Key BIM Adoption Drivers to Improve Performance of Infrastructure Projects in the Ethiopian Construction Sector: A Structural Equation Modeling Approach

ORCID

Belay: https://orcid.org/0000-0002-5931-9089; Rokooei: https://orcid.org/0000-0001-8543-653X; Sousa: https://orcid.org/0000-0002-4569-1090

MSU Affiliation

College of Architecture, Art and Design; Department of Building Construction Science

Creation Date

2026-10-30

Abstract

The aim of this paper is to explore the critical BIM adoption drivers across the Ethiopian public infrastructure construction sector. In this regard, a comprehensive systematic literature review was employed to identify potential BIM implementation attributes in developing countries and validated through a pilot test. Then, quantitative data was collected from experts working in various organizations using a structured questionnaire survey. A structural equation model was then developed based on five key BIM adoption constructs and 14 adoption drivers. Based on the path analysis, Application, Environment, and Project related factors positively affect BIM adoption in infrastructure projects, whereas Organization and Information Management are insignificant and negatively affect BIM adoption in the Ethiopian construction industry. The study highlighted key BIM adoption attributes that are helpful to enhance the overall project management performance in infrastructure projects. The proposed action plan is beneficial to various professionals, government, and stakeholders in an effort to improve the current level of BIM uptake in the horn of Africa. More so, the findings of this paper can be used to facilitate and promote BIM adoption in public infrastructure construction projects across the Ethiopian construction market.

Publication Date

9-24-2021

Publication Title

Advances in Civil Engineering

Publisher

Wiley

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Rights

© 2021 Solomon Belay et al.

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Digital Object Identifier (DOI)

https://doi.org/10.1155/2021/7473176