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The penetration of mobile social media has given birth to various online collective spaces (OCSs) within cross-functional project teams (CFPTs), which have significantly transformed knowledge sharing (KS) practice. While existing literature has endeavored a lot on KS factors within CFPTs, our understanding of KS in OCSs is limited, thus constituting the central of the present study.

Centering on communication visibility, the core feature of KS in OCSs, this study combines social power theory and knowledge governance theory to shed light on the influence of two agents, i.e. members in the OCS and the project team. Data were collected by administering a questionnaire survey to 205 project team members in China. Ordinary least squares regression and fuzzy-set qualitative comparative analysis (QCA) techniques are used to analyze the data.

The ordinary least squares (OLS) regression results suggest that expert power of oneself, formal knowledge governance mechanism (KGM) and group size positively influence both KS quantity and quality. Besides, hierarchical distance is found to negatively influence KS quantity, and informal KGM is found to positively affect KS quality. The fuzzy-set QCA results reveal three configurations sufficient for KS quantity and four configurations sufficient for KS quality. Notably, the combination of expert power of oneself, formal and informal KGMs is essential for both KS quantity and quality.

This study provides valuable insights into KS in OCSs by synthesizing multi-level contextual antecedents from multiple theoretical perspectives. The results of OLS regression and fuzzy-set QCA are discussed, offering a comprehensive understanding of the mechanisms impacting KS. Ultimately, this study aims to offer guidance to managers on effectively facilitating KS within the contemporary digital era.

Due to the flexibility and proactivity emphasized by job crafting in today’s uncertain and complex era, scholars have preliminarily proved its positive consequences in the project context. Nevertheless, little is known about the task-related antecedents of job crafting in the construction project environment and whether these task factors can be further converted into project performance through job crafting of project teams, thereby motivating this study.

Drawing on the person-environment fit theory, this study hypothesized job crafting of construction project teams as a mediator that links the project task-related job resources (job autonomy and informal coordination) and job demands (time urgency and task complexity) with project performance. The proposed conceptual model was empirically validated with survey data from 244 members of construction project teams.

The results show that job autonomy and informal coordination motivate project teams to engage in job crafting and thus improve project performance. Further, the direct effect of task complexity on team job crafting and the indirect effect on project performance are negative. Moreover, informal coordination is found to buffer the negative impact of task complexity on team job crafting and project performance.

This research explores how construction task characteristics can be leveraged to boost project performance from a team proactivity perspective. The findings of this study offer novel insights into effectively incentivizing job crafting of project teams and transforming task characteristics into project performance within the construction management field.

Drawing on the stakeholder theory, this study aims to empirically analyse the impact of platform enterprises’ corporate social responsibility (CSR) announcements on corporate stock market value. This study also estimates the moderating effect of stakeholder orientation and responsibility categories of CSR announcements, the platform enterprise type and the degree of CSR disclosure.

The event study method is used to analyse the change in stock market value of 191 CSR announcements from 137 Chinese platform enterprises. In addition, a case analysis is presented for two platform enterprises with the best practices to validate and complement study findings.

CSR announcements improve platform enterprises’ stock market value. Specifically, CSR announcements responding to platform enterprises’ external stakeholders, and CSR announcements with economic responsibility, have obvious positive impacts on stock market value. Furthermore, the maker platform’s CSR announcement has a more positive impact on stock market value than the exchange platform.

To the best of the authors’ knowledge, this study is the first attempt to identify the link between platform enterprises’ CSR announcements and stock market performance by empirical evidence, and it contributes to new knowledge of operating and evaluating platform enterprises’ CSR.

Recently, due to the practicability in several domains, generative adversarial network (GAN) has successfully been adopted in the field of natural language generation (NLG). The purpose of this paper focuses on improving the quality of text and generating sequences similar to human writing for several real applications.

A novel model, GAN², is developed based on a GAN with dual adversarial architecture. We train the generator by an internal discriminator with a beam search technique to improve the quality of generated sequences. Then, we enhance the generator with an external discriminator to optimize and strengthen the learning process of sequence generation.

The proposed GAN² model could be utilized in widespread applications, such as chatbots, machine translation and image description. By the proposed dual adversarial structure, we significantly improve the quality of the generated text. The average and top-1 metrics, such as NLL, BLEU and ROUGE, are used to measure the generated sentences from the GAN² model over all baselines. Several experiments are conducted to demonstrate the performance and superiority of the proposed model compared with the state-of-the-art methods on numerous evaluation metrics.

Generally, reward sparsity and mode collapse are two main challenging issues when adopt GAN to real NLG applications. In this study, GAN² exploits a dual adversarial architecture which facilitates the learning process in the early training stage for solving the problem of reward sparsity. The occurrence of mode collapse also could be reduced in the later training stage with the introduced comparative discriminator by avoiding high rewards for training in a specific mode. Furthermore, the proposed model is applied to several synthetic and real datasets to show the practicability and exhibit great generalization with all discussed metrics.

This paper aims to propose a real-time evolutionary multi-objective semi-analytical inverse kinematics (IK) algorithm (EMSA-IK) for solving the multi-objective IK of redundant manipulators.

Within EMSA-IK, the parameterization method is applied to reduce the number of optimization variables of the evolutionary algorithm and calculate semi-analytical solutions that meet high target pose accuracy. The original evolutionary algorithm is improved with the proposed adaptive search sub-space strategy so that the improved evolutionary algorithm can be used to efficiently perform global search within the parametric joint space to obtain the global optimal parametric joint angles that satisfy multi-objective constraints.

Ablation experiments show the effectiveness of the improved strategy used for evolutionary algorithms. Comparative experiments on different manipulators demonstrate the advantages of EMSA-IK in terms of generalizability and balancing multiple objectives, for example, motion continuity, joint limits and obstacle avoidance. Real-world experiments further validate the effectiveness of the proposed algorithm for real-time application.

The semi-analytical IK solution that simultaneously satisfies high target pose accuracy and multi-objective constraints can be obtained in real time. Compared to existing semi-analytical IK algorithms, the proposed algorithm achieves obstacle avoidance for the first time. The proposed algorithm demonstrates superior generalizability, applicable to not only redundant manipulators with revolute joints but also those with prismatic joints.

In the current uncertain working environment, how and when employees adapt their performance is an essential issue. Based on the integrative model of uncertainty tolerance, this study aims to investigate the relationship between uncertainty and adaptive performance by testing the mediating role of negative affect and mindfulness as a moderator.

To test the model, 159 participants took part in a diary study during 10 working days (n = 159 x 10 = 1,590).

The multilevel results showed that negative affect mediated the within-person effect of uncertainty on adaptive performance. Further, mindfulness moderated the relationship between uncertainty and adaptive performance in such a way that it became stronger for individuals who scored lower on mindfulness (versus higher mindfulness).

Understanding what may sustain and amplify responses to uncertain working conditions may help managers to design interventions that may support their employees’ responses to effectively cope with them.

This study shed light on the relevance of both negative affect and mindfulness on the relationship between uncertainty and adaptive performance. In addition, the findings expand the theoretical knowledge of the mechanism underlying the relationship between uncertainty and adaptive performance, and the condition that may strengths this relationship.

This study investigates the factors influencing the saving behaviour of Chinese Generation Z (Gen Z) through Internet wealth management (IWM) services. It adopts the unified theory of acceptance and use of technology (UTAUT) as the theoretical framework, focusing on key determinants such as performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC). The research also explores the mediating role of the intention to save via IWM and its subsequent influence on actual saving behaviour.

The hypotheses were assessed using data collected from 274 Gen Z users in China. The data were analysed using the partial least squares structural equation modelling.

The results suggest that the formation of intention among Gen Z to save through IWM services is directly affected by factors such as PE, EE, SI and FC. Intention to save via IWM positively influences actual saving behaviour. Mediation analysis further confirms the mediating role of intention to save via IWM in these relationships.

The findings have direct implications for financial institutions and policymakers engaged in promoting the practice of saving via IWM services among Gen Z, thereby fostering a culture of proactive financial management and encouraging saving behaviour.

The study contributes to the existing literature by being among the first to examine Gen Z’s IWM adoption as a personal saving tool through the theoretical lens of the UTAUT.

This study aims to construct a system dynamics model for industrial ecosystems, focusing on the sustainable development of the manufacturing industry. It seeks to promote coordinated development among industry, society and nature by integrating interactions among stakeholders, supporting institutions and the external environment.

This study utilizes data from China’s manufacturing industry between 2011 and 2018 and employs a system dynamics model to analyze the key factors influencing the sustainable development of the manufacturing ecosystem. Additionally, policy recommendations are proposed based on the synergistic effects of factors within three distinct subsystems driving sustainable development.

The results demonstrate a trade-off between industrial growth and environmental sustainability. Financial development and technology import drive output and innovation but increase pollution emission and energy consumption. Resource recycling is key to reducing pollution emissions and fostering greener ecosystems. While technological adoption and talent boost product growth, they fail to lower environmental impact. Macro-environmental improvements promote growth but add ecological pressure. Therefore, policies should focus on green technologies, resource recycling, environmental assessments and targeted funding for sustainable innovation to achieve balanced industrial development and ecological sustainability.

This paper presents a generic system dynamics model for evaluating and guiding the sustainable development of the manufacturing industry. It provides valuable insights for policymakers to identify key factors for sustainable manufacturing development, understand the evolution of the industrial ecosystem and enhance economic sustainability at the industrial level.

Pigments, as essential colorants, are extensively used in industries such as coatings, inks and plastics. However, the dispersibility of pigments during application critically affects their performance. Therefore, it is crucial to enhance pigment dispersion to improve their application performance. This paper aims to offer valuable insights into the enhancement of pigment dispersion.

Drawing upon existing research on pigment dispersion, this study synthesizes key achievements into two main categories: pigment dispersion mechanism and pigment dispersion technology.

Concerning pigment dispersion mechanism, the article summarizes the three pivotal processes and their respective mechanisms: wetting, dispersion and stabilization. In addition, research advances in computational modeling and structure–activity relationship studies are introduced. Regarding pigment dispersion technology, the paper reviews technological advancements in the utilization of surfactants, solid solution preparation, application of potential pigments, manufacture of pigment derivatives and encapsulation of polymer materials.

Understanding the dispersion mechanism and dispersion technology is instrumental in guiding pigment dispersion practices. Effective application of these principles can enhance pigment performance and expand their application scope.

This paper proposes a novel visual-inertial-ranging odometry (VIRO) algorithm that tightly incorporates bundled ultra-wideband (UWB) sensors into existing visual-inertial odometry (VIO), with the aim to provide fully observable robot state estimation. The VIRO estimator proposed in this paper is the first VIRO estimator to demonstrate full observability.

This paper proposes a tightly coupled fusion model of visual-inertial-ranging measurements based on extended Kalman filter (EKF), where UWB anchors are integrated into a cohesive sensor network. This paper modifies the Schmidt Kalman filter formulation to efficiently incorporate the robot’s poses into state vector, facilitating online UWB network initialization and consistent state augmentation.

Observability analysis proves that the proposed VIRO system has four unobservable directions in the VIO reference frame, and achieves fully observable estimation in the gravity-aligned UWB network frame. Extensive simulation and real-world experiments are conducted to validate the proposed method.

Most existing approaches treat UWB anchors as independent sensing sources, overlooking the potential of leveraging the relative poses between these cooperative targets to enhance localization accuracy. Or, the UWB network and other VIO components are loosely coupled, with the robot pose estimation being solved independently before fusion. However, this approach fails to account for UWB sensing errors, which is unestimable, leading to manual intervention in parameter settings. This work proposed the first proved fully observable VIRO estimator based on a tightly coupled EKF framework.