Big data framework for quantity surveying firms in Malaysia

Abstract

Big data emerges as a technology that improves decision making capability, optimizing productivity, and capable of generating a financial return in organizations across industries. Like many others, the benefit of big data is imminent, prompting construction organizations to redesign the conventional construction processes, thus stimulating change to the construction practices. While big data does improve productivity, any construction organizations which aspire to leverage its benefit will require a refreshed mindset and a new set of capabilities. Recognizing the importance of big data to the future of construction in Malaysia, there has been a strong push by the construction authorities for big data initiatives across organizations given the Construction Industry Transformation Programme (CITP) 2016-2020. Though the initiatives from CITP 2016-2020 managed to introduce big data to the construction organizations, there appear to be a fraction of construction organizations in Malaysia that are lagging behind the others to embrace big data. A clear case is Malaysian quantity surveying (QS) firms, where a limited big data adoption strategy was observed, creating a knowledge gap that hinders the Malaysian QS firm's capability to move forward with big data. Against this background, this research aims to develop a big data conceptual framework as a basis to support Malaysian QS firm's strategic big data adoption. The research outlines four objectives which include identifying big data potentials for QS, identifying attributes supporting QS firm's big data success, developing a conceptual big data framework for QS firms in Malaysia, and validating the big data framework for QS firms to support their strategic big data adoption. Adopting the TOE framework and the 5G innovation model as theoretical underpinnings, the research adopted Charmaz's grounded theory approach where sixteen QS with known experience in handling big data were contacted and interviewed. Data analysis revealed nine big data potentials for QS which are optimized data access, national cost data establishment, cost control data-driven decision making, project management data-driven decision making, development management data-driven decision making, work synchronization, data commercialization, diversifying professional services and strategic policy establishment. Likewise, seven big data attributes supporting the QS firm's big data success were identified which are data, people, technology, financial investment, strategic alignment, power, and collaboration. The conceptual framework demonstrates QS strategic big data adoption sequentially follows 'creating big data', 'big data buy-in', and 'revolutionizing through big data' phases. Each phase detailed specific big data potentials that the Malaysian QS firms can achieve, subject to the firm's resources and facilities availability. Framework validation was administered with the research participants and big data experts using a questionnaire survey to establish conformity. It was concluded that big data is a universal technology for the QS firms but, requires a unique set of big data attributes appraised from the peculiarities of its context of adoption. This research contributes by identifying big data potentials and attributes supporting big data success for QS firms. Further, it provides insights for policymakers, regulators, and authority bodies to strategically maximize their capabilities in advancing Malaysia's big data agenda.