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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.

The purpose of this paper is to develop a novel integrated risk assessment method from the system perspective to evaluate the risk of the cross-regional mega construction project (CMCP). Furthermore, this paper aims to confirm the core risk source factors and refine the risk management strategies of CMCPs through a case study.

Based on the analysis of the risk system of CMCPs, the concept model and risk assessment principles of integrated risk assessment were confirmed. The risk source factors and project objectives of the CMCP were identified from a literature review, export interview and case analysis. According to the vulnerability theory, the integrated risk assessment model was developed by involving vulnerabilities, threats, objectives and interaction of those factors synthetically. Then, ZW high-speed railway from China was analyzed to verify the feasibility and effectiveness of the proposed method.

As a result, 12 threat factors and 12 vulnerability factors were identified. Based on the case study, the main external threat comes from T13 (conflicts of interest between local governments) and T23 (harsh natural environment); the most easily exploited internal vulnerabilities were V11 (complexity of technology), V13 (lack of experience in technical application), V21 (inadequate experience) and V23 (lack of interest coordination mechanism). Moreover, the economic objective was most affected.

It is essential to develop an interest coordination mechanism for CMCPs. The harsh natural environment is a critical factor, but it also promotes technological innovation and iteration. Public opinions in different regions are critical for CMCPs, and more emphasis should be placed on public opinion surveys of CMCPs. Moreover, diverse and flexible environmental protection strategies should apply in CMCPs.

This research has the following three contributions. First, based on vulnerability theory, an integrated risk assessment approach of CMCPs is developed, which enriches the risk measurement method system and provides inspiration for future research on risk in the construction industry. Second, the risk sources of CMCPs are identified from the perspective of vulnerability and threat to provide clear guidance for the risk management of CMCPs. Third, the core risk source factors and management strategies confirmed by the case study will be beneficial for various governments in different regions and project managers to optimize the project management scheme, as they are transferable management experiences.

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.

Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.

This research aims to investigate how emerging destinations cope with digitalization of information, where they are in the process and how digitalization of information takes place in destination marketing organizations (DMOs). As a case for emerging destinations that must deal with the negative consequences of the digital divide, the Vietnamese Mekong Delta (VMD) will be examined. A new framework, solutions in general, and potential innovative approaches will be presented.

A mixed methods approach was used. Firstly, a content analysis comprising 68 criteria to examine 10 destination websites was conducted to evaluate the performance of provincial destination websites of VMD. Secondly, the authors interviewed five managers from VMD DMOs to reveal the strategy, status quo and their challenges with digitalization.

Some digitalization is evident in VMD DMOs, with the digitization of tourist information provision developing from analog formats to digital modes. The content analysis of the websites shows that provincial destination websites of VMD perform well with regard to communication but need improvements for transaction, and especially relationship aspects. Emerging destinations like VMD DMOs are reaching the second or third level in the digitalization process. Yet they face challenges with human and financial resources.

This research provides recommendations concerning destination website performance, the process of digitalization and how to promote digitalization and apply more digital instruments to move to the next stages of destination digitalization. Also, suggestions on how to overcome existing challenges/barriers in similar areas of the world are provided.

A new, extended more granulated version of the digitalization framework by Karpova et al. (2019) has been developed. The new model acknowledges the continued importance of printed information, provides information about the sequence of steps how to implement website dimensions, and which instruments are realistic to implement in different levels of digitalization considering the challenges and barriers developing destinations face.

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

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.

This study aims to explore the relationship between network position and innovation under major environmental turbulence.

The authors use a difference-in-differences identification approach using the 2009 Industry Revitalization Plan in response to the global financial crisis as a natural experiment with a sample of Chinese listed firms from 2001 to 2017.

The findings show that a major environmental turbulence can facilitate firm innovation, and firms that occupy central positions in the interlock network show worse innovation performance while firms with high brokerage show better innovation performance.

The literature on environmental implication has largely focused on the threats and overlooked the potential opportunities. Moreover, social network literature has elaborated on the benefits and constraints of network positions from a static perspective but largely overlooked their implications facing environmental change. By exploring the bright side of major environmental turbulence and including this factor as a key contingency in exploring the effects of centrality and brokerage, this study integrates external environmental context with social network research and provides empirical evidence responding to the call for more attention to network dynamics and extends our understanding of the context-contingent network effects on firm innovation.

Cultivating loyal customers is a pressing concern for streamers. The present study investigates how to build interpersonal relationships with streamers and whether different interpersonal relationship factors lead to repurchase intention and WOM intention in live streaming commerce. The moderating effect of gender is also examined.

A self-administered questionnaire was completed by 429 live streaming commerce users in mainland China. Partial least squares structural equation modeling was used to test the research hypotheses.

The results indicate that all four streamer attributes (expertise, authenticity, attractiveness, and homophily) have a positive influence on swift guanxi, and swift guanxi is effective in predicting both calculative commitment and affective commitment. In addition, all interpersonal relationship factors (swift guanxi, calculative commitment, and affective commitment) significantly affect repurchase intentions, with only affective commitment being linked to WOM intention. Also, the moderating role of gender was confirmed in expertise – swift guanxi, attractiveness – swift guanxi, cognitive commitment – repurchase intention and affective commitment – repurchase intention linkages.

This paper contributes to the live streaming commerce literature by integrating swift guanxi, calculative commitment, and affective commitment to understand the repurchase intention and WOM intention from the relationship-building process perspective. In addition, this paper enriches the source credibility and source attractiveness models by identifying gender boundaries on the effectiveness of these models in predicting swift guanxi.