Haibing Liu

Artificial intelligence (AI) is a general-purpose technology driving technological and industrial transformation. In this study, we construct an AI adoption indicator based on textual analyses of firms’ annual reports and measure innovation quality using the cumulative three-year forward citation counts—excluding self-citations—of invention patents at the firm-year level. Based on a sample of Chinese A-share manufacturing firms spanning 2011–2021, we found that AI adoption is positively associated with innovation quality. This conclusion remains robust across a range of endogeneity-mitigating specifications and robustness checks. Exploratory mechanism analyses suggest that this positive association is partly accounted for by three channels—R&D investment, human capital, and R&D efficiency—although these results should be interpreted as indicating an association rather than causal mechanisms. Heterogeneity analysis further showed that such association is concentrated in larger enterprises, and it is stronger for enterprises with lower financialization and those in asset-intensive industries. However, no significant difference was observed across firms with different ownership or regional locations. This study expands the literature on the innovation effects of AI and offers new insights into the factors influencing innovation quality in the AI era. It also has implications for business management and public policy with regard to leveraging AI to foster high-quality innovation capabilities, thereby providing a knowledge foundation for industrial upgrading and sustainable development.

Amid rapid digital transformation in the global economy, digital innovation capability has emerged as a strategic asset for enterprises to cultivate competitive advantages. However, challenges such as the complexity of digital resource integration, limitations of traditional innovation paradigms, and ecosystem fragmentation hinder the development of this capability. Combining innovation ecosystem theory with dynamic capability perspectives, this study analyzed data from 312 Chinese enterprises to examine how enterprise innovation ecosystems shape digital innovation capability. Key findings include the following: (1) the composition, operation, and relationships of enterprise innovation ecosystems exert significant positive effects on digital innovation capability, with the composition dimension demonstrating the most substantial impact.; (2) digital innovation capability comprises three hierarchical components—Digital technological capability (Foundational Capability), Digital technology-enabled Innovation Capability (Evolutionary Capability), and Value reconfiguration capability (Transformational Capability)—all dynamically supported by ecosystem interactions; (3) dynamic capabilities serve as critical mediators between ecosystems and digital innovation capability. Theoretically, this research integrates ecosystem and capability theories to reveal a transformation pathway of “ecosystem embeddedness → dynamic capabilities → digital innovation”. Practically, it offers guidance for resource allocation, ecosystem optimization, and governance, advancing sustainable digital innovation ecosystems.

Residents have substantial control over their daily lifestyles, and their behavior change has a considerable potential to reduce emissions. Understanding the adoption of sustainable lifestyles and recycling behaviors and how behavioral policies might shape this decision-making is critical to the transition to sustainable consumption patterns. This paper developed a multi-agent model on a multiplex complex network that integrates evolutionary game theory to simultaneously capture information transmission and behavioral interaction dynamics. In this model, residents determine decision utilities under the influence of internal and external factors and information transmission and then perform social interactions according to an evolutionary dynamics model. A real case of residents’ green express packaging purchase decisions in China was used for parameter initialization. Explorative simulations and scenario analyses were conducted to investigate the adoption patterns of sustainable lifestyles under different policy scenarios. Results indicate that the dynamic evolution of residents’ sustainable lifestyle decisions relies on social interactions and social networks. Government subsidies are effective in fostering sustainable decisions, but this effect is sensitive to the size of complex networks. Information campaigns and government subsidies have a significant marginal contribution to promoting sustainable lifestyles than green labeling schemes. Implementing subsidies and information campaigns as policy mixes can exert complementary effects and improve aggregate outcomes of policy packages.

This study investigates the distributed heterogeneous hybrid flow‐shop scheduling problem (DHHFSP) with the tardiness and energy consumption criteria. A decomposition‐based multi‐objective artificial bee colony (MOABC/D) algorithm is developed to solve the scheduling problem. In the MOABC/D algorithm, a tri‐level encoding scheme combined with domain‐specific heuristic rules are designed to enable comprehensive solution space exploration. A local search framework incorporating five novel critical path‐based neighbourhood structures to intensify subproblem investigation. An adaptive optimisation strategy integrating similarity‐based prioritisation, dynamic neighbourhood relationships, and coordinated information sharing among adjacent subproblems. A solution exchange strategy is proposed to assist the algorithm jump out of the local optimum, and continue searching for solutions in various directions. Comprehensive simulation trials validate the algorithm's ability to balance scheduling efficiency and energy conservation in the DHHFSP. It shows great promise for multi‐objective optimisation in complex distributed manufacturing systems with heterogeneous resources.

In the context of increasingly fierce global competition, utilizing digital technology to realize disruptive innovation is an effective way for enterprises to gain access to the mainstream market and form long-term competitive advantages. From the perspective of affordance, this study classifies digital technology affordance into cumulative affordance and variant affordance and explores the influence mechanism of digital technology affordance on enterprise disruptive innovation according to three core innovation phases: “fuzzy front-end–development–commercialization”. Based on 241 data points from different industries and types of enterprises, this empirical study found that (1) cumulative affordance and variant affordance have a significant positive impact on disruptive innovation and its “fuzzy front-end—development—commercialization” phases; (2) dynamic capabilities play a mediating role between digital technology affordance and disruptive innovation; and (3) the three aspects of dynamic capabilities—sensing, seizing, and reconfiguring—are greatly enhanced by cumulative affordance and variable affordance. These findings contribute to the research on the relationship between digital technology affordance and disruptive innovation and provide a micro-level perspective on how firms realize disruptive innovation through digital technology affordance.

Based on the structural hole theory, this paper takes China's A-share automobile manufacturing listed companies in 2021 as the research object, builds a mediation effect model, and explores the impact mechanism of digital technology on enterprise innovation performance. The results show that, first, Digital technology has a significant positive impact on enterprise innovation performance; second, structural holes have a positive impact on enterprise innovation performance; third, structural holes play a part of the intermediary role in the role of digital technology on enterprise innovation performance. This research conclusion expands the discussion boundary between digital technology and enterprise innovation performance, enriches the element analysis of the internal mechanism, and provides guidance and reference for the innovation of manufacturing enterprises.

This paper selects 74 Chinese automobile manufacturing enterprises as the sample data, and analyzes the relationship between digital technology and innovation capability from the perspective of the heterogeneous role of enterprise size. The research conclusion shows that digital technology can significantly improve the innovation capability, and the size of enterprises is significantly positively correlated with the innovation capability. Enterprise size has a moderating effect in the process of promoting the innovation capability of digital technology. When the enterprise size is larger, the digital technology has a stronger impact on innovation capability.

Strategic management of Enterprise is a comprehensive course, the teaching process integrated financial management, marketing human resources management and etc. Most of the enterprises will adopt diversified management as their strategic target to make profits by the impact of globalization. How to make students participate in learning process ,then use the management methods in real business management situation, try to identify four steps in strategic management by practicing in the enterprises, establish a strategic partnership platform for teachers-enterprises-students, make a effectively improvement though learning and teaching for all of the three in the end.

There are many problems in home decoration industry, such as imperfect industry system, lack of integrity of enterprises, low quality of construction personnel, etc. However, Qingdao Youzhu Home Decoration Co., Ltd. has achieved rapid growth with the innovation of business model. Therefore, based on Qingdao Youzhu Home Decoration Co., Ltd., this paper attempts to analyze the company’s business model innovation. The conclusion shows that the company’s business model canvas includes nine modules of important partners, value proposition, key business, core resources, customer segmentation, customer relationships, Channel access, revenue sources and cost structure, and each module plays an important role.