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Artificial Intelligence (AI) has revolutionized teaching and learning methods in higher education, especially in English language teaching and learning. This chapter contributes to the existing knowledge by exploring how AI has developed within the framework of teaching and learning of English, highlighting the challenges, dangers, and moral issues associated with its application. The typical classroom environment has significantly changed because of the integration of AI-powered tools and platforms in English instruction. Chatbots, automated grading systems, and language learning apps driven by AI have streamlined language education, increasing its effectiveness and accessibility. But these benefits accompany a variety of challenges and worries. Ethical concerns about data privacy, algorithmic biases, and the depersonalization of education arise as AI becomes more deeply ingrained in educational methods. Reliance on AI may inadvertently exacerbate educational disparities as long as learners' access to technology and its advantages remain unequal. In addition, significant thought must be given to the ethical ramifications of AI-generated content as well as the possible loss of human connection in language learning settings. This chapter examines these dangers and challenges and makes the case for a well-rounded strategy that maximizes AI's benefits while minimizing any potential downsides. Together, educators and legislators need to create moral guidelines that balance the potential of AI with human-centered learning experiences. To ensure responsible and fair AI integration and promote an inclusive learning environment that prioritizes students' holistic development while exploiting technology breakthroughs, comprehensive assessment of the associated obstacles, risks, and ethical issues is necessary.

The purpose of this study is to examine the impact of green bonds on common prosperity in China. Green bonds have gained significant attention as a means to address financial challenges and promote environmental protection. This research aims to investigate the influence of green bonds on common prosperity by utilizing the system-generalized method of moments (SYS-GMM) and analyzing panel data from prefecture-level cities. The study also explores the theoretical mechanisms and heterogeneous relationships between green bonds and common prosperity, providing valuable guidance for advancing economic and social well-being in China.

This study employs a system-generalized method of moments (SYS-GMM) as the methodology to investigate the influence of green bonds on common prosperity in China. Panel data from prefecture-level cities for the period 2014 to 2020 are utilized for analysis. The SYS-GMM approach allows for the examination of dynamic relationships and control of endogeneity issues. By utilizing this methodology, the study aims to provide robust and reliable findings on the impact of green bonds on common prosperity, considering the specific context of China's ecological civilization development and financial challenges faced by energy-saving and environmental protection enterprises.

The findings of this research indicate several important outcomes. Firstly, common prosperity in China experienced substantial growth between 2014 and 2020. Secondly, green bonds have demonstrated a clear and positive impact on common prosperity. They contribute to the enhancement of common prosperity by driving industrial structure upgrading and fostering green technology innovation. Lastly, the study reveals that the positive influence of green bonds on common prosperity is particularly pronounced in the western region of China. These findings highlight the significance of green bonds in promoting sustainable economic development and societal well-being.

This study contributes to the existing literature by examining the impact of green bonds on common prosperity in China, utilizing the system-generalized method of moments (SYS-GMM) and panel data analysis. The research not only adds to the understanding of the relationship between green bonds and economic well-being but also provides insights into the theoretical mechanisms and heterogeneous relationships involved. The findings showcase the positive influence of green bonds on common prosperity, emphasizing their role in addressing financial challenges, promoting environmental protection, and driving sustainable development. The study's conclusions offer valuable guidance for policymakers, financial institutions, and stakeholders in advancing common prosperity in China.

This paper investigates how various emergent theoretical perspectives in entrepreneurship research, representing diverse decision-making logics, influence firm growth and evolution. It explores the interaction among decision-making logics, including experimentation, affordable loss, flexibility and pre-commitment as components of effectuation, alongside causation and bricolage and their synergistic effects on firm growth.

This study uses a multi-phase, discovery-oriented approach. Initially, insights from existing literature on decision-making logic were combined with in-depth interviews with 10 Iranian entrepreneurs within the food sector. This phase used alternative template research to evaluate the principles of effectuation, causation and bricolage within case study data depicting firm growth. Subsequently, a self-administered survey was developed based on these insights and distributed to 205 entrepreneurs in Iran. The survey data was analysed using fuzzy-set qualitative comparative analysis (fsQCA) to identify key factors and pathways influencing firm growth.

Using a discovery-oriented approach, this study formulates a comprehensive framework detailing decision-making logics that influence firm growth. Through fsQCA, 12 distinct paths are identified, highlighting the complex interplay of causation, effectuation and bricolage in high-growth firms within the food sector.

This study has limitations. FsQCA identifies only logically sufficient combinations, suggesting potential for exploring alternative pathways in future research. Given COVID-19’s impact on the food sector, examining decision-making logic across diverse contexts and industries is advisable. Additionally, exploring how bricolage, causation and effectuation affect outcomes like new product development and innovation is essential in a growth-focused context. It is also important to consider environmental and organizational factors influencing growth.

This paper pioneers the examination of emerging theoretical paradigms in entrepreneurship and their impact on firm growth. It identifies critical configurations of causation, effectuation and bricolage, providing actionable insights for navigating dynamic business environments.

This study aims to solve the problems of large training sample size, low data sample quality, low efficiency of the currently used classical model, high computational complexity of the existing concern mechanism, and high graphics processing unit (GPU) occupancy in the current visualization software defect prediction, proposing a method for software defect prediction termed recurrent criss-cross attention for weighted activation functions of recurrent SE-ResNet (RCCA-WRSR). First, following code visualization, the activation functions of the SE-ResNet model are replaced with a weighted combination of Relu and Elu to enhance model convergence. Additionally, an SE module is added before it to filter feature information, eliminating low-weight features to generate an improved residual network model, WRSR. To focus more on contextual information and establish connections between a pixel and those not in the same cross-path, the visualized red as integer, green as integer, blue as integer images are inputted into a model incorporating a fused RCCA module for defect prediction.

Software defect prediction based on code visualization is a new software defect prediction technology, which mainly realizes the defect prediction of code by visualizing code as image, and then applying attention mechanism to extract the features of image. However, the challenges of current visualization software defect prediction mainly include the large training sample size and low sample quality of the data, and the classical models used today are not efficient, and the existing attention mechanisms have high computational complexity and high GPU occupancy.

Experimental evaluation using ten open-source Java data sets from PROMISE and five existing methods demonstrates that the proposed approach achieves an F-measure value of 0.637 in predicting 16 cross-version projects, representing a 6.1% improvement.

RCCA-WRSR is a new visual software defect prediction based on recurrent criss-cross attention and improved residual network. This method effectively enhances the performance of software defect prediction.

This study aims to empirically investigate how knowledge paradox affects collaboration performance. Knowledge paradox, which arises from the simultaneous need for knowledge sharing and protection, is common in interorganizational collaboration. Using the ambidexterity perspective, this paper aims to reexamine the effect of the knowledge paradox on collaborative performance to explore the moderating roles of structural and contextual ambidexterity.

This study used a sample of 153 firms involved in vertical and horizontal collaboration, collected via questionnaires. Hypotheses were tested using hierarchical regression analysis.

This study demonstrates that the stronger the knowledge paradox is, the higher the potential for value creation. Thus, knowledge paradox has a positive impact on collaborative performance. The functions of structural ambidexterity and contextual ambidexterity strengthen this positive relationship.

This paper not only expands the theoretical application of the knowledge paradox and ambidexterity theory in the context of interorganizational relationships but also provides significant managerial implications. By comprehending the dynamics of the knowledge paradox and the role of ambidexterity, managers can make well-informed decisions to enhance their collaborative performance.

With the uptake of prefabricated construction (PC) facing serious obstacles in China, networked innovation can break the technical constraints while also containing the risks in individual innovation. However, the construction community has paid little attention to PC innovation, especially networked innovation. This study aims to gain deep insights into what impacts the formation and dynamics of a prefabricated construction innovation network (PCIN). With the uptake of PC facing serious obstacles in China, networked innovation can break the technical constraints while also containing the risks in individual innovation.

The research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. It first conceptualizes the PCIN based on the quadruple helix model and formulates a corresponding system dynamics model based on causality analysis. After validating the PCIN model using empirical data, simulations are carried out using Vensim PLE software. Finally, this study identifies key factors that promote the formation of PCIN in China through sensitivity analysis.

The results show that PC predicts a continuous increase in practice as of 2030. The tested drivers all positively influence the formation of the PCIN, with market demand and risk sharing having the greatest influence, followed by competitive pressure, profit government support, scientific and technological advancement and collaborative innovation strategy.

The study makes three major contributions. First, it provides a novel angle for a deeper understanding of the PC innovation. Second, it proposes a new approach for probing the formation and dynamics of the PCIN. Finally, it offers a theoretical reference for promoting the formation of innovation networks and the development of PC.

In recent decades, advancements in biosensors technology have made fluorescent biosensor pivotal for biomolecular recognition. This paper aims to provide an in-depth analysis of polymerase chain reaction (PCR) fluorescent biosensor detection technology for identifying Escherichia coli (E. coli), setting the stage for future developments in the field.

The review of literature on PCR fluorescent biosensor detection technology for E. coli over the past decades includes discussions on traditional biological fluorescent detection, quantitative PCR fluorescent detection and digital fluorescent detection technology.

Advancements in fluorescent biosensor technology enable precise measurement of fluorescent signals, and when integrated with microfluidic technology, produce compact, reagent-efficient digital sensor devices.

This paper provides a comprehensive review of recent fluorescent detection technology for pathogenic E. coli, assessing method efficiencies and offering insights to advance the field.

The purpose of this research paper is to explore the determinants of patients' service choices between telephone consultation and text consultation in online health communities (OHCs).

This study utilized an empirical model based on the elaboration likelihood model and examined the effect of information, regarding service quality (the central route) and service price (the peripheral route), using online health consultation data from one of the largest OHCs in China.

The logistic regression results indicated that both physician- and patient-generated information can influence the patients' service choices; service price signals will lead patients to cheaper options. However, individual motivations, disease risk and consulting experience change a patients' information processing regarding central and peripheral cues.

Previous researchers have investigated the mechanism of patient behavior in OHCs; however, the researchers have not focused on the patients' choices regarding the multiple health services provided in OHCs. The findings of this study have theoretical and practical implications for future researchers, OHC designers and physicians.

In the quest to build a sense of human contact, e-retailers are increasingly depending on the scalability of chatbots to promote assistive dialogue during online shopping. Not much is known about the experiential value of customer interaction. This research proposes and evaluates a conceptual model for understanding the value perceptions emanating from the experiences of fashion shoppers utilising e-retail chatbots.

Data were collected using an online survey administered to 460 online panellists. Structural equation modelling was used to test the proposed research model.

Continued chatbot use intentions (CUIs) are influenced positively by perceived hedonic and utilitarian experiential value. Perceived social experiential value had a negative effect on shoppers’ continued intention to use the chatbot. Both perceived chatbot anthropomorphism and perceived chatbot intelligence positively and significantly affect shoppers’ experiential value while perceived chatbot risk yields a significantly negative effect.

By using conversational artificial intelligence chatbots, engagement at e-retail stores can be driven based on the user data and made more interactive.

The study introduces an e-retail chatbot model which asserts the power of selected chatbot attributes as catalysts of shoppers’ experiential value. Cumulatively, the model is a first-step approach providing a novel and balanced (both positive attributes and negative risks) view of chatbot continued use intentions.

As technology increasingly integrates into the restaurant industry, avatar servers present a promising avenue for promoting healthier dining habits. Grounded in the halo effect theory and social comparison theory, this study aims to delve into the influence of avatars' appearance, humor and persuasion on healthier choices and customer satisfaction.

This paper comprises three experimental studies. Study 1 manipulates avatar appearance (supermodel-looking vs normal-looking) to examine its effects on perceived attractiveness, warmth and relatability. These factors influence customer satisfaction and healthy food choices through the psychological mechanisms of social comparison and aspirational appeal. Studies 2 and 3 further refine this theoretical model by assessing the interplay of appearance with humor (presence vs absence) and persuasion (health-oriented vs beauty-oriented), respectively.

Results suggest that avatars resembling supermodels evoke stronger aspirational appeal and positive social comparison due to their attractiveness, thus bolstering healthier choices and customer satisfaction. Moreover, humor moderates the relationship between appearance and attractiveness, while persuasion moderates the effects of appearance on social comparison and aspirational appeal.

This research bridges the halo effect theory and social comparison theory, offering insights enriching the academic discourse on technology’s role in hospitality.

The findings provide actionable insights for managers, tech developers and health advocates.

Despite its significance, avatar design research in the hospitality sector has been overlooked. This study addresses this gap, offering a guideline for crafting attractive and persuasive avatars.