Big Data Adoption in Construction and Demolition Waste Management: Prevailing Challenges in Developing Nations

The composition of the construction and demolition waste (C&DW) materials produced in each area is not random; rather, it is influenced by the most popular building materials, technologies, and recycling rates. Big data is progressively promoted as a potent tool for effectively managing C&DW. However, big data applications in C&DW have gained little attention. Hence, this study uses the South African construction industry as a case study to examine the prevailing challenges to big data adoption in C&DW management. The study utilised a survey design. 125 questionnaires were distributed, and 96 were returned and considered appropriate for the study. The data analysis involved various statistical methods, including percentages, mean item scores, standard deviation, one-sample t-tests, and Kruskal-Walli tests. The results indicate that the significant prevailing challenges to big data adoption in construction and demolition waste management in developing nations are data integration mechanism, data analysis ability, data application ability, lack of organisational cooperation, data creditability, unwillingness to share data, and high initial import costs. This study, therefore, strongly recommends addressing these challenges, which will be crucial for successfully adopting big data adoption in C&DW. Strategies to overcome these challenges, such as targeted training programs, investment in infrastructure, and fostering a data-driven culture, should be considered. Also, by acknowledging and actively working to mitigate these challenges, stakeholders in the construction sector in developing nations can pave the way for more efficient, sustainable, and data-informed C&DW practices.