Purpose: Construction 4.0 technology has the capabilities for improving the design, management, operations and decision making of construction projects. Therefore, this study aimed at examining the willingness of construction professionals towards adopting construction 4.0 technologies.Design/methodology/approach: The study adopts a survey design, and construction professionals in South Africa are assessed using a convenience sampling technique through a structured questionnaire. The questionnaire was analysed with SPSS while statistical test like; mean score, t-test and principal component analysis was used to present the data.Findings: The findings, from the analysis, revealed that the construction professionals are willing to adopt construction 4.0 technologies for construction project. However, the possibility of fully integrating the technologies into the construction industry is low. This is because the major technologies such as; Internet of things, robotics, human-computer interaction and cyber-physical systems that encourage smart construction site are rated as not important by the construction professionals.Practical implications: It is believed that the findings emanating from this study will serve as an indicator for investors that are interested in procuring construction 4.0 technologies for the construction industry.Originality/value: This paper presents a framework for the application of construction 4.0 technologies for the construction industry. It also contributes to the development of digitalising construction industry in South Africa.

Machine Learning (ML), a subset of Artificial Intelligence (AI), is gaining popularity in the architectural, engineering, and construction (AEC) sector. This systematic study aims to investigate the roles of AI and ML in improving construction processes and developing more sustainable communities. This study intends to determine the various roles played by AI and ML in the development of sustainable communities and construction practices via an in-depth assessment of the current literature. Furthermore, it intends to predict future research trends and practical applications of AI and ML in the built environment. Following the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines, this study highlights the roles that AI and ML technologies play in building sustainable communities, both indoors and out. In the interior environment, they contribute to energy management by optimizing energy usage, finding inefficiencies, and recommending modifications to minimize consumption. This contributes to reducing the environmental effect of energy generation. Similarly, AI and ML technologies aid in addressing environmental challenges. They can monitor air quality, noise levels, and waste management systems to quickly discover and minimize pollution sources. Likewise, AI and ML applications in construction processes enhance planning, scheduling, and facility management.

As the world continues to experience significant and dynamic changes, the concept of graduate employability remains a well-discussed subject in the body of knowledge. Consequently, the concept has attracted the interest of educators, policymakers, researchers and graduates themselves. As a vital cog in the employability conversation, the quality of present-day graduates is highly dependent on the effectiveness of training received from higher education institutions. This formal training provides learners with discipline-specific skills (academic skills) and knowledge which helps them obtain a firm foundation in their chosen discipline or profession. This study seeks to unearth the various discipline-specific skills (DSS) that built-environment graduates need to possess to thrive in the labor market after graduation. A quantitative research approach was adopted to achieve this study’s objective with close-ended questionnaires developed and administered to built environment professionals based in the Gauteng province of South Africa. Retrieved data were analyzed using several statistical tools such as percentage, frequency, Mean Item Score, One-Sample T-test and Exploratory Factor Analysis. Findings revealed four clusters highlighting the key DSS required by built environment graduates. These include lifelong learning, hands-on experience, digital literacy and knowledge of the subject area. The outcomes of this study will be beneficial to several stakeholders involved in construction education and employability skills discussion.

The major prevention for the COVID-19 pandemic is staying indoors. But research has discovered that COVID-19 can be spread indoors through the touching of building components. Therefore, this study makes a case for the development of contactless smart buildings for preventing the spread of coronavirus. However, most studies related to smart buildings are focused on energy consumption and occupants’ satisfaction while neglecting maintenance management challenges. Thus, this study examines the challenges of maintenance management of smart buildings. A random sampling technique was used in collecting data from facility, property and project managers. The data were collected with the aid of questionnaire and analysed using SPSS V 24, adopting mean score and factor analysis. The study discovered that there is a high prospect for developing smart buildings as a preventive mechanism against coronavirus. The major challenges confronting maintenance management of smart buildings are maintenance cost, occupants behaviour and maintenance strategy. The study recommends that the occupants must be involved in the maintenance management strategies of smart buildings. The study provides the roadmap for creating an innovative method of reducing the spread of coronavirus through construction management.

Purpose: The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.Design/methodology/approach: This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.Findings: The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.Originality/value: Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.