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Risk Management Strategies and Performance of Small Scale Agribusiness Firms in Kiambu County

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Date
2017
Author
Ondiek, Samson
Muathe, Stephen M. A.
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Abstract
Purpose: This study sought to establish the risk management strategies and performance of small agribusiness firms in Kiambu County. Methodology: The study employed descriptive research design. The population of the study was 11,120 small agribusinesses businesses (SME) in Kiambu County. The selection was done in random manner ensuring that all types of firms are considered. This was achieved through stratified random sampling. Data of the list of firms and type was obtained from the County office in-charge of industrialization. The study used both primary data. Primary data was gathered by use of closed ended questionnaires, which was self-administered. A multiple linear regression model was used to test the significance of the influence of the independent variables on the dependent variable. Data was analyzed mainly by use of descriptive and inferential statistics. SPSS was used to produce the descriptive and inferential statistics. Descriptive statistics included mean, and standard deviation. Inferential statistical techniques included correlation and regression analysis. Results: The study findings indicated that financial risk management strategy, operational risk management strategy, human resource risk management strategy, regulatory risk management strategy and disaster risk management strategy affected organizational performance. The study indicated that keeping previous record enables to forecast future risks, financial distress affects performance, keeping informed of various risks reduces the risk of poor performance and that having contingent measures to reduce financial risks improves the organizational performance. Unique contribution to theory, practice and policy: The study recommends that it is important for a company to reduce the volatility of earnings or cash flows due to financial risk exposure as the reduction enables the firm to perform better forecasts.
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http://ir-library.ku.ac.ke/handle/123456789/18040
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