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The purpose of this study is to develop a new hybrid method that combines interpretative structural modeling (ISM) and matrix cross-impact multiplication applied to classification (MICMAC) to investigate the influencing factors of sustainable infrastructure vulnerability (SIV).

(1) Literature review and case study were used to identify the possible influencing factors; (2) a semi-structured interview was conducted to identify representative factors and the interrelationships among influencing factors; (3) ISM was adopted to identify the hierarchical structure of factors; (4) MICMAC was used to analyze the driving power (DRP) and dependence power (DEP) of each factor and (5) Semi-structured interview was used to propose strategies for overcoming SIV.

Results indicate that (1) 18 representative factors related to SIV were identified; (2) the relationship between these factors was divided into a five-layer hierarchical structure. The 18 representative factors were divided into driving factors, dependent factors, linkage factors and independent factors and (3) 12 strategies were presented to address the negative effects of these factors.

The findings illustrate the factors influencing SIV and their hierarchical structures, which can benefit the stakeholders and practitioners of an infrastructure project by encouraging them to take effective countermeasures to deal with related SIVs.

This paper aims to explore augmented reality (AR) applications in construction safety academic literature and propose possible improvements for future scholarly works. The paper explicitly focuses on AR integration with Construction 4.0 technologies as an effective solution to safety concerns in the construction industry.

This study applied a systematic review approach. In total, 387 potentially relevant articles from databases were identified. Once filtering criteria were applied, 29 eligible papers where selected. The inclusion criteria were being directly associated with construction safety focused on an AR application and AR interactions associated with the Construction 4.0 technologies.

This study investigated the structure of AR applications in construction safety. To this end, the authors studied the safety purposes of AR applications in construction safety: pre-event (intelligent operation, training, safety inspection and hazard alerting), during-event (pinpointing hazard) and post-event (safety estimation) applications. Then, the integration of AR with Construction 4.0 technologies was elaborated. The systematic review also revealed that the AR integration has contributed to developing several technical aspects of AR technology: display, tracking and human–computer interaction. The study results indicate that AR integration with construction is effective in mitigating safety concerns; however, further research studies are required to support this statement.

This study contributes to exploring applications and integrations of AR into construction safety in order to facilitate the leverage of this technology. This review can help encourage practitioners and researchers to conduct further academic investigations into AR application in construction safety.

This study explores the potential of artificial intelligence (AI) in fostering sustainable entrepreneurship within the tourism industry. The rapid growth of the tourism sector has raised concerns regarding its environmental impact, social equity and economic sustainability. Sustainable entrepreneurship offers a promising approach to address these challenges by integrating environmental, social and economic considerations into business practices. AI technologies, with their ability to process vast amounts of data, analyse patterns and make predictions, have the potential to support sustainable entrepreneurship initiatives in the tourism industry. By analysing the current literature, this study provides insights into the effective utilisation of AI to promote sustainable entrepreneurship in the tourism industry, while acknowledging the need for responsible and ethical AI implementation. The findings contribute to the understanding of how AI can be harnessed as a tool for driving sustainable practices and innovation in the tourism sector, ultimately leading to a more sustainable and responsible tourism industry.

Project portfolio benefit (PPB) evaluation is crucial for project portfolio management decisions. However, PPB is complex in composition and affected by synergy and ambidexterity. Ignoring these characteristics can result in inaccurate assessments, impeding the management and optimization of benefit. Considering the above complexity of PPB evaluation, this study aims to propose a refined PPB evaluation model to provide decision support for organizations.

A back propagation neural network optimized via genetic algorithm and pruning algorithm (P-GA-BPNN) is constructed for PPB evaluation. First, the benefit evaluation criteria are established. Second, the inputs and expected outputs for model training and testing are determined. Then, based on the optimization of BPNN via genetic algorithm and pruning algorithm, a PPB evaluation model is constructed considering the impacts of ambidexterity and synergy on PPB. Finally, a numerical example was applied to validate the model.

The results indicate that the proposed model can be used for effective PPB evaluation. Moreover, it shows superiority in terms of MSE and fitting effect through extensive comparative experiments with BPNN, GA-BPNN, and SVM models. The robustness of the model is also demonstrated via data random disturbance experiment and 10-cross-validation. Therefore, the proposed model could serve as a valuable decision-making tool for PPB management.

This study extends prior research by integrating the impacts of synergy and ambidexterity on PPB when conducting PPB evaluation, which facilitates to manage and enhance PPB. Besides, the structural redundancy of existing assessment methods is solved through the dynamic optimization of the network structure via the pruning algorithm, enhancing the effectiveness of PPB decision-making tools.

As an important load-bearing component of cable-stayed bridge, the cable-stayed cable is an important load-bearing link for the bridge superstructure and the load transferred directly to the bridge tower. In order to better manage the risk of the cable system in the construction process, the purpose of this paper is to study a new method of dynamic risk analysis of the cable system of the suspended multi-tower cable-stayed bridge based on the Bayesian network.

First of all, this paper focuses on the whole process of the construction of the cable system, analyzes the construction characteristics of each process, identifies the safety risk factors in the construction process of the cable system, and determines the causal relationship between the risk factors. Secondly, the prior probability distribution of risk factors is determined by the expert investigation method, and the risk matrix method is used to evaluate the safety risk of cable failure quantitatively. The function expression of risk matrix is established by combining the probability of risk event occurrence and loss level. After that, the topology structure of Bayesian network is established, risk factors and probability parameters are incorporated into the network and then the Bayesian principle is applied to update the posterior probability of risk events according to the new information in the construction process. Finally, the construction reliability evaluation of PAIRA bridge main bridge cable system in Bangladesh is taken as an example to verify the effectiveness and accuracy of the new method.

The feasibility of using Bayesian network to dynamically assess the safety risk of PAIRA bridge in Bangladesh is verified by the construction reliability evaluation of the main bridge cable system. The research results show that the probability of the accident resulting from the insufficient safety of the cable components of the main bridge of PAIRA bridge is 0.02, which belongs to a very small range. According to the analysis of the risk grade matrix, the risk grade is Ⅱ, which belongs to the acceptable risk range. In addition, according to the reverse reasoning of the Bayesian model, when the serious failure of the cable system is certain to occur, the node with the greatest impact is B3 (cable break) and its probability of occurrence is 82%, that is, cable break is an important reason for the serious failure of the cable system. The factor that has the greatest influence on B3 node is C6 (cable quality), and its probability is 34%, that is, cable quality is not satisfied is the main reason for cable fracture. In the same way, it can be obtained that the D9 (steel wire fracture inside the cable) event of the next level is the biggest incentive of C6 event, its occurrence probability is 32% and E7 (steel strand strength is not up to standard) event is the biggest incentive of D9 event, its occurrence probability is 13%. At the same time, the sensitivity analysis also confirmed that B3, C6, D9 and E7 risk factors were the main causes of risk occurrence.

This paper proposes a Bayesian network-based construction reliability assessment method for cable-stayed bridge cable system. The core purpose of this method is to achieve comprehensive and accurate management and control of the risks in the construction process of the cable system, so as to improve the service life of the cable while strengthening the overall reliability of the structure. Compared with the existing evaluation methods, the proposed method has higher reliability and accuracy. This method can effectively assess the risk of the cable system in the construction process, and is innovative in the field of risk assessment of the cable system of cable-stayed bridge construction, enriching the scientific research achievements in this field, and providing strong support for the construction risk control of the cable system of cable-stayed bridge.

The aim of this study is to examine in detail the impact of technological advancements on the workforce within the tourism industry. Specifically, it seeks to understand the effects of information and communication technologies (ICT), social media, the internet and websites, mobile technologies and other technological developments on workforce dynamics, skill requirements and job descriptions. The research intends to analyze how technological innovations are transforming the workforce and how these transformations are influencing practices within the industry.

This study employs a comprehensive literature review to understand the impact of technological innovations on the workforce in the tourism industry. The research aims to conduct an in-depth examination of empirical data obtained from extensive databases in the fields of technology and tourism and detail the effects of technological advancements on the workforce. Additionally, it includes a general assessment of trends and transformation processes within the sector by synthesizing findings from existing literature on the relationship between technology and workforce.

The results of the research reveal that technological innovations have fundamentally transformed workforce dynamics and job descriptions. Developments in information technology have led to the automation of routine tasks and the creation of high-skilled new job roles. Social media has contributed to the emergence of new job roles and skill requirements, while the internet and websites have altered digital marketing strategies. Mobile technologies have increased the necessity for the workforce to develop mobile skills. Furthermore, big data and artificial intelligence applications have enhanced the workforce’s data management and analysis capabilities.

This study makes a significant contribution to understanding the impact of technological innovations on the workforce in the tourism industry. The findings emphasize how technological changes have altered skill requirements and job descriptions, highlighting the increased need for continuous education and skill development.

Research on corporate social responsibility (CSR) within the real estate sector is limited, and the precise workings of its impact are still unclear. Under the premise that real estate enterprises face environmental uncertainty in China, this study explored the impact of CSR on real estate enterprise value.

To investigate the impact of CSR on enterprise value, we studied 111 real estate enterprises with A-shares listed on Shanghai and Shenzhen stock exchanges from 2010 to 2020, and performed empirical tests to determine the moderating effect of environmental uncertainty on this relationship.

(1) The fulfillment of corporate social responsibility (CSR) significantly influences the value of real estate enterprises. A sub-dimensional analysis reveals that fulfilling stakeholder and social welfare responsibilities within CSR positively impacts enterprise value, whereas environmental responsibility does not exert a notable effect. (2) The uncertainty associated with environmental changes profoundly affects the relationship between CSR and the value of real estate enterprises. More precisely, as environmental uncertainty increases, it amplifies the beneficial impact of CSR on enterprise value.

These findings are valuable for real estate enterprises as they navigate the transition towards sustainable development, and they also provide insight for the government in formulating policies aimed at regulating the real estate sector.

This study complements the existing discussion and research on corporate social responsibility (CSR) and enterprise value in the real estate industry, while elucidating the underlying mechanism of how environmental uncertainty mediates the relationship between the two.

With the continuous deepening of China's mixed-ownership reform, the participants in the reform have gradually expanded from state-owned enterprises to private enterprises. Whether state-owned equity participation in private enterprises can facilitate the development of environmental, social and governance (ESG) performance in private enterprises is a question that needs urgent examination. This study aims to investigate the impact of state-owned equity participation on the ESG performance of private enterprises.

Using Chinese listed companies as the research sample, this study uses econometric methods such as multiple regression to analyze the relationship between state-owned equity and the ESG performance of private enterprises. Additionally, it explores the underlying mechanisms and influencing factors of this relationship.

There is a significant inverted U-shaped relationship between state-owned equity and the ESG performance of private enterprises. Mechanism analysis reveals that resource effects and governance effects play a mediating role in this nonlinear relationship. Furthermore, the authors find that environmental regulation and managers' attention to the environment positively moderate the relationship between state-owned equity participation and ESG performance.

A reasonable equity structure is crucial for enhancing corporate ESG performance. Moderate state-owned equity participation helps to leverage resource integration and governance advantages, which will assist private enterprises in maximizing ESG performance and achieving sustainable development.

In advancing the process of mixed-ownership reform, the government should maintain an appropriate proportion of state-owned equity to avoid excessive intervention in enterprise decision-making. At the same time, it should ensure that enterprises can genuinely undertake their social and environmental responsibilities while pursuing economic benefits. This is of great significance for promoting sustainable economic and social development.

This study integrates state-owned equity, ESG and nonlinear relationships into a single research framework. It explores the internal mechanisms and influencing factors of their relationship, overcoming the limitations of previous studies and provides a new perspective for understanding the impact of state-owned equity on corporate ESG performance.

Scientific impact is traditionally assessed with citation-based metrics. Recently, altmetric indices have been introduced to measure scientific impact both within academia and among the general public. However, little research has investigated the association between the linguistic features of research article titles and received online attention. To address this issue, the authors examined in the present study the relationship between a series of title features and altmetric attention scores.

The data included 8,658 titles of Science articles. The authors extracted six features from the title corpus (i.e. mean word length, lexical sophistication, lexical density, title length, syntactic dependency length and sentiment score). The authors performed Spearman’s rank analyses to analyze the correlations between these features and online impact. The authors then conducted a stepwise backward multiple regression to identify predictors for the articles' online impact.

The correlation analyses revealed weak but significant correlations between all six title features and the altmetric attention scores. The regression analysis showed that four linguistic features of titles (mean word length, lexical sophistication, title length and sentiment score) have modest predictive effects on the online impact of research articles.

In the internet era with the widespread use of social media and online platforms, it is becoming increasingly important for researchers to adapt to the changing context of research evaluation. This study identifies several linguistic features that deserve scholars’ attention in the writing of article titles. It also has practical implications for academic administrators and pedagogical implications for instructors of academic writing courses.