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Green innovation in supply chains is crucial for socioeconomic development and stability. Factors that influence collaborative green innovation in the supply chain are complex and diverse. Exploring the main influencing factors and their mechanisms is essential for promoting collaborative green innovation in supply chains. Therefore, this study analyzes how upstream and downstream enterprises in the supply chain collaborate to develop green technological innovations, thereby providing a theoretical basis for improving the overall efficiency of the supply chain and advancing green innovation technology.

Based on evolutionary game theory, this study divides operational scenarios into pure market and government-regulated operations, thereby constructing collaborative green innovation relationships in different scenarios. Through evolutionary analysis of various entities in different operational scenarios, combined with numerical simulation analysis, we compared the evolutionary stability of collaborative green innovation behavior in supply chains with and without government regulation.

Under pure market mechanisms, the higher the green innovation capability, the stronger the willingness of various entities to collaborate in green innovation. However, under government regulation, a decrease in green innovation capability increases the willingness to collaborate with various entities. Environmental tax rates and green subsidy levels promote collaborative innovation in the short term but inhibit collaborative innovation in the long term, indicating that policy orientation has a short-term impact. Additionally, the greater the penalty for collaborative innovation breaches, the stronger the intention to engage in collaborative green innovation in the supply chain.

We introduce the factors influencing green innovation capability and social benefits in the study of the innovation behavior of upstream and downstream enterprises, expanding the research field of collaborative innovation in the supply chain. By comparing the collaborative innovation behavior of various entities in the supply chain under a pure market scenario and government regulations, this study provides a new perspective for analyzing the impact of corresponding government policies on the green innovation capability of upstream and downstream enterprises, enriching theoretical research on green innovation in the supply chain to some extent.

This study aims to investigate the corrosion inhibitor effect of migrating corrosion inhibitor (MCI) on Q235 steel in high alkaline environment under cathodic polarization.

This study investigated the electrochemical characteristics of Q235 steel with and without MCI by polarization curve and electrochemical impedance spectroscopy. Besides, the surface composition of Q235 steel under different environments was analyzed by X-ray photoelectron spectroscopy. In addition, the migration characteristic of MCI and the adsorption behavior of MCI under cathodic polarization were studied using Raman spectroscopy.

Diethanolamine (DEA) and N, N-dimethylethanolamine (DMEA) can inhibit the increase of Fe(II) in the oxide film of Q235 steel under cathodic polarization. The adsorption stability of DMEA film was higher under cathodic polarization potential, showing a higher corrosion inhibition ability. The corrosion inhibition mechanism of DEA and DMEA under cathodic polarization potential was proposed.

The MCI has a broad application prospect in the repair of damaged reinforced concrete due to its unique migratory characteristics. The interaction between MCIs, rebar and concrete with different compositions has been studied, but the passivation behavior of the steel interface in the presence of both the migrating electric field and corrosion inhibitors has been neglected. And it was investigated in this paper.

Green innovation, particularly in manufacturing firms, is one of the most advocated methods to curb the effects of climate change. This study aims to investigate the impact of the integration of green customers and suppliers on the green innovation performance of food manufacturing firms in Pakistan. The institutional and resource-based view theories determine the moderating role of regulatory pressure and the mediating role of green knowledge integration capability (GKIC).

Data was collected from 511 middle management-level employees of food manufacturing firms in Pakistan. The questionnaire was tested for reliability and validity. Hierarchical regression is used to test the proposed hypothesis.

A marginal improvement in integration with green customers can increase the green innovation performance (GIP) of a firm by 23.6%. Green supplier integration can improve the GIP by 14.2%, whereas the GKIC mediates the relationship between Green Customers Integration (GCI) and GIP but not for green suppliers integration (GSI). The moderating effect of regulatory pressure was significant for the relationship between GCI and GIP but insignificant for GSI.

Food manufacturing accounts for approximately 16% of global green house gases (GHG) emissions. Sustainable development goals (SDGs) cannot be achieved without a significant decrease in GHG emissions by food manufacturing companies. Therefore, it is crucial to investigate firms' green innovation performance in this sector. The findings of this study can help policymakers develop policies for achieving SDGs.

This study aims to provide insights into wine consumers in Hong Kong and Mainland China, and the diversity of their behavior, by investigating their motivations, consumption and purchasing behavior and segmenting the sample based on wine knowledge and frequency of consumption to identify three distinct clusters that are then profiled.

Data was collected through an online survey of wine consumers of ethnic Chinese origin residing in Mainland China and Hong Kong, using two methods of recruitment (a convenience sample from wine-related databases, a panel survey undertaken by an international market research firm). The final sample comprised 477 respondents, about equally divided among Mainland Chinese and Hong Kong wine consumers. Analysis of the data using the Statistical Package for Social Sciences for Windows (SPSS) 29 included a series of descriptive analyses, followed by a two-step cluster analysis using an Euclidean distance method.

Wine knowledge and frequency of wine consumption were relatively higher within the sample as a whole than previous studies, but three distinct consumer segments are evident. There is evidence of a democratization of wine consumption, and a greater focus on wine consumption for enjoyment in relaxed and informal settings. rather than consumption driven by health and status, occurring primarily in commercial or business settings. The dominance of red wine in this market is also declining, and intrinsic cues – specifically, prior experience of the wine – are becoming important in wine purchase decisions.

This study uses a broad sample of wine consumers to identify trends in wine consumption patterns and motivations, and purchase decision-making behavior, of Mainland Chinese and Hong Kong wine consumers. The identification of three clusters of wine consumers offers both points of comparison with previous segmentation research globally and within China, whilst also identifying scope for further research.

This study examines the effect of supply chain (SC) learning (i.e. supplier and customer learnings) on green innovation (i.e. green product and process innovations) and investigates the moderating role of green transformational leadership in the SC learning-green innovation linkage in the construction industry.

Data are gathered from construction firms in Vietnam by a questionnaire survey. Hypotheses of the study framework are tested by hierarchical regression analysis.

Both supplier and customer learnings have positive effects on green innovation (both green process and product innovations). Furthermore, green transformational leadership moderates the linkage between supplier learning and green innovation but does not moderate the linkage between customer learning and green innovation.

Construction firms need to constantly develop capabilities of SC learning for promoting their green innovation.

The present study is one of the first attempts in construction that investigates the importance of SC learning to achieving green innovation as well as the role of green transformational leadership for strengthening the effect of green learning on green innovation.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

This paper aims to identify the impact of the new ambient air quality standards (AAQS) on firm innovation.

Taking listed companies from 2009 to 2014 as the research object, the authors focus on the quasinatural experiment of the pilot policy of the new AAQS and apply the time-varying difference-in-differences (DID) method to conduct the empirical test.

The authors find that the new AAQS has a negative effect on firm innovation, and this negative impact may be caused by the increased environmental expenditures following the implementation of the new AAQS. Furthermore, the authors find that firm profitability and state ownership weaken this negative effect, but the effect of the degree of industrial pollution is statistically insignificant.

The study is an initial effort to explore the causal effect of the new AAQS on firm innovation. This study enriches the literature on the impact of environmental regulations on firm innovation and provides some reference value for the formulation of environmental regulation policies in developing countries.

The purpose of this research is to address the existing gap in the study of construction and demolition waste (C&DW) by focusing on its impact on human health throughout the entire life cycle. And this research provides a comprehensive assessment model that incorporates the release of gaseous pollutants and particulate matter during the whole life cycle of C&DW, thereby contributing to a more holistic understanding of its impact on human health.

The research was conducted in two stages. Firstly, the quantitative model framework of pollutants emitted by C&DW was established. Three types of pollutants were considered, namely nitrogen dioxide (NO₂), sulfur dioxide (SO₂) and inhalable particulate matter (PM10). Second, disability-adjusted life year (DALY) and willingness to pay (WTP) assessments were used to provide a monetary quantified health impact for pollutants released by C&DW.

The results show that the WTP value of PM10 is the highest among all pollutants and 8.68E+07 dollars/a, while the WTP value in the disposal stage accounts for the largest proportion compared to the generation and transportation stage. These findings emphasize the importance of PM10 and C&DW treatment stage for pollutant treatment.

The results of this study are of great significance for the management department to optimize the construction management scheme to reduce the total amount of pollutants produced by C&DW and its harm to human health. Meanwhile, this study fills the gap in existing research on the impact assessment of C&DW on human health throughout the whole life cycle, and provides reference and basis for future research and policy formulation.

Social recommendation has been recognized as a kind of e-commerce with large potential, but how social recommendations influence consumer decisions is still unclear. This paper aims to investigate how recommendations from different social ties influence consumers’ purchase intentions through both behavior and brain activity.

Utilizing behavioral (N = 70) and electroencephalogram (EEG) (N = 49) experiments, this study explored participants’ behavior and brain responses after being recommended by different social ties. The data were analyzed using statistical inference and event-related potential (ERP) analysis.

Behavioral results show that social tie strength positively impacts purchase intention, which can be fitted by a logarithmic model. Moreover, recommender-to-customer similarity and product affect mediate the effect of tie strength on purchase intention serially. EEG findings show that recommendations from weak tie strength elicit larger N100, N200 and P300 amplitudes than those from strong tie strength. These results imply that weak tie strength may motivate individuals to recruit more mental resources in social recommendation, including unconscious processing of consumer attention and conscious processing of cognitive conflict and negative emotion.

This study considers the effects of continuous social ties on purchase intention and models them mathematically, exploring the intrinsic mechanisms by which strong and weak ties influence purchase intentions through recommender-to-customer similarity and product affect, contributing to the applications of the stimulus-organism-response (SOR) model in the field of social recommendation. Furthermore, our study adopting EEG techniques bridges the gap of relying solely on self-report by providing an avenue to obtain relatively objective findings about the consumers’ early-occurred (unconscious) attentional responses and late-occurred (conscious) cognitive and emotional responses in purchase decisions.

Work and leisure, as important activity domains, play important roles in the lives of individuals. However, most previous studies focused on only the interference and negative effects of work on leisure, with little focus on the facilitation of work and the positive effects of work on leisure. In view of the shortcomings of previous studies, this study focuses on the facilitation effect of work on leisure and its impact on individual psychology. This study aims to explore the relationship between work–leisure facilitation (WLF) and turnover intention and the role of positive emotions and perceived supervisor support in this relationship.

In this study, the method of multipoint data collection was adopted to measure the subjects; 180 employees were sampled for 5 consecutive working days, and a multilevel structural equation model was established for analysis.

The results show that WLF is negatively related to turnover intention, and positive emotions play a mediating role in this relationship. Perceived supervisor support significantly positively moderates not only the relationship between WLF and positive emotions but also the indirect effect of WLF on turnover intention through positive emotions.

Based on affective events theory, this study explored the relationship between WLF and turnover intention and its mechanism by using the daily diary sampling method for the first time, to the best of the authors’ knowledge. The results not only deepen the understanding of affective events theory but also provide management suggestions for reducing employees’ turnover intentions.