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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.

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.

An optimal control model is built considering the private sector's opportunistic effort diversion and reciprocal effort improvement, while a numerical study is conducted to draw some managerial implications.

In infrastructure PPP projects, private sectors may opportunistically divert part of their effort from the current projects to other projects to allocate their limited human resources. Nevertheless, this effort diversion can be inhibited by dynamic incentives since the private sectors reciprocally exert greater effort into the current projects when receiving the dynamic incentives. This article investigates how the government specifies the output standard that the private sector should meet and offers dynamic incentives to mitigate the private sector's opportunistic effort diversion.

The output standard for the private sector to acquire the dynamic incentives should be specified as the output level corresponding to the private sector's optimal long-run stationary equilibrium (OLSE) effort level, which decreases with its reciprocal preference level but increases with its effort-diverting level. The optimal dynamic incentives comprise an initial incentive and a periodic OLSE incentive, which declines with the reciprocal preference level but improves with the effort-diverting level. Besides, the numerical study reveals that the government should distinguish whether the bidders have high effort-diverting levels and, if so, should focus on their reciprocal preference levels and decline the bidders with low reciprocal preference to avoid utility loss.

This article provides a theoretical model combining opportunistic behavior with reciprocal preference through an optimal control lens, thus embedding the problem of incentive design into a broader socioeconomic framework.

This study aims to develop a Stackelberg Game Model for seeking the optimal subsidy plans with varying levels of government financial capability (GFC). Furthermore, the scenario-based analysis is conducted and will enable governments to identify a comprehensive subsidy plan as follows: improve project performance and optimise social welfare.

A Stackelberg Game Model is developed to optimise the effectiveness of subsidies on the performance of public-private partnerships (PPPs).

According to the scenarios that are generated from the model, governments that are confronting with limited public budgets could reduce the intensity of performance incentives and increase the participation-oriented subsidy. Whilst a participation-oriented subsidy can stimulate private organisations’ willingness to participate in infrastructure PPPs, a performance-oriented subsidy is capable of facilitating the projects’ performances. Intuitively, the performance-oriented subsidy enables the private entities of PPPs to improve their efforts on the projects to realise higher profits. However, the participation-oriented subsidy is unable to affect the level of their effort spent on the projects. To satisfy both parties’ expectations in a PPP, the performance-oriented subsidy needs to be prioritised for a purpose of enabling higher quality outputs.

The game model developed in this study contributes to the literature by offering new insight into the underlying mechanism of governments and private entities, in terms of their decision-making for subsidy planning and contributions (i.e. resource allocation and spending) during the life-cycle of PPPs. This research enriches the government subsidy model by revealing the effects of the GFC and clarifies the impacts of two different schemes of subsidy on the performance of PPPs.

The government has been conventionally viewed as being omnipotent to provide PPPs with a wide range of subsidies. However, the subsidies are not unlimited, due to GFC. In addressing this void, this study has modelled the impacts of government subsidy plans with a consideration of GFC-related constraints. The combined effects of the participation- and performance-oriented subsidies on the project performance of PPPs have been examined.

Currently, engineering project design lacks a design framework that fully combines subjective experience and objective data. This study develops an aided design decision-making framework to automatically output the optimal design alternative for engineering projects in a more efficient and objective mode, which synthesizes the design experience.

A database of design components is first constructed to facilitate the retrieval of data and the design alternative screening algorithm is proposed to automatically select all feasible design alternatives. Then back propagation (BP) neural network algorithm is introduced to predict the cost of all feasible design alternatives. Based on the gray relational degree-particle swarm optimization (GRD-PSO) algorithm, the optimal design alternative can be selected considering multiple objectives.

The case study shows that the BP neural network-cost prediction algorithm can well predict the cost of design alternatives, and the framework can be widely used at the design stage of most engineering projects. Design components with low sensitivity to design objectives have been obtained, allowing for the consideration of disregarding their impacts on design objectives in such situations requiring rapid decisions. Meanwhile, design components with high sensitivity to design objective weights have also been obtained, drawing special attention to the effects of changes in the importance of design objectives on the selection of these components. Simultaneously, the framework can be flexibly adjusted to different design objectives and identify key design components, providing decision reference for designers.

The framework proposed in this paper contributes to the knowledge of design decision-making by emphasizing the importance of combining objective data and subjective experience, whose significance is ignored in the existing literature.

In construction projects, engineering variations are very common and create breeding grounds for opportunistic claims. This study investigates the complementary effect between an inspection mechanism and a reputation system in deterring opportunistic claims, considering an employer with limited inspection accuracy and a contractor, which can be either reputation-concerned or opportunistic.

This paper applies a signaling game to investigate the complementary effect between the employer's inspection and a reputation system in deterring the contractor's possible opportunistic claim, considering the information-flow influence of claiming prices.

This study finds that in the exogenous-inspection-accuracy case, the employer does not always inspect the claim. A more stringent reputation system complements a less accurate inspection only when the inspection cost is lower than a threshold, but may decline the employer's surplus or social welfare. In the optimal-inspection-accuracy case, the employer always inspects the claim. However, only a sufficiently stringent reputation system can guarantee the effectiveness of an optimal inspection in curbing opportunistic claims. A more stringent reputation system has a value-stepping effect on the employer's surplus but may unexpectedly impair social welfare, whereas a higher inspection cost efficiency always reduces social welfare.

This article contributes to the project management literature by combing the signaling game theory with the reputation theory and thus embeds the problem of inspection mechanism design into a broader socio-economic framework.

Early termination of public–private partnerships (PPPs) in China is caused by various risk factors, resulting in significant losses. This study aimed to clarify the key factors and identify the causal relationships among these factors.

Social network analysis (SNA) was used to analyze 37 risk factors that were summarized from 97 early terminated PPP cases and to identify the relationships among these key risk factors. Interpretive structural modeling (ISM) was conducted to explore the causal relationships. Data were collected from case documents, questionnaires and interviews.

A total of 17 key risk factors were identified and distributed in a hierarchical structure with six tiers. Among these key risk factors, the root causes affecting the early termination of PPP projects were government oversight in decision-making, local government transition, policy and law changes and force majeure. The direct cause was insufficient returns. Furthermore, local government and private sector defaults were essential mediating factors. Local government transition and the low willingness of the private sector were highlighted as potential key risks.

The cases and experts were all from China, and outcomes in other countries or cultures may differ from those of this study. Therefore, further studies are required.

This research provides knowledge regarding the key risk factors leading to the early termination of PPP projects and guidance on avoiding these factors and blocking the factors' transmission in the project lifecycle.

This study contributes to the knowledge of risk management by emphasizing the importance of local government transition, the low willingness of the private sector and project cooperation and operation, whose significance is ignored in the existing literature. The proposed ISM clarifies the role of risk factors in causing early termination and explains their transmission patterns.

Previous research has little specific guidance on how to improve large infrastructures’ risk analysis. This paper aims to propose a practical risk analysis framework across the project lifecycle with Bayesian Networks (BNs).

The framework includes three phases. In the qualitative phase, primary risks were identified by literature reviews and interviews; questionnaires were used to determine key risks at each project stage and causal relationships between stage-related risks. In the quantitation, brainstorming and questionnaires, and techniques of ranked nodes/paths, risk map and Bayesian truth serum were adopted. Then, a BN-based risk assessment model was developed, and risk analysis was conducted with AgenaRisk software.

Twenty key risks across the lifecycle were determined: some risks were recurring and different risks emerged at various stages with the construction and feasibility most risky. Results showed that previous stages’ risks significantly amplified subsequent stages’ risks. Based on the causality of stage-related risks, a qualitative model was easily constructed. Ranked nodes/paths facilitated the quantification by requiring less statistical knowledge and fewer parameters than traditional BNs. As articulated by a case, this model yielded very simple and easy-to-understand representations of risks and risk propagation pathways.

Rare research has developed a BN risk assessment model from the perspective of project stages. A structured model, a propagation network among individual risks, stage-related risks, and the final adverse consequence, has been designed. This research provides practitioners with a realistic risk assessment approach and further understanding of dynamic and stage-related risks throughout large infrastructures’ lifecycle. The framework can be modified and used in other real-world risk analysis where risks are complex and develop in stages.

This study aims to investigate the impact of experience product portal page aesthetics on bounce rate.

This research collected data from an online shop selling original design furniture on Taobao.com. It employed deep learning algorithm and manual coding to operationalize image and text aesthetics.

The empirical results indicate that text aesthetics has a U-shaped relationship with bounce rate, whereas the relationship between image aesthetics and bounce rate is insignificant. Moreover, the U-shaped relationship between text aesthetics and bounce rate is weakened by image aesthetics.

This study addresses an important but understudied topic – the bounce rate of experience products in the context of e-commerce. Although the high bounce rate has increasingly gained attention from practitioners, there remains a scarcity of research that addresses the effect of product portal page aesthetics in the specific context of experience products. The authors theorize product portal page aesthetics as the design elements of an e-commerce website and deeply analyzed the role of product portal page aesthetics by classifying it into text aesthetics and image aesthetics. The authors’ findings provide implications for online sellers and platforms to effectively design product profile pages to reduce the bounce rate.

Even though many studies have been conducted in exploring the determinants of job stress, limited research has been conducted in exploring how the internal factors of religious orientation and the relatively new variable in the field of psychology, psychological capital (PsyCap), may relate to job stress. This study aims to examine the influence of PsyCap as a mediating variable on the relationship between religious orientation and job stress.

This study used self-administered surveys. Data was collected through the completion of online questionnaires. The participants include 208 accountants and business practitioners in Indonesia. Data were analysed using structural equation model-partial least squares.

The results show that intrinsic (extrinsic) religious orientation is negatively (positively) related to job stress and positively (negatively) related to PsyCap. Additionally, PsyCap mediates the relationship between religious orientation (intrinsic and extrinsic) and job stress. Other results indicate a positive relationship between PsyCap and job stress.

Firstly, the sample used comes from various professions in companies or agencies that function differently. Secondly, this study asks individuals to assess their own job stress, which increases the chance of bias.

The findings of this study will provide a recommendation to the company, particularly the human resources division, to consider candidates’ religious orientation and PsyCap levels during the recruitment process. Thus, the company may observe the employees or individuals who can effectively manage job stress.

This study provides new empirical evidence regarding the relationship between religious orientation, PsyCap and job stress. It shows that the individual’s religious orientation (intrinsic and extrinsic) affects their level of PsyCap (higher and lower), which is a determinant factor in the experience of job stress.