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Green technology innovation is the organic combination of green development and innovation driven. It is also a powerful guarantee for shaping sustainable competitive advantages of manufacturing enterprises. To explore what kind of executive incentive contracts can truly stimulate green technology innovation, this study aims to distinguish the equity incentive and reputation incentive, upon their contractual elements characteristics and green governance effects, and then put forward suggestions for green technology innovation accordingly.

This study establishes an evaluation model and uses empirical methods to test. Concretely, using data from A-share listed manufacturing companies for the period from 2007 to 2020, this study compares and analyzes the impact of equity and reputation incentive on green technology innovation and explores the relationship between internal green business behavior and external green in depth.

This study finds that reputation incentives focus on long-term and non-utilitarian orientation, which can promote green technology innovation in enterprises. While equity incentives, linked to performance indicators, have a inhibitory effect on green technology innovation. Internal and external institutional factors such as energy conservation measures, the “three wastes” management system, and environmental recognition play the regulatory role in the relationship between incentive contracts and green technology innovation.

Those findings validate and expand the efficient contracting hypothesis and the rent extraction hypothesis from the perspective of green technology innovation and provide useful implications for the design of green governance systems in manufacturing enterprises.

Autonomous underwater vehicle (AUV) is widely used in resource prospection and underwater detection due to its excellent performance. This study considers input saturation, nonlinear model uncertainties and external ocean current disturbances. The containment errors can be limited to a small neighborhood of zero in finite time by employing control strategy. The control strategy can keep errors within a certain range between the trajectory followed by AUVs and their intended targets. This can mitigate the issues of collisions and disruptions in communication which may arise from AUVs being in close proximity or excessively distant from each other.

The tracking errors are constrained. Based on the directed communication topology, a cooperative formation control algorithm for multi-AUV systems with constrained errors is designed. By using the saturation function, state observers are designed to estimate the AUV’s velocity in six degrees of freedom. A new virtual control algorithm is designed through combining backstepping technique and the tan-type barrier Lyapunov function. Neural networks are used to estimate and compensate for the nonlinear model uncertainties and external ocean current disturbances. A neural network adaptive law is designed.

The containment errors can be limited to a small neighborhood of zero in finite time so that follower AUVs can arrive at the convex hull consisting of leader AUVs within finite time. The validity of the results is indicated by simulations.

The state observers are designed to approximate the speed of the AUV and improve the accuracy of the control method. The anti-saturation function and neural network adaptive law are designed to deal with input saturation and general disturbances, respectively. It can ensure the safety and reliability of the multiple AUV systems.

In the era of the digital economy, higher demands are placed on versatile talents, and the cultivation of students with innovative and entrepreneurial abilities has become an important issue for the further development of higher education, thus leading to extensive and in-depth research by many scholars. The study summarizes the characteristics and patterns of dual-innovation education at different stages of development, hoping to provide a systematic model for the development of dual-innovation education in China and make up for the shortcomings.

This paper uses Citespace software to visualize and analyze the relevant literature in CNKI and Web of Science databases from a bibliometric perspective, focusing on quantitative analysis in terms of article trends, topic clustering, keyword co-linear networks and topic time evolution, etc., to summarize and sort out the development of innovation and entrepreneurship education research at home and abroad.

The study found that the external characteristics of the literature published in the field of bi-innovation education in China and abroad are slightly different, mainly in that foreign publishers are more closely connected and have formed a more stable ecosystem. In terms of research hotspots, China is still in a critical period of reforming its curriculum and teaching model, and research on the integration of specialization and creative education is in full swing, while foreign countries focus more on the cultivation of students' entrepreneurial awareness and the enhancement of individual effectiveness. In terms of cutting-edge analysis, the main research directions in China are “creative education”, “new engineering”, “integration of industry and education” and “rural revitalization”.

Innovation and entrepreneurship education in China is still in its infancy, and most of the studies lack an overall overview and comparison of foreign studies. Based on the econometric analysis of domestic and foreign literature, this paper proposes a path for domestic innovation and entrepreneurship education reform that can make China's future education reform more effective.

Amidst the increasing global emphasis on environmental sustainability, manufacturing firms seek to integrate eco-conscious practices into their innovation processes. This study aims to explore the intricate relationships between green learning orientation (GLO), knowledge management capability (KMC), resource orchestration capability (ROC) and two dimensions of green innovation (GI): green product innovation (GPDI) and green process innovation.

Partial least squares structural equation modelling (PLS-SEM) and moderated mediation techniques were used to investigate the relationships among the constructs using data gathered from a survey of 167 manufacturing firms in the United Arab Emirates.

This study indicates that GLO significantly influences GPDI and green process innovation. Although KMC mediates the relationship between GLO and process innovation, it does not mediate the GPDI relationship. Moreover, ROC significantly strengthens the links between GLO, KMC and both the aspects of GI.

This study emphasises the importance of fostering a green learning culture and integrating it into product development without complex knowledge management systems. This study also highlighted the role of effective resource allocation in maximising environmental learning benefits for sustainable innovation. Organisations can achieve environmental progress by integrating green knowledge into product and process development and by investing in sustainable practices.

By examining various mechanisms involving moderation and mediation, this study has made a notable contribution to advancing the field of knowledge-based view theory. This study also offers enhanced insights into the interconnections among GLO, knowledge management capability, ROC and a firm’s capacity for GI.

In the context of the environmental degradation challenge in manufacturing firms, greening the supply chain (SC) is the most widely endorsed method to mitigate the adverse repercussions of climate change. Based on organizational learning and resource dependence theories, the aim of this research is to know how green supply chain external integration (GSCEI) and green supply chain internal integration (GSCII) influence ambidextrous green innovation (AGI). It also examines the mediating roles of green absorptive capacity (GAC) and green knowledge integration capability (GKIC), as well as the moderating role of green technology dynamism (GTD).

To assess the hypothesized model, data were obtained with 386 questionnaires from managers employed in manufacturing firms in Pakistan applying a cross-sectional approach. A partial least square structural equation modeling technique was implemented to evaluate the data.

The results revealed that GSCEI and GSCII substantially impact AGI. Moreover, GAC and GKIC serve as mediators between GSCEI and AGI. GAC and GKIC also intervene in the relationship between GSCII and AGI. GTD was significant as a moderator for the correlation between GSCEI and AGI. However, it does not moderate the relationship between GSCII and AGI.

This research offers significant comprehension and an innovative approach for manufacturing organizations to curb environmental corrosion by stimulating AGI through green SC integration. It suggests to practitioners that integrating internal knowledge with external partners expands communication and collaboration to ensure that resources connected with environmental preservation flow smoothly.

This research is a valuable addition to the field, as it explores for the first time the missing link among the studied constructs. It opened the black box of how knowledge-related capabilities facilitate knowledge resources to elicit AGI, an area that has not yet been explored.

This study aims to determine whether knowledge creation processes (KCPs) – knowledge exchange and knowledge integration affect digital innovation (DI), including information technology (IT)-enabled capabilities (ITECs) as a mediator and absorptive capacity (AC) as a moderator.

With a survey data set of 390 employees from Pakistani software small- and medium-sized enterprises (SMEs), the current study employed Structural Equation Modeling (SEM) using Smart Partial Least Squares to estimate the structural relationships in the conceptual model.

The results confirm that KCPs – knowledge exchange and knowledge integration positively enhance software SME's DI; ITECs play a partial mediating role in the linkage between KCPs and DI; AC positively moderates the relationship between knowledge integration and ITECs, and ITECs and DI, while AC doesn’t moderate the relationship between knowledge exchange and ITECs. The AC positively moderates the mediating role of ITECs amongst KCPs (knowledge exchange and knowledge integration) and DI, respectively.

This research uniquely integrates the knowledge-based view and dynamic capability theory to present a comprehensive framework that explains the interdependencies between knowledge process, ITECs and AC in driving DI. This approach advances the understanding of how software SMEs can strengthen internal knowledge and IT resources to achieve superior innovation outcomes.

This study aims to use the knowledge management perspective to examine the mechanism through which entrepreneurship drives firms’ technological innovation in the digital age. The objective is to develop a multi-stage integrated theoretical model to explain how entrepreneurship exerts its influence on firms’ technological innovation with a particular focus on the knowledge management perspective. The findings can be used for the cultivation of entrepreneurship and for the promotion of continuous technological innovation activities.

This study uses a case-based qualitative approach to examine the relationship between entrepreneurship and technological innovation. The authors first analyze the case of SANY and then explore the mechanism of how entrepreneurship can promote a firm’s technological innovation from the perspective of knowledge management based on the technology-organization-environment framework. An integrated theoretical model is then developed in this study.

Based on a case study, the authors propose that there are three main processes of knowledge management in firms’ technological innovation: knowledge acquisition, knowledge integration and knowledge creation. In the process of knowledge acquisition, the joint effects of innovation spirit, learning spirit, cooperation spirit and global vision drive the construction and its healthy development of firms’ innovation ecosystem. In the process of knowledge integration, the joint effects of innovation spirit, cooperation spirit and learning spirit help complete the integration of knowledge and further the accumulation of firms’ core knowledge resources. In the process of knowledge creation, the joint effects of mission spirit, learning spirit and innovation spirit encourage the top management team to establish long-term goals and innovation philosophy. This philosophy can promote the establishment of a people-oriented incentive mechanism that helps achieve the transformation from the accumulation of core knowledge resources to the research and innovation of core technologies. After these three stages, firms are passively engaged in the “reverse transfer of knowledge” step, which contributes to other firms’ knowledge management cycle. With active knowledge acquisition, integration, creation and passive reverse knowledge transfer, firms can achieve continuous technological innovation.

This study has important theoretical implications in entrepreneurship research. This study helps advance the understanding of entrepreneurship and literature on the relationship between entrepreneurship and technological innovation in the digital age, which can broaden the application of knowledge management theories. It can also help better understand how to develop healthy firm-led innovation ecosystems to achieve continuous optimization of knowledge and technological innovation in the digital age.

This study proposes an integrated theoretical model to address the issues of entrepreneurship and firms’ technological innovation in the digital age, and it is also one of few studies that focuses on entrepreneurship and innovation from a knowledge management perspective.

With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.

With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.

Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.

The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.