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Psychosocial factors have received increasing attention regarding significantly influencing safety in the construction industry. This research attempts to comprehensively summarize psychosocial factors related to safety performance of construction workers. In the context of coronavirus disease 2019, some typical psychosocial factors are selected to further analyze their influence mechanism of safety performance.

First, a literature review process was conducted to identify and summarize relevant psychosocial factors. Then, considering the impact of the epidemic, hypotheses on the relationship between six selected psychosocial factors (i.e. work stress, role ambiguity, work–family conflict, autonomy, social support and interpersonal conflict) and safety performance were proposed, and a hypothetical model was developed based on job demands-resources theory. Finally, a meta-analysis was used to examine these hypotheses and the model.

The results showed these psychosocial factors indirectly influenced workers’ safety performance by impacting on their occupational psychology condition (i.e. burnout and engagement). Work stress, role ambiguity, work–family conflict and interpersonal conflict were negatively related to safety performance by promoting burnout and affecting engagement. Autonomy and social support were positively related to safety performance by improving work engagement and reducing burnout.

This research is the pioneer systematically describing the overall picture of psychosocial factors related to the safety performance of construction workers. Through deeply discussed the mechanism of psychosocial factors and safety performance, it could provide a reference for the theory and application of psychosocial factors in the field of construction safety management.

Capable service employees are increasingly scarce and costly. Many organizations opt to partially replace, support or augment human employees with AI systems. This study builds a framework to help managers map and understand the challenges of crafting a service climate that fosters synergies between AI and human employees, where customers require value-added, personalized and excellent service.

This conceptual article identifies barriers and facilitators of building a service climate for organizations using both human and AI-based employees through an eclectic review of relevant literature.

A conceptual framework is built, and a future research agenda is brought forth.

By identifying barriers and facilitators for AI–human synergies in service settings, this article clarifies how AI can be made to complement human employees, especially in delivering personalized, value-added services, while also highlighting knowledge gaps.

This study provides a practical framework for integrating AI into the workforce. It offers insights into addressing challenges in creating a service climate that combines human and AI capabilities to maintain service excellence. Identifying key barriers and facilitators, the framework guides managers to improve efficiency and customer satisfaction in a rapidly changing service landscape.

This research offers insights on incorporating AI to address labor shortages while maintaining high-quality, personalized service. It provides a pathway to improving service experiences, especially in sectors facing staffing challenges from an aging population.

This research builds on Bowen and Schneider’s (2014) seminal service climate framework to account for a mix of human and AI-based employees.

The current study examines the impact of big data analytics capabilities (BDAC) on supply chain performances of the manufacturing firms. Based on the underpinning of resource-based view (RBV) theory, the current study will highlight the significance of BDAC on green dynamic capabilities (GDC), supply chain agility (SCA) and green competitive advantage (GCA). Furthermore, the study examines the moderating effect of supply chain innovativeness (SCI) on the relationship between GCA and firm performance (FP).

Online survey method was employed for the data collection from the 331 managers employed in Pakistan Stock Exchange (PSX)-listed manufacturing firms. The hypothesized model was tested using partial least squares structural equation modeling (PLS-SEM) technique.

The study results indicate that BDAC has a positive influence on both GDC and SCA, leading to enhanced GCA. Furthermore, the results demonstrate that GCA significantly and positively impacts FP, and the relationship between them is positively moderated by SCI.

This study developed a novel theoretical perspective based on RBV theory and provided empirical evidence that manufacturing firms' performances are significantly influenced by BDAC, GDC and SCA. The study results provide valuable practical implications top management regarding the effectiveness of BDAC and SCA in the supply chain. The findings further highlight the significance of SCI strengthening relationship between GCA and FP.

This study aims to analyze the capabilities and external conditions of womenpreneur owners of micro, small and medium enterprises (MSMEs) Fashion EcoPrint Indonesia in green business practices and their implications for sustainable business performance.

This study chooses a quantitative approach with a sustainable business internal-external capability model. Using the structural equation modeling-partial least square analysis tool, the analysis was conducted on a sample of womenpreneurs who owned MSMEs EcoPrint Indonesia (n = 493).

In the internal capability dimension, total quality environment and green core competence affect green supply chain management, affecting green product innovation performance. Meanwhile, women entrepreneurs’ external capability dimensions (regulation, customer awareness of the environment and technological innovation infrastructure) are connected to sustainable development business capabilities, which affect the performance of green process innovation. The study confirms that green products and processes have the potential to influence sustainable business performance. A key finding is the strong influence of environment-based total quality management governance on both sides. At the same time, womenpreneurs have a significant impact on their respective dimensions.

This study has implications for increasing competency, Sustainable MSME industrial infrastructure, and protecting women in developing countries. The theoretical implications of creating a model that examines the impact of womenpreneurs’ internal and external abilities on eco-friendly businesses’ success are significant for developing nations’ promising growth.

This study explores women’s contributions to family well-being and environment-based economies, focusing on eco-friendly supply chain management and sustainable external capabilities of women entrepreneurs in Indonesia, using a gender equality approach in developing economies.

The purpose of this paper is to study the electrochemical properties of electrode material on activated carbon double layer capacitors. It also tries to develop a prediction model to evaluate pore size value.

Back-propagation neural network (BPNN) prediction model is used to evaluate pore size value. Also, an improved heuristic approach genetic algorithm (HAGA) is used to search for the optimal relationship between process parameters and electrochemical properties.

A three-layer ANN is found to be optimum with the architecture of three and six neurons in the first and second hidden layer and one neuron in output layer. The simulation results show that the optimized design model based on HAGA can get the suitable process parameters.

HAGA BPNN is proved to be a practical and efficient way for acquiring information and providing optimal parameters about the activated carbon double layer capacitor electrode material.

The relationship between farm size and greenhouse gas (GHG) emissions has not been clearly defined. This paper aims to assess and compare the impact of farm size on greenhouse gas (GHG) emissions derived from wheat and maize production in the North China Plain (NCP), one of the most important agricultural regions in China.

A field survey through face-to-face interviews was conducted to collect the primary data, and life cycle assessment method, a worldwide comparable framework, was then adopted to characterize the farm-size effect on greenhouse gas (GHG) wheat and maize production in NCP.

It was confirmed that GHG emissions from N fertilizer production and use were the primary contributor to total carbon footprint (CF). As farm size increased, maize yield increased but wheat yield barely changed, while area-scaled and yield-scaled CF declined for both crops. These results were supposed to relate to utilize the inputs more efficiently resulting from increased application of modern agriculture methods on larger operations. It was also found maize not only had higher grain yields, but possessed much smaller CFs. More notably, the reduction of CF with farm size seemed to be more sensitive for maize as compared to wheat. To further mitigate GHG emissions, farm size should better be larger for wheat than for maize.

This study provides useful information guide for Chinese agriculture in increasing crop production, raising farm income and relieving environmental burdens caused by the misuse of agricultural resources.

Nitrogen oxides (NO_x) have been considered as primarily responsible for many serious environmental problems. Removing NO is the key task to remove NO_x hazards. To clarify, NO removal process for pitch-based spherical-activated carbons (PSACs), an online prediction and optimization technique in real-time based on support vector machine algorithm in regression (support vector regression [SVR]) is discussed. The purpose of this paper is to develop a predictor and optimizer system on selective catalytic reduction of NO (SCRN) using experimental data and data-driven SVR intelligence methods.

Predictor and optimizer using developed SVR have been proposed. To modify the training efficiency of SVR, the authors especially customize batch normalization and k-fold cross-validation techniques according to the unique characteristics of PSACs model.

The results present that SVR provides a property regression model since it can linkage linear and non-linear process and property relationships in few experimental data sets. Also, the integrated normalization and k-fold cross-validation show a satisfying improvement and results for SVR optimization. The predicted results of predictor and optimizer in single and double factor systems are in excellent agreement with the experimental data.

SCRN-PO for predicting and optimization SCRN problems is developed by data-driven methods. The outperformed SCRN-PO system is used to predict multiple-factors property parameters and obtain optimum technological parameters in real-time. Also, experiment duration is greatly shortened.

Because of increasing wealth inequality, China has been confronted with resentment against the rich (referred to hereafter as RAR or Choufu in Chinese), which is a growing concern owing to its potential to foment social conflict. Drawing on social comparison and deonance theories, this paper aims to provide theoretical insights into RAR within the Chinese context and to develop an RAR scale. Following spillover theory, the attitudinal and behavioral outcomes of RAR in organizational settings will be explored.

This research consists of two studies. Study 1 conceptualizes RAR and develops an RAR scale by using three separate samples. Exploratory and confirmatory factor analyses are conducted to establish scale reliability and validity. Study 2 uses hierarchical linear regression analysis to test whether employees’ RAR attitude spills over from the societal to the organizational setting.

Results suggest that RAR can be conceptualized as two distinct but related dimensions – emotional RAR and moral RAR. These two forms spill over to the workplace, influencing employees’ work attitudes and behaviors. Emotional RAR relates negatively to life satisfaction and prosocial organizational behaviors and positively to unethical organizational behaviors. Moral RAR relates negatively to pay satisfaction and positively to prosocial behaviors.

This research suggests that RAR has spillover effects from societal to organizational settings and demonstrates that a more robust understanding of employees’ workplace experience requires acknowledging social experiences, such as conflicts beyond the workplace.

This research contributes to the conflict management literature by exploring RAR as a negative attitude that serves to potentially ignite social conflict. It not only develops a theory-grounded, conceptual RAR model and a reliable RAR scale but also for the first time explores RAR attitudinal and behavioral outcomes beyond the social domain. This study serves as a meaningful touchstone for future research to incorporate social attitudes into organizational behavior research.

This paper believes that while implementing the gradual delay retirement age policy in China, the impact should be considered comprehensively; we should pay attention to impacts brought by the delayed retirement policy and introduce policies to deal with the impacts in a timely manner.

This paper aims to explore the delayed retirement’s impact on women’s labor supply and to clarify the elderly care’s role in it.

The results found that the delayed retirement has a positive effect on the women’s overall and young women’s labor supply, with a more significant promotion for young women’s labor supply. The mediation results suggest that delayed retirement promotes women’s labor supply by affecting elderly care. Therefore, we believe that while implementing the gradual delay retirement policy in China, it is important to implement it on the correct estimation basis so as to reduce the volatility in the labor market.

This paper may produce marginal contributions in the following two aspects: From the research perspective, we construct a model containing delayed retirement, elderly care and women’s labor supply and illustrate how the delayed retirement promotes women’s labor supply by affecting elderly care. Secondly, from the research content, this paper expands the delayed retirement on macro-employment and further explores the micro-impact on employment.

This paper aims to explore the influence of corrosion-deformation interactions (CDI) on the corrosion behavior and mechanisms of 316LN under applied tensile stresses.

Corrosion of metals would be aggravated by CDI under applied stress. Notably, the presence of nitrogen in 316LN austenitic stainless steel (SS) would enhance the corrosion resistance compared to the nitrogen-absent 316L SS. To clarify the CDI behaviors, electrochemical corrosion experiments were performed on 316LN specimens under different applied stress levels. Complementary analyses, including three-dimensional morphological examinations by KH-7700 digital microscope and scanning electron microscopy coupled with energy dispersive spectroscopy, were conducted to investigate the macroscopic and microscopic corrosion morphology and to characterize the composition of corrosion products within pits. Furthermore, ion chromatography was used to analyze the solution composition variations after immersion corrosion tests of 316LN in a 6 wt.% FeCl₃ solution compared to original FeCl₃ solution. Electrochemical experiment results revealed the linear decrease in free corrosion potential with increasing applied stress. Electrochemical impedance spectroscopy results indicated that high tensile stress level damaged the integrity of passivation film, as evidenced by the remarkable reduction in electrochemical impedance. Ion chromatography analyses proved the concentrations increase of NO₃⁻ and NH₄⁺ ion concentrations in the corrosion media after corrosion tests.

The enhanced corrosion resistance of 316LN SS is attributable to the presence of nitrogen.

The scope of this study is confined to the influence of tensile stress on the electrochemical corrosion of 316LN at ambient temperatures; it does not encompass the potential effects of elevated temperatures or compressive stress.

The resistance to stress electrochemical corrosion in SS may be enhanced through nitrogen alloying.

This paper presents a systematic investigation into the stress electrochemical corrosion of 316LN, marking the inaugural study of its impact on corrosion behaviors and underlying mechanisms.