Implementation of Artificial Intelligence for Labour Controlling in the Construction Site - Lim, Ai Nee; Kasim, Narimah; Shareh Musa, Sharifah Meryam; Mohd Noh, Hamidun (Research in Management of Technology and Business, 2024)
This research presents a survey on the implementation of artificial intelligence for labour control in construction sites. The construction industry is famously slow to accept new technologies, and this has been a persistent issue for years. The common problems of low-skilled labour participation in construction have been attributed to unfair wages, poor safety on construction sites, a lack of defined career paths, a decline in skilled labour training programs, and delays in the on-site work schedule. Therefore, this research was carried out to identify challenges, potential, and steps to improve the Artificial intelligence implementation for labour control in the construction site. To accomplish the objectives, the research was conducted with the quantitative approach by using questionnaires as the instrument to collect the primary data. There were 234 respondents among G7 contractors selected at Penang state for this research. For this research, 132 respondents have been successfully collected. Statistical Package for the Social Science (SPSS) software was utilized to analyse the collected data in descriptive methods to determine the frequency and mean score values. Besides, secondary data information was obtained through various references such as journals, articles, websites, or reports related to the implementation of artificial Intelligence for labour control in the construction site. Moreover, the result showed that the biggest challenges of AI implementation was higher cost for hiring AI expert, and the potential of AI implementation was providing critical information on threats to labour, as well as the improvement steps of AI implementation was providing an AI training program. In conclusion, this research provided a better understanding of the AI implementation for labour controlling at construction sites.