Adoption of Big Data analytics in construction: development of a conceptual model. Issue 4 (9th September 2019)

• Record Type: Journal Article
• Authors: Ram, Jiwat; Afridi, Numan Khan; Khan, Khawar Ahmed
• Is Part Of: Built environment project and asset management. Volume 9:Issue 4(2019)
• Page Start: 564
• Page End: 579
• Publication Date: 2019-09-09
• Subjects: Sustainability -- Technological innovation -- BIM -- Project management -- Asset management -- Data analysis; Construction industry -- Management -- Periodicals; Construction projects -- Management -- Periodicals; Public works -- Management -- Periodicals; Sustainable buildings -- Periodicals; 624.068
• DOI: 10.1108/BEPAM-05-2018-0077
• Languages: English
• ISSNs: 2044-124X

Abstract

Abstract : Purpose: Big Data (BD) is being increasingly used in a variety of industries including construction. Yet, little research exists that has examined the factors which drive BD adoption in construction. The purpose of this paper is to address this gap in knowledge. Design/methodology/approach: Data collected from literature (55 articles) were analyzed using content analysis techniques. Taking a two-pronged approach, first study presents a systematic perspective of literature on BD in construction. Then underpinned by technology–organization–environment theory and supplemented by literature, a conceptual model of five antecedent factors of BD adoption for use in construction is proposed. Findings: The results show that BD adoption in construction is driven by a number of factors: first, technological: augmented BD–BIM integration and BD relative advantage; second, organizational: improved design and execution efficiencies, and improved project management capabilities; and third, environmental: augmented availability of BD-related technology for construction. Hypothetical relationships involving these factors are then developed and presented through a new model of BD adoption in construction. Research limitations/implications: The study proposes a number of adoption factors and then builds a new conceptual model advancing theories on technologies adoption in construction. Practical implications: Findings will help managers (e.g. chief information officers, IT/IS managers, business and senior managers) to understand the factors that drive adoption of BD in construction and plan their own BD adoption. Results will help policy makers in developing policy guidelines to create sustainable environment for the adoption of BD for enhanced economic, social and environmental benefits. Originality/value: This paper develops a new model of BD adoption in construction and proposes some new factors of adoption process.