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Enterprises use social media for their daily work. The use of social media in the workplace is crucial for social connections, the growth and evolution of the enterprise, and it opens up new avenues for voice behavior. Employee voice involves the expression of ideas or opinions towards enterprise and is beneficial for employee work and enterprise development. Extant studies of voice behavior usually focus on the leadership and employee factors. However, the internal mechanism of voice behavior, especially the interrelationship between different kinds of social media use and voice behavior has not been well investigated. To fill that research gap, this study analyzes the internal mechanism of voice behavior, taking the effects of social media use and social capital into consideration.

Using structural equation model, this study collected data from employees using social media and analyzed the data using the software of Smartpls 3.0, SPSS and AMOS, in order to analyze the internal mechanism of voice behavior among employees.

Based on social capital theory, this study investigates the relationship between social media use, social capital and voice behavior, and provides some insights into the mechanism of voice behavior. The social media use, social capital and voice behavior are divided into several kinds in order to clarify the internal mechanism of voice behavior more comprehensively. The empirical results show that: (1) Social media use for both work and social-related purposes could positively affect employees’ promotive and prohibitive voice behaviors. (2) Social capital mediates the relationship between social media use and voice behavior. (3) In the process of social media use influencing employees’ voice behavior, employees of different genders and ages show significant differences in social capital and voice behavior.

This study explored the internal mechanism of voice behavior, which could help to elicit the relationship between social media use and voice behavior. By integrating the roles of social capital, individual differences, this study could uncover the deep internal mechanism of employee voice behavior more comprehensively, broadening social capital theory and enriching the researches of voice behavior among employees.

This study aims to explore the internationalization of multinational enterprises (MNEs) from China and aims to examine the relationship between Chinese MNEs’ duration of internationalization and export intensity, and the contingent roles of the home country government.

By extending the springboard theory with institutional and cost-benefit analyses, the authors elaborate a two-phase framework of internationalization to explain how Chinese MNEs develop their international business under the influences of the home country government. Furthermore, the authors apply the Heckman two-stage method based on a panel data set of 19,994 firm-year observations of Chinese listed firms in 2008–2018 to test the hypotheses.

The research findings demonstrate an inverted U-shape relationship between the duration of internationalization and the export intensity of MNEs from China. The export intensity of MNEs from China increases during the initial phase of internationalization, and decreases during the subsequent. A further study reveals that the inverted U-shape of Chinese non-SOEs is steeper than that of SOEs, and this moderating effect is more salient after the Belt and Road Initiative. These results highlight the influence of the home government through state ownership and policies on the inverted U-shaped relationship.

This study helps to refine the understanding of Chinese MNEs’ global expansion by addressing time as an explicit dimension and revealing the mechanism of state ownership and the home country governmental policy in the dynamic internationalization process.

This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.

The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.

Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.

This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.

First, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.

In the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.

This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.

Megaprojects exert a significant impact on sustainable development, and it is imperative for stakeholders to collectively ensure their development occurs in a socially responsible manner. While there has been a growing focus on the involvement of megaprojects in social responsibility, scant attention has been given to understanding the collective actions of stakeholders in implementing social responsibility within these projects. Specifically, the institutional mechanism leading megaproject stakeholders to engage in socially responsible collective action is largely unexplored. To fill this gap, this study primarily aims to explore the institutional antecedents influencing socially responsible collective action in megaprojects.

Drawing on institutional theory, this study empirically examines the factors influencing socially responsible collective action in megaprojects. An online questionnaire survey was administered to collect data from 365 participants engaged in mega water transfer projects in China. The data analysis employed the partial least squares structural equation modeling technique.

The findings from the partial least squares analyses indicate that coercive isomorphism, mimetic isomorphism, and normative isomorphism all demonstrate positive associations with stakeholders’ intention to engage in socially responsible collective action. Moreover, the findings also show a positive correlation between stakeholders’ intention and their behavior in participating in socially responsible collective action within megaprojects. Additionally, coercive isomorphism positively moderates the connection between mimetic isomorphism and the intention to engage in SRCA, while negatively moderates the relationship between normative isomorphism and the intention to undertake socially responsible collective action.

This study enriches the existing body of knowledge by identifying coercive, mimetic, and normative isomorphism as antecedents to adopting socially responsible collective action in megaprojects. Furthermore, the study enhances our comprehension by demonstrating that stakeholders’ intention to fulfill social responsibility translates into tangible actions. The implications and recommendations provided shed light on how various types of institutional isomorphism can be used to encourage stakeholders to embrace socially responsible collective action in megaproject management.

The incompatibility of natural fibers with polymer matrices is one of the key obstacles restricting their use in polymer composites. The interfacial connection between the fibers and the matrix was weak resulting in a lack of mechanical properties in the composites. Chemical treatments are often used to change the surface features of plant fibers, yet these treatments have significant drawbacks such as using substantial amounts of liquid and chemicals. Plasma modification has recently become very popular as a viable option as it is easy, dry, ecologically friendly, time-saving and reduces energy consumption. This paper aims to explore plasma treatment for improving the surface adhesion characteristics of sisal fibers (SFs) without compromising the mechanical attributes of the fiber.

A cold glow discharge plasma (CGDP) modification using N₂ gas at varied power densities of 80 W and 120 W for 0.5 h was conducted to improve the surface morphology and interfacial compatibility of SF. The mechanical characteristics of unmodified and CGDP-modified SF-reinforced epoxy composite (SFREC) were examined as per the American Society for Testing and Materials standards.

The cold glow discharge nitrogen plasma treatment of SF at 120 W (30 min) enhanced the SFREC by nearly 122.75% superior interlaminar shear strength, 71.09% greater flexural strength, 84.22% higher tensile strength and 109.74% higher elongation. The combination of improved surface roughness and more effective lignocellulosic exposure has been responsible for the increase in the mechanical characteristics of treated composites. The development of hydrophobicity in the SF had been induced by CGDP N₂ modification and enhanced the size of crystals and crystalline structure by removing some unwanted constituents of the SF and etching the smooth lignin-rich surface layer of the SF particularly revealed via FTIR and XRD.

Chemical and physical treatments have been identified as the most efficient ways of treating the fiber surface. However, the huge amounts of liquids and chemicals needed in chemical methods and their exorbitant performance in terms of energy expenditure have limited their applicability in the past decades. The use of appropriate cohesion in addition to stimulating the biopolymer texture without changing its bulk polymer properties leads to the formation and establishment of plasma surface treatments that offer a unified, repeatable, cost-effective and environmentally benign replacement.

The authors are sure that this technology will be adopted by the polymer industry, aerospace, automotive and related sectors in the future.

The purpose of this study is to systematically analyze the wear of cylindrical needle bearings in rotary vector reducers under temperature rise and identify the influencing factors.

Based on the dynamic characteristics of the RV-20E reducer, the time-varying contact force of the cylindrical needle bearing and the entrainment speed of the inner and outer raceways were calculated. A mixed elastohydrodynamic lubrication model of the needle bearing, considering friction and temperature rise, was established using a dynamic rough tooth surface model. The model solved for the oil film thickness, contact stress and wear conditions of the bearing raceway contact area. The effects of the number of rolling needles, the diameter of rolling needles and surface strength on the wear characteristics were analyzed.

The results of this study show that the oil film thickness, oil film pressure and surface scratches of cylindrical needle bearings exhibit an uneven, patchy distribution under the combined effects of friction and temperature rise. When the radius of the rolling needle is less than 1.44 mm, inner ring wear is less than outer ring wear. Conversely, when the radius exceeds 1.44 mm, inner ring wear is greater. The optimal rolling needle radius is 1.6 mm. Increasing the number of rolling needles and enhancing the yield strength of the contact surface significantly extend bearing life.

This study provides valuable recommendations for optimizing bearing structural parameters and material characteristics in the design of rotary vector reducers.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0242/

This paper explores how blockchain technology can enhance information quality within project management information systems (PMIS), thereby positively affecting knowledge management, learning capabilities and project portfolio success.

We employ a literature review and a theory-based approach to develop a conceptual framework and set of propositions that integrate key principles from blockchain technology, project management and dynamic capabilities theory. Subsequently, a focus group is conducted to refine our propositions, providing insights and examples demonstrating the potential value of blockchain in project management.

The findings suggest that blockchain significantly impacts the information quality within PMIS. This improvement in information quality enhances traceability, reliability and security of project data, facilitating better decision-making and governance. The focus group revealed blockchain’s benefits in managing confidential data and streamlining knowledge sharing processes, ultimately contributing to project portfolio success.

This research offers a novel conceptual framework and original insights into the application of blockchain in project management, particularly within the context of Industry 4.0, paving the way for future research on digital transformation in project management.

By extending corporate social responsibility research to a megaproject context, the purpose of this study is to examine the effect of megaproject external social responsibility (SR) on community acceptance through the mediating role of public trust.

Drawing from legitimacy theory as well as corporate social responsibility literature, this study developed hypotheses linking the constructs of SR, (public) trust and community acceptance of a megaproject. Thereby, a household survey questionnaire was administered to collect data from randomly selected 328 households in the local community who reside near the construction site of a mega hydropower project in Ethiopia. Guided by structural equation modelling, Smart Partial Least Squares4 (SmartPLS4) was utilized to analyze the data.

The empirical findings show that community-targeted SR has a significant positive effect on community acceptance of a megaproject, whereas the direct effect of natural environment-targeted SR on community acceptance was found to be non-significant. More importantly, the study reveals that trust in the contractor of the megaproject mediates the relationships between a megaproject SR (fully in the case of the natural environment and partially for community-targeted responsibility) and community acceptance.

The present study contributes to legitimacy theory by illustrating the consequences of megaproject external SR in terms of trust and community acceptance, an issue which has not received adequate attention in the project management literature.

This study aims to investigate an intersecting single-walled structure fabricated using wire-arc directed energy deposition (waDED). Because of the highly complex geometrical features of this structure, characterisation is used to identify potential weak points and provide a benchmark for future complex components.

A structural component with a process-specific design is built using additive manufacturing of an Al-Mg alloy and analysed using micro-computed tomography. Scans are carried out at different resolutions and subsequently compared to microsections. The chemical composition and hardness are also examined. These investigations provide an enhanced understanding of defects and overall quality of the manufactured parts.

The results show that very high-quality parts can be achieved using ER5183 alloy, even in intersecting areas. Defects in these regions are primarily caused by converging and diverging waDED paths and discontinuous waDED operations.

In addition to demonstrating the feasibility of complex structures using waDED, this study provides an overview of problem areas and potential improvements in waDED manufacturing.