scholarly journals Social and Cognitive Aspects of Women Entrepreneurs: Evidence from India

2020 ◽  
Vol 45 (4) ◽  
pp. 223-239
Author(s):  
Mohd Yasir Arafat ◽  
Javed Ali ◽  
Amit Kumar Dwivedi ◽  
Imran Saleem
Keyword(s):  
Social Capital ◽  
Social Networks ◽  
Risk Perception ◽  
Adult Population ◽  
Predictive Ability ◽  
Binary Logistic Regression ◽  
Executive Summary ◽  
Data Set ◽  
Women Entrepreneurship ◽  
Factors Affecting

Executive Summary In the present era, the role of women entrepreneurship has been recognized in the process of economic development worldwide; hence, it must be promoted. Before designing any policy intervention to boost women entrepreneurship, it is important to understand the factors driving women to become entrepreneurs. The previous research on women entrepreneurship was preoccupied with performance of businesses run by women. This research aimed at answering the question: ‘What motivates or discourages the women of a society or an economy from becoming an entrepreneur?’ More specifically, this research investigates factors affecting the entrepreneurial propensity of Indian women through the lenses of cognitive and social capital perspectives. The present study is steered to enhance the understanding of women entrepreneurship at a niche level. Scholars have tried to explain factors affecting women entrepreneurship using myriad of approaches. However, these approaches have been criticized on methodological, conceptual and predictive ability weaknesses. Recently, cognitive and social capital perspectives have gained currency in explaining entrepreneurship. The purpose of this study was to examine the influence of cognitive factors—opportunity perception (Hypothesis 1), risk perception (Hypothesis 2) and perceived capabilities (Hypothesis 3)—and social capital factors—social networks (Hypothesis4) and informal investment (Hypothesis 5)—on women’s entrepreneurial propensity in India, a developing country. A data set of Global Entrepreneurship Monitor Adult Population Survey including a sample of 1305 Indians was used and binary logistic regression technique was employed to analyse the data. The finding shows that the entrepreneurial opportunities have no significant influence on women entrepreneurship; risk perception discourages women from becoming entrepreneurs, and perceived capabilities influence the decision of women to engage in entrepreneurship; social network motivates women to be entrepreneurial, and being an informal investor encourages them to start their venture. Surprisingly, we do not find support for opportunity perception. Therefore, policymakers should pay more attention to these factors of perception and social networks so that, the propensity of a woman to become entrepreneur would be increased.

scholarly journals Economic violence against women: A case in Turkey

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248630
Author(s):  
Ömer Alkan ◽  
Şenay Özar ◽  
Şeyda Ünver
Keyword(s):  
Violence Against Women ◽  
Reference Group ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Age Group ◽  
Data Set ◽  
Factors Affecting ◽  
Economic Violence ◽  
Policies And Programs ◽  
Source Of Information

The aim of this study was to determine the factors affecting the exposure of women in the 15–59 age group in Turkey to economic violence by their husbands/partners. The micro data set of the National Research on Domestic Violence against Women in Turkey, which was conducted by the Hacettepe University Institute of Population Studies, was employed in this study. The factors affecting women’s exposure to economic violence were determined using the binary logistic regression analysis. In the study, women in the 15–24, 25–34 and 35–44 age group had a higher ratio of exposure to economic violence compared to the reference group. Women who graduated from elementary school, secondary school, and high school had a higher ratio of exposure to economic violence compared to those who have never gone to school. Women’s exposure to physical, sexual and verbal violence was also important factor affecting women’s exposure to economic violence. The results obtained in this study are important in that they can be a source of information for establishing policies and programs to prevent violence against women. This study can also be a significant guide in determining priority areas for the resolution of economic violence against women.

Selection of Factors Affecting the Presence of Brook Trout (Salvelinus fontinalis) in Adirondack Lakes: A Case Study

1992 ◽  
Vol 49 (3) ◽  
pp. 597-608 ◽  
Author(s):  
J. J. Beauchamp ◽  
S. W. Christensen ◽  
E. P. Smith
Keyword(s):  
Logistic Regression ◽  
Brook Trout ◽  
Salvelinus Fontinalis ◽  
Model Development ◽  
Predictive Ability ◽  
Absolute Magnitude ◽  
Regression Diagnostics ◽  
Data Set ◽  
Factors Affecting ◽  
Adirondack Lakes

We used multiple logistic regression techniques to develop models for estimating the probability of brook trout (Salvelinus fontinalis) presence/absence as a function of observable water chemistry variables and watershed characteristics. The data set consists of the Adirondack Lakes Survey Corporation data collected on 1469 lakes during 1984–87. Two models fitted to a randomly selected development subset of lakes, using two sets of candidate explanatory/predictor variables of particular interest, were compared on the basis of coefficient consistency and predictive ability. In addition to the usual maximum likelihood logistic regression results, we also applied collinearity and other associated diagnostics and variable-selection procedures designed specifically for the logistic regression model to arrive at parsimonious models. Both models correctly predicted fish presence in more than 85% of the model development set and more than 80% of the lakes in the verification data. For those variables appearing in both models, the signs of the estimated coefficients were the same and in agreement with expectation. The removal of influential observations, as indicated by the logistic regression diagnostics, caused all of the estimated coefficients to increase in absolute magnitude. This results in a model which is more sensitive to changes in the explanatory variables.

scholarly journals Factors Affecting Intention to Disclose HIV Status among Adult Population in Sarawak, Malaysia

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Aren Sinedeh Lemin ◽  
Md Mizanur Rahman ◽  
Cliffton Akoi Pangarah
Keyword(s):  
Logistic Regression ◽  
Adult Population ◽  
Binary Logistic Regression ◽  
Future Research ◽  
Binary Logistic Regression Analysis ◽  
Cluster Sampling ◽  
Hiv Status ◽  
Factors Affecting ◽  
Significant Difference ◽  
Hiv Aids

Background. Disclosure of HIV-positive status is an essential prerequisite for the prevention and care of person living with HIV/AIDS as well as to tackle hidden epidemic in the society. Objective. To determine the intention to disclose the HIV/AIDS status among adult population in Sarawak, Malaysia, and factors affecting thereof. Methods. This cross-sectional community-based study was conducted among adult population aged 18 years and above in Sarawak, Malaysia. A gender-stratified multistage cluster sampling technique was adopted to select the participants. A total of 900 respondents were successfully interviewed by face-to-face interview using interview schedule. Stepwise binary logistic regression models were fitted in SPSS version 22.0 to identify the factors associated with the disclosure of HIV/AIDS status. A p value less than 0.05 was considered as statistically significant. Results. The mean (SD) age of male and female respondents was 41.57 (13.45) and 38.99 (13.09) years, respectively. A statistically significant difference of intention to disclosure of HIV status was found between males and females (p<0.05). A stepwise binary logistic regression analysis revealed that age, occupation, knowledge on HIV transmission, and content of discussion about HIV/AIDS appeared to be potential predictors for male respondents to disclose HIV status, while ethnicity and content of discussion on HIV/AIDS were found to be important predictors among the female respondents (p<0.05). Conclusion and Recommendation. Though the study did not depict the national prevalence of disclosure of HIV/AIDS status, the findings of the study would provide an important basic information for programme intervention, policy, and future research agenda.

scholarly journals Finding of the factors affecting the severity of COVID-19 based on mathematical models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jiahao Qu ◽  
Brian Sumali ◽  
Ho Lee ◽  
Hideki Terai ◽  
Makoto Ishii ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Test ◽  
Recognition Rate ◽  
Predictive Ability ◽  
Training Group ◽  
Acute Onset ◽  
Prognostic Indicators ◽  
Test Results ◽  
Data Set ◽  
Factors Affecting

AbstractSince 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.

scholarly journals Logit business failure prediction in V4 countries

2019 ◽  
Vol 11 (4) ◽  
pp. 54-64 ◽  
Author(s):  
Marek Durica ◽  
Katarina Valaskova ◽  
Katarina Janoskova
Keyword(s):  
Logistic Regression ◽  
Failure Prediction ◽  
Predictive Ability ◽  
Binary Logistic Regression ◽  
Business Failure ◽  
Data Set ◽  
Prediction Ability ◽  
Regression Approach ◽  
High Prediction ◽  
One Year

Abstract The paper presents the creation of the model that predicts the business failure of companies operating in V4 countries. Based on logistic regression analysis, significant predictors are identified to forecast potential business failure one year in advance. The research is based on the data set of financial indicators of more than 173 000 companies operating in V4 countries for the years 2016 and 2017. A stepwise binary logistic regression approach was used to create a prediction model. Using a classification table and ROC curve, the prediction ability of the final model was analysed. The main result is a model for business failure prediction of companies operating under the economic conditions of V4 countries. Statistically significant financial parameters were identified that reflect the impending failure situation. The developed model achieves a high prediction ability of more than 88%. The research confirms the applicability of the logistic regression approach in business failure prediction. The high predictive ability of the created model is comparable to models created by especially sophisticated artificial intelligence approaches. The created model can be applied in the economies of V4 countries for business failure prediction one year in advance, which is important for companies as well as all stakeholders.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

Sign in / Sign up

Export Citation Format

Share Document