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.

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.

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.

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.”

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.

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.

This research investigates the impact of education expenditure, labor force and literary rates on economic growth, measured by GDP per capita. Using panel data from a cross-country level covering 267 nations and regions, the study examines how human capital influence growth. The analysis reveals that education expenditure is highly associated with GDP per capita through all country groups, especially for non-OECD and developing countries, where institutional and educational systems are not that strong compared to advanced economies. Literary rates show positive relationships with more obvious in advanced economies. The finding emphasizes the crucial role of human capital in the economy field. And it should boost nations development: investment in education contributes growth on economy and fosters workforce quality and productivity for the country.

Fintech may not only guide capital to return to the real economy, but also aggravate capital idling due to lowering the threshold of financial investment. Based on this, this study empirically analyzes the impact of Finance on the "de real to virtual" of enterprises, combining with the data of Chinese listed entity enterprises from 2012 to 2023. The research finds that, first, Fintech has helped enterprises "shift from real to virtual", and has passed a series of robustness tests; Second, Fintech can release a large number of financial resources by alleviating enterprise financing and enhancing the resilience of enterprise supply chain, which further increases the tendency of enterprises to "shift from real to virtual"; Third, for private enterprises, enterprises in the eastern region and enterprises without important defects, the impact of Fintech on enterprises' de reality to virtual is more significant, while for state-owned enterprises, enterprises in the central and western regions and enterprises with important defects, this effect is not obvious.

As the e-commerce sector develops quickly, clients' decision-making has been greatly affected by live-streaming sales. While paying attention to the live-streaming promotion scene, this research digs whether scarcity cues can activate clients' loss aversion emotions to improve purchase transformation rates, reduce decision-making cycles, and decrease abandonment rates. Adopting a controlled experimental design, the research makes the experiment on Douyin and other platforms: the implantation of experimental group with scarcity cues such as "Only 5 pieces left of the product" is made, while regular good data is only offered by the control group. While keeping the same host and speech modes, both groups control variables by alternating live-streaming in two daily time slots. The platform's backend offers live-streaming data, and the quantification of loss aversion scores is made by pop-up questionnaires. The key indexes for information processing encompass purchase transformation rate, average decision-making time, payment abandonment rate, and loss aversion score. The results show that the purchase conversion rate of the experimental group (28.01%) is significantly higher than that of the control group (21.03%) (t = 21.36, p < 0.001), the average decision-making time (9.32 minutes) is shorter than that of the control group (13.64 minutes) (t = -24.18, p < 0.001), the payment abandonment rate (15.89%) is lower than that of the control group (23.05%) (t = -12.75, p < 0.001), and the loss aversion score (3.61 points) is higher than that of the control group (2.47 points) (t = 25.89, p < 0.001), verifying the two hypotheses: "Scarcity cues activate loss aversion and enhance conversion" (H1) and "Compression of dual constraints shortens decision-making cycle" (H2). This study fills the literature gap of scarcity cues in live-streaming scenarios and provides a reference for optimizing marketing strategies in e-commerce. However, it does not distinguish the types of scarcity cues, and the sample focuses on a single platform. With future studies, the research can be expanded to various platforms and across cultures.

Based on the data of Chinese listed companies from 2012 to 2023, this study examines the impact of financial technology on enterprises' short-term borrowing and long-term investment behaviors. The findings are as follows:Fintech exerts a significantly negative impact on both short-term borrowing and long-term investment of enterprises, which indicates that fintech can curb such behaviors.After a series of model reconstruction and tests—including replacing core explanatory variables, adopting random effects models and fixed effects models—the aforementioned negative impact remains significant.Mechanism tests show that fintech can further inhibit enterprises' short-term borrowing and long-term investment behaviors by alleviating corporate financing constraints and improving the level of corporate financialization.For enterprises in high-tech industries and those without major operational defects, the negative impact of fintech on their short-term borrowing and long-term investment is significant; whereas this effect is not obvious in non-high-tech industries and enterprises with major operational defects.