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This study aims to investigate the effect of top management team (TMT) stability on outward foreign direct investment (OFDI) of Chinese firms and the moderating effects of state ownership and managerial ownership on this relationship.

An empirical analysis based on the ordinary least square regression model is conducted using Chinese A-share listed firms that engaged in OFDI from 2008 to 2021.

TMT stability has a positive effect on firms’ OFDI. Moreover, state ownership significantly strengthens the positive relationship between TMT stability and OFDI, while managerial ownership weakens this positive relationship.

The findings help firms to effectively retain TMT talents and promote the smooth internationalization of firms, thereby enhancing their long-term development capabilities and competitive advantages.

This study expands the investigation of the factors influencing OFDI at the micro level of the TMT, providing valuable decision-making insights for firms.

This study aims to comprehensively analyze the current developments and applications of paper-based electrochemical platforms for blood glucose detection, focusing on their potential to revolutionize point-of-care testing through cost-effective and accessible diagnostic solutions.

The review systematically examines fundamental principles of paper-based platforms, including substrate properties, fluid transport mechanisms and electrochemical detection methods. It critically evaluates recent technological advances in materials science, fabrication techniques and signal amplification strategies while analyzing various case studies demonstrating successful implementations.

Recent innovations in paper-based glucose sensors have achieved remarkable performance metrics, with detection limits reaching sub-millimolar ranges and response times within seconds. The integration of nanomaterials, particularly graphene-based composites and carbon nanotubes, has significantly enhanced sensor sensitivity and stability. Advanced enzyme immobilization techniques using layer-by-layer assembly have demonstrated sustained activity for up to 10 weeks, while novel signal amplification strategies incorporating bimetallic nanoparticles have pushed detection limits into the sub-picogram range.

This review uniquely synthesizes the latest developments in paper-based electrochemical glucose sensing, providing critical insights into the synergistic integration of advanced materials, fabrication methods and detection strategies. It offers valuable perspectives on overcoming current technical challenges and highlights emerging opportunities in smart device integration and artificial intelligence applications, serving as a comprehensive resource for researchers and practitioners in the field of point-of-care diagnostics.

Existing research on innovation has mainly focused on how to promote technological innovation in megaprojects and management innovation (MI) in megaprojects is still an unknown research field. The purposes of this study are to examine the effect of MI on megaproject performance and how the top management team (TMT) regulatory focus affects the use of MI in projects. At the same time, the moderating effects of project uncertainties are also tested.

On the basis of an explorative/exploitative ambidextrous analysis framework, this study divides MI into two dimensions: explorative and exploitative MI, and integrates the theoretical perspectives of the TMT regulatory focus and project uncertainties into a research model. Taking 314 responses from megaprojects’ TMTs in China as research data, this study empirically tests the above model.

Results show that exploratory MI has a U-shaped relationship with megaproject performance; whereas exploitative MI has an inverted U-shaped relationship with megaproject performance. The TMT promotion focus has a positive effect on exploratory and exploitative MI; and the TMT prevention focus has a negative effect on exploratory MI but has a positive effect on exploitative MI. Project uncertainties have a positive moderating effect on the positive relationship between TMT promotion focus and exploratory MI, whereas it has a negative moderating effect on the negative relationship between the TMT prevention focus and exploratory MI.

By empirically measuring the relationship between two types of MIs and megaproject performance, this study clarifies the differential mechanism of the effect of different MIs on megaproject performance. This study also examines the MI of megaprojects from the perspective of the TMT regulatory focus and expounds how changes in uncertainties affect the relationship between the TMT regulatory focus and MI.

Performance optimization algorithms based on node attributes are of great importance for sharding blockchain systems. Currently, existing studies on blockchain sharding algorithms consider only random selection sharding strategies. However, the random selection strategy does not perfectly utilize the performance of a node to break the bottleneck of blockchain performance.

This paper proposes a blockchain sharding algorithm called TOPSIS Optimization Sharding System (TOSS), which is based on entropy weight method, relative Euclidean distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It defines a multi-attribute matrix to assess node performance and applies TOPSIS for scoring nodes. Then, an algorithm based on the TOPSIS method is proposed to calculate the performance score of each data node. In addition, an entropy weighting method is introduced to obtain the weights of each attribute to balance the impact of dimensional differences of attributes on the attribute weights. Nodes are ranked by composite scores to guide partitioning.

The effectiveness of the proposed algorithm in this paper is verified by comparing it with various comparative algorithms. The experimental results show that the TOSS algorithm outperforms the comparison algorithms in terms of performance improvement for the blockchain system, and the throughput metrics are improved by about 20% in comparison.

This study introduces a novel approach to blockchain sharding by incorporating the entropy weight method and relative Euclidean distance TOPSIS into the sharding process. This approach allows for a more effective utilization of node performance attributes, leading to significant improvements in system throughput and overall performance, addressing the limitations of the random selection strategy commonly used in existing algorithms.

This paper aims to an analytical investigation of a consequent poles (CP) permanent magnet machine, to enhance the accuracy of the predicted air gap flux density and thus of the machine performance.

The machine under study is obtained by the substitution of the poles machines (PMs) that corresponds to south poles, of a conventional surface PM (SPM) machine, by iron pieces of same geometry. First, the analytical model of the air gap flux density generated by a SPM topology is presented. To fit the CP concept, such model has been rearranged based on a virtual SPM machine. Then, an improved prediction of the CP machine air gap flux density is addressed by the incorporation of a south pole rotor correction function.

An improved prediction of the consequent pole PM machines air gap flux density is addressed by the incorporation of a south pole rotor correction function.

The paper proposes an original approach to enhance the prediction of the air gap flux density of consequent pole machines despite the different magnetic permeability of the north and south poles.

This study aims to provide empirical evidence to verify the dimensional structure of artificial intelligence (AI) Chatbot quality and examine the impact of these dimensions on consumer satisfaction and brand advocacy among Gen Z in the fast food industry in Egypt.

The empirical data was obtained with an electronic self-administered survey instrument from 397 young consumers who had prior experience using AI Chatbots across multiple fast food brands in Egypt. Structural equation modeling was used to analyze the formulated hypotheses.

The results showed that AI Chatbot quality dimensions, specifically information authenticity and system compliance, significantly enhance young consumers’ satisfaction. In addition, information authenticity of AI Chatbot quality was observed to wield a significant influence on young consumers’ advocacy. In contrast, an insignificant relationship was noticed between satisfaction and advocacy. Moreover, the mediating role of consumer satisfaction was not established.

Given that Gen Z is more technology savvy and computer literate, marketers and practitioners of fast food brands should invest in AI tools to respond to young consumers’ expectations and improve their perception of their services.

This study uses stimulus-organism-response theory to understand the mediating effect of young consumers’ satisfaction in the relationship between AI Chatbot quality and consumer brand advocacy within the fast food industry. Also, it introduced two novel main constructs of AI Chatbot quality, namely, information authenticity and system compliance.

Data transformation has prompted enterprises to rethink their strategic development. Scholars have frequently acknowledged the vast potential value of supply chain data and realised that simply owning data resources cannot guarantee excellent innovation performance (IP). Therefore, this study focussed on the mediating and moderating issues between data-driven supply chain orientation (DDSCO) and IP. More specifically, the purpose was to explore (1) whether DDSCO promotes enterprise innovation through dynamic and improvisational capabilities and (2) how information complexity (INC) plays a moderating role between capabilities and performance.

An empirical study was performed using the results of a questionnaire survey, and a literature review was used to build the premises of this study. A sample was conducted on 296 Chinese enterprises, and the data collected were used to test the hypothesis by successive regression.

This research has implications for the theoretical development of DDSCO, as well as the dynamic capabilities (DC) and improvisation capabilities (IC) in innovation strategic literature. The empirical results show that DDSCO has a direct, positive impact on both DC and IC, which thus positively impact IP. Meanwhile, IC has a negative moderating effect on the path joining DC and IP. Conversely, IC has a positive moderating effect on the path joining IC and IP.

Although this study has limitations, it also creates opportunities for future research. The survey comes from different industries, so the possibility of unique influences within industries cannot be ruled out. Second, the authors' survey is based on cross-sectional data, which allow for more comprehensive data verification in the future. Third, this study also provides opportunities for future research, because it proves that DC and IC, as partial mediators of DDSCO and IP, can mine other paths of the data-driven supply chain in IP. For example, the perspective of the relationship between supply chain members, knowledge perspective, etc.

The research findings offer a novel perspective for enterprise managers. First, enterprises can leverage supply chain data to gain competitive advantages in innovation. Second, it is imperative for enterprises to acknowledge the significance of developing dynamic and IC. This also requires enterprises to acknowledge innovations in DDSCO necessitate a focus on dynamic and IC. Third, it is recommended that managers take into account both sides of IC and encourage enterprises to prioritise the utilisation of IC.

Empirical research results revealed how DDSCO improves IP and is an extension of digital transformation in the supply chain field, providing new opportunities and challenges for enterprise innovation. It can also expand the enterprise's understanding of DDSCO. Second, based on resource-based theory, it is possible to develop and test theoretical arguments regarding the importance of dynamic and IC as intermediaries in the DDSCO-IP. Third, the authors conducted simulations of highly dynamic data environments to develop and test theoretical arguments about the importance of IC as a moderator of capabilities-performance relationships.

Promoting technological innovation is important to address the complexity of major engineering challenges. Technological innovations include short-term innovations at the project level and long-term innovations that can enhance competitive advantages. The purpose of this study is to develop an incentive mechanism for the public sector that considers short-term and long-term efforts from the private sector, aiming to promote technological innovation in major engineering projects.

This study constructs an incentive model considering the differences in short-term and long-term innovation efforts from the private sector. This model emphasizes the spillover effect of long-term efforts on current projects and the cost synergy effect between short-term and long-term efforts. It also explores the factors influencing the optimal incentive strategies for the public sector and innovation strategies for the private sector.

The results indicate that increasing the output coefficient of short-term and long-term efforts and reducing the cost coefficient not only enhance the innovation efforts of the private sector but also prompt the public sector to increase the incentive coefficient. The spillover effect of long-term innovation efforts and the synergy effect of the two efforts are positively related to the incentive coefficient for the public sector.

This research addresses the existing gap in understanding how the public sector should devise incentive mechanisms for technological innovation when contractors acting as the private sector are responsible for construction within a public-private partnership (PPP) model. In constructing the incentive mechanism model, this study incorporates the private sector's short-term efforts at the project level and their long-term efforts for sustained corporate development, thus adding considerable practical significance.