Big data framework for quantity surveying firms in Malaysia

Big data framework for quantity surveying firms in Malaysia

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 conventiona...

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Main Author: Maaz, Zafira Nadia
Format: Thesis
Language:English
Published: 2020
Subjects:
TH434-437 Quantity surveying
Online Access:http://eprints.utm.my/id/eprint/96225/1/ZafiraNadiaMaazPFABU2020.pdf.pdf
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spelling my-utm-ep.962252022-07-05T03:34:27Z Big data framework for quantity surveying firms in Malaysia 2020 Maaz, Zafira Nadia TH434-437 Quantity surveying 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. 2020 Thesis http://eprints.utm.my/id/eprint/96225/ http://eprints.utm.my/id/eprint/96225/1/ZafiraNadiaMaazPFABU2020.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:142745 phd doctoral Universiti Teknologi Malaysia Faculty of Built Environment & Surveying
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
language English
topic TH434-437 Quantity surveying
spellingShingle TH434-437 Quantity surveying
Maaz, Zafira Nadia
Big data framework for quantity surveying firms in Malaysia
description 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.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Maaz, Zafira Nadia
author_facet Maaz, Zafira Nadia
author_sort Maaz, Zafira Nadia
title Big data framework for quantity surveying firms in Malaysia
title_short Big data framework for quantity surveying firms in Malaysia
title_full Big data framework for quantity surveying firms in Malaysia
title_fullStr Big data framework for quantity surveying firms in Malaysia
title_full_unstemmed Big data framework for quantity surveying firms in Malaysia
title_sort big data framework for quantity surveying firms in malaysia
granting_institution Universiti Teknologi Malaysia
granting_department Faculty of Built Environment & Surveying
publishDate 2020
url http://eprints.utm.my/id/eprint/96225/1/ZafiraNadiaMaazPFABU2020.pdf.pdf
_version_ 1747818649726484480

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