Lingling Wang

As climate governance increasingly shifts toward consumption-side intervention, digital platforms such as the Carbon Generalized System of Preferences have become important tools for promoting low-carbon behavior. However, existing studies have mainly focused on participation and engagement, paying limited attention to users’ advocacy intentions. Drawing on the perceived value perspective and Social Exchange Theory, this study examines how perceived rewards and gameful experiences influence advocacy intentions through perceived benefits and low perceived costs. A three-wave survey of Chinese respondents was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA). The results show that perceived rewards enhance perceived benefits, while gameful experiences increase perceived benefits and reduce users’ actual perceived burden. In turn, perceived benefits and lower perceived costs both promote advocacy intentions. The mediation analysis confirms the important roles of perceived benefits and low perceived costs, while the fsQCA results identify three distinct configurations leading to high advocacy intentions. This study extends CGSP research from participation to advocacy and offers practical implications for designing digital low-carbon platforms.

China's chemical manufacturing sector is under growing pressure to pursue greener, cleaner production in response to increasingly stringent environmental regulations and carbon neutrality goals. Specialized, Refined, Differential, and Innovative (SRDI) enterprises—recognized for their technological focus and market niches—are becoming key actors in driving green innovation in China's chemical SRDI sector. This study explores how different configurations of internal and external factors influence green innovation performance in chemical SRDI firms. Using data from 48 chemical SRDI enterprises in Zhejiang Province, we apply the Technology–Organization–Environment framework and conduct a configurational analysis through NCA and fsQCA. Our results reveal three distinct innovation pathways: Scale and Dynamism‐Driven, Digitalization‐Driven, and Technology Leadership‐Driven, reflecting heterogeneous strategies for sustainable transition. Each pathway demonstrates that green innovation is not determined by any single factor but rather by synergistic combinations tailored to an enterprise's context. These findings provide theoretical and managerial implications for promoting sustainable transformation in the chemical industry and supporting the green upgrading of niche enterprises.

Despite numerous enterprises embracing crowdsourcing to access several innovative solutions, the prevalence of information asymmetry among different participants has led to an increase in the submission of low‐quality solutions and payment disputes. To improve the efficiency of crowdsourcing solutions for innovation, this study aims to employ an evolutionary game model to capture the dynamic interaction and decision‐making process of the requesters, platforms, and solvers. Initially, we dissect the relevant factors influencing the behavioral decisions of participants to construct a tripartite evolutionary game model. Subsequently, we analyze five potential evolutionarily stable strategies and conditions. Ultimately, we simulate the dynamic evolution of participant decision‐making behavior and the sensitivity of related parameters. The simulation results depict that the initial selection probabilities of populations bear no correlation to the system stability, which only influences the time required to reach equilibrium. The participant's behaviors are affected by price, loss, penalty, compensation, cost, and reputation recognition. Reward and punishment mechanisms help effectively mitigate the emergence of free‐riding and collusion. These findings provide important implications for the sustainable development of crowdsourcing solutions for innovation.

In view of the current teaching situation and challenges faced by the Introduction to Artificial Intelligence course, this paper analyzes the core teaching objectives of the course, and explores the online and offline hybrid teaching methods of it. This paper analyzes the advantages of online and offline hybrid teaching, and probes into the development mode of online and offline hybrid teaching of Introduction to Artificial Intelligence from the aspects of teaching content, teaching mode, teaching effect, examination method, etc.

With the advent of the digital economy era, the relationship between consumers and brands is changing. The mode of marketing, especially the paradigm of brand management, also needs to be adapted to change. Brand orientation has triggered a heated discussion on the dominant paradigm of market orientation and a new revolution in brand management. In view of the primary position of brand orientation in the management domain, it is necessary to sort out a systematic scientific knowledge mapping, clarify the research context and progress, and discover research focuses and limitations for strengthening the construction of brand-oriented theories. This study conducts a scientific quantitative analysis of 169 literatures and 7187 references from the Web of Science in the field of brand orientation by comprehensively using methods of scientific knowledge mapping and traditional literature review. The findings show that: (1) Concentrating on the core issue that “whether and how brand orientation becomes an effective strategic orientation of an organization”, brand orientation research includes six major hot spots and has been extended to fields including non-profit organizations, retail, service, manufacturing, e-commerce, and tourism. (2) As a multi-dimensional construct, brand orientation affects organizational performance directly through internal branding and external customer perception, and it is influenced by organizational culture, leadership, competition environment, funding sources, and brand cooperation. The relationship between brand orientation and market orientation has evolved from mutual substitution to synergy.

With the rapid growth of global demand for water and energy, the two increasingly restrict economic and social development. The total energy consumption and water use are positively correlated. Identifying the key drivers influencing the energy-water development can realize national resource management and sustainable supplement. In this context, this study aims to capture the key driving forces that affect the sustainable energy-water development characteristics in Chinese change processes throughout 2000–2017. Five driving forces, the EW intensity effect, industrial structure effect, GDP value-added effect, income improvement effect, and population-scale effect, were further decomposed by the logarithmic mean Divisia index (LMDI) model to explore the energy consumption and water use. Our findings indicated that the largest and lowest energy consumers were the manufacturing and construction sectors, while agriculture accounted for the largest share in water use. During the three time intervals, the cumulative effects increased the EW use, but the contributions were declining. Further, these effects had a more prominent influence on water use than energy consumption; GDP value-added effect, income improvement effect, and population-scale effect increased the EW use, while intensity effect played a vital role in decreasing EW use during the study period. Notably, the industrial structure effect had a seesaw role during 2000–2006, which led to a tradeoff between various driving factors. In future sustainable issues, policymakers should pay more attention to energy-saving than water-saving to achieve the national energy and water conservation targets.

With the characteristics of low cost and open call, crowdsourcing has been widely adopted in many fields, particularly to support the use of surveys, data processing, and the monitoring of public health. The objective of the current study is to analyze the applications, hotspots, and emerging trends of crowdsourcing in the field of public health. Using CiteSpace for the visualization of scientific maps, this study explores the analysis of time-scope, countries and institutions, authors, published journals, keywords, co-references, and citation clusters. The results show that the United States is the country with the most publications regarding crowdsourcing applications for public health. Howe and Brabham are the two leading authors in this field. Further, most of the articles published in this field are found in medical and comprehensive journals. Crowdsourcing in public health is increasing and diversifying. The results of this study will enable and support the analysis of the specific role of crowdsourcing in the public health ecosystem.