." Revue des questions scientifiques 190, no. 1-2 (January 1, 2019): 7–26. http://dx.doi.org/10.14428/qs.v190i1-2.69423.
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Cet article passe en revue les multiples applications de la théorie des groupes aux problèmes de symétrie en physique. En physique classique, il s’agit surtout de la relativité : euclidienne, galiléenne, einsteinienne (relativité restreinte). Passant à la mécanique quantique, on remarque d’abord que les principes de base impliquent que l’espace des états d’un système quantique a une structure intrinsèque d’espace préhilbertien, que l’on complète ensuite en un espace de Hilbert. Dans ce contexte, la description de l’invariance sous un groupe G se base sur une représentation unitaire de G. On parcourt ensuite les différents domaines d’application : physique atomique et moléculaire, matière condensée, optique quantique, ondelettes, symétries internes, symétries approchées. On discute ensuite l’extension aux théories de jauge, en particulier au Modèle Standard des interactions fondamentales. On conclut par quelques indications sur des développements récents. * * * The present article reviews the multiple applications of group theory to the symmetry problems in physics. In classical physics, this concerns primarily relativity : Euclidean, Galilean, Einsteinian (special). Going over to quantum mechanics, we first note that the basic principles imply that the state space of a quantum system has an intrinsic structure of pre-Hilbert space, that one completes into a genuine Hilbert space. In this framework, the description of the invariance under a group G is based on a unitary representation of G. Next we survey the various domains of application : atomic and molecular physics, condensed matter physics, quantum optics, wavelets, internal symmetries, approximate symmetries. Next we discuss the extension to gauge theories, in particular to the Standard Model of fundamental interactions. We conclude with some remarks about recent developments.
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Rastogi, Krati, and Divya Lohani. "Edge Computing-Based Internet of Things Framework for Indoor Occupancy Estimation." International Journal of Ambient Computing and Intelligence 11, no. 4 (October 2020): 16–37. http://dx.doi.org/10.4018/ijaci.2020100102.
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Indoor occupancy estimation has become an important area of research in the recent past. Information about the number of people entering or leaving a building is useful in estimation of hourly sales, dynamic seat allocation, building climate control, etc. This work proposes a decentralized edge computing-based IoT framework in which the majority of the data analytics is performed on the edge, thus saving a lot of time and network bandwidth. For occupancy estimation, relative humidity and carbon dioxide concentration are used as inputs, and estimation models are developed using multiple linear regression, quantile regression, support vector regression, kernel ridge regression, and artificial neural networks. These estimations are compared using execution speed, power consumption, accuracy, root mean square error, and mean absolute percentage error.
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Miranda-Soberanis, V. F., and T. W. Yee. "New Link Functions for Distribution–Specific Quantile Regression Based on Vector Generalized Linear and Additive Models." Journal of Probability and Statistics 2019 (May 7, 2019): 1–11. http://dx.doi.org/10.1155/2019/3493628.
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In the usual quantile regression setting, the distribution of the response given the explanatory variables is unspecified. In this work, the distribution is specified and we introduce new link functions to directly model specified quantiles of seven 1–parameter continuous distributions. Using the vector generalized linear and additive model (VGLM/VGAM) framework, we transform certain prespecified quantiles to become linear or additive predictors. Our parametric quantile regression approach adopts VGLMs/VGAMs because they can handle multiple linear predictors and encompass many distributions beyond the exponential family. Coupled with the ability to fit smoothers, the underlying strong assumption of the distribution can be relaxed so as to offer a semiparametric–type analysis. By allowing multiple linear and additive predictors simultaneously, the quantile crossing problem can be avoided by enforcing parallelism constraint matrices. This article gives details of a software implementation called the VGAMextra package for R. Both the data and recently developed software used in this paper are freely downloadable from the internet.
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He, Qiang, Xin Deng, Chuan Li, Zhongcheng Yan, and Yanbin Qi. "Do Internet Skills Increase Farmers’ Willingness to Participate in Environmental Governance? Evidence from Rural China." Agriculture 11, no. 12 (November 29, 2021): 1202. http://dx.doi.org/10.3390/agriculture11121202.
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Environmental pollution is threatening the sustainable development of rural areas. Increasing farmers’ willingness to participate in environmental governance (FWPEG) can effectively reduce this threat. Fortunately, the internet can speed up the process. However, it is unclear whether and to what extent the mastery of internet skills will increase FWPEG. This study uses data from 3503 farmers in 30 provinces in mainland China. It uses the TE and IVQTE models to correct selection bias and quantitatively assess the impact of mastery of internet skills on FWPEG. The results show: (1) mastering internet skills can significantly increase FWPEG, and after correcting the endogenous deviation, the marginal benefit of farmers mastering internet skills is 0.124; (2) in the 34–81% quantile range, internet skills show a declining development trend in FWPEG, which is in line with “the law of diminishing marginal utility”, and mastery of the impact of internet skills on FWPEG has “leaping” (33% → 34%)” and “sagging (81% → 82%)” characteristics; (3) compared to that of the east, internet skills in central and western regions have a more significant role in promoting FWPEG. In general, internet skills can effectively increase FWPEG, and the impact will be more pronounced in underdeveloped areas. The influence of internet skills on FWPEG will gradually weaken with the increase of FWPEG. The results of this research help to coordinate the relationship between government environmental governance and rural environmental autonomy and provide some new ideas for realizing global rural revitalization.
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Bryson, Bethany P. "Polarizing the middle: internet exposure and public opinion." International Journal of Sociology and Social Policy 40, no. 1/2 (December 11, 2019): 99–113. http://dx.doi.org/10.1108/ijssp-09-2019-0181.
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Purpose Research on opinion polarization in the USA repeatedly finds more divergence among politically privileged groups: respondents who are college educated, politically interested, party identified or have a liberal/conservative orientation. The purpose of this paper is to examine whether their excluded counterparts can be polarized by exposure to political information on the internet. Design/methodology/approach Quantile regression and visual analysis of raw data from the online and face-to-face samples in the 2012 and 2016 American National Election Studies (n=9,563) assessed the impact of online political information on opinion polarization among ideological moderates, political Independents, respondents without a college degree, and those with low interest in politics. Findings Exposure to online political information during the survey was associated with significant polarizing shifts toward more consistent ideological positions in all four groups. Practical implications Engaging the middle is a social justice issue as much as a matter of political conflict, and evidence suggests that politically excluded groups use the internet to translate their own views into the language of policy opinions and popular (polarized) politics. Recommended policy interventions include information literacy programs. Further research should use experimental models and browser histories. Originality/value Current research on political polarization leaves open the question of whether larger portions of the electorate are available to join the fray. This study shows that excluded publics can be polarized via exposure to online information.
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Chen, Yuyan, Lin Wu, and Zenghua Guo. "Does Mobile Internet Use Affect the Depression of Young Chinese Adults? An Instrumental Variable Quantile Analysis." International Journal of Environmental Research and Public Health 19, no. 8 (April 8, 2022): 4473. http://dx.doi.org/10.3390/ijerph19084473.
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Background: With the advancement of the digital age, the links between mobile Internet use (MIU) and mental health have attracted the attention of scholars. This paper focuses on the relationship between MIU and depression across the entire distribution of young adults’ depression. Methods: Based on nationally representative data from the 2018 China Family Panel Studies (CFPS), we explore whether and to what extent MIU affects depression in young adults in China through instrumental variables. In addition, we employ a quantile regression approach to explore the depression–MIU gradients and examine potential mediation mechanisms by exploiting variation in several potential intermediates available. Results: 2SLS estimate suggests that MIU is associated with a decrease in young adults’ depression by 1.526 points. Besides, the effect of MIU was only significantly negative at the 0.8 to 0.96 quantiles. Discussions: MIU reduces the level of depression in people with higher levels of depression, older age, and who use the Internet for communicative purposes. However, there is no significant gender difference in MIU. In addition, young people will improve their feeling of social comparison when using mobile Internet, thus making them less depressed. Conclusions: MIU has a significant positive impact on depression among young Chinese adults.
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Deh, Amon Aniké. "Système de gouvernance interne et performance des banques de l’Union Économique et Monétaire Ouest-Africaine." Recherches en Sciences de Gestion N° 157, no. 4 (September 28, 2023): 95–120. http://dx.doi.org/10.3917/resg.157.0095.
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Cette recherche vise à analyser l’impact des mécanismes internes de gouvernance sur la performance des banques de l’Union Économique et Monétaire Ouest-Africaine (UEMOA). L’approche méthodologique porte sur un panel dynamique en système (GMM) et une régression quantile. L’échantillon est composé de quatre-vingt-trois banques des huit pays de l’UEMOA sur les périodes de 2012 à 2019. Nos résultats sur la base d’une analyse affinée des banques par pays, montrent une volatilité et une dichotomie de l’influence de certaines variables des mécanismes internes de gouvernance sur la performance de ces banques. D’où l’importance des tests de robustesse afin d’éviter des biais d’analyse.
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Takele, Kasahun, Temesgen Zewotir, and Denise Uwamariya. "Quantile Regression for Identifying the Determinants of Child Malnutrition in Ethiopia." African Journal of Applied Statistics 7, no. 1 (January 1, 2020): 755–76. http://dx.doi.org/10.16929/ajas/2020.755.241.
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Child malnutrition is serious health, socioeconomic and demographic problems in Ethiopia. The objective of this study was to investigate the determinants for childhood malnutrition in Ethiopia. We used the 2016 Ethiopian Demographic and Health Survey data. Quantile regression model with particular emphasis on the 10th , 25th, 50th, 75th and 90th percentiles were used to identify the risk factors of malnutrition at a more comprehensive spectrum. The results show that the child's age, sex, preceding birth interval, birth order, breast feeding, mother's body mass index, use of internet mother's education level, types of toilet and region are all significant factors related with childhood malnutrition in Ethiopia
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Gao, Jiacheng, and Yue Liu. "Has Internet Usage Really Narrowed the Gender Wage Gap?: Evidence from Chinese General Social Survey Data." Human Behavior and Emerging Technologies 2023 (February 2, 2023): 1–15. http://dx.doi.org/10.1155/2023/7580041.
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Based on the data from a sample of 4832 from the Chinese General Social Survey in 2018, this study examines the impact of Internet usage on the wage equation for males and females by using the robust least squares regression method, the threshold regression method, and the quantile regression method and decomposes the gender wage gap on this basis by using the RIF regression decomposition method. The findings show that, firstly, in either linear or nonlinear effects, Internet usage has a significant wage premium effect on the male wage equation, and this premium effect increases as male wage income rises, but Internet usage consistently does not have a significant effect on the female wage equation. Secondly, 26.73% of the overall gender wage gap is due to Internet usage, and the presence of gender discrimination in the cyberspace is a significant contributor to this result. Thirdly, Internet usage causes the gender wage gaps at different wage levels to be further widened to different degrees. Specifically, Internet usage has the strongest effect on the gender wage gap at lower wage levels, the second strongest effect on the gender wage gap at higher wage levels, and the weakest effect on the gender wage gap at middle wage levels. The different levels of gender discrimination experienced by females in the cyberspace are an important cause of the differential widening effect of Internet usage on gender wage gaps at different wage levels. Therefore, this study proposes policy recommendations in terms of regulating gender discrimination and introducing relevant female protection policies.
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Tsoi, Kelvin K. F., Nicholas B. Chan, Karen K. L. Yiu, Simon K. S. Poon, Bryant Lin, and Kendall Ho. "Machine Learning Clustering for Blood Pressure Variability Applied to Systolic Blood Pressure Intervention Trial (SPRINT) and the Hong Kong Community Cohort." Hypertension 76, no. 2 (August 2020): 569–76. http://dx.doi.org/10.1161/hypertensionaha.119.14213.
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Visit-to-visit blood pressure variability (BPV) has been shown to be a predictor of cardiovascular disease. We aimed to classify the BPV levels using different machine learning algorithms. Visit-to-visit blood pressure readings were extracted from the SPRINT study in the United States and eHealth cohort in Hong Kong (HK cohort). Patients were clustered into low, medium, and high BPV levels with the traditional quantile clustering and 5 machine learning algorithms including K-means. Clustering methods were assessed by Stability Index. Similarities were assessed by Davies-Bouldin Index and Silhouette Index. Cox proportional hazard regression models were fitted to compare the risk of myocardial infarction, stroke, and heart failure. A total of 8133 participants had average blood pressure measurement 14.7 times in 3.28 years in SPRINT and 1094 participants who had average blood pressure measurement 165.4 times in 1.37 years in HK cohort. Quantile clustering assigned one-third participants as high BPV level, but machine learning methods only assigned 10% to 27%. Quantile clustering is the most stable method (stability index: 0.982 in the SPRINT and 0.948 in the HK cohort) with some levels of clustering similarities (Davies-Bouldin Index: 0.752 and 0.764, respectively). K-means clustering is the most stable across the machine learning algorithms (stability index: 0.975 and 0.911, respectively) with the lowest clustering similarities (Davies-Bouldin Index: 0.653 and 0.680, respectively). One out of 7 in the population was classified with high BPV level, who showed to have higher risk of stroke and heart failure. Machine learning methods can improve BPV classification for better prediction of cardiovascular diseases.
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Li, Huan, Lanjing Yi, Bo Tang, Hua Lu, and Christian S. Jensen. "Efficient and error-bounded spatiotemporal quantile monitoring in edge computing environments." Proceedings of the VLDB Endowment 15, no. 9 (May 2022): 1753–65. http://dx.doi.org/10.14778/3538598.3538600.
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Underlying many types of data analytics, a spatiotemporal quantile monitoring (SQM) query continuously returns the quantiles of a dataset observed in a spatiotemporal range. In this paper, we study SQM in an Internet of Things (IoT) based edge computing environment, where concurrent SQM queries share the same infrastructure asynchronously. To minimize query latency while providing result accuracy guarantees, we design a processing framework that virtualizes edge-resident data sketches for quantile computing. In the framework, a coordinator edge node manages edge sketches and synchronizes edge sketch processing and query executions. The co-ordinator also controls the processed data fractions of edge sketches, which helps to achieve the optimal latency with error-bounded results for each single query. To support concurrent queries, we employ a grid to decompose queries into subqueries and process them efficiently using shared edge sketches. We also devise a relaxation algorithm to converge to optimal latencies for those subqueries whose result errors are still bounded. We evaluate our proposals using two high-speed streaming datasets in a simulated IoT setting with edge nodes. The results show that our proposals achieve efficient, scalable, and error-bounded SQM.
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Lakshmi, G. S. Devi. "Towards Contrastive and Data Driven Cloud Data Center Energy Prediction using Regression Analysis." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 5492–96. http://dx.doi.org/10.22214/ijraset.2024.61234.
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Abstract: Data centers are the backbone of todays Internet and cloud computing systems. Due to the increasing demand for electrical energy by data centers, it is necessary to account for the vast amount of energy they consume. Energy modeling and prediction of data centers plays a pivotal role in this context. In this study, we address the challenge of predicting energy consumption in cloud data centers, crucial for managing their significant electricity demand. Despite numerous existing methods, there remains a lack of robust methodologies. To fill this gap, we propose a novel approach that incorporates aleatoric uncertainty estimation. Our method utilizes regression distributions to model this uncertainty, with parameters derived from regressive techniques. This yields energy consumption predictions as random variables drawn from these distributions. Additionally, we illustrate how these random variables can be aggregated to form probabilistic forecasts for diverse data center portfolios. Our methodology achieves three key advancements: 1) introducing a simple multiple linear regression model for fundamental series; 2) devising a unique method that combines quantile regression and empirical copulas to estimate joint distribution; 3) enhancing prediction accuracy through a weighted correction technique based on constrained quantile regression.
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Zhang, Hai Liang, and Ming Guo. "On the Identifying of Influencing Factors of E-Commerce Based on the Quantile Regression." Applied Mechanics and Materials 411-414 (September 2013): 2276–79. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.2276.
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In this paper, the semi-annual data such as the number of mobile users, the total amount of social logistics, the total amount of the private placement and financing from 2000 to 2012 in China. The following conclusions are drawn. The mobile internet market, the financial capital market and the logistics service market are main factors influencing the development of e-commerce in China. From a dynamic perspective, these influencing factors would become differentiated. Along with the gradually becoming booming and mature of the e-commerce market, the mobile communication market and the financial capital market play a greater and greater role in promoting the development of e-commerce, while the logistics service market plays a smaller and smaller role in influencing e-commerce.
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Dirir, Sadik Aden. "Performing a Quantile Regression to Explore the Financial Inclusion in Emerging Countries and Lessons African Countries Can Learn from Them." European Journal of Development Studies 2, no. 5 (November 11, 2022): 1–9. http://dx.doi.org/10.24018/ejdevelop.2022.2.5.153.
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Financial inclusion is a concept that promotes the accessibility and admittance of people and small businesses to financial assistance such as credit, banking features, and insurance items. There is a significant poof that adequate financial services have advantageous gains for women, young people, clients, and underprivileged individuals. Efficient and sound expansion of financial inclusion in emerging countries is frequently upheld by adequate strategies, innovative reforms, and favorable regulations that ought to help small firms, poor and marginalized individuals, and empower communities. Various emerging countries are executing reforms to extend financial diffusion. For that reason, this study will explore the factors that promote financial inclusion in emerging countries and the lessons that African countries could learn from them. Thus, 13 emerging countries which are (India, Saudi Arabia, Thailand, Malaysia, the Philippines, Singapore, Indonesia, Nigeria, Mexico, South Africa, Brazil, Russia, and China) from the period of 2005 to 2020 were nominated. Additionally, to determine the elements that influence financial inclusion factors such as bank branches per 100 000 individuals, net income per capita, percentage of individuals using the internet, gross domestic product, total employment, inflation, and population density were selected. A simple OLS and quantile regression model were performed in different percentiles. Furthermore, the findings exposed that variables such as national income per individual, the increase in internet usage, and inflation regulations promote financial inclusion in emerging countries. Whereas, employment displayed a negative effect with the OLS model. However, it presented a positive influence after performing the quantile regression. This implies at a larger scale the employment rate does have a positive impact on the availability of bank branches. Finally, population density presented a neutral effect on the availability of bank branches while the GDP of emerging countries exhibited a negative impact on the availability of bank branches for individuals.
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Volkmer, Sara Alida, Susanne Gaube, Martina Raue, and Eva Lermer. "Troll story: The dark tetrad and online trolling revisited with a glance at humor." PLOS ONE 18, no. 3 (March 10, 2023): e0280271. http://dx.doi.org/10.1371/journal.pone.0280271.
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Internet trolling is considered a negative form of online interaction that can have detrimental effects on people’s well-being. This pre-registered, experimental study had three aims: first, to replicate the association between internet users’ online trolling behavior and the Dark Tetrad of personality (Machiavellianism, narcissism, psychopathy, and sadism) established in prior research; second, to investigate the effect of experiencing social exclusion on people’s motivation to engage in trolling behavior; and third, to explore the link between humor styles and trolling behavior. In this online study, participants were initially assessed on their personality, humor styles, and global trolling behavior. Next, respondents were randomly assigned to a social inclusion or exclusion condition. Thereafter, we measured participants’ immediate trolling motivation. Results drawn from 1,026 German-speaking participants indicate a clear correlation between global trolling and all facets of the Dark Tetrad as well as with aggressive and self-defeating humor styles. However, no significant relationship between experiencing exclusion/inclusion and trolling motivation emerged. Our quantile regression findings suggest that psychopathy and sadism scores have a significant positive effect on immediate trolling motivation after the experimental manipulation, whereas Machiavellianism and narcissism did not explain variation in trolling motivation. Moreover, being socially excluded had generally no effect on immediate trolling motivation, apart from participants with higher immediate trolling motivation, for whom the experience of social exclusion actually reduced trolling motivation. We show that not all facets of the Dark Tetrad are of equal importance for predicting immediate trolling motivation and that research should perhaps focus more on psychopathy and sadism. Moreover, our results emphasize the relevance of quantile regression in personality research and suggest that even psychopathy and sadism may not be suitable predictors for low levels of trolling behavior.
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Hsieh, Lu-Chen, and Ying-Shing Lin. "Inflows and outflows of mutual funds: a performance comparison of funds offered by traditional banks, insurance companies and mutual fund companies." Investment Management and Financial Innovations 15, no. 4 (December 5, 2018): 258–72. http://dx.doi.org/10.21511/imfi.15(4).2018.21.
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The transformations in internet technology and financial innovation have led to the prevalence of direct finance, causing indirect finance to contract and concerns among traditional banks and insurance channel operators to seek transformation to innovate traditional services with advanced technology applications. The research compares the sales revenue flows of traditional banks, insurance companies, and mutual fund institutions, using quantile regression methods with five mutual fund factors: Jensen’s indexes, expenses, risks, sizes, and turnover rates. The sample statistics from 2001 to 2016 were evident, showing the results that sales revenue flows of bank and insurance companies did not decrease when compared to institutional fund investors, but instead, grew substantially, owing to the significant relationship of better technological services and financial innovation by banks and insurance companies. The research contribution is to point out that financial industry should focus, review and strengthen its most competitive core services inside, which are less challenged by outside competitors. By adhering to financial innovation and internet technology, it is still possible for traditional banks and insurance channels to gain substantial market shares with concentration on their core competitive services.
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Luo, Jing. "Short-term Traffic Flow Prediction Method in Bayesian Networks Based on Quantile Regression." Promet - Traffic&Transportation 32, no. 6 (November 11, 2020): 821–35. http://dx.doi.org/10.7307/ptt.v32i6.3394.
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With the popularization of intelligent transportation system and Internet of vehicles, the traffic flow data on the urban road network can be more easily obtained in large quantities. This provides data support for shortterm traffic flow prediction based on real-time data. Of all the challenges and difficulties faced in the research of short-term traffic flow prediction, this paper intends to address two: one is the difficulty of short-term traffic flow prediction caused by spatiotemporal correlation of traffic flow changes between upstream and downstream intersections; the other is the influence of deviation of traffic flow caused by abnormal conditions on short-term traffic flow prediction. This paper proposes a Bayesian network short-term traffic flow prediction method based on quantile regression. By this method the trouble caused by spatiotemporal correlation of traffic flow prediction could be effectively and efficiently solved. At the same time, the prediction of traffic flow change under abnormal conditions has higher accuracy.
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Zaighum, Isma, Ameenullah Aman, Arshian Sharif, and Muhammad Tahir Suleman. "Do energy prices interact with global Islamic stocks? Fresh insights from quantile ARDL approach." Resources Policy 72 (August 2021): 102068. http://dx.doi.org/10.1016/j.resourpol.2021.102068.
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Casinillo, Leomarich F. "Quantile Regression Analysis for Students’ Difficulty Level in Learning Statistics Online." JPI (Jurnal Pendidikan Indonesia) 12, no. 4 (December 23, 2023): 607–14. http://dx.doi.org/10.23887/jpiundiksha.v12i4.66752.
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Statistics learning in the time of distance education makes students uneasy due to the difficulties in the learning environment and processes. This article aims to analyze the level of difficulty faced by students in statistics distance education and captures the causal factors affecting it. Cross-sectional secondary data were used from a current study in literature and described by standard statistical measures. The study utilized the level of difficulty in learning statistics during online setup as the dependent variable in the form of a scale of 1 to 10. The study used standard descriptive measures. Moreover, this study also use quantile regression models were constructed. Results depicted that students were facing "difficulty" as they learn statistics during distance education. The quantile regression revealed that the learning environment, inaccessible places (rural areas), and more family members are the statistically significant factors that influence the difficulty level in learning statistics online. This implies that due to the distractive place of learning, students cannot focus and penetrate their lessons. In addition, students were struggling with communication technology and internet access wherein it is vital in classroom engagement and the acquisition of learning resources. Hence, the study suggests that students must be supported by the government in regard to their learning needs and teachers must promote a positive and interesting educational environment. Furthermore, teachers must undergo training and workshops on how to be equipped in teaching statistics online.
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Drapella, Antoni. "A wrong advice concerning the test F Snedecor." Wiadomości Statystyczne. The Polish Statistician 61, no. 3 (March 28, 2016): 61–67. http://dx.doi.org/10.5604/01.3001.0014.0957.
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Readers of some domestic statistical textbooks and Internet publications related to F test are advised to accomplish the following test scheme: After having sample variances calculated use quotient of greater to smaller of them as the test statistics. Then take 1 quantile of the F distribution as the critical value. This paper identifies this advice to be wrong and gives reason for it: test statistics in question definitely does not follow the F distribution. So, derivation of the proper test statistics named WF as well as the method of calculating WF' s cumulative distribution function is given. Analytical considerations are confirmed by two Monte Carlo experiments. These show that following the advice one makes first type error two times greater than wanted.
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Enache, Andreea, and Jean-Pierre Florens. "IDENTIFICATION AND ESTIMATION IN A THIRD-PRICE AUCTION MODEL." Econometric Theory 36, no. 3 (March 8, 2019): 386–409. http://dx.doi.org/10.1017/s0266466618000440.
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The first novelty of this paper is that we show global identification of the private values distribution in a sealed-bid third-price auction model using a fully nonparametric methodology. The second novelty of the paper comes from the study of the identification and estimation of the model using a quantile approach. We consider an i.i.d. private values environment with risk-averse bidders. In the first place, we consider the case where the risk-aversion parameter is known. We show that the speed of convergence in process of our nonparametric estimator produces at the root-n parametric rate, and we explain the intuition behind this apparently surprising result. Next, we consider that the risk-aversion parameter is unknown, and we locally identify it using exogenous variation in the number of participants. We extend our procedure to the case where we observe only the bids corresponding to the transaction prices, and we generalize the model so as to account for the presence of exogenous variables. The methodological toolbox used to analyse identification of the third-price auction model can be employed in the study of other games of incomplete information. Our results are interesting, also from a policy perspective, as some authors recommend the use of the third-price auction format for certain Internet auctions. Moreover, we contribute to the econometric literature on auctions using a quantile approach.
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Dong, Zhang, and Sana Ullah. "Towards a Green Economy in China? Examining the Impact of the Internet of Things and Environmental Regulation on Green Growth." Sustainability 15, no. 16 (August 18, 2023): 12528. http://dx.doi.org/10.3390/su151612528.
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The idea of green growth stresses the necessity for economic expansion while resolving environmental issues, notably climate change. The Internet of Things (IoT) and environmental regulations have the potential to support green growth. Therefore, this study intends to examine the empirical link between the IoT, environmental regulations, and green growth in China by utilizing the autoregressive distributed lag (ARDL) and quantile autoregressive distributed lag (QARDL) methods to analyze data from 1997 to 2021. Data are obtained from reputable local and international sources like the Organisation for Economic Co-operation and Development (OECD), World Development Indicators (WDI), the Energy Information Administration (EIA), and the National Bureau of Statistics of China. Findings derived from the baseline ARDL model prove that the IoT, environmental regulations, renewable energy consumption, and research and development (R&D) encourage long-run green growth. Likewise, the robust model also highlights that the internet, environmental policy stringency, renewable energy consumption, and R&D help encourage green growth. In the short run, environmental policy stringency and the internet are favorably linked to green growth in the robust model, and renewable energy consumption is favorably linked to green growth in the baselines model; however, environmental regulation is negatively linked to green growth. The findings from the QARDL analysis show that the impact of the IoT on promoting green growth is significant across all quantiles. On the other hand, the effects of environmental regulation are more pronounced at higher levels of green growth. These findings imply that policymakers should try to increase the role of digitalization in society by promoting the IoT and the internet to decouple economic growth and environmental pollution. Moreover, the digitalization policy should be supported by implementing strict environmental laws and regulations.
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Zhang, Wei, Yan-Chun Zhu, Jian-Bo Wen, and Yi-Jie Zhuang. "Growth and Firm Size Distribution." Journal of Electronic Commerce in Organizations 14, no. 2 (April 2016): 61–73. http://dx.doi.org/10.4018/jeco.2016040105.
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Studies on the firm's size distribution (FSD) can set a good foundation to know about the growth path and mechanism of e-commerce firms. The purpose of this paper is to understand features of the China's listed e-commerce firms by testing Gibrat's law and Zipf's law within the Internet sectors. From a macroscopic perspective, with the approach of OLS estimation, Zipf's coefficient of the FSD is calculated to test whether Zipf's law holds. From a microscopic perspective, the relationship between e-commerce firm size and growth is explored by quantile regression method. The results indicate that from 2005 to 2014, Zipf's law cannot be rejected, with the relationship changing over time, Gibrat's law holds partly. It implies that competition status among China's e-commerce firms becomes more stable.
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Wang, Su Huan, and Jian Yin. "Color Filter Based on Image ROI Extraction." Applied Mechanics and Materials 246-247 (December 2012): 1121–24. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.1121.
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With the rapid development of Internet, more and more enterprises establish business sites to achieve the purpose of online transactions. Taking taobao.com for example, hundreds of millions of goods trade on the trading platform. In front of the huge commodity image database, extraction of image features is very convenient for people to find out images of user requirement. This paper focus mainly on the color feature of images. Firstly, we segment ROI of images using grabCut algorithm; secondly, we extract primary color of images by using dominant color descriptor of MPEG 7; Thirdly, we adopt RGB color quantization to quantize the primary color. Finally achieve the purpose of image color navigation. I have done experiment to compare with some other methods, and find that the algorithms I adopted make a better performance.
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Casinillo, Leomarich F., and Emily L. Casinillo. "Analyzing the difficulty level in learning mathematics online using a quantile regression approach." EDUCATUM Journal of Science, Mathematics and Technology 10, no. 2 (December 30, 2023): 38–46. http://dx.doi.org/10.37134/ejsmt.vol10.2.5.2023.
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Online learning is a difficult type of educational process due to its limitations, particularly in college mathematics courses. The essential aim of this article is to elucidate the students' difficulty level in learning mathematics amid online learning and predict its statistically significant influencing factors. The data used in this study is secondary from a paper in the mathematics education literature on the form of cross-sectional. The data were summarized employing some descriptive measures and regression modeling as an inference. The result of the study showed that, on average, students are facing "difficulty" in learning their mathematics lessons during distance learning due to some problems. The quantile regression revealed that younger and female students are experiencing higher levels of difficulty. Plus, students who spent more money on the internet are facing higher difficulty in learning. In addition, a not conducive learning environment and social distractions are predictors of difficulty in learning. Conclusively, the difficulty in learning mathematics which adversely affects their performance is due to the distractions and problems in the learning environment, low coping mechanisms, and unprecedented educational process in the form of online setup. Hence, the study suggested that teachers must be flexible with students and provide digital simulations of mathematical problems, and use interactive models in their classes.
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Sing, Ting Zhao, and Norhashidah Awang. "Determinants of hotel room price in Penang, Malaysia: Evidence from multiple linear regression and quantile regression." International Journal Artificial Intelligent and Informatics 2, no. 2 (April 1, 2022): 79–85. http://dx.doi.org/10.33292/ijarlit.v2i2.37.
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Since Penang, Malaysia was recognized and given World Heritage status by UNESCO in year 2008, the growth in tourist arrivals has been found increasing. More hotels are seen mushrooming to accommodate for the increasing number of hotel guests. Surprisingly, hotel room price in Penang are found to vary largely across different hotels despite the close proximity. This study aims to examine the significant hotel attributes associated with the hotel room price in Penang. The data are about 83 different hotels in Penang and are obtained from an internet hotel reservation website and the attribute, lowest hotel room price is used as the dependent variable. The results of the analysis show that multiple linear regression and quantile regression share common characteristics and differences in some aspects. The empirical results are hoped to serve as a reference for hoteliers to develop effective strategies to maximize hotel revenue.
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Özyılmaz, Ayfer, and Yüksel Bayraktar. "Internal Migrations as a Driving Force of Regional Disintegration: An Empirical Analysis of NUTS-2 Regions in Turkey." Journal of Humanity and Society (insan & toplum) 11, no. 3 (September 15, 2021): 215–48. http://dx.doi.org/10.12658/m0632.
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Internal migrations, which involve population movements within the borders of a country for economic, political or social reasons, is seen as both a cause and a result of regional imbalances. In this framework, the effect increasing internal migrations have on developed and underdeveloped regions may differ through the effect of the different socio-cultural and economic conditions between regions. The aspect of imbalance is directly related to the extent to which migration affects parameters such as wage, production, consumption, human capital levels, entrepreneurial migration, unemployment, and household income in regions with different stages of development. This study analyzes the effect internal migration has on regional imbalances in Turkey’s NUTS-2 regions during 2008-2019 using the bootstrap quantile regression method. According to the analysis findings, internal migration increases growth in all NUTS-2 regions, but this effect is stronger at higher income levels. In this context, as a region’s income levels increase, the effect of net migration on growth also increases. When considering the migration direction to be from low-income regions to high-income regions, internal migration has been found to increase interregional disintegration in Turkey.
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Devi, S. Suguna, and A. Bhuvaneswari. "Quantile Regressive Fish Swarm Optimized Deep Convolutional Neural Learning for Reliable Data Transmission in IoV." International journal of Computer Networks & Communications 13, no. 2 (March 31, 2021): 81–97. http://dx.doi.org/10.5121/ijcnc.2021.13205.
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Route path identification on the Internet of Vehicles (IoV) is complicated due to the nature of high dynamic mobility, bandwidth constraints, and traffic load. A vehicle present on the IoV communicates with each other to find the status of the road and location of other vehicles for reliable data transmission. However, the existing routing algorithm does not effectively improve the packet delivery ratio and reduce the delay. To resolve these issues, A Quantile Regressive Fish Swarm Optimized Deep Convolutional Neural Learning (QRFSODCNL) technique is introduced reliable data transmission with minimum end to end delay in IoV. The Do Convolutional Neural Learning uses multiple layers such as one input layer, three hidden layers, and one output layer for vehicle location identification and optimal route path discovery. The different node characteristics of vehicle nodes are analyzed in the hidden layers using the quantile regression function. Depends on the regression analysis, the neighbouring node is identified with minimal time. To improve the throughput and reduce the packet loss rate, the artificial fish swarm optimization technique is applied to choose the best route among the population based on the fitness function. Simulation is carried out to analyze the performance of QRFSODCNL technique and existing methods with different metrics such as packet delivery ratio, packet loss rate, average end to end delay, and throughput. The discussed outcome proves that the QRFSODCNL technique outperforms well as compared to the stateof-the-art methods.
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Khakiki, Diana Noviani, and Mohammad Faizal Amir. "DAPIC Problem-solving Process Towards Elementary Students’ Statistical Literacy." JPI (Jurnal Pendidikan Indonesia) 12, no. 4 (December 23, 2023): 722–31. http://dx.doi.org/10.23887/jpiundiksha.v12i4.67310.
Full textAbstract:
Statistics learning in the time of distance education makes students uneasy due to the difficulties in the learning environment and processes. This article aims to analyze the level of difficulty faced by students in statistics distance education and captures the causal factors affecting it. Cross-sectional secondary data were used from a current study in literature and described by standard statistical measures. The study utilized the level of difficulty in learning statistics during online setup as the dependent variable in the form of a scale of 1 to 10. The study used standard descriptive measures. Moreover, this study also use quantile regression models were constructed. Results depicted that students were facing "difficulty" as they learn statistics during distance education. The quantile regression revealed that the learning environment, inaccessible places (rural areas), and more family members are the statistically significant factors that influence the difficulty level in learning statistics online. This implies that due to the distractive place of learning, students cannot focus and penetrate their lessons. In addition, students were struggling with communication technology and internet access wherein it is vital in classroom engagement and the acquisition of learning resources. Hence, the study suggests that students must be supported by the government in regard to their learning needs and teachers must promote a positive and interesting educational environment. Furthermore, teachers must undergo training and workshops on how to be equipped in teaching statistics online.
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Liu, Huimeng, Yutong Wang, Binyan Zhang, Yating Huo, Suixia Cao, Jingchun Liu, Lingxia Zeng, Hong Yan, Shaonong Dang, and Baibing Mi. "Preference for Stronger Taste Associated with a Higher Risk of Hypertension: Evidence from a Cross-Sectional Study in Northwest China." International Journal of Hypertension 2022 (November 22, 2022): 1–11. http://dx.doi.org/10.1155/2022/6055940.
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Background. Dietary modulation is a primary lifestyle approach for reducing the risk of hypertension. However, evidence of the potential role that a dietary taste preference plays in the risk of hypertension remains limited. Methods. A cross-sectional analysis was conducted based on the Shaanxi baseline survey of the Regional Ethnic Cohort Study. We used self-reported salt consumption and intensity preferences for sourness and spiciness to calculate the taste preference score, which was categorized into bland, moderate, and strong. A generalized linear mixed model and quantile regression were performed to estimate associations between taste preferences and hypertension/blood pressure. Results. Among 27,233 adults, 72.2% preferred a moderate taste and 21.4% preferred a strong taste. Compared with a bland taste, a stronger taste preference might be associated with a higher risk of hypertension (adjusted OR for a moderate taste = 1.25, 95% CI: 1.06, 1.49; adjusted OR for a strong taste = 1.41, 95% CI: 1.15, 1.71; Ptrend = 0.002), especially in females (adjusted OR for a moderate taste = 1.43, 95% CI: 1.24, 1.66; adjusted OR for a strong taste = 1.55, 95% CI: 1.32, 1.83; P t r e n d < 0.001 ). Quantile regression showed that the taste preference was positively associated with diastolic blood pressure (DBP) (P5-P80) in females, with an average increase of 3.31 mmHg for a strong taste (β = 3.31, P < 0.001 ) and 1.77 mmHg for a moderate taste (β = 1.77, P = 0.008). Conclusions. A preference for stronger multitastes of salty, sour, and spicy might be associated with a higher risk of hypertension, especially in females. This relationship possibly occurs through increasing DBP. Dietary modulation with the promotion of a bland taste is encouraged.
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Li, Li, Qifa Song, and Xi Yang. "K-means clustering of overweight and obese population using quantile-transformed metabolic data." Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy Volume 12 (August 2019): 1573–82. http://dx.doi.org/10.2147/dmso.s206640.
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Awan, Ashar, Kashif Raza Abbasi, Soumen Rej, Arunava Bandyopadhyay, and Kangjuan Lv. "The impact of renewable energy, internet use and foreign direct investment on carbon dioxide emissions: A method of moments quantile analysis." Renewable Energy 189 (April 2022): 454–66. http://dx.doi.org/10.1016/j.renene.2022.03.017.
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Byaro, Mwoya, Anicet Rwezaula, and Nicholaus Ngowi. "Does internet use and adoption matter for better health outcomes in sub-Saharan African countries? New evidence from panel quantile regression." Technological Forecasting and Social Change 191 (June 2023): 122445. http://dx.doi.org/10.1016/j.techfore.2023.122445.
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Raptou, Elena, and Georgios Papastefanou. "Analyzing the Influence of Wine and Beer Drinking, Smoking, and Leisure Time Screen Viewing Activity on Body Weight: A Cross-Sectional Study in Germany." Nutrients 13, no. 10 (October 11, 2021): 3553. http://dx.doi.org/10.3390/nu13103553.
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The increasing global prevalence of overweight and obesity highlights an urgent need to explore modifiable obesogenic factors. This study investigated the impact of lifestyle factors, such as beer and wine drinking, cigarette smoking, and leisure time screen viewing activities, on body weight and the development of obesity. Individual level data were selected from a random sample of 3471 German adults using a two-stage disproportionate random sampling procedure. The empirical analysis employed a two-stage equations system and combined the endogenous treatment effects model with the quantile regression technique. Our estimations showed that the decisions to smoke and consume wine and beer were positively interrelated, especially in women. Frequent beer/wine drinkers of normal weight were found to have a lower BMI in the male subsample. Quantile regression estimates indicated a significant influence of smoking on BMI in both genders, with smokers’ BMI following an upward trend, especially in the upper quantiles of the distribution. Leisure time screen activity was found to have a major impact on females’ BMI. Prolonged television viewing and regular computer gaming had a strong relationship with weight increase in overweight women, whereas internet surfing was inversely correlated with the BMI of normal weight and slightly overweight female participants. Nutrition and health policies should direct individuals toward alternative recreational activities in order to substitute screen usage and reduce sedentary time. This study also raised doubts about the general belief that smokers have a lower body weight. As unhealthy behaviors usually co-occur or cluster together, obesity prevention interventions might also contribute to a decrease in smoking.
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Dudás, Gábor, Tamás Kovalcsik, György Vida, Lajos Boros, and Gyula Nagy. "Price determinants of Airbnb listing prices in Lake Balaton Touristic Region, Hungary." European Journal of Tourism Research 24 (May 7, 2020): 2410. http://dx.doi.org/10.54055/ejtr.v24i.412.
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The aim of the paper was to investigate the impact of different accommodation attributes on Airbnb listing prices in a touristic area. The study applied hedonic price modeling utilizing a sample of 2417 Airbnb accommodation rental offers in the Lake Balaton Touristic Region in Hungary. Our results revealed that property-related attributes significantly influence Airbnb prices although the magnitude of these effects is very diverse and complex. The OLS findings showed that the provision of air conditioning, free internet, and free parking are the main determinants of Airbnb price in the sample area, while the number of available photos and the presence of a kitchen does not significantly influence the price. The quantile regression results further demonstrated that capacity, the provision of breakfast, and TV leads to higher prices among the higher-priced accommodations, while the number of bedrooms and bathrooms, smoking, and free parking influence more the prices of lower-end accommodations.
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Cox, Joe, Eun Young Oh, Brooke Simmons, Gary Graham, Anita Greenhill, Chris Lintott, Karen Masters, and Jamie Woodcock. "Doing Good Online: The Changing Relationships Between Motivations, Activity, and Retention Among Online Volunteers." Nonprofit and Voluntary Sector Quarterly 47, no. 5 (July 5, 2018): 1031–56. http://dx.doi.org/10.1177/0899764018783066.
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Advances in Internet technology are making it possible for individuals to volunteer online and participate in research-based activities of nonprofit organizations. Using survey data from a representative sample of such contributors, this study investigates their motivations to volunteer for five online volunteering projects using the Volunteer Functions Inventory. We explore relationships between these six categories of motivation and actual recorded measures of both volunteer activity and retention. We also use quantile regression analysis to investigate the extent to which these motivations change at different stages in the volunteer process. Our results show that volunteers’ activity and retention tend to associate significantly and positively with the motivations of understanding and values, as well as significantly and negatively with the social and career motivations. We also find the importance of motivations changes significantly across the stages of volunteer engagement. In some cases, especially the understanding motivation, the changes observed for activity and retention are markedly different.
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Işık, Cem, Umit Bulut, Serdar Ongan, Hasibul Islam, and Muhammad Irfan. "Exploring how economic growth, renewable energy, internet usage, and mineral rents influence CO2 emissions: A panel quantile regression analysis for 27 OECD countries." Resources Policy 92 (May 2024): 105025. http://dx.doi.org/10.1016/j.resourpol.2024.105025.
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Gherghina, Ștefan Cristian, Mihai Alexandru Botezatu, and Liliana Nicoleta Simionescu. "Exploring the Impact of Electronic Commerce on Employment Rate: Panel Data Evidence from European Union Countries." Journal of Theoretical and Applied Electronic Commerce Research 16, no. 7 (November 18, 2021): 3157–83. http://dx.doi.org/10.3390/jtaer16070172.
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The objective of this study is to explore the impact of electronic commerce on employment rate for a sample covering the whole 27 Member States of the European Union (EU-27), from 2010 to 2019. Moreover, this research explores the clusters of nations with reference to electronic commerce adoption and employment rate dynamics. The outcomes of cluster analysis show that Western Europe reveals the most developed e-commerce marketplace in EU-27, shown by Internet accessibility and high penetration rate of digital tools, and the lowest figures are registered in the Eastern part of Europe. Furthermore, the empirical findings of the panel data fixed-effects and the generalized least squares regressions suggest that electronic commerce influences employment rate positively. By including country-level control variables (real GDP growth rate, research and development expenditure, employed ICT specialists, enterprises with Internet access), the outcomes reveal that one percentage change in enterprises’ total turnover from e-commerce sales, enterprises’ turnover from web sales, and enterprises with e-commerce sales of at least 1% turnover will increase employment rate by 0.205, 0.258, and 0.350 percentage points. Furthermore, the econometric evidence from the method of moments quantile regression models with fixed effects reinforces our findings. Enterprises’ total turnover from e-commerce sales and the percentage of enterprises with e-commerce sales of at least 1% turnover positively influence employment rate for all quintiles, but in the case of enterprises’ turnover from web sales, the effect is positive only for the quintiles ranging from 0.5–0.8.
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Makowsky, Mark J., Shahnaz Davachi, and Charlotte A. Jones. "eHealth Literacy in a Sample of South Asian Adults in Edmonton, Alberta, Canada: Subanalysis of a 2014 Community-Based Survey." JMIR Formative Research 6, no. 3 (March 30, 2022): e29955. http://dx.doi.org/10.2196/29955.
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Background Digital health interventions are efficient and flexible methods for enhancing the prevention and management of cardiovascular disease and type 2 diabetes. However, little is known about the characteristics associated with eHealth literacy in the Canadian South Asian population. Objective The aim of this study is to describe perceived eHealth literacy and explore the extent to which it is associated with sociodemographic, health status, and technology use variables in a subset of South Asian Canadians. Methods We analyzed data from the e-Patient Project survey, a mixed-mode cross-sectional survey that occurred in 2014. The eHealth Literacy Scale (eHEALS) was used to measure eHealth literacy in a convenience sample of 511 English- or Punjabi-speaking South Asian adults recruited from a community pharmacy, a family physician office, and community events in Edmonton, Alberta. Multivariable quantile regression was used to explore variables associated with eHealth literacy. Results The analysis was restricted to 301 internet users (mean age 39.9, SD 14.8 years; 166/301, 55.1% female) who provided responses to all 8 eHEALS questions and complete demographic information. The mean overall eHEALS score was 29.3 (SD 6.8) out of 40, and 71.4% (215/301) agreed to at least 5 out of the 8 eHEALS items. The eHEALS item with the lowest level of agreement was “I can tell high-quality health resources from low-quality health resources on the internet” (182/301, 60.5%). Although there were statistically significant differences in eHEALS scores according to age, educational achievement, language preference, and the presence of chronic medical conditions, multivariable regression analysis indicated that language preference was the only variable independently associated with eHealth literacy (coefficient –6.0, 95% CI –9.61 to –2.39). Conclusions In our sample of South Asian Canadian internet users, preference for written health information in languages other than English was associated with lower eHealth literacy. Opportunities exist to improve eHealth literacy using culturally and linguistically tailored interventions.
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Kuzmenko, O., A. Boyko, and T. Dotsenko. "RISK OF LEGALIZATION OF FUNDS BY BANK CLIENTS FROM GAMBLING CONDUCTED ON THE INTERNET: APPROACHES TO MEASUREMENT." Vìsnik Sumsʹkogo deržavnogo unìversitetu 2022, no. 3 (2022): 31–41. http://dx.doi.org/10.21272/1817-9215.2022.3-3.
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Today's fraudsters try to use modern financial services and products provided by banking institutions to legalize criminal proceeds and finance terrorism. Moreover, money laundering through banks using operations related to gambling occupies a significant place and has already turned into a serious financial problem. Currently, Internet gambling is a multi-billion dollar, extensive, widely used industry; specific economic activity, which includes illegal ways of exchanging virtual assets for real money, which causes an imbalance in economic processes. The most acute problems with money laundering arise in the direction of activity on the Internet, and especially through gambling, games and sports totalizers. The purpose of this study is to identify money laundering schemes by bank clients through participation in game and sports totalizers, as well as to determine approaches to assessment, modeling aspects of the risk of legalization of funds from online gambling. Theoretical research methods, such as: abstraction, synthesis, grouping, were used to conduct research and obtain results. and empirical methods, namely: observation and description. In the course of the study, the existing schemes of money laundering through participation in Internet gaming and sports totalizers were highlighted. They point out that the existing regulatory measures regarding online gambling are mainly based on a passive policy of dealing with already received negative consequences. Global approaches to modeling, evaluation, and forecasting of certain aspects of gambling have been identified, which partially help in identifying and assessing the risk of laundering illegal funds: longitudinal modeling; a model for assessing the effectiveness of gaming companies in preventing fraud and money laundering, including on the Internet; a model of national and supranational risk assessment of the financial and non-financial sectors from the point of view of the threat of money laundering; a quantile regression model of in-game bets on a large online gambling data set to detect money laundering; a model for predicting the behavior of Internet players with the establishment of restrictions using machine learning algorithms using account data to identify the risk of legalization of illegal funds. A clear understanding of the types, ways, schemes of threats that can contribute to the laundering of illegal funds, as a result, will provide a practical opportunity for financial institutions to form automatic notifications about suspicious financial transactions, predict and control potential risks, for more efficient organization of their functioning and conducting financial transactions. The results of the conducted research will help, among other things, the state regulatory bodies to make certain changes to the existing state policy of combating the laundering of criminal funds and the financing of terrorism.
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Dolja-Gore, Xenia, Melissa L. Harris, Hal Kendig, and Julie E. Byles. "Factors associated with length of stay in hospital for men and women aged 85 and over: A quantile regression approach." European Journal of Internal Medicine 63 (May 2019): 46–55. http://dx.doi.org/10.1016/j.ejim.2019.02.011.
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Parsons, Brandon. "Political Polarization and Internal Conflict: A Cross-National Analysis Using Popular Support and Government Cohesion as Proxies." International Journal of Economics and Financial Issues 14, no. 1 (January 20, 2024): 15–27. http://dx.doi.org/10.32479/ijefi.15368.
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The study uses proxies of political polarization, popular support of the government, and government cohesion, to examine its role in explaining internal conflict and the specific types of political violence and civil disorder. The study uses panel data from 135 countries from 1990 to 2021. The research uses two econometric models, Quantile via Moments, to examine the effects of popular support and government cohesion across the distribution of internal conflict, and two-way fixed effects with Driscoll and Kraay standard errors. The two models account for heteroscedasticity, cross-sectional dependence, and autocorrelation. The study finds popular support of the government and its cohesion robustly explain internal conflict and its specific forms of political violence and civil disorder. The research also finds political polarization that reduces popular support and government cohesion influences internal conflict regardless of regime type. The variables are significant from autocratic to democratic, suggesting political polarization that reduces popular support and government cohesion can negatively affect internal conflict levels irrespective of regime. Finally, the study finds the internet has a minor mitigating impact on internal conflict, while its interaction with popular support and government cohesion slightly exacerbates internal conflict.