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Sustainable development is inseparable from rational and responsible use of resources and promotion of green entrepreneurship. The contemporary green development agenda encompasses climate, economic, technical, social, cultural, and political dimensions. International efforts to greening the global development are conducted by the major economies, including China as the world’s largest consumer of energy and the biggest emitter of greenhouse gases. China is aware of its environmental problems, as well as of its part of the overall responsibility for the accomplishment of the sustainable development goals. By means of the decarbonization efforts, the latter are integrated both into the national development agenda (the concept of ecological civilization) and China’s international initiatives (the greening narrative within the Belt and Road Initiative). Over the past decade, China has made a breakthrough on the way to promoting green entrepreneurship and greening of its development (better quality of air and water, renewable energy, electric vehicles, and organic farming). On the other hand, emissions remain high, agricultural land loses productivity, and freshwater resources degrade due to climate change. In conventional industries (oil, coal mining, and electric and thermal energy), decarbonization faces an array of impediments. In this chapter, the authors summarize fundamental provisions of China’s approach to building an ecological civilization and measures to reduce emissions and achieve the carbon neutrality status within the nearest decades. The analysis of obstacles to the decarbonization of the economy and possible prospects for the development of green entrepreneurship summarizes China’s practices for possible use in other countries.

While the importance of supply chain learning (SCL) is widely recognized by both academia and industry, the mechanisms through which SCL is developed and its effects on environmental performance remain insufficiently understood. This study conceptualizes and empirically investigates the role of relational capital and information technology (IT) in enhancing SCL and improving environmental performance.

Drawing on socio-technical system theory (STS) and a knowledge-based view, this research employs structural equation modeling to test the proposed model, utilizing survey data collected from Chinese manufacturing companies.

The results demonstrate that relational capital with supplier and customer significantly enhances both supplier and customer learning. While IT does not directly impact supplier and customer learning, it exerts an indirect influence through its positive effect on relational capital. In other words, relational capital mediates the relationship between IT and supplier and customer learning. Additionally, this study finds that both supplier and customer learning enhance environmental performance.

This study provides actionable insights for managers, emphasizing the importance of relational capital in fostering SCL and improving environmental performance. By understanding these relationships, managers can develop more effective strategies for leveraging SCL as a tool for sustainability.

This study adds to the existing body of knowledge in supply chain management by offering a more nuanced and holistic model to explain how relational capital, IT and SCL interact to influence environmental performance, particularly within the context of Chinese manufacturing companies.

The potential of generative AI (GenAI) to stimulate employee creativity has received extensive attention from industry and academia. However, there is still limited research on strategically using GenAI to leverage its positive effects on employee creativity. This study aims to clarify the effects of different GenAI use purposes on employee creativity.

Based on self-determination theory, this study explores the effects of work-related and nonwork-related GenAI use on incremental and radical creativity through the mediator role of exploratory and exploitative learning and the boundary role of perceived ease of use. This study constructs a theoretical model and uses structural equation modeling to test the model by analyzing survey data from 330 employees.

(1) Work-related and nonwork-related GenAI use positively impacts incremental and radical creativity through exploratory and exploitative learning; (2) work-related GenAI use contributes more to exploitative learning than to exploratory learning, while nonwork-related GenAI use contributes more to exploratory learning than to exploitative learning; (3) exploitative learning has a stronger positive impact on incremental creativity, and exploratory learning has a stronger positive impact on radical creativity; (4) perceived ease of use weakens the positive effects of nonwork-related GenAI use on exploratory and exploitative learning.

First, this study enriches employee creativity research by revealing the relationship between different GenAI use purposes and incremental and radical creativity. Second, this study enriches employee creativity research by revealing the mediating role of exploratory and exploitative learning between GenAI use and incremental and radical creativity. Finally, this study enriches GenAI use research by revealing the moderating role of perceived ease of use between GenAI use and employee learning.

The purpose of this paper was to compare the electrochemical homogeneity of AZ91D after various heat treatment processes, and its influence on the growth, composition, microstructure and corrosion resistance of phosphate conversion coatings.

The electrochemical activity of different heat-treated Mg alloys was evaluated via scanning vibrational electrode technique; the characterization of the microstructure and phase composition of coatings was conducted using a scanning electron microscope and X-ray diffraction. The corrosion resistance was evaluated by electrochemical tests and accelerated neutral salt spray tests.

T6 treatment increased the electrochemical homogeneity, while T4 treatment decreased the microstructure homogeneity of AZ91D magnesium alloy, due to the existence of residual Al-Mn impurity phase. The phosphate conversion coating (PCC) on T6 heat-treated Mg alloys showed the most compact microstructure and the best corrosion resistance, while the coating on the T4 heat-treated Mg alloy exhibited the worst microstructure and corrosion resistance.

The microstructure and protectiveness of coatings are related to the homogeneousness of Mg alloy: an Mg substrate with a more heterogeneous electrochemical reactivity yields a PCC with less protectiveness, which could be explained by the difference of precipitation kinetics at the metal/electrolyte interface.

This study aims to develop a modelling framework of housing supply dynamics within the context of urban microsimulation systems. Housing markets have witnessed substantial investigation over recent decades, predominantly concerning residential demand. However, comparatively limited attention has been directed towards comprehending the housing supply dynamics. Housing policy disconnects with the developers’ market behaviours, which leads to significant mismatch between the housing construction and affordable housing needs of the population. Research attention should be made in comprehending the residential construction market activities. To address this gap, this study developed an autoregressive distributed lag (ARDL) model and analyzed the temporal evolution of housing construction.

An ARDL model was developed to address the issue of temporal modelling of the housing supply. An empirical study was conducted in the Greater Toronto and Hamilton Area (GTHA) based on a longitudinal housing starts data set from 1998 to 2020. The model integrates diverse variables, including macroeconomic conditions, property development costs, dwelling prices and opportunity costs. Notably, the model captures both the path-dependent effects stemming from supply market fluctuations and the temporal lag effect of influential factors.

The findings reveal that the supply-side’s responsiveness to market condition alterations may span up to 18 months. The model has reasonable and satisfying performance in fitting the observed starts. The methodological foundations laid will facilitate future modelling of housing supply dynamics.

This study innovatively separated the modelling of housing supply within the context of urban microsimulation, into two parts, the modelling of housing starts and completion. The housing starts are determined in a complex and regressive process influenced by both the micro-economic environment and the construction cost and housing market trends. Through the temporal modelling method, this study captures how long it would take for the housing supply to respond to multiple factors and provides insight for urban planners in regulating the housing market and leveraging various policies to influence the housing supply.

With the competitive nature of golf club operations, understanding the loyalty formation process is crucial for maintaining a competitive edge. This study investigated the sequential developmental stages of consumer loyalty, progressing through cognitive, affective, and conative stages.

The study conducted a survey targeting consumers who have experienced golf club services in the United States. This study operationalized reliability and trust as key indicators of the cognitive stage, hedonic motivation and social engagement as indicators of the affective stage, and loyalty as the indicator of the conative stage.

Using structural equation modeling (SEM), this study found that reliability has a significantly positive influence on trust. Trust has a significantly positive influence on hedonic motivation and loyalty. Furthermore, hedonic motivation has a significantly positive influence on social engagement and loyalty.

By integrating these findings within the Cognitive-Affective-Conative (CAC) framework, this study contributes to both the theoretical literature on consumer loyalty and provides practical insights for golf club management.

Prioritising economic, environmental and social requirements of society has become imperative for attaining a competitive edge in the global marketplace. This study examines how internal and external factors influence sustainable strategic orientations of Chinese small technology enterprises. It uses an initial framework grounded in institutional theory and resource-based view (RBV).

The study surveyed small technology enterprises using quantitative techniques. A combination of partial least squares structural equation modelling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) was used to analyse 402 valid samples with statistical significance.

The PLS-SEM results show that absorptive capacity, innovation culture and coercive pressure positively influence small enterprises’ sustainability orientation. Additionally, the fsQCA method identifies five complex combinations of causal antecedents that can facilitate small enterprises’ adoption of sustainability orientation.

The findings reaffirm the importance of maintaining ideal environmental cognition and external institutional pressure. Specifically, enterprises need to strategically allocate their limited resources to optimal levels and combinations to achieve sustainability orientation.

This study enhances the understanding of sustainability orientation by integrating the RBV and institutional theory, highlighting various factors that promote sustainability at the firm level. Furthermore, asymmetric research perspectives show the triggers of sustainability orientation from both symmetrical and asymmetrical perspectives. Set theory approaches represent a pioneering approach that captures the intricacies of sustainability orientation, advancing beyond previous variance-based findings by providing optimal mix solutions.

The investigation of pro-environmental behaviors (PEB) among college students is essential for future sustainability endeavors. Existing research seldomly concentrated on college students and their PEB. This study aims to address the gap in understanding PEB among college students.

This study constructed an integrated model combining the theory of planned behavior (TPB) and the value-belief-norm (VBN) theory, with the novel addition of environmental risk perception. Through an empirical study involving 844 college students, this research analyzed the data with the structural model.

The authors identified that environmental values, attitudes, perceived behavioral control, subjective norms and risk perception play crucial roles in shaping PEB. This study also revealed age-related differences, highlighting that older students might be less influenced by attitudes and subjective norms due to more established habits. Findings underscore the importance of fostering PEB through environmental education, promotion of low-carbon lifestyle choices and incentives. This investigation not only enriches the theoretical framework for PEB but also offers practical insights for policymakers and educators to enhance sustainable practices among the youth.

Though the authors offer valuable findings, this research has two key limitations: the use of observational data for hypothesis testing, which weakens causal inference, and the collection of data through questionnaires, which may be biased by social desirability. Respondents of self-report tend to behave in the socially desired ways. Consequently, they usually exaggerate their pro-environmental intention or PEB. To comprehend the influencing aspects more thoroughly, future research should consider incorporating experimental methods and objective data, such as digitalized data.

The findings provide valuable evidence for guiding college students’ PEB, including strengthening environmental education, promoting of low-carbon fashion and providing incentives for PEBs.

First, the authors examine the internal factors influencing PEB among Chinese university students within the “dual-carbon” initiative framework. Second, this research pioneers the use of structural equation modeling to merge TPB and VBN theories, offering a predictive model for university students’ PEB. Third, the authors introduce “environmental risk perception” as a novel variable derived from both TPB and VBN, enhancing the model’s explanatory power.

The structure and protective effect of Al-coated Nd-Fe-B magnets before and after grain boundary diffusion were studied to explore the feasibility of improving the corrosion resistance of Nd-Fe-B magnets by Al coating and Al grain boundary diffusion.

The Al coating was deposited on sintered Nd-Fe-B magnets by magnetron sputtering, and then the Al-coated Nd-Fe-B magnets were put into the vacuum tube furnace for grain boundary diffusion process. The influence of Al coating and grain boundary diffusion process on the corrosion resistance of Nd-Fe-B magnets was investigated using electrochemical tests.

Results showed that the Al coating thickness increases, the corrosion current density of Al-coated magnets first increases and then decreases with increasing coating time. The Al coating particles transform from small millet shaped particles to equiaxed polygonal particles, and finally to big millet shaped particles with increasing coating time. The diffusion temperature has little effect on the corrosion potential, and the corrosion current density of Al-diffused magnets after grain boundary diffusion is much higher than that of Al-coated magnets before grain boundary diffusion. The corrosion potential and corrosion current density of magnets first increase, and then decrease with increasing tempering temperature.

As for high intrinsic coercivity Nd-Fe-B magnets, Poor preparation of Al coatings can result in Al coatings lacking protective properties, and the Al coating should be used cautiously as the surface protective coating of magnets. Grain boundary diffusion leads to the disappearance of Al coating, and reduces the corrosion resistance of Nd-Fe-B magnets, while tempering treatment can improve the corrosion resistance of Al-diffused magnets.

In online exchange platforms' sponsored search advertising, the array of product quality signals within a keyword search results list plays a crucial role in shaping buyers' purchasing decisions. This research seeks to explore the impact of various quality signals – namely, ranking position, seller reputation and product price – on ad clicks. Additionally, it examines the role of keyword attributes, such as specificity and popularity, in modulating the effects of these quality signals on advertising clicks.

A total of 5,763 effective data points were collected from a leading B2B electronic platform company, and we employed negative binomial regression with Heckman correction methods to test the hypotheses.

The results indicate that in online exchange platforms, search ad clicks are significantly and positively affected by displayed signals such as ranking position, seller reputation and product price information. Notably, a U-shaped relationship emerges between product price and ad clicks. Furthermore, keyword specificity and popularity distinctly moderate the impact of these displayed signals on ad clicks within online exchange platforms.

This paper addresses the gap in existing research on search advertising by methodically analyzing the impact of various signals displayed in search results and how keyword attributes moderate ad clicks, all through a signaling theory lens.