Data ownership can either be considered as an indivisible monistic right or separated into ownership and usufruct, but the economic significance of data ownership and usufruct separation remains controversial. Digital enterprises' data-based services incentivize users to provide data, but also result in loss of privacy for users through personalized pricing. This paper constructs a monopoly firm model to explore the impact of data collection behavior and welfare under the definition of lack of usufruct and possession of usufruct when there is personalized pricing and privacy loss. The study finds that without usufruct, data collection will not occur if the privacy cost is high, but excessive data collection will occur if the privacy cost is low. When the collector has the initial usufruct and the user has ownership of the data, efficient data collection can be achieved, improving user surplus and social welfare. Data ownership only affects the distribution of social welfare between the collector and user. Therefore, we should consider the right definition framework of data ownership and usufruct separation and discuss the ownership of data usufruct in different scenarios. When the privacy cost is low, data collectors should be granted usufruct, and users should be allowed to exercise ownership through the "deletion right" and other ways to improve social welfare and user surplus. When the cost of privacy is high or users underestimate the cost of privacy, the method of prohibiting data collection should be adopted to protect sensitive data, rather than confirming rights.

In the era of the digital economy, the overlap between antitrust and information protection issues poses a great challenge to the formulation and implementation of public policies. This paper constructs a duopoly model where there are vertical differences in product quality and consumers' sensitivity to the quality of different products is negatively correlated. It explores the impact of different information protection policies with different strength on the pricing strategies of duopolists and consumer decisions and analyzes the specific welfare effects of policies. The conclusion indicates that in the market where consumers' sensitivity to the quality of different products is negatively correlated, a strong information protection policy leads to the impairment of social welfare, and vice versa. The reason is that information weakens the monopoly power by promoting competition. Meanwhile, considering different product characteristics, a strong protection policy and a weak one exert different effects on consumers' surplus and manufacturers' profits because information will bring about the "monopoly effect" and "competition effect", both of which play against each other to make the market present different welfare distribution patterns. Thus, the conclusion of this paper has certain policy implications for promoting and deepening the actualization of China's antitrust and information protection.

By considering the imperfect competition among platforms and sellers, this paper investigates the incentives behind the "pick one of two" exclusive dealing used by asymmetric platforms, as well as potential effects on market competition, consumers, and welfare. The study finds that only when the platform differentiation is sufficiently small and the homogeneity among sellers is strong, can dominant platform exclude its competitor by signing exclusive contracts with sellers in the form of "pick one of two." At this time, the "pick one of two" behavior will increase the commission level set by the platform, raise the price of seller's products, and ultimately reduce consumer surplus and social welfare. Therefore, prohibiting platforms from implementing "pick one of two" may not necessarily increase the profits of sellers and competing platforms, but it can increase consumer surplus and welfare. This suggests that the purpose of prohibiting the use of exclusive dealing such as "pick one of two" by platforms should not be to protect sellers or small competitive platforms, but to maintain consumer interest and guarantee the efficiency of platform markets. The paper clarifies some of the controversies in practice, provides theoretical support for the judgment of the anti-competitive effects of platform "pick one of two," and provides practical guidance for regulating such behaviors in the platform economy. To determine whether the dominant platform's "pick one of two" behavior should be considered as an abuse of market dominance, it is necessary to comprehensively consider factors such as the number of sellers within the platform, the contract forms provided by the platforms, and the contract information disclosed by the platform, in addition to focusing on the degree of differentiation between platforms and the degree of competition among sellers within the platform.

This paper is based on the bilateral market theory and considers the asymmetric market scale between platforms. We divide the merger model into two types: Strong expansion and weak self-rescue. The decision-making changes of platforms are analyzed when adopting different operation strategies (independent or combined operation). This paper also examines the merger strategies of asymmetric platforms under the competitive conditions and the changes in total social welfare and anti-monopoly supervision. The results indicate that, first, if the platform chooses the strong expansion merger mode, it tends to pursue the strategy of merger operation to obtain greater profits, assuming there is no "merger paradox" phenomenon. Second, the merger strategy of the self-help of the weak platform is related to the difference in market size between platforms. Third, the platform's merger behavior under different strategies is likely to enhance overall social welfare. Finally, the market supervision department should focus on different aspects of anti-monopoly supervision under different cross-network externalities and post-merger operation strategies.

The ongoing progress in digital technology has simplified the collection, dissemination, interaction, and realization of multi-sourced and heterogeneous data, resulting in changes in the economic operation mechanisms. This paper measures the "data-driven ability" of enterprises from a technological innovation perspective and integrates data elements and an enterprise's "data-driven ability" into the production function. This provides fresh theoretical insights into the intrinsic mechanism by which data elements enable manufacturing companies to reduce costs and improve efficiency. We then get the following results: 1) The generation, sharing, and application of data can significantly enhance the production efficiency of manufacturing companies; 2) Further comparison of the processes and differences in the release of data value in companies of different technology intensities reveals that the current application of digital technology and the input of data elements can effectively reduce the manufacturing cost of low tech (LT) and medium tech (MT) manufacturing companies and the sales cost of high-tech (HT) companies; 3) Interestingly, when HT companies exhibit stronger data-driven abilities, their production costs tend to rise, indicating that China's HT manufacturing companies are yet to achieve a successful transition in production models. Furthermore, based on the industrial correlation theory and industrial agglomeration theory, this paper incorporates spatial factors into the analytical framework. The findings indicate that, from the industrial chain perspective, the integration and development utilization of data across the entire manufacturing process, industry chain, and product lifecycle can improve the collaborative efficiency of the regional industrial chain. This in turn can further enhance the production efficiency of companies. This research enriches the theory and mechanism analysis of digital economy and provides evidence support for further promoting the value release of data elements and optimizing the efficiency of resource allocation.

Developing the digital economy to boost common prosperity is a focus topic at the moment that has attracted wide attention. As an important measure of common prosperity, whether social intergenerational mobility can be improved in the digital economy era is rarely discussed in the literature. This paper seeks to systematically examine the role of the digital economy on intergenerational income mobility, both theoretically and empirically. The research found that: 1) On average, the digital economy significantly reduces intergenerational income autocorrelation, which reflects the improvement of intergenerational mobility. After various robustness tests, the conclusion still holds. 2) The mechanism is that the development of the digital economy promotes the accumulation of human capital among family offspring, stimulates urban entrepreneurial vitality, and optimizes the economic structure to improve intergenerational mobility. 3) In rural and inland areas, the effect of intergenerational mobility in the development of the digital economy is better. The research in this paper can provide new policy insights on how to smooth social mobility and achieve common prosperity in the digital age.

Based on the research sample of Chinese A-share listed enterprises from 2010 to 2021, this paper empirically tested the synergistic effect of bank digitalization and enterprise digitalization in promoting the green transformation of enterprises. The study found that both bank digitalization and enterprise digitalization contribute to promoting enterprise green transformation, and bank-enterprise digitalization has a synergistic effect on promoting enterprise green transformation. This conclusion is still valid after considering endogeneity problems and a series of robustness tests. The results of mechanism test show that the synergistic effect of bank-enterprise digitization in promoting enterprise green transformation is due to the "information synergistic effect", "credit allocation synergistic effect" and "internal governance synergistic effect". Based on the test of the enterprise characteristics, it is found that the synergistic effect of bank-enterprise digitalization is more effective for non-state-owned enterprises, small-scale enterprises, heavy polluting enterprises and high-tech enterprises. Based on the test of the external environment of enterprises, it is found that the synergistic effect of bank-enterprise digitalization is more effective for regions with stronger environmental regulations and more intense bank competition, as well as for competitive industries. The additional test of the effects of enterprise green transformation found that green transformation promoted the carbon emission reduction without sacrificing the development of enterprises. This study provides some policy implications for giving full play to the synergistic effect of bank digitalization and enterprise digitalization, so as to empower enterprises with green transformation through digitalization.

As an important way of integrating digital economy with real economy, enterprise digital transformation has become an important way of enterprise strategic change in recent years. Using the sample of A-share listed companies in the Shanghai and Shenzhen Stock Exchange from 2007 to 2022, this paper empirically analyzes the influence of enterprise digital transformation on executives' opportunistic stock selling and its influencing mechanism. The results indicate that the enterprise digital transformation has significantly promoted the possibility of executives' opportunistic stock selling. This conclusion still holds after a series of endogenous and robustness tests such as instrumental variable estimation (IV) and propensity score matching (PSM). The mechanism analysis shows that the enterprise digital transformation affects executives' opportunistic stock selling by improving investors' optimistic sentiment and innovation investment. Further tests show that the positive relationship between enterprise digital transformation and opportunistic stock selling is more significant in companies with high divergence in analyst predictions, poor legal environment, and low audit quality. This paper combines digital transformation with the executives' opportunistic stock selling, enriching the relevant research in these two fields. The conclusion provides empirical evidence for controlling the chaos of opportunistic stock selling and promoting high-quality development of capital markets. At the same time, this paper provides a useful exploration for empowering the real economy with digital technology and promoting industrial digitization.

While the application of industrial robots enhances production efficiency, does it also impact the health of workers? This paper, leveraging data from the IFR (International Federation of Robotics), investigates the health effects of industrial robot application both from a broader perspective of workers' right to life and health and a detailed examination of individual mental and physical well-being. The research reveals that the application of industrial robots has created a health dividend for workers. At the macro level, it has significantly reduced the rate of occupational accidents and the number of casualties, safeguarding workers' right to life. A micro-level analysis indicates that the mental and physical health of workers, particularly those in lower-tier positions, has improved. The utilization of industrial robots has shortened their working hours and enhanced job safety and workplace satisfaction, thereby playing a crucial "safety-net" role. The findings of this paper contribute to a deeper understanding of the social benefits of the current application of industrial robots. They offer more precise policy recommendations for balancing safety with development, genuinely enhancing workers' sense of well-being and accomplishment, and achieving high-quality development.

There is wide consensus that social trust is a fundamental asset for economic development. A large literature suggests that trust crucially depends on social interaction and information. The use and popularity of mobile social networks (MSN) have profoundly changed the way of social interaction and information dissemination. Yet, evidences on whether and how they affect social trust are rare. This paper uses the establishing of community WeChat group as a quasi-natural experiment to estimate the impact of MSN on social trust. Using data from China household financial survey (CHFS) and taking the difference-in-differences (DID) and difference-in-difference-in-differences (DDD) as empirical strategy, we find that WeChat group can increase the level of social trust by 0.109 standard deviations during the sample coverage period (2013-2017), which slows down the decline of social trust by about 27%. Further mechanism analyses show that the enhancing effect of MSN on social trust does not arise through the channel of facilitating social interactions, but rather through the superimposed effect positive and negative information dissemination. This paper emphasizes that the content of information dissemination in MSN is the key to reshape social trust.

The digital infrastructure is an important foundation for the construction of a strong network and manufacturing power, and it is also a strategic public resource for the socio-economic development of contemporary China. In this context, this paper mainly focuses on the impact of China's digital infrastructure construction on urban labor employment and its operating mechanism. By treating the "Broadband China" pilot policy as a quasi-natural experiment, this paper is based on panel data from 285 prefecture-level and above cities in China from 2007 to 2019, and employs a multi-period difference-in-differences (DID) model to thoroughly examine the direct effects and underlying mechanisms of digital infrastructure construction on labor employment. The study finds that the use of digital infrastructure construction represented by the "Broadband China" pilot policy can significantly improve the level of labor employment in pilot cities, a conclusion that holds true even after multiple robustness and heterogeneity tests. Mechanism analysis reveals that digital infrastructure construction enhances labor employment levels through the effects of optimizing industrial structure, investing in human capital, and mitigating labor force distortions. The promotional effect of the "Broadband China" pilot policy on labor employment is more pronounced in cities with higher administrative ranks, better geographical locations, and among high-skilled labor. The research findings of this paper provide important policy implications for promoting the construction of "Digital China" and high-quality employment of labor.

In the current era of the digital economy, the transformation of digital technology has become a significant driving force for enterprises to achieve high-quality development. Taking advantage of the pilot program of "integration of informatization and industrialization" as a quasi-natural experiment and using Chinese listed manufacturing companies from 2007 to 2021 as the sample, this paper empirically investigates the impact of digital technology transformation on the stock price crash risk using a staggered difference-in-differences approach. The findings are as follows: 1) With the pilot program significantly promoting the digital technology transformation of enterprises, the risk of future stock price crash shows a notable decrease. This conclusion is further supported by a series of robustness tests. 2) Mechanism analysis reveals that the pilot program reduces the risk of future stock price crash through improving internal control quality, but not attracting more external monitoring. 3) The effects of the pilot program in reducing the risk of stock price crash are more pronounced when accounting conservatism is low, earnings aggressiveness is high, there is less analyst coverage, and media attention is low. These indicate the existence of complementary effects between emerging digital technologies and traditional governance mechanisms. Our research enriches the empirical evidence supporting the improvement of corporate governance in the digital economy. The conclusions provide decision-making references for enhancing corporate quality through digital governance for listed companies.

E-commerce is an important application field in the digital economy industry. In the interaction sessions between users and goods on the e-commerce platform, the micro-operation behaviors can gain insight into the user's fine-grained purchase interests, and at the same time, the macro multi-period session sequence can reflect the dynamic evolution of user's interests. Therefore, it has become a hot and difficult problem that how to integrate the two to conduct accurate and comprehensive user interest modeling, and then carry out accurate recommendation. The deep recurrent neural network (RNN) has outstanding advantages in processing serial data with periodic and long-term dependencies, and is one of the core methods of AI. Based on this, a hierarchical RNN network: Session-level LSTM_{m ses}, block-level LSTM_{m blo} and user-level LSTM_{m usr}, is designed in the framework of the proposed model MpUIP. Firstly, the user's fine-grained interests are first learned from the micro behavior details in the session. Further, the evolution of user's short-term, medium-term and long-term interests are learned, and the multi-period interests are fused. Finally, on two real datasets, four experiments: model ablation, benchmark model comparison, sparsity evaluation, and real case analysis are conducted. On the two typical indicators of recommendation: Recall@K and MRR@K, the experimental results verify that the proposed model Mp-UIP has best performance than the existing classical models. This confirms that the model Mp-UIP can indeed build a more accurate and comprehensive user interest model by combining the user's fine-grained interests with the multi-period interest evolution representation, so as to serve for the accurate and personalized E-commerce recommendations.

The new generation of artificial intelligence has more significant cognitive, communicate, and creative abilities, and the attribute of general purpose technology is increasingly prominent. The new generation of artificial intelligence will enable high-quality social development through three mechanisms: Innovation enabling effect, promoting the cross-integration of emerging technologies; Promoting the transformation role, promoting the intelligent transformation of thousands of industries; System change potential, will promote the comprehensive innovation of social productive forces and production relations. The technological and social attributes of the new generation of artificial intelligence are highly integrated and unified, and currently, there are still technical problems in its development, such as inadequate infrastructure, imperfect open-source ecology, and insufficient technology implementation, At the same time, there are also social problems such as externality to society, difficulty in defining legal rights and responsibilities, and new challenges to employment reform. It is necessary to grasp the development law of the new generation of artificial intelligence and promote its high-quality development through strengthening system planning, improving ethical regulations, cultivating an innovative ecosystem, improving laws and regulations, innovating regulatory methods, and promoting employment.

Against the backdrop of economic globalization, the rapid advancement of cutting-edge digital technology has catalyzed a new wave of technological revolution. The AIGC technology, represented by ChatGPT, disrupts the technical landscape of traditional artificial intelligence. And it is widely embraced for its enhanced human-like functionalities, thus emerging as a pivotal milestone in the advancement of general artificial intelligence. Through the analysis of ChatGPT's impacts on the education and employment market, this research reveals that the implementation of AIGC technology can enhance social value exchange efficiency and invigorate the education and employment market. However, it also gives rise to legal and ethical concerns such as data privacy infringement. Therefore, management and supervision recommendations are proposed to address potential risks in order to ensure seamless operation of the economy and society.

With the decreasing cost of intelligent capital compared to the labor costs of medium-skilled workers, artificial intelligence (AI) is poised to replace jobs in the middle-skilled segment, leading to labor market polarization. This article constructs a dynamic multi-sector general equilibrium model to compare three strategies to cope with this phenomenon: 1) improving the labor productivity of medium-skilled workers, 2) transitioning medium-skilled workers into low-skilled roles, and 3) upskilling medium-skilled workers into high-skilled positions. Findings reveal that all three strategies can mitigate wage polarization, but transforming medium-skilled workers into high-skilled workers can enhance the overall labor force skill level, reduce income inequality, and promote quality employment and shared prosperity. Automation of high (low) skill tasks will decrease the wages and labor income share of high (low) skilled workers, while the creation of high (low) skill tasks will increase their wages and labor income share. The government increasing the proportion of investment in new infrastructure and reducing the proportion of investment in education can increase total social output, but it will intensify wage polarization. The government increasing the proportion of investment in education and reducing the proportion of investment in new infrastructure will help reduce income inequality, but the economic growth effect will not be as good as Invest in new infrastructure.

Being "too interconnected to fail" has made the risk correlation of financial institutions and its influencing factors a crucial issue in maintaining financial stability. Drawing inspiration from gene regulatory research using random forests, this paper proposes a method to construct a network that captures the relations between different indicators, for the purpose of exploring the influences between risk correlation and its related factors. It is achieved by integrating forest fusion and random permutation. The proposed method overcomes the limitations of traditional regression analysis, Granger causality test, and Bayesian networks, while the introduction of random permutation enhances the model's capability to handle variable heterogeneity. Empirical results based on 46 listed financial institutions in China from 2012 to 2022 demonstrate that the constructed network can identify the direct or indirect impact of different factors on risk correlation and reveal the influence paths of factors. This provides more comprehensive empirical evidence of complex relationships, highlighting the applicability of the proposed approach in addressing this issue and potentially offering a useful tool for financial regulation and risk management.

The generative AI technology represented by ChatGPT, considered as the second information revolution, have transformed the depth of data analysis, offering new perspectives for intelligent internal audits in enterprises. In response to the limitations in the existing audit risk warnings, such as the limited improvement in the generalization capability of traditional machine learning and the insufficient feature analysis dimensions, we propose a method based on the core technology of ChatGPT—A deep autoencoder network. This method aims to pre-determine risks in the critical accounting activity of incoming funds. First, based on influencing factors, audit features are selected and extracted from various perspectives including business matching, term structure, impairment loss, related transactions, individual statistics, and text information. Subsequently, considering the imbalance of risk samples and the temporal characteristics of financial indicators over the operating cycle, an unsupervised and deep learning-based approach is employed. This involves constructing a deep autoencoder (DAE) pre-training model with the addition of an attention mechanism and employing bidirectional long short-term memory (Bi-LSTM) as the network. Additionally, drawing from the concept of multi-task learning, an integrated framework with model transfer is utilized to quantify audit risk probabilities, ensuring the stability of warnings. Finally, real data from enterprise transactions and finances are collected by using big data technology for comprehensive comparative validation of the proposed method. Experimental results indicate that this method effectively and accurately extracts audit features under different warning time windows. In comparison to common practices like supervised learning and iterative clustering, it significantly enhances the precision and robustness of audit risk warnings. Moreover, it also identifies key factors leading to risk, enabling quickly swift localization of audit suspicions. Our study can provide intelligent decision support for enterprises to improve the quality and efficiency of internal audit.

With the explosive growth of investable assets and asset information, portfolio selection faces the dual challenges of high dimensionality in both assets and characteristics. This paper proposes a portfolio selection framework based on machine learning and asset characteristics. Leveraging the inherent advantages of machine learning, the framework utilizes asset characteristics to directly predict portfolio weights, bypassing return distribution prediction in the conventional two-step portfolio management paradigm. The framework is applied to asset allocation research in the Chinese stock market. The research results show that: 1) The proposed investment strategies capture incremental information within high-dimensional characteristics and uncover both linear and non-linear relationships between asset characteristics and portfolio weights, resulting in a significant enhancement of investment performance. 2) Trading friction-related characteristics are the most important indicators for predicting portfolio weights. 3) These strategies yield higher returns on stocks with stricter arbitrage restrictions while exhibiting lower sensitivity to changes in macroeconomic conditions. Under other economic constraints, these strategies remain robust. This paper expands the research framework of modern portfolio theory, contributing to the development of artificial intelligence and quantitative investment.

In recent years, driven by the commercialization of internet of things (IoT) platforms and support from governments at all levels, the process of intelligent and digital transformation in the manufacturing industry has accelerated. Manufacturers are utilizing IoT platforms to develop and produce smart products, providing consumers with smart products and services. Through game theory analysis, the study found that when the government does not provide subsidies, IoT platforms adjust commission rates and transfer payments to incentivize manufacturers to innovate and yield higher market returns. When the government provides subsidies, these subsidies have a similar effect on promoting smart product innovation for equipment manufacturers or consumers. However, when research and development costs are high, government subsidies may not be sufficient to fully incentivize manufacturers to expand smart product production, and platform transfer payments can strengthen the positive effect of government subsidies. Additionally, government subsidies have shown to bring more pronounced economic benefits in promoting manufacturer innovation and increasing market demand for smart products compared to platform transfer payments. This research provides constructive insights for enterprise digital transformation, platform transfer payments, and government policy formulation.

The integration of local aggregation and global integrity in the structure of intelligent collectives arouses the exploration of its underlying mechanisms in the academic community. The current mainstream explanation is the combination of structural embeddedness and structural holes based on social capital, and the discussion of homophily and heterophily based on individual attributes is relatively insufficient. In addition, related work often adopts the method of agent-based modeling, which is divorced from the real network to a certain extent and cannot predict the formation of relationships. Accordingly, this paper uses the method of data analysis and deep learning to explore the driving role of homophily and heterophily in the structural evolution of intelligent collectives. The results show that the interactive networks of two intelligent groups in two different fields investigated in this paper have experienced three stages (namely, loose clustering, chain structure, and small world state), showing good local aggregation and global integrity. During the network evolution, homophily promotes similar individuals to converge and form clusters; heterophily promotes the formation of connections between clusters. This study not only helps to further understand the underlying mechanisms of the structural evolution in intelligent collectives, but also provides a new idea for the analysis of evolutionary mechanisms in complex networks from the perspective of deep learning. Meanwhile, our work also has certain reference significance for the management practice of using social intelligence resources to implement open innovation.

Using natural language processing (NLP) techniques to gain key information from unstructured data, such as corporate texts, news coverage and self-media language, to do financial and economic research which has attracted extensive attention from numerous scholars in recent years and a wealth of research literature has existed. This paper summaries the latest research progress on the application of NLP in financial problems to expatiate text analysis methods using NLP techniques, and focuses on literature about how to use annual reports and news text to study issues in financial areas including corporate finance, asset pricing, risk management, macro-finance and green finance. We evaluate some rough edges in the existing research literature and provide certain research directions for further research in the end.