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New generation of construction workers (NGCWs) who were born in the 1980s and later have gradually become the main workforce of Chinese construction industry. They may behave differently when dealing with knowledge-related activities due to divergent characteristics caused by generational discrepancy. To provide a theoretical foundation for construction companies and safety managers to improve safety management, this research explores the factors and paths impacting the NGCWs' ability to share their safety knowledge.

Based on literature review, main factors that influence the safety knowledge sharing of the NGCWs were identified. Decision-Making Trial and Evaluation Laboratory and Interpretive Structural Modeling were applied to identify the hierarchical and contextual relations among the factors influencing the safety knowledge sharing of the NGCWs.

The results showed that sharing atmosphere ranked first in centrality and had a high degree of influence and being influenced, indicating itself an extremely important influencing factor of safety knowledge sharing of NGCWs. Six root influencing factors were identified, including individual characteristics, work pressure, sharing platform, incentive mechanism, leadership support and safety management system.

The number of influencing factors of safety knowledge sharing of the NGCWs identified in this study is limited, and the data obtained by the expert scoring method is subjective. In future studies, the model should be further developed and validated by incorporating experts from different fields to improve its integrity and applicability.

The influencing factors identified in this paper can provide a basis for construction companies and safety managers to improve productivity and safety management by taking relevant measures to promote safety knowledge sharing. The research contributes to the understanding knowledge management in the context of the emerging market. It helps to answer the question of how the market can maintain the economic growth success through effective knowledge management.

This paper investigates the influencing factors of NGCWs' safety knowledge sharing from the perspective of intergenerational differences, and the 13 influencing factor index system established expands the scope of research on factors influencing safety knowledge sharing among construction workers and fills the gap in safety knowledge sharing research on young construction workers. Furthermore, this paper establishes a multi-layer recursive structure model to clarify the influence path of the influencing factors and contributes to the understanding of safety knowledge sharing mechanism.

In recent years, several high‐profile cross‐border acquisitions made by Chinese enterprises have attracted the world's attention. However, none of these acquisitions were related to Chinese construction enterprises despite their expanding role in the international construction market. The purpose of this paper is to evaluate the objectives and impediments faced by Chinese construction enterprises in cross‐border acquisitions based on existing theories and research studies.

Based on a literature review and the analysis of the current status of cross‐border acquisitions in China, an industry‐wide questionnaire survey was conducted to investigate the objectives and impediments of cross‐border acquisitions faced by Chinese construction enterprises.

The main objective of Chinese construction enterprises towards cross‐border acquisitions is to create new markets and to optimize the industrial structure. Although a majority of the construction enterprises demonstrated an intention for cross‐border acquisitions which is driven by their past and current overseas activities, their preparation for cross‐border acquisitions is impeded by concerns over post‐acquisition issues and a lack of knowledge and experience.

The paper originally contributes to a better understanding of the current status of cross‐border acquisitions by Chinese construction enterprises based on an analysis of their objectives and impediments, which will be useful for researchers and practitioners.

To explore how the blockchain affects the pricing and financing decisions in a low-carbon platform supply chain.

Considering the dual roles of the e-commerce platform as a seller and an initiator, a typical game-theoretical method is applied to analyze the behavior of supply chain decision-makers and the impact of key variables on equilibriums.

When loan interest rates are symmetric, whether blockchain is used or not, the e-commerce platform financing mode will always produce higher wholesale price and unit carbon emission reduction, while the retail price is the opposite. Higher unit additional income brought by the blockchain can bring higher economic and environmental performances, and the e-commerce platform financing mode is superior to bank financing mode. The application of blockchain may cause the manufacturer to change his/her financing choice. For bank financing, with the increase of loan interest rates, the advantages brought by blockchain will gradually disappear, but this situation will not occur under e-commerce platform financing.

Blockchain is known for its information transparency properties and its ability to enhance user trust. However, the impacts of applying blockchain in a low-carbon platform supply chain with different financing options are not clear. The authors examine the manufacturer's strategic choices for platform financing and bank financing, whether to adopt blockchain, and the impact of these decisions on carbon emissions reduction, consumer surplus and social welfare. The research conclusion can provide reference for the operation and financing decisions of platform supply chain under the carbon reduction target in the digital economy era.

The purpose of this study is to examine Bitcoin's price behavior across market conditions, focusing on the influence of Bitcoin's historical prices, news sentiment and market indicators like oil prices, gold and the S&P index. The authors also assess the stability of Bitcoin-inclusive hedging portfolios under different market conditions, for example, bearish, bullish and moderate market states.

This study uses the Quantile Autoregressive Distributed Lag model to explore the effects of different factors on Bitcoin's prices across various market situations. This method allows for a detailed analysis of historical trends, investor expectations and external market influences on Bitcoin's price movements and systematic stability.

Key findings reveal historical prices and investor expectations significantly influence Bitcoin in all market scenarios, with news sentiment exhibiting substantial volatility. This study indicates that oil prices have minimal impacts on Bitcoin, whereas gold is a stabilizing asset in bear markets, with the S&P index influencing short-term fluctuations. At the same time, Bitcoin's volatility varies with market conditions, proving more efficient as a hedging tool in bear and stable markets than in bull ones.

This study highlights the intrinsic correlation between Bitcoin's prices, news sentiment and financial market indicators, enhancing understanding of Bitcoin's market dynamics. The authors demonstrate Bitcoin's weak direct correlation with commodities like oil, the stabilizing role of gold in crypto portfolios and the stock market's indirect effect on Bitcoin prices. By examining these factors' impacts across various market conditions, the findings offer strategies for investors to improve hedging and portfolio management in cryptocurrency markets.

This paper seeks to address the following two research questions. RQ1: What are the influential user archetypes in the social question-answering (SQA) community? RQ2: To what extent does user feedback affect influential users in changing from one archetype to another?

Based on a sample of 13,840 influential users drawn from the Covid-19 community on Zhihu, the archetypes of influential users were derived from their ongoing participation behavior in the community using the Gaussian mixture model. Additionally, user feedback characteristics such as relevance and volume from 222,965 commenters who contributed 546,344 comments were analyzed using the multinomial logistic regression model to investigate the archetype change of influential users.

Findings suggest that influential users could be clustered into three distinctive archetypes: touch-and-go influential users, proactive influential users and super influential users. Moreover, feedback variables have various impacts on the influential user archetype change, including a shift toward creating higher-quality content and fostering increased interaction, a shift toward generating lower-quality content and decreased interaction but improved speed and having mixed effects due to differences in information processing among these archetypes.

This study expands the existing knowledge of influential users and proposes practical approaches to cultivate them further.