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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 paper is to investigate how boundary-spanning search affects sustainable competitive advantage under the conditions that competitors also search and to test the moderating role of knowledge integration capability.

This paper classifies boundary-spanning search into proactive search and responsive search by considering competition and develops a theoretical model in which knowledge integration capability moderates the effects of proactive and responsive searches on sustainable competitive advantage. Empirical analyses were conducted on the data of 245 Chinese advanced manufacturing firms.

The results show that proactive and responsive searches have inverted U-shaped relationships with sustainable competitive advantage. Moreover, the relationships between proactive and responsive searches and sustainable competitive advantage are moderated by knowledge integration capability. Specifically, as knowledge integration capability increases, the inverted U-shaped relationship between proactive search and sustainable competitive advantage becomes flatter, whereas the inverted U-shaped relationship between responsive search and sustainable competitive advantage becomes almost linear.

This paper enriches the research of boundary-spanning search by considering competition and uncovers how boundary-spanning search affects sustainable competitive advantage under the conditions that competitors also search. Furthermore, this paper sheds light on that the effects of proactive and responsive searches on sustainable competitive advantage are even more complex than inverted U-shaped patterns and provides a contingent viewpoint to deeply understand the relationship between boundary-spanning search and sustainable competitive advantage.

The rapid advancement of digital transformation requires a shift in firms’ focus from past met needs to both latent future and unmet past needs. However, how boundary-spanning search with future orientation and past orientation affects breakthrough innovation remains unclear. This study thus aims to investigate the relationship between boundary-spanning search and breakthrough innovation from the perspective of search orientation.

In terms of search orientation, this study divides boundary-spanning search into forward-looking search and backward-looking search. Drawing on resource-based view, this study develops a theoretical model in which big data analytics capability moderates the effects of forward-looking and backward-looking searches on breakthrough innovation. Empirical analyses were conducted on data from China’s advanced manufacturing firms. Research model and hypotheses were tested through multiple regression.

The results confirm that forward-looking search has a positive effect on breakthrough innovation, and big data analytics capability strengthens this positive effect. Furthermore, backward-looking search has an inverted U-shaped effect on breakthrough innovation. Interestingly, as big data analytics capability increases, this inverted U-shaped curve flattens and becomes almost linear.

This study uncovers the different effects of boundary-spanning search with different orientations on breakthrough innovation and extends the research on the relationship between boundary-spanning search and breakthrough innovation by incorporating search orientation. Furthermore, by demonstrating the moderating role of big data analytics capability, this study provides a crucial condition under which boundary-spanning search can enhance breakthrough innovation.

In the context of global economic downturn and intense competition, firms are increasingly resorting to supply chains to acquire capital support and achieve sustainability. This study aims to investigate the effect of supply chain finance (SCF) on corporate sustainability performance (CSP) and identifies SCF-related recipes for CSP.

Based on a sample of 1,038 firms that disclose CSP – namely, corporate financial performance (CFP) and environmental, social and governance performance (ESGP) – the authors use a quasi-replication method consisting of empirical analysis with fuzzy-set qualitative comparative analysis (fsQCA) to investigate SCF’s effects on CSP.

The authors find that SCF has a “doing well by doing good” effect on CSP. CFP can promote the positive effect of SCF and ESGP while ESGP’s positive effect on SCF and CFP is nonsignificant. In addition, heterogeneity tests show that SCF’s promoting effect on CSP is affected by high-low CFP and ESGP. The fsQCA results verify the empirical findings and reveal five SCF-related recipes for achieving high CSP.

This study has the following two limitations. First, we do not consider how SCF affects CSP in different industries. There is a need to investigate whether industry heterogeneity changes SCF’s effects on CSP, especially in prominent industries, such as the energy industry, with its high susceptibility to ESGP, and the manufacturing industry, with its extensive application of SCF. It will be important to investigate these industries to better understand SCF’s role in sustainability. Second, we study the secondary supply chain – namely, core firm–suppliers and core firm–customers. The authors do not consider financial institutions (e.g. banks and guarantee institutions). SCF modes that include the participation of financial institutions, such as factoring financing and reverse factoring financing, cater more to the capital needs of diversified firms. In the future, studying specific industries that have made significant contributions to the application of SCF along with others that are more sensitive to environmental governance could better highlight the effect of SCF on sustainability and help supply chain managers understand the application value of SCF. Future research could also extend SCF participants into multiple roles to explore separate effects. Tracking financing demanders, fund providers and credit guarantors could capture SCF characteristics more comprehensively. Methodologically, it will be challenging to accurately measure SCF networks in terms of quantification. In future work, this could be performed with the help of artificial intelligence.

First, our findings indicate that SCF has a “doing well by doing good” effect on core firms. SCF can not only overcome the capital shortage of SMEs but also provide significant benefits to core firms. Second, our findings provide SCF-related recipes to help firms fulfil ESGP obligations without sacrificing CFP under the pressure to “do good.” The authors provide valuable insights and diverse recommendations to help supply chain managers, marketing executives and researchers adjust supply chain management strategies. Third, this work can guide executives in various fields to adopt SCF to achieve sustainability as a risk-mitigation strategy by means of marketing.

This study identifies better, more straightforward SCF-related recipes for CSP (consisting of CFP and ESGP) using a quasi-replication analysis that improves upon conventional methods such as regression analysis, which have limited power. The authors provide valuable insights and diverse recommendations to help managers pursue sustainable development. The findings point to practical guidelines and feasible solutions that can support well-founded operational strategic and management decision-making, which can enhance a firm’s competitiveness under uncertainty and a sluggish economy.

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.

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.

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.

The purpose of the study is to evaluate the severity of public events in real time from the perspective of social media and to construct the early warning mechanism of public events.

This study constructed the severity assessment system of public events from the dimensions of the netizens' role, the Internet media's role, the spread of public events and the attitudes and feelings of netizens. The method of analyzing the influence tendency of the public event severity indicators was proposed. A total of 1,107,308 microblogging entries regarding four public events were investigated. The severity of public events was divided into four levels.

It is found that serious public events have higher indicator values than medium level events on the microblogging platform. A quantitative severity classification standard for public events was established and the early warning mechanism of public events was built.

Microblogging and other social media platforms provide rich clues for the real-time study and judgment of public events. This study only investigated the Weibo platform as the data source. Other social media platforms can also be considered in future.

Different from the ex-post evaluation method of judging the severity of public events based on their physical loss, this study constructed a quantitative method to dynamically determine the severity of public events according to the clues reflected by social media. The results can help the emergency management departments judge the severity of public events objectively and reduce the subjective negligence and misjudgment.

Based on the organizational learning theory, this study regards market orientation as market-based learning and seeks to advance insight into how proactive and responsive market orientations affect two kinds of open innovation strategies, sourcing and selling. A firm’s information and communication technology (ICT) capability is considered an essential moderator in these relationships.

This study adopted a quantitative design and used the questionnaire survey method to collect data. The authors finally collected data on samples in China. Multiple regression analysis was used to test the hypotheses.

The results show that proactive and responsive market orientations act as antecedents of open innovation, showing linear and curvilinear relationships between them. Specifically, responsive market orientation positively affects selling, and proactive market orientation positively affects sourcing. Responsive market orientation has an inverted U-shaped relationship with sourcing, and proactive market orientation has a U-shaped relationship with selling. In addition, ICT capability strengthens the positive effects of market orientation on open innovation and weakens the negative effects.

Drawing on organizational learning theory, this study provides a novel perspective to explain the complex mechanism between market orientation and open innovation. This study also explores the moderating role of ICT capability in this process, which advances research on how to select open innovation strategies under different conditions.

Health-related online fake news (HOFN) has become a major social problem. HOFN can lead to the spread of ineffective and even harmful remedies. The study aims to understand Internet users' responses to HOFN during the coronavirus (COVID-19) pandemic using the protective action decision model (PADM).

The authors collected pandemic severity data (regional number of confirmed cases) from government websites of the USA and China (Studies 1 and 2), search behavior from Google and Baidu search engines (Studies 1 and 2) and data regarding trust in two online fake news stories from two national surveys (Studies 2 and 3). All data were analyzed using a multi-level linear model.

The research detected negative time-lagged relationships between pandemic severity and regional HOFN search behavior by three actual fake news stories from the USA and China (Study 1). Importantly, trust in HOFN served as a mediator in the time-lagged relationship between pandemic severity and search behavior (Study 2). Additionally, the relationship between pandemic severity and trust in HOFN varied according to individuals' perceived control (Study 3).

The authors' results underscore the important role of PADM in understanding Internet users' trust in and search for HOFN. When people trust HOFN, they may seek more information to implement further protective actions. Importantly, it appears that trust in HOFN varies with environmental cues (regional pandemic severity) and with individuals' perceived control, providing insight into developing coping strategies during a pandemic.