Construction practitioners’ perception of key drivers of reputation in mega-construction projects

Purpose: The purpose of this study is to commence the discourse on the non-inclusiveness of the dynamics of reputation within the construction industry by identifying and examining the key product and process drivers of reputation in mega-construction projects. Design/methodology/approach: Data was collected through an exploratory sequential mixed methods approach which commences with a qualitative study and culminates with a quantitative study to identify product and process drivers of reputation in mega-construction projects. Findings: The findings suggest that “project quality”, “robust social and environmental sustainability plan”, “project team competence and interpersonal relationship” and “project process efficacy” are the four key drivers influencing the reputation of mega-construction projects. Research limitations/implications: The findings of this study are solely based on the perception of UK construction practitioners; therefore, the results may only be considered valid in this context. The identification of these key drivers provides a pathway where stakeholders, professionals and organisations can identify and prioritise critical issues associated with enhancing and sustaining the reputation of mega-construction projects. Originality/value: Findings of this research make a significant contribution to the discourse on the concept of reputation within the construction industry by identifying its specific drivers of reputation

Critical success factors (CSFs) for motivating end-user stakeholder’s support for ensuring sustainability of PPP projects in Nigerian host communities

© 2021, Emerald Publishing Limited. This is the accepted manuscript version of an article which has been published in final form at http://dx.doi.org/10.1108/JEDT-04-2021-0202Purpose This study aims to investigate two public private partnership (PPP) road projects in Nigeria for exploring factors that can motivate end-user stakeholders for contributing towards sustaining a PPP project in the long-term. Design/methodology/approach Using a case study methodology approach, this study adopts two-way data collection strategies via in-depth interviews with PPP experts and end-user stakeholders in Nigeria host communities and a questionnaire survey to relevant stakeholders. Findings The study identifies an eight-factor structure indicating critical success factors for ensuring end-user stakeholders support PPP projects on a long-term basis in their host communities. Originality/value Results of the study have huge implications for policymakers and project companies by encouraging the early integration of far-sighted measures that will promote long-term support and sustainability for PPP projects amongst the end-user stakeholders.Peer reviewe

Insolvency of small civil engineering firms: Critical strategic factors

© 2016 American Society of Civil Engineers. Construction industry insolvency studies have failed to stem the industry's high insolvency tide because many focus on big civil engineering firms (CEF) when over 90% of firms in the industry are small or micro (S&M). This study thus set out to uncover insolvency criteria of S&M CEFs and the underlying factors using mixed methods. Using convenience sampling, the storytelling method was used to execute interviews of 16 respondents from insolvent firms. Narrative and thematic analysis were used to extract 17 criteria under 2 groups. Criteria were used to formulate a questionnaire, of which 81 completed copies were received and analyzed using Cronbach's alpha coefficient and relevance index score for reliability and ranking, respectively. The five most relevant criteria were economic recession, immigration, too many new firms springing up, collecting receivables, and burden of sustainable construction. The four underlying factors established through factor analysis were market forces, competence-based management, operations efficiency and other management issues, and information management. The factors were in line with Mintzberg's and Porter's strategy theories. The results demonstrate that insolvency factors affecting big and small CEF can be quite different and, sometimes, even opposite. This research will provide a unique resource on the factors that should make potential owners of S&M CEF cautious. The criteria are potential variables for insolvency prediction models for S&M CEFs

Reducing waste to landfill: A need for cultural change in the UK construction industry

© 2015 Elsevier Ltd. All rights reserved. Owing to its contribution of largest portion of landfill wastes and consumption of about half of mineral resources excavated from nature, construction industry has been pressed to improve its sustainability. Despite an adoption of several waste management strategies, and introduction of various legislative measures, reducing waste generated by the industry remains challenging. In order to understand cultural factors contributing to waste intensiveness of the industry, as well as those preventing effectiveness of existing waste management strategies, this study examines cultural profile of construction industry. Drawing on four focus group discussions with industry experts, the study employs phenomenological approach to explore waste inducing cultural factors. Combining findings from phenomenological research with extant literatures, the study suggests that in order to reduce waste intensiveness of the construction industry, five waste inducing cultural factors need to be changed. These include (i) "make-do" understanding that usually result in "make-do waste" (ii) non-collaborative culture, which results in reworks and other forms of wasteful activities (iii) blame culture, which encourages shifting of waste preventive responsibilities between designers and contractors, (iv) culture of waste behaviour, which encourages belief in waste inevitability, and (v) conservatism, which hinders diffusion of innovation across the industry. Changing these sets of cultural and behavioural activities is not only important for engendering waste management practices; they are requisite for effectiveness of existing strategies. Improvement in the identified areas is also required for overall improvement and general resource efficiency of the construction industry. Thus, this paper advocates cultural shift as a means of reducing waste landfilled by the construction industry, thereby enhancing sustainability and profitability of the industry

Critical factors for insolvency prediction: Towards a theoretical model for the construction industry

© 2016 Informa UK Limited, trading as Taylor & Francis Group. Many construction industry insolvency prediction model (CI-IPM) studies have arbitrarily employed or simply adopted from previous studies different insolvency factors, without justification, leading to poorly performing CI-IPMs. This is due to the absence of a framework for selection of relevant factors. To identify the most important insolvency factors for a high-performance CI-IPM, this study used three approaches. Firstly, systematic review was used to identify all existing factors. Secondly, frequency of factor use and accuracy of models in the reviewed studies were analysed to establish the important factors. Finally, using a questionnaire survey of CI professionals, the importance levels of factors were validated using the Cronbach's alpha reliability coefficient and significant index ranking. The findings show that the important quantitative factors are profitability, liquidity, leverage, management efficiency and cash flow. While important qualitative factors are management/owner characteristics, internal strategy, management decision making, macroeconomic firm characteristics and sustainability. These factors, which align with existing insolvency-related theories, including Porter's five competitive forces and Mintzberg's 5Ps (plan, ploy, pattern, position and perspective) of strategy, were used to develop a theoretical framework. This study contributes to the debate on the need to amalgamate qualitative and quantitative factors to develop a valid CI-IPM

Designing out construction waste using BIM technology:Stakeholders’ expectations for industry deployment

The need to use Building Information Modelling (BIM) for Construction and Demolition Waste (CDW) minimisation is well documented but most of the existing CDW management tools still lack BIM functionality. This study therefore assesses the expectations of stakeholders on how BIM could be employed for CDW management. After a review of extant literature to assess the limitations of existing CDW management tools, qualitative Focus Group Interviews (FGIs) were conducted with professionals who are familiar with the use of BIM to understand their expectations on the use of BIM for CDW management. The 22 factors identified from the qualitative data analyses were then developed into a questionnaire survey. The exploratory factor analysis of the responses reveals five major groups of BIM expectations for CDW management, which are: (i) BIM-based collaboration for waste management, (ii) waste-driven design process and solutions, (iii) waste analysis throughout building lifecycle, (iv) innovative technologies for waste intelligence and analytics, and (v) improved documentation for waste management. Considering these groups of factors is key to meeting the needs of the stakeholders regarding the use of BIM for CDW management. These groups of factors are important considerations for the implementation and acceptance of BIM-based tools and practices for CDW management within the construction industry.<br/

Big Data in the construction industry: A review of present status, opportunities, and future trends

© 2016 Elsevier Ltd The ability to process large amounts of data and to extract useful insights from data has revolutionised society. This phenomenon—dubbed as Big Data—has applications for a wide assortment of industries, including the construction industry. The construction industry already deals with large volumes of heterogeneous data; which is expected to increase exponentially as technologies such as sensor networks and the Internet of Things are commoditised. In this paper, we present a detailed survey of the literature, investigating the application of Big Data techniques in the construction industry. We reviewed related works published in the databases of American Association of Civil Engineers (ASCE), Institute of Electrical and Electronics Engineers (IEEE), Association of Computing Machinery (ACM), and Elsevier Science Direct Digital Library. While the application of data analytics in the construction industry is not new, the adoption of Big Data technologies in this industry remains at a nascent stage and lags the broad uptake of these technologies in other fields. To the best of our knowledge, there is currently no comprehensive survey of Big Data techniques in the context of the construction industry. This paper fills the void and presents a wide-ranging interdisciplinary review of literature of fields such as statistics, data mining and warehousing, machine learning, and Big Data Analytics in the context of the construction industry. We discuss the current state of adoption of Big Data in the construction industry and discuss the future potential of such technologies across the multiple domain-specific sub-areas of the construction industry. We also propose open issues and directions for future work along with potential pitfalls associated with Big Data adoption in the industry

Disassembly and deconstruction analytics system (D-DAS) for construction in a circular economy

© 2019 Despite the relevance of building information modelling for simulating building performance at various life cycle stages, Its use for assessing the end-of-life impacts is not a common practice. Even though the global sustainability and circular economy agendas require that buildings must have minimal impact on the environment across the entire lifecycle. In this study therefore, a disassembly and deconstruction analytics system is developed to provide buildings’ end-of-life performance assessment from the design stage. The system architecture builds on the existing building information modelling capabilities in managing building design and construction process. The architecture is made up of four different layers namely (i) Data storage layer, (ii) Semantic layer, (iii) Analytics and functional models layer and (iv) Application layer. The four layers are logically connected to function as a single system. Three key functionalities of the disassembly and deconstruction analytics system namely (i) Building Whole Life Performance Analytics (ii) Building Element Deconstruction Analytics and (iii) Design for Deconstruction Advisor are implemented as plug-in in Revit 2017. Three scenarios of a case study building design were used to test and evaluate the performance of the system. The results show that building information modelling software capabilities can be extended to provide a platform for assessing the performance of building designs in respect of the circular economy principle of keeping the embodied energy of materials perpetually in an economy. The disassembly and deconstruction analytics system would ensure that buildings are designed with design for disassembly and deconstruction principles that guarantee efficient materials recovery in mind. The disassembly and deconstruction analytics tool could also serve as a decision support platform that government and planners can use to evaluate the level of compliance of building designs to circular economy and sustainability requirements

Investigating profitability performance of construction projects using big data: A project analytics approach

© 2019 The Authors The construction industry generates different types of data from the project inception stage to project delivery. This data comes in various forms and formats which surpass the data management, integration and analysis capabilities of existing project intelligence tools used within the industry. Several tasks in the project lifecycle bear implications for the efficient planning and delivery of construction projects. Setting up right profit margins and its continuous tracking as projects progress are vital management tasks that require data-driven decision support. Existing profit estimation measures use a company or industry wide benchmarks to guide these decisions. These benchmarks are oftentimes unreliable as they do not factor in project-specific variations. As a result, projects are wrongly estimated using uniform rates that eventually end up with entirely unusual margins either due to underspends or overruns. This study proposed a project analytics approach where Big Data is harnessed to understand the profitability distribution of different types of construction projects. To this end, Big Data architecture is recommended, and a prototype implementation is shown to store and analyse large amounts of projects data. Our data analysis revealed that profit margins evolve, and the profitability performance varies across several project attributes. These insights shall be incorporated as knowledge to machine learning algorithms to predict project margins accurately. The proposed approach enabled the fast exploration of data to understand the underlying pattern in the profitability performance for different types of construction projects

Cloud computing in construction industry: Use cases, benefits and challenges

Cloud computing technologies have revolutionised several industries (such as aerospace, manufacturing, automobile, retail, etc.) for several years. Although the construction industry is well placed to also leverage these technologies for competitive and operational advantage, the diffusion of the technologies in the industry follows a steep curve. This study therefore highlights the current contributions and use cases of cloud computing technologies in construction practices. As such, a systematic review was carried out using ninety-two (92) peer-reviewed publications, published within a ten-year period of 2009-2019. A key highlight of the research findings is that cloud computing is an innovation delivery enabler for other emerging technologies (building information modelling, internet of things, virtual reality, augmented reality, big data analytics, mobile computing) in the construction industry. As such, this paper brings to the fore, current and future application areas of cloud computing vis-à-vis other emerging technologies in the construction industry. The paper also identifies barriers to the broader adoption of cloud computing in the construction industry and discusses strategies for overcoming these barriers