Supply chain management (SCM), which generally refers to horizontal integration management, has steadily become the core competitiveness of company rivalry and an essential approach to developing national comprehensive and national strength since the end of the 20th century due to the numerous needs arising from a competitive international economy. Manufacturers develop a community of interest by forming long-term strategic partnerships with suppliers and vendors throughout the supply chain. This paper defines supply chain management by reviewing the existing literature and discusses the current state of supply chain management research, as well as prospective research directions. Specifically, we conducted a bibliometric analysis of the influential studies of SCM in terms of various aspects, such as research areas, journals, countries/regions, institutions, authors and corresponding authors, most cited publications, and author keywords, based on the 8998 reviews and articles collected from the SCI and SSCI database of the Web of Science (WoS) between 2010 and 2020. The results show that the major research areas were Management (3071, 34.13%), Operations Research & Management Science (2680, 29.78%), and Engineering, Industrial (1854, 20.60%) with TP and TPR%. The most productive journal and institution were J. Clean Prod and Hong Kong Polytech Univ with a TP of 554 and 238, respectively. China, USA, and UK were the top three contributing countries. Furthermore, “sustainability”, “green supply chain (management)”, and “sustainable supply chain (management)” were the most popular author keywords in recent three years and since 2010, apart from the author keywords of SCM. When combined with the most cited articles in recent years, the application of block chain and Industry 4.0 in supply chain management increased rapidly and generated great attention.

Enterprise Digital Intelligence Transformation is based on the Digital Conversion of information and process service upgrading, further touching the enterprise’s core business, with the goal of building a new business model of Digital Intelligence Transformation at a higher level. Based on dynamic capability theory, this paper conducts an in-depth study on the mechanism of enterprise Digital Intelligence Transformation and firm performance. This paper selects manufacturing companies listed in China’s Shanghai and Shenzhen A-shares from 2013 to 2022 as the research sample, and analyzes and tests the sample data using empirical research methods in order to explore the actual impact of Digital Intelligence Transformation on firm performance, including the specific pathways of action and moderating effects. This study helps enterprises to positively face the megatrend of Digital Intelligence Transformation and upgrading and the challenges it brings, and to grasp the new opportunities in the digital era. This study finds that enterprises carry out digital empowerment transformation and development strategies, and implement information Digital Conversion, service upgrading, and Digital Intelligence Transformation to promote firm performance to different degrees. Enterprise innovation resilience has a mechanism effect between information digitalization conversion enterprise performance and process service upgrading enterprise performance. The higher the environmental uncertainty, the greater the positive contribution of information digitalization to firm performance. Digital Conversion is the base and service upgrading is the process. The current sample enterprises have limited years of data collection, and most of them have only carried out the strategic implementation of Digital Conversion or servitization, and have not reached the high-level stage of digital and intellectual transformation. Therefore, it is found that enterprise innovation resilience has not yet shown a significant role mechanism effect between digital–intelligent transformation and enterprise performance at present. And environmental uncertainty has not yet shown a significant moderating effect in the stage of Servitization Upgrading and digital–intelligent transformation. The marginal contributions of this paper are mainly reflected in the following: (1) This study introduces the dynamic capability theory to explore the role of Digital Intelligence Transformation (Digital Conversion + service upgrading) on enterprise performance. (2) This paper investigates the role of Digital Intelligence Transformation in influencing the performance of enterprises from the Digital Conversion and service upgrading phases, and enriches the relevant role and regulatory mechanisms. (3) This study provides new ideas and strategic suggestions on Digital Intelligence Transformation for enterprises with different factor intensities, at different stages of development, and in different regions.

In the context of developing the digital platform economy, trade-in programs have become an effective strategy for e-commerce platforms to stimulate consumption. Many head e-commerce platforms have launched their own trade-in programs. However, the existing research on trade-in programs is still stuck in the traditional trade-in model. The purpose of this study is to explore whether there is a new and more beneficial trade-in program. In this paper, we construct the Stackelberg game model between a brand owner and a B2C e-commerce platform under two trade-in programs and use optimization theory to obtain the equilibrium results of the model. The results indicate that the performance improvement of the new-generation product will promote the increase in two-generation products’ price under traditional trade-in programs, the price of the new-generation product will increase, and the price of the previous-generation product will decrease under new trade-in programs. The brand owner always prefers traditional trade-in to new trade-in. However, the e-commerce platform prefers traditional trade-in to new trade-in just when the previous-generation product is durable enough and the performance improvement of a new-generation product is small enough; otherwise, it prefers new trade-in to traditional trade-in. These findings are beneficial to the operational practices of e-commerce platforms and brand owners.

In the era of big data, data-driven services (DDS) have become critical competitive strategies for digital platform-based enterprises. This paper considers two operational modes of e-commerce platforms, which are self-operated and third-party modes, respectively, and they each lead a platform system. The Hotelling model is adopted to describe the competitive market of both platforms. We characterize their system performance functions. The optimization models are built using game theory to discuss the DDS and price decisions. We obtain the implementation conditions of DDS strategies for both platforms and the dominant situations of their respective DDS levels. We find that a platform adopting the price reduction strategy can improve the performance of its platform system while reducing the competitor’s system performance. From the system performance perspective, continuous improvement of the DDS level may appear “harming others may not benefit oneself”; that is, continuously improving the DDS level leads to a decrease in the competitor’s system performance but not necessarily an increase in its system performance. Further, consumer welfare within both platform systems shows the law of “as one falls then another rises”. As the big data industry matures, self-operated platforms would demonstrate the advantages of service level, profit, and system performance. In contrast, third-party platforms would have an advantage in consumer welfare. These conclusions have important implications for e-commerce platforms developing data-driven operations-based strategies.

With the global climate problem becoming increasingly severe, governments have adopted policies to encourage enterprises to invest in low-carbon technologies. However, the opacity of the carbon emission reduction process leads to incomplete consumer trust in low-carbon products as well as higher supply chain transaction costs. Based on this, this paper constructs Stackelberg game models with and without blockchain under different power structures and compares the impact of these models on low-carbon emission reduction decisions. The results show that: (1) blockchain does not necessarily improve enterprise profits and can only help enterprises maintain optimal profits within a certain range when the carbon emission cost is low; (2) when consumers’ environmental awareness is high, the blockchain can incentivize manufacturers to enhance carbon emission reduction, and it has an obvious promotional effect on retailers’ profits; and (3) the profit gap between enterprises in the supply chain is larger under different power structures, and the implementation of blockchain can coordinate profit distribution and narrow the gap between enterprises. Compared with the manufacturer-dominated model, the emission reduction in products is maximized under the retailer-dominated model. Our study provides theoretical support for the government to regulate greenhouse gas emissions as well as for the optimization of enterprises’ decision-making supported by blockchain.