Bamidele Temitope Arijeloye

PURPOSE The study investigates the adoption of digital technologies (DTs) for sustainable waste management (SWM) in Lagos and Abuja construction sites. It aims to explore how these technologies improve construction sustainability and circular economy practices. DESIGN / APPROACH / METHODOLOGY The quantitative study method involved a structured survey of 342 Lagos and Abuja construction professionals. The study examined the importance and usage of IoT, BIM, AI, UAVs, and predictive analytics tools. Technologies were functionally clustered using exploratory factor analysis. FINDINGS The analysis revealed four primary clusters of DTs: predictive and monitoring tools, digital modeling platforms, smart data integration systems, and AI-based recognition tools. Although these technologies were perceived as highly relevant for SWM, their actual adoption was found to be limited. This gap was attributed to infrastructural deficiencies and regulatory constraints. RESEARCH LIMITATIONS / IMPLICATIONS While the survey provides significant insights, it is limited to Lagos and Abuja and might not adequately represent construction professionals in other regions of Nigeria. Further studies could include other urban and rural locations for greater generalizability. The findings show that SWM DT adoption requires integrated digital strategies, enhanced infrastructure, and enabling regulations. The study offers recommendations for stakeholders in the industry, government institutions, and professional bodies aiming to promote digital transformation in construction. ORIGINAL / VALUE This study expands empirical research on digital technology adoption for SWM in developing countries. It advances theoretical understanding and practical implementation of Construction 4.0 and circular economy in Nigeria’s construction sector by categorizing relevant DTs and identifying critical barriers.

PURPOSE This study investigates the barriers hindering the adoption of digital technologies (DTs) for sustainable waste management (SWM) in the Nigerian construction industry, with a focus on promoting circular economy practices and improved resource efficiency. DESIGN / APPROACH / METHODOLOGY A quantitative research design was adopted using a structured survey administered to 342 construction professionals across Lagos and Abuja. Exploratory factor analysis (EFA) was used to categorize and analyze the perceived barriers to DT adoption. FINDINGS The analysis revealed five major categories of barriers: financial, technical, legal, institutional, and behavioural. Key issues include limited access to finance, high implementation costs, lack of technical expertise, poor digital infrastructure, and weak regulatory frameworks. Notably, the Kruskal-Wallis test indicated no significant variation in how different professional groups perceive these barriers, highlighting the systemic nature of the barriers. RESEARCH LIMITATIONS / IMPLICATIONS This survey is limited to construction professionals in Lagos and Abuja, Nigeria, which may not reflect regional variations. However, the findings demonstrate specific strategies using empirical evidence. Policymakers, professional bodies, and construction firms can use the evidence to identify strategic investments, capacity building, and regulatory reforms to improve DT adoption for SWM. ORIGINAL / VALUE This study is one of the first to categorize DT adoption barriers for sustainable waste management in Nigeria’s construction sector. It supports data-driven policy and institutional reforms to accelerate digital transformation in emerging economies.

Public–Private Partnership (PPP) procurement is a relatively new approach in Nigeria’s housing sector. This study introduces a Fuzzy Risk Allocation Decision Model (FRADM) designed to address the complex and subjective nature of risk allocation in PPP-procured Mass Housing Projects (MHPs). A structured quantitative approach involving 40 purposively selected PPP housing experts was employed. Using a fuzzy synthetic evaluation (FSE) technique, critical risk factors were assessed based on partners’ risk management capabilities and allocation criteria. Constants (Ci) normalized the risk-carrying capacity indices (RCCIs) of both public and private sectors. Results show that risk attitude ranks highest among nine allocation criteria (MIS = 6.21), with the private sector demonstrating higher overall risk management capability. For instance, the availability of finance risk is optimally shared 53.48% to the private and 46.52% to the public sector. The FRADM was validated as reliable, practical, and replicable. Implications point to enhanced transparency, equitable risk-sharing, and support for SDG 11. The model is a strategic tool for decision-makers in PPP housing delivery in Nigeria and can inform similar efforts in other emerging economies. Further research should examine applications across other infrastructure sectors.