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There is lack of literature on the evaluation of PPP projects performance based on critical success factors (CSFs). Thus, the purpose of this paper is to investigate and establish which of the CSFs are good predictor of PPP projects performance in terms of success criteria.

A questionnaire survey was developed based on PPP performance indicators and CSFs identified through a rigorous literature review. It was administrated among experts in PPP from the UK and the UAE. The respondents were selected purely on their work experience in PPP projects. The sites for collecting data were selected based on the similarity of the procurement methods between the two countries. The data were initially analysed using descriptive statics to identify the association between CSFs and PPP performance indicators. Multiple regression analysis was used to examine which of the CSFs were significant predictor of PPP projects performance.

The results demonstrated that “project technical feasibility, social support and local financial market assessment” contribute significantly to time performance. Detailed cost/benefits assessment contributed significantly to the cost, time and quality performance. Appropriate risk allocation and multi-benefit objectives of all stakeholders were found to be significant predictors of the service performance. CSFs “social support and detailed cost/benefits assessment” contribute positively to profit and variation performances. CSFs “profit and transparent procurement” are negatively associated with the variation performance. Cost and quality were the least performance criteria that could be predicted by the factors associated with this study.

The findings are expected to benefit the upper management of local governments and stakeholders to make informed decisions by understanding the link between the CSFs and the generic performance success measures at the onset of the of PPP project.

This study expands the existing literature by using the CSFs to predict the performance success of PPP projects.

Scaling up smart city infrastructure projects will require a large financial investment. Using public–private partnerships is one of the most effective ways to address budget constraints. Numerous factors have varying degrees of influence on the performance of Public private partnerships (PPP) projects; certain PPP factors are more crucial to the success of a smart city infrastructure project than others, and their influence can be greatly increased when they are fulfilled collectively. This study aims to find out what factors are unique to smart city PPP initiatives, as well as how these factors work together, so that successful smart city infrastructure PPP projects can be scaled up.

The methodology included three sequential stages: identifying the critical success factors (CSF) of PPP for smart cities based on an extensive literature review, collecting data from a sample of 90 PPP practitioners using a Likert scale questionnaire and estimating interrelationships among the CSF and their emergent clusters using structural equation modelling.

The best fit model developed in this study demonstrated the significance of each factor and their interrelationships within their categories in enhancing the performance of PPPs in smart city infrastructure projects. Five categories of critical success factors for PPPs in smart city infrastructure projects have been established: partnership and collaboration; financial sustainability; contractual duties and outsourcing; smart integration; and contract governance.

The proposed model represented the causal interrelationships among relevant critical success factors derived from literature, which may help in directing the organization’s attention and resources to more critical areas, leading to the effective fulfilment of the smart city infrastructure project’s objectives. In addition to the theoretical and methodological contributions, this study produced a usable and readily adaptable list and clusters of critical success factors for research in the area of the implementation of PPP in smart city infrastructure projects.

To the best of the authors’ knowledge, this is the first study to identify PPP critical success factors and their themed clusters for smart city infrastructure projects.

The purpose of this paper is to develop a public–private partnership (PPP) framework for newcomers from developing countries with the United Arab Emirates (UAE).

For this purpose, through the meta-analysis review of literature and applied qualitative content analysis to the international PPP best practices, the authors identified key relevant PPP processes, terminologies and vocabulary that can assist new entrants in establishing their PPP infrastructure.

The outcome was a generic best practice framework for PPP implementation that consisted of five phases which further consisted of groups of functions and sub-groups of functions. These phases are establish the PPP framework, PPP implementation, contract design, bid management and PPP contract management.

This framework is expected to benefit the upper management of local government departments and the federal ministries to understand the full process for local PPPs and to help them make informed decision for what to develop as PPPs and how to develop them.

This systematic approach to the development of PPPs in the UAE is expected to increase the number of PPP tenders, as there will be more awareness on what PPPs stand for, how they balance risks, improve efficiency and effectiveness of projects, improve facilities and services, etc.

This framework is the first to lay the foundations for a standardised PPP practice in the UAE for practitioners.

Building information modelling (BIM) is not without risk, as the greater reliance on information technology has associated technical risks. Thus, the purpose of this paper is to assess the perceptions of the users of BIM regarding the likelihood of emergence of technical risks which might influence the successful application of BIM, to facilitate the successful implementation of BIM in the construction industry.

The primary data were collected via a questionnaire to document the BIM risks, where 105 responses were recorded from constructors, consultants, cost consultants and other professionals from the UK construction industry. Subsequently, the analysis of the results was driven by univariate and inferential statistics (ANOVA) to assess the perception of risk emergence.

The study found the most likely technical risks that might emerge from BIM application. These risks are complexity of transferring modelling data from one program to another, lack of understanding of the BIM for the different software platforms, interoperability shortcomings, failure to discover errors in the model and risks of different software platforms.

The results will certainly intensify the discussion about BIM risks, risk allocation and all other aspects that are related to BIM contractual processes. Also, the compiled list of risks will help stakeholders in assessing financial implications that may result from BIM application.

Important technical risks have been identified in the application of BIM. This renders a new understanding of the risks that might influence the successful application of BIM. The respondents generally agreed on the importance of the following risks: “complexity of transferring modelling data between programs from one program to another”, “lack of understanding of the BIM for the different software platforms”, “interoperability shortcomings”, “failure to discover errors in the model” and “risks of different software platforms”, which are in line with current literature.

The amount of expenditure required to scale up smart infrastructure projects is often enormous. Public–private partnership (PPP) is one of the proposed and viable solutions for addressing the financial issues of smart infrastructure projects. However, the most important criterion in choosing PPP over other procurement methods is that the project under the PPP method should deliver the best value for money (VFM) while also including defined economic and social objectives, rather than relying exclusively on efficiency factors. While PPP provides a variety of advantages for developing infrastructure, significant challenges may arise as a result of smart infrastructure initiatives. Diverse PPP approaches have been used to build smart infrastructure around the world, with varying degrees of success. The purpose of this study is to identify the VFM factors that are suitable for smart infrastructure projects and to examine the impact of their interrelationships.

The methodology for this study consisted of three stages: identifying VFM factors in PPP for smart cities based on an extensive literature review, analyzing data from a sample of 90 PPP practitioners using a Likert scale questionnaire and estimating interrelationships among VFM factors using structural equation modeling (SEM).

After performing a SEM analysis on the gathered data, the best fitted measurement model consisted of 11 VFM factors acting as indicators of three latent variables for smart infrastructure projects (clear output specification for measuring performance, efficient dispute resolutions, optimized risk allocation and business models, improved and integrated community services, economic sustainability, appropriate capital structure and collaterals, smart asset management, diffusion of smart technologies, technical innovation, Ince) and three clusters of their interrelations (economic sustainability, integration drive, optimization and smart technology).

This research has resulted in a useful and readily applicable list of factors and clusters of value for money criteria for the implementation of PPP in smart infrastructure projects, assisting public sector management by providing a measure of pre-conditions that can be used as an assessment tool when determining whether a PPP should be used instead of conventional methods.

In addition to the theoretical and methodological contributions, this study produced a usable and readily adaptable list and clusters of value for money factors for the implementation of PPP in smart infrastructure projects.

Building information modelling (BIM) technology adoption is growing rapidly because of its perceived benefits. The purpose of this paper is to assess the perceptions of the users of BIM regarding the likelihood of managerial risks emergence that might influence the successful application of BIM, to facilitate the successful implementation of BIM in the construction industry.

Emerging managerial BIM risks were extracted from the literature. The primary data were collected via a questionnaire survey. The analysis of the results was driven by univariate and inferential statistics (analysis of variance ) to assess the emergence of managerial risks.

The study confirmed the nine most likely managerial risks that might emerge from BIM adoption, which are lack of understanding of the expectations from BIM modelling, lack of experienced and skilled personnel, lack of clarity on integration of BIM with the current business practice, conflict because of dissimilar expectations from BIM, lack of collaborative work processes and standards, lack of understanding of BIM processes, lack of understanding modelling behaviours, lack of expertise within the project team, lack of expertise within the organizations and lack of criteria for BIM project implementation.

The results will intensify the discussion about BIM risks, risk allocation and other aspects that are related to BIM methodology. The compiled list of managerial risks will help stakeholders in assessing financial implications that may result from BIM application. The list of risks could be used in pricing consultancy and construction services. More importantly, the list might be useful in developing an international standard for BIM risk management. The results showed that BIM success depends on the close collaboration, at the outset of the project, with contractors, consultants, designers and client.

Important managerial risks have been identified in the adoption of BIM. It renders a new understanding of the risks that might influence the successful application of BIM.

The net-present-value (NPV) method is well-known for its drawbacks. To overcome some of these NPV weaknesses this paper aims to provide a methodology to determine an optimal concession period that treats risk and time separately. The purpose of this paper is to apply the notion of risk-adjusted decoupled net present value (risk-adjusted DNPV) to determine a conception period taken into consideration synthetic insurance premiums as compensation for risks.

This paper conducts theoretical and empirical analysis and provides an integrated model for deriving concession periods of any PPP projects. The model is able to capture several contractual issues such risks costing and other contractual scenarios. Methodologically, the paper addressees both the issues of risk-based cost–benefit analysis and cash flow analysis bearing an emphasis of risk-adjusted DNPV to compute an optimum concession period.

The results show that using DNPV will produce a shorter concession period comparatively to NPV. The consequence of this is that the public sector will gain financially from an earlier transfer of the concession.

This paper contributes to the PPP literature by combing DNPV and risk to determine the PPP concession period for the mutual benefits both the private and public sectors. The decoupling of risk from traditional NPV computation will allow for risk pricing and tradability through insurance and allocation.

The attempt to decouple time and risk in the computation of NPV is the added value to the body of knowledge.

This paper aims to examine the performance efficiency of 56 real estate assets within the rental sector in the UAE to evaluate the relative operation efficiency in relation to revenue generation.

The data envelopment analysis (DEA) approach was used to measure the relative operational efficiency of the studied assets in relation to the revenue performance. This method could produce a more informed and balanced approach to performance measurement.

The outcomes show that scores of efficiencies ranging from 7% to 99% in some of the models. The results showed that on average buildings are 75% relatively less efficient in maintenance, in term of revenue generation, than the benchmark set. Likewise, on average, the inefficient buildings are 60% relatively less efficient in insurance. Result also shows that 95% of the building assets in the sample are by and large operating at decreasing returns to scale. This implies that managers need to considerably reduce the operational resources (input) to improve the levels of revenue.

This study recommends that the FM operational variables that were found to inefficiently contribute to the revenue should be re-examined to test the validity of the findings. This is necessary before generalising or interpolating the results that are presented in this study.

The information obtained about operational performance can help FM managers to understand which improvements in the productivity of inefficient FM resources are required, providing insight into how to reduce operating costs and increase revenue.

This paper adds value in using new FM operational parameters to evaluate the efficiency of the performance of built assets.

The internet of things (IoT) is becoming an increasingly inescapable part of society. IoT paradigm cannot function without the networking infrastructure. High-speed data networks are essential to enable the IoT future. Thus, the purpose of this study is on the identification of risks that influence the development, installation and operation of information and communication technology (ICT) infrastructure network project cost outcomes. So far, there has been little attention has been paid to risks problems in these types of IoT enabling projects.

This research follows a quantitative analysis approach. Data for this study were collected by a survey from 209 professionals. Multiple regression analysis was used to model the relationship between risks and outturn cost of infrastructure needed to enable the operation of IoT technologies.

The main risk factors that were identified were planning and development, people and management, operations, technology and hardware.

This research has expanded the existing literature by documenting and clustering ICT infrastructure network project risks into themes, and has developed a scale (risk statements) for measuring such risks. Further, the research has advanced the understanding by identifying the most likely risks that will contribute to the overrun of these projects.

This research establishes a reliable regression method for the assessment of the risks that influence the development, installation and operation of ICT infrastructure network projects outturn cost. No other research has measured or studied the risks in this type of project.

The different scenarios of climate change, such as floods, temperature change and storms, are considered the main drivers influencing the building sector. Understanding how and when these climatic risks will emerge, specifically financial risks, is pivotal in dealing with these risks and applying the adaptation and mitigation strategies so as to minimise the effects and damages. Thus, the purpose of this paper is to discover the financial risks emerging from climate change impact on the building sector and determine the timescale of occurrence for such risks.

The research methodology formulated in this study is founded on a systematic literature review and statistical analysis. Built on this, the potential financial risks emerging from climate change scenarios (CCS) were identified and designed as a questionnaire to collect data from UK expert professionals. Statistical methods were used to rank and compare the outcomes of the survey.

The research observed that around 40 per cent of the participants in this study indicated that one-third of the total identified financial risks (23 factors) would emerge within 5-10 years. The most important factors are increased insurance excess and additional expense in insuring buildings in flood risk zones, whilst the least important financial risks are inability to repay debts and un-insurability because of climate change.

This study is limited to the UK, and regional implications are not covered. However, it is a starting point.

The main contribution of this research project is establishing and developing clusters of the potential risks emerging from CCS, which can assist professionals in the building sector in the management and development of strategies to cope with these emerging risks.