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Stakeholder interests are complex, sensitive and highly uncertain and may influence the development of net zero carbon building (NZCB). However, this study aims to conduct a systematic literature review to explore the stakeholder interests towards achieving NZCB.

A total of 62 articles were identified from the Scopus database and thoroughly reviewed to extract relevant information on stakeholders' interest towards achieving NZCB.

A total of 28 stakeholder interests influencing the development of NZCB were identified from the literature and were classified into six major groups according to their uniqueness (economic, social, environmental, technological, political, regulatory and legal).

The findings of this study provide insight into the specific stakeholder interests towards achieving NZCB. Thus, the findings of this study could serve as a guide for future research, policy formulation and implementation to expedite the practice of building towards net zero carbon (NZC). Empirical studies are suggested in future studies to test and consolidate the theoretical claims of this study.

This paper undertakes a comprehensive systematic review of studies on stakeholder interests towards achieving NZCB, which is the least investigated in the literature.

This article explores how the social media usage affect team creative performance via transactive memory system, knowledge interaction and expertise coordination.

The study is based on the perspective of transaction memory system and expertise coordination theory. A research model was constructed and tested, involving 289 individuals from 67 distributed agile software development teams.

The results indicate that social media usage is positively correlated with transactive memory system, and social media usage and transactive memory system have positive relations to knowledge interaction and expertise coordination. Moreover, this analysis shows that knowledge interaction has a positive relationship with expertise coordination, and expertise coordination positively affects team creative performance. However, knowledge interaction has no direct relationship on team creative performance, and its indirect impact on team creative performance was fully mediated by expertise coordination. This research shows that social media usage by distributed agile software development teams can support the development of transactive memory system and promote expertise coordination. In addition, knowledge interaction alone is not enough, and expertise coordination must be achieved to increase team creative performance.

First, this paper explores the mechanism of transactive memory system in distributed Agile Software Development teams from the perspective of social media, which is different from the previous information processing theory framework that confined transactive memory system to the cognitive aspects of knowledge coding, storage and retrieval. Second, this research focuses on the knowledge interaction and expertise coordination formed by team members in the process of communication in the context of social media usage, which confirms the crucial roles of social media usage and transactive memory system in team knowledge management and team creative performance. Then, this research also shows that the development of transactive memory system in the team is indeed an important factor to promote knowledge interaction and professional expertise coordination.

In today’s digital era, talent recruitment can help address the growing shortage of skilled labor in the construction industry and promote sustainable growth. While existing research has explored the association between talent acquisition and local labor productivity or economic progress, the impact on construction growth deserves further study. This study aims to (1) explore the influence of talent recruitment on the growth of the construction industry and (2) analyze whether different regional characteristics shape the differential impact of talent acquisition on construction growth.

This research employs a quantitative approach, focusing on 35 major cities in China. A panel data regression model is utilized to analyze annual data from 2013 to 2018, considering variables like the construction talent recruitment index, value added in construction, gross regional product per capita and others. The study also examines regional heterogeneity and conducts robustness tests to validate the findings.

The results reveal a positive and significant correlation between talent recruitment and construction industry growth. This correlation is more pronounced in economically advanced and infrastructure-rich regions. The study also finds that factors like capital investment, educational attainment and housing prices significantly contribute to industry growth. Talent recruitment not only transforms local labor market dynamics but also drives demand for construction services, promoting industry growth through economies of scale.

This research constructs a new measurement for talent recruitment and provides new insights into the pivotal role of talent recruitment in the sustainable growth of the construction industry. It underscores the need for construction firms to tailor talent acquisition policies to their specific circumstances and regional developmental conditions. The findings offer practical guidance for driving regional growth within the sector, emphasizing the importance of talent recruitment as a key yet previously underappreciated factor in industry development.

To help decision-makers choose appropriate infrastructure project delivery systems (IPDS) and keep up with the construction industry’s rapid growth, this study aims to develop a goal optimization technique.This looks into team integration, large production and optimum sustainability. The suggested approach for meeting several infrastructure project objectives is flexible and expandable. This research overcomes the significant discrepancy between the construction industry’s progress and the rate at which project delivery methods evolve.

This study examined pertinent literature to choose an appropriate project delivery method and gave information on several elements that affect that decision. After optimization using a genetic algorithm (GA), a Pareto front of solutions has been found. The three construction goals of sustainability, mass production and team integration are all met by the chosen best solution. The four most popular possibilities for studying the suggested approach are five primary categories, each of which has 22 variables, and the weight of each variable was established using Simo’s procedure. This is optimized, demonstrating the accuracy of the optimization model.

Sustainability, mass production and team integration are the major goals of selecting the finest IPDS. The Pareto-optimal solutions discovered through analysis demonstrated that the created GA is reliable and generates solid outcomes. In fact, it enables decisions that were based on a single criterion to be overturned. The process has therefore demonstrated its efficacy in identifying the ideal answer. First integrated project delivery (IPD), second design-build (DB), third design-bid-build (DBB) and last construction manager at risk (CMR) are the best options. The weight of the aims function has found these rankings to be satisfactory.

The findings demonstrate that the suggested strategy can lead to optimization, providing the government with a wide range of options for attaining certain project objectives. The ability of this study to evaluate the combined effects of three objectives in choosing the best IPDS, the production of optimal selection solutions (IPDS), which can help with better decision-making when many objectives are present, and the flexibility and extendibility of the suggested approach for achieving priorities in infrastructure projects are what make it unique. This approach was able to select IPDS to meet developments in the construction project.

To confirm the validity of the GA, the factor of error was calculated, which is equal to 1.7599e-08.

This paper studies the determinants for the desirability of the public-private partnership (PPP) mode in infrastructure development.

The author manually collects data on over 12,000 PPP projects in China, and regard the successful transition and abnormal termination as signals for the mode’s desirability and undesirability, respectively. Then, guided by relevant theories in the literature, the author investigates the impact of various project characteristics on the projects’ successful transition and abnormal termination.

First, execution-stage projects in industries where government support is indispensable, or where quality improvement is more important than cost reduction, face higher likelihood of abnormal termination. But such negative effects are mitigated if state-owned enterprises (SOEs) participate in the social party. Second, the structure of social party matters. The participation by private firms in the social party increases the termination likelihood, while the decentralization of the social party decreases it. Third, pre-execution projects with government payment or subsidies are more likely to enter into the execution stage.

Regulations on participation by SOEs in PPPs, such as policy [2023 No. 115] announced by State Council, should take industrial heterogeneity into consideration.

Using a large sample, the author empirically tests the seminal PPP-related theories in the literature. The author also uncovers some unique stylized facts about PPPs in China, especially the impact of SOE participation in the social party on PPP survival.

The six-axis force/torque sensor based on a Y-type structure has the advantages of simple structure, small space volume, low cost and wide application prospects. To meet the overall structural stiffness requirements and sensor performance requirements in robot engineering applications, this paper aims to propose a Y-type six-axis force/torque sensor.

The performance indicators such as each component sensitivities and stiffnesses of the sensor were selected as optimization objectives. The multiobjective optimization equations were established. A multiple quadratic response surface in ANSYS Workbench was modeled by using the central composite design experimental method. The optimal manufacturing structural parameters were obtained by using multiobjective genetic algorithm.

The sensor was optimized and the simulation results show that the overload resistance of the sensor is 200%F.S., and the axial stiffness, radial stiffness, bending stiffness and torsional stiffness are 14.981 kN/mm, 16.855 kN/mm, 2.0939 kN m/rad and 6.4432 kN m/rad, respectively, which meet the design requirements, and the sensitivities of each component of the optimized sensor have been well increased to be 2.969, 2.762, 4.010, 2.762, 2.653 and 2.760 times as those of the sensor with initial structural parameters. The sensor prototype with optimized parameters was produced. According to the calibration experiment of the sensor, the maximum Class I and II errors and measurement uncertainty of each force/torque component of the sensor are 1.835%F.S., 1.018%F.S. and 1.606%F.S., respectively. All of them are below the required 2%F.S.

Hence, the conclusion can be drawn that the sensor has excellent comprehensive performance and meets the expected practical engineering requirements.

Express freight transportation is in rapid development currently. Owing to the higher speed of express freight train, the deformation of the bridge deck worsens the railway line condition under the action of wind and train moving load when the train runs over a long-span bridge. Besides, the blunt car body of vehicle has poor aerodynamic characteristics, bringing a greater challenge on the running stability in the crosswind.

In this study, the aerodynamic force coefficients of express freight vehicles on the bridge are measured by scale model wind tunnel test. The dynamic model of the train-long-span steel truss bridge coupling system is established, and the dynamic response as well as the running safety of vehicle are evaluated.

The results show that wind speed has a significant influence on running safety, which is mainly reflected in the over-limitation of wheel unloading rate. The wind speed limit decreases with train speed, and it reduces to 18.83 m/s when the train speed is 160 km/h.

This study deepens the theoretical understanding of the interaction between vehicles and bridges and proposes new methods for analyzing similar engineering problems. It also provides a new theoretical basis for the safety assessment of express freight trains.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

This study explores the mechanism by which intellectual capital (IC) [i.e. human capital (HC), structural capital (SC) and relational capital (RC)] influences small and medium-sized enterprise (SME) efficiency in the presence of business model innovation (BMI) as a mediator.

Data collection is conducted through a survey completed by 319 owners and top managers of SMEs operating in the manufacturing sector in three cities in Pakistan. A simple random sampling method is used. A structural equation modeling artificial neural network (SEM-ANN)-based approach is applied to evaluate the role of IC predictors. The mediation results are authenticated using PROCESS.

The results indicate that HC, SC and RC significantly influence SME efficiency and BMI. Furthermore, BMI fully mediates the relationship between human capital and SME efficiency, while partially mediating the relationship between structural capital and SME efficiency, as well as between SC and SME efficiency.

This study pioneers research into the link between IC and SME efficiency. It contributes to the literature by defining IC as an antecedent of SME efficiency. It further contributes to the literature by defining IC as an antecedent and BMI as an intervening variable of SME efficiency.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.