Currently, the research on corporate digital transformation is mainly explored from the perspective of independent decision-making, but pays less attention to the interactive impact among peer firms. Taking the listed equipment manufacturing enterprises in China as its research sample, this paper uses theoretical analysis and empirical tests to test the impact of peer effect in corporate digital transformation and the moderating effect of top management team (TMT) characteristics. The results show that there are industry peer effects and regional peer effects in corporate digital transformation in which TMT characteristics play a moderating effect. Furthermore, the higher the proportion of females, the younger the average age, the higher the average education, and the longer the average tenure in TMT, the more significant the positive impact of industry peer effect and regional peer effect in corporate digital transformation. The above conclusions remain valid when using change variables, Tobit tests, lag period tests, and IV methods for robustness tests. Further analysis of the results shows that there is a non-linear impact between digital transformation and green technological innovation, and there are multiple mediating effects among industry and regional peer effects, digital transformation, R&D, and green technological innovation. In addition, it is necessary to further examine the social network peer effect in corporate digital transformation as well as the mechanism and economic consequences of peer effect in the future. These findings contribute to a more comprehensive understanding of the driving factors that affect the digital transformation of equipment manufacturing enterprises in China and provide new evidence and theoretical contributions for enterprises to enhance the efficiency of digital transformation and strengthen the construction of a TMT.

With the development of the country’s social and economic transformation, the topic of innovation and entrepreneurship has received widespread attention from society. However, from the current situation, due to the high uncertainty and challenges of entrepreneurship, China’s college graduates’ willingness to start their own business is generally low, and entrepreneurship policy, as a pioneering factor, has an important influence on college graduates’ willingness to start their own business. This study investigates college graduates in more than 20 representative cities, such as Shenyang, Xi’an, and Hangzhou, and examines the influence path and mechanism of entrepreneurship policy on entrepreneurship behavior based on MOA (Model of Attraction) model. This study used SPSS and AMOS to empirically analyze 288 valid questionnaires, verifying that there is a significant positive relationship between entrepreneurship policy, entrepreneurship willingness, and entrepreneurship behavior of college graduates. Entrepreneurship willingness plays a part in mediating the effect between entrepreneurship policy and entrepreneurship behavior, and the willingness to use entrepreneurship policy moderates the path of influence of entrepreneurship policy on entrepreneurship willingness. At the same time, it provides a reference for the promulgation of government entrepreneurship policies and the optimization of the implementation effect of entrepreneurship policy, provides suggestions for universities to improve entrepreneurship education, and has guiding significance for stimulating the entrepreneurship behavior of college graduates.

Chinese Golden Courses was proposed by the Ministry of Education of China in 2018. It consists of five types. Virtual Simulation Golden Course is one of them. The college students are often faced with the problems of few opportunities, higher cost, higher risk and poorer effect during the internship in logistics courses. Virtual simulation experiment course is an important means to solve this kind of practical teaching problem. Green Logistics Virtual Simulation Experiment (GLVSE), a course built according to Virtual Simulation Golden Course, as a case was reported. The development process of GLVSE such as designing reasonable talent training architecture, reflecting Two Properties and One Degree, cooperating between schools and enterprises, and reforming the teaching mode with the way of "mixed online and offline" were introduced in detail. Six successful experiences and a model of building virtual simulation golden course are summed up. The report provides important references for developing high-quality virtual simulation courses not only for Chinese universities but also for other universities in the world.

Research background: M&A (Mergers and acquisitions) is a strategic measure for enterprises to expand their scale, enhance their competitiveness and improve productivity in the market competition. As a new factor of production, data is changing the factor input model and value creation path of enterprises. Research objectives: From the perspective of serial M&A, this study explores the impact of serial M&A on enterprises’ TFP (total factor productivity) and the mechanism of digital transformation between them. Research methods: Take the serial M&A transactions of China’s A-share listed companies from 2010 to 2019 as samples, using the theory of organizational learning to analyze the relationship among serial M&A, enterprises’ TFP and the degree of digital transformation. Three-step regression is used to construct a model that serial M&A indirectly affects enterprises’ TFP through intermediary variable digital transformation. Research finding: There is a significant inverse U-shaped relationship between serial M&A and enterprises’ TFP, and digital transformation plays a mediating role in this relationship. The impact of serial M&A on enterprises’ TFP shows an upward trend at first and then a downward trend and this relationship is indirectly realized through digital transformation. The results are still valid after considering the change-explained variables, lag test, Sobel-Goodman test, and Bootstrap test. Heterogeneity analysis shows that for enterprises with non-state-owned property rights, smaller enterprise scale, and higher business environment index, serial M&A has a more obvious effect on TFP indirectly through the degree of digital transformation. Research value: It further enriches the existing literature on the decision-making of M&A from the perspective of serial M&A and profoundly reveals the mechanism of the degree of digital transformation in the relationship between serial M&A and enterprises’ TFP. The research provides theoretical support and empirical evidence for enterprises to achieve high-quality development.

Based on the sample of serial M&A of Chinese listed companies from 2010–2019, this paper intends to investigate the impact of serial M&A on innovation performance and the impact of financing constraints and digital inclusive finance (DIF). The empirical results show that an inverted U-shaped relationship exists between serial M&A and innovation performance that first goes up and then goes down. The results of mechanism analysis show that financing constraints play a mediating role in the inverted U-shaped relationship between serial M&A and innovation performance, while DIF plays a moderating role in the mediating effect of serial M&A on innovation performance through the financing constraint. The heterogeneity analysis finds that the inverted U-shaped relationship between serial M&A and innovation performance is more significant in firms with non-state ownership property, a higher business environment index, and medium and large-scale firm size. The research results not only help to promote the in-depth analysis of the impact of serial M&A on innovation performance, but also help to provide targeted theoretical reference and practical guidance for corporate management decision making.

Data, as a novel factor of production, brings new opportunities for enterprise development. Using a sample of Chinese heavily polluting listed companies, this study measured corporate green total factor productivity (GTFP) by employing the Super-SBM Malmquist index and explored the underlying mechanism through which digital transformation affects GTFP.This study employs an OLS benchmark regression model to investigate the impact of corporate digital transformation on green total factor productivity (GTFP). Additionally, it constructs mediation and moderation effect models to examine the mechanism through which digital transformation affects GTFP, specifically assessing the mediating role of M&A activity and the moderating role of green innovation. The research results indicate that digital transformation has a positive promoting effect on corporate green total factor productivity. M&A activity plays a partial mediating role between the two, meaning that the impact of digital transformation on corporate green total factor productivity is indirectly generated through M&A activity. Corporate green innovation plays a positive moderating role between the two, meaning that the higher the level of corporate green innovation, the more significant the promoting effect of digital transformation on corporate green total factor productivity. The research results remain valid after considering robustness tests such as variable substitution, model transformation, Sobel-Goodman, and Bootstrap methods. Heterogeneity analysis reveals that the positive effect of digital transformation on corporate green total factor productivity (GTFP) is significantly amplified under three conditions: in non-state-owned enterprises, in regions with superior business environments, and in firms with higher information transparency. For the mediation mechanism, M&A activity exhibits partial mediation in non-state-owned firms and in high-index business environments, but transitions to complete mediation in low-index contexts. Correspondingly, the moderating effect of green innovation is markedly stronger under these same conditions. These contingent findings delineate critical boundary conditions, thereby extending theories of digital transformation and corporate sustainability, while providing empirically grounded insights for regulatory policymakers and corporate managers of heavily polluting enterprises.