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This study aims to present the critical role of knowledge management in digital entrepreneurship by reviewing the literature and proposing future research directions for digital entrepreneurship and knowledge management through an interdisciplinary framework.

This study uses the Derwent Data Analyzer to identify and visualise the extant studies on digital entrepreneurship. This study qualitatively analyses the hot topics and trends in digital entrepreneurship research to understand digital entrepreneurship from the knowledge management perspective.

The authors found two dominant trends in existing research: logical and development trend exploration at the theoretical background and empirical research at the practical dimension. To understand digital entrepreneurship from a knowledge management perspective, the authors summarised the theoretical logic and internal and external reasons why knowledge management is required in digital entrepreneurship. Moreover, the authors analysed the new features of digital entrepreneurship under five aspects: management concept, object, content, scope and focus. The authors concluded that existing research on integrating knowledge management and digital entrepreneurship is primarily conducted from three perspectives: technology, platform and ecosystem.

This study provides an in-depth analysis of digital entrepreneurship from a knowledge management perspective. The findings can further promote the theoretical research and practical development of digital entrepreneurship and knowledge management. This approach provides a new direction for interdisciplinary study and enriches entrepreneurship research. In addition, this study proposes a knowledge management framework for digital entrepreneurship research. The findings contribute to understanding the role and function of knowledge management in digital entrepreneurship.

The study aims to analyze how the characteristics of intellectual capital (IC) facilitate green entrepreneurship development in the context of ecology, environment and sustainability. Specifically, the evolution of IC and green entrepreneurship was explored through a systematic review, including the relationships and interactions between human, structural and relational capital and green entrepreneurship.

Meticulously combing the Web of Science Core Collection, the researcher conducted a bibliometric analysis of 800 English-language articles from 2002 to 2023. Employing co-word analysis and visualization, the literature on IC and green entrepreneurship was synthesized and systematized, exploring core topics, knowledge architectures and their evolutionary trajectories.

The IC elements such as human, structural and relational capital interact with green entrepreneurship; IC enhances the innovation and competitiveness of green entrepreneurship, while green entrepreneurship orientation influences the accumulation and reshaping of IC. The flow of IC impacts the establishment of green start-ups and the emergence of green industries, promoting sustainable growth.

The dynamic interplay between IC and green entrepreneurship is marked by intricate relationships and diverse attributes. Currently, no comprehensive theoretical model has been established to address the complexities intrinsic to this study. The evidence suggests that the green entrepreneurial orientation influences corporate initiatives to bolster human and structural capital, with structural capital serving as both a constraint and catalyst for human capital. The paper presents an embryonic framework of IC for green entrepreneurship, highlighting its critical role in the aggregation and reconfiguration of IC or venture creation and industry evolution. This contributes to a more profound understanding of IC in entrepreneurial contexts, providing a basis for future research and practical strategy.

Based on social cognitive theory, this study aims to investigate the influence of perceived overqualification (POQ) on employees’ cyberloafing behavior. The mediating role of moral disengagement and the moderating roles of organizational identification (OID) and organizational decline are further examined.

The authors collected 740 valid questionnaires from participants across multiple organizations. To minimize common method bias (CMB) and enhance the reliability of the findings, data were gathered at two different time points, with a 30-day interval.

POQ positively impacts cyberloafing through the mechanism of moral disengagement. Additionally, the indirect relationship between POQ and cyberloafing via moral disengagement is moderated by OID and organizational decline. Specifically, a higher degree of OID weakens the indirect effect of POQ on cyberloafing, while a higher level of perceived organizational decline strengthens this effect.

While most existing studies on cyberloafing focus on insufficient resources, such as role conflict and workload, the authors propose that surplus personal resources, exemplified by POQ, can also lead to cyberloafing. This research contributes to a broader understanding of antecedents of cyberloafing, highlighting the mechanism of ethical considerations and the interplay between personal qualifications, organizational identification and organizational decline.

The garment industry will be one of the major beneficiaries of advances in smart manufacturing, as it is highly labor-intensive and heavily depends on labor force. Manipulating robots in human environments has made great strides in recent years. However, the main research has focused on rigid, solid objects and core capabilities such as grasping, placing remain a challenging problem when dealing with soft textiles. The experimental results indicate that adopting the proposed bionic soft finger will provide garment manufacturers with smart manufacturing capabilities. Then, the purpose of this paper is to utilize the flexibility of the soft finger to transfer fabric layer by layer without damage in garment automation.

In this paper, a new way to separate layer by layer pieces of fabric has been inspired by the rise of soft robotics and their applications in automation. Fabric gripping is accomplished by wiping deformation and pinching the fabric. A single fabric piece is separated from cutting pile by the soft finger in four steps: making an arch by pressing, wiping deformation, grasping and separating, and placing.

The case study demonstrated that the soft finger arrangement for automated grasping of fabric pieces of a garment can be successfully applied to delicate fabric. A combination of cloth shape and weight determines the number of soft fingers. In addition, the soft finger was tested on different types of fabrics to determine its performance and application capabilities. The technology may be used to produce clothing intelligently in the future, such as intelligent stacking, intelligent transportation and intelligent packaging, to increase clothing industry productivity.

An industrial bionic soft finger gripping system is proposed in this paper for application in the field of fabric automatic manipulation. A piece of fabric could be picked up and released layer by layer from a stack by the proposed gripper without creating any damage to it. Soft grippers have the right proportion of softness and rigidity like a human being. A soft finger has a potential affinity for soft materials such as fabrics without damaging either their surface or their properties.

Energy conservation and environmental protection industry (ECEPI) is a strategic choice to promote energy conservation and emission reduction, develop green economy and circular economy. However, China’s ECEPI is still in the stage of rapid development and the overall scale is relatively small, what development periods have the ECEPI experienced? This study aims to contribute to a better understanding of collaborative innovation evolution based on social network analysis from the perspective of multi-dimensional proximity.

Methodologically, this study uses social network analysis method to explore the co-evolution of multidimensional collaboration networks. It divides China’s ECEPI into four periods based on national policies from 2001 to 2020. This contribution constructs collaborative innovation networks from geographical, technological and organizational proximity.

The results show that the collaborative innovation network was initially formed in the central region of China, gradually expanded to neighboring cities and the core positions of Beijing, Jiangsu and Guangdong have been continuously consolidated. C02F has been the core of the collaboration networks, and the research focus has gradually shifted from the treatment of wastewater, sewage or sludge to the separation field. Enterprises always occupy a dominant position in the collaboration networks.

This research investigates the dynamic evolution process of collaborative innovation network in China’s ECEPI from the perspective of multidimensional proximity, explores the community structure, important nodes and multidimensional proximity features in the network, expands the research perspective on evolution characteristics of innovative network and the research field of social network analysis. Theoretically, this study enriches collaborative innovation theory, social network theory and multi-dimensional proximity theory.

The main goal of this paper is to present a synthetic multiple criteria group decision-making (MCGDM) methodology for assessing the enterprise digital maturity with linear Diophantine fuzzy (LDF) setting.

This paper utilizes the presented LDF generalized Dombi operator to aggregate assessment information of experts. The developed combined weight model through merging the rank sum (RS) model and symmetry point of criterion (SPC) method is used to ascertain the comprehensive importance of criterion. The evaluation based on distance from average solution (EDAS) approach based upon regret theory (RT) is presented to achieve the sorting of candidate enterprises.

Firstly, the proposed method has strong stability. Secondly, the proposed method takes into consideration the psychological behavior of experts during the decision-making process which further enhances the rationality of the decision results. Finally, the proposed method integrates expert and criterion weight determination models which provides a practical evaluation framework for assessing the digital maturity of enterprises. The research outcomes confirm that the proposed approach fails to resolve the decision problems with unknown weight information flexibly, but also reflect the psychological behavior of expert in decision process. The presented weight approach also provides a rational algorithm to ascertain the weight more accurate.

A composite LDF group decision-making approach is presented by aggregating the proposed generalized Dombi operator, combined weight model and the EDAS model, which make the outcome more reasonable. Sensitivity analysis and comparison study are conducted to reflect the superiority of the proposed approach.

The proposed model aims to tackle the data quality issues in multivariate time series caused by missing values. It preserves data set integrity by accurately imputing missing data, ensuring reliable analysis outcomes.

The Conv-DMSA model employs a combination of self-attention mechanisms and convolutional networks to handle the complexities of multivariate time series data. The convolutional network is adept at learning features across uneven time intervals through an imputation feature map, while the Diagonal Mask Self-Attention (DMSA) block is specifically designed to capture time dependencies and feature correlations. This dual approach allows the model to effectively address the temporal imbalance, feature correlation and time dependency challenges that are often overlooked in traditional imputation models.

Extensive experiments conducted on two public data sets and a real project data set have demonstrated the adaptability and effectiveness of the Conv-DMSA model for imputing missing data. The model outperforms baseline methods by significantly reducing the Root Mean Square Error (RMSE) metric, showcasing its superior performance. Specifically, Conv-DMSA has been found to reduce RMSE by 37.2% to 63.87% compared to other models, indicating its enhanced accuracy and efficiency in handling missing data in multivariate time series.

The Conv-DMSA model introduces a unique combination of convolutional networks and self-attention mechanisms to the field of missing data imputation. Its innovative use of a diagonal mask within the self-attention block allows for a more nuanced understanding of the data’s temporal and relational aspects. This novel approach not only addresses the existing shortcomings of conventional imputation methods but also sets a new standard for handling missing data in complex, multivariate time series data sets. The model’s superior performance and its capacity to adapt to varying levels of missing data make it a significant contribution to the field.

With the development of Web information systems, steel e-commerce platforms have accumulated a large number of quality objection texts. These texts reflect consumer dissatisfaction with the dimensions, appearance and performance of steel products, providing valuable insights for product improvement and consumer decision-making. Currently, mainstream solutions rely on pre-trained models, but their performance on domain-specific data sets and few-shot data sets is not satisfactory. This paper aims to address these challenges by proposing more effective methods for improving model performance on these specialized data sets.

This paper presents a method on the basis of in-domain pre-training, bidirectional encoder representation from Transformers (BERT) and prompt learning. Specifically, a domain-specific unsupervised data set is introduced into the BERT model for in-domain pre-training, enabling the model to better understand specific language patterns in the steel e-commerce industry, enhancing the model’s generalization capability; the incorporation of prompt learning into the BERT model enhances attention to sentence context, improving classification performance on few-shot data sets.

Through experimental evaluation, this method demonstrates superior performance on the quality objection data set, achieving a Macro-F1 score of 93.32%. Additionally, ablation experiments further validate the significant advantages of in-domain pre-training and prompt learning in enhancing model performance.

This study clearly demonstrates the value of the new method in improving the classification of quality objection texts for steel products. The findings of this study offer practical insights for product improvement in the steel industry and provide new directions for future research on few-shot learning and domain-specific models, with potential applications in other fields.

The aim of this study is to establish the nonlinear dynamics equations of roller bearings with surface faults on outer raceway, inner raceway and rolling elements to analyze the dynamic characteristics of the double row self-aligning roller bearings, and provided theoretical basis for bearing fault diagnosis and life prediction.

First, based on the momentum theorem, the formulas for quantitative calculation of impact load were established, when roller was in contact with the fault of the inner or outer raceway. Then, the fault position piecewise functions and the load-carrying zone piecewise functions were established. Based on these, the nonlinear dynamic equations of double row self-aligning roller bearings are established, and Matlab is used to simulate the faulty bearings at different positions, sizes and rotational speeds. Finally, the vibration test of the fault bearings are completed, and the correctness of the nonlinear dynamic equations of the rolling bearing are verified.

The simulation and test results show that: the impact load increased with the increasing rotate speed and fault size, and the larger the fault size, the longer the impact load existed and the shorter vice versa.

The nonlinear dynamic equation of double row self-aligning roller bearings is established, which provides a theoretical basis for bearing faults diagnosis and fatigue life prediction.

This paper aims to understand how quantum leaders influence employee work behavior through effective tasks.

In this study, 516 questionnaires were collected using the interval data method to explore the triggering mechanisms and paths of emerging quantum leadership on constructive deviance.

The findings indicate that quantum leadership promotes constructive deviance through facilitating recovery experience (affective path), job crafting (task path) and the chained mediation path between the two. Additionally, the moderating effect of openness to experience strengthens the pathways between quantum leadership and recovery experience, and between quantum leadership and job crafting.

This study focuses closely on the mechanism of leadership behavior on employees, neglecting the psychological state and behavior of the leader as a key resource element in the work environment. Quantum leadership emphasizes value-bound characteristics, so the role played by quantum leaders may vary in different cultures and values.

First, this study calls for the organizational management focusing on the advantages of quantum leadership thinking and its positive effects in practice. Second, the mediating mechanisms of recovery experience and job crafting provide insights into how quantum leadership can be used to enhance constructive deviance. Third, this study elucidates how individual responses to organizational environment and leadership style vary in management practices. Our study helps managers better understand how individual characteristics, such as openness to experience, influence managerial behavior.

This study enriches the qualitative research on emerging “quantum” perspectives of leadership, expands the mechanism of employee constructive deviance and highlights the need for organizations to take measures that encourage constructive deviance by their employees, as this can lead to high-quality and long-term growth.

Based on conservation of resources theory, authors revealed the mechanisms by which quantum leadership influences employees’ constructive deviance, confirming the mediating role of recovery experience and job crafting as well as the moderating role of openness to employee experience. We explored the moderating mechanisms of the individual trait of openness to experience in the quantum leadership-to-job crafting and the recovery experience-to-job crafting.