Enterprise AI Analysis
Visual Analysis of Corporate Social Responsibility and Corporate Governance Based on CiteSpace
This paper systematically reviews the research progress on CSR governance of domestic and foreign platforms from 2004 to 2024, and reveals the evolution mode of the research topic and its correlation with the policy environment and social events.
Executive Impact Summary
Research found corporate governance mechanisms (equity structure, board characteristics, internal controls) significantly impact CSR fulfillment. Internal control reduces agency costs and promotes non-state-owned enterprises' CSR. State-owned enterprises have better CSR performance. CSR fulfillment relates to corporate performance and risk management (improves capital allocation, reduces debt default risks, but mandatory disclosure may exacerbate bankruptcy risks). External institutional environment (media attention, social trust, legal environment, property rights) regulates CSR. Research evolved from crisis-driven policy response to institutionalization, property rights reform, media supervision, agency costs. Future research needs to integrate ESG with AI, explore AI company governance, and address AI ethics.
20
Publication Years Analyzed
365
Total Papers Reviewed
0.49
Top Centrality Score (CSR)
Post-2025
Key Insight (AI Ethics)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Evolution of CSR Research
Governance Mechanisms & CSR
CSR Performance & Risk Management
This category examines the historical progression of corporate social responsibility (CSR) research, highlighting shifts from crisis-driven policy responses to institutionalization and property rights reforms. It also identifies the accelerating influence of artificial intelligence on future CSR governance studies and the emerging focus on AI ethics.
This section delves into how various corporate governance mechanisms—such as equity structure, board characteristics, and internal controls—impact the fulfillment of CSR. It differentiates the effects on state-owned versus non-state-owned enterprises, noting the role of internal control in reducing agency costs and promoting CSR in the latter.
This tab explores the critical link between corporate social responsibility performance and overall corporate health, including capital allocation efficiency and debt default risks. It also discusses the potential double-edged sword of mandatory CSR disclosure, which, while promoting transparency, might also intensify bankruptcy risks due to agency issues.
0.49
Highest Centrality Score for 'Social responsibility' keyword, indicating its significant influence and interconnectedness in the research domain.
Impact of Internal Control on CSR Fulfillment
| Feature | Non-State-Owned Enterprises | State-Owned Enterprises |
|---|---|---|
| Effect of Internal Control |
|
|
| Key Mechanisms |
|
|
Evolution of CSR Research Topics (2008-2025)
Crisis-driven Policy Response (2008-2009)
→
Institutionalization of CSR (2012-2014)
→
Deepening Property Rights Reform (After 2015)
→
Media Supervision & Agency Costs (After 2019)
→
ESG & AI Ethics Integration (Post 2023)
The 'Collinridge Dilemma' in AI Governance
The paper highlights the need to address social responsibility issues of AI enterprises in advance to prevent future crises, contrasting with past hasty policy formulations. The rapid development of AI (exponential progress) necessitates proactive research on challenges like deep fraud, high energy consumption, demand for nuclear power, labor replacement, algorithmic discrimination, and social injustice risks. Failing to act proactively risks falling into the 'Collinridge Dilemma', where the potential negative impacts of a technology are only fully understood when it's too late to control them. This calls for integrating AI ethics and corporate governance from the outset.
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Roadmap to Enhanced CSR & Governance with AI
Our phased approach ensures a smooth integration of AI-driven analytical tools to optimize your corporate social responsibility and governance strategies.
Phase 1: Discovery & Strategy Alignment
Initial consultations to understand existing CSR and governance frameworks, data sources, and strategic objectives. Define KPIs and AI application scope.
Phase 2: Data Integration & Model Development
Integrate relevant internal and external data. Develop and train custom AI models for CSR impact assessment, risk prediction, and governance optimization.
Phase 3: Pilot Implementation & Iteration
Deploy AI solutions in a pilot environment, gather feedback, and iterate on model performance and user experience. Refine outputs based on initial results.
Phase 4: Full-Scale Deployment & Training
Roll out the AI-powered platform across the organization. Provide comprehensive training to teams for effective utilization and continuous monitoring.
Phase 5: Continuous Optimization & Reporting
Establish ongoing monitoring and feedback loops. Provide regular reports on CSR performance, governance effectiveness, and AI-driven insights for strategic adjustments.
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