Approaches on Mitigating Variation Orders in Road Construction Industry in Kenya: The Case of Kenya National Highways Authority (KeNHA)

  • J. N. Gatitu Sustainable Materials Research & Technology Centre, Jomo Kenyatta University of Agriculture and Technology, Kenya
  • C. K. Kabubo Sustainable Materials Research & Technology Centre, Jomo Kenyatta University of Agriculture and Technology, Kenya
  • P. Ajwang Department of Agricultural and Biosystems Engineering, Jomo Kenyatta University of Agriculture and Technology, Kenya
Volume: 10 | Issue: 5 | Pages: 6195-6199 | October 2020 |


Variation orders have been globally identified as a significant reason for poor performance of construction projects. Several variation orders have been identified in the Kenyan construction industry, however, there is a paucity of information on the approaches to minimize their occurrence. The aim of this study is to identify the practices for variation order mitigation in the public road construction sector in Kenya. This is a cross-sectional study that was carried out among the stakeholders (clients, consultants, and contractors) involved in road construction projects. The study used the purposive sampling technique whereby the data were collected with the use of distributed questionnaires among clients, consultants, and contractors’ staff with a technical background in civil engineering. The Relative Importance Index (RII) was used for data analysis. According to the findings of the study, the top five recommended approaches for minimizing variation orders include the provision of adequate planning before the initiation of road construction projects, understanding of the contract provisions by all the parties before project implementation, solving of land disputes before the project tender awards, provision of complete project designs, and provision of a brief project scope during the tendering process. This research, therefore, contributes to a pool of knowledge that will enable practitioners and researchers in the construction industry to develop effective models for minimizing variation orders.

Keywords: variation orders, relative importance index


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