As the Digital China strategy accelerates, enterprise digital transformation has been recognized as a central pathway to high-quality growth. However, opportunistic behavior—characterized by rhetoric exceeding action—remains widespread, thereby undermining policy effectiveness. A Digital Government Development Index is constructed for China’s prefecture-level cities using machine-learning methods. A panel fixed-effects model is employed to examine how digital government development affects the rhetoric–action gap in enterprise digital transformation. The gap is significantly reduced by digital government development. Three mechanisms are identified: the easing of financing constraints, the reduction of institutional transaction costs, and the stimulation of substantive innovation. More substantial governance effects are observed in low-competition, low-tech, and highly labor-intensive industries. New evidence is provided on how digital governance shapes firm behavior in the digital era, and an actionable policy framework is proposed for building a dual-incentive constraint system under resource constraints in developing countries.

This paper develops a new explanation based on a resource–institutional compatibility framework in which the value of artificial intelligence (AIs) depends not just on firm-level capabilities but more so on national labour institutional compatibility. This perspective addresses a key limitation inherent in most traditional formulations of the Resource-Based View (RBV), namely their tendency to overlook the role of institutional frictions. The study suggests that firms with high socio-political capital through strong unionisation and collective bargaining will be environments where AI-induced restructuring runs the risk of greater socio-political imperatives undermining AI value potential. The implication is that multinational enterprises (MNEs) must factor in both labour relations and the institutional configurations in their host destinations—over and above the level of technological readiness—when taking AI across borders. The strategic advantages of the AI boom are not ubiquitous – they depend on other forms of “institutional legitimacy and social license.”

Enterprise green innovation is an important way to achieve the goal of "double carbon" in China and promote high-quality economic growth. This paper constructs a fixed-effect econometric model based on the annual reporting data of A-share listed companies from 2011 to 2021, and studies the influence of green finance on enterprises' green innovation behavior from all aspects. The main results of the analysis are as follows: (1) Green finance has a significant positive impact on the green innovation of enterprises, and it still holds after many robustness tests. (2) Its role in green innovation is mainly through two channels, namely, alleviating the financing friction faced by enterprises and improving the standard of information disclosure. (3) Heterogeneity analysis shows that green finance plays a stronger role in promoting state-owned enterprises and large enterprises, but has no statistical significance in promoting non-state-owned entities and small and medium-sized enterprises. It not only provides reliable empirical support for the theoretical path of linking green finance with green innovation, but also brings practical policy impact to government agencies to improve the policy coordination framework and enterprises to rationally allocate innovation resources.

The US semiconductor industry is positioned as a key pillar in contemporary global manufacturing and technology. Predicting the semiconductor price index is critical in deriving investment decisions and evaluating policies. This study applies two time-series prediction models: ARIMA and Exponential Smoothing (ETS) in forecasting the US Semiconductor Industry price index using monthly data from 2015 to 2025. The author runs the time series data against the two models to evaluate which one offers a better prediction. The findings of the analysis demonstrate that the two models effectively capture the hypothesized growth in the semiconductor industry’s price index; however, the ETS model demonstrates superior forecasting accuracy with a lower root-mean-square error (RMSE = 10.68) relative to the ARIMA model (RMSE = 20.73). The findings of the study showed that exponential smoothing offers a more stable short-term forecasting framework, while ARIMA remains valuable for analyzing cyclical and seasonal fluctuations. Overall, this paper provides a quantitative perspective on the trajectory of the U.S. semiconductor industry within an evolving technological and macroeconomic landscape.

With the surge in the digital platform ecosystem over the last few years, competition in the game sector has become more prominent, compelling the leading game console producers to consider a critical strategic dilemma: whether or not the platform should retain exclusivity in order to maintain a lock-in effect on consumers or implement a compatibility feature like cross-platform play in order to increase the scale of the network. This paper investigates the extent to which the adoption level of cross-platform compatibility affects the results in the bilateral digital game console market, specifically on the sale of games and the platform market share. This paper uses a Difference-in-Differences approach and focuses on the adoption level of cross-play on multiple-player game consoles between 2018 and 2022. This paper aims to give real-world evidence on the level of influence on the extent to which the adoption level affects user engagement on digital platforms. Our results provide corresponding evidence that the adoption level of cross-platform play can increase the sale of games and the level of market dynamism. Even more importantly, the results reveal that the leading platforms are less benefited compared to the platforms with lower initial market shares, indicating that cross-platform compatibility can act as a highly effective competitive tool for the challenging platforms.

China’s 1994 housing privatization decoupled employer-provided housing from state-sector employment, transforming a key component of the danwei welfare system into private property. This reform plausibly reduced job lock, by allowing workers to change jobs without losing housing, and relaxed liquidity institutional considerations through a one-time wealth transfer. Using panel data from the China Health and Nutrition Survey (CHNS) between 1989 to 2004, this paper applies a difference-in-differences-in-differences (DDD) design comparing treated households that received employer-tied housing with untreated households and contrasting women with men. The results show that the reform substantially increased labor mobility among treated men, who were 18 percentage points more likely to enter non-state wage employment after 1994 (s.e. 0.017, p < 0.01). However, the gender differential in this transition is small and statistically insignificant (−3.4 percentage points, s.e. 0.021), suggesting that women did not experience a comparable expansion in private-sector opportunities. Event-study estimates confirm that no systematic differences existed between treated and control groups before the reform, supporting the validity of the identification strategy. Overall, the findings indicate that unbundling housing from employment successfully reduced job lock and promoted market-based mobility but did not narrow gender differences. These results highlight the importance of complementary policies, such as childcare support and equal-opportunity measures, to ensure inclusive labor-market benefits from structural reforms. Overall, the analysis highlights the 1994 reform as a landmark achievement in China’s urban welfare modernization, strengthening household property rights and supporting orderly, high-quality labor-market development.

Utilizing empirical evidence extracted from the sample pool of Chinese A-share listed entities within the 2018–2023 temporal frame, an inquiry has been undertaken herein regarding the impact exerted by digital transformation upon corporate performance parameters, while a particular focus is concurrently allocated to interrogating the contingent moderating agency displayed by media attention. To be discerned from these investigative undertakings are several salient outcomes: (1) Enhanced corporate performance is engendeblue, demonstrably and sustainably, by instances wherein digital transformation processes are deployed—such regularity remains substantiated when tested for robustness in methodology. (2) Upon disaggregated examination through heterogeneity analysis, the amplification effect wrought by digital initiatives upon organizational performance manifests with elevated conspicuity among state-owned enterprises; less intensively does such elevation reveal itself within non-state entities—a pattern of divergence that reflects variation across property-rights demarcations. (3) Negative moderation by media attention becomes apparent, where interaction between external scrutiny intensity and firm-level digital advancement yields a weakening influence on performance increments otherwise associated with digital measures. Thus, in periods marked by heightened media surveillance, digitalization’s resultant augmentation of enterprise outcomes shows attenuation. Arising from the corpus of empirical exploration, nuanced recommendations are articulated, addressing managerial practitioners, regulatory authorities, journalistic actors, and societal constituencies, each cohort envisaged as instrumental in fostering China’s ongoing transition toward digitally-enabled, qualitatively-driven economic trajectories.

Based on the data of A-share listed companies in China from 2010 to 2024, this study uses A panel model to analyze and finds that higher ESG disclosure quality is generally conducive to improving corporate performance. From a multi-dimensional perspective, the promoting effect of governance disclosure is the strongest, mainly due to the reduction of agency costs. Social disclosure also has a positive impact, but to a lesser extent, and the path lies in improving stakeholder relationships. Environmental disclosure does not bring significant financial returns in the short term, and its value may need to be manifested over the long term. This indicates that the influence mechanisms and timeliness of each dimension of ESG are different, and enterprises and policy-making need to adopt differentiated strategies.

ESG (Environmental, Social, and Governance) investment has been building up as a values based special purpose strategy to a theoretically sound and empirically valid constituent of the contemporary portfolios. The current research summary provides a conceptual description of the observed empirical steps through which ESG characteristic can influence the prices of assets, the communications of risk and capital decision. In line with nonpecuniary utility, information asymmetry, and stakeholder theory models, we examine empirical evidence concerning the presence or absence of pricing of ESG attributes as risk factors, safeguarding of downside risk, or, merely,, simply industry and firm-specific factors. We research in terms of techniques methodological instruments of econometric identification panel regressions, quasi-natural experiments, and factor-model extensions and novel methods of forecasting ESG-adjusted returns with machine-learning and identifying non-linear relationships. The literature has shown that the ESG integration is always associated with reduced volatility and tail-risk exposure but the return premia is sporadic and is contingent on the design of the measure. The review also displays the increased importance of the ESG factors in the decision in allocating venture capital particularly venture capital in capital-intensive green technology. The typical issues include the data fragmentation, the difference in scores, the absence of inference of causality and the complicatedness of the dynamics modeling of the formation of ESG factors. Our research areas of interest will include standardized ESG data construction, and introduction of ESG into multi-factor models of asset-pricing, and construction of ESG valuation of more diversity of assets other than household and alternative assets.