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Infrastructure financialization plays a critical role in infrastructure development and urban growth around the world. However, on the one hand, the existing research on the infrastructure financialization focuses on qualitative and lacks quantitative country-specific studies. On the other hand, the spatial heterogeneity and influencing factors of infrastructure financialization are ignored. This study takes China as a typical case to identify and analyze the spatial characteristics, development process and impact factors of infrastructure financialization.

To assess the development and characteristics of infrastructure financialization in China, this study constructs an evaluation index of infrastructure financialization based on the infrastructure financialization ratio (IFR). This study then analyzes the evolution process and spatial pattern of China's infrastructure financialization through the spatial analysis method. Furthermore, this study identifies and quantitatively analyzes the influencing factors of infrastructure financialization based on the spatial Dubin model. Finally, this study offers a policy suggestion as a governance response.

The results demonstrate that infrastructure financialization effectively promotes the development of infrastructure in China. Second, there are significant spatial differences in China’s infrastructure financialization. Third, many factors affect infrastructure financialization, with government participation having the greatest impact. In addition, over-financialization of infrastructure has the potential to lead to government debt risks, which is a critical challenge the Chinese Government must address. Finally, this study suggests that infrastructure financialization requires more detailed, tailored,and place-specific policy interventions by the government.

This study not only contributes to enriching the knowledge body of global financialization theory but also helps optimize infrastructure investment and financing policies in China and provides peer reference for other developing countries.

This study proposes a novel method utilising a speech-word pair bipartite network to examine the correlation structure between members of parliament (MPs) in the context of the UK- China relationship.

We construct MP-word pair bipartite networks based on the co-occurrence relationship between MPs and words in their speech content. These networks are then mapped into monopartite MPs correlation networks. Additionally, the study calculates correlation network indicators and identifies MP communities and factions to determine the characteristics of MPs and their interrelation in the UK-China relationship. This includes insights into the distribution of key MPs, their correlation structure and the evolution and development trends of MP factions.

Analysis of the parliamentary speeches on China-related affairs in the British Parliament from 2011 to 2020 reveals that the distribution and interrelationship of MPs engaged in UK-China affairs are centralised and discrete, with a few core MPs playing an integral role in the UK-China relationship. Among them, MPs such as Lord Ahmad of Wimbledon, David Cameron, Lord Hunt of Chesterton and Lord Howell of Guildford formed factions with significant differences; however, the continuity of their evolution exhibits unstableness. The core MP factions, such as those led by Lord Ahmad of Wimbledon and David Cameron, have achieved a level of maturity and exert significant influence.

The research has several limitations that warrant acknowledgement. First, we mapped the MP-word pair bipartite network into the MP correlation network for analysis without directly analysing the structure of MPs based on the bipartite network. In future studies, we aim to explore various types of analysis based on the proposed bipartite networks to provide more comprehensive and accurate references for studying UK-China relations. In addition, we seek to incorporate semantic-level analyses, such as sentiment analysis of MPs, into the MP-word -pair bipartite networks for in-depth analysis. Second, the interpretations of MP structures in the UK-China relationship in this study are limited. Consequently, expertise in UK-China relations should be incorporated to enhance the study and provide more practical recommendations.

Firstly, the findings can contribute to an objective understanding of the characteristics and connotations of UK-China relations, thereby informing adjustments of focus accordingly. The identification of the main factions in the UK-China relationship emphasises the imperative for governments to pay greater attention to these MPs’ speeches and social relationships. Secondly, examining the evolution and development of MP factions aids in identifying a country’s diplomatic focus during different periods. This can assist governments in responding promptly to relevant issues and contribute to the formulation of effective foreign policies.

First, this study expands the research methodology of parliamentary debates analysis in previous studies. To the best of our knowledge, we are the first to study the UK-China relationship through the MP-word-pair bipartite network. This outcome inspires future researchers to apply various knowledge networks in the LIS field to elucidate deeper characteristics and connotations of UK-China relations. Second, this study provides a novel perspective for UK-China relationship analysis, which deepens the research object from keywords to MPs. This finding may offer important implications for researchers to further study the role of MPs in the UK-China relationship.

This study proposes a novel scheme for analysing the correlation structure between MPs based on bipartite networks. This approach offers insights into the development and evolving dynamics of MPs.

The global prevalence of vaccine misinformation has underscored the crucial necessity to combat false information and explore innovative solutions like chatbots. These artificial intelligence (AI)-powered tools play a pivotal role in disseminating accurate information and mitigating the adverse effects of misinformation. This study aimed to investigate what factors motivated users to combat vaccine misinformation using chatbot tools, and their active communication actions and anti-misinformation behaviors.

Researchers surveyed 612 chatbot users in the United States and utilized structural equation modeling for data analysis.

The findings of this study revealed that both situational and gratification motivations of chatbot users significantly contributed to three essential types of communicative actions: information-seeking, forwarding and forfending. Meanwhile, the data demonstrated that except for information forfending, both information-seeking and forwarding communicative actions could enhance user engagement with anti-misinformation behavior.

The originality of this study lies in its integration of two key motivational frameworks – gratification and situational motivations – within the context of AI-driven tools like chatbots, particularly in combating misinformation. While previous research has explored the use of chatbots or the role of situational motivations in communication separately, this study uniquely combines these concepts to enhance the situational theory of problem-solving (STOPS) model and uses and gratifications (U&G) theory. Additionally, the practical implications for chatbot design and communication strategies targeted at misinformation are a significant contribution, demonstrating how motivation-driven interactions can be used to improve user engagement and public health outcomes.

The rural revitalization is a significant indicator of social development in China. Despite the crucial role that local governments play in coordinating urban and rural development, rural areas continue to face limited investment opportunities and scholarship on the impact of local government competition on urban–rural income disparity is scant, especially lacking empirical research from the spatial perspective. The authors contribute to closing the knowledge gap by exploring the spatial spillover effect of local government competition using the sample of China.

This study evaluated the urban–rural income inequality using the Theil index and constructed the spatial Durbin model to analyze the data by the method of maximum likelihood estimation.

The findings from the Theil index showed that the income gap is significant between city and countryside in China, with being wider in the western region compared to the eastern region. The evidence from the spatial Durbin model revealed that, in the same province, local government competition widens this gap, while competition from neighboring regions prompts a reduction in this gap in this province.

The authors recommended some policy implications, such as supporting the development in underdeveloped regions and improving the promotion evaluation criteria for local officials.

This article is original in exploring the spatial effects of local government competition on the urban–rural income gap. In addition, instead of using a single proxy to measure the urban–rural income gap, in the current paper, the Theil index for evaluating the disparity is incorporated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2023-0582

To predict the price of battery-grade lithium carbonate accurately and provide proper guidance to investors, a method called MFTBGAM is proposed in this study. This method integrates textual and numerical information using TCN-BiGRU–Attention.

The Word2Vec model is initially employed to process the gathered textual data concerning battery-grade lithium carbonate. Subsequently, a dual-channel text-numerical extraction model, integrating TCN and BiGRU, is constructed to extract textual and numerical features separately. Following this, the attention mechanism is applied to extract fusion features from the textual and numerical data. Finally, the market price prediction results for battery-grade lithium carbonate are calculated and outputted using the fully connected layer.

Experiments in this study are carried out using datasets consisting of news and investor commentary. The findings reveal that the MFTBGAM model exhibits superior performance compared to alternative models, showing its efficacy in precisely forecasting the future market price of battery-grade lithium carbonate.

The dataset analyzed in this study spans from 2020 to 2023, and thus, the forecast results are specifically relevant to this timeframe. Altering the sample data would necessitate repetition of the experimental process, resulting in different outcomes. Furthermore, recognizing that raw data might include noise and irrelevant information, future endeavors will explore efficient data preprocessing techniques to mitigate such issues, thereby enhancing the model’s predictive capabilities in long-term forecasting tasks.

The price prediction model serves as a valuable tool for investors in the battery-grade lithium carbonate industry, facilitating informed investment decisions. By using the results of price prediction, investors can discern opportune moments for investment. Moreover, this study utilizes two distinct types of text information – news and investor comments – as independent sources of textual data input. This approach provides investors with a more precise and comprehensive understanding of market dynamics.

We propose a novel price prediction method based on TCN-BiGRU Attention for “text-numerical” information fusion. We separately use two types of textual information, news and investor comments, for prediction to enhance the model's effectiveness and generalization ability. Additionally, we utilize news datasets including both titles and content to improve the accuracy of battery-grade lithium carbonate market price predictions.

Environmental, social and governance (ESG) greenwashing is a form of social responsibility response that appears compliant but is substantively oppositional. As an abnormal social behavior, existing research has rarely focused on the deep-seated strategic logic behind ESG greenwashing. Business strategy emerges as the linchpin for companies undertaking a series of decision-making actions. Consequently, this research seeks to provide new insights into the strategic drivers behind corporate greenwashing and the role of institutional investors in mitigating these practices.

The research utilizes empirical analysis based on data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2010 to 2022. ESG performance data is sourced from the Bloomberg ESG Disclosure Ratings and Thomson Reuters’ Asset4 database. Business strategy is assessed using six key indicators. The study employs institutional theory as the analytical framework, examining the impact of business strategy on ESG greenwashing and investigating the internal mechanisms driving these behaviors.

The study finds that compared with defender strategies, prospector strategies are more likely to lead to ESG greenwashing behavior. Specifically, aggressive business strategies tend to facilitate corporate ESG greenwashing. Mechanism analysis indicates that, compared to defenders, prospectors induce ESG greenwashing by increasing information asymmetry (reputation effect) and being constrained by financing limitations (profit-seeking effect). From an external governance perspective, this study finds that institutional investor ownership can mitigate the impact of business strategy on ESG greenwashing. Furthermore, additional research confirms that in heavily polluting industries, the positive effect of business strategy on ESG greenwashing is more pronounced, whereas implementing the Environmental Protection Tax Law curtails the impact of business strategy on ESG greenwashing.

This study analyzes the role of business strategy in ESG greenwashing, particularly in the context of emerging economies such as China, contributing uniquely to the literature on corporate decision-making and green management. The research extends the application of institutional theory to the field of corporate environmental strategy and introduces the concepts of reputation and profit-seeking effects, offering fresh perspectives on understanding ESG greenwashing behavior. It also provides empirical evidence of the governance role of institutional investors in addressing managerial opportunism related to ESG greenwashing, enriching the existing theoretical framework. Finally, the study highlights the need to establish stronger institutional and managerial mechanisms to effectively tackle corporate greenwashing, offering valuable insights for future research and practice.

The increased use of Information Systems (IS) as a working tool for employees increases the number of accounts and passwords required. Despite being more aware of password entropy, users still often participate in deviant password behaviors, known as “password workarounds” or “shadow security.” These deviant password behaviors can put individuals and organizations at risk, resulting in a data breach. This paper aims to engage IS users and Subject Matter Experts (SMEs), focused on designing, developing and empirically validating the Password Workaround Cybersecurity Risk Taxonomy (PaWoCyRiT) – a 2x2 taxonomy constructed by aggregated scores of perceived cybersecurity risks from Password Workarounds (PWWAs) techniques and their usage frequency.

This research study was a developmental design conducted in three phases using qualitative and quantitative methods: (1) A set of 10 PWWAs that were identified from the literature were validated by SMEs along with their perspectives on the PWWAs usage and risk for data breach; (2) A pilot study was conducted to ensure reliability and validity and identify if any measurement issues would have hindered the results and (3) The main study data collection was conducted with a large group of IS users, where also they reported on coworkers' engagement frequencies related to the PWWAs.

The results indicate that statistically significant differences were found between SMEs and IS users in their aggregated perceptions of risks of the PWWAs in causing a data breach, with IS users perceiving higher risks. Engagement patterns varied between the two groups, as well as factors like years of IS experience, gender and job level had statistically significant differences among groups.

The PaWoCyRiT taxonomy that the we have developed and empirically validated is a handy tool for organizational cyber risk officers. The taxonomy provides organizations with a quantifiable means to assess and ultimately mitigate cybersecurity risks.

Passwords have been used for a long time to grant controlled access to classified spaces, electronics, networks and more. However, the dramatic increase in user accounts over the past few decades has exposed the realization that technological measures alone cannot ensure a high level of IS security; this leaves the end-users holding a critical role in protecting their organization and personal information. Thus, the taxonomy that the authors have developed and empirically validated provides broader implications for society, as it assists organizations in all industries with the ability to mitigate the risks of data breaches that can result from PWWAs.

The taxonomy the we have developed and validated, the PaWoCyRiT, provides organizations with insights into password-related risks and behaviors that may lead to data breaches.

Fashion marketers are adopting attractive virtual personalities to replace human influencers on social media, but the impact of consumer bias against virtual influencer acceptance is not fully understood. Drawing upon match-up hypothesis, attribution theory and speciesism against artificial intelligence (AI), this research investigates how speciesism shapes the influencer-product attractiveness transference in AI-powered influencer marketing for fashion products.

Three studies were conducted (N = 1,385) to test the influencer-product attractiveness transference, the moderating role of influencer type and the moderated moderating role of speciesism against AI.

Our studies validated the attractiveness transference and revealed that influencers’ attractiveness promotes purchase intention via perceived product attractiveness. The adoption of virtual (vs human) influencers weakens the attractiveness transference and attenuates the mediating effect. Low speciesism boosts the effectiveness of virtual influencers, such that attractiveness transference disappears only when high-speciesism consumers react to virtual influencers.

Our findings clarify how influencers’ physical appearance, AI application and speciesism together impact interactive fashion marketing, offering practical insights into successful influencer strategies on social media.

Explore the impact of Party organization embedding on firm green governance.

The regression analysis method.

The findings show that Party organization embedding significantly enhances the green governance effects of firms, with this effect being more pronounced in environments with high-quality internal control. Moreover, the study reveals that Party organization embedding facilitates green governance through mechanisms such as reducing agency costs and optimizing management decisions. Agency costs have a negative transmission effect, while management decisions have a positive transmission effect, with the quality of internal control playing a crucial moderating role.

Most existing studies on firm green governance have focused on aspects such as the heterogeneity of management teams (Liu, 2019; Wu et al., 2019), executive green cognition (Fineman and Clarke, 1996; Huang and Wei, 2023), organizational structure and the involvement of controlling families (Bertrand and Schoar, 2006; Symeou et al., 2019), with limited attention to the unique role of Party organizations’ incentive and restraint mechanisms, supervisory power and management functions in firm green governance. Additionally, while scholars have examined the impact of political embedding in firms, including Party organization embedding as a specific form of political embedding, and find that it affects various aspects of business performance (Chang and Wong, 2004; Gu and Yang, 2023), governance quality (Li et al., 2020; Huang and Yang, 2024), agency costs (Qian, 2000; Wang and Ma, 2014), excessive management compensation (Chang and Wong, 2004; Chen et al., 2014), social externalities and audit needs (Faccio, 2006; Cheng, 2022), there is still insufficient discussion on how Party organization embedding promotes firm green governance. Particularly in the context of China’s unique system and using Chinese data, there is a need for more in-depth research on the impact of Party organization embedding on firm green governance. This paper addresses this research gap by empirical analysis.

Overall, this study has significant theoretical and practical implications. Theoretically, it enriches the literature on Party organization embedding and firm green governance, filling a gap in the intersection research of firm governance and green governance. Practically, on the one hand, this paper’s findings demonstrate that the involvement of Party organizations in firm governance plays a significant role in enhancing green governance. This supports the modernization of firm governance in China, establishes a micro-level foundation for achieving the strategic goals of “carbon peaking and carbon neutrality” and offers empirically-backed insights into green transformation for policymakers. The research also provides practical policy recommendations for strengthening Party building efforts within firms and optimizing government-business relations, thereby facilitating the deep integration of Party building with business operations. On the other hand, this study highlights that the unique feature of China’s corporate governance system, Party organization embedding, can effectively enhance green governance. This offers empirical support for leveraging the strengths of China’s firm governance model and provides valuable governance strategies for firms in other countries and regions to improve their green governance practices.

This study’s social implications are significant as it highlights the broader societal benefits that arise from integrating Party organization involvement into firm governance structures, especially within the context of green governance. By improving the green governance practices of firms, Party organization embedding helps to address pressing environmental issues such as pollution, carbon emissions and resource depletion, which ultimately contributes to healthier living environments and a more sustainable society. The emphasis on green governance supports China’s national strategy for sustainable development and demonstrates a governance model that balances economic growth with environmental stewardship. Additionally, the study underscores the role of Party organizations in fostering social responsibility, equity and cohesion by ensuring that firm decision-making aligns with both economic and social welfare goals. This model of governance provides a framework that can serve as a reference for other countries and regions looking to enhance environmental protection efforts while maintaining social stability and economic progress.

This study offers original insights by exploring the distinctive role of Party organization embedding in enhancing firm green governance within the unique context of China’s political and economic systems. Unlike previous research, which has primarily focused on conventional governance structures, this paper delves into the underexplored area of how Party organizations influence firm-level green governance. By examining the direct and indirect effects of Party organization embedding, this study expands current understanding of corporate governance models that integrate political structures, providing a novel perspective on how firms can achieve both economic and environmental objectives. The findings not only contribute to the literature on green governance but also present a valuable model for emerging economies that are pursuing sustainable development. This research thus provides a meaningful addition to the dialogue on corporate governance innovation and environmental responsibility.

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.