A capability assessment model for implementing digital technologies in Nigerian heavy construction firms

Abstract

Purpose

Due to the critical importance of digital transformation in enhancing industrial growth and competitiveness, especially in heavy construction, this study introduces a tailored capability assessment model and self-appraisal tool for firms in this sector. These resources enable them to gauge their readiness for adopting digital technology effectively.

Design/methodology/approach

Utilizing the Technology—Organization—Environment (TOE) and Natural Resource Dependence Theory (NRDT) frameworks, 22 markers were identified to structure a questionnaire distributed to construction professionals. Descriptive analysis and fuzzy synthetic evaluation (FSE) were used to develop the capability assessment model. A validation survey assessed the validity of both the model and the self-appraisal instrument.

Findings

The study identified the top five significant markers: (1) leadership commitment to digital transformation, (2) workforce readiness for technology integration, (3) potential ROI through efficiency gains, (4) technology maturity for construction applications and (5) complexity of integrating new technologies with existing workflows. Through FSE, the most critical factors were technology-related, organizational and resource optimization markers.

Originality/value

By employing the TOE and NRDT frameworks, the study identifies the most critical factors influencing digital adoption in heavy construction. Also, the user-friendly self-appraisal instrument developed in this study can be considered a valuable contribution, as it provides heavy construction firms with a practical tool for ongoing monitoring and improvement of their digital transformation efforts.

Citation

Oke, A.E., Aliu, J., Farhana, M.Z., Jesudaju, O.T. and Lee, H.-P. (2024), "A capability assessment model for implementing digital technologies in Nigerian heavy construction firms", Smart and Sustainable Built Environment, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/SASBE-04-2024-0112