Hybrid organizations that combine multiple institutional logics face unique challenges in pursuing Corporate Social Innovation (CSI), yet existing literature lacks an integrative framework explaining how organizational identity drives innovation in these contexts. This paper develops a theoretical framework linking organizational identity configurations to CSI through the mediating mechanism of Organizational Learning Capability (OLC). Drawing on institutional logic theory, paradox theory, and organizational learning literature, the framework conceptualizes hybrid organizational identity along two dimensions—logic centrality and logic compatibility—yielding four identity archetypes: aligned, dominant, estranged, and contested. Eight formal propositions are advanced, arguing that tension-laden identity configurations (estranged and contested) stimulate greater social innovation through enhanced organizational learning, whereas harmony-driven configurations (aligned and dominant) suppress innovation through learning complacency. The framework contributes to management theory by bridging fragmented literatures on organizational identity, institutional complexity, and social innovation, offering a paradox-based explanation of why internal tensions can serve as catalysts rather than impediments to innovation in hybrid organizations.

High equity concentration is common in high-growth start-ups, but its impact on corporate governance and financial risks has not been fully studied - this is a key research gap in the existing literature. Equity concentration will lead to the failure of internal control and aggravate the risk of financial fraud through factors such as centralization of control, information asymmetry and weakening of governance mechanisms. The critical threshold of shareholders' rights or voting rights will lead to governance imbalance and weakening supervision, thus facilitating fraudulent activities. The main contribution of this paper includes the construction of a dynamic threshold model, which explains this nonlinear transformation: moderate concentration can improve efficiency through entrusted responsibility, while excessive concentration will trigger systemic fraud risks, because the solidification effect dominates. The main contributions also include proposing and supporting this nonlinear relationship in a consistent way, building a new start-up enterprise governance research and analysis framework that integrates agency theory and stewardship theory, and systematically analyzing the potential threats of equity concentration to emerging enterprises. This is to optimize the equity structure and improve the regulatory policy system, and provide theoretical basis and practical advice for high-growth start-ups.

As the global climate change situation continues to intensify and China's "dual carbon" strategy is deeply implemented, ESG performance has become a key indicator for measuring a company's sustainable development capability. Taking Chinese manufacturing enterprises as a sample, this paper studies the impact path and dynamic effects of ESG performance on the financial performance of manufacturing enterprises, thereby revealing and explaining the proactive behaviors of enterprises in practicing ESG. This paper adopts a two-way fixed effects model and empirically tests the impact and mechanism of ESG performance on corporate financial performance using unbalanced panel data from 2019 to 2023. The research shows that improving a company's ESG performance helps to enhance its financial performance. The improvement in ESG performance positively affects corporate financial performance by reducing financing costs, and this effect is more significant in non-heavy polluting and high-tech enterprises.

Driven by consumption upgrading and sustained economic growth, the global pet industry has expanded rapidly. As a representative emerging market, China's pet economy has exceeded RMB 500 billion in scale; however, it faces challenges such as intensifying market competition, low export dependence, and fluctuations in international policies. This study employs a combination of the ARIMA time series model, Holt's linear trend method, Principal Component Analysis (PCA), and Ordinary Least Squares (OLS) regression to systematically examine the development trends of China's pet industry, the structural differentiation of the global pet food market, and China's production capacity and export potential in pet food. The findings indicate that China's pet industry is transitioning from a phase of rapid growth to a more mature stage characterized by moderated expansion. Over the next three years, the market size is projected to grow at an average annual rate of 10.5%, with the focus of competition shifting toward product differentiation and service upgrading. Global demand for pet food exhibits pronounced regional disparities: the U.S. market is approaching saturation, the European market maintains a competitive advantage in quality and safety, and emerging markets such as China are becoming the primary engines of growth. Although China's pet food production reached RMB 270.3 billion in 2023, exports accounted for less than 2%, highlighting a structural imbalance characterized by demand-driven domestic consumption and relatively weak international competitiveness. PCA results reveal that trade barriers and production costs are the primary factors constraining exports. Based on these quantitative findings, this study proposes strategies including short-term optimization of supply chain costs, long-term expansion into high-potential markets in Southeast Asia and Latin America, and the strengthening of digital marketing capabilities. These recommendations aim to provide data-driven decision support for the sustainable development of the industry and the enhancement of firms' international competitiveness.

From the perspective of the new development philosophy, this paper explores the influence mechanism and effect of the digital economy on the high-quality development of the cultural industry. The Entropy Weight Method is adopted to measure the development levels of the digital economy and the high-quality development of the cultural industry in 31 provinces of China from 2012 to 2023, and empirical tests are conducted by using panel regression, threshold model and regional heterogeneity analysis. The results show that: (1) On the whole, the digital economy significantly promotes the high-quality development level of the cultural industry, especially exerting an obvious upgrading effect on the dimensions of innovation, coordination and sharing, but has a negative impact on the opening-up dimension. This suggests that the opening-up mode of the cultural industry may be shifting from the traditional form relying on physical cross-border flow to a new form more dependent on digital platforms and ecological connection. (2) The impact of the digital economy has a single threshold effect based on the level of industrial agglomeration, and its driving effect is significantly enhanced only when the agglomeration degree crosses a certain threshold. (3) The analysis of regional heterogeneity shows that the digital economy has a more prominent promoting effect on the central and western regions, while its impact on the eastern region is not significant. The study suggests that efforts should be made to consolidate digital infrastructure, promote industrial agglomeration and ecological construction, implement regionally differentiated empowerment strategies, and improve the digital cultural governance and opening-up sharing mechanism, so as to systematically promote the deep integration of the digital economy and the cultural industry and achieve high-quality and inclusive development. This paper empirically analyzes the impact of the digital economy on the high-quality development of the cultural industry through panel regression model, threshold effect model and other methods.

In the digital transformation of enterprises, event logs are generated in large quantities, but mining process diagrams alone is insufficient to produce actionable value. This paper proposes the Diagnosis-Detection-Verification (DDV) framework, a novel process optimization methodology that bridges the gap between event logs and explainable interventions. The framework integrates three key components: (1) bottleneck diagnosis using criticality-indexed activity analysis, (2) hybrid anomaly detection combining rule-based filtering with Isolation Forest, and (3) data-driven intervention rule generation validated through log replay simulation. We demonstrate that DDV achieves state-of-the-art performance on BPI Challenge datasets (F1 = 0.82, + 5.1% improvement over Deep SVDD) while maintaining computational efficiency (26.5 × faster training than deep learning methods). Intervention rules generated by DDV reduce the average throughput time by 18.7% and improve SLA compliance from 67.3% to 82.6%.

Against the backdrop of the deepening implementation of the rural revitalization strategy, the development of the rural market economy has become a central lever for activating endogenous rural growth dynamics. As a critical component of rural infrastructure construction, public transport capital investment exhibits a profound interactive relationship with the development of the rural market economy. Grounded in public goods theory, coupling coordination theory, and industrial integration theory, this study employs the entropy weight method, coupling degree model, and coupling coordination degree model, combined with provincial panel data from 2010 to 2020, to systematically examine the interactive mechanisms between public transport capital investment and rural market economy development. The findings indicate that public transport capital investment promotes rural market economy development through three primary channels: reducing circulation costs, facilitating industrial agglomeration, and expanding market boundaries. In turn, the development of the rural market economy guides the efficient allocation of public transport capital via demand-driven forces, financial feedback mechanisms, and structural optimization. The coupling coordination degree between the two systems shows a year-by-year upward trend; however, pronounced regional disparities persist. The central and western regions lag behind the eastern region in terms of coordination levels, and within rural areas there exist dual imbalances characterized by "transport lag–market contraction" and "transport advance–resource misallocation". Based on these findings, this paper proposes policy recommendations including optimizing the investment structure, strengthening supply–demand alignment, and improving coordination mechanisms, thereby providing a reference for promoting the synergistic development of rural public transport and the market economy.

Based on my more than four years of work experience in the pharmaceutical industry, I once regarded "compliance" as an external constraint rather than a core competitiveness. In the era of short videos, numerous peers have released content that skirts regulatory rules to drive traffic and achieve considerable profits. This situation made me somewhat eager for quick success and instant benefits, as I was overly eager to achieve better results and more career development opportunities. Consequently, pharmaceutical supervision laws and regulations such as Decree No.24 once became a straitjacket that hindered me from creating outstanding and influential promotional content. Later, when contemplating entrepreneurship, I realized this issue—what was a constraint for me could also be an opportunity from another perspective. Upon reflection, the particularity of the pharmaceutical industry (e.g., the stringent restrictions on publicity stipulated in the Advertising Law and the Drug Administration Law) is precisely the cornerstone for building user trust. Some practitioners have resorted to edge-ball practices, leading to market chaos and a decline in consumer trust. During a business trip for market research last year, I found that traditional pharmaceutical marketing models (e.g., offline promotion) are experiencing diminishing efficiency. These models have failed to fully embrace the digital trend, resulting in the loss of early traffic dividends. The number of in-store purchasers has decreased, and more young people choose to buy pharmaceutical products through online food delivery platforms.

Green finance is a pivotal market-oriented instrument for coordinating economic growth with ecological sustainability. Against the backdrop of China's Green Finance Reform and Innovation Pilot Zone (GFRIPZ) policy, this paper utilizes this policy implemented in 2017 as a quasi-natural experiment to investigate its impact on corporate environmental investment. Based on data from Shanghai and Shenzhen A-share listed companies from 2011 to 2024, this study conducts an empirical test using the Difference-in-Differences (DID) model. The results indicate that the pilot policy significantly promotes corporate environmental investment. Mechanism analysis demonstrates that the policy drives investment through two main channels: first, by increasing government environmental subsidies to provide fiscal incentives; and second, by strengthening regional environmental regulation intensity to exert external pressure. Furthermore, heterogeneity analysis reveals that the promoting effect of the pilot policy on environmental investment is more pronounced for non-state-owned enterprises and non-heavily polluting firms. This paper provides empirical evidence for assessing the micro-level effectiveness of green finance policies and offers theoretical support for optimizing resource allocation and driving a comprehensive green economic transformation.

This paper looks at how systematic risk—measured through beta coefficients—behaves differently depending on whether we use daily or monthly return data. We pick nine major industry sectors and run standard market model regressions at both frequencies over a three-year window (March 2017 to March 2020, using S&P 500 as the benchmark). What we find is pretty striking: for most sectors, the sign of the average beta actually flips between daily and monthly estimates. Daily betas tend to be noisy and jump around a lot, while monthly ones give much smoother trajectories. These results suggest that anyone relying on just one frequency for risk assessment might be getting an incomplete—or even misleading—picture. We also argue that simple OLS-based approaches, rather than fancy machine learning models, work quite well here precisely because complex models tend to overfit the short-term noise.