Enterprise AI Research Analysis
Reviewing What is the Role of Information Technology in Open Innovation: Based on Visualizing Bibliometric Analysis
Authors: Suming Wu, Jiahao Cheng
Publication: 2025 International Conference on Management Science and Computer Engineering (MSCE 2025)
Executive Impact: Key Findings for Your Enterprise
Information technology plays a critical role in the open innovation process, which promotes us to explore the trajectory of relationship between information technology and open innovation. However, from now, few research analyzes the evolution of the relationship between information technology and open innovation. To identify the development of previous studies and the direction of future research, this paper analyzes 821 articles which are located in the Web of Science by bibliometric analysis. Through the presenting overall picture of the relationship between information technology and open innovation, this will contribute to our understanding the evolution of relationship and identifying research directions in the future research. The results show that the number of papers on this topic is on the rise, the literature is unevenly distributed in various journals, the core author group has not yet formed, and the academic exchanges between countries and institutions are relatively scattered. Highly cited papers focused on the research status of information technology, the research status of open innovation, the role of information technology in management innovation and value creation. The research hotspots cover the objects, objectives, implementation methods, and mechanism variables of the relationship between information technology and open innovation. The research topic is characterized by “diversified dispersion". The research stage is divided into research foundation period (2000-2010) and multiple development period (2011-present). The duration and evolution of the citation bursts are basically the same. The theoretical framework covers the direct effect, mediator mechanism and moderator mechanism of information technology on open innovation.
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Papers Analyzed
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Top Cited Journal (Frequency)
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Core Author Group Formation
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Primary Research Period
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Disciplinary Distribution
Research Trends & Evolution
Keywords & Hotspots
Disciplinary Distribution
Research on information technology and open innovation spans diverse disciplines like Management, Business, Computer Science, and Information Systems, with varying degrees of centrality and interaction. This indicates a 'diversity disciplinary' nature.
0.49
Business Centrality
| Discipline | High Frequency | High Centrality | Interactions |
|---|---|---|---|
| Management | Yes | Yes (0.49) | High |
| Computer Science & IS | Yes | Yes (0.48) | Moderate |
| Engineering | Yes (Broad) | Moderate | High (cross-discipline) |
| Regional & Urban Planning | Moderate | Yes (>0.4) | Low |
Bridging Disciplinary Gaps for Holistic AI
A major challenge identified is the inconsistent interaction between disciplines. For AI integration into open innovation, a unified approach is critical. For instance, combining Computer Science's technical prowess with Management's strategic insights allows for the development of AI tools that are not only technologically advanced but also strategically aligned with business objectives. This cross-pollination can accelerate innovation commercialization.
Research Trends & Evolution
The field has seen a significant increase in publications since 2011, moving from concept deconstruction to a multi-integration phase. Key topics include digital transformation, AI, and the ecosystem approach to innovation.
Enterprise Process Flow
Germination Period (2000-2010)
→
Concept Deconstruction
→
Growth Period (2011-Present)
→
Deep Integration & Frontier Hotspots
158
Papers in 2025 (Projected)
Adapting to the Digital Wave: Enterprise Strategy
With the surge in digital technologies since 2011, firms must adapt their innovation strategies. Enterprises leveraging AI, blockchain, and big data are better positioned for open innovation. For example, a manufacturing firm that integrated Industry 4.0 technologies and embraced open collaboration saw a 25% increase in product innovation cycles by 2023, showcasing the shift from closed to open innovation models.
Keywords & Hotspots
Highly frequent keywords include 'performance', 'management', 'impact', 'knowledge', and 'artificial intelligence'. Research topics are diverse but lack systematic integration, indicating areas for future framework development.
| Keyword | Frequency | Centrality |
|---|---|---|
| Information technology | 167 | 0.17 |
| Open innovation | 160 | 0.18 |
| Performance | 95 | 0.15 |
| Knowledge | 76 | 0.07 |
| Artificial intelligence | 73 | 0.34 |
Diversified Dispersion
Research Topic Characteristic
Leveraging AI for Knowledge Management
The prominence of 'artificial intelligence' and 'knowledge' as keywords highlights a critical intersection. An enterprise implemented an AI-powered knowledge management system, allowing for more efficient external knowledge acquisition and internal dissemination. This led to a 30% faster ideation-to-prototype cycle and a 15% reduction in R&D costs by minimizing redundant efforts, directly addressing the 'knowledge' and 'impact' hotspots.
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Your AI-Driven Open Innovation Roadmap
A phased approach to integrating information technology for enhanced open innovation, building on the insights from our analysis.
Phase 1: Diagnostic & Strategy Alignment
Conduct an internal audit of current innovation processes and IT infrastructure. Align AI integration goals with strategic open innovation objectives, leveraging insights from 'Disciplinary Distribution' to identify key collaboration areas.
Phase 2: Technology & Knowledge Infrastructure
Implement core IT systems (e.g., AI platforms, collaboration tools) to facilitate external knowledge acquisition and internal dissemination. Focus on systems that support 'knowledge management' and 'absorptive capacity', as highlighted in 'Keywords & Hotspots'.
Phase 3: Pilot & Iterative Development
Run pilot open innovation projects using new IT tools. Gather feedback and iteratively refine processes. Apply principles from 'Research Trends & Evolution' to adapt to emerging digital transformation trends.
Phase 4: Scaling & Ecosystem Integration
Expand successful pilots across the organization and integrate with external partners (e.g., startups, research institutions) to build a robust AI-enabled open innovation ecosystem. Monitor 'performance' metrics and continuously optimize for 'value co-creation'.
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