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The 21st century has brought the business model earth-shaking changes, especially since the Corona Virus Disease 2019 (COVID-19) epidemic at the end of 2019. Now, the epidemic normalization is slowing down China's rapid development. However, technological development, like artificial intelligence (AI), is unstoppable and is transforming China's economic growth modes from factor-driven to innovation-driven systems. Therefore, it is necessary to study further the new changes in labor entrepreneurship and innovation business models and their mechanism of action on economic growth.

This work studies how innovative human capital (IHC) uses AI and other scientific and technological (S&T) innovation technologies to promote China's innovation-driven economic growth model transformation from the labor entrepreneurship and innovation perspective.

The research shows that the entrepreneurial innovation ability of IHC can increase marginal return and output multiplier effect. It changes the traditional business model and promotes China's economic growth and innovation development. At the same time, this work analyzes China's inter-provincial panel data through the panel smooth transition regression (PSTR) model. It concludes that there is a nonlinear relationship between IHC and the output of innovative achievements. The main body presents three stages of nonlinear changes: first rising, then slightly declining, and rising so far.

The finding provides a direction for solving the problem of slow economic growth and accelerating the transformation of economic growth mode under epidemic normalization.

With the uptake of prefabricated construction (PC) facing serious obstacles in China, networked innovation can break the technical constraints while also containing the risks in individual innovation. However, the construction community has paid little attention to PC innovation, especially networked innovation. This study aims to gain deep insights into what impacts the formation and dynamics of a prefabricated construction innovation network (PCIN). With the uptake of PC facing serious obstacles in China, networked innovation can break the technical constraints while also containing the risks in individual innovation.

The research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. It first conceptualizes the PCIN based on the quadruple helix model and formulates a corresponding system dynamics model based on causality analysis. After validating the PCIN model using empirical data, simulations are carried out using Vensim PLE software. Finally, this study identifies key factors that promote the formation of PCIN in China through sensitivity analysis.

The results show that PC predicts a continuous increase in practice as of 2030. The tested drivers all positively influence the formation of the PCIN, with market demand and risk sharing having the greatest influence, followed by competitive pressure, profit government support, scientific and technological advancement and collaborative innovation strategy.

The study makes three major contributions. First, it provides a novel angle for a deeper understanding of the PC innovation. Second, it proposes a new approach for probing the formation and dynamics of the PCIN. Finally, it offers a theoretical reference for promoting the formation of innovation networks and the development of PC.

The purpose of this research is to explore how an innovation organization's orientation toward the digital economy influences its position within R&D networks. By using institutional theory, the study aims to forecast market changes and understand how organizations can navigate the digital economy to secure essential resources and minimize dependencies.

This study employs a longitudinal panel dataset with 11,763 entries from 1995 to 2018, covering strategic emerging industries in China to analyze the impact of digital economy orientation on R&D networks. Utilizing advanced statistical models, it assesses the role of the legal environment as a moderator. This methodological approach facilitates a robust examination of the nexus between digital orientation and network dynamics within the context of institutional theory.

The study reveals that an organization's digital economy orientation enhances its centrality in R&D networks but reduces its control over structural holes. The legal environment negatively moderates the impact of digital economy orientation on network centrality, while positively influencing the relationship with network structural holes. These findings offer new insights into how institutional forces shape the strategic positioning of organizations in R&D collaborations.

This research offers a fresh perspective on the digital economy's impact on R&D networks, particularly in the Industry-University-Research (IUR) context. It extends the discourse by integrating institutional theory to elucidate the adaptation of R&D networks in the digital era. By identifying the legal environment as a moderator, the study provides a nuanced understanding of the strategic alignment within networks influenced by digital advancements. The unique focus on China's R&D networks presents a valuable contribution to the global discussion on digital integration and innovation ecosystems, highlighting the intersection of policy, academia, and industry in shaping research and development trajectories.

The ongoing paradigm shift in the energy sector holds paramount implications for the realization of the sustainable development goals, encompassing critical domains such as resource optimization, environmental stewardship and workforce opportunities. Concurrently, this transformative trajectory within the power sector possesses a dual-edged nature; it may ameliorate certain challenges while accentuating others. In light of the burgeoning research stream on open innovation, this study aims to examine the intricate dynamics of knowledge-based industry-university-research networking, with an overarching objective to elucidate and calibrate the equilibrium of ambidextrous innovation within power systems.

The authors scrutinize the role of different innovation organizations in three innovation models: ambidextrous, exploitative and exploratory, and use a multiobjective decision analysis method-entropy weight TOPSIS. The research was conducted within the sphere of the power industry, and the authors mined data from the widely used PatSnap database.

Results show that the breadth of knowledge search and the strength of an organization’s direct relationships are crucial for ambidextrous innovation, with research institutions having the highest impact. In contrast, for exploitative innovation, depth of knowledge search, the number of R&D patents and the number of innovative products are paramount, with universities playing the most significant role. For exploratory innovation, the depth of knowledge search and the quality of two-mode network relations are vital, with research institutions yielding the best effect. Regional analysis reveals Beijing as the primary hub for ambidextrous and exploratory innovation organizations, while Jiangsu leads for exploitative innovation.

The study offers valuable implications to cope with the dynamic state of ambidextrous innovation performance of the entire power system. In light of the findings, the dynamic state of ambidextrous innovation performance within the power system can be adeptly managed. By emphasizing a balance between exploratory and exploitative strategies, stakeholders are better positioned to respond to evolving challenges and opportunities. Thus, the study offers pivotal guidance to ensure sustained adaptability and growth in the power sector’s innovation landscape.

The primary originality is to extend and refine the theoretical understanding of ambidextrous innovation within power systems. By integrating several theoretical frameworks, including social network theory, knowledge-based theory and resource-based theory, the authors enrich the theoretical landscape of power system ambidextrous innovation. Also, this inclusive examination of two-mode network structures, including the interplay between knowledge and cooperation networks, unveils the intricate interdependencies between these networks and the ambidextrous innovation of power systems. This approach significantly widens the theoretical parameters of innovation network research.

This study aims to develop a polymer cement-based waterproof coating with self-healing capability to efficiently and intelligently solve the building leakage caused by cracking of waterproof materials, along with excellent durability to prolong its service life.

Ion chelators are introduced into the composite system based on ethylene vinyl acetate copolymer emulsion and ordinary Portland cement to prepare self-healing polymer cement-based waterproof coating. Hydration, microstructure, wettability, mechanical properties, durability, self-healing performance and self-healing products of polymer cement-based waterproof coating with ion chelator are investigated systematically. Meanwhile, the chemical composition of self-healing products in the crack was examined.

The results showed that ion chelators could motivate the hydration of C₂S and C₃S, as well as the formation of hydration products (C-S-H gel) of the waterproof coating to improve its compactness. Compared with the control group, the waterproof coating with ion chelator had more excellent water resistance, alkali resistance, thermal and UV aging resistance. When the dosage of ion chelator was 2%, after 28 days of curing, cracks with a width of 0.29 mm in waterproof coating could fully heal and cracks with a width of 0.50 mm could achieve a self-healing efficiency of 72%. Furthermore, the results reveal that the self-healing product in the crack was calcite crystalline CaCO₃.

A novel ion chelator was introduced into the composite coating system to endow it with excellent self-healing ability to prolong its service life. It has huge application potential in the field of building waterproofing.

The study aims to equip robots with the ability to precisely maintain interaction forces, which is crucial for tasks such as polishing in highly dynamic environments with unknown and varying stiffness and geometry, including those found in airplane wings or thin, soft materials. The purpose of this study is to develop a novel adaptive force-tracking admittance control scheme aimed at achieving a faster response rate with higher tracking accuracy for robot force control.

In the proposed method, the traditional admittance model is improved by introducing a pre-proportional-derivative controller to accelerate parameter convergence. Subsequently, the authors design an adaptive law based on fuzzy logic systems (FLS) to compensate for uncertainties in the unknown environment. Stability conditions are established for the proposed method through Lyapunov analysis, which ensures the force tracking accuracy and the stability of the coupled system consisting of the robot and the interaction environment. Furthermore, the effectiveness and robustness of the proposed control algorithm are demonstrated by simulation and experiment.

A variety of unstructured simulations and experimental scenarios are designed to validate the effectiveness of the proposed algorithm in force control. The outcomes demonstrate that this control strategy excels in providing fast response, precise tracking accuracy and robust performance.

In real-world applications spanning industrial, service and medical fields where accurate force control by robots is essential, the proposed method stands out as both practical and straightforward, delivering consistently satisfactory performance across various scenarios.

This research introduces a novel adaptive force-tracking admittance controller based on FLS and validated through both simulations and experiments. The proposed controller demonstrates exceptional performance in force control within environments characterized by unknown and varying.

For undergraduate and graduate students in universities, social media are playing an important role in their study/research because a large amount of academic information has been accumulated on social media. Indeed, social media are complementing university libraries. Given that intelligent recommender systems have been widely implemented on social media, this paper aims to examine the adoption mechanism of intelligently recommended information by university students in their study/research.

Building upon the updated information system success model and herding theory, this study developed a research model to examine the determinants of recommended information adoption in mobile applications for social media. Data were collected through an online questionnaire and analyzed with partial least squares structural equation modelling.

The results suggest that herding belief is a valid second-order construct, comprising two first-order dimensions of imitating others and discounting their own information. Information quality, system quality and service quality directly impact satisfaction with the intelligent recommender system. Furthermore, satisfaction with the intelligent recommender system and herding belief directly impact recommended information adoption by university students in their study/research.

This study draws on the updated information system success model and incorporates herding belief as an extended component to investigate recommended information adoption, providing a new lens for understanding recommended information adoption by university students in their study/research.

This study examines the impact of corporate governance on succession planning and organizational performance. Drawing on agency theory, the main purpose of this study is to identify the effect of corporate governance on succession planning by measuring the different characteristics of the board of directors.

This multi-quantitative research used primary and archival data. A total of 281 valid questionnaires were collected from Chinese listed family firms to gauge succession planning. Relevant archival data were obtained to measure board characteristics and organizational performance. All hypotheses were examined through structural equation modeling.

The outcomes indicate that corporate governance positively influences succession planning and, in turn, boosts superior organizational performance, which uncovers the mediating effect of succession planning on the relationship between corporate governance and organizational performance. Our findings reveal that board independence and education facilitate the development of succession planning, which is crucial in the family business’s life cycle.

The results of this study contribute to management succession, strategic management and leadership research by demonstrating how corporate governance fosters organizational performance through succession planning, thereby expanding the application scenarios of agency theory in family firms. Additionally, the article also enriches our understanding of how family businesses apply sound governance structures to promote organizational strategic decision-making during the succession process.

Integrating green (sustainable) practices in logistics management play a crucial role in accelerating the transition to a circular economy, realizing its sustainability potential and position in the net zero emission target by 2050. Over the past decade, this integration has attracted significant attention in both academic and industrial discourse. Despite the increasing recognition of the benefits of green logistics practices (GLPs), only a few firms have implemented green-oriented or sustainable logistics practices; hence, a comprehensive understanding of what could drive its implementation as well as how and when firms can benefit from GLPs is of key importance for theory, policy and practice. Drawing on dual theoretical lenses, this study investigated how supply chain ethical leadership (SCEL) could stimulate green logistics practices by building green core competencies (GCC) under varying conditions of corporate green culture (CGC).

An integrated model motivated by social learning and contingency theories was tested using responses from 208 managers of logistics firms in Ghana. SPSS 23 and covariance-based structural equation modeling (CB-SEM) were used for data analyses.

Both SCEL and GCC significantly influenced GLPs. The findings also showed that GLPs significantly enhanced carbon-neutral supply chain performance (CNSCP). The results further showed that GCC mediates the SCEL–GLPs link. We also found that the effect of SCEL on GLPs was amplified at a high level of CGC.

This study offers fresh insight into how managers can leverage SCEL to support GLP and when they can also combine green competence and green culture to achieve enhanced benefits in the form of carbon-neutral SCP. This further implies that building ethical leadership and competencies alone may not be sufficient to drive superior emission reduction and subsequent sustainability; rather, cultivating a green-oriented culture that values sustainable logistics is crucial to fully realize the potential of ethical leadership and competencies in enabling the implementation of GLPs and subsequently enhancing carbon-neutral SCP.

The novelty of the present study lies in the integration of social learning and contingency theories to unearth the mechanism and conditional roles of green competence and green culture in optimizing the SCEL–GLPs–GLP relationship. The study is also among the few attempts to shed light on how firms can leverage GLPs to enhance carbon-neutral supply chain performance, which is rare.

The purpose of this study was to examine the relationship between environmental pressure, green logistics strategy (GLS) and sustainability performance as well as the moderating role of competitive intensity in the relationship between environmental pressure and GLS in the context of the Ghanaian Manufacturing firms.

The study included a thorough review of the literature and an empirical questionnaire-based data collection with responses from 220 participant manufacturing firms in Ghana. The data collected was statistically analysed using the PLS-SEM software.

The findings of the study indicated that environmental pressure positively influences the implementation of GLS. Again, it was revealed that there is a significant relationship between GLS and sustainability performance. Likewise, the study also found that environmental pressure significantly influences sustainability performance. Also, competitive intensity was found to moderate the relationship between environmental pressure and GLS.

This study gives insight into GLS and sustainability performance and also suggested that when managers in manufacturing industries adopt green practices as a result of environmental pressure, sustainability performance will be achieved. The geographic scope of the study area and time constraints were some of the research's limitations.

Although there have been studies carried out on the subject of green logistics, this study is the first of its kind to examine the relationship between environmental pressure, GLS and sustainability performance within the context of developing economies such as Ghana. Also, this study shows how intense competition in the market can moderate the adoption of GLS.