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Considering the frequency of extreme events, enhancing the global financial system's stability has become crucial. This study aims to investigate the contagion effects of extreme risk events in the international commodity market on China's financial industry. It highlights the significance of comprehending the origins, severity and potential impacts of extreme risks within China's financial market.

This study uses the tail-event driven network risk (TENET) model to construct a tail risk spillover network between China's financial market and the international commodity market. Combining with the characteristics of the network, this study employs an autoregressive distributed lag (ARDL) model to examine the factors influencing systemic risks in China's financial market and to explore the early identification of indicators for systemic risks in China's financial market.

The research reveals a strong tail risk contagion effect between China's financial market and the international commodity market, with a more pronounced impact from the latter to the former. Industrial raw materials, food, metals, oils, livestock and textiles notably influence China's currency market. The systemic risk in China's financial market is driven by systemic risks in the international commodity market and network centrality and can be accurately predicted with the ARDL-error correction model (ECM) model. Based on these, Chinese regulatory authorities can establish a monitoring and early warning mechanism to promptly identify contagion signs, issue timely warnings and adjust regulatory measures.

This study provides new insights into predicting systemic risk in China's financial market by revealing the tail risk spillover network structure between China's financial and international commodity markets.

This study has incorporated competitiveness by considering it a significant factor behind determining as well as moderating industrial value added in the environmental Kuznets curve (EKC) framework. This study aims to explore the moderating role of competitiveness policy in EKC with an aim to promote business led sustainability at national level.

Considering the environmental deterioration aspect of industrialization, this study tests the existence of EKC for SAARC countries using the data from 1996 to 2021 using second-generation static panel data model.

Estimated results have validated that moderating effect is responsible for improving environmental sustainability in SAARC countries. Furthermore, population density is responsible for increasing while trade openness is responsible for decreasing carbon emissions.

Higher industrial activities are a symbol of upward-moving economic growth. But its other impact is in the form of environmental deterioration. However, the relationship between industrialization and environmental quality can be identified through EKC.

This paper aims to investigate the preparation of AlN and Al₂O₃, as well as the effect of nano-AlN and nano-Al₂O₃, on friction and wear properties of copper-steel clad plate immersed in the lubricants.

Nano-AlN or nano-Al₂O₃ (0.1, 0.2, 0.3, 0.4 and 0.5 Wt.%) functional fluids were prepared. Their tribological properties were tested by an MRS-10A four-ball friction tester and a ball-on-plate configuration, and scanning electron microscope observed the worn surface of the plate.

An increase in nano-AlN and Al₂O₃ content enhances the extreme pressure and anti-wear performance of the lubricant. The best performance is achieved at 0.5 Wt.% of nano-AlN and 0.3 Wt.% of nano-Al₂O₃ with PB of 834 N and 883 N, a coefficient of friction (COF) of approximately 0.07 and 0.06, respectively. Furthermore, the inclusion of nano-AlN and nano-Al₂O₃ particles in the lubricant enhances its extreme pressure performance and reduces wear, leading to decreased wear spot depth. The lubricating effect of the nano-Al₂O₃ lubricant on the surface of the copper-steel composite plate is slightly superior to that of the nano-AlN lubricant, with a COF reaching 0.07. Both lubricants effectively fill and lubricate the holes on the surface of the copper-steel composite plate.

AlN and Al₂O₃ as water-based lubricants have excellent lubrication performance and can reduce the COF. It can provide some reference for the practical application of nano-water-based lubricants.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0255/

The choice of a sales channel for fresh vegetables is an important decision a farmer can make. Typically, the farmers rely on their personal experience in directing the produce to a sales channel. This study examines how sales forecasting of fresh vegetables along multiple channels enables marginal and small-scale farmers to maximize their revenue by proportionately allocating the produce considering their short shelf life.

Machine learning models, namely long short-term memory (LSTM), convolution neural network (CNN) and traditional methods such as autoregressive integrated moving average (ARIMA) and weighted moving average (WMA) are developed and tested for demand forecasting of vegetables through three different channels, namely direct (Jaivasree), regulated (World market) and cooperative (Horticorp).

The results show that machine learning methods (LSTM/CNN) provide better forecasts for regulated (World market) and cooperative (Horticorp) channels, while traditional moving average yields a better result for direct (Jaivasree) channel where the sales volume is less as compared to the remaining two channels.

The price of vegetables is not considered as the government sets the base price for the vegetables.

The existing literature lacks models and approaches to predict the sales of fresh vegetables for marginal and small-scale farmers of developing economies like India. In this research, the authors forecast the sales of commonly used fresh vegetables for small-scale farmers of Kerala in India based on a set of 130 weekly time series data obtained from the Kerala Horticorp.

This study examines the relationship between streamers' product descriptions, customer comments and online sales and focuses on the moderating effect of streamer–viewer relationship strength.

Between June 2021 and April 2022, the structured data of 965 livestreaming and unstructured text data of 42,956,147 characters from two major live-streaming platforms were collected for the study. Text analysis and regression analysis methods were employed for data analysis.

First, the authors' analysis reveals an inverted U-shaped relationship between comment length and product sales. Notably, comment volume and comment emotion positively influence product sales. Furthermore, the semantic richness, emotion and readability of streamers' product descriptions also positively influence product sales. Secondly, the authors find that the strength of streamer–viewer relationship weakens the positive effects of comment volume and comment emotion without moderating the inverted U-shaped effect of comment length. Lastly, the strength of streamer–viewer relationship also diminishes the positive effects of emotion, semantics and readability of streamers' product descriptions on product sales.

This study is the first to concurrently examine the direct and interactive effects of user-generated content (UGC) and marketer-generated content (MGC) on consumer purchase behaviors in livestreaming e-commerce, offering a novel perspective on individual decision-making and cue utilization in the social retail context.

In this paper, the authors aim to study the effect of hydrogen on the pitting corrosion behavior of Incoloy 825, a commonly used material for heat exchanger tubes in hydrogenated heat exchangers.

The pitting initiation and propagation behaviors were investigated by electrochemical and chemical immersion experiments and observed and analyzed by scanning electron microscope and energy dispersive spectrometer methods.

The results show that hydrogen significantly affects the electrochemical behavior of Incoloy 825; the self-corrosion potential decreased from −197 mV before hydrogen charging to −263 mV, −270 mV and −657 mV after hydrogen charging, and the corrosion current density increased from 0.049 µA/cm² before hydrogen charging to 2.490 µA/cm², 2.560 µA/cm² and 2.780 µA/cm² after hydrogen charging. The pitting susceptibility of the material increases.

Hydrogen is enriched on the precipitate, and the pitting corrosion also initiates at that location. The synergistic effect of hydrogen and precipitate destroys the passive film on the metal surface and promotes pitting initiation.

This paper investigates the logistics management in the e-grocery sector. It contrasts the key issues faced by practitioners and the topics addressed in the academic literature, to identify potential misalignments between research and practice and propose avenues for future efforts.

This work adopts a twofold methodological approach. From an academic perspective, a systematic literature review (SLR) is performed to define the topics addressed so far by scholars when analysing e-grocery logistics. From a managerial perspective, a Delphi study is accomplished to identify the most significant issues faced by logistics practitioners in the e-grocery context and the associated significance.

The study develops a conceptual framework, identifying and mapping the 9 main logistics challenges for e-grocery along 4 clusters, in the light of a logistics-related revision of the SCOR model: distribution network design (area to be served, infrastructures), order fulfilment process (picking, order storage, consolidation, delivery), logistics-related choices from other domains (product range, stock-out management) and automation. These elements are discussed along three dimensions: criticalities, basic and advanced/automation-based solutions. Finally, the main gaps are identified – in terms of both under-investigated topics (order storage and stock-out management) and investigated topics needing further research (picking and automation) – and research questions and hypotheses are outlined.

This paper provides a threefold contribution, revolving around the developed framework. First, it investigates the state of the art about e-grocery logistics, classifying the addressed themes. Second, it explores the main issues e-grocery introduces for logistics practitioners. Third, it contrasts the two outcomes, identifying the misalignment between research and practice, and accordingly, proposing research directions.

Development of urban-rural integration is essential to fulfill sustainable development goals worldwide, and comprehension about urban-rural integration types has been highlighted as increasingly relevant for an efficient policy design. This paper aims to utilize an unsupervised machine learning approach to identify urban-rural integration typologies based on multidimensional metrics regarding economic, population and social integration in China.

The study introduces partitioning around medoids (PAM) for the identification of urban-rural integration typologies. PAM is a powerful tool for clustering multidimensional data. It identifies clusters by the representative objects called medoids and can be used with arbitrary distance, which help make clustering results more stable and less susceptible to outliers.

The study identifies four clusters: high-level urban-rural integration, urban-rural integration in transition, low-level urban-rural integration and early urban-rural integration in backward stage, showing different characteristics. Based on the clustering results, the study finds continuous improvement in urban-rural integration development in China which is reflected by the changes in the predominate type. However, the development still presents significant regional disparities which is characterized by leading in the east regions and lagging in the western and central regions. Besides, achievement in urban-rural integration varies significantly across provinces.

The machine learning techniques could identify urban-rural integration typologies in a multidimensional and objective way, and help formulate and implement targeted strategies and regionally adapted policies to boost urban-rural integration.

This is the first paper to use an unsupervised machine learning approach with PAM for the identification of urban-rural integration typologies from a multidimensional perspective. The authors confirm the advantages of this machine learning techniques in identifying urban-rural integration types, compared to a single indicator.

As China's only ruling party, will the Communist Party influence corporate decisions? The purpose of this paper is to examine whether and how the political ideology of CEOs affects the environmental responsibility of Chinese family firms and its effects on Corporate Environmental Responsibility (CER), in addition to a cohesive set of corporate governance contingency factors.

This paper uses a series of the Ordinary Least Squares (OLS) regression estimates and two-stage approach to examine four main hypotheses, based on 7,824 observations corresponding to 1,919 family firms in China from 2004 to 2015.

The study's findings show that CEOs imprinted with communist ideology are significantly positively related to CER in family firms, that the moderating role of ownership concentration is not significant, that board independence positively moderates the focal relationship and that CEO duality negatively moderates this relationship.

The paper expands the research of CEOs' political ideology to the ecological context, which are of significance to both theory and practice.

Given the growing preference for internet celebrity restaurants, it is crucial to explore how internet celebrity restaurants can maintain customer loyalty. Therefore, this study aims to examine the connections between brand cognition [emotion value, brand symbol (BS) and brand experience (BE)], brand resonance (BR) and revisit intention.

In this paper, the authors use a theoretical model to test the relationship between cognition and intention. A total of 366 volunteers were recruited to participate in this research. Hypothesis testing and a moderated mediation model were used to measure the results.

BR acts as a mediator in the interaction between emotion value, BS, experience and repurchase intention (RI). Surprisingly, the authors also discover that electronic word-of-mouth (e-WOM) acceptance negatively modifies the relationship between brand cognition and BR. Internet exposure (IE) helps consumers perceive BE and BSs more favorably.

Managers should be aware of how internet celebrity BR is built. Specifically, they can use cultural or emotional elements to maintain relationships with consumers. Furthermore, to lessen the negative consequences of e-WOM, managers should work to maintain positive WOM consistency.

The research advances our knowledge of RI in internet celebrity restaurants settings. This study pioneers an investigation of how brand cognition is related to RI through BR’s mediating effect. It enriches this research perspective of the emerging restaurant literature. By analyzing the boundary impact of internet transmission on resonance, it also advances the literature.