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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.

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.

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.

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.

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.

Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.

This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.

Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.

The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.

The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.

The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.

The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.

The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of health and safety risks, which are usually sparse and noisy.

The study focuses on using the big data frameworks for designing a robust architecture for handling and analysing (exploratory and predictive analytics) accidents in power infrastructure. The designed architecture is based on a well coherent health risk analytics lifecycle. A prototype of the architecture interfaced various technology artefacts was implemented in the Java language to predict the likelihoods of health hazards occurrence. A preliminary evaluation of the proposed architecture was carried out with a subset of an objective data, obtained from a leading UK power infrastructure company offering a broad range of power infrastructure services.

The proposed architecture was able to identify relevant variables and improve preliminary prediction accuracies and explanatory capacities. It has also enabled conclusions to be drawn regarding the causes of health risks. The results represent a significant improvement in terms of managing information on construction accidents, particularly in power infrastructure domain.

This study carries out a comprehensive literature review to advance the health and safety risk management in construction. It also highlights the inability of the conventional technologies in handling unstructured and incomplete data set for real-time analytics processing. The study proposes a technique in big data technology for finding complex patterns and establishing the statistical cohesion of hidden patterns for optimal future decision making.

A major challenge for foreign lenders in financing public private partnerships (PPP) infrastructure projects in an emerging market (EM) is the bankability of country-related risks. Despite existing studies on country risks in international project financing, perspectives of foreign lenders on bankability of country-specific risks in an EM is yet to be explored. Hence, using a mixed methodology approach, three private finance initiatives/PPP projects in Sub Saharan Africa (Nigeria) were used to investigate political risk, sponsor, concession and legal risks in PPP loan applications. The paper aims to discuss these issues.

The study adopted mixed methodological approach comprising focus group discussions and analysis of loan documents obtained from foreign project lenders, in addition to the questionnaire survey distributed to local and international project financiers with experiences in PPPs within Nigeria.

Results identified seven topmost bankability criteria for evaluating country-related risks (political risk, sponsor, concession and legal risks) in EM PPPs. In addition, a “Risk and Bankability Framework Model” was developed from the study presenting critical parameters for gaining foreign funding approval for EM’s PPP loan applications.

Since the study only explored bankability of PPPs in Sub Saharan Africa with the exclusion of other geographical regions, the proposed framework model should be taken in context of EMs as a mind-map for foreign lenders and local private investors seeking to finance PPPs in an EM.

Results from the study represent critical parameters for winning foreign loan approval for PPP infrastructure projects within an EM context.

Study proposed “Risk and Bankability Framework Model” relevant for evaluating PPP loan applications at the pre-approval stage for EM PPPs.