Oluwatayo Timothy Jesudaju

The fourth industrial revolution has introduced a range of digital technologies (DTs) that possess the potential to significantly enhance the operations and competitiveness of heavy-construction firms. Grounded in the Technology–Organization–Environment (TOE) Framework, the Resource-Based View (RBV) and the Diffusion of Innovation Theory (DOI), this study investigates the relationship between the adoption of digital technologies and the competitive edge (CE) of heavy-engineering firms. Specifically, this research seeks to assess how the adoption of DTs impacts four critical competitive-edge metrics: efficient resource management (CE1), real-time monitoring and control (CE2), data-driven decision-making (CE3) and improved collaboration and communication (CE4). A quantitative research approach was employed, using a structured questionnaire distributed to construction professionals in Lagos State, Nigeria. The principal results of the study revealed that firms adopting artificial intelligence (AI), cloud-based technology and the Internet of Things (IoT) exhibited significantly higher competitive-edge metrics compared to their counterparts. Notably, AI and cloud-based technology were found to have a particularly strong association with improved resource management, real-time monitoring, and decision-making processes. A major contribution of this research is the development of a DT-adoption model which can serve as a benchmarking tool for firms to assess their current adoption levels and identify areas for improvement. This model can also guide policymakers and regulators in developing strategies to encourage the integration of digital technologies within the heavy-construction industry. The originality of this study lies in its holistic approach, examining a broad spectrum of digital technologies and their collective impact on enhancing the competitive edge of construction firms.

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.