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This study aims to investigate the optimal pricing decisions and shared channel strategy selection of battery manufacturers considering heterogeneous consumers' range anxiety.

Amidst the rapid growth of the electric vehicle sector, countries are promoting upgrades in the automotive industry. However, insufficient driving range causes consumer range anxiety. The study utilizes the Stackelberg game model to assess how range anxiety influences battery manufacturers' pricing and channel strategy decisions across three strategies.

We find that electric vehicle battery manufacturers' decisions to cooperate with third-party sharing platforms (TPSPs) are primarily influenced by fixed costs and consumer range anxiety levels. As range anxiety increases, the cost threshold for joining shared channels rises, reducing cooperation likelihood. However, considering diverse consumer needs, especially a higher proportion of leisure-oriented consumers, increases the likelihood of cooperation. Furthermore, higher battery quality makes direct participation in shared channels more probable.

In the electric vehicle industry, range anxiety is a significant concern. While existing literature focuses on its impact on consumer behavior and charging infrastructure, this study delves into battery manufacturers' strategic responses, offering insights into channel options and pricing strategies amidst diverse consumer segments.

The rapid growth of professional esports has highlighted the lack of a universally recognised governing body to standardise operations and competition rules. This absence presents many challenges. A key concern is the well-being of professional esports players (e-pro-players), who often suffer from exhaustion. This study aims to examine the factors contributing to exhaustion among e-pro-players.

Using the conservation of resources theory, we developed a framework to explain the factors leading to e-pro-players’ exhaustion and the conditions under which it occurs. We tested this framework with 126 responses in a dyadic survey from e-pro-players and their coaches in China. Additionally, we gathered qualitative insights from 50 interviews with esports stakeholders to provide more context for our quantitative findings.

Our study found that e-pro-players’ intrinsic motivation to engage in training reduces their exhaustion, while their struggle to cope with uncertainty in esports environments (intolerance of uncertainty) increases it. The effect of intrinsic motivation is weaker for those who believe their talent for playing esports is fixed (entity belief) but stronger for those with high relational identification with their coaches. Additionally, the link between uncertainty intolerance and exhaustion is stronger in players with strong entity beliefs.

Our study sheds light on the factors contributing to e-pro-players’ exhaustion within the partially regulated professional esports environment, a phenomenon that significantly influences their overall well-being. Through the identification and examination of these factors and the conditions under which they affect exhaustion, we deepen the understanding of the drivers of exhaustion for e-pro-players who operate in an industry lacking standardised regulations.

The persistence and virality of conspiracy theories online have raised significant concerns. This study revisits Rogers’ Diffusion of Innovations theory to examine the spread of conspiracy theories on social media, specifically focusing on how factors influencing their diffusion evolve over time.

The study analyzes over 1.18 million COVID-19-related tweets using a combination of natural language processing, social network analysis and machine learning techniques. It explores the dynamic roles of novelty, content negativity, influencers, echo chamber members and social bots in the diffusion of conspiracy theories.

The results indicate that novelty, influencers, echo chamber members and social bots are positively associated with the spread of conspiracy theories. The initial dissemination of conspiracy theories is primarily driven by content novelty and influencer involvement. Over time, the perpetuation of these theories becomes increasingly influenced by content negativity and the involvement of echo chamber members and social bots. Social bots serve as important connectors within echo chambers and their removal significantly reduces network cohesion.

The findings provide practical guidance for social media platforms and policymakers in monitoring diffusion patterns and applying targeted interventions.

This study introduces a time-sensitive approach to understanding the spread of conspiracy theories on social media. By identifying the key drivers at different stages of the diffusion process, this study offers valuable insights for developing effective strategies to counteract the proliferation of conspiracy theories at various points in their lifecycle.

This study aims to explore the impact of green inclusive leadership (GIL) on green creativity (GCRY) within the context of higher education institutions (HEIs) in China. Specifically, it aims to examine the mediating roles of green intrinsic motivation (GIM), environmental knowledge (EK) and green thinking (GT) according to the componential theory of creativity (CTC).

The study employed a series of questionnaire surveys to collect data at three different time points from various sources. A total of 583 leader-faculty matched samples were obtained from two universities in China. The hypothesized relationships were tested using PROCESS macro in SPSS.

The findings indicate a beneficial influence of GIL on GCRY, mediated by GIM, EK and GT. Noteworthy interaction effects were observed, with GIM fostering EK and GT, and EK laying the groundwork for GT.

This research contributes to the existing literature by confirming the implementation of GIL and supporting the CTC, offering insights into the motivational processes driving GCRY and with practical implications discussed for the effective management of GIL and GCRY in higher education settings.

The novelty of this research model lies in its operationalization of environmental sustainability within the CTC. This study is the initial investigation highlighting the role of GIL in fostering GCRY within HEIs. The key contribution of the study is the investigation of GIM, EK and GT as potential mediators in the relationship between GIL and GCRY. This expands the theoretical boundaries of the CTC framework.

This study explores how the high-end equipment manufacturing industry (HEMI) achieves intelligent development through the digital innovation ecosystem. While this industry urgently needs to achieve intelligent development through innovation breakthroughs, existing research lacks a deep analysis in conjunction with the digital innovation ecosystem. Considering the sophisticated nature of HEMI and the unique characteristics of the digital innovation ecosystem, this paper aims to uncover the innovation potential and synergetic development opportunities that arise from their integration.

This study uses Dynamic Qualitative Comparative Analysis (QCA) to explore the evolving relationship between the digital innovation ecosystem and intelligent development in HEMI enterprises. Data from 60 HEMI enterprises were collected from 2015 to 2022, and the study window was divided into two-year intervals for analysis. Compared to traditional QCA methods, this approach overcomes the limitations of cross-sectional analysis, fully accounting for time’s influence on causal relationships for more accurate results.

The study reveals that the digital innovation ecosystem of HEMI drives intelligent development through the coordinated interactions of its elements within each time window. Configuration paths and key driving factors evolve dynamically, reflecting the complexity of the ecosystem’s role in driving intelligent development. The study suggests that enterprises dynamically adjust their strategies to different stages, enhancing the effectiveness of intelligent transformation.

The paper proposes and validates a digital innovation ecosystem framework for HEMI, systematically exploring its role in driving intelligent development. The study fills a research gap and extends innovation ecosystem theory by identifying core driving factors and their evolutionary trends through Dynamic QCA. It offers a new perspective on the dynamic role of digital innovation ecosystems in intelligent transformation.

This study aims to explore the spatio-temporal dynamic characteristics and influencing factors of the coordination degree of the three systems of digital economy, energy and human habitat in Western China and to provide academic research support for promoting coordinated and sustainable development in similar regions of the world.

Based on system theory and sustainable development theory, this study primarily uses the coupled coordination degree model to assess the degree of coordination between the three systems.

The findings of this study indicate that: The three systems’ overall coordination is low. The distribution of the degree of coordination has spatial differences and its coefficient of variation is small. The probability of the coordination type changing for the better is greater than that of the opposite, and neighboring provinces interact with one another. The old-age dependence ratio, the resident population’s urbanization rate and public budget expenditure have the strongest gray association with the degree of coordination.

This study’s findings will be valuable for policymakers in developing policies to promote the coordinated and sustainable growth of the region’s digital economy, energy and human habitat. Additionally, the findings will aid in facilitating regional exchanges and cooperation to enhance the level of sustainable development.

This study’s findings will contribute to increased social interest in coordinating sustainable growth in the digital economy, energy and human habitat.

This study examines the digital economy, energy and human habitat within the same framework and investigates spatial spillover effects using spatial Markov chains.

It is a complex and dynamic process to provide high-quality rural infrastructure. However, there lacks a holistic performance evaluation method for rural infrastructure provision that reflects changing rural social needs and takes a village as a whole. This study aims to develop a holistic and dynamic performance evaluation model for rural infrastructure in Mainland China.

This study established an evaluation index system by combining the lifecycle theory and the economy, efficiency, effectiveness and equity (4E) theory. This study developed an evaluation model by using the analytic network process (ANP) and matter-element analysis theory (MEAT). The model was validated by two representative villages in Mainland China.

The developed model can reflect dynamic social needs and effectively evaluate the overall infrastructure provision performance of a village. The weight of indicators reflects the changes in Mainland China’s contemporary rural social needs, with particular emphasis on the impact and output performance. The evaluation result shows that the overall performance of the representative villages was excellent but had a tendency toward good. Although the output performance was excellent, different input, process and impact performances resulted in different downgrade trends.

This study provides a theoretical basis for disaggregating the complex issue of the performance of rural infrastructure provision. The results can be used by relevant authorities to make a holistic and dynamic evaluation of the performance of rural infrastructure provision and timely revise planning and management policies.

This paper aims to improve the refractive index sensor performance for analytes with large refractive index by adopting the technology of microstructured fiber (MF) and surface plasmon resonance (SPR).

The structure adopts an MF with a hexagonal lattice cladding structure composed of all-circular air holes, and three defect regions are introduced. The liquid analyte that needs to be tested is filled in the defect area. The surface plasmon polarition mode is generated and coupled with the core mode, thus forming a refractive index sensing channel. When the resonance conditions are satisfied, the resonance wavelength will be changed with the refractive index of the liquid analyte. All parameters that may affect the performance of the sensor are numerical simulated, and the structure is optimized through a large number of calculations.

The results demonstrate that the maximum dynamic sensitivity (S_R) can reach to 24,260 nm/RIU, and the average sensitivity (S_R-AV) can reach to 18,046 nm/RIU when the refractive index range is from 1.42 to 1.47. Besides, the sensitivity linearity (R²) is approximately 0.965, and its resolution is 4.1 × 10^–6 RIU. The comparison with some literature results shown that the proposed sensor has certain advantages over the sensors reported in these literatures.

This work proposed an SPR-based refractive index sensor with a simple MF structure. It has a certain reference significance for the design and optimization of SPR-based MF sensors. Moreover, owing to its simple structure, high refractive index sensitivity and linear sensing performance, this sensor will play an important role in the detection of high refractive index liquid analytes.

China is experiencing an economic revolution focused on reducing carbon emissions (CEs). Various technological research and development (R&D) frameworks also hasten the growth of the digital economy, which then fuels this economic revolution. Nevertheless, several correlation uncertainties in China have been observed between R&D investment and CE reduction with green economic transformation. This phenomenon is attributed to insufficient spatial impact considerations.

Therefore, this article explored the spatial impacts of the digital economy and R&D expenditures regarding environmental quality using Chinese-related panel data between 2012 and 2021. This study uses the Moran I index to test whether there is a spatial relevance between regional carbon emissions in China and assess the digital economic advancement level using the entropy weight approach. In addition, this article analyzes the direct and indirect impacts following the partial differentiation approach, and then creates an interaction term between the digital economy and R&D investment to assess the moderating effect for examining the influence of investing in R&D on reducing CO2 levels of the digital economy.

A positive spatial relevance between the digital economy and CEs was then highlighted from the empirical findings. The digital economy expansion also demonstrated higher local CEs while negatively impacting nearby regions. Notably, the digital economy concurrently lowered and increased local CEs in the Eastern and Central zones, respectively. Overall, a larger R&D investment directly impacted the capacity of the digital economy in decreasing the carbon emission intensity (CEI) at a regional level. An accelerated digital economy expansion and lower CEI were recorded in the Eastern zone owing to more significant R&D investments.

China has gradually shifted its focus from reducing CEs to implementing “dual control of carbon” to achieve the “dual carbon” target. Future studies should then involve additional studies concerning the impact mechanism and path selection related to “dual carbon control.”

Investment in R&D plays a key role in reducing carbon emissions from the digital economy. By fostering innovation and technological advances, R&D investment activities can create more energy-efficient digital infrastructures, develop sustainable practices and optimize resource use. In addition, these R&D investments can facilitate the transition to renewable energy sources, enhance data management systems to minimize waste and promote the adoption of green technologies by businesses and consumers. As the digital economy continues to evolve, prioritizing R&D in this area is critical to achieving long-term sustainable development goals and addressing the pressing challenges of climate change. Stakeholders across industries must therefore recognize the importance of investment in research and development as a strategic approach that not only drives economic growth but also ensures environmental stewardship in an increasingly digital world.

Investments in research and development not only foster innovation and technological progress, but also promote sustainable practices, which can have significant environmental benefits. In addition, they have the potential to create new jobs, improve public health through better air quality and drive economic growth in a manner consistent with climate goals. As society becomes increasingly dependent on digital solutions, it is critical to harness the power of the digital economy to achieve a more sustainable and inclusive society.

Research development investment is critical to all aspects of regulation. Research on R&D investment can provide direction to local governments in formulating digital economy policies and can be beneficial to local governments in considering regional differences in resource availability. The research and technical innovation strategies in the policies for developing the digital economy can substantially expedite carbon neutrality achievement by 2060.

As the complex influencing factors for financing decisions and limited information at the early project stage often render inappropriate financing mode and scheme (FMS) selection in the large-scale urban rail transit (URT) field, this study aims to identify the multiple influencing factors and establish a revised case-based reasoning (CBR) model by drawing on experience in historical URT projects to provide support for effective FMS decisions.

Our research proposes a two-phase, five-step CBR model for FMS decisions. We first establish a case database containing 116 large-scale URT projects and a multi-attribute FMS indicator system. Meanwhile, grey relational analysis (GRA), the entropy-revised G1 method and the time decay function have been employed to precisely revise the simple CBR model for selecting high-similarity cases. Then, the revised CBR model is verified by nine large-scale URT projects and a demonstration project to prove its decision accuracy and effectiveness.

We construct a similarity case indicator system of large-scale URT projects with 11 indicators across three attributes, in which local government fiscal pressure is considered the most influential indicator for FMS decision-making. Through the verification with typical URT projects, the accuracy of our revised CBR model can reach 89%. The identified high-similarity cases have been confirmed to be effective for recommending appropriate financing schemes matched with a specific financing mode.

This is the first study employing the CBR model, an artificial intelligence approach that simulates human cognition by learning from similar past experiences and cases to enhance the accuracy and reliability of FMS decisions. Based on the characteristics of the URT projects, we revise the CBR model in the case retrieval process to achieve a higher accuracy. The revised CBR model utilizes expert experience and historical information to provide a valuable auxiliary tool for guiding the relevant government departments in making systematic decisions at the early project stage with limited and ambiguous project information.