Optimisation Framework for Enhancing Construction Productivity of Medium-Level Construction Projects (Kenyan Counties Perspective)

Extensive studies have been conducted on optimisation frameworks for construction projects to determine project productivity, albeit biased towards the BIM Principle, Architectural, Engineering, and Construction (AEC) Models, and those that focus on large and complex projects. The objective of this study was, therefore, to develop an integrated (Preconstruction & construction factors) optimisation framework for medium-level projects. Key parameters considered as independent variables were the cost of construction, Project management techniques, Pre-project planning and Lean technology. Project completion time, quality, and costs were listed as aspects of the dependent variable (Project productivity). Structured questionnaires were administered to six professional subgroups and one subgroup of key stakeholders as the study participants. These comprised landlords/homeowners, architects, structural engineers, quantity surveyors, building contractors, construction project managers and Ministry of Housing and Infrastructure officials. Descriptive and diagnostic research methods were used to analyse data. The results on the performance of the framework based on multivariate regression analysis show that there is a strong relationship between performance (Y) and variable factors X1(Cost of construction), X2(Project management techniques), X3(Pre-project planning), and X4(Lean technology). On the importance of the independent variables, Project management techniques took the lead, followed by cost, pre-project planning & lean technology. The results on the performance of the framework based on case studies show the capacity of the framework to optimise by as much as 23.3% & 24.9% on Case studies 1 &2, respectively; however, the percentages should be unique for each specific project. The model was overwhelmingly endorsed by key stakeholders through a validation process over its Key Performance Indicators, adequacy in measuring &scoring indicators and its ability to determine project productivity with a mean rating of 8.63± 0.890 on a scale out of 10. Further research is recommended on the models’ applicability to small-level construction & civil engineering projects & new/unique projects.

Authors

Institutions

Publication Details

Journal
East African Journal of Engineering
Published
2025-10-14
DOI
https://doi.org/10.37284/eaje.8.2.3828
Primary Topic
Construction Project Management and Performance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimisation Framework for Enhancing Construction Productivity of Medium-Level Construction Projects (Kenyan Counties Perspective)

James Mithamo Muthike, Abednego Oswald Gwaya, Christopher Luchebeleli Kanali
East African Journal of Engineering
Construction Project Management and Performance
article

Optimisation Framework for Enhancing Construction Productivity of Medium-Level Construction Projects (Kenyan Counties Perspective)

James Mithamo Muthike, Abednego Oswald Gwaya, Christopher Luchebeleli Kanali
article en

Abstract

Extensive studies have been conducted on optimisation frameworks for construction projects to determine project productivity, albeit biased towards the BIM Principle, Architectural, Engineering, and Construction (AEC) Models, and those that focus on large and complex projects. The objective of this study was, therefore, to develop an integrated (Preconstruction & construction factors) optimisation framework for medium-level projects. Key parameters considered as independent variables were the cost of construction, Project management techniques, Pre-project planning and Lean technology. Project completion time, quality, and costs were listed as aspects of the dependent variable (Project productivity). Structured questionnaires were administered to six professional subgroups and one subgroup of key stakeholders as the study participants. These comprised landlords/homeowners, architects, structural engineers, quantity surveyors, building contractors, construction project managers and Ministry of Housing and Infrastructure officials. Descriptive and diagnostic research methods were used to analyse data. The results on the performance of the framework based on multivariate regression analysis show that there is a strong relationship between performance (Y) and variable factors X1(Cost of construction), X2(Project management techniques), X3(Pre-project planning), and X4(Lean technology). On the importance of the independent variables, Project management techniques took the lead, followed by cost, pre-project planning & lean technology. The results on the performance of the framework based on case studies show the capacity of the framework to optimise by as much as 23.3% & 24.9% on Case studies 1 &2, respectively; however, the percentages should be unique for each specific project. The model was overwhelmingly endorsed by key stakeholders through a validation process over its Key Performance Indicators, adequacy in measuring &scoring indicators and its ability to determine project productivity with a mean rating of 8.63± 0.890 on a scale out of 10. Further research is recommended on the models’ applicability to small-level construction & civil engineering projects & new/unique projects.

East African Journal of EngineeringVol. 8(2)
Jomo Kenyatta University of Agriculture and Technology (KE), Technical University of Mombasa (KE), Dedan Kimathi University of Technology (KE)
Openalex Percentile: Top 66%
Construction Project Management and Performance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.