Prospects and Problems of Women Entrepreneurship in India: With Special Reference to Punjab, Himachal and Haryana

Abstract

Women entrepreneurs are growing day by day in the 21st century as compared to 18th century when women were restricted to house boundaries. As part of government’s efforts to improve the situation for women entrepreneurs, various measures have been taken. It is well known that women are an essential part of the country and can boost its economic growth. Global Entrepreneur Monitoring (GEM) 2020 states that women start-ups are approximately 274 million globally, with 139 million managers/ owners of established businesses and 144 million women informal investors. As per GEM reports, there are 15.7 million women- owned enterprises in India. It is 22percent of all enterprises in India, which requires attention of the government and policy makers. It is also important to highlight that India's number of women entrepreneurs is far less than in other developed countries. The government needs to work on why the number of women entrepreneurs is far less after so many government schemes. In this context an attempt is made in this study to determine the prospects and problems faced by women-owned micro-enterprises. Also, a model was formulated to evaluate the performance of women-owned micro enterprises in selected states. Primarily, this study focused on the women-owned micro-enterprises in Northern India (Punjab, Himachal Pradesh, and Haryana). The performance of the women-owned micro- enterprises was analysed with the help of supporting factors, prospects, problems faced, whether working efficiently or not, and the risk-taking abilities of the women entrepreneurs. With the help of a questionnaire, data were collected from 606 respondents. According to the feedback, a detailed analysis was done using SPSS and SEM PLS. In the initial phase of model formulation, there were seven variables namely, support business problems, health problems, risk, prospects, operating cost, and business performance. However, in the final model, the health problem variable was excluded from the analysis as it was found to be insignificant. In the final model, the six latent variables were business problems which include human resources, marketing, operational and technical. The support variable includes external support, educational support, and governmental support. The variable Risk includes risk 1 (financial) and risk 2 (environmental); prospects have success and the ability to overcome problems, and performance is judged by net profit margin, average sales turnover, gross profit, and family income. Thus, there were six latent variables: support business problems, risk, prospects, operating cost, and business performance. In the PLS Path model, the business performance represents the target variable v

DocuSign Envelope ID: BD368CA8-D406-4C53-815E-8B11A5CA7FEF directly predicted by prospects and operating costs. Furthermore, support variable and business problems indirectly impact the business performance via risk or prospects. Direct or indirect, but the overall impact is on the business performance. The results of multiple regression model in the case of net profit margin model showed that financial support and mobility are the main determinants of family support. These factors affect the profit margin and consequently impact the performance of women enterprises positively. In the case of sales turnover model, the moral support is the main determinant of the sales performance. Multiple regressions also reveal that in the case of profit model legal ownership, nature of a business and sales turnover are main contributing factor. So, a lot of these elements positively affect the profit of women owned organizations besides nature of business and number of products offered by the establishments. While in the case of models for sales family income, the number of employees and number of products are the main variable affecting the sales performance. These are the essential aspects that effects the sales of the enterprises positively but, the number of products offered by an enterprise effect the sales of the enterprises negatively. Results of study validate all the hypotheses and consistent with previous work. In the study the impact of demographic factors was also analysed on the sales and profit of women-owned micro-enterprises (WOME). In the field of entrepreneurship most of the studies have focused on two broad categories of factors explaining the determinants of financial performance of women-owned micro enterprises 1) performance parameters that explain all the parameters used to check the performance of the women-owned enterprises, and 2). The performance of the women-owned micro enterprises was stated with the help of demographic factors. Also, the study tried to explore the major business problems and risk factors as prevailing in WOME through an outer model of PLS-SEM. Further, it also investigated the major significant prospects and supports as available WOME again through the outer model. The major business problems as found to be significant were related to marketing, technical, operational, and human resource. Risk 1 (financial) and Risk 2 (environmental) were the main reasons which encouraged Indian women entrepreneurs to take risk. Both types of risks showed a significant impact on overall risk-taking the capability of women entrepreneurs. External, governmental, family, education were the various supports available to WOME. External, government and educational supports have a significant role in overall supports vi

DocuSign Envelope ID: BD368CA8-D406-4C53-815E-8B11A5CA7FEF available to WOME. The business performance of WOME is measured by gross profit, net profit, and family income. All these sub-factors were showed significant impact on overall business performance. There were two sub-factors of prospects which were found to be significant. As per this research work, the government schemes were found to be unimportant for business performance of WOME. However, this could be because women entrepreneurs lost hope in lengthy, time consuming, corrupt government procedure. educational support is shifting from neutral to important in case of better understanding and knowledge and competitive edge. The highest correlation is between competitive edge and better understanding and knowledge 72.5% rest all the variables are good and significant. The major reason could be that our education is not related to what we really do in day-to-day life in business. There are many flaws in our education system that need to be amended. No doubt, education gives us all the conceptual knowledge of the subjects. However, the industrial scenario is different. Furthermore, ANOVA is applied to government, family, external and educational supporting factors based on family income, age, education, and marital status. Government, family, external and educational support is taken as the dependent variable, and family income, age, education, and marital status as the independent variables. Most variables ' f values (ANOVA value) were significant at 1 %, as the p-value is <0.01. The policy implication of this study is that it might be useful for existing as well as budding entrepreneurs to identify various governments, external and family-supporting factors responsible for business performance. It will enlighten society about the association of various government, external, and family-supporting factors with different performance parameters for women-owned micro-enterprises. This will also help in promoting women entrepreneurs that ultimately leads to women empowerment in India.