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Atuahene, B T, Kanjanabootra, S and Gajendran, T (2018) Towards an integrated framework of big data capabilities in the construction industry: A systematic literature review. In: Gorse, C and Neilson, C J (Eds.), Proceedings 34th Annual ARCOM Conference, 3-5 September 2018, Queen’s University, Belfast, UK. Association of Researchers in Construction Management, 547–556.
- Type: Conference Proceedings
- Keywords: improvement; information technology; innovation; monitoring; sensors; systematic literature review
- ISBN/ISSN: 978-0-9955463-2-5
- URL: http://www.arcom.ac.uk/-docs/proceedings/f09eff5747ca10d2378ebafb902e4c00.pdf
The construction industry is gradually expanding to blend its technical knowledge with innovative principles geared towards value creation. This is motivated by the industry’s attempt to overcome inefficiencies and ineffectiveness in the construction process. The desire for improvement has consequently necessitated construction researchers to champion the innovation agenda in the construction industry. Within the past decades, the wind of innovation has swept through industries causing the high demand on information technology applications. The construction industry depends on digital devices through the use of sensors, surveillance footages, smart devices and real-time in monitoring projects. Subsequently, the devices have led to generation of large datasets (volume), at an exponential and faster rate (velocity) and in different formats (variety), requiring data analytics to mine these data to improve processes. The volume, velocity and variety are key characteristics of big data as well as the analytics. Getting valuable insights from such data, has currently taken the centre-stage of digitalization in the business world, through big data applications. Though, extant literature have revealed benefits of big data in construction, but cognizance is not given to what it takes for the industry to realize to the fullest, the benefits of big data. This study presents a systematic literature review on big data capabilities across different disciplines. Further synthesis of articles led to the identification of four major big data capabilities. The findings together with antecedents of innovation from the Schumpeter theory of innovation led to conceptualization of a process framework for big data capabilities in the construction industry. The framework consists of antecedents to innovation, big data capabilities and outcomes (insight from data). The study addressed the gap of big data capabilities deficit in the construction industry. Practically, the framework can serve as a guide for construction organizations interested in adopting big data in their operations.