Augmented and virtual reality have the potential to provide a step-change in productivity in the construction sector; however, the level of adoption is very low. This paper presents a systematic study of the factors that limit and drive adoption in a construction sector–specific context. A mixed research method was employed, combining qualitative and quantitative data collection and analysis. Eight focus groups with 54 experts and an online questionnaire were conducted. Forty-two limiting and driving factors were identified and ranked. Principal component analysis was conducted to group the identified factors into a smaller number of factors based on correlations. Four types of limiting factors and four types of driving factors were identified. The main limitation of adoption is that AR and VR technologies are regarded as expensive and immature technologies that are not suitable for engineering and construction. The main drivers are that AR and VR enable improvements in project delivery and provision of new and better services. This study provides valuable insights to stakeholders to devise actions that mitigate the limiting factors and that boost the driving factors. This is one of the first systematic studies to present a detailed analysis of the factors that limit and drive adoption of AR and VR in the construction industry. The main contribution of this study is that it grouped and characterized myriad limiting and driving factors into easily understandable categories, so that the limiting factors can be effectively mitigated and the driving factors potentiated. A roadmap with specific short-term and medium-term actions for improving adoption was outlined.
Augmented and Virtual Reality in Construction: Drivers and Limitations for Industry Adoption
Technical Papers
Augmented and Virtual Reality in Construction: Drivers and Limitations for Industry Adoption
Juan Manuel Davila Delgado, Ph.D., A.M.ASCE; Lukumon Oyedele, Ph.D.;
Thomas Beach, Ph.D.; and Peter Demian, Ph.D.

Abstract
Authors:
Associate Professor, Big Data Enterprise and Artificial Intelligence Laboratory, Univ. of West of England Bristol, Coldharbour Lane, Bristol BS16 1WD, UK. Email: [email protected]
Chair Professor of Enterprise and Project Management, Big Data Enterprise and Artificial Intelligence Laboratory, Univ. of West of England Bristol, Coldharbour Lane, Bristol, BS16 1WD, UK (corresponding author). Email: [email protected]
Senior Lecturer, School of Engineering, Cardiff Univ., The Parade, Cardiff CF24 3AA, UK. ORCID: https://orcid.org/0000-0001-5610-8027. Email: [email protected]
Reader, School of Architecture, Building and Civil Engineering, Loughborough Univ., Loughborough LE11 3TU, UK. Email: [email protected]
Received: March 04, 2019
Accepted: December 12, 2019
Published online: May 11, 2020
©2020 American Society of Civil Engineers