Factors Affecting the Growth of Technology Startups in the Small and Medium Entreprises Sector in Nairobi County

Abstract

The purpose of this study was to investigate factors affecting the growth of technology startups in the SME sector in Nairobi County. The study was guided by the following research questions. How does access to finance affect the growth of technology startups in the SME sector? How does human capital affect the growth of technology startups in the SME sector? How does access to market affect the growth of technology startups in the SME sector? This study used a descriptive correlation research design approach. The target population for this study consisted of 177 owners and managers of tech startups based in Nairobi County. Stratified random sampling was used to select a sample of 137 tech startups out of the total population. A questionnaire was used as the data collection tool. Data was analysed using descriptive statistical techniques such as mean and standard deviation. Inferential statistics used included correlation, ANOVA and linear regression. Statistical Package for Social Sciences (SPSS) was used for data analysis. Thereafter, the results and findings were presented in figures and tables. The findings on the effect of access to finance on the growth of tech startups revealed that there was a positive and significant correlation between access to finance and growth of the startups, r (137) =.162, p<.05. The results from One Way ANOVA revealed that there was a significant difference for duration of business existence F (18,109) = 2.418, p<.05. The regression model summary results indicated that access to finance explained about 3% of the variability of growth of tech startups (R2 =0.026, F (1,108) =2.926, p<.05). The linear regression coefficient results showed that access to finance significantly predicted the growth of tech startups (β=.165, p<.05). The results also revealed that there was a positive and significant correlation between human capital and growth of tech startups, r (137) =.174, p<.05. One Way ANOVA revealed that there was a significant difference for annual staff turnover F (13, 80) =2.819, p<0.05; and age structure F (14, 82) =2.326, p<.05. Linear regression analysis was used to establish how human capital affected growth. The regression model summary results also indicated that human capital explained about 3% of the variability of growth of start-ups (R2 = 0.030, F (1,107) =3.331, p<.05). The linear regression coefficient results showed that human capital significantly predicted the growth of tech startups (β=.185, p<.05). There was a positive and significant correlation between access to market and growth of tech startups, r (137) =0.373, p<.05. Linear regression analysis was used to establish how access to market affected growth. The results from One Way ANOVA revealed that there was a significant difference for duration of business existence F (16,105) =1.936, p<.05. The regression model summary results also indicated that access to market explained about 14% of the variability of growth of start-ups (R2 =0.139, F (1,106) =17.120, p<.05). The linear regression coefficient results showed that access to market significantly predicted the growth of tech startups (β=.404, p<.05). The study concluded that access to finance affects the growth of tech startups and this can be attributed to the lack of collateral which leads to the SMEs not getting access to long-term credit. Exposure to high cost of credit also meant that borrowing was not attractive and this resulted in less finance for growth for a majority of the start-ups and thus less expansion. The study further concluded that the firm’s capacity to offer on the job training and development of management skills affected its growth. Finally, the study concluded that access to market was a major factor in the growth of startups as lack of access to new customers and business information on market trends led to the firms having low sales turnover. The study recommends that training should be provided to all personnel on the life cycle of startups to address the challenge caused by changes throughout the different startup stages; creation of affordable loan products in order for more start-ups to have access to finance; crowd funding and grants from non-governmental organisations; innovativeness and fresh ideas of acquiring customers to boost sales turnover. The study also recommends that further studies should be conducted on the effects of entrepreneurial competencies on the growth of technology startups in the SME sector in Kenya.