Strategic determinants of big data analytics in the AEC sector: a multi-perspective framework

Abstract

With constant flow of large data sets generated by different organisations, big data analytics promises to be a revolutionary game changer for Architecture, Engineering and Construction (AEC) industry. Despite the potential of Big Data, there has been little research conducted thus far to understand the Big Data phenomenon, specifically in the AEC industry. The objective of this research therefore is to understand the contributing factors for adopting big data in AEC firms. The investigation combined the perceived strategic value of BDA with the TOE framework (technology, organization, and environment), to develop and test a holistic model on big data adoption. A set of hypotheses derived from the extant literature was tested on data from structured surveys of about 365 firms, categorised as construction service firms (engineering and architecture) and construction firms (firms engaged in managing construction projects). The results indicated that the inhibitors and facilitators of BDA adoption are different in the construction services (architecture and engineering) and construction firms. For effective adoption of BDA solutions, the findings will guide the business managers to have realistic expectations of BDA integration challenges in AEC sector.

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

“…At the individual-level, the tendency of resistance to ‘Data Science’ project execution either by an employee or by a mid-level manager is either due to fear of ‘failure’ or ‘loss of control’ or ‘operational disruption’ (Mikalef et al 2020c ; Shahbaz et al 2019 ). The enablers in the form of developing dynamic capability, such as experience in ‘dealing with complexity’, ‘high tolerance for complexity’ (Gong and Janssen 2021 ; Walker and Brown 2019 ) and ‘Top-management-Team’ support (Alaskar et al 2020 ; Behl et al 2019 ; Chaurasia and Verma 2020 ; Foshay et al 2015 ; Halaweh and Massry 2015 ; Lai et al 2018 ; Lamba and Singh 2018 ; Lautenbach et al 2017 ; Popovič et al 2018 ; Ransbotham et al 2017 ; Verma and Bhattacharyya 2017 ; Walker and Brown 2019 ; Wang et al 2018c ) are a must to address the barriers to considerable extent. Organizational environment for an individual in communicating the benefits of ‘Data Science’ (Chakravorty 2020 ; Gong and Janssen 2021 ; Verma 2017 ) is also a barrier for ‘Data Science’ project success.…”

“…Establishing ‘Data Science’ projects require huge investments on skills and infrastructure (Behl et al 2019 ; De Luca et al 2020 ; Holland et al 2020 ; Lee et al 2017 ; Wu et al 2017 ), which is quite a big challenge for most organizations. Though it may not be avoided completely, infrastructure flexibility in identifying and using of compatible and complementary resources (Alaskar et al 2020 ; Chaurasia and Verma 2020 ; Mikalef and Gupta 2021 ; Moreno et al 2019 ; Shokouhyar et al 2020 ; Verma and Bhattacharyya 2017 ; Walker and Brown 2019 ) already existing in the organization can considerably reduce the burden on new investments. Lack of skills and knowledge (Ahmed et al 2018 ; Behl et al 2019 ; Dubey et al 2019b ; Lamba and Singh 2018 ; Foshay et al 2015 ; GalbRaith 2014 ; Mikalef et al 2020a , 2019b , 2020c ; Rialti et al 2019 ) required to execute the ‘Data Science’ projects can be addressed by setting up ‘Training & Knowledge Management’ capabilities and processes (Calvard 2016 ; Dam et al 2019 ; Ferraris et al 2019 ; Harlow 2018 ; Rialti et al 2020 ).…”

Review of Big Data Integration in Construction Industry Digitalization

“…3D printing, BIM, real-time data, cloud computing, AR, VR, drone scanning, BD, and The Internet of Things (IoT) are not all the tools used in today's world; the integration and deployment of these technologies is the future (You and Feng, 2020). Utilization of these technologies is the solution to breaking the challenges faced by the construction industry; also, it requires stakeholders who think fast and take smart decisions for the gross profits of their organizations and customers (Chaurasia and Verma, 2020). This adoption lets the construction sector players from the project manager to the workers practice the right and quick decisions, optimize design and sector automation, and decrease the risk of construction.…”

Big data analytics usage in the banking industry in Tanzania: does perceived risk play a moderating role on the technological factors

“…As organizations rely on erroneous or low-quality data for their analytics, it can result in incorrect insights and poor decisionmaking, ensuring data quality and accuracy is essential, and the risk of doing so can be a big worry (Alyoussef & Al-Rahmi, 2022). Accordingly, Chaurasia and Verma (2020) found that organizations must employ strong security measures and adhere to data protection laws to reduce the dangers of PRs. For instance, firms' worries about the future return on investment, the resources needed for data infrastructure, qualified staff, and the difficulty of developing and managing systems can impact BDA adoption decisions.…”