Impact of Building Information Modelling on Construction Project Performance in Abuja Municipal Area Council, Nigeria

This study examined the impact of BIM on construction project performance by assessing awareness, adoption, perceived benefits, and barriers to implementation among construction professionals. A mixed-methods research design was employed, involving a questionnaire survey of 529 construction professionals across in AMAC, Abuja, Nigeria. A total of 515 valid responses were analysed, representing a 97.4% response rate, while qualitative insights were obtained through interviews with 15 BIM experts. Quantitative data were analysed using descriptive statistics, and qualitative data were subjected to thematic analysis. The findings revealed a moderately high level of BIM awareness (overall mean = 3.39), with awareness of the BIM concept recording the highest mean score (3.54). BIM adoption was highest for design visualization and quantity take-off, while advanced applications such as 4D scheduling and facility management remained limited. Respondents agreed that BIM positively influences project performance (overall mean = 3.44), particularly in design quality (3.55) and collaboration (3.47). The principal barriers were the absence of a national BIM policy (3.88), inadequate technical skills (3.78), and high software costs (3.75). The study concludes that strengthening policy support, professional capacity, and organizational investment is essential for accelerating BIM adoption and improving construction project performance in Nigeria.

Building Information Modelling; construction project performance; adoption; digital transformation; Technology Acceptance Model; Nigeria.

1. Abdullahi, A., Saka, A. B., & Ibrahim, Y. M. (2022). Digital transformation and Building Information Modelling adoption in the Nigerian construction industry. Journal of Engineering, Design and Technology. [Google Scholar] [Crossref]

2. Abou-Ibrahim, H., & Hamzeh, F. (2021). Building Information Modelling implementation and its impact on construction project performance. Engineering, Construction and Architectural Management. [Google Scholar] [Crossref]

3. Abubakar, M., Ibrahim, Y., Saka, A. B., & Oyewobi, L. O. (2023). Building Information Modelling adoption and construction project performance in Nigeria. International Journal of Construction Management. [Google Scholar] [Crossref]

4. Adebayo, A. A., & Ibrahim, M. A. (2022). Organisational factors influencing Building Information Modelling implementation in Nigeria. Journal of Construction in Developing Countries. [Google Scholar] [Crossref]

5. Adeleke, A. Q., Bamgbose, O. A., Nawanir, G., & Sorooshian, S. (2023). Digital technologies and construction project performance in developing countries. Buildings, 13(4), 1–20. [Google Scholar] [Crossref]

6. Aghimien, D. O., Aigbavboa, C. O., Oke, A. E., & Musenga, C. (2022). Organisational readiness for Building Information Modelling adoption in the construction industry. Journal of Engineering, Design and Technology. [Google Scholar] [Crossref]

7. Akinlolu, M., Haupt, T., Edwards, D. J., & Aigbavboa, C. (2021). Drivers and barriers to Building Information Modelling adoption in the South African construction industry. Journal of Engineering, Design and Technology. [Google Scholar] [Crossref]

8. Alenezi, A., & Almalki, M. (2022). Critical success factors influencing Building Information Modelling adoption in construction projects. International Journal of Construction Management. [Google Scholar] [Crossref]

9. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa [Google Scholar] [Crossref]

10. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications. [Google Scholar] [Crossref]

11. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]

12. Enshassi, A., & El-Ghandour, S. (2023). Building Information Modelling and project performance in the construction industry. International Journal of Construction Management. [Google Scholar] [Crossref]

13. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. [Google Scholar] [Crossref]

14. Latiffi, A. A., Mohd, S., Kasim, N., & Fathi, M. S. (2021). Building Information Modelling implementation and project delivery performance: A review. International Journal of Construction Management. [Google Scholar] [Crossref]

15. Mahamadu, A. M., Manu, P., Booth, C., & Mahdjoubi, L. (2021). Technology Acceptance Model applications in Building Information Modelling research: A systematic review. Automation in Construction. [Google Scholar] [Crossref]

16. Maina, P., Mutisya, M., & Karanja, P. (2022). Adoption of Building Information Modelling in the Kenyan construction industry. International Journal of Construction Management. [Google Scholar] [Crossref]

17. Olawumi, T. O., & Chan, D. W. M. (2020). Critical success factors for implementing Building Information Modelling and smart technologies in construction. Engineering, Construction and Architectural Management. [Google Scholar] [Crossref]

18. Oyewobi, L. O., Ibrahim, A. D., Ganiyu, B. O., & Abubakar, M. (2022). Building Information Modelling adoption and project performance in the Nigerian construction industry. Journal of Engineering, Design and Technology. [Google Scholar] [Crossref]

19. Saka, A. B., Chan, D. W. M., & Siu, F. M. F. (2020). Drivers and barriers influencing Building Information Modelling adoption in developing countries. Engineering, Construction and Architectural Management. [Google Scholar] [Crossref]

20. Saka, A. B., Chan, D. W. M., & Olawumi, T. O. (2023). Institutional factors influencing Building Information Modelling implementation in developing economies. Journal of Engineering, Design and Technology. [Google Scholar] [Crossref]

21. Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson Education. [Google Scholar] [Crossref]

22. Umar, U. A., Ibrahim, A. D., & Aigbavboa, C. O. (2023). Digital transformation and construction project performance in developing economies. International Journal of Construction Management. [Google Scholar] [Crossref]

23. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926 [Google Scholar] [Crossref]

24. Yamane, T. (1967). Statistics: An introductory analysis (2nd ed.). Harper & Row. [Google Scholar] [Crossref]