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Reducing carbon emissions in agriculture is vital for fostering sustainable agricultural growth and promoting ecological well-being in rural areas. The adoption of Low-Carbon Agriculture (LCA) by farmers holds great potential to accomplish substantial reductions in carbon emissions. The purpose of this study is to explore the farmers' preference and willingness to engage in LCA.

This study employs the Choice Experiment (CE) method to examine farmers' preferences and willingness to adopt LCA, using field survey data of 544 rural farmers in the Weihe River Basin between June and July 2023. We further investigate differences in willingness to pay (WTP) and personal characteristics among different farmer categories.

The empirical results reveal that farmers prioritize government-led initiatives providing pertinent technical training as a key aspect of the LCA program. Farmers' decisions to participate in LCA are influenced by factors including age, gender, education and the proportion of farm income in household income, with their evaluations further shaped by subjective attitudes and habits. Notably, we discovered that nearly half of the farmers exhibit indifference towards LCA attributes.

To the best of the authors' knowledge, this study is the first to investigate farmers' attitudes toward LCA from their own perspectives and to analyze the factors influencing them from both subjective and objective standpoints. This study presents a fresh perspective for advocating LCA, bolstering rural ecology and nurturing sustainable development in developing nations.

The purpose of this study is to investigate the influence of ambidextrous strategy on supply chain resilience and its impact on firm performance, employing the Dynamic Capabilities View.

Through a survey of 215 supply chain professionals, the research employs a structural equation modeling analysis to examine the relationships between ambidexterity, agile operations, resilience, and performance.

The findings demonstrate that the ambidextrous strategy significantly enhances both agile operations and supply chain resilience. In turn, agile operations and resilience positively impact firm performance. The study also reveals that agile operations and supply chain resilience partially mediate the relationship between ambidextrous strategy and firm performance.

This research contributes to the supply chain management literature by highlighting the importance of an ambidextrous approach in fostering agile operations and resilience, thereby improving firm performance. It extends the dynamic capabilities view framework by elucidating how ambidexterity acts as a pivotal mechanism for adapting to disruptions and securing competitive advantage in volatile markets. Finally, measurements of ambidextrous strategy and resilience are provided to further enhance practitioners’ understanding of building these important components in networks.

Based on traditional Chinese filial piety, this article examines the impacts and mechanisms of the two-dimensional filial piety concept “Qinqin – Zunzun” on gender wages in China via China Family Panel Studies (CFPS) conducted in 2014 and 2018.

This article construct regression models to examine the relationship between filial piety concepts and wages. Also, it uses unconditional quantile regression and decomposition to explore the impact of filial piety concepts on the wage gap.

It is found that: (1) The effects of two-dimensional filial piety are heterogeneous in terms of gender. Specifically, authoritarian filial piety significantly suppresses individual wages and has a stronger suppressive effect on women’s wages, whereas affinity filial piety significantly enhances individual wages without gender heterogeneity; (2) Parents' time support in the intergenerational exchange model is a crucial mechanism by which filial piety affects wages, exhibiting significant gender heterogeneity; (3) Regarding wage distribution, authoritarian filial piety mainly widens the gender income gap in the low and middle income-groups, while affinity filial piety narrows the gender wage gap by “raising the floor”, with its converging effect being most significant in the middle and high-income groups. This article deepens the understanding of the gender wage gap and intergenerational income mobility, providing policy references for better utilizing the social governance function of culture.

The article deepens the understanding and mechanisms of the gender wage gap and inter-generational income mobility, providing policy reference for better utilizing the social governance function of culture.

Based on the theory of organizational socialization, this article broadens the knowledge in the field of organizational commitment by determining the relationship between the organizational climate (OC) and the employees’ commitment, as well as the moderating role of the person-environment (P-E) fit.

We conducted quantitative research using three psychometric questionnaires. We investigated a large sample (N = 1,032) of employees hired in Poland.

We found strong relationships between the OC, the employees’ fit and their commitment. Moreover, both supplementary and complementary fit significantly moderated the relationships between the majority of climate dimensions and, especially, affective commitment. Interestingly, highly fitted employees with longer tenure showed a stronger relationship between material climate dimensions and commitment compared to similarly fitted newcomers, for whom the most important were relationships with co-workers and superiors.

We analyzed only a subjective fit among employees working in Poland. Although it was beneficial for developing the OC knowledge of non-American sample, the results require cautious generalization.

Assessing a candidate’s fit with the organization through detailed interviews, behavioral questions or practical tasks during the selection process can improve candidates' and employees’ P-E fit. A better fit can increase commitment, even if the OC or other factors are not perfect. Socialization tactics aimed at strengthening the fit can facilitate better alignment with the climate and higher commitment among employees with longer tenure.

This study is the first to empirically verify the moderating role of the P-E fit on the relations between OC and organizational commitment. It also considers the comparison between more experienced employees and newcomers.

Green technology, characterized by its environmentally friendly attributes and sustainable practices, has emerged as a crucial tool in harmonizing the economic and ecological benefits. However, the challenge lies in selecting the most effective strategies for acquiring green technology. This paper aims to explore how chemical enterprises choose green technology acquisition strategies across diverse scenarios.

Considering the influence of competition effects, spillover effects and their interactions on selecting green technology acquisition strategies, this paper develops three decision models (independent R&D, cooperative R&D and technology introduction). Drawing on the duopoly game theory as its theoretical framework, this paper delves into the examination of the economic and environmental benefits within distinct scenarios.

Cooperative R&D excels in promoting green technology R&D when spillover effects are strong, while independent R&D demonstrates superiority when spillover effects are weak. The threshold for the strength of spillover effects is related to competition effects. Additionally, cooperative R&D typically yields greater financial advantages than independent R&D and technology introduction. Moreover, the economic and environmental benefits may not be optimized simultaneously. Only enterprises that satisfy low competition and spillover effects as well as high competition and spillover effects, can achieve win-win economic and environmental benefits.

Although green technology R&D and introduction are alternative strategies, they have typically been considered separately in prior literature. This study attempts to incorporate green technology R&D and introduction into a strategic system to investigate the selection of green technology acquisition strategies, taking into account competition effects, spillover effects and their interactions.

Nanotechnology (NT) advancements in personal protective textiles (PPT) or personal protective equipment (PPE) have alleviated spread and transmission of this highly contagious viral disease, and enabled enhancement of PPE, thereby fortifying antiviral behavior.

Review of a series of state of the art research papers on the subject matter.

This paper expounds on novel nanotechnological advancements in polymeric textile composites, emerging applications and fight against COVID-19 pandemic.

As a panacea to “public droplet prevention,” textiles have proven to be potentially effective as environmental droplet barriers (EDBs).

PPT in form of healthcare materials including surgical face masks (SFMs), gloves, goggles, respirators, gowns, uniforms, scrub-suits and other apparels play critical role in hindering the spreading of COVID-19 and other “oral-respiratory droplet contamination” both within and outside hospitals.

When used as double-layers, textiles display effectiveness as SFMs or surgical-fabrics, which reduces droplet transmission to <10 cm, within circumference of ∼0.3%.

NT advancements in textiles through nanoparticles, and sensor integration within textile materials have enhanced versatile sensory capabilities, robotics, flame retardancy, self-cleaning, electrical conductivity, flexibility and comfort, thereby availing it for health, medical, sporting, advanced engineering, pharmaceuticals, aerospace, military, automobile, food and agricultural applications, and more. Therefore, this paper expounds on recently emerging trends in nanotechnological influence in textiles for engineering and fight against COVID-19 pandemic.

The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.

This study used a survey method to gather information from 225 food processing SMEs registered with the Ghana Enterprise Agency (GEA) in Ghana’s eastern region. A structural equation modeling (SEM) path analysis was used to assess the impact of marketing analytics capability (MAC) on the performance of SMEs.

The results of the study show that MAC significantly and positively affect the financial performance (FP), customer performance (CF), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs. The findings of this study also illustrated the significance of MAC determinants, including marketing analytics skills (MAS), data resource management (DRM) and data processing capabilities (DPC), in achieving SME success in Ghana.

The research’s conclusions give RBV theory strong credence. The results of this study also provide credence to previous research finding that SMEs should view MAC and its determinants (i.e. DRM, DPC, MAS) as a crucial strategic capability to improve their performance (i.e. FP, CF, IBPP, LGP). With regard to its contribution, this study broadens the body of knowledge on MAC and SME performance, particularly in the context of an emerging economy.

This paper aims to contribute to understanding Brazilian fashion consumer behavior. The subsequent research question is formulated as follows: How are the consumers purchasing new clothes and disposing of used ones, and how is their awareness of sustainable fashion consumption and disposal of used clothes?

An online questionnaire was sent to nearly one thousand e-mails. A database was formed with 182 complete answers to 13 questions concerning consumer behavior toward sustainability, especially clothing acquisition, use and disposal. A multimethod approach was used to analyze the initial attributes, applying descriptive statistics, cluster analysis and data mining.

This survey obtained valuable answers from Brazilian fashion consumers grouped into four clusters. Age and yearly income were more critical in determining the clusters. Only four attributes were chosen by the algorithm to build the trees (age, annual income, yearly spending on clothes and how long the clothes are worn). The consumer's profile may help the fashion industry redirect investments in sustainability. The most critical factor leading to the sustainability of clothing fashion was the duration of the clothes. The study dealt with a limited sample size that was not representative of Brazil's broader population. Despite numerous attempts to seek responses through e-mail, the participant pool was predominantly composed of highly educated individuals.

This assessment of Brazilian consumer behavior toward sustainability and fashion presents essential knowledge to understand the relationships among variables affecting the purchase and discharge of clothes.

The waste-to-energy (WtE) project plays a significant role in the sustainable development of urban environments. However, the inherent “Not in my backyard” (NIMBY) effect presents substantial challenges to site selection decisions. While effective public participation is recognized as a potential solution, research on incorporating it into site selection decision-making frameworks remains limited. This paper aims to establish a multi-attribute group decision-making framework for WtE project site selection that considers public participation to enhance public satisfaction and ensure project success.

Firstly, based on consideration of public demand, a WtE project site selection decision indicator system was constructed from five dimensions: natural, economic, social, environmental and other supporting conditions. Next, the Combination Ordered Weighted Averaging (C-OWA) operator and game theory were applied to integrate the indicator weight preferences of experts and the public. Additionally, an interactive, dynamic decision-making mechanism was established to address the heterogeneity among decision-making groups and determine decision-maker weights. Finally, in an intuitive fuzzy environment, an “acronym in Portuguese of interactive and multi-criteria decision-making” (TODIM) method was used to aggregate decision information and evaluate the pros and cons of different options.

This study develops a four-stage multi-attribute group decision-making framework that incorporates public participation and has been successfully applied in a case study. The results demonstrate that the framework effectively handles complex decision-making scenarios involving public participation and ranks potential WtE project sites. It can promote the integration of expert and public decision-making preferences in the site selection of WtE projects to improve the effectiveness of decision-making. In addition, sensitivity and comparative analyses confirm the framework’s feasibility and scientificity.

This paper provides a new research perspective for the WtE project site selection decision-making, which is beneficial for public participation to play a positive role in decision-making. It also offers a valuable reference for managers seeking to effectively implement public participation mechanisms.

Online portfolio selection sequentially allocates wealth among a set of assets and aims to maximize the investor’s cumulative return in the long run. Various existing algorithms in the finance and accounting area adopt an indirect approach to exploit one asset characteristic through the channel of assets’ expected return and thus cannot fully leverage the power of various asset characteristics found in the literature. This study aims to propose new algorithms to overcome this issue to enhance investment performance.

We propose a parameterized portfolio selection (PPS) framework, which directly incorporates multiple asset characteristics into portfolio weights. This framework can update parameters timely based on final performance without intermediate steps and produce efficient portfolios. We further append L1 regularization to constrain the number of active asset characteristics. Solving the PPS formulation numerically, we design two online portfolio selection (OLPS) algorithms via gradient descent and alternating direction method of multipliers.

Empirical results on five real market datasets show that the proposed algorithms outperform the state of the arts in cumulative returns, Sharpe ratios, winning ratios, etc. Besides, short-term characteristics are more important than long-term characteristics, and the highest return category is the most important characteristic to improve portfolio performance.

The proposed PPS algorithms are new end-to-end online learning approaches, which directly optimize portfolios by asset characteristics. Such approaches thus differ from existing studies, which first predict returns and then optimize portfolios. This paper provides a new algorithmic framework for investors’ OLPS.