Micro- and small-scale enterprises' financing preference in line with POH and access to credit: Empirical evidence from entrepreneurs in Ethiopia - Hayelom Abrha Meressa (Journal of Innovation and Entrepreneurship, 2022)
The purpose of this study was to examine factors that determine micro- and small-scale enterprises' financing preference in line with pecking order theory and access to credit in Benishangul-Gumuz Regional State of Ethiopia. The study used primary data collected using cross-sectional survey questionnaire. The sample of this study was 296 enterprises selected using proportional stratified random sampling technique. The data were analyzed using descriptive and logistic regression analysis. The results of probit estimation revealed that business experience, collateral, gender, motivation and enterprises' sectoral engagement affect financing preference. To investigate access to credit determinants, only enterprises that need to raise capital through credit were considered. The empirical results, therefore, revealed that business experience, size, sectoral engagement, collateral, interest rate, loan repayment period, and preparation of business plan, financial reporting, location and educational background of entrepreneurs affect access to credit of enterprises. Before any generalization of the results can be made, more evidence is needed on enterprises' financing preference and access to credit determinants for the fact that the empirical tests were conducted only on 296 entrepreneurs since 2019. Therefore, the findings are valid and practicable only for the entrepreneurs under the study and the results cannot be assumed to extend beyond this group of entrepreneurs to different study periods. The study makes an original contribution to the literature of small business finance by investigating determinants of micro- and small-scale enterprises' financing preference and access to credit in Benishangul Gumuz Regional State of Ethiopia as a developing country.
Keywords: 650; Assosa Zone; Credit; Financing; Logistic regression; Micro- and small enterprises