Search results

Public-private partnership (PPP) projects for construction waste recycling have become the main approach to construction waste treatment in China. Risk sharing and income distribution of PPP projects play a vital role in achieving project success. This paper is aimed at building a practical and effective risk sharing and income distribution model to achieve win–win situation among different stakeholders, thereby providing a systematic framework for governments to promote construction waste recycling.

Stakeholders of construction waste recycling PPP projects were reclassified according to the stakeholder theory. Best-worst multi–criteria decision-making method and comprehensive fuzzy evaluation method (BWM–FCE) risk assessment model was constructed to optimize the risk assessment of core stakeholders in construction waste recycling PPP projects. Based on the proposed risk evaluation model for construction waste recycling PPP projects, the Shapley value income distribution model was modified in combination with capital investment, contribution and project participation to obtain a more equitable and reasonable income distribution system.

The income distribution model showed that PPP Project Companies gained more transaction benefits, which proved that PPP Project Companies played an important role in the actual operation of PPP projects. The policy change risk, investment and financing risk and income risk were the most important risks and key factors for project success. Therefore, it is of great significance to strengthen the management of PPP Project Companies, and in the process of PPP implementation, the government should focus on preventing the risk of policy changes, investment and financing risks and income risks.

The findings from this study have advanced the application methods of risk sharing and income distribution for PPP projects and further improved PPP project-related theories. It helps to promote and rationalize fairness in construction waste recycling PPP projects and to achieve mutual benefits and win–win situation in risk sharing. It has also provided a reference for resource management of construction waste and laid a solid foundation for long-term development of construction waste resources.

PPP mode is an effective tool for construction waste recycling. How to allocate risks and distribute benefits has become the most important issue of waste recycling PPP projects, and also the key to project success. The originality of this study resides in its provision of a holistic approach of risk allocation and benefit distribution on construction waste PPP projects in China as a developing country. Accordingly, this study adds its value by promoting resource development of construction waste, extending an innovative risk allocation and benefit distribution method in PPP projects, and providing a valuable reference for policymakers and private investors who are planning to invest in PPP projects in China.

Utilizing data from the 2017 Chinese General Social Survey (CGSS2017), the paper aims to investigate the impact of information and communication technology (ICT) adoption on residents' self-rated health and reveals the mechanisms behind ICT.

In the study, ICT adoption is defined as a dummy variable, which takes the value of one if respondents adopt the computers or mobile phone. Meanwhile, respondents' perceptions on five categories of self-rated health are used to construct the dependent variable. Then, based on a fixed-effects regression model, the ordinary least squares (OLS) and ordered probit approaches are applied to estimate their association. Moreover, the two-stage least squares (2SLS) and instrumental variable (IV)-oprobit methods are used to tackle the potential endogeneity of ICT adoption. Finally, the heterogeneity across individuals and regions as well as the underlying mechanisms are discussed.

The results indicate that ICT adoption significantly improves residents' self-rated health, which confirms the health utility model with ICT adoption. The conclusion is robust after overcoming the endogeneity issues with IV. In addition, heterogeneity analysis shows that ICT adoption is more beneficial to the health of residents who are male, young, better educated and those who live in the rural areas and in central and western China. Furthermore, the study demonstrates that ICT adoption for searching health-related information and improving social capital are two crucial mechanisms underlying its health effects.

The findings of this research can help Chinese Government improve population health by issuing corresponding digital and health policies at the regional and individual level.

First, the study provides fresh microscopic evidence on health outcomes of ICT adoption based on data from the latest wave of CGSS2017. Second, individual and regional heterogeneity is extensively discussed in contrast to most related macro studies that consider average effects. Third, the study addresses underlying mechanisms that have not been thoroughly tested or studied primarily on a theoretical level.

The application of circular economy (CE) has received wide coverage in the built environment, including public-private partnership (PPP) infrastructure projects, in recent times. However, current studies and practical implementation of CE are largely associated with construction demolition, waste and recycling management. Few studies exist on circular models and success factors of public infrastructures developed within the PPP contracts. Thus, the main objective of this article is to identify the models and key success factors associated with CE implementation in PPP infrastructure projects.

A systematic review of the literature was undertaken in this study using forty-two (42) peer-reviewed journal articles from Scopus, Web of Science, Google Scholar and PubMed.

The results show that environmental factors, sustainable economic growth, effective stakeholder management, sufficient funding, utilization of low-carbon materials, effective supply chain and procurement strategies facilitate the implementation of CE in PPP infrastructure projects. Key CE business models are centered around the extension of project life cycle value, circular inputs and recycling and reuse of projects.

Although the study presents relevant findings and gaps for further investigations, it has a limited sample size of 42 papers, which is expected to increase as CE gain more prominence in PPP infrastructure management in future.

The findings are relevant for decision-making by PPP practitioners to attain the social, economic and environmental benefits of transitioning to circular infrastructure management.

This study contributes to articulating the key models and measures toward sustainable CE in public infrastructure development.

Public–private partnership (PPP), a project financing arrangement between private investors and the public sector, has revolutionized the approach to the funding and development of public infrastructure worldwide. However, the increasing cases of financial risks and poor financial risk management related to the model threaten the sustainability and financial success of PPP projects leading to huge financial investment losses. This study aims to review existing literature to establish the key measures to control the financial risks of sustainable PPP projects.

A PRISMA-compliant systematic literature review method was used in this study. Data were sourced from academic databases consisting of 56 impactful peer-reviewed journal articles.

The review outcomes demonstrate 41 critical factors (measures) in mitigating the financial risks of sustainable PPP projects. They include minimum revenue guarantee, strategic alliance with private investors, financial transparency and accountability and sound macroeconomic policies. The principal results of the study were categorized and conceptualized into a financial risk management maturity model for sustainable PPP projects. Lastly, the study reveals that further studies and project policies must focus more on addressing financial challenges relating to climate risks, and health and safety concerns such as COVID-19 outbreak that have negative impacts on PPP projects.

The results provide essential research gaps and directions for future studies on measures to mitigate the financial risks of sustainable PPP projects. However, this study used small but significant existing publications.

A checklist and a conceptual maturity model are provided in this study to help practitioners to learn and improve upon their practices to mitigate the financial risks of sustainable PPP projects.

This study contributes to managerial measures to reduce huge losses in financial investments of PPP projects and the attainment of sustainability in public infrastructure projects with a financial risk maturity model.

Multi-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

In this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.

In order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.

In order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

Drawing from resource orchestration theory, this research proposed an integrative model that leverages insights into counter resource constraints and uncertainty in start-up business model innovation (BMI). It investigated the influences of entrepreneurial networks and effectuation on BMI through bricolage in uncertain environments.

The research surveyed 481 start-ups in China. LISREL 8.80 and SPSS 22.0 were employed to test the validity and reliability of key variables, respectively. Additionally, hypotheses were examined through multiple linear regression.

First, entrepreneurial networks and effectuation were positively related to BMI, and combining these two factors improved BMI for start-ups. Second, bricolage contributed to BMI and played mediating roles in translating entrepreneurial networks and effectuation into BMI. Third, environmental uncertainty weakened the linkage between bricolage and BMI.

Future research should replicate the results in other countries because only start-ups in China were investigated in the study, and it is necessary to extend this research by gathering longitudinal data. This research emphasized the mediating effects of bricolage and the moderating influence of environmental uncertainty, and new potential mediating and moderating factors should be explored between resources and BMI.

There are three significant theoretical contributions. First, the findings enrich the literature on the complex antecedents of BMI by combining the impacts of entrepreneurial networks and effectuation. Second, an overarching framework is proposed explaining how bricolage (resource management) links entrepreneurial networks and effectuation and BMI. Third, it demonstrates the significance of environmental uncertainty in the bricolage–BMI linkage, deepening the understanding of the bricolage boundary condition.

The study uses machine learning techniques to cluster regional retail egg prices after 2000 in China. Furthermore, it combines machine learning results with econometric models to study determinants of cluster affiliation. Eggs are an inexpensiv, nutritious and sustainable animal food. Contextually, China is the largest country in the world in terms of both egg production and consumption. Regional clustering can help governments to imporve the precision of price policies and help producers make better investment decisions. The results are purely driven by data.

The study introduces dynamic time warping (DTW) algorithm which takes into account time series properties to analyze provincial egg prices in China. The results are compared with several other algorithms, such as TADPole. DTW is superior, though it is computationally expensive. After the clustering, a multinomial logit model is run to study the determinants of cluster affiliation.

The study identified three clusters. The first cluster including 12 provinces and the second cluster including 2 provinces are the main egg production provinces and their neighboring provinces in China. The third cluster is mainly egg importing regions. Clusters 1 and 2 have higher price volatility. The authors confirm that due to transaction costs, the importing areas may have less price volatility.

The machine learning techniques could help governments make more precise policies and help producers make better investment decisions.

This is the first paper to use machine learning techniques to cluster food prices. It also combines machine learning and econometric models to better study price dynamics.

The literature on how emerging market multinational enterprises (EMNEs) overcome the liability of emergingness/origin has sidestepped a prerequisite for any efforts to overcome liability, namely, corporate compliance. The authors argue that EMNEs build corporate compliance capability as a knowledge-based firm-specific advantage (FSA) to adapt to institutional norms in advanced economies. In this study, the authors empirically examine the intricate relationships between corporate compliance capability and performance in the US subsidiaries of Chinese firms.

In this study, the authors use survey data to empirically examine the intricate relationships between corporate compliance capability and performance in the US subsidiaries of Chinese firms.

The findings reveal a positive relationship between corporate compliance capability and subsidiary performance, as mediated by local financing.

The study suggests that corporate compliance capability helps a subsidiary gain legitimacy, which leads to local resource acquisition and utilization. Corporate compliance capability thus serves as a source of a knowledge-based FSA for EMNEs in developed economies.

This study aimed to analyze existing problems in the dissemination and management of emergency information on social media platforms, improve social media users' experience regarding such information, increase the efficiency of emergency information dissemination and curb the spread of misinformation.

In this study, the emergency information quality on social media platforms was examined. Based on the evaluation principles of the quality of mature information, social media information characteristics and the rules of emergency information dissemination, combined with relevant academic research results, an index to evaluate the quality of emergency information on social media was constructed. In addition, the authors have introduced cloud theory as an information quality evaluation method and used social media users' emotional characteristics to assess information quality evaluation results. A comprehensive system for evaluating emergency information quality, including indexes, methods and detection strategies was established. Based on a comprehensive system, a case study was conducted on the forest fires in Sichuan Province and the African swine fever events as reported on the Zhihu platform. In accordance with the results of the case study, the authors expanded the research and introduced the emotional characteristics of social media users as an independent evaluation dimension to evaluate the quality of emergency information on social media.

The comprehensive system's effectiveness was verified through the case study. Further, it was found that users' emotional characteristics (reflected in their information behavior) are inconsistent with their evaluation of websites' information quality regarding major emergencies. Integrating users' emotional characteristics into the information evaluation system can enhance its effectiveness following major emergencies.

First, an evaluation index system of emergency information quality on social media about major emergencies was offered. Unlike the commonly available index system for information quality evaluation, this proposed evaluation index system not only accounted for the characteristics of social media, such as massive disordered information, multiple information sources and rapid dissemination, but also for the characteristics of emergency events, such as variability and the absence of precursors. This proposed evaluation index system enhances the pertinence of the information quality evaluation and compensates for the shortcoming that the current research only focuses on evaluating social media information quality in a broad context, but pays insufficient attention to major emergencies. Second, cloud theory was introduced as a method to evaluate the emergency information quality found on social media. Existing research has primarily included the use of traditional statistical methods, which cannot transform numerical values into qualitative concepts effectively. Various indeterminate factors inevitably affect the quality of emergency information on social media platforms, and the traditional methods cannot eliminate this uncertainty in the evaluation process. The method to assess emergency information quality based on cloud theory can effectively compensate for the gaps in the research and improve the accuracy of information quality assessment. Third, the inspection and the dynamic adjustment of assessment results are absent in the research on information quality assessment, and the research has relied principally on the information users' evaluation and has paid insufficient attention to their attitudes and behaviors toward information. Therefore, the authors incorporated users' emotional characteristics into the evaluation of emergency information quality on social media and used them to test the evaluation results so that the results of the information quality assessment not only include the users' explicit attitudes but also their implicit attitudes. This enhances the effectiveness of the information quality assessment system. Finally, through this case study, it was found that an inconsistency exists between user evaluation and user emotional characteristics after major emergencies. The reasons for this phenomenon were explained, and the necessity of integrating user emotional characteristics into information quality assessment was demonstrated. Based on this, the users' emotional characteristics were used as a separate evaluation dimension for assessing the quality of emergency information on social media. Compared with assessing the quality of general information, integrating the user's emotional characteristics into the evaluation index system can lead the evaluation results to include not only the users' cognitive evaluation but also their emotional experience, further enhancing their adaptability.

The problem of stress is increasingly gaining attention in the construction industry in recent years. This study is aimed at examining the causes, effects and possible alleviation of stress of project management (PM) practitioners so that their stress could be appropriately managed and reduced, which would contribute to improved mental health.

Primary data were collected in an online questionnaire survey via Qualtrics. Questions ranged from PM practitioners’ stressors, stress and performance under stress to stress alleviation tools and techniques. One hundred and five PM practitioners completed the questionnaire. Their responses were compiled and analyzed using descriptive statistics, correlation and regression.

The results confirmed that the identified stressors tended to increase stress of PM practitioners. All stressors tested in this study were found to have negative impact on the performance of PM practitioners. In particular, the burnout stressors were seen as the key stressors that influence the performance of PM practitioners and have a strong correlation with all the other stressors. It was also found that a number of tools and techniques can reduce the impact of stressors on PM practitioners.

This study has taken a specific focus on stress-related issues of PM practitioners in the construction industry due to their critical role in this project-dominated industry. Using the Job Demand-Resource theory, a holistic examination was not only conducted on stress and stressors but also on alleviation tools and techniques. This study has thus made significant contribution to the ongoing research aimed at finding solutions to mental health-related problems in the project-dominated construction industry, thereby achieving the United Nations’ social sustainability development goals.