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This paper identifies the critical factors that influence the attitudes of construction workers towards safe behaviour on construction sites. It studies these attitudes by using a research model that links three themes: safety management implementation strategies, attitudes of workers about safety and behavioural factors displayed by construction workers. This model is used to frame the responses of 126 directly employed construction workers in 10 companies. Some 56 variables were identified as having a potential influence upon attitudes to safety. The initial data analysis found that 12 technical factors significantly correlated to the development of strong positive attitudes towards safety management. Second‐order analysis, using factor analysis, isolated five variables that had a major influence on safety attitudes. The five factors were: organizing for safety supervision and equipment management, industry norms and culture, attitudes to risk taking and management behaviour.

The heightened state of flux in the construction industry in general and construction procurement strategies in particular, provides welcome opportunities to inject innovative improvements. While some improvements are generated from within the construction industry itself, these evolve sluggishly along prolonged learning curves. These are compared with lessons to be learnt and examples to be drawn from manufacturing in the development of a marketable product. A product development focus is thus advocated in re‐integrating segregated groups and in empowering and inspiring the innovations that are needed to achieve the dramatic productivity gains now demanded from the construction industry.

In order to retain and motivate employees, organizations must respond to their expectations, both in terms of meeting formal aspects of their employment contracts and in addressing their less formal expectations of the employment relationship. Within the current human resources management (HRM) literature, these informal expectations are known as psychological contracts. This paper reports on research that explored psychological contracts within the construction industry. In‐depth interviews were held with more than 80 construction managers and professional staff who worked for five large UK contracting organizations. The interviewees were asked to describe their career histories, and to discuss any tensions between the personnel policies of their organizations and their personal career aspirations and expectations. It emerged that responsibility for human resource development (HRD) had been largely devolved to divisional and operational management. This led to HRD becoming fragmented and unresponsive, and to employees becoming disillusioned by their employers' failure to meet their expectations. It is argued that construction companies require a more sophisticated understanding of their employees' expectations of the employment relationship if they are to be retained in the long term.

The purpose of this research is to study the strategies used by construction organizations in dealing with environmental issues. It identifies the factors which govern a firm's performance in respect of environmental management and explores the management strategies which are used to generate good environmental performance. The paper also seeks to consider the techniques adopted in pursuit of these strategies and to compare their effectiveness.

The research reviews the strategic management and environmental management and then proceeds to a case study investigation of six construction organizations. The case studies seek to uncover the variables that influence strategies for managing environmental issues. A cross‐case analysis provides an indication of the characteristics of those companies that demonstrate a higher environmental concern.

As environmental strategies are developed, the competencies in managing environmental issues will grow and lead to improved business performance. The growth in an organization's environmental competence provides the opportunity for increased competitive advantage.

A model is developed which illustrates the relationship between environmental management, business strategy and competitive advantage.

Outlines a model for the development of quality systems in New South Wales schools. Also considers quality assurance as a means of auditing such a system and the role of leadership in the development of such systems. Looks into the nature of quality systems and international quality standards. Notes the problems and challenges inherent in the introduction of a quality system concept into schools and suggests further research to look into leadership factors and training required to support such an introduction.

This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best form of variable for this problem. Because of MapReduce’s unsuitability for iteration problems involved in developing CB-FPMs, various BDA initiatives for iteration problems were identified. A BDA framework for developing CB-FPM was proposed. It was validated by using 150,000 datacells of 30,000 construction firms, artificial neural network, Amazon Elastic Compute Cloud, Apache Spark and the R software. The BDA CB-FPM was developed in eight seconds while the same process without BDA was aborted after nine hours without success. This shows the issue of not wanting to use large dataset to develop CB-FPM due to tedious duration is resolvable by applying BDA technique. The BDA CB-FPM largely outperformed an ordinary CB-FPM developed with a dataset of 200 construction firms, proving that use of larger sample size with the aid of BDA, leads to better performing CB-FPMs. The high financial and social cost associated with misclassifications (i.e. model error) thus makes adoption of BDA CB-FPMs very important for, among others, financiers, clients and policy makers.

This chapter aimed to uncover the gaps in the existing construction supply chain management (CSCM) models. Organisational culture and the fourth industrial revolution (4IR) components are the two gaps that were identified through reviewing existing CSCM models. The 4IR is driven by three components which are smart management, virtualisation and cyber-physical system. It was proposed in this chapter that the practice of CSCM should be in tandem with the components of 4IR. This chapter recommended that for the effective practice of the construction supply chain (CSC) in the 4IR era, construction stakeholders should adopt an innovative and collaborative organisational culture. The organisational culture adopted by a construction firm performs a crucial role in encouraging construction stakeholders in adopting 4IR components for CSCM. Each of the 4IR components is driven by technologies like autonomous robots, building information modelling (BIM), radio frequency identification (RFID), the internet of things (IoT) and others. Among all the technologies, it was discovered that RFID and BIM had gained prominence in most CSC literature. The chapter recommended that blockchain, digital twins and the cyber-physical system are the next trending technology for CSCM.

The way in which clients or their consultants undertake to select firms to tender for a given project is a highly complex process and can be very problematic. This is also true for public authorities as, for them, ‘compulsory competitive tendering’ is a relatively new concept. Despite its importance, contractors' prequalification is often based on heuristic techniques combining experience, judgement and intuition of the decision makers. This, primarily, stems from the fact that prequalification is not an exact science. For any project, the right choice of the contractor is one of the most important decisions that the client has to make. Therefore, it is envisaged that the development of an effective decision‐support model for contractor prequalification can yield significant benefits to the client. By implication, such a model can also be of considerable use to contractors: a model of this nature is an effective marketing tool for contractors to enhance their chances of success to obtain new work. To this end, this work offers a decision‐support model that predicts whether or not a contractor should be selected for tendering projects. The focus is on local authorities because, in the absence of a viable universal selection system, there are significant variations in the way they conduct prequalification. The model is based on the use of artificial neural networks (ANN) and uses data relating to 42 local authorities (clients). With the aid of a questionnaire and a scaling system, the prequalification attributes that are considered to be important by clients are identified. The survey indicates significant variations in the level of importance given to different attributes. Statistical methods are adopted to generate additional data representing disqualified instances. Following a preprocessing exercise, the data form the basis of the input and output layers for training the neural‐net model. An independent set of data is subjected to a similar preprocessing for testing the model. Tests reveal that the model has a highly satisfactory predictive accuracy and that the ANN technique is a viable tool for the prediction of success or failure of the contractor to qualify to tender for local authority projects.