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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 level at which risk is priced and the magnitude of risks transferred to the private sector will have a significant impact on the cost of the public–private partnership (PPP) deals as well as on the value for money analysis and on the section of the optimum investment options. The price of risk associated with PPP schemes is complex, dynamic and continuous throughout the concession agreement. Risk allocation needs to be re-evaluated to ensure the optimum outcome of the PPP contract.

This paper provides a coherent theoretical framework for dealing with scenarios of potential gain and loss from retaining or transferring risks.

The outcome indicates that using the proposed framework will provide innovative ways of deriving risk prices in PPP projects using several risk determinants strategies.

In costing risks, analysts have to take into consideration the balance between the cost of risk transfer and the cost of losses if risk is retained.

This paper contributes to the PPP literature and practice by proposing a framework which is consistent with a risk allocation approach in PPP projects, where the key proposition is that risk pricing can overload project debt leading to loss of value.

A unique aspect of PPP is the opportunity for the transfer of risk ownership to the private sector. The purpose of this paper is to investigate how risk cost influences risk allocation.

A questionnaire survey was used to collect data. The questionnaire included nine sub-categories of risks. To quantify the influence of risk cost on risk allocation, a dependency risk matrix was employed. Heat maps techniques were used to visualise the results of the survey.

The findings show which risks within the endogenous or exogenous groups are to be allocated to the public sector, the private sector, or to be shared. The finding from this research provides a baseline for the PPP stakeholders in developing guidelines for estimating the value of risk costs in the risks register as well as serving as a mechanism for risk allocation.

The context of the study may limit the generalisability of the results.

The study provides practical guidance to PPP stakeholders on risk allocation appetite.

This study extends the processes and methods by which PPP project’s risk is allocated to create a better value for all the stakeholders.

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.

There has been a mounting interest in building information modelling (BIM) in the construction industry sector worldwide due to its perceived benefits. However, reliance on information technology is associated with risks. The purpose of this paper is to offer a better understanding of the emerging contractual and legal risks, which might influence the successful adoption of BIM, in order to facilitate the successful implementation of BIM in the construction industry.

The risks used in the study were documented from the literature, and primary data were collected by a questionnaire survey. The analysis of the results was driven by univariate and inferential statistics (Analysis of Variance) to identify the emerging contractual and legal risks.

The findings showed that there were little significant differences in the mean rating of the occurrence of contractual and legal risks between the respondents. The study confirmed that emerging risks are likely to be related to BIM documentations, intellectual rights and liability, missing data and misplaced assumptions among project stakeholders. The results showed that BIM success depends on close collaboration, at the outset of the project, with the client, designers, contractors and consultants.

The findings suggest that contract documents and contract agreements may need to be created in accordance with the identified risks, so that the questions of contractual and legal responsibilities are appropriately defined and allocated among the participants.

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

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 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.

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.