{"title":"Evaluating credit accessibility predictors among small and medium contractors in the South African construction industry","authors":"O. Balogun, J. Agumba, N. Ansary","doi":"10.18820/24150487/AS25I2.3","DOIUrl":null,"url":null,"abstract":"The importance of small and medium construction enterprises (SMEs) in the South African economy has been recognised. However, construction SMEs are faced with difficulties in accessing credit from financial institutions. Furthermore, past research has failed to reach consensus on the demographic and socio-economic factors that predict credit accessibility for construction SMEs in South Africa. This study determines the predicting demographic and socio-economic factors for credit accessibility for construction SMEs from financial institutions in South Africa. A quantitative research approach was used and data was collected, using a questionnaire survey from 250 construction SMEs who were conveniently sampled. The demographic and company profile factors predicting credit accessibility were modelled and set as the independent variables with credit accessibility to the construction SMEs as the dependent variable, irrespective of the amount obtained from financial institutions. The data was analysed using the Statistical Package for the Social Sciences (SPSS) version 22. Binary logistic regression analysis was used to analyse the predictors of obtaining credit. In the first model, the results revealed that the credit accessed irrespective of the amount and those who did not receive credit at all, when modelled with the conceptualised predictors suggested, showed no significant predictors of obtaining credit. However, in the second model, when the conceptualised predictors were modelled with full and partial credit, the results established that age group, current position in the organisation, tax number and location were good predictors of obtaining full credit. The findings of this study cannot be generalised across South Africa, as the study was conducted only in the Gauteng province. The value of this study informs owners of SMEs in the construction industry to provide their age and current position in the organisation when applying for credit. They should also provide the tax number and the location of the business in order to improve their chances of obtaining full credit from financial institutions. Keywords : Credit accessibility, determinants of credit accessibility, full credit, small and medium enterprises","PeriodicalId":42571,"journal":{"name":"Acta Structilia","volume":" ","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2018-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Structilia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18820/24150487/AS25I2.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 2
Abstract
The importance of small and medium construction enterprises (SMEs) in the South African economy has been recognised. However, construction SMEs are faced with difficulties in accessing credit from financial institutions. Furthermore, past research has failed to reach consensus on the demographic and socio-economic factors that predict credit accessibility for construction SMEs in South Africa. This study determines the predicting demographic and socio-economic factors for credit accessibility for construction SMEs from financial institutions in South Africa. A quantitative research approach was used and data was collected, using a questionnaire survey from 250 construction SMEs who were conveniently sampled. The demographic and company profile factors predicting credit accessibility were modelled and set as the independent variables with credit accessibility to the construction SMEs as the dependent variable, irrespective of the amount obtained from financial institutions. The data was analysed using the Statistical Package for the Social Sciences (SPSS) version 22. Binary logistic regression analysis was used to analyse the predictors of obtaining credit. In the first model, the results revealed that the credit accessed irrespective of the amount and those who did not receive credit at all, when modelled with the conceptualised predictors suggested, showed no significant predictors of obtaining credit. However, in the second model, when the conceptualised predictors were modelled with full and partial credit, the results established that age group, current position in the organisation, tax number and location were good predictors of obtaining full credit. The findings of this study cannot be generalised across South Africa, as the study was conducted only in the Gauteng province. The value of this study informs owners of SMEs in the construction industry to provide their age and current position in the organisation when applying for credit. They should also provide the tax number and the location of the business in order to improve their chances of obtaining full credit from financial institutions. Keywords : Credit accessibility, determinants of credit accessibility, full credit, small and medium enterprises