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The purpose of this paper is to form propositions about the relationship between top management team (TMT) heterogeneity and peer effects in investment decision-making and explore the mediating role of social learning processes.

To investigate the correlations between TMT heterogeneity and investment peer effects, we considered the TMT heterogeneity category, team process and contextual factors. With a sample of 8,467 firm-year observations from Chinese listed companies, we used the mean linear model and instrumental variable method to empirically examine their relationships. To identify the mediating role of social learning processes, we introduced a social learning model to find out the contextual factors influencing corporate social learning demands from three aspects and subsequently used comparative statics analysis to explore the variations in the main effect under these contextual factors.

For task-oriented heterogeneity (e.g. functional background, education and tenure heterogeneity), the opposite effects of information elaboration and social categorization processes make it a nonlinear multiplex correlation with investment peer effects. For relation-oriented heterogeneity (e.g. age and gender heterogeneity), the sole effect of social categorization processes leads to a negative linear correlation. Further, we identify the mediating role of social learning processes. In summary, we established a connection from the TMT heterogeneity, to information elaboration theory or social categorization theory, to social learning processes and ultimately to investment peer effects.

The results of this study provide a comprehensive perspective to predict the decision-making outcomes of team heterogeneity and contribute to heterogeneity research and practice.

Human-artificial intelligence (AI) collaboration, as a new form of cooperative interaction, has been applied in brainstorming activities. This study aims to explore the impact of performance-reward expectancy (PRE) and creative motivation (CM), along with the search for ideas in associative memory (SIAM) theory, on participants' AI collaboration intent (AICI).

The research employs an online survey targeting users with brainstorming experience. Structural equation modeling (SEM) is applied to analyze the data and validate the proposed hypotheses.

PRE shows a positive correlation with both intrinsic motivation (IM) and extrinsic motivation (EM). Furthermore, EM significantly and positively influences AICI, while IM has a negative significant effect. Additionally, the study confirms the mediating role of social inhibition (SI) between EM and AICI.

This study examines the intent to collaborate with AI in brainstorming, filling a gap in existing research. It integrates SIAM theory to analyze how performance rewards and creative motivation influence this intent. Findings reveal that performance-based rewards effectively motivate creative engagement, but high intrinsic motivation may lead to lower intent to collaborate due to autonomy concerns and trust issues. The study emphasizes the need for an open environment and offers practical insights for fostering AI collaboration while addressing challenges like social inhibition and resistance among participants.

This study provides practical insights for creative teams and individuals, emphasizing the importance of integrating AI in brainstorming to unlock its full potential. While performance rewards are effective, social inhibition may still lead participants to have negative attitudes toward AI collaboration. Creating an open and inclusive environment is essential. Additionally, the “individual + AI” model may provoke resistance among highly intrinsically motivated participants, necessitating training and improved AI transparency to build trust. Although focused on the Chinese market, the findings are applicable globally, highlighting the need to explore effective AI integration methods for innovation.

Our study found that PRE can positively influence intrinsic and extrinsic motivation in creative activities. This finding provides new evidence for our understanding of the role of performance-reward mechanisms in stimulating creativity. At the same time, we also explored how factors such as social inhibition and production blocking can affect individuals’ willingness to work with AI by influencing creativity motivation. This provides new insights to better understand how AI in teams affects individual psychology and team dynamics. These findings not only enrich our understanding of innovation and teamwork but also provide valuable references and directions for future research.

This study systematically examines the influence of PRE on CM within the context of AI-assisted brainstorming for the first time. It further investigates how SIAM theory regulates this process and ultimately shapes participants' willingness to engage in AI collaboration. The findings offer theoretical and practical guidance on designing incentive mechanisms to enhance engagement in AI-supported brainstorming and provide new perspectives on the application of AI in team innovation activities.

This paper aims to explore the impact of Sc element on the microstructure and corrosion properties of Mg-0.5Zn alloy.

Three kinds of Mg-0.5Zn-xSc (x = 0.1, 0.3 and 0.5 Wt.%) alloys were obtained, and the microstructure and corrosion properties were both analyzed.

As the Sc concentration increases, the corrosion resistance of the alloys initially improves and subsequently deteriorates. The trace addition of Sc can effectively reduce the grain size of Mg-Zn-Sc alloys and enhance the density of the corrosion products film. Consequently, an appropriate amount of Sc can reduce the corrosion rate of Mg-0.5Zn alloy.

However, the addition of Sc also introduces the second phase particles in the alloy, leading to galvanic corrosion, which adversely affects the corrosion resistance of Mg-0.5Zn alloy. Therefore, the amount of Sc added should be carefully controlled.

This paper explores what factors influence household textile washing behaviour and how these factors relate to greenhouse gas emissions during the textile use stage.

A questionnaire survey related to textile summer washing and care behavior was conducted among households in 16 administrative districts of Shanghai. This study used the modified Consumer Lifestyle Approach framework of the washing and care ecosystem. The research hypotheses were established by selecting related factors from four aspects: household demographic characteristics, economy and consumption characteristics, washing machines and detergents characteristics.

First, we have demonstrated how some course factors do not significantly affect greenhouse emissions. None of the demographics, detergent-related activities, economy and consumption constructs significantly affect greenhouse emissions. Second, we have identified that washing machine and related activities has a direct positive effect on GHG emissions. The washing machine is not only the de facto carrier of all washing activities but also the core of washing activities. Washing machine is crucial in reducing greenhouse emissions and adjusting consumer behaviors.

This paper conducts a study related to the washing and care behavior of households in Shanghai. The paper examines the factors influencing household washing behavior and the relationship between these factors and greenhouse gas emissions during the textile use phase.

The purpose of this study is to map the intellectual structure of sustainable finance and accounting (SFA) literature by identifying the influential aspects, main research streams and future research directions in SFA.

The results are obtained using bibliometric citation analysis and content analysis to conduct a bibliometric review of the intersection of sustainable finance and sustainable accounting using a sample of 795 articles published between 1991 and November 2023.

The most influential factors in the SFA literature are identified, highlighting three primary areas of research: corporate social responsibility and environmental disclosure; financial and economic performance; and regulations and standards.

SFA has experienced rapid development in recent years. The results identify the current research domain, guide potential future research directions, serve as a reference for SFA and provide inspiration to policymakers.

SFA typically encompasses sustainable corporate business practices and investments. This study contributes to broader social impacts by promoting improved corporate practices and sustainability.

This study expands on previous research on SFA. The authors identify significant aspects of the SFA literature, such as the most studied nations, leading journals, authors and trending publications. In addition, the authors provide an overview of the three major streams of the SFA literature and propose various potential future research directions, inspiring both academic research and policymaking.

The purpose of this study is to characterize the galvanic corrosion behavior of a simulated X80 pipeline steel welded joint (PSWJ) reconstructed by the wire beam electrode (WBE) and numerical simulation methods.

The galvanic corrosion of an X80 PSWJ was studied using WBE and numerical simulation methods. The microstructures of the coarse-grained heat affected zone, fine-grained heat affected zone and intercritical heat affected zone were simulated in X80 pipeline steel via Gleeble thermomechanical simulation processing.

Comparing the corrosion current density of coupled and isolated weld metal (WM), base metal (BM) and heat-affected zone (HAZ), the coupled WM exhibited a higher corrosion current density than isolated WM; the coupled BM and HAZ exhibited lower corrosion current densities than isolated BM and HAZ. The results exhibited that the maximum anodic galvanic current fitted the Gumbel distribution. Moreover, the numerical simulation results agreed well with the experimental data.

This study provides insight into corrosion evaluation of heterogeneous welded joints by a combination of experiment and simulation. The method of reconstruction of the welded joint has been proven to be a feasible approach for studying the corrosion behavior of the X80 PSWJ with high spatial resolution.

This paper examines the evolving landscape of accounting and finance research on the Chinese capital market, building on a previous study published at Abacus in 2018.

By incorporating data from 1999 to 2023, our analysis offers a detailed examination of shifts in academic focus, methodological advancements and thematic expansions over the last quarter-century.

The study reveals a substantial increase in accounting and finance publications related to the Chinese capital market in both Tier 1 and Asia-Pacific journals. The dynamic growth of the Chinese capital market during this period reflects profound economic transformations, characterized by technological innovations, sustainability commitments and regulatory reforms.

We conclude that the globally important Chinese capital market has attracted increasing academic attention, significantly advancing the understanding of accounting and finance research in China’s capital market.

The study aims to examine the impact of cultural dependency stemming from exchange rate fluctuations (specifically the US dollar) on herding behavior in the housing market across 31 provinces of Iran from Q2 2011 to Q1 2022, using a spatial econometrics approach. After confirming the presence of spatial effects, the Dynamic Spatial Durbin Panel Model with Generalized Common Effects (SDM-DPD(GCE)) was selected from various spatial models for these provinces.

The study examines the impact of cultural dependency stemming from exchange rate fluctuations (specifically the US dollar) on herding behavior in the housing market across 31 provinces of Iran from Q2 2011 to Q1 2022, using a spatial econometrics approach. After confirming the presence of spatial effects, the Dynamic Spatial Durbin Panel Model with Generalized Common Effects (SDM-DPD(GCE)) was selected from various spatial models for these provinces.

The model estimation results indicate that fluctuations in the free market exchange rate of the dollar significantly and positively impact the housing market in both target and neighboring regions, fostering herding behavior characterized by cultural dependency within the specified timeframe. Additionally, the study found that variables such as the inflation rate, population density index and the logarithm of stock market trading volume have significant and positive impacts on the housing market. Conversely, the variable representing the logarithm of the distance from the provincial capital, Tehran, significantly and negatively impacts the housing market across Iranian provinces.

Given that housing is a fundamental need for households, the dramatic price increases in this sector (for instance, a more than 42-fold increase from 2011–2021) have significantly impacted the welfare of Iranian families. Currently, considering the average housing price in Tehran is around 50 million Tomans, and the average income of worker and employee groups is 8 million Tomans (as of 2021), the time required to purchase a 100-square-meter house, even with a 30% savings rate and stable housing prices, is approximately 180 years. Moreover, the share of housing and rent expenses in household budgets now constitutes about 70%. The speculative behavior in this market is so acute that, despite 25 million of Iran’s 87 million population being homeless or renting, over 2.5 million vacant homes (12% of the total housing stock) are not used. Therefore, various financial behaviors and decisions affect Iran’s housing market. Herd behavior is triggered by the signal of national currency devaluation (with currency exchange rates increasing more than 26-fold between 2011 and 2021) and transactions at higher prices in certain areas (particularly in northern Tehran) (Statistical Center of Iran, 2023). Given the origins of housing price surges, a price increase in one area quickly spreads to other regions, resulting in herd behavior in those areas (spillover effect). Consequently, housing market spikes in Iran tend to follow episodes of currency devaluation. Therefore, considering the presented discussions, one might question whether factors other than economic ones (such as herd behavior influenced by dependence culture) play a role in the rising housing prices. Or, if behavioral factors were indeed contributing to the increase in housing prices, what could be the cause of this herd movement? Has the exchange rate, particularly fluctuations in the free market dollar rate, triggered herd behavior in the housing market across Iran’s provinces? Or has the proximity and neighborhood effect been influential in the increase or decrease in housing prices in the market?

The research attempts to estimate how the use of WeChat, the most popular mobile social networking application in contemporary China, affects rural household income.

Our materials are 4,552 rural samples from the Chinese General Social Survey, and a treatment effect (TE) model is employed to address the endogeneity of WeChat usage.

The results prove that WeChat usage has a statistically significant and positive correlation with rural household income. This conclusion remains robust after using alternative variables to replace the explanatory and dependent variables. Our research provides two channels through which WeChat usage boosts rural household income, namely, it can promote their off-farm employment and participation in investment activities.

Theoretically, the study provides several micro-evidences for understanding the impact of mobile social networks on rural household welfare. Further, our findings may shed light on the importance of digital technology applications in rural poverty alleviation for developing countries.

Clarifying the relationship between building carbon emissions and economic development can help sustainable construction in the field of construction, and this paper provides a constructive suggestion for ensuring economic development while realizing energy efficiency and emission reduction in buildings.

The study focuses on the building sector and firstly analyzes the complex relationship between economic agglomeration (EA) and carbon emission intensity (CEI) of commercial buildings at the city level through the spatial Durbin model and the threshold effect model, and then discusses the regional heterogeneity of this complex relationship from the dimensions of economic density and climate zones, respectively, and finally analyzes in depth the intrinsic influencing mechanism of EA on the CEI of commercial buildings.

The authors found that (1) there is an inverted U-shaped nonlinear relationship between EA and CEI of commercial buildings, and the inflection point of the EA level is 2.42, i.e. 1.125 bn RMB/km². (2) Significant regional differences exist in the inverted U-shaped relationship for cities with different economic densities and cities in different climate zones. (3) EA mainly affects the CEI of commercial buildings through externalities such as commercial building size and tertiary industry share, of which commercial building size is the most important factor hindering the decoupling of urban economic development from the CEI of commercial buildings.

This paper discusses for the first time the relationship between economic development and carbon emissions at the city level and clarifies the spatial differences and influencing mechanism of this relationship, providing a fuller reference for policymakers to develop differentiated building energy efficiency and emission reduction strategies.