Explores how digital technologies influence women's retention in construction, suggesting strategies for improvement.
Women make up less than 15% of the UK construction workforce and continue to face major retention challenges, driven by structural biases that lead to feelings of disrespect, insufficient support, and being undervalued. This study takes a novel approach by applying Actor-Network Theory (ANT) to investigate how digital technologies (as non-human actors) influence the retention of women (as human actors) in the industry- a perspective that has been overlooked in previous research. Utilising data from 23 qualitative interviews with women involved in digitally enabled projects, the research develops a socio-technical framework that connects the functions of digital technology to the concepts of respect, support, and value (RSV). The interviews were analysed thematically using NVivo 13 to identify retention challenges and how women interact with digital technologies. The findings reveal several retention issues, including rigid work practices, a predominantly masculine culture, and occurrences of bullying and harassment. Importantly, the study shows that technologies like Building Information Modelling (BIM), Artificial Intelligence (AI), and online mentoring platforms do more than enhance operational efficiency; they actively reshape workplace dynamics to promote inclusivity and improve women's perceptions of respect, support, and value. By employing ANT, this research underscores the strategic potential of digital technologies in addressing systemic challenges within the construction sector. This is the first study to establish a conceptual link between digitalisation and gender equity, and it offers practical strategies for construction firms to improve retention by focusing on respect, support, and perceived value.
No takes yet. Share an insight, caveat, or question.
Blay et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: