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This study examines the direct and indirect effects of organizational environmental orientation (EO)/culture, quality management practices (QMP) and sustainability experience (SE) on the relationship between organizational green practices (GP) and the triple bottom line (TBL) of sustainability performance (SuP).

To test the seven hypotheses, a structured questionnaire was used to collect data. The responses of 365 managers from various USA businesses in the service industries were analyzed using IBM SPSS and structural equation modeling (SEM)-AMOS.

The empirical results indicate that positive SuP in the economic, environmental and social dimensions and organizational GP can be improved by a strong culture of EO, effective QMP and substantial SE.

This research fills the gap in existing research between important organizational and environmental priorities and SuP. Consequently, the study provides managers with important strategic guidance: for environmental practices to achieve profitability and sustainability success, companies must promote an environmental-mindful culture and strategically invest in integrated QM systems.

This research is one of the first that explores how organizational environmental culture and QMP affect directly and indirectly the relationship between GP and SuP. These results provide empirical evidence to support the claim that environmental culture and QMP have significant direct and indirect effects on the relationship between GP and SuP dimensions.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

Abusive supervision provokes subordinates’ interpersonal deviant behavior. It is, therefore, essential to explore the contingent factors of this relationship. Drawing upon gender role theory, this study aims to explore how subordinate and leader genders moderate the relationship between abusive supervision and subordinate interpersonal deviance. Furthermore, this study posits a three-way interaction effect of abusive supervision with leader and subordinate genders on interpersonal deviance.

Multisource survey data were collected from 45 supervisors and 170 subordinates in eight companies in China. The data were analyzed using the PROCESS macro in SPSS.

The results showed that the positive relationship between abusive supervision and interpersonal deviance was stronger among female leaders than male leaders. Furthermore, the authors found a three-way interaction effect between abusive supervision and leader and subordinate genders on subordinates’ interpersonal deviance. Compared with female subordinates, male subordinates engaged in significantly more interpersonal deviance when experiencing abusive supervision from a female leader than from a male leader.

The authors reveal that gender differences exist in the effect of abusive supervision on subordinates’ interpersonal deviant behavior. Furthermore, the authors demonstrate that subordinate and leader genders jointly influence the effect of abusive supervision. Finally, the findings extend the literature on gender’s moderating effects from constructive and neutral leader behaviors to destructive leader behaviors.

The globalisation of supply chains has contributed to modern slavery by degrading labour standards and work practices. The inherent difficulties involved in monitoring extremely fragmented production processes also render workers in and from developing countries vulnerable to labour exploitation. This research adopts a benchmark methodology that will help examine the inherent modern slavery challenges.

This study examines how the benchmark model, including governance, risk assessment, purchasing practice, recruitment and remedy of victims, addresses supply chain modern slavery challenges. The proposed hypotheses are tested based on the reoccurring issues of modern slavery in global supply chains.

Estimations suggest that modern slavery is a growing and increasingly prominent international problem, indicating that it is the second largest and fastest growing criminal enterprise worldwide except for narcotics trafficking. These social issues in global supply chains have drawn attention to the importance of verifying, monitoring and mapping supply chains, especially in lengthy and complex supply chains. However, the advent of digital technologies and benchmarking methodologies has become one of the existing key performance indicators (KPIs) for measuring the effectiveness of modern slavery initiatives in supply chains.

This review provides an understanding of the current situation of global supply chains concerning the growing social issue of modern slavery. However, this includes various individual specialities relating to global supply chains, modern slavery, socially sustainable supply chain management (SCM), logistic social responsibility, corporate social responsibility and digitalisation. Furthermore, the review provided important implications for researchers examining the activities on benchmarking the effectiveness of the existing initiatives to prevent modern slavery in the supply chains.

This paper introduced the simple synthesis process of self-cleaning coating with fog-resistance property using hydrophobic polydimethylsiloxane (PDMS) polymer and nano-calcium carbonate (nano-CaCO₃) and titanium dioxide (TiO₂).

The synthesis method of PDMS/nano-CaCO₃-TiO₂ is based on sol-gel process. The crosslinking between PDMS and nanoparticles is driven by the covalent bond at temperature of 50°C. The 3-Aminopropyltriethoxysilane is used as binder for nanoparticles attachment in polymer matrix. Two fabrication methods are used, which are dip- and spray-coating methods.

The prepared coated glass fulfilled the requirement of standard self-cleaning and fog-resistance performance. For the self-cleaning test BS EN 1096-5:2016, the coated glasses exhibited the dust haze value around 20%–25% at tilt angle of 10°. For the antifog test, the coated glasses showed the fog haze value were below 2% and the gloss value were above 85%. The obtained results completely achieved the standard antifog value ASTM F659-06 protocol.

Findings will provide an infrastructure support for the building glass to enhance building’s energy efficiency, cleaning performance and friendly environment.

This study proposed the simple synthesis method using hydrophobic polymer and nano-CaCO₃ and nano-TiO₂, which can achieve optimum self-cleaning property at low tilt angle and fog-resistance performance for building glass.

The research findings have high potential for building company, cleaning building company and government sector. The proposed project capable to reduces the energy consumption about 20% per annum due to labor cost, time-consuming and safety during manual cleaning.

The novel method to develop self-cleaning coating with fog-resistance using simple synthesis process and fabrication method for building glass application.

This paper aims to present the fabrication of 6061 aluminum alloy (AA6061) using a promising laser additive manufacturing process, called the laser-foil-printing (LFP) process. The process window of AA6061 in LFP was established to optimize process parameters for the fabrication of high strength, dense and crack-free parts even though AA6061 is challenging for laser additive manufacturing processes due to hot-cracking issues.

The multilayers AA6061 parts were fabricated by LFP to characterize for cracks and porosity. Mechanical properties of the LFP-fabricated AA6061 parts were tested using Vicker’s microhardness and tensile testes. The electron backscattered diffraction (EBSD) technique was used to reveal the grain structure and preferred orientation of AA6061 parts.

The crack-free AA6061 parts with a high relative density of 99.8% were successfully fabricated using the optimal process parameters in LFP. The LFP-fabricated parts exhibited exceptional tensile strength and comparable ductility compared to AA6061 samples fabricated by conventional laser powder bed fusion (LPBF) processes. The EBSD result shows the formation of cracks was correlated with the cooling rate of the melt pool as cracks tended to develop within finer grain structures, which were formed in a shorter solidification time and higher cooling rate.

This study presents the pioneering achievement of fabricating crack-free AA6061 parts using LFP without the necessity of preheating the substrate or mixing nanoparticles into the melt pool during the laser melting. The study includes a comprehensive examination of both the mechanical properties and grain structures, with comparisons made to parts produced through the traditional LPBF method.

This study highlights the impact of mental accounts on a user's decision-making regarding payment schemes and aims to determine the pricing strategy for the first-enjoy-after-pay service offered by the two-sided media platforms.

This study establishes a game-theoretic model and utilizes backward induction to derive the equilibrium price by maximizing the monopolist's profit.

The findings indicate that the conditions for a two-sided media platform to offer the first-enjoy-after-pay service depend on the trade-off between pleasure attenuation and pain buffering and the effect of time discounts. Moreover, the authors found that the time discount is a critical factor in determining pricing strategies under various payment schemes offered by the platform.

This work adopts a uniform pricing strategy for users who opt for either immediate or post-payment schemes. Nevertheless, it is important to note that this approach has limitations in terms of offering discriminatory pricing for those who choose both payment schemes.

This analytical work provides valuable insights for two-sided media platforms to optimize their payment scheme strategies and pricing considering the influence of a user's mental account.

In a two-sided media platform, the authors provide applicable conditions for the platform to offer first-enjoy-after-pay service considering the effect of mental accounts. Further, the authors show the optimal pricing strategy under different payment schemes provided by the platform.

This study aims to identify an antecedent that hinders knowledge sharing, namely, the perceived climate of Cha-xu. Based on the social exchange perspective, the authors propose a theoretical model that links the perceived climate of Cha-xu to employee knowledge sharing. This model focuses on the mediating role of two types of trust (vertical and horizontal trust) and the moderating role of task interdependence in influencing the mediation.

Using a sample of 509 Chinese employees, this study carried out a survey on an online platform. This study developed a structural equation model and tested the moderated mediation hypothesis by using Mplus 8.0.

The results showed that two types of trust act as mediators in the relationship between the perceived climate of Cha-xu and knowledge-sharing processes. The mediating effect of horizontal trust is stronger. Most significantly, findings show that this mediated relationship is contingent on the level of task interdependence.

This paper provides evidence for distinguishing vertical trust and horizontal trust in the field of knowledge management. From a managerial perspective, this study identifies traditional cultural factors for hindering knowledge-sharing processes within Chinese organizations.

To contribute to achieving the Sustainable Development Goals, this study aims to explore how to encourage innovative green behaviors among college students and the mechanisms behind the formation of green innovation behavior. Specifically, this study examines the influences of schools, mentors and college students themselves.

A multilevel, multisource study involving 261 students from 51 groups generally supported this study’s predictions.

Proenvironmental and responsible mentors significantly predicted innovative green behavior among college students. In addition, creative motivation mediated the logical chain among green intellectual capital, emotional intelligence and green innovation behavior.

The study findings offer new insights into the conditions required for college students to engage in green innovation. In addition, they provide practical implications for cultivating green innovation among college students.

The authors proposed and tested a multilevel theory based on the ability–motivation–opportunity framework. In this model, proenvironmental and responsible mentors, green intellectual capital and emotional intelligence triggered innovative green behavior among college students through creative motivation.