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 bigdata potentials and attributes supporting big data success for QS firms. Further,  insights for policymakers, regulators, and authority bodies to strategically maximize their capabilities in advancing Malaysia’s big data agenda.

Nigerian construction sector relies on the ability of the industry to train and produce sufficient skilled workers to sustain its operation. To maintain consistent entrants into the construction labor market, TVET was exerted to enhance the capacity-building, which offered a pathway to train and provide the skilled workers required for the industry. Though the TVET was expected to produce out turns which will be able to fulfil the construction industry needs, it was reported that only 50 percent of the total enrollment made their way to join the rank in the industry. For this reason, past studies on construction skilled workers capacity building framework were examined and several social, economic, and organizational factors were identified for hindering construction skilled workers required in construction. It appears that the problem lies with the construction industry respond to the changing labor market requirements, while trying to meet projected demand of construction skilled workers required in construction. Hence, against this backdrop, this research aimed to develop a construction skilled workers capacity building framework with a view for improving the construction skilled workers required by the industry. This aim was achieved through the investigation of the types of construction skilled workers required, evaluating the factors hindering the construction skilled workers required and the approaches that negate the factors hindering the construction skilled workers required. A self-administered questionnaire was administered on a stratified random sample of 576 construction professionals in Abuja with 290 valid responses recorded. Data were first analysed by way of Analysis of Variance (ANOVA) which showed that the groups of construction professionals surveyed did not significantly differ in their views (p<0.05), followed by Relative Importance Index (RII), Rank Agreement Factor (RAF) and Percentage Rank Agreement Factor (PRAF). Findings revealed that the types of construction skilled workers required are equipment operators, glaziers, asphalt/tar sprayers, insulating specialist, fabricators, scaffolding specialist, suspended ceiling specialist, plumbers, electricians, and roofers. While factors hindering the construction, skilled workers required are health and safety issues on construction sites, low remuneration, absence of clear career path, lack of motivation, poorly equipped training workshops, government policies on construction industry, workforce high mobility, technological advancement and diminishing building trades training programs. The six significant approaches that negate the factors hindering construction skilled workers required are the provision of safety and health care services, increase remuneration of skilled workers, redesign of career paths and promotion systems of skilled workers, provision of incentives scheme, development of construction industry central regulatory agency and funding of vocational training centers, which together form the basis in developing the research conceptual framework. The conceptual framework provides construction regulatory bodies with a basis to make and prioritized decisions aiming in forming an adequate pool of construction skilled workers in line with the planned investment under Economic Recovery and Growth Plan (ERGP) of the new Nigerian government. Hence, it is recommended that there should be a collaboration between construction regulatory bodies in the introduction of policies and incentives to encourage new entrants and reduce attrition of existing skilled workers in the construction industry.

Developing countries will attain the benefits from using resources efficiently, reduce waste and pollution, and assess actions they made towards the environment. These endeavors are what sustainability really is, which traverses many industries which include construction. Sustainable construction which aligns construction and Sustainable Development Goal (SDG) tenets adopted and implemented in several developed and developing countries appears to bring advantage to life. For this reason, there is an urgency for the Nigerian construction industry to adapt to sustainability following the advantages it proffers. Though evidence on the benefit of practicing sustainable construction in other countries abound, it was observed that its practice in Nigeria remained an occasional undertaking. Sustainable construction strategies offered in the literature, despite seemed suitable for developing countries like Nigeria somehow are less relatable to the local context, where the disintegration of practices and poor grasp among construction professionals had curtailed the strategies normative and dogmatic. Hence, it is postulated that its practice pivots upon a complex understanding by construction professionals of its concept and application, where current discordance somehow leads to its diminutive consideration in the industry.  Therefore, this research aims to develop a sustainable construction framework for professionals in the Nigerian construction industry. The framework outlines the effect of the significant factors determined to characterize sustainable construction understanding among Nigerian construction professionals to establish a framework suitable for Nigeria. The research adopted a quantitative research design by firstly identifying factors that characterize sustainable construction understanding and secondly determining the significant factors for implementing sustainable construction practices. The research also determined the effects of the factors that characterize sustainable construction understanding on factors significant to implementing sustainable construction practices. A questionnaire was designed where a small-scale pilot study was first carried out before the actual survey to test the instrument reliability. It was found that the value of α was greater than 0.9 hence confirming its reliability. The self-administered survey was administered through a stratified random sample of 585 construction professionals in Abuja, with 290 feedbacks were received. The coded data were first subjected to a normality test where the Shapiro-Wilk test showed that the data were normally distributed. An analysis of variance showed there is no significant difference in the responses from the respondents (p < 0.05). Sequentially, exploratory and confirmatory factor analyses using PLS-SEM were carried out in conformance to the objectives of this research. Findings showed that factors that characterize sustainable construction understanding in Nigeria are attitude, awareness, demand, financial, knowledge, passive culture, and political. At the same time, significant factors for implementing sustainable construction practices are design and techniques, education and training, environment, economic, procurement, and social. The factors that characterize sustainable construction have a significant positive effect (β=0.572) on factors significant for implementing sustainable construction practices, which means the practice of sustainable construction appears to rely much on a progressive understanding of the implementation factors identified in this research. The sustainable construction framework proposed provides construction regulatory bodies in Nigeria with proactive approaches in implementing sustainable construction practices in the industry.

Considerable effort to leverage digital data was exerted as construction adopts innovative work processes. Among these, BIM sat at the backbone of digital strategy, with the capability to create huge digital data through its realizable multifunctionality and integration capacity. Ubiquitous to construction, the explosion of huge data through BIM sparked the relevancy of big data. While it was logical that the volume of BIM data would swell from the adoption of BIM, question arose whether the intensity from the effort expanded to create BIM data led to big data progression. With an incognizant state of intensity being imposed as reported in previous studies, it was rather perplexing to concede the infusion of big data in construction and anticipate its way forward. It was rather persuasive therefore, to explore the relationship between the effort vested from creating BIM data and the big data, with a view to proffer big data progression in construction.  Hence, the aim of this research was to establish a preliminary framework of BIM data creation for big data progression in construction. The objectives of this research were to identify the associations between the types of BIM data and big data attributes, to determine the relationship between the effort of BIM data creation and big data progression and to develop a framework for the initial indication of BIM data creation and big data progression. A quantitative research design was adopted with a survey questionnaire develop from the output of the systematic review. A pilot study was conducted before the actual survey, where α was greater than 0.8, hence confirming its reliability. The survey was administrated to 400 BIM modellers in the Klang Valley identified through simple random sampling where 112 feedbacks were received. The coded data were first subjected to a normality test which the data found not normally distributed. Thus, Spearman’s Rho non-parametric analysis has been carried out. A preliminary analysis using the scatterplots was conducted to check outliers and data points prior to the analysis. Results from data analysis showed that out of the 20 groups of variables tested for correlation, 11 exhibited strong correlation (r = .5 – 1.), 5 exhibited medium correlation (r = .3 – .49) while 4 exhibited weak to almost no correlation (r = .0 – .29). Findings also showed that once the categories were classified into their respective main classifications, 3 out of 5 categories which are effort intensity of material, design and schedule data creation reflected a strong correlation with big data progression. On contrary, cost and operation and maintenance data creation appeared to be negatively low and medium correlation respectively which is justified with irrefutable reasons. Thus, it was observed from the findings that despite the considerable effort vested to create BIM data, not all types of BIM data created so far would lead towards the big data progression. This despite collectively, the coefficient value for correlation between effort intensity of BIM data creation and big data progression was r = .60, which illustrated a strong relationship. Overall, it was concluded that the alternative hypothesis was accepted where the higher the effort intensity in BIM data creation, the higher the fulfilment of big data attributes in the BIM platform. This research contributes by developing a preliminary framework for earlier attestation of effort in BIM data creation and precursor for future research which is expected to gradually lead to the realization of big data in construction.