Sustainable supply chain management practices and the performance of united nations agencies in Nairobi, Kenya
Mulwa, Victoria Mwikali
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This study set out to establish the influence of Sustainable Supply Chain Practices on the performance of UN Agencies in Nairobi, Kenya. The study was guided by the following research objectives; to establish the SSCP adopted by UN agencies in Kenya and the extent to which these practices have been adopted; to determine the relationship between SSCP adopted by UN agencies and their performance; to establish the challenges faced by UN agencies in implementing SSCP. The study adopted a correlation cross-sectional research design in collecting data from the respondents. The study involved a census of the UN Agencies in Nairobi. The primary tool for collecting data was questionnaires which were administered by the researcher to allow for further probing on issues that were not clear to the respondents. The data was analyzed using descriptive statistics, regression analysis, correlation analysis and factor analysis with the aid of SPSS 20.0. The findings show that stakeholder engagement, having a diverse supplier network, ensuring suppliers have a sustainable policy, good working conditions for employees, employee health and safety and ethical sourcing, production and distribution were highly adopted. The findings also show that through adoption of SSC practices, UN agencies were able to get new market opportunities, increased their operational and production efficiencies, reduced their costs and improved the organizations corporate image. Funding limitation and delays, procurement delays, strains in control, and staff resistance to adopting change were shown to be some of the challenges faced by UN Agencies. It is suggested that training of employees, community training and having a sustainable policy would help in curbing the challenges. It is evident from the correlation analysis that adopting SSC practices had an effect on the performance of these organizations.
University of Nairobi