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In the context of Industry 4.0, intelligent construction technologies (ICT) represented by information technology and networking will undoubtedly provide new impetus to the development of the prefabricated building supply chain (PBSC), but they will also bring various potential risks. So far, there is a large lack of research on the comprehensive consideration of the risks associated with the intelligent transformation of PBSC based on the information sharing perspective, and the critical risks and interactions are still unclear, making it difficult to identify efficient risk mitigation strategies. Therefore, this paper aims to reveal the interactions between stakeholders and clarify the critical risk nodes and interactions in information sharing of PBSC (IS-PBSC), and propose targeted risk mitigation strategies.

Firstly, this paper creatively delineates the risks and critical stakeholders of IS-PBSC. Secondly, Data is collected through questionnaires to understand the degree of risks impact. Thirdly, with the help of NetMiner 4 software, social network analysis is conducted and IS-PBSC risk network is established to reveal critical risk nodes and interactions. Finally, further targeted discussion of critical risk nodes, the effectiveness and reasonableness of the risk mitigation strategies are proposed and verified through NetMiner 4 software simulation.

The results show that the critical risks cover the entire process of information sharing, with the lack of information management norms and other information assurance-related risks accounting for the largest proportion. In addition, the government dominates in risk control, followed by other stakeholders. The implementation of risk mitigation strategies is effective, with the overall network density reduced by 41.15% and network cohesion reduced by 24%.

In the context of Industry 4.0, ICT represented by information technology and networking will undoubtedly provide new impetus to the development of the PBSC, but they will also bring various potential risks. So far, there is a large lack of research on the comprehensive consideration of the risks associated with the intelligent transformation of PBSC based on the information sharing perspective, and the critical risks and interactions are still unclear, making it difficult to identify efficient risk mitigation strategies.

Based on the results of risk network visualization analysis, this paper proposes an ICT-based IS-PBSC mechanism that promotes the development of the integration of ICT and PBSC while safeguarding the benefits of various stakeholders.

Learning from past construction accident reports is critical to reducing their occurrence. Digital technology provides feasibility for extracting risk factors from unstructured reports, but there are few related studies, and there is a limitation that textual contextual information cannot be considered during extraction, which tends to miss some important factors. Meanwhile, further analysis, assessment and control for the extracted factors are lacking. This paper aims to explore an integrated model that combines the advantages of multiple digital technologies to effectively solve the above problems.

A total of 1000 construction accident reports from Chinese government websites were used as the dataset of this paper. After text pre-processing, the risk factors related to accident causes were extracted using KeyBERT, and the accident texts were encoded into structured data. Tree-augmented naive (TAN) Bayes was used to learn the data and construct a visualized risk analysis network for construction accidents.

The use of KeyBERT successfully considered the textual contextual information, prompting the extracted risk factors to be more complete. The integrated TAN successfully further explored construction risk factors from multiple perspectives, including the identification of key risk factors, the coupling analysis of risk factors and the troubleshooting method of accident risk source. The area under curve (AUC) value of the model reaches up to 0.938 after 10-fold cross-validation, indicating good performance.

This paper presents a new machine-assisted integrated model for accident report mining and risk factor analysis, and the research findings can provide theoretical and practical support for accident safety management.

This paper aims to examine the relationship between environmental, social and governance (ESG) performance and systemic risk sensitivity of Chinese listed companies. From the consumer loyalty and investor structure perspectives, the relationship between ESG performance and systemic risk sensitivity is analyzed.

Since Morgan Stanley Capital International (MSCI) ESG officially began to analyze and track China A-shares from 2018, 275 listed companies in the SynTao Green ESG testing list for 2015–2021 are selected as the initial model. To measure the systematic risk sensitivity, this study uses the beta coefficient, from capital asset pricing model (CPAM), employing statistics and data (STATA) software.

The study reveals that high ESG rating companies have high corresponding consumer loyalty and healthy trading structure of institutional investors, thereby the systemic risk sensitivity is lower. This paper reveals that companies with high ESG rating are significantly less sensitive to systemic risk than those with low ESG rating. At the same time, ESG has a weaker impact on the systemic risk of high-cap companies than low-cap companies.

The study helps the companies understand the influence of market value on the relationship between ESG performance and systemic risk sensitivity. Moreover, this paper explains explicitly why ESG performance insulates a firm’s stock from market downturns with the lens of consumer loyalty theory and investor structure theory.

The paper provides new insights on the company’s ESG performance that significantly affects the company’s systemic risk sensitivity.

This paper aims to examine how the diversity of educational levels within bank boards influences the efficiency and stability of banks operating in the Middle East and North Africa (MENA) region. Unlike previous studies, this analysis also investigates the role of board gender diversity in moderating the relationship between board educational level diversity and bank efficiency and financial stability in MENA.

In this study, a sample of 77 banks in the MENA region spanning the years 2011 to 2018 is used. The relationship between the presence of highly educated directors on the board, bank efficiency and stability is assessed using the ordinary least squares method. Additionally, the authors use the Generalized Method of Moments technique to correct endogeneity problem.

This study establishes a positive association between the presence of directors with advanced educational backgrounds on bank boards and bank efficiency and stability. Furthermore, the inclusion of women on the board strengthens this relationship.

These findings have important implications for policymakers and regulators in the MENA region, suggesting that promoting diversity policies that encourage the participation of highly educated directors on bank boards can contribute to enhanced efficiency and financial stability. Policymakers may also consider implementing quotas or guidelines to improve gender diversity in board appointments, thereby fostering bank performance in the region.

This study stands out for its innovation and distinctiveness, as it delves into the connection between board educational level diversity and bank efficiency in the MENA region. Notably, it surpasses previous research by investigating the moderating role of board gender diversity, thus offering valuable insights into the complex interplay between these two facets of board diversity. This contribution enriches the existing literature by providing novel perspectives on board composition dynamics and its influence on bank efficiency and stability.

The novel coronavirus (COVID-19) has caused financial stress and limited their lending agility, resulting in more non-performing loans (NPLs) and lower performance during the II wave of the coronavirus crisis. Therefore, it is essential to identify the risky factors influencing the financial performance of Indian banks spanning 2018–2022.

Our sample consists of a balanced panel dataset of 75 scheduled commercial banks from three different ownership groups, including public, private and foreign banks, that were actively engaged in their operations during 2018–2022. Factor identification is performed via a fixed-effects model (FEM) that solves the issue of heterogeneity across different with banks over time. Additionally, to ensure the robustness of our findings, we also identify the risky drivers of the financial performance of Indian banks using an alternative measure, the pooled ordinary least squares (OLS) model.

Empirical evidence indicates that default risk, solvency risk and COVAR reduce financial performance in India. However, high liquidity, Z-score and the COVID-19 crisis enhance the financial performance of Indian banks. Unsystematic risk and systemic risk factors play an important role in determining the prognosis of COVID-19. The study supports the “bad-management,” “moral hazard” and “tail risk spillover of a single bank to the system” hypotheses. Public sector banks (PSBs) have considerable potential to achieve financial performance while controlling unsystematic risk and exogenous shocks relative to their peer group. Finally, robustness check estimates confirm the coefficients of the main model.

This study contributes to the knowledge in the banking literature by identifying risk factors that may affect financial performance during a crisis nexus and providing information about preventive measures. These insights are valuable to bankers, academics, managers and regulators for policy formulation. The findings of this paper provide important insights by considering all the risk factors that may be responsible for reducing the probability of financial performance in the banking system of an emerging market economy.

The empirical analysis has been done with a fresh perspective to consider unsystematic risk, systemic risk and exogenous risk (COVID-19) with the financial performance of Indian banks. Furthermore, none of the existing banking literature explicitly explores the drivers of the I and II waves of COVID-19 while considering COVID-19 as a dependent variable. Therefore, the aim of the present study is to make efforts in this direction.

This study aims to investigate the contribution of blockchain technology to supply chain risk management and its impact on performance among Indian manufacturing companies.

Drawing on a resource-based view, dynamic capability and system of systems theory, this study examines the direct relationships between blockchain, supply chain risk management and supply chain performance. The authors validate the mediating effects of three supply chain risk management components, namely supply risk management, demand risk management and cyber security management, on financial transaction reliability and information reliability. Data were collected from 204 Indian manufacturing companies that have adopted blockchain technology.

The results demonstrate that companies adopting blockchain technology have experienced positive outcomes in managing supply chain-related risks, financial transaction reliability and information reliability. These findings provide valuable guidance to managers, highlighting blockchain as a competitive advantage for supply chain management.

To the best of the authors’ knowledge, no previous research on blockchain-based risk management capabilities has been conducted.

This study investigates the economic outcomes of the strategic deviation (SD), the fundamental and crucial question in institutional theory and strategic management. Previous studies have yielded contradictory findings. This study reconciles conflicting results by distinguishing the effects of the SD on financial and market performance, examining the mechanism of financing constraints and the boundary condition of institutional investor heterogeneity.

This research collected data from Chinese A-shares listed manufacturing firms from 2009 to 2021 from the CSMAR and Wind databases. This study conducted empirical tests using OLS models with Stata 15.

Empirical results demonstrate that the SD has different impacts on different dimensions of performance. The SD negatively impacts financial performance while positively impacts market performance. Financing constraints mediate the main effects. Moreover, transactional institutional investors positively moderate the negative effect of the SD on financial performance, whereas stable institutional investors negatively moderate the positive effect of the SD on market performance.

By systematically revealing how the SD has different effects on financial and market performance, this study reconciles the debate on the SD between institutional theorists and strategy scholars. This research makes contributions to the research stream by providing reasonable explanations for conflicting conclusions. Furthermore, by introducing the overlooked perspective of financing constraints, this research identifies crucial mediating mechanisms and highlights the double-edged effect of financing constraints, enriching our understanding of financing constraints. Finally, this study investigates the moderating effects of institutional investor heterogeneity, thereby making valuable contributions to the comprehension of boundary conditions.

The authors investigate the effects of Chinese acquirer’s chief executive officer (CEO) risk preference on mergers and acquisitions (M&A) payment method and the moderating roles played by acquirer’s ownership, industry relatedness and whether the M&A is cross-border.

Using 4,624 worldwide M&A deals conducted by Chinese firms from 2009 to 2021, the authors conduct multiple linear regression and ordered probit regression. And comprehensive indexes constructed based on the observed features of acquirer’s CEOs are used to be the proxy for CEO risk preference.

The results show that the higher-level Chinese acquirer’s CEO risk preference is overall positively associated with using more stock in payment. Moreover, the above relationship is strengthened if the ownership of the acquirer is state-owned.

The authors highlight the importance of the non-economic factors and demonstrate a relationship between the Chinese acquirer’s CEO risk preference and the M&A payment method, providing support for and enriching the upper echelons theory (UET). Moreover, the unique risk priorities of Chinese acquirers’ CEOs are revealed.

This research paper aims to explore the concerns and determinants of travelers’ behavior toward ChatGPT in the hospitality and tourism context. It also examines the weight of risk factors versus that of motivation and innovation characteristics influencing travelers’ approach behaviors toward ChatGPT.

A cumulative prospect theory was used to determine travelers’ responses to ChatGPT. This study, using a fuzzy-set qualitative approach, explored risk, motivation and innovation factors as determinants of approach behaviors for ChatGPT.

Findings revealed that risk, motivation and innovation factors were the key triggers of approach behaviors for ChatGPT. An intricate combination effect of the perceived risk, motivation and innovation characteristics was found, and the necessary predictors were determined.

The findings of this study will expand our current knowledge and offer practical insights for the development of ChatGPT in the hospitality and tourism sector.

This study makes a significant contribution to the existing literature by providing a nuanced understanding of the intricate interplay between the various factors that shape customer behavior in the context of technology adoption in hospitality and tourism studies.

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.