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Construction projects, and particularly highway infrastructures, are known to be major contributors to the socio-economic growth of developing countries. However, these types of projects are infamous for being highly risky due to the interplay of numerous risk factors. This study aims to explore the key risk factors impacting on the performance of highway infrastructure projects in Nigeria from the contextual viewpoint of key industry stakeholders.

Qualitative data was collected using semi-structured interviews. Specifically, 17 in-depth expert interviews were conducted with experienced stakeholders in the highway sector of the Nigerian construction industry. The collected data was transcribed and analysed using an established coding framework (grounded on case study approach, principles of thematic analysis and saliency analysis).

Overall, 17 key risks were identified from the data analysis process, and 6 risks were recognised as the most significant, based on the combination of prevalence of occurrence and significance of the coded information. The six top risks were: change in government, corruption, cost of construction materials, inflation, project funding issues and construction project delay. However, the first two of these risks (change in government and corruption) are politically related, which is specific and unique to the setting of Nigeria and thus might be seen as discouraging indicators that could have an impact on attracting foreign investors/contractors to Nigeria.

The study addressed the gap related with identifying context-specific risk factors impeding the performance of highway projects in Nigeria from the viewpoints of industry experts. It is expected that the findings will provide a better insight into the various risk factors and thus aid relevant policymakers to provide context-specific mitigating strategies.

This study aims to optimize the energy consumption of residential buildings in mild and humid climates. It investigates the use of thermal insulation to reduce thermal load through energy simulation analysis.

A residential building located in Rasht city, Iran (a mild and humid climate zone), is simulated using DesignBuilder software. Subsequently, the minimum thermal resistance for external walls and roof is analyzed along with its impact on building energy consumption and carbon emissions.

The simulation results indicated a 26.5% reduction in heat loss through the walls and a 14.2% reduction through the roof due to optimal thermal insulation. Furthermore, optimal insulation led to a 19.2% reduction in cooling system energy use, a 12% reduction in heating system energy use and a combined 15.3% reduction in total energy consumption for cooling and heating.

This optimization process leads to several benefits: reduced costs associated with thermal and cooling energy losses in buildings, improved building performance against atmospheric factors and, ultimately, a reduction in energy consumption across the building industry. This research can be valuable to various stakeholders, including the construction industry and building sector, municipalities and engineering systems, building owners and contractors and environmental organizations. By implementing these findings, they can improve the state of modern building insulation and achieve greater energy efficiency.

Social media usage has been documented to affect the psychological well-being of its users. This study aims to examine how social media overload influences cognitive fatigue among individuals in Malaysia.

This study employed a comprehensive research framework based on the stressor-strain-outcome (SSO) model to examine how perceived overload affects social media cognitive fatigue through emotional exhaustion and anxiety. Survey data were gathered from 451 social media users in Malaysia, and data analysis was performed using PLS-SEM.

The findings revealed that information overload, communication overload and interruption overload are antecedents of emotional exhaustion. Communication overload, interruption overload and cognitive overload were identified as antecedents of anxiety, while emotional exhaustion and anxiety were confirmed as predictors of social media cognitive fatigue. However, pathway analysis indicated no relationship between emotional exhaustion and anxiety.

Our study contributes to the literature on media technology and media psychology by examining the psychological mechanisms (emotional exhaustion and anxiety). The findings offer implications for service providers, practitioners and social media users, as they facilitate measures and strategies to mitigate the adverse effects of social media while elevating psychological well-being.

Digital technologies promise efficiency gains and untapped opportunity. Adoptions of digital technology lead firms to rethink their organizational setup and existing practices. This paper aims to present a management innovation-based framework that describes new processes and practices for digital transformation.

This study uses a structural equation modeling approach to test the framework with survey responses from a sample of 901 Taiwanese organizations – both local firms and multinational subsidiaries – to explore the linkages between adoption of digital technologies and digital maturity.

The results reveal that management innovation mediates the relationship between digital technology adoption and digital maturity. Moreover, fast-paced environments have a greater impact of management innovation toward digital maturity than slow-paced environments.

This study adds to emerging research that considers the role of organizational learning in digital transformation efforts. The extent to which organizations link the lessons from direct experience to digital routines through which management innovation is implemented determines to a large extent whether this strategic initiative is optimized by the firm. More generally, the findings point to the mutual importance of digital maturity and experiential learning efforts, and suggest a specific means by which learning processes are mobilized by innovating organizations. This study contributes to digital transformation research by providing insight into how a firm can restart failed transformation initiatives of this kind.

Knowledge adoption is the key to effective knowledge exchange in virtual question-and-answer (Q&A) communities. Although previous studies have examined the effects of knowledge content, knowledge source credibility and the personal characteristics of knowledge seekers on knowledge adoption in virtual Q&A communities from a static perspective, the impact of answer deviation on knowledge adoption has rarely been explored from a context-based perspective. The purpose of this study is to explore the impact of two-way deviation on knowledge adoption in virtual Q&A communities, with the aim of expanding the understanding of knowledge exchange and community management.

The same question and the same answerer often yield multiple answers. Knowledge seekers usually read multiple answers to make adoption decisions. The impact of deviations among answers on knowledge seekers' knowledge adoption is critical. From a context-based perspective, a research model of the impact of the deviation of horizontal and vertical answers on knowledge adoption is established based on the heuristic-systematic model (HSM) and empirically examined with 88,287 Q&A data points and answerer data collected from Zhihu. Additionally, the moderation effects of static factors such as answerer reputation and answer length are examined.

The negative binomial regression results show that the content and emotion deviation of horizontal answers negatively affect knowledge seekers' knowledge adoption. The content deviation of vertical answers is negatively associated with knowledge adoption, while the emotion deviation of vertical answers is positively related to knowledge adoption. Moreover, answerer reputation positively moderates the negative effect of the emotion deviation of horizontal answers on knowledge adoption. Answer length weakens the negative correlation between the content deviation of horizontal and vertical answers and knowledge adoption.

This study extends previous research on knowledge adoption from a static perspective to a context-based perspective. Moreover, information deviation is expanded from a one-way variable to a two-way variable. The combined effects of static and contextual factors on knowledge adoption are further uncovered. This study can not only help knowledge seekers identify the best answers but also help virtual Q&A community managers optimize community design and operation to reduce the cost of knowledge search and improve the efficiency of knowledge exchange.

The purpose of this study is to explore the impact of online reviews on answer adoption in virtual Q&A communities, with an eye toward extending knowledge exchange and community management.

Online reviews contain rich cognitive and emotional information about community members regarding the provided answers. As feedback information on answers, it is crucial to explore how online reviews affect answer adoption. Based on signaling theory, a research model reflecting the influence of online reviews on answer adoption is established and empirically examined by using secondary data with 69,597 Q&A data and user data collected from Zhihu. Meanwhile, the moderating effects of the informational and emotional consistency of reviews and answers are examined.

The negative binomial regression results show that both answer-related signals (informational support and emotional support) and answerers-related signals (answerers’ reputations and expertise) positively impact answer adoption. The informational consistency of reviews and answers negatively moderates the relationships among information support, emotional support and answer adoption but positively moderates the effect of answerers’ expertise on answer adoption. Furthermore, the emotional consistency of reviews and answers positively moderates the effect of information support and answerers’ reputations on answer adoption.

Although previous studies have investigated the impacts of answer content, answer source credibility and personal characteristics of knowledge seekers on answer adoption in virtual Q&A communities, few have examined the impact of online reviews on answer adoption. This study explores the impacts of informational and emotional feedback in online reviews on answer adoption from a signaling theory perspective. The results not only provide unique ideas for community managers to optimize community design and operation but also inspire community users to provide or utilize knowledge, thereby reducing knowledge search costs and improving knowledge exchange efficiency.

The purpose of this paper is to investigate dynamics of causal interactions and financial risk contagion among BRICS stock markets under rare events.

Two methods are adopted: The new causal inference technique, namely, the Liang causality analysis based on information flow theory and the dynamic causal index (DCI) are used to measure the financial risk contagion.

The causal relationships among the BRICS stock markets estimated by the Liang causality analysis are significantly stronger in the mid-periods of rare events than in the pre- and post-periods. Moreover, different rare events have heterogeneous effects on the causal relationships. Notably, under rare events, there is almost no significant Liang's causality between the Chinese and other four stock markets, except for a few moments, indicating that the former can provide a relatively safe haven within the BRICS. According to the DCIs, the causal linkages have significantly increased during rare events, implying that their connectivity becomes stronger under extreme conditions.

The obtained results not only provide important implications for investors to reasonably allocate regional financial assets, but also yield some suggestions for policymakers and financial regulators in effective supervision, especially in extreme environments.

This paper uses the Liang causality analysis to construct the causal networks among BRICS stock indices and characterize their causal linkages. Furthermore, the DCI derived from the causal networks is applied to measure the financial risk contagion of the BRICS countries under three rare events.

This study aims to address challenges in the distribution of social donations during epidemic emergencies, focusing on issues such as uneven distribution and material stacking. The goal is to propose optimized strategies that enhance equity and efficiency in the allocation of donated resources.

Firstly, the satisfaction function is constructed from two perspectives of the designated hospital and the Red Cross. On this basis, the fairness perception level of the two is portrayed. Then, we set the time weights, and construct a multi-objective programming model by combining the resource constraints in the social donation distribution process. The combined algorithm of NSGA-II and TOPSIS is also designed for model solving. Finally, an example of social donation distribution of the Red Cross Society of China Wuhan Branch is conducted for numerical analysis.

Numerical analysis reveals that timely transmission of demand information favors a demand-oriented distribution strategy for optimal efficiency. However, in scenarios with poor demand information transmission, an equal distribution of social donations proves to be a more effective strategy. Equal distribution ensures rapid allocation while minimizing perceived unfairness at designated hospitals, ultimately improving overall satisfaction levels and emergency response effectiveness.

The findings provide practical insights for emergency response planners. These include translating the developed methods into guiding principles, establishing real-time monitoring systems, enhancing training for relevant departments, and implementing evaluation mechanisms. Practitioners can utilize this knowledge to optimize the efficiency of social donation distribution during sudden outbreaks.

The equitable distribution of social donations ensures efficient resource allocation and minimizes perceived unfairness, contributing to improved social satisfaction levels. This has broader implications for community resilience and support during emergencies.

This research contributes to the field by proposing a comprehensive model for optimizing social donation distribution in emergencies. The integration of fairness perception, time weights, and a multi-objective planning approach, along with the application of the combined algorithm of NSGA-II and TOPSIS, adds novelty and practical value to the existing literature. The study serves as a decision-making reference for enhancing emergency response theories in sudden event.

Providing quality emergency supplies is crucial to mitigate and respond to emergencies. However, despite government and consumer oversight of emergency supplies’ quality, a troubling trend persists among some enterprises to sacrifice product quality for financial gain. This paper examines the influence of strategy selections among governments, enterprises, and consumers to enhance the quality of emergency supplies.

We develop a tripartite evolutionary game model consisting of three stakeholders: government, enterprises, and consumers, considering factors including subsidies and penalties. After analysing three stakeholders’ strategic choices to ascertain system stability, parametric analyses were conducted.

Excessive or insufficient subsidies are not conducive to encouraging enterprises to adopt an authentic production strategy; excessive subsidies may result in consumers enduring counterfeiting. Furthermore, the government’s supervision strategy can stabilise the system quickly, suggesting that consumer reporting cannot replace government supervision. Additionally, incentivising enterprises to adopt an authentic production strategy can be achieved by increasing penalties and enhancing compensation while reducing consumer reporting, government supervision, and raw materials costs.

We present a preliminary exploration of how to promote the production of qualified emergency supplies in the early stages of an emergency event. The model and findings proposed in this paper can be generalised and applied to various emergency events, including epidemics and earthquakes.

Clarifying the relationship between building carbon emissions and economic development can help sustainable construction in the field of construction, and this paper provides a constructive suggestion for ensuring economic development while realizing energy efficiency and emission reduction in buildings.

The study focuses on the building sector and firstly analyzes the complex relationship between economic agglomeration (EA) and carbon emission intensity (CEI) of commercial buildings at the city level through the spatial Durbin model and the threshold effect model, and then discusses the regional heterogeneity of this complex relationship from the dimensions of economic density and climate zones, respectively, and finally analyzes in depth the intrinsic influencing mechanism of EA on the CEI of commercial buildings.

The authors found that (1) there is an inverted U-shaped nonlinear relationship between EA and CEI of commercial buildings, and the inflection point of the EA level is 2.42, i.e. 1.125 bn RMB/km². (2) Significant regional differences exist in the inverted U-shaped relationship for cities with different economic densities and cities in different climate zones. (3) EA mainly affects the CEI of commercial buildings through externalities such as commercial building size and tertiary industry share, of which commercial building size is the most important factor hindering the decoupling of urban economic development from the CEI of commercial buildings.

This paper discusses for the first time the relationship between economic development and carbon emissions at the city level and clarifies the spatial differences and influencing mechanism of this relationship, providing a fuller reference for policymakers to develop differentiated building energy efficiency and emission reduction strategies.