There is a large literature on public-private-partnership, covering many different areas and aspects. This article deals with a specific but important aspect: the decision-making mechanisms to choose the management of PPP enterprises. In this sector, a suitable choice of managers is of particular importance because the persons chosen must balance the public and private interests. This is often difficult to achieve. Two new procedures are discussed, “Directed Random Choice” and “Rotating CEOs”. In each case, the advantages and disadvantages of the procedure of choosing the managers of PPP enterprises are discussed and evaluated. It is concluded that the two novel mechanisms should be seriously considered when choosing the managers of PPP enterprises.

In the rapidly evolving landscape of China’s pharmaceutical industry, this study investigates how pharmaceutical enterprises can achieve profitable sales innovation amid the process of digital transformation. Grounded in the Affordance theory, it posits that the positive impact of digital transformation on sales innovation is driven by the affordance afforded by digital technology and ubiquity. The research focuses on A-share pharmaceutical companies in China, utilizing data from 2012 to 2022 and employing multiple regression analysis to examine the influence of digital transformation on corporate sales innovation. The results demonstrate a significant positive effect of digital transformation on sales innovation. The study further categorizes digital transformation into technological affordance and ubiquity affordance, separately validating their roles in promoting sales innovation. Moreover, by considering synergistic effects, the research unveils the intricate relationship between digital transformation and corporate innovation performance. The findings provide a fresh perspective on understanding how digital technology propels sales innovation and offer concrete guidance for the digital transformation practices in the pharmaceutical industry.

In today's highly competitive environment, enterprises strive for competitive advantages by actively responding to changes in the network environment through digital technology. This approach fosters continuous innovation and establishes new paradigms by creating new network structures and relationships. However, research on the relationship and transmission mechanisms between digital technology and innovation performance in dynamic environments is still in its early stages, which does not fully address the demands of current social practice. Therefore, exploring the impact mechanisms of digital technology applications on enterprise innovation performance is an important research area. Based on the dynamic capability theory, this paper utilized SPSS 26.0 and AMOS 24.0 software to conduct an empirical analysis of 490 valid samples from the network perspective, exploring the pathways through which digital technology capability influences enterprise innovation performance. The results indicate that (1) digital technology capability is positively correlated with enterprise innovation performance; (2) digital technology capability is positively correlated with network responsiveness; (3) network responsiveness is positively correlated with enterprise innovation performance; (4) network responsiveness plays a mediating role in the impact of digital technology capability on enterprise innovation performance; (5) environmental dynamism positively moderates the relationship between digital technology capability and enterprise innovation performance. This paper enhances the understanding of how digital technology capability influences enterprise innovation performance in dynamic environments, offering new insights for future research. The results suggest that enterprises should focus on enhancing their digital technology capabilities, optimizing network structures, and strengthening network relationships to drive digital innovation.

This paper aims to investigate the impact of China’s central state-owned enterprises (SOEs) relocation policy from the capital city of Beijing on the economy and local fiscal revenue. We find that these enterprises play a critical role in implementing national strategies, promoting industrial upgrading, and enhancing the competitiveness of the industry chain. At the same time, their relocation has also dispersed the pressure of economic development in Beijing, promoted regional economic coordination and development, and increased local fiscal revenue. However, attention should be paid to the particularity and diversity of local areas in the process of policy formulation to avoid “one-size-fits-all” solutions. Therefore, when formulating corresponding policies, the central government should guide enterprises to handle relocation issues correctly and safeguard the legitimate rights and interests of employees and their families. Meanwhile, local governments should also formulate corresponding support policies to facilitate enterprise settlement. The ultimate goal is to solve problems and contradictions through development and achieve common prosperity. Therefore, we suggest that the government and enterprises work together to bring prosperity to everyone and jointly promote the sustainable development of the Chinese economy.

Since the proposal of the low-carbon economy plan, all countries have deeply realized that the economic model of high energy and high emission poses a threat to human life. Therefore, in order to enable the economy to have a longer-term development and comply with international low-carbon policies, enterprises need to speed up the transformation from a high-carbon to a low-carbon economy. Unfortunately, due to the massive volume of data, developing a low-carbon economic enterprise management model might be challenging, and there is no way to get more precise forecast data. This study tackles the challenge of developing a low-carbon enterprise management mode based on the grey digital paradigm, with the aim of finding solutions to these issues. This paper adopts the method of grey digital model, analyzes the strategy of the enterprise to build the model, and makes a comparative experiment on the accuracy and performance of the model in this paper. The results show that the values of MAPE, MSE and MAE of the model in this paper are the lowest. And the r^2 of the model in this paper is also the highest. The MAPE value of the model in this paper is 0.275, the MSE is 0.001, and the MAE is 0.003. These three indicators are much lower than other models, indicating that the model has high prediction accuracy. r² is 0.9997, which is much higher than other models, indicating that the performance of this model is superior. With the support of this model, the efficiency of building an enterprise model has been effectively improved. As a result, developing an enterprise management model for the low-carbon economy based on the gray numerical model can offer businesses new perspectives into how to quicken the shift to the low-carbon economy.