Determinants of Women Entrepreneurs Business Performance: Evidence from Micro and Small Scale Enterprises in Arba Minch Town, Southern Ethiopia

Abstract

Women’s business performance is influenced by individual, legal, economic and technological factors. Thus,   the purpose of this study was to identify determinants of women’s business performance with reference to MSEs in Arba Minch town. In order to achieve this objective, the researcher has used explanatory research design with quantitative research approach in which six hypotheses have been tested. Primary data has been collected from 281women entrepreneurs who selected using stratified and simple random sampling technique by using structured questionnaires. Both descriptive and inferential statistics has been used to analyze the data through SPSS version 21.0. According to the finding of the study the descriptive result of the study indicates that  variables such as access to technology, access to land premise, communication skill and tax amount has moderate/medium mean because the mean score of the variables is  ranges from 2.60 to 3.39 (average value).  In other way, two remaining variables such as access to finance and lack of training has low mean because the mean score of the variables is  ranges from 1.80 to 2.59 (low  value of mean).  Based on the Pearson correlation coefficient analysis of the study lack of training and increase in tax amount have negative and significant relationship with the business performance of women entrepreneurs whereas the rest variables such as access to finance, access to land premise, access to technology and communication skill has positive and significant relationship with the women business performance of the MSEs in Arbaminch Town. According to the multiple regressions analysis of the study access to land was the best predictor of women business performance and lack of training was the least predictor of women business performance.

Keywords: Women Entrepreneurs, Business Performance, Determinants, Micro and Small Scale Enterprise, Multiple regressions.

DOI: 10.7176/JPID/63-02

Publication date: January 31^st 2024