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Given that the current sustainability assessment in higher education institutions primarily relies on qualitative methods with relatively limited quantitative tools, the purpose of this study is to design a tool that could be used to comprehensively assess the overall state of higher education institutions’ sustainability.

The authors based the “Model to Assess the Sustainability of Higher Education Institutions” on the Triple Bottom Line (TBL) framework of economic, environmental and social factors, and established its primary dimensions as educational level, research capacity, community outreach, campus operations, campus experience and assessment reports. They designed the College Organisational Sustainability Scale (CO-SS) based on this research model, drawing their inspiration from the qualitative research tool, the Sustainability Assessment Questionnaire, and taking the following validation steps: expert review (n = 10), pilot testing (n = 150) and formal experiments (n = 1108). These steps were taken to optimise the scale items, test the model’s validity and assess its reliability.

After undergoing rigorous scientific validation, CO-SS was unequivocally confirmed as an effective and reliable tool, demonstrating its accurate reflection of the level of sustainability in higher education institutions.

The authors took an industry-specific approach by relying on the TBL and the Sustainability Assessment Questionnaire to construct and validate the CO-SS. Furthermore, the CO-SS has the potential to evolve into a self-assessment tool for higher education institutions, and a reliable foundation for data-driven decision-making in the realm of organisational sustainability at universities.

This study aims to explore the attitudes and perceptions of Chinese coffee consumers towards robot baristas, considering the proliferation of automated entities within China's coffee sector.

Employing the extended Technology Acceptance Model 2 as its theoretical framework, this research conducts in-depth interviews with 30 Chinese coffee consumers. The laddering technique is utilized, supplemented by video simulation. Thematic analysis is subsequently employed to scrutinize the data.

The findings delineate six pivotal themes encapsulating Chinese coffee consumers' perceptions of robot baristas – Perceived Introvert Friendliness, Perceived Novelty, Perceived Intellectual Discrepancies, Perceived Efficiency and Reliability, Perceived Emotional Disconnection, and Perceived Labour Market Disruption. Moreover, six motivational themes are identified - Social Status Boosting, Openness to Experience, Ease of Use, Tech-Driven Affordability, Reliable and Uncompromising Quality, and Resistance to Overbearing Service.

The study is limited by its focus on a specific cultural context. Future research could explore cross-cultural perspectives.

The findings of this study offer guidance on how to market and position robotic barista services to appeal to consumer preferences and drive adoption.

Understanding consumer perceptions of robotic baristas has broader social implications, particularly in terms of labour market disruption and the potential impact on traditional coffee professions. Businesses can navigate the social implications of automation more effectively and foster greater acceptance of technological innovations within society.

This study offers insights into the inclinations of Chinese coffee consumers, thereby facilitating informed decision-making and the formulation of effective strategies to expedite the adoption of robotic service.

The purpose of this teaching case is to introduce students to an alternative business model as practiced by the Haier Group, a Chinese company, and how that model was used to turn around an acquired company in the United States of America (USA). The explicit teaching objectives are threefold. First, the case provides insights into Haier’s unique employee-centered culture. Second, Haier’s efforts to transfer parts of its culture to the newly acquired General Electric (GE) Appliance Division are discussed. Third, to improve the performance of the acquired division, Haier had to invest in people, equipment and new product technology.

This case is based on both primary and secondary research. The authors interviewed the chief executive officer. In addition, one of the authors spent a week in China observing and participating in meetings at the Haier Group. Both before and after the interviews, the authors searched both print and electronic media for additional information.

The case describes how Haier, a large Chinese appliance manufacturer, learned from its own difficult growth in building an employee- and client-driven organization. Haier was able to transfer key aspects of its culture, management practices and technology to help turnaround the well-known American appliance brand GE. The results of the first five years have been financially successful and investment in new product technology (Smart Home) bodes well for the future.

This case represents only one successful turnaround of one acquisition, GE Appliances, by the Haier Group in the USA. Whether the changes introduced would work with other acquisitions in other countries is unknown. Thus, additional follow-up research needs to be done.

This case brings into question the traditional, hierarchical, bureaucratic, spreadsheet-driven model of management that is the predominant model taught and practiced in the USA. Others, such as Gary Hamel and Michele Zanini, have also raised the same question. This case introduces readers to another way and is just one more example of how managers who practice a high engagement style and share financial success with workers can outperform those who do not.

This case documents the introduction of nontraditional management and human resource management practices by a foreign, Chinese company, into what at one time had been a highly regarded division of a traditional major American corporation. GE had become known for strident labor-management relationships. The employees’ acceptance of the alternative approach to management was in doubt prior to the acquisition, but over the past five years, the labor–management relationship appears to have improved as well as the profitability of the division.

Although the acquisition of the GE Appliance Division by the Haier Group has been widely reported in the press, knowledge of how the acquisition was successfully turned around from a money-losing GE Division to a profitable and growing portion of the Haier Group has been largely ignored in both the popular press and in instructional material for the classroom. This case study seeks to ameliorate the latter deficiency.

As social networks have developed to be a ubiquitous platform of public opinion spreading, it becomes more and more crucial for maintaining social security and stability by accurately predicting various trends of public opinion dissemination in social networks. Considering the fact that the dissemination of online public opinion is a dynamic process full of uncertainty and complexity, this study establishes a novel conformable fractional discrete grey model with linear time-varying parameters, namely the CFTDGM(1,1) model, for more accurate prediction of online public opinion trends.

First, the conformable fractional accumulation and difference operators are employed to build the CFTDGM(1,1) model for enhancing the traditional integer-order discrete grey model with linear time-varying parameters. Then, to improve forecasting accuracy, a base value correction term is introduced to optimize the iterative base value of the CFTDGM(1,1) model. Next, the differential evolution algorithm is selected to determine the optimal order of the proposed model through a comparison with the whale optimization algorithm and the particle swarm optimization algorithm. The least squares method is utilized to estimate the parameter values of the CFTDGM(1,1) model. In addition, the effectiveness of the CFTDGM(1,1) model is tested through a public opinion event about “IG team winning the championship”. Finally, we conduct empirical analysis on two hot online public opinion events regarding “Chengdu toddler mauled by Rottweiler” and “Mayday band suspected of lip-syncing,” to further assess the prediction ability and applicability of the CFTDGM(1,1) model by comparison with seven other existing grey models.

The test case and empirical analysis on two recent hot events reveal that the CFTDGM(1,1) model outperforms most of the existing grey models in terms of prediction performance. Therefore, the CFTDGM(1,1) model is chosen to forecast the development trends of these two hot events. The prediction results indicate that public attention to both events will decline slowly over the next three days.

A conformable fractional discrete grey model is proposed with the help of conformable fractional operators and a base value correction term to improve the traditional discrete grey model. The test case and empirical analysis on two recent hot events indicate that this novel model has higher accuracy and feasibility in online public opinion trend prediction.

Modular capacity units enable rapid reconfiguration, providing tactical flexibility to efficiently meet customer demand during disruptions and ensuring sustainability. Moreover, the Physical Internet (PI) enhances the potential of modular capacity in addressing efficiency, sustainability, and resilience challenges. To evaluate the sustainability and resilience advantages of the PI-enabled reconfigurable modular system (PI-M system), this paper studies a PI-enabled sustainable and resilient production-routing problem with modular capacity.

We develop a multi-objective optimization model to assess the sustainability and resilience benefits of combining PI and modular capacity in a chemical industry case study. A hybrid solution approach, combining the augmented e-constraint method, construction heuristic, and hybrid adaptive large neighborhood search, is developed.

The experimental results reveal that the proposed solution approach is capable of obtaining better solutions than the Gurobi and the existing heuristic in a shorter running time. Moreover, compared with the traditional system, the PI only and traditional with modular capacity systems, PI-M system has significant advantages in both sustainability and resilience.

To the best of our knowledge, this study is the first to integrate the PI and modular capacity and investigate sustainability and resilience in the production-routing problem.

Express freight transportation is in rapid development currently. Owing to the higher speed of express freight train, the deformation of the bridge deck worsens the railway line condition under the action of wind and train moving load when the train runs over a long-span bridge. Besides, the blunt car body of vehicle has poor aerodynamic characteristics, bringing a greater challenge on the running stability in the crosswind.

In this study, the aerodynamic force coefficients of express freight vehicles on the bridge are measured by scale model wind tunnel test. The dynamic model of the train-long-span steel truss bridge coupling system is established, and the dynamic response as well as the running safety of vehicle are evaluated.

The results show that wind speed has a significant influence on running safety, which is mainly reflected in the over-limitation of wheel unloading rate. The wind speed limit decreases with train speed, and it reduces to 18.83 m/s when the train speed is 160 km/h.

This study deepens the theoretical understanding of the interaction between vehicles and bridges and proposes new methods for analyzing similar engineering problems. It also provides a new theoretical basis for the safety assessment of express freight trains.

Purpose: In this study, monolith analysis methods, microservice identification, and decomposition methods proposed for the transition to microservice architectures that enable the development of appropriate solutions by adapting to the complex demands that will shape the technological infrastructure of the future are evaluated.

Need for the study: Decomposition from monolithic architectures to microservices has become a popular approach in organizations and companies with Industry 5.0. This transformation of Industry 5.0 enables businesses to gain a competitive advantage and can provide a quick solution to personalized problems such as personal service systems.

Methodology: The study, decomposition from monolith to microservice, initially includes monolith analysis, followed by microservice decomposition review. Various classification methods have been proposed for microservice identification and decomposition and are aligned with Industry 5.0 principles, focusing on artificial intelligence (AI)-based approaches, especially human-centered AI.

Findings: Four analysis methods (domain, static, dynamic, and version) are identified for monolith analysis, with static and dynamic being the most common. Version analysis is not typically used alone. In the decomposition phase, clustering-based methods are prevalent due to the uncertain dimensions of microservices. Rule-based and unsupervised methods are identified for decomposition, with AI algorithms like affinity propagation, Kmeans clustering, hierarchical clustering, Hungarian algorithm, genetic algorithm, latent Dirichlet allocation (LDA), and minimum spanning tree (MST) being employed.

Practical implications: Microservice architecture enables flexibility, scalability, and resilience compared to monolithic structures. Decomposing large-scale monolith projects into microservices is challenging, requiring selection of appropriate monolith analysis methods based on project details (e.g., domain analysis for detailed Unified Modelling Language (UML) diagrams) before proceeding with decomposition. This transformation improves deployment, maintenance, fault isolation, and scalability, while allowing for diverse service-specific databases and programming languages.

This paper explores the association between directors’ and officers’ liability insurance (D&O insurance) and management tone manipulation.

This study uses data from A-share listed non-financial companies from 2009 to 2021 as its sample for empirical tests. In addition, the study relies on text analysis and the construction of models to investigate the relationship between D&O insurance and management tone manipulation.

The authors find that the purchase of D&O insurance will lead to management tone manipulation in the “management discussion and analysis” part of companies’ annual reports, and operating risk and agent cost are the two paths for the effect. Further analysis shows that having a male CEO and employing high-quality auditors can weaken the positive impact of D&O insurance on tone manipulation.

This paper provides a new approach for studying the literature related to D&O insurance and management behavior, and the findings enrich our understanding of the influencing factors and the mechanism of management tone manipulation, thus revealing policy implications for further standardization of the terms and system of D&O insurance in China.

The purpose of this paper is to examine the potential relationship between green finance and greenwashing to rationalize resource allocation better. Additionally, it explores the interconnections among various subgroups of green finance products (GFPs) and identifies any overlooked or underrepresented subgroups.

This paper uses a mixed-method exploratory sequential design. Initially, the authors collected a sample of 313 relevant documents. Thematic analysis and hierarchical coding were then performed using NVivo software to uncover correlations between various nodes and address our research questions. Additionally, a word cloud analysis was conducted to assess the potential research value of stakeholders as moderating variables. Following this, the role of stakeholders was reevaluated, leading to the selection of 58 samples for separate content analysis.

First, there is a negative relationship between green finance and greenwashing. Second, a negative relationship is observed between GFPs and greenwashing. The authors’ correlation coefficient analysis suggests that environmental, social and governance funds, as a non-mainstream research focus within GFPs, deserve further in-depth investigation.

While a significant portion of the existing literature focuses on the relationship between green bonds and greenwashing, a noticeable gap exists regarding the broader spectrum of GFPs and their potential association with greenwashing. The lack of a direct connection between broader GFPs and greenwashing suggests that this area is underexplored in literature. This paper fills this gap by investigating the role of broader GFPs in either promoting or mitigating greenwashing.

This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.