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The purpose of this study is for examining the evolution of stakeholder influence and the trans-period effect (TPE) of process performance of public–private partnerships (PPPs). TPE refers to the ripple effect of project performance across different phases of a PPP.

Social network analysis is used to analyze each stakeholder’s influence on PPP performance. For examining the TPE, partial least squares structural equation modelling is conducted.

The performance in the five phases (e.g. initiation and planning, procurement, construction, operation and transition) of PPPs exhibits significant TPE. The stakeholder network varies in different phases. The most influential stakeholder is a public authority, followed by a public initiator and a private consortium.

The project type of PPPs is not considered in the stakeholder network analysis. Future work should focus on developing a multidimensional stakeholder network by considering the typology of the project. Moreover, the TPE cannot reflect the relationships between the KPIs in the different phases, and thus, further study is required.

This research provides a useful tool for measuring the life cycle outputs and outcomes of PPPs through enhanced process-oriented performance measurement. The developed PMS enable practitioners to have a better understanding of the process performance of the projects and then ensure informed decision-making about actions to be required and taken to improve future performance.

This study contributes to knowledge of performance management by simultaneously addressing the process and stakeholder management theories within the context of PPPs. The proposed PMS provides an insight into managing stakeholders’ influences to enhance the life cycle performance of PPPs.

This paper investigates the policy changes made towards infrastructure public–private partnerships (PPPs). The purpose of this study is to empirically identify the policy risks associated with the development of PPPs and to assess their impacts on the projects.

A case study of the policy changes that have been implemented for PPPs in China over the past seven years has been undertaken and is presented in this study. The causal loop diagrams are applied to assess and illustrate the potential impacts of the risks as a result of such changes on PPPs.

A sequence of the policy risks, which relate to PPP risk allocation, contract management and implementation, payment and abatement mechanisms and financing, has been identified. It is also found that the identified risks will generate significant but negative impacts on PPPs, leading to an ineffective project delivery, low revenue, poor service quality and even contract breach.

This research provides the private-sector entities that will embark on PPPs with an insight into managing and controlling policy risks over the project's lifecycle.

PPPs have been critical for infrastructure development worldwide. Nevertheless, they have been a controversy, as many of them were subjected to poor outputs. Consequently, a variety of political mechanisms has been implemented to enhance the governance for PPPs. Policy can bring not only benefits but also risks and, however, policy risks of PPPs with a particular assessment for their potential impacts have received limited attention. Therefore, the study presented in this paper will contribute to the identification and assessment of policy risks within the context of PPPs.

In Hong Kong, over 20,000 private residential buildings will be 50 plus years old by 2039. However, building maintenance has not been owners’ popular interest because of the high cost as well as the complexities in justifying whether the quantities and prices of the maintenance works are reasonable. This paper therefore aims to validate the practicality of adopting Scan-to-BIM: Terrestrial Laser Scan (TLS) and Building Information Modelling (BIM) to perform quantity take-offs (QTO) for estimating building maintenance costs.

A 64-year-old tenement building was selected to conduct a case study. In this instance, the building had undergone a Scan-to-BIM survey approach to generate QTO for the bills of quantities for external painting works. The Scan-to-BIM approach includes site visit, positioning of scanning equipment, assignment of circular scan routes, point cloud registration and identification of residual error. After that, time, cost and quality data were logged into contrast with QTO on as-built plans for external wall plastering works.

The “time”, “cost” and “quality” of the Scan-to BIM practice were then examined and compared with the prevailing practices of manual measurements on as-built drawings. As noted from the results, the initial cost of Scan-to BIM is high, owing to the cost of equipment, software and capable available operators. However, the authors identified that the time and cost can be significantly minimised by developing and implementing efficient practices such as preparing a detailed scan plan, equipping modeller with quantity surveying knowledge, using automated object recognition and 5D BIM software packages such as Vico Office and CostX.

The upshot is that Scan-to-BIM could be one of the measures to advance the clarity in the QTO and estimated price of the maintenance projects.

The practicability of Scan-to-BIM has received limited attention on existing building maintenance project. The Scan-to-BIM approach was examined using a case building of a 64-year-old tenement building. The approach demonstrated in this research study is promised to advance the clarity in the QTO and estimated price of maintenance project.

The performance of public–private partnerships (PPPs) can be determined by a variety of factors, i.e. influencing factors (IFs). This study is undertaken for a purpose of identifying how such factors determine the project's performance (i.e. factor transmission patterns), particularly from the key stakeholders' perspectives.

A hybrid approach, which comprises a Social Network Analysis, ISM (i.e. Interpretive Structural Modeling) and an improved DEMATEL (i.e. Decision-Making Trail and Evaluation Laboratory), was developed to analyze the causal relationships between the identified IFs as well as the transmission patterns of their impacts on PPPs. Data were collected from interviews and questionnaire surveys.

The transmission patterns of the identified IFs cascade from project environment and features and stakeholders' relationship to the project company capabilities and project process. It is identified that the public authority has a higher level than that of the private entity in PPPs.

It lacks longitudinal studies to investigate the dynamics of PPP stakeholder relationships and social networks. Future research needs to explore the transmission patterns of sub-factors affecting PPP performance and extend the applicability of the developed hybrid approach.

This research provides practitioners with a robust tool that is useful for and insights into enhancing the management of lifecycle performance. It ensures the public authorities and private entities embarking on PPPs will make an informed decision about the monitoring of the life cycle performance.

This study contributes to knowledge of managerial mechanisms that can be adopted to manage factors determining the performance of PPPs. It enables an understanding of stakeholders' roles in driving the life cycle performance of PPPs.

Drawing on institution embeddedness and the resource-based view, the authors develop a theoretical framework and empirically examine how intra-national innovation ecosystems and environmental institutions impact logistics service providers' (LSPs) technological innovation (TI) and green practices.

The authors test the theoretical framework based on survey data of 328 Chinese LSPs. Archival datasets complement the survey data.

The research reveals that intra-national institutional forces of formal and informal environment-related institutions can mitigate LSPs' reliance on their firm-specific advantages when engaging in TI and green practices. Results from a three-way interaction indicate that intra-national innovation ecosystems positively moderate the effects of environmental institutions.

The research has three critical implications. First, the study reveals the contingency role of intra-national environment-related institutions and innovation ecosystems in shaping green logistics. Second, the study finds new results about the roles of informal environmental institutions. Finally, intra-national innovation ecosystems can override environmental institutions in influencing the green practices of LSPs.

Taking a unique angle of institution embeddedness coupled with the resource-based view, the authors examined how intra-national ecosystems and environmental institutions impact LSPs' TI and green practices.

This paper develops a platform that can be used to determine how to effectively and efficiently deal with a large number of temporary facilities under a constrained site condition(s). The ultimate goal is to reduce the material handling costs between transformation phases of construction works occurring during the project's development period.

Empirical and deductive research is first adopted to mathematical model dynamic site layout planning using the branch and bond algorithm (B&B). Second, a real-life construction project is examined to illustrate how dynamic site layout planning (using the aforementioned B&B algorithm and a computer software program called LINGO) can reduce the material handling costs. The application of the proposed methodology is then showcased against a case study that utilizes a comparative analysis between the “dynamic” and “statistic” site planning approaches.

By dividing the construction period into different phases, the developed model is shown to be capable of optimizing the material handling costs between the phases of transformation during construction works. Optimal costs are also considered using the site boundary and unit cost for moving construction materials between two facilities. The comparative analysis results illustrate that the B&B algorithm reduces material handling costs by 33%.

The proposed model offers an effective planning algorithm for the site layout and location of temporary facilities. More specifically, it can make a substantial improvement in reducing the travel time and material handling cost between the temporary facilities in the construction sites.

The primary knowledge contribution of this study to the site layout is successfully deal with the unequal area problem of temporary site facilities and incorporates the concept of dynamics site planning into the algorithm.

The accuracy and reliability of subjectively assessing a construction project's complexity at the pre-construction stage is questionable and relies upon the project manager's tacit experiences, knowledge and background. The purpose of this paper is to develop a scientifically robust analytical approach by presenting a novel classification mechanism for defining the level of project complexity in terms of work contents (WCs), scope, building structures (BSs) and site conditions.

Empiricism is adopted to deductively analyze variables obtained from secondary data within extant literature and primary project data to develop project type classifications. Specifically, and from an operational perspective, a two-stage “waterfall process” was adopted. In stage one, the research identified 56 variables affecting project complexity from literature and utilized a structured questionnaire survey of 100 project managers to measure the relevance of these. A total of 27 variables were revealed to be significant and exploratory factor analysis (EFA) is adopted to cluster these variables into six-factor thematic groups. In stage two, data from 62 real-life projects (including the layout and structural plans) were utilized for computing the factor score using the six-factor groups. Finally, hierarchical cluster analysis (HCA) is adopted to classify the projects into collected distinctive groups and each of a similar nature and characteristics.

The developed theoretical framework (that includes a novel complex index) provides a robust “blueprint platform” for main contractors to compile their project complexity database. The research outputs enable project managers to generate a more accurate picture of complexity at the pre-construction stage.

While numerous research articles have provided a comprehensive framework to define project complexity, scant empirical works have assessed it at the pre-construction stage or utilized real-life project samples to classify it. This research addresses this knowledge gap within the prevailing body of knowledge.

Modular construction is considered a well-established construction method for improving the efficiency of the construction industry worldwide. However, the industry struggles to achieve higher levels of modularisation in urban areas. Previous studies on decision-making for modularisation have, so far, not focussed much on its application in urban areas. As modular construction could bring lots of advantages such as speed of construction, This study aims to develop a decision-making tool that can assist the project planners in deciding whether the modular construction techniques should be applied in their urban area project.

Based on the literature review, a total of 35 decision-making factors of modularisation were identified for this study. The decision-making model is then developed to evaluate the significance of each factor using the analytic hierarchy process (AHP) approach. A total number of 72 valid responses were obtained and analysed. The geometric mean of priorities is adopted to obtain the par-wise comparison between the critical factors in which each factor’s weighting in the decision-making model is calculated. Afterwards, the robustness of the decision-making model is demonstrated by the real-life projects in China, Hong Kong and the UK, respectively.

A total of 35 decision-making factors allocated in five criteria for modular construction selection in urban areas were identified. The criteria include site attributes, project characteristics, labour consideration, environmental and organisation and project risk. Their impact was calculated using the AHP to indicate the relative importance with respect to the adoption of modularisation in urban areas. Afterwards, a two-level decision-making model was developed that can be used as a decision-making tool for the adoption of modular construction.

The outcome of this research will be beneficial to industrial practitioners and academics in understanding the critical attributes that affect the adoption of modular construction in an urban area. It further enables the building professionals to assess the feasibility of using modular construction in their projects, especially at the early stage, so as to facilitate its use.

There is a number of literature on the decision-making model on the adoption of modular construction. However, previous studies did not provide specific concerns related to urban areas, whereas there is an urgent need to have an updated analysis that can be catered to the modular construction in the urban area. In this research study, the 35 decision-making factors were ranked by the experienced project managers and then a pair-wise comparison was conducted. With this information, the robust decision-making model is formulated to offer a kept promised indicator in adopting modularisation in the urban area.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.