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This paper aims to develop an optimization model to enhance pipeline assembly performance. It focuses on predicting the pipeline’s assembly pose while ensuring compliance with clamp constraints.

The assembly pose of the pipeline is quantitatively assessed by a proposed indicator based on joint defects. The assembly interference between the pipeline and assembly boundary is characterized quantitatively. Subsequently, an analytical mapping relationship is established between the assembly pose and assembly interference. A digital fitting model, along with a novel indicator, is established to discern the fit between the pipeline and clamp. Using the proposed indicators as the optimization objective and penalty term, an optimization model is established to predict the assembly pose based on the reinforced particle swarm optimization, incorporating a proposed adaptive inertia weight.

The optimization model demonstrates robust search capability and rapid convergence, effectively minimizing joint defects while adhering to clamp constraints. This leads to enhanced pipeline assembly efficiency and the achievement of a one-time assembly process.

The offset of the assembly boundary and imperfections in pipeline manufacturing may lead to joint defects during pipeline assembly, as well as failure in the fit between the pipeline and clamp. The assembly pose predicted by the proposed optimization model can effectively reduce the joint defects and satisfy clamp constraints. The efficiency of pipeline modification and assembly has been significantly enhanced.

On the premise of verifying whether the platformization organization of DEEs is born, this work aims to explore the evolutionary process of the organizational structure of digital entrepreneurial enterprises (DEEs) and to further reveal the drivers of organizational structure evolution from the perspective of data resources.

The authors use a longitudinal two-case approach to analyze rich archival and interview data from two DEEs in China.

The findings reveal that the organizational structure of DEEs evolves from hierarchy, network and flatlization to platformization, that the drivers of evolution include building data flow channels, removing barriers of data flow and forming data rules. Meanwhile, the coordination devices in this process have gradually evolved from hierarchy to standard operating procedures, shared culture, norms, etc. to achieve a balance between commercial and creative success.

This work develops a framework for the evolution of organizational structure of DEEs from organization design theory lens and provide some management insights into the development of DEEs.

Given the importance of teacher leadership in influencing, motivating and inspiring student learning engagement and associated learning outcomes, a robust instrument to assess this construct is critical. Although there are some teacher leadership instruments available in existing literature, efforts to adapt robust psychometric instruments to measure teachers' leadership practices in Ethiopian higher education institutions have been limited. Therefore, this study attempted to address this gap by adapting the Teacher Leadership Scale (TLS) based on the Multifactor Leadership Questionnaire (MLQ-5X) and validating its psychometric properties for use in higher education settings.

Using a cross-sectional design, the study involved 409 undergraduate university students who were randomly selected from public universities. Factor analytic methodologies, including exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), were used to analyze the data collected.

The result confirmed a set of 36 items arranged in nine factors, which have a theoretically supported factor structure, excellent model fit and robust evidence for validity, and reliability and measurement invariance. These results demonstrate that the scale is a strong psychometric tool for measuring the leadership profile and practice of higher education teachers.

It can be concluded that the TLS can assist stakeholders in several ways. Researchers can benefit from the scale to measure teachers' leadership practices and predict their influence on student learning outcomes. In addition, the scale can help practitioners and policymakers collect relevant data to rethink teacher professional development initiatives, leadership training programs and other practices aimed at improving teacher leadership effectiveness.

This study aims to optimize control strategies for multi-unmanned aerial vehicle (UAV) systems by integrating differential game theory with sliding mode control and neural networks. This approach addresses challenges in dynamic and uncertain environments, enhancing UAV system coordination, operational stability and precision under varying flight conditions.

The methodology combines sliding mode control, differential game theory and neural network algorithms to devise a robust control framework for multi-UAV systems. Using a nonsingular fast terminal sliding mode observer and Nash equilibrium concepts, the approach counters external disturbances and optimizes UAV interactions for complex task execution.

Simulations demonstrate the effectiveness of the proposed control strategy, showcasing enhanced stability and robustness in managing multi-UAV operations. The integration of neural networks successfully solves high-dimensional Hamilton–Jacobi–Bellman equations, validating the precision and adaptability of the control strategy under simulated external disturbances.

This research introduces a novel control framework for multi-UAV systems that uniquely combines differential game theory, sliding mode control and neural networks. The approach significantly enhances UAV coordination and operational stability in dynamic environments, providing a robust solution to high-dimensional control challenges. The use of neural networks to solve complex Hamilton–Jacobi–Bellman equations for real-time multi-UAV management represents a groundbreaking advancement in autonomous aerial vehicle research.

This paper aims to regulate the oil retention rate and tribological properties of pored polytetrafluoroethylene (PPTFE) using polyvinyl alcohol (PVA)-based oil gel.

PPTFE was first prepared by using citric acid (CA) as an efficient pore-making agent. Subsequently, PVA and chitosan solution was introduced into the pores and experienced a freezing-thawing process, forming PVA-based gels inside the pores. Then, the PPTFE/PVA composite was impregnated with polyethylene glycol 200 (PEG200), yielding an oil-impregnated PPTFE/PVA/PEG200 composite.

It was found that the oil-impregnated PPTFE/PVA/PEG200 composite exhibited advanced tribological properties than neat PTFE with reductions of 53% and 70% in coefficient of friction and wear rate, respectively.

This study shows an efficient strategy to regulate the tribological property of PTFE using a PVA-based oil-containing gel.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0432/

This study empirically investigates whether and how boycott attitudes and subjective norms influence the impact of the perceived behavioral control of boycotts on boycott intention.

To test our hypotheses, we perform a hierarchical linear regression analysis using data from Japanese (n = 500) and South Koreans (n = 571).

Boycott attitudes strengthen the positive effect of perceived behavioral control on boycott intentions for Japanese and South Koreans. Contrary to our assumptions, while the direct impact of perceived behavioral control on boycott intention is not significant, there is a negative moderating effect of subjective norms for Japanese consumers.

We argue that when perceived behavioral control is evident in boycotts, consumers listen more to themselves than to others. Our cross-national analysis of actual boycott campaigns is the first study on boycott research to offer implications for the interaction effects among the three key antecedents of psychological motivation factors in the theory of planned behavior.

Achieving sustainability and sustainable performance has emerged as a critical area of focus for both academic research and practice. However, this pursuit faces challenges, particularly concerning the inadequacy of supply chain information. To address this issue, our study employs the organizational information processing theory to explore how adopting blockchain technology enables firms to learn from and collaborate with their supply chain partners, ultimately facilitating their sustainable performance even in the presence of organizational inertia.

Underpinned by the organizational information processing theory and drawing data from 220 manufacturing firms in China, we use structural equation modeling to test our conceptual model.

Our results demonstrate that blockchain technology adoption can significantly enhance sustainable performance. Furthermore, supply chain learning acts as a mediator between blockchain technology adoption and sustainable performance, while organizational inertia plays a negative moderating role between blockchain technology adoption and supply chain learning.

These findings extend the existing literature on blockchain technology adoption and supply chain management, offering novel insights into the pivotal role of blockchain in fostering supply chain learning and achieving sustainable performance. Our study provides valuable practical implications for managers seeking to leverage blockchain technology to enhance sustainability and facilitate organizational learning.

This study aims to investigate the role of emoji and the moderating effects of recycling messages in encouraging customers’ attitudes toward recycling and their recycling intentions via processing fluency.

Two 2 × 2 between-subject experiments are conducted to investigate the role of emojis, the moderating role of recycling messages and the mediating role of processing fluency on customers’ recycling experiences.

Study 1 identifies that customers’ attitudes toward recycling are enhanced when a positive emoji is used with a promotion-focused message, compared to a prevention-focused message. Study 2 finds that customers’ attitudes and recycling intentions are enhanced when a positive emoji is used with an upcycling message, compared to a recycling message. Both Study 1 and Study 2 identify the mediating role of processing fluency.

Results suggest strategically using emojis and various recycling messages to enhance customers’ recycling experience.

This study investigates the role of emojis as an effective recycling communication strategy.

This paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.

This paper provides a comprehensive overview of the definition, connotations, characteristics and key technologies of digital twin technology. It also conducts a thorough analysis of the current state of digital twin applications, with a particular focus on the overall requirements for intelligent operation and maintenance of high-speed railway infrastructure. Using the Jinan Yellow River Bridge on the Beijing–Shanghai high-speed railway as a case study, the paper details the construction process of the twin system from the perspectives of system architecture, theoretical definition, model construction and platform design.

Digital twin technology can play an important role in the whole life cycle management, fault prediction and condition monitoring in the field of high-speed rail operation and maintenance. Digital twin technology is of great significance to improve the intelligent level of high-speed railway operation and management.

This paper systematically summarizes the main components of digital twin railway. The general framework of the digital twin bridge is given, and its application in the field of intelligent operation and maintenance is prospected.

Drawing on social information processing theory, this study constructs a multilevel moderated mediation model. This model seeks to delve into the intricate and previously overlooked interplay between supervisor bottom-line mentality (BLM) and knowledge hiding. Within this context, we introduce self-interest as a mediating factor and incorporate performance climate as a team-level moderating variable.

The time-lagged data involve 336 employees nested in 42 teams from 23 automobile sales companies in five regions of China. The analysis was meticulously executed using Hierarchical Linear Modeling, complemented by bias-corrected bootstrapping techniques.

The findings reveal that self-interest acts as a full mediator in the positive link between supervisor BLM and knowledge hiding. Furthermore, the performance climate plays a moderating role in both the relationship between supervisor BLM and self-interest, and the entire mediation process. Notably, these relationships are intensified in environments with a high performance climate compared to those with a low one.

This research stands as one of the pioneering efforts to integrate supervisor BLM into the discourse on knowledge hiding, elucidating the underlying psychological mechanisms and delineating the boundary conditions that shape the “supervisor BLM–knowledge hiding” relationship. Further, our insights provide organizations with critical guidance on strategies to curtail knowledge hiding among their employees.