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The purpose of this paper is to identify the key facilitating factors for smart sustainable practices (SSP) and develop a project evaluation model (PEM) for SSP implementation in Nigeria and Hong Kong. SSP is coined from the integration of digital technologies such as Building Information Modelling (BIM) to facilitate sustainability practices.

The study employed a quantitative research design approach using empirical questionnaire surveys to solicit the opinions of 69 and 97 construction practitioners in Nigeria and Hong Kong. Purposive and snowball sampling techniques were used to identify the potential survey respondents. The fuzzy synthetic evaluation technique was used to develop the PEMs.

The findings revealed that adequate technical expertise of the SSP processes is critical in enhancing its implementation in Hong Kong and Nigeria; as well as the provision of training programs for specialists in smart and sustainable initiatives. Meanwhile, the study's findings advocated that for an SSP-enabled construction project, its project performance is mainly influenced by the client's satisfaction level and the early involvement of the project teams.

The study's results are limited to the Nigeria and Hong Kong construction industries.

Construction stakeholders such as the clients, developers, contractors can utilize the PEMs to determine and track SSP initiatives implementation in building projects in a reliable and practical way.

No tool has been developed for evaluating SSP initiatives at the project level in the construction industry. Using case studies of Hong Kong and Nigeria, PEM indices were developed to measure and track SSP implementation in construction projects.

Guaranteed maximum price (GMP) and target cost contracting (TCC) with a pain‐share/gain‐share arrangement have been adopted to integrate the construction delivery process and motivate service providers to seek continuous improvements in project outcomes. However, there is still a lack of research evidence to evaluate the levels of success and lessons learned from these innovative procurement strategies. Based on the analysis of a series of in‐depth interviews on the perceptions of various relevant experienced industrial practitioners, this paper aims to explore the key attributes of GMP/TCC including the underlying motives, perceived benefits, potential difficulties, critical success factors, key risk factors involved and optimal project conditions for adopting GMP/TCC. The research findings are useful in assisting key project stakeholders in minimising the detriments brought about by potential difficulties in and maximising the benefits derived from implementing GMP/TCC concepts. The study is also significant in contributing to new knowledge and practical information of GMP/TCC applications and implementation, in both a national and international context.

The workforce management model conceptualised for the effective management of the construction workforce was subjected to expert scrutiny to determine the suitability and applicability of the identified practices and their attributed variables to the construction industry. In achieving this, a Delphi approach was adopted using experts from construction organisations in South Africa. These experts comprised workforce management personnel and construction professionals in senior management positions. The data were analysed using appropriate statistical tools such as interquartile deviation, Kendell’s coefficient of concordance, and chi square to determine consensus among these experts. After a two-round Delphi, the seven constructs proposed in the conceptualised workforce management model were adjudged to be important and worthy of adoption by construction organisations seeking to improve workforce management in the current fourth industrial revolution era.

The construction industry has been evolving in recent years through the adoption of smart tools such as building information modeling to reduce the complexity in the construction process and optimize the project's goals. This paper aims to identify and assess the key drivers for the implementation of smart sustainable practices in the construction industry.

Inferential and descriptive statistical techniques were employed in analyzing the data collected via an international empirical questionnaire survey deployed in soliciting the perceptions of 220 construction professionals across 21 countries. Factor analysis was used to categorize the identified key drivers into their underlying clusters for further discussion. Also, the data were analyzed based on the various groups and regions of the study's respondents.

The key drivers (KDs) are related to the technical competence of staff as well as knowledge and awareness level within the industry, issues related to organizational and project's strategy and policies, availability of financial resources and development of relevant standards and policies to aid its execution among others. A comparative analysis of the perceptions of the different respondents' groups was undertaken and discussed.

The analysis of the key drivers for the implementation of smart and sustainable practices in the construction industry is expected to aid the decision-making of the relevant stakeholders as well as serve as a consultation instrument for government agencies in their design of localized policies and guidelines to aid smart and sustainable urbanization. The findings revealed the gaps in the implementation of smart and sustainable practices in various climes and organization setups and provided useful and practical strategies for addressing the current hindrances during implementation.

The study has generated valuable insights into the significant drivers that can enhance the implementation of smart and sustainable practices across regions. It is evident that synergy among the relevant stakeholders in the built environment will help accelerate the implementation of smart sustainable practices in the construction industry. The study findings have provided profound contributions to theory and research as well as to industry practice.

Allocating risk in public–private partnership (PPP) projects based on public–private parties’ risk management (RM) capabilities is a condition for success of these projects. In practice, however, risks are allocated to these parties beyond their respective RM capabilities. Too much risk is often assigned to the private or public party, resulting in poor RM and costly contract renegotiations and terminations. This chapter proposes a methodology based on fuzzy set theory (FST) in which decision makers (DMs) use linguistic variables to assess and calculate RM capability values of public–private parties for risk events and to arrive at risk allocation (RA) decisions. The proposed methodology is based on integrating RA decision criteria, the Delphi method and the fuzzy synthetic evaluation (FSE) technique. The application of FSE allows for the introduction of linguistic variables that express DMs’ evaluations of RM capabilities. This provides a means to deal with the problems of qualitative, multi-criteria analysis, subjectivity and uncertainty that characterise decision-making in the construction domain. The methodology is outlined and demonstrated based on empirical data collected through a three-round Delphi survey. The public–private parties’ RM capability values for land acquisition risk are calculated using the proposed methodology. The methodology is helpful for performing fuzzy-based analysis in PPP projects, even in the event of limited or no data. This chapter makes the contribution of presenting a RA decision-making methodology that is easy to understand and use in PPP contracting and that enables DMs to track calculations of RM capability values.

Demands for Industrialized Construction (IC) have intensified with growing construction industry imperatives to (A) boost performance; (B) reduce reliance on “in-situ and on-site” operations; and (C) strengthen supply chain resilience (SCR) not just for survival but also to fulfill obligations to clients in the coronavirus disease 2019–induced (COVID-19–induced) “new normal”. In addressing these imperatives, this paper targets more effective leveraging of latent efficiencies of off-site-manufacture, based on findings from a Hong Kong (HK)–based study on assessing and improving SCR in IC in a high-density city.

Starting with the identification of critical supply chain vulnerabilities (CSCVs), this study developed a multilevel–multicriteria mathematical model to evaluate the vulnerability levels of IC supply chains (SCs) in HK based on an in-depth questionnaire survey followed by experts' inputs and analyzing them using fuzzy synthetic evaluation (FSE).

The overall vulnerability index indicates that IC in HK is substantially vulnerable to disruptions, while production-based vulnerabilities have the highest impact. Top management attention is needed to address these CSCVs in IC in HK.

To the authors' knowledge, this is the first structured evaluation model that measures the vulnerability level of IC, providing useful insights to industry stakeholders for well-informed decision-making in achieving resilient, sustainable and performance-enhanced SCs.

Public–private partnerships (PPPs) are viewed as a reform tool for resolving inefficiency and absence of dynamism in water supply delivery in developing countries. However, the requirements for their successful implementation have received very little attention. This paper aims to describe a set of critical success factors (CSFs) that, when given special and continual attention, would ensure a successful project implementation and to provide a predictive tool to aid implementers to evaluate the likelihood of a successful PPP water supply project.

Fourteen perceived CSFs were initially derived from project cases and extant literature, and verified through a two-round Delphi survey. Factor analysis established five critical success factor groups (CSFGs) that were then used to develop a fuzzy synthetic evaluation tool for assessing the chance of a successful project.

The five key CSFGs are commitment of partners, strength of consortium, asset quality and social support, political environment, and national PPP unit. The model output showed that, overall, these factors have a “very high” positive impact on a successful implementation of a water supply project. Hence, there is an excellent correlation between achievement of the CSFGs and project success. Success indices of individual principal factors are also “very high”.

The study presents a tool to public clients and private audience, and it is hoped that the study will trigger policy development towards PPP practice in developing countries, because these findings have wider implications for legal and regulatory systems, public capacity, financing, public procurement and politics.

This paper aims to make a systematic review of the manpower prediction model of the construction industry. It aims to determine the forecasting model's development trend, analyse the use limitations and applicable conditions of each forecasting model and then identify the impact indicators of the human resource forecasting model from an economic point of view. It is hoped that this study will provide insights into the selection of forecasting models for governments and groups that are dealing with human resource forecasts.

The common search engine, Scopus, was used to retrieve construction manpower forecast-related articles for this review. Keywords such as “construction”, “building”, “labour”, “manpower” were searched. Papers that not related to the manpower prediction model of the construction industry were excluded. A total of 27 articles were obtained and rated according to the publication time, author and organisation of the article. The prediction model used in the selected paper was analysed.

The number of papers focussing on the prediction of manpower in the construction industry is on the rise. Hong Kong is the region with the largest number of published papers. Different methods have different requirements for the quality of historical data. Most forecasting methods are not suitable for sudden changes in the labour market. This paper also finds that the construction output is the economic indicator with the most significant influence on the forecasting model.

The research results discuss the problem that the prediction results are not accurate due to the sudden change of data in the current prediction model. Besides, the study results take stock of the published literature and can provide an overall understanding of the forecasting methods of human resources in the construction industry.

Through this study, decision-makers can choose a reasonable prediction model according to their situation. Decision-makers can make clear plans for future construction projects specifically when there are changes in the labour market caused by emergencies. Also, this study can help decision-makers understand the current research trend of human resources forecasting models.

Although the human resource prediction model's effectiveness in the construction industry is affected by the dynamic change of data, the research results show that it is expected to solve the problem using artificial intelligence. No one has researched this area, and it is expected to become the focus of research in the future.

As construction of commercial and recreational complex building projects (CRCBPs) is one of the most important issues in many developing countries and requires a very high cost of implementation, it is important to identify and prioritize the risks of such projects. Therefore, the purpose of this study is to identify and rank the risks of CRCBPs by studying the case of the “Hamedanian Memorial,” a CRCBP in Iran.

To pursue this aim, a descriptive-survey method was used. The statistical population of the study consists of 30 experienced experts (consultants, contractors and employers) of the “Hamedanian Memorial” project selected according to the Cochran formula and minimum population census. A questionnaire was used as the data collection tool, administered in all stages of risk identification and evaluation, and was devised by using library and field methods based on the literature and research background, as well as interviewing experts in the risk identification and evaluation stages. Kendall’s coefficient of agreement was used to validate the experts’ opinions in the risk identification stage. The ranking in qualitative evaluation was done based on the risk intensity and the cumulative risk index.

The results show that the risks are associated with exchange rate fluctuation, inflation fluctuation, access to skilled workers, contractors’ claims and foreign threats from international relations.

The results and findings of the present study can be of interest to the executives of large commercial, leisure, public and private projects in developing and developed countries; understanding risks can significantly improve the decision-making process of CRCBPs.

This paper aims to empirically investigate the differences and similarities on the implementation constraints in public–private partnership (PPP) in developing and developed economies/countries, represented by Ghana and Hong Kong, respectively.

A questionnaire survey was conducted with relevant experienced PPP practitioners in Ghana and Hong Kong. One hundred and three completed questionnaires were received for analysis. Kendall’s coefficient of concordance analysis, mean score ranking, Mann–Whitney U test and quartile grouping were used for data analysis.

The results show six implementation constraints with significant differences. Constraints related to the general investment climate of PPP projects (i.e. ecological conditions of PPP) are ranked higher in Ghana than in Hong Kong, whereas constraints related to the organisation and negotiations of PPP projects are higher in Hong Kong than in Ghana. Further, two constraints, lengthy delay in finalising negotiations and lengthy delay due to political debate, are very critical in both jurisdictions, whereas “negative public perceptions on PPP transactions” and “high use of unsolicited proposals” are of less challenge in the implementation of PPP in both jurisdictions.

The findings of this study contribute to knowledge on the international best practices of PPP. In addition, international private bidders would be informed of the mitigation measures to adopt when engaging in PPP arrangements in any part of the world, whether in a developing or developed economy country.