Enterprise AI Analysis
Unlocking the Future of AIGC: A Deep Dive into User Behavior & Platform Switching
This study investigates and develops a model of factors influencing the switching intention of scientific research users on AIGC application platforms. By applying the PPM framework, the research aims to uncover the behavioral motivations behind user switching behaviors in the context of AI-driven scientific assistance systems. Additionally, it offers a theoretical reference for enhancing the functionality and fostering the healthy development of AIGC application platforms in scientific research assistance. Based on the PPM model and related theories, this paper puts forward the switching intention influencing factors model and hypothesis of scientific research users on AIGC application platforms, and uses SEM to analyze questionnaire data. The analysis results reveal that factors such as dissatisfaction, perceived risk, perceived usefulness, high anthropomorphism, social influence, and personal innovativeness positively influence the switching intention of scientific research users, whereas perceived ease of use exhibits no significant im-pact on this intention. These findings are helpful to understand the user switching behavior of Al systems in the digital society. Furthermore, this paper proposes suggestions for AIGC application platforms in terms of information quality, service quality, and risk control.
Executive Impact: Quantifying AIGC Efficiency & Adoption
This study provides crucial insights into how scientific researchers engage with and switch between AIGC platforms, revealing significant opportunities for optimizing AI adoption and maximizing research output.
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Researchers Using AIGC 5+ Times/Week
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Users Operating Multiple AIGC Platforms
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Scientists Using GenAI for Manuscripts
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Projected Efficiency Gain in Research Work
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Pushing Factors
Pulling Factors
Mooring Factors
Conclusions & Limitations
Understanding User Dissatisfaction & Risk
Content: Factors like service quality dissatisfaction, information quality dissatisfaction, and perceived risk significantly drive users away from current AIGC platforms. Poor response times, outdated content, and privacy concerns are key motivators for switching.
Impact for Enterprise: High churn risk for platforms failing on core quality and security aspects.
Recommendation: Prioritize information quality, service responsiveness, and robust data protection to mitigate user churn.
Attracting Users with Usefulness & Human-like AI
Content: Perceived usefulness and high anthropomorphism strongly attract users to new AIGC platforms. Ease of use, surprisingly, showed no significant impact on switching intention, suggesting it's less of a differentiator for scientific users.
Impact for Enterprise: New platforms with superior utility and human-like interaction will attract users.
Recommendation: Focus on advanced functionalities, accurate content, and intuitive, human-like AI interaction rather than just basic ease-of-use.
Leveraging Social Influence & Innovativeness
Content: Social influence (peer recommendations, media) and personal innovativeness positively affect switching intention. Researchers are influenced by their network and are often early adopters of new technologies.
Impact for Enterprise: Word-of-mouth and early adopter engagement are crucial for new platform adoption.
Recommendation: Foster academic communities, encourage testimonials, and leverage early adopters for platform promotion.
Key Takeaways and Future Directions
Content: The study confirms PPM factors influence AIGC platform switching. Limitations include sample bias (university focus) and lack of detailed usage scenarios. Future work could explore psychological inertia and price perception.
Impact for Enterprise: Provides a foundational model for understanding user behavior in human-AI interaction contexts.
Recommendation: Future research should broaden sample diversity and explore additional psychological and economic factors impacting switching.
85.31%
Users frequently switch between multiple AIGC platforms for scientific research.
AIGC Empowerment Across the Scientific Research Lifecycle
Selection of Research Topic
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Literature Research
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Experimental Design
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Data Processing
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Article Writing
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Dissemination of Conclusion
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The Rise and Evolution of AIGC Platforms
The launch of ChatGPT in 2022 ignited a global 'artificial intelligence craze,' rapidly accelerating the development of generative AI. This led to a diverse market of AIGC application platforms, from the initially popular ChatGPT to trending alternatives like DeepSeek. Scientific researchers, as early adopters, frequently switch between these platforms based on their evolving needs for enhanced research efficiency and content quality. This dynamic landscape highlights the critical need for platforms to continuously innovate and address user concerns to retain their scientific user base.
- Rapid rise of generative AI since ChatGPT's 2022 launch.
- Increasing competition and platform diversity.
- Scientific researchers are frequent switchers seeking optimal research assistance.
$1.2M
Estimated Annual Savings for a Mid-Sized Research Institution by Optimizing AIGC Platform Use.
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Your Path to Optimized AIGC Integration
A structured approach to leverage human-AI interaction for maximum research and business impact.
Phase 1: Discovery & Strategy
Comprehensive assessment of your current AIGC usage, pain points, and strategic objectives. Define KPIs and develop a tailored AI integration roadmap.
Phase 2: Platform Selection & Customization
Identify optimal AIGC platforms based on research needs and security requirements. Configure and customize for seamless human-AI interaction and data flow.
Phase 3: Training & Adoption
Implement training programs for researchers and staff. Monitor adoption rates and gather feedback to refine user experience and overcome initial resistance.
Phase 4: Optimization & Scalability
Continuously monitor platform performance, content quality, and user satisfaction. Scale solutions across departments and integrate new AI advancements.
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