scholarly journals Network Position and Crowdsourcing Innovation Contribution Behavior: The Moderating Role of Knowledge Absorption Capacity

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Qingliang Meng ◽  
Yi Hang ◽  
Xiaojun Chen
Keyword(s):  
Negative Binomial ◽  
Positive Impact ◽  
Knowledge Exchange ◽  
Negative Binomial Regression ◽  
Absorption Capacity ◽  
Negative Binomial Regression Model ◽  
Network Position ◽  
The Individual ◽  
The Relationship ◽  
Knowledge Absorption

Purpose. The purpose of this study is to examine the relationship between network position and crowdsourcing innovation contribution behavior and the moderating effects of knowledge absorption capacity on the aforementioned relationship. Design/Methodology/Approach. Focusing on the Chinese context, the study conducts empirical research with the user’s knowledge-sharing network of the MIUI community to test the research model. The negative binomial regression model which is suitable for processing discrete data is used to examine the main effects of the network position, knowledge absorption capacity, and crowdsourcing innovation contribution behavior. Findings. The findings reveal that the closer the user gets to the center of the network, the more likely they will contribute. The users’ knowledge absorption capacity can help stimulate the users’ crowdsourcing innovation contribution behavior, and the users with stronger knowledge absorption capacity are more likely to transform their network position advantages into innovative contribution behaviors. Practical Implications. The study provides evidence that network position has a positive impact on their crowdsourcing innovation contribution behavior, and knowledge absorption capacity promotes the crowdsourcing innovation behavior of users. Managers should encourage users to occupy a favorable network position and increase knowledge exchange with other users, while at the same time continuously improving their own knowledge absorption capacity. Originality/Value. This study combines social network theory and the individual mindset to introduce knowledge absorptive capacity into the relationship model of the user’s network position and crowdsourcing innovation contribution behavior, thereby constructing a complete path of “knowledge supply-knowledge acquisition-knowledge application-knowledge output.” The study contributes to provide a theoretical basis for an in-depth understanding of the influence relationship between network position and crowdsourcing innovation contribution behavior. Also, it provides a reference for enterprises to carry out practical crowdsourcing innovation community governance and improve innovation performance.

scholarly journals The relationship between fleas and small mammals in households of the Western Yunnan Province, China

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jia-Xiang Yin ◽  
Xiao-Ou Cheng ◽  
Yun-Yan Luo ◽  
Qiu-Fang Zhao ◽  
Zhao-Fei Wei ◽  
...  
Keyword(s):  
Small Mammals ◽  
Host Species ◽  
Yunnan Province ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Strong Relationship ◽  
Negative Binomial Regression Model ◽  
Small Male ◽  
Western Yunnan ◽  
The Relationship

Abstract The Yunnan province has one of the most serious outbreaks of the plague epidemic in China. Small mammals and fleas are risk factors for the occurrence of plague in commensal plague foci. Understanding the relationship between fleas and small mammals will help control fleas and prevent the onset of the plague. Four hundred and twenty-one small mammals, belonging to 9 species, were captured. Of these, 170 small mammals (40.4%) were found infested with fleas. A total of 992 parasitic fleas (including 5 species) were collected. The number of Leptopsylla segnis and Xenopsylla cheopis accounted for 91.03% (903/992). The final multiple hurdle negative binomial regression model showed that when compared with Rattus tanezumi, the probability of flea infestation with Mus musculus as well as other host species decreased by 58% and 99%, respectively, while the number of flea infestations of the other host species increased by 4.71 folds. The probability of flea prevalence in adult hosts increased by 74%, while the number of fleas decreased by 76%. The number of flea infestations in small male mammals increased by 62%. The number of fleas in small mammals weighing more than 59 g has been multiplied by about 4. R. tanezumi is the predominant species in households in the west Yunnan province, while L.segnis and X. cheopis were dominant parasitic fleas. There is a strong relationship between the abundance of fleas and the characteristics of small mammals (e.g. Species, age, sex, and body weight).

scholarly journals Geographically Weighted Negative Binomial Regression Model Predicts Wildfire Occurrence in the Great Xing’an Mountains Better Than Negative Binomial Model

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 377 ◽  
Author(s):  
Zhangwen Su ◽  
Haiqing Hu ◽  
Mulualem Tigabu ◽  
Guangyu Wang ◽  
Aicong Zeng ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Stable Model ◽  
Wildfire Occurrence ◽  
Great Xing’An Mountains ◽  
Binomial Regression ◽  
The Impact ◽  
The Relationship ◽  
Better Than

Wildfire is a major disturbance that affects large area globally every year. Thus, a better prediction of the likelihood of wildfire occurrence is essential to develop appropriate fire prevention measures. We applied a global negative Binomial (NB) and a geographically weighted negative Binomial regression (GWNBR) models to determine the relationship between wildfire occurrence and its drivers factors in the boreal forests of the Great Xing’an Mountains, northeast China. Using geo-weighted techniques to consider the geospatial information of meteorological, topographic, vegetation type and human factors, we aimed to verify whether the performance of the NB model can be improved. Our results confirmed that the model fitting and predictions of GWNBR model were better than the global NB model, produced more precise and stable model parameter estimation, yielded a more realistic spatial distribution of model predictions, and provided the detection of the impact hotpots of these predictor variables. We found slope, vegetation cover, average precipitation, average temperature, and average relative humidity as important predictors of wildfire occurrence in the Great Xing’an Mountains. Thus, spatially differing relations improves the explanatory power of the global NB model, which does not explain sufficiently the relationship between wildfire occurrence and its drivers. Thus, the GWNBR model can complement the global NB model in overcoming the issue of nonstationary variables, thereby enabling a better prediction of the occurrence of wildfires in large geographical areas and improving management practices of wildfire.

Negative Binomial regression model for analysis of the relationship between hospitalization and air pollution

2018 ◽  
Vol 9 (2) ◽  
pp. 333-341 ◽  
Author(s):  
Leda G. Ardiles ◽  
Yara S. Tadano ◽  
Silvano Costa ◽  
Viviana Urbina ◽  
Maurício N. Capucim ◽  
...  
Keyword(s):  
Air Pollution ◽  
Regression Model ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Binomial Regression ◽  
The Relationship

scholarly journals Why Students Have Conflicts in Peer Assessment? An Empirical Study of an Online Peer Assessment Community

2019 ◽  
Vol 11 (23) ◽  
pp. 6807 ◽  
Author(s):  
Yanqing Wang ◽  
Zheng Zong
Keyword(s):  
Learning Environments ◽  
Fixed Effects ◽  
Negative Binomial ◽  
Positive Impact ◽  
Peer Assessment ◽  
Negative Binomial Regression ◽  
Task Conflict ◽  
Negative Binomial Regression Model ◽  
Learning Groups ◽  
Learning Platform

This study highlights the issues in the process of peer assessment in an online environment. As an interactive learning platform, peer assessment will likely lead to conflicts among students, which will hinder the sustainability of peer assessment learning environments. It is still unclear about the particular factors that influence and cause the behavioral conflicts which arise within learning groups and learning environments. To overcome this issue, the current study explores why peer assessment could trigger conflict over a student’s task. The results of a negative binomial regression model with user fixed effects indicate that student’s knowledge self-efficacy, cognitive diversity of general knowledge, and network density have a positive impact on task conflict. Interactive experience and cognition diversity of specific knowledge are not powerful motivations for task conflict in the peer assessment. The findings of this study may be helpful for educators in understanding why students have task conflict in a specific learning environment.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

Evaluation of Strategies to Mitigate Culvert-Involved Crashes

2021 ◽  
pp. 036119812199207
Author(s):  
Hitesh Chawla ◽  
Megat-Usamah Megat-Johari ◽  
Peter T. Savolainen ◽  
Christopher M. Day
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
Negative Binomial Regression Model ◽  
Promising Alternative ◽  
Roadside Safety ◽  
Adverse Conditions ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Binomial Regression

The objectives of this study were to assess the in-service safety performance of roadside culverts and evaluate the potential impacts of installing various safety treatments to mitigate the severity of culvert-involved crashes. Such crashes were identified using standard fields on police crash report forms, as well as through a review of pertinent keywords from the narrative section of these forms. These crashes were then linked to the nearest cross-drainage culvert, which was associated with the nearest road segment. A negative binomial regression model was then estimated to discern how the risk of culvert-involved crashes varied as a function of annual average daily traffic, speed limit, number of travel lanes, and culvert size and offset. The second stage of the analysis involved the use of the Roadside Safety Analysis Program to estimate the expected crash costs associated with various design contexts. A series of scenarios were evaluated, culminating in guidance as to the most cost-effective treatments for different combinations of roadway geometric and traffic characteristics. The results of this study provide an empirical model that can be used to predict the risk of culvert-involved crashes under various scenarios. The findings also suggest that the installation of safety grates on culvert openings provides a promising alternative for most of the cases where the culvert is located within the clear zone. In general, a guardrail is recommended when adverse conditions are present or when other treatments are not feasible at a specific location.

scholarly journals Public or Private Religiosity: Which Is Protective for Adolescent Substance Use and by What Pathways?

2016 ◽  
Vol 49 (2) ◽  
pp. 228-253 ◽  
Author(s):  
Christopher P. Salas-Wright ◽  
Michael G. Vaughn ◽  
Brandy R. Maynard ◽  
Trenette T. Clark ◽  
Susanna Snyder
Keyword(s):  
Substance Use ◽  
Sensation Seeking ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
National Sample ◽  
Adolescent Substance Use ◽  
Public And Private ◽  
Adolescent Substance ◽  
The Relationship ◽  
Private Religiosity

While it is well understood that adolescent religiosity is associated with the use and abuse of licit and illicit substances, few studies have revealed the pathways through which religiosity buffers youth against involvement in such behavior. The aim of this study is to examine the complexity of the relationships between religiosity, sensation seeking, injunctive norms, and adolescent substance use. Using a national sample of adolescents ( N = 18,614), negative binomial regression and path analysis were used to examine the various components of the relationship between religiosity and the use of cigarettes, alcohol, and marijuana. Results indicate that private religiosity moderates the relationship between key risk factors and substance use. Public and private religiosity were associated with tolerant injunctive substance use norms which, in turn, were associated with substance use. Implications for research and theory related to religiosity and adolescent substance use are discussed.

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