Influence of Risk Mitigation Strategies on Supply Chain Resilience in the Petroleum Industry in Kenya

Abstract

The study sought to determine the influence of risk mitigation strategies on supply chain resilience in the petroleum industry in Kenya. The specific objectives for the study were: to determine the influence of Risk Avoidance, to establish the influence of Risk Acceptance, to determine the influence of Risk Reduction and to establish the influence of Risk Transfer on Supply Chain Resilience in Petroleum Industry in Kenya. Also, the study examined the moderating effect of management control policies, rules and procedures on risk mitigation strategies and supply chain resilience. The study adopted descriptive and correlation research design with a target population of the 87 active oil-marketing companies licensed by the Energy Regulatory Commission to import and trade with petroleum products in Kenya. The study employed a census survey technique to collect data from those firms. The data was collected using questionnaires respondents who were depot manager and either supply chain or logistics managers from each firm. Prior study was conducted using 10 companies to test the validity and reliability of the research instrument and a Cronbach’s alpha equal to 0.787 was obtained from the analysis of the 20 questionnaires. After eliminating the 10 companies that participated in the pilot study, 77 oil marketing firms remained out of which 75 accepted to participate in the survey and the study obtained 150 fully completed questionnaires. The data was analyzed using SPSS version 22 to obtain descriptive and inferential statistics which were presented in tables and figures and was used to accept or reject the study hypotheses which were tested at five percent significant level. The first regressions analysis was carried out to test the relationship between each of the four risk mitigation strategies (risk avoidance, risk acceptance, risk reduction and risk transfer) and supply chain resilience followed by a test on the moderating effect of management control policies, rules and procedures using Baron and Kenney (1986) technique and finally multiple regression analysis for risk mitigation strategies and supply chain resilience. The first regression analysis that tested each of the four risk mitigation strategies and supply chain resilience indicated that risk avoidance and risk acceptance had significant values at 0.515 and 0.915 higher than p value 0.05 and negative coefficient at -0.032 and -0.012 respectively. Therefore, both risk avoidance and risk acceptance had a negative and statistically insignificant influence on supply chain resilience. Risk reduction and risk transfer had a positive and statistically significant influence on supply chain resilience because their significant values were less than p values at 0.037 and 0.008 respectively and also their coefficients were positive. The second regression which tested moderation effect determined that management control policies, rules and procedures have a positive effect on the relationship between risk mitigation strategies and supply chain resilience because it improved the size of R in the multiple regression analysis from 0.293 to 0.340 and R-Square from 0.086 to 0.116. In addition, it improved the prediction power of the multiple regression model as the p-value for ANOVA, which tests model’s statistical significance in predicting the relationship between the study variables reduced from 0.011 to 0.001. The study concluded that control policies and procedures moderates positively the influence of risk mitigation strategies and supply chain resilience. Thirdly, the multiple regression analysis obtained positive correlation coefficient (R) coefficient of determination (R-Square) equal to 0.293 and 0.086 respectively which indicated a positive multiple relationship between the risk mitigation strategies and supply chain resilience. However, coefficients for risk avoidance and acceptance were both negative (-0.349 and -0.144) and their significant values were 0.099 and 0.763 respectively which are larger than alpha value of 0.05 hence they are not statistically significant in predicting the supply chain resilience. On the other hand, coefficients for risk reduction and transfer were positive (0.497 and 0.508) respectively and were significant since their corresponding p-values were 0.031 and 0.008 respectively which are less than 0.05. The study concluded that risk mitigation strategies have a positive influence on supply chain resilience. The study recommended that firms in the petroleum industry should implement risk reduction and transfer strategies much more rather than the risk avoidance and acceptance as the latter which show preference to status quo have a negative influence on supply chain resilience. Future studies may focus on drivers for vulnerability in the point of view of product or process value stream, infrastructure or asset dependencies, firm-specific or inter-organizational networks, and influences from natural and social environments that could influence oil product supply chain frameworks.