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The discourse traverses the intricate landscape of the metaverse, exploring its evolution, intricacies, and the symbiotic integration of artificial intelligence (AI). The metaverse, a virtual realm where individuals interact with digital entities, burgeons from a decades-old concept to a contemporary reality, captivating attention with its immersive potential. The union of AI and the metaverse heralds unprecedented possibilities and challenges. It fuels personalized recommendations, realistic avatars, intelligent Non-Playable Character (NPCs), and predictive analytics. However, concerns loom, spanning addiction, privacy, and security, as users immerse themselves in virtual realms, potentially neglecting real-world responsibilities and sharing sensitive information has been discussed in this chapter. The narrative further delves into the metaverse's anatomy, delineating its infrastructure, hardware, software, content creation, and commerce. The integration of AI into metaverse security epitomizes a confluence of innovation and growth. Balancing the potential benefits and risks, stakeholders embark on a journey toward a secure, immersive digital realm. The discourse advocates for proactive and responsible AI usage, encompassing transparency, accountability, and trustworthiness. Regulatory frameworks and standards emerge as essential guardrails, protecting user privacy and forestalling AI misuse.

We aim to determine the subsidy mechanism that can help participants of green supply chain financing (GSCF) maximize their benefits. Then, the optimal subsidy mechanism is designed to promote the development of GSCF.

To better understand the impact of different government subsidy measures on the optimal strategy for GSCF, we treat the motivation of the participants in the supply chain as a cost–benefit decision-making process. Then, a Stackelberg game model is developed that accounts for consumers' green preferences and government subsidies. In addition, the factors influencing supply chain members' earnings are analyzed via computational experiments.

(1) When consumers 2019 green sensitivity reaches a certain threshold relative to that of core enterprises (CEs), the optimal order quantity of these enterprises is greater when the government subsidizes small and medium-sized enterprises (SMEs). Conversely, the optimal order quantity is greater when CEs are subsidized. (2) When the government subsidizes CEs, financial institutions (FIs) and SMEs at the same time, these forms of subsidies have a cumulative effect on the supply chain, and the supply chain and all participants generate the highest earnings.

We analyze the benefits of each participant of GSCF under different government subsidies and then determine the optimal subsidy measures.

This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.

The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.

Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.

While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.

This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

Prior research has extensively examined how bringing technology from work into the non-work life domain creates conflict, yet the reverse pathway has rarely been studied. The purpose of this study is to bridge this gap and examine how the non-work use of smartphones in the workplace affects work–life conflict.

Drawing from three literature streams: technostress, work–life conflict and role boundary theory, the authors theorise on how limiting employees' ability to integrate the personal life domain into work, by means of technology use policy, contributes to stress and work–life conflict. To test this model, the authors employ a natural experiment in a company that changed its policy from fully restricting to open smartphone access for non-work purposes in the workplace. The insights gained from the experiment were explored further through qualitative interviews.

Work–life conflict declines when a ban on using smartphones for non-work purposes in the workplace is revoked. This study's results show that the relationship between smartphone use in the workplace and work–life conflict is mediated by sensed stress. Additionally, a post-hoc analysis reveals that work performance was unchanged when the smartphone ban was revoked.

First, this study advances the authors' understanding of how smartphone use policies in the workplace spill over to affect non-work life. Second, this work contributes to the technostress literature by revealing how, in specific situations, engagement with ICT can reduce distress and strain.

Access to good menstrual products is a human rights issue. Yet, there are rising health concerns of women regarding traditional–plastic sanitary napkins. The usage of these products has raised apprehensions towards the environmental pollution due to the plastic content. The solution for both these issues lies in the sustainable menstrual products. As menstruation is a public health issue, this study aims to identify the role of health beliefs on attitude of women towards sustainable menstrual products.

A survey was conducted for the purpose of data collection. The study uses questionnaire as a research instrument to gain an insight on women health beliefs towards sustainable menstrual products. Data was collected from 527 women respondents through convenience sampling. SPSS and Smart-PLS 4 were used for analysing the data.

Results of the study indicate that all the health belief perceptions had a significant impact on attitude. Women with more positive health belief and less negative health beliefs are the potential consumers for sustainable menstrual products.

To the best of the authors’ knowledge, this the first study that uses health belief model to explore and add to the menstruation literature especially sustainable menstrual hygiene.

Access to good menstrual products is a human rights concern. It is essential for normalising the life during menstrual cycles by making available all the resources necessary for managing them. Sustainable menstrual products are the novel products which can improve the women menstrual lifestyles and reduce the discomfort associated with menstruation. However, these products are not readily adopted by the women due to their perception resulting from various factors. Hence, this study aims to figure out these perceptions, which can promote and hinder the adoption of these products.

A survey was conducted for the purpose of data collection. The study uses questionnaire as a research instrument to gain an insight on women health beliefs towards sustainable menstrual products. Data was collected from the women respondents through convenience sampling. Structural equation modelling was applied to analyse the data.

The findings suggest that women with appropriate knowledge had a positive perception towards the sustainable menstrual products, further leading to positive behaviour. It was also noted that marketing efforts through cues to action had an impact on their behavioural patterns. The policymakers and the marketers can focus on such prospects to advocate the use of sustainable menstrual products so that healthcare is improved.

To the best of the authors’ knowledge, this is the first study that provides a comprehensive framework for analysing the impact of women health beliefs towards behaviour of sustainable menstrual products. The study adds meaningful insights to the sustainable menstrual literature.

This study provides a bibliometric analysis of business literature on the interlinks of fintech, climate risks, and sustainable finance. Fintech growth promotes national environmental efficiency and green finance by decreasing carbon intensity toward the net-zero target. National fintech growth moderates the impact of environmental, social, and governance investment on bank efficiency. Fintech mitigates the loan bankruptcy risk imposed by climate risks with strict mortgage lending decisions due to climate concerns. Fintech applications in banking systems optimize financing costs and increase the accessibility of money for firms, decreasing corporate greenwashing behaviors and promoting green innovation. The existing literature leaves room for future studies on fintech to promote climate finance with important policies for climate action toward Sustainable Development Goals.

Social commerce (s-commerce) offers community-based platforms that facilitate customer-to-customer interactions and the development of customers' social shopping-based experience. While prior research has addressed the role of customer engagement (CE) in boosting s-commerce-based sales and performance, insight into the effect of s-commerce attributes on CE remains tenuous. Addressing this gap, this study examines the role of specific s-commerce attributes (i.e. community, collaboration, interactivity and social dynamics) on CE, which is, in turn, proposed to impact customers' repurchase- and electronic word of mouth (eWOM) intention.

A web-based survey was deployed to target users of a popular s-commerce platform, Etsy.com. Partial least squares structural equation modeling (PLS-SEM) was, then, used to analyze the survey data collected from 390 users.

The results reveal that the four examined attributes positively affect CE. The findings also demonstrate CE's positive effect on customers' repurchase- and eWOM intention.

Though CE has been identified as a key s-commerce performance indicator, little remains known about the role of specific s-commerce attributes in driving CE, as, therefore, explored in this research. Specifically, the authors examine the role of s-commerce-based community, collaboration, interactivity and social dynamics on CE. Their analyses also corroborate that CE, in turn, drives customers' post-purchase (i.e. repurchase/eWOM) intention. Managerially, our findings can be used to develop more engaging s-commerce platforms.

With the development of Web information systems, steel e-commerce platforms have accumulated a large number of quality objection texts. These texts reflect consumer dissatisfaction with the dimensions, appearance and performance of steel products, providing valuable insights for product improvement and consumer decision-making. Currently, mainstream solutions rely on pre-trained models, but their performance on domain-specific data sets and few-shot data sets is not satisfactory. This paper aims to address these challenges by proposing more effective methods for improving model performance on these specialized data sets.

This paper presents a method on the basis of in-domain pre-training, bidirectional encoder representation from Transformers (BERT) and prompt learning. Specifically, a domain-specific unsupervised data set is introduced into the BERT model for in-domain pre-training, enabling the model to better understand specific language patterns in the steel e-commerce industry, enhancing the model’s generalization capability; the incorporation of prompt learning into the BERT model enhances attention to sentence context, improving classification performance on few-shot data sets.

Through experimental evaluation, this method demonstrates superior performance on the quality objection data set, achieving a Macro-F1 score of 93.32%. Additionally, ablation experiments further validate the significant advantages of in-domain pre-training and prompt learning in enhancing model performance.

This study clearly demonstrates the value of the new method in improving the classification of quality objection texts for steel products. The findings of this study offer practical insights for product improvement in the steel industry and provide new directions for future research on few-shot learning and domain-specific models, with potential applications in other fields.

Megaprojects contribute greatly to China’s socio-economic development. However, the diverse stakeholders of megaprojects tend to be prone to conflict and plunge the project into lower performance than expected. This study aims to investigate (1) the optimal supervision mechanism under direct supervision of the owner and (2) the optimal indirect supervision mechanism of the owner, with involvement of integrated construction consultancy (ICC) in supervision and considerations into the impact of collusion between the ICC and the general contractor on supervision efficiency.

This study applies the principal–agent theory and game theory to design effective incentive and punishment mechanisms for direct and indirect supervision by the owner. Through the theoretical framework of game theory, it analyses the strategic interactions between the owner, the ICC and the general contractor, and evaluates the effectiveness of various supervision mechanisms based on Nash equilibrium theory.

The results showed adopting the direct supervision mechanism when the probability of the general contractor’s opportunistic behavior being detected was higher or the occurrence had a greater impact on the owner. However, the increase in supervision cost would reduce the motivation of owner’s direct supervision and prompt the owner to choose the indirect supervision mechanism. Additionally, the indirect supervision mechanism was more likely to inhibit the collusion between the ICC and the general contractor. Thus, increasing penalties for ICC would result in improved supervision of ICC, thereby improving the benefits to the owner.

This study examines the dynamic interaction between the owner, the general contractor and the ICC. The study establishes a game tree and game matrix based on principal–agent theory, and analyses direct and indirect supervision models to determine the Nash equilibrium and optimal supervision strategy that is in the best interest of the owner. The study advocates for improved management and supervision mechanisms and argues that tailored supervision mechanisms can significantly improve the performance of megaprojects, which is well validated in megaprojects in China.