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Purpose This paper aims to focus on 11 digital technologies (i.e. building information modeling, artificial intelligence and machine learning, 3D scanning, sensors, robots/automation, digital twin, virtual reality, 3D printing, drones, cloud computing and self-driving vehicles) that are portrayed in future trend reports and hype curves. The study concentrates on the current usage and knowledge of digital technologies in the Swedish architecture, engineering and construction (AEC) industry to gain an insight in the possible expectations and future trajectory of these digital technologies. Design/methodology/approach The study applies an abductive approach which is based on three different types of methods. These methods are a literature and document study which focused on 11 digital technologies, two workshops with industry (13 participants) and an online survey (N = 84). Findings The paper contributes to a current state analysis of the Swedish AEC industry concerning digital technologies and discusses the trajectory of these technologies for the AEC industry. The paper identifies hype factors, in which the knowledge of a digital technology is related to its usage. From the hype factors, four zones that show different stages of digital technology usage and maturity in the industry are induced. Originality/value The contribution of the paper is twofold. The paper shows insight into opportunities, the current barriers, use and knowledge of digital technologies for the different actors in the AEC industry. Furthermore, the study shows that the AEC industry is behind the traditional Gartner hype curves and contributes with defining four zones for digital technologies for the Swedish AEC industry: confusion, excitement, experimentation and integration.

PurposeOff-site construction (OSC) has been regarded as a clean and efficient production approach to help the construction industry towards sustainability. Different levels of OSC technologies vary greatly in their implementations and adoptions. Compared to low OSC level technologies have been applied widely, the adoption of high OSC level technologies (HOSCLTs) in practice remains limited. The adoption mechanism for HOSCLTs by firms has not been clear, hindering their promotion. This study aims to explore the mechanism combining subjective and objective adoption for HOSCLTs.Design/methodology/approachThis study developed an integrated model illustrating mechanism for HOSCLTs adoption based on the technology acceptance model (TAM), which has strong capacity to explain potential adopters' subjective intentions to adoption, and the task-technology fit (TTF) theory, which well describes the linkages between the task, technology and performance in technology adoption. The proposed model was then empirically evaluated through a survey of 232 practitioners in the Chinese OSC industry using partial least squares structural equation modeling.FindingsThe results indicate that both task characteristics (TAC) and technology characteristics (TEC) positively affect TTF of HOSCLTs. TAC, TTF, firm conditions and stakeholder influence have significant positive effects on perceived usefulness (PU), which further positively influence attitude towards adoption. TEC and firm conditions are significantly related to perceived ease of use (PEU). TTF, PEU and attitude towards adoption are good predictors of behavior intention to HOSCLTs adoption. PEU only significantly influences adoption intention and is not observed to influence attitudes and PU, unlike prior research on common OSC adoption.Originality/valueThis study contributes to the body of knowledge by exploring HOSCLTs adoption in the industry based on distinguishing the levels of OSC technologies and supplementing an integrated model for explaining the mechanism with the combination of subjective and objective adoption. The study also provides useful insights into understanding and promoting HOSCLTs adoption for policy makers and stakeholders actively involved in the OSC field.

Purpose The practice of artificial intelligence (AI) is increasingly being promoted by technology developers. However, its adoption rate is still reported as low in the construction industry due to a lack of expertise and the limited reliable applications for AI technology. Hence, this paper aims to present the detailed outcome of experimentations evaluating the applicability and the performance of AI object detection algorithms for construction modular object detection. Design/methodology/approach This paper provides a thorough evaluation of two deep learning algorithms for object detection, including the faster region-based convolutional neural network (faster RCNN) and single shot multi-box detector (SSD). Two types of metrics are also presented; first, the average recall and mean average precision by image pixels; second, the recall and precision by counting. To conduct the experiments using the selected algorithms, four infrastructure and building construction sites are chosen to collect the required data, including a total of 990 images of three different but common modular objects, including modular panels, safety barricades and site fences. Findings The results of the comprehensive evaluation of the algorithms show that the performance of faster RCNN and SSD depends on the context that detection occurs. Indeed, surrounding objects and the backgrounds of the objects affect the level of accuracy obtained from the AI analysis and may particularly effect precision and recall. The analysis of loss lines shows that the loss lines for selected objects depend on both their geometry and the image background. The results on selected objects show that faster RCNN offers higher accuracy than SSD for detection of selected objects. Research limitations/implications The results show that modular object detection is crucial in construction for the achievement of the required information for project quality and safety objectives. The detection process can significantly improve monitoring object installation progress in an accurate and machine-based manner avoiding human errors. The results of this paper are limited to three construction sites, but future investigations can cover more tasks or objects from different construction sites in a fully automated manner. Originality/value This paper’s originality lies in offering new AI applications in modular construction, using a large first-hand data set collected from three construction sites. Furthermore, the paper presents the scientific evaluation results of implementing recent object detection algorithms across a set of extended metrics using the original training and validation data sets to improve the generalisability of the experimentation. This paper also provides the practitioners and scholars with a workflow on AI applications in the modular context and the first-hand referencing data.

PurposeThis study aims to evaluate and investigate the dynamics of the barriers to building information modelling (BIM) adoption from the perspective of small and medium-sized enterprises (SMEs) in developing countries with the Nigerian construction industry as a case study.Design/methodology/approachAn interpretive structural modelling approach was adopted to develop a hierarchical model of the interrelationships of the barriers. Also, the Matrice d'Impacts croises-multipication applique a classement analysis was used for categorisation of the barriers.FindingsThe findings revealed that the barriers are from a sociotechnical context and that SMEs have the will to drive BIM adoption by focussing more on their internal environment.Originality/valueThis study presented the adoption of BIM in SMEs, which is underrepresented in extant studies. Also, it contributes to the nascent discussion of BIM from the perspective of SMEs in developing countries.

Purpose Emerging Construction Industry 4.0 technologies raise serious questions for construction companies when deciding whether to adopt or reject emerging technologies. Vendors seek to understand what factors are involved in how construction companies make these decisions and how they might vary across different companies. This paper aims to present a systematic, technology adoption decision-making framework for the construction industry which includes the key steps required for the final decision being made by companies up to the commencement of the operation of the technology. Design/methodology/approach A total of 123 experienced practitioners were interviewed to identify a broad range of tasks relevant to decision-making. Participants known as customers or vendors were chosen to validate the findings of each group by using data triangulation methods. A systematic thematic analysis method was applied in the NVivo environment to analyse the data. Findings This study identifies the active role of vendors who need to understand how their customers arrive at decisions to increase the rate of technology adoption. This paper also provides insights to new companies and late adopters (reported greater than 50%) about how others arrived at their decisions. Originality/value Unlike other technology adoption models, this paper investigates vendors’ corresponding interactions during the decision-making process. This paper also goes beyond previous studies, which focussed on the individual customer’s intention to use a specific technology at a single-stage by developing a multi-stage framework to enable understanding the details of the decision process at the organisational level.

PurposeThis paper aims to explore UAE's transition towards circular economy (CE) through construction and demolition waste (CDW) management in the pre-construction stage. The extent of circularity is assessed by five key aspects of CE, such as policies and strategic frameworks, design for waste prevention, design for disassembly or deconstruction, use of prefabricated elements and CDW management plans.Design/methodology/approachMultiple case studies were conducted in the context of the Dubai construction industry (UAE). Three significant and unique construction projects were selected as the cases. Semi-structured interviews were carried out to collect data, and the thematic analysis technique and NVIVO 12 software were used for data analysis.FindingsFindings reveal several positive initiatives towards CE in the UAE context; yet it is identified that the transition is still at the initial stage. Selected case studies, the best-case scenarios of UAE (i.e. influential cases), demonstrated adequate measures in relation to four key CE aspects out of five. For instance, (a) policies and strategic frameworks such as lean standards, green building standards and standards developed by the local authorities, (b) design for waste prevention (e.g. adherence to the 3R principle, and construction planning with BIM), (c) use of prefabricated elements and application of innovative construction technologies (e.g. 3DPC, DfMA) and (d) CDW management planning such as 3R principle were evident. However, the selected cases hardly showcase designing for disassembly or deconstruction.Research limitations/implicationsThe existing CDW practices are mostly conventional, as most constructions in UAE are procured through conventional building materials and methods. Therefore, there is a necessity of encouraging CE principles in CDW management. Even though the transition towards CE was evident in four key CE aspects out of five, the UAE construction industry has yet to adopt more effective CE-based CDW management practices to accelerate the circularity. Hence, it is necessary to enforce standard waste management guidelines, including the 3R principle, to standardise CDW management in UAE and encourage construction practitioners to adhere to CE principles.Originality/valueThe findings of this study provide valuable insights for decision-making processes around CDW management towards a CE. This paper contributes to the literature by bridging the CE concept with CDW management in the pre-construction stage. The study provides insights for industry practitioners for planning CE in terms of policies and strategic frameworks, CDW management planning, construction planning and application of innovative construction technologies.

Due to the complexity, high-risk and conservative character of construction companies, advanced digital technologies do not become widely adopted in the short term, while vendors make determined efforts to overcome this and disseminate their technologies. This paper presents the methods of an investigation addressing the extremely complex issues related to the current practices of digital technology adoption in construction. It discusses how construction companies follow a specific logical process linked to need, project objectives, the characteristics of the adopting organization and the characteristics of the new technology to be adopted. The study aims to demonstrate a novel method of data collection and analysis, such as data and methodological triangulation techniques, including the use of NVivo and AHP to explore how companies make the decision to uptake new technology (e.g., advanced crane, tunnel boring machine or drones) by focusing on customer and vendor activities, their interactions, contributing factors and people involved in the process. The major original contribution of this paper is developing an innovative methodological cube for investigating the Construction Technology Adoption Process (CTAP) covering technology adoption, acceptance, diffusion and implementation concepts. CTAP is a framework that delineates the phases of the process that customer organizations use when deciding to adopt a new digital technology and the parallel vendor activities. The significance of these contributions is that they enable vendors to understand how to match their strategies with customer expectations in each phase of CTAP. It also provides a benchmark for new construction companies to use the current best practices of decision making. Future research is warranted to more clearly delineate any differences with respect to developing nations or related industries such as mining and property management.

Purpose Government initiatives to improve construction have increasingly become more focused on introducing a repertoire of technologies to transform the sector. In the literature on construction industry transformation through policy-backed initiatives, how firms will respond to the demands to adopt and use innovative technologies and approaches is taken for granted, and there is scarcely any attention given to the institutional implications of transformation agenda. The purpose of this paper is to discuss these gaps and offer directions for future research. Design/methodology/approach Following a synthesis of literature on the UK’s industry transformation agenda, the authors use the concepts of institutional logics, arrangements, complexity and strategic responses to suggest seven research questions that are at the nexus of policy-backed transformation and institutional theory. Findings In this paper, the authors argue that increasing demands for the adoption and use of digital technologies, platforms, manufacturing approaches and other “industry-4.0”-related technologies will reconfigure existing logics and arrangements in the construction industry, creating a problem of institutional complexity for general contracting firms in particular. Originality/value The questions are relevant for our understanding of the nature of institutional complexities, change, strategic firm responses, field-level dynamics and implications for the construction industry in relation to the transformation agenda. This paper is positioned to spur future research towards exploring the consequences of industry transformation through the lens of institutional theory.

Purpose This study aims to examine relationships between several key technology acceptance variables that predict workers’ wearable sensing devices (WSDs) acceptance in the construction industry by using technology acceptance model, theory of planned behavior and unified theory of acceptance and use of technology (UTAUT) model. The study proposes a hybrid conceptual model to measure construction field workers’ intentions to use WSDs and their usage behaviors. The study introduces variables that are instrumental in understanding and improving WSD acceptance in construction. Design/methodology/approach The study was carried out using a structured literature review, online survey and structural equation modeling. A total of 195 field workers across the USA, with experience in using WSDs, participated in the study. Findings Results indicate that all three theories predict WSD acceptance with variables explaining at least 89% of the variance in actual use, with the UTAUT outperforming other models (91%). However, the differences between the predictive power of these models were not statistically significant. A hybrid conceptual model is proposed using findings from the present study. Practical implications The study contributes to knowledge and practice by highlighting key variables that influence WSD acceptance. Findings from this study should provide stakeholders with critical insights needed to successfully drive WSD acceptance in the construction industry. Originality/value To the best of the authors’ knowledge, this is the first study that evaluates the predictive strength of multiple technology acceptance theories and models within the construction worker safety technology domain. Additionally, the study proposes a hybrid conceptual model which could provide practitioners and researchers with information pertinent to enhancing WSD acceptance.

Purpose The purpose of this paper is to examine the relationship between overcoming the value management (VM) implementation barriers and VM implementation in the Egyptian building sector. Design/methodology/approach A critical review of the literature on VM was used to through bibliometric analysis has been conducted to highlight the studies’ gap and establish the VM barriers. These obstacles were then contextually transformed via a semi-structured interview and a pilot study, and subsequently organized in the form of a theoretical model. The primary data was collected from 335 building stakeholders in Egypt through the administration of questionnaire surveys. Consequently, structural equation models of partial least squares were applied to statistically assess the final model of VM barriers. Findings The bibliometric analysis shows that there is an inadequate study on VM implementation barriers in the Egyptian construction industry and insufficient studies on implementing VM in developing countries. Results obtained from the proposed model showed that overcoming the VM barriers has a major connection with successful VM implementation. This is indicated with the value of ß = 0.743, which is necessary when the firm is overcoming 1 unit of VM barriers. Originality/value This study fills the knowledge gap by identifying and emphasizing the critical obstacles to VM implementation.