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The purpose of this paper is to investigate the impact of land approval decentralization on energy efficiency and then analyze the mechanisms that land decentralization improves energy efficiency.

This paper has been based on the dynamic slack based measure (DSBM) method to measure energy efficiency using input-output data at the city level in China. In the empirical analysis, this paper employed difference in differences (DID), instrumental variable (IV), propensity score matching–difference in differences (PSM–DID) and triple differences (DDD) to examine the research hypotheses.

Firstly, this paper finds unexpected good news and shows that decentralizing land approvals significantly improves regional energy efficiency. Secondly, this paper explores two mechanisms for this effect, one is the mitigation of land misallocation and the other one is the cleaner allocation of land resources across industries. Thirdly, this paper provides micro-evidence from Chinese listed firms and finds that the decentralization of land approvals similarly improve firms’ energy efficiency.

Existing studies have mainly explored the environmental impacts of land marketization reforms, with limited attention to decentralization of land approval. As a result, the promotion effect of decentralization of land approval on energy efficiency is still unclear, and the mechanism need to be further refined. Based on empirical analysis, this paper not only helps to clarify the relationship between land decentralization reform and energy efficiency, but also explores the mechanisms of transmission.

This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.

Drawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.

The results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.

Although images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.

This study aims to identify an antecedent that hinders knowledge sharing, namely, the perceived climate of Cha-xu. Based on the social exchange perspective, the authors propose a theoretical model that links the perceived climate of Cha-xu to employee knowledge sharing. This model focuses on the mediating role of two types of trust (vertical and horizontal trust) and the moderating role of task interdependence in influencing the mediation.

Using a sample of 509 Chinese employees, this study carried out a survey on an online platform. This study developed a structural equation model and tested the moderated mediation hypothesis by using Mplus 8.0.

The results showed that two types of trust act as mediators in the relationship between the perceived climate of Cha-xu and knowledge-sharing processes. The mediating effect of horizontal trust is stronger. Most significantly, findings show that this mediated relationship is contingent on the level of task interdependence.

This paper provides evidence for distinguishing vertical trust and horizontal trust in the field of knowledge management. From a managerial perspective, this study identifies traditional cultural factors for hindering knowledge-sharing processes within Chinese organizations.

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.

As the core technology of blockchain, various consensus mechanisms have emerged to satisfy the demands of different application scenarios. Since determining the security, scalability and other related performance of the blockchain, how to reach consensus efficiently of consensus mechanism is a critical issue in the blockchain.

The paper opted for a research overview on the blockchain consensus mechanism, including the consensus mechanisms' consensus progress, classification and comparison, which are complemented by documentary analysis.

This survey analyzes solutions for the improvement of consensus mechanisms in blockchain that have been proposed during the last few years and suggests future research directions around consensus mechanisms. First, the authors outline the consensus processes, the advantages and disadvantages of the mainstream consensus mechanisms. Additionally, the consensus mechanisms are subdivided into four types according to their characteristics. Then, the consensus mechanisms are compared and analyzed based on four evaluation criteria. Finally, the authors summarize the representative progress of consensus mechanisms and provide some suggestions on the design of consensus mechanisms to make further advances in this field.

This paper summarizes the future research development of the consensus mechanisms.

Digital product and service innovations (DPSI) has attracted widespread attention from both management scholars and practitioners. Previous studies have documented that information technology (IT) capability and digital orientation positively influence DPSI performance. However, the question of whether and how digitalization capability can facilitate DPSI performance remains unresolved. This paper fills these gaps by investigating the mediating role of improvisation capability and the moderating role of technological turbulence.

This study used two-wave data from 240 matched digital transformation department leaders and senior managers from Chinese firms and examined the hypotheses deploying hierarchical regression and bootstrapping.

Our analyses reveal positive, significant links between digitalization capability and improvisation capability and between improvisation capability and DPSI performance. The findings further show that the effect of digitalization capability on DPSI performance is partially mediated by improvisation capability and that technological turbulence strengthens the indirect relationship between digitalization capability and DPSI performance through improvisation capability.

Integrating resource-based view, this research provides evidence that the extent to which improvisation capability mediates the relationship between digitalization capability and DPSI performance depends on technological turbulence. It provides a new direction for digitalization capability and DPSI performance.

The purpose of this study is to analyze factors affecting decision-making on libraries’ electronic resources management under the situation of tight budgets in Canadian research universities.

Interview was adopted to investigate library resources management leaders’ opinions from English-speaking university members of the Group of Canadian Research University Libraries.

A comprehensive model is developed for library resources’ management. Subject specialists are the key of the model integrating marketing roles and evaluation roles.

The main limitations of this study are the small sample size of interview candidates, which may have application limitations on other types of libraries and universities in different areas.

This study generates a comprehensive model based on past research, contributing to future library decision-making practices.

It develops a subject specialist-centric model of library resources’ value assessment and brings the element of culture into future studies of academic library.

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

This investigation endeavors to examine the routes by which environmental–social–governance (ESG) performance influences the competitive landscape for logistics enterprises, with a particular emphasis on the function of digitalization in this complex process. The research underscores the significance of the ESG context in the realm of digitalization, providing valuable insights into its impact on the overall competitiveness of logistics enterprises.

This research gathers information from a total of 90 logistics enterprises that are publicly traded on the Shanghai and Shenzhen A-share stock markets for analysis and model testing. Due to the multiple pathways of influence and the constrained size of the sample, it has been decided that the Piecewise structural–equation–modeling (SEM) approach will be employed.

The research reveals that ESG factors positively impact enterprises' competitiveness (EC). The augmentation of competitiveness is attributed to the moderating role of green technology innovation (GTI) and agency costs between ESG and EC. In the context of digitalization, the level of digitalization of logistics enterprises may create a capital squeeze effect on environmental performance, weakening competitiveness. Conversely, the level of digitalization positively regulates the promoting effect of governance performance on competitiveness.

This research provides a sound theoretical foundation for understanding how ESG contributes to boosting the competitiveness of Chinese logistics enterprises and extends the application of Piecewise SEM in the research field of logistics enterprise competitiveness. Furthermore, it offers a practical pathway for companies to implement ESG practices and foster competitiveness in digital environments.

The purpose of this study is to assess how managerial capability affects artificial intelligence (AI) adoption and employee well-being now in a dynamic context of organizational change. This study investigated the role that managerial capability and organizational support play in facilitating successful AI technology implementation within organizations. The study seeks to provide an integrated perspective on how organizations can help mitigate the effects of AI anxiety and improve the well-being of employees.

A survey questionnaire was administered to collect data from 324 employees and managers working in small- and medium-sized enterprises (SMEs) located in Pakistan. Partial least squares-structural equation modeling (PLS-SEM) was employed using Smart PLS version 4.1.0.3 to analyze the relationships between the study variables.

The findings of the study show that AI anxiety can significantly impact employee well-being. However, the relationship was moderated by organizational support. When organizational support was high, the effects of AI anxiety decline on employee well-being.

This study offers three important implications; it adds to our understanding regarding AI adoption and its effect on employee well-being by addressing how managerial interventions may facilitate the smooth integration of AI technology and examining the moderating effect that organizational support might have over the association between anxiety and employee well-being. Additionally, we have offered a nuanced view of the potential impact of AI adoption on employees and offered practical recommendations for organizations to undertake to address AI anxiety and promote employee well-being during AI implementation.