Research on driving factors of consumer purchase intention of artificial intelligence creative products based on user behavior
Unlocking Consumer Intent for AI-Generated Creative Products
A Deep Dive into User Behavior and Market Dynamics
Executive Summary
The advent of AI-generated content (AIGC) is reshaping industries, with a phenomenal 653.3% year-on-year growth in monthly active users. Our analysis reveals key drivers for successful market penetration of AIGC creative products, offering strategic insights for enterprise adoption.
653.3%
AIGC User Growth
61.7M
Monthly Active Users
1.55B
Top Platform Visits (OpenAI)
Deep Analysis & Enterprise Applications
The study employs a combination of SEM (structural equation modeling) and ANN (artificial neural networks) to construct the basic methodology of this research, with the specific process shown in Fig. 3. SEM is a method used to analyze complex variable relationships by combining multiple statistical techniques, allowing researchers to study various causal relationships. SEM integrates the advantages of factor analysis and path analysis and is commonly used to validate theoretical models, including the relationships between observed variables and latent variables112. ANN, on the other hand, is a computational model inspired by biological neural networks, and it is one of the key methods in machine learning. ANN constructs a network structure with learning capabilities by simulating the connection patterns of human brain neurons, which can handle complex nonlinear relationships and pattern recognition problems. ANN has stronger data fitting and predictive capabilities, especially suitable
Methodology
Key Findings
Implications
This research integrates Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) to provide a robust framework for analyzing complex user behavior in the context of AIGC creative products. SEM is utilized to validate theoretical models and causal relationships, while ANN captures nonlinear interactions and enhances predictive accuracy.
The combined approach offers a comprehensive understanding of factors influencing purchase intention, moving beyond traditional linear models. This dual methodology ensures both theoretical rigor and practical applicability, making the findings highly reliable for enterprise decision-making.
Key findings indicate that behavioral attitude, hedonic motivation, perceived price, perceived value, and generative quality are primary factors influencing user purchase intention for AIGC creative products. Cultural experience also plays a significant role.
Self-innovation acts as a moderator, influencing the relationship between perceived value/price and perceived behavioral control. Users prioritize pleasurable experiences and cultural uniqueness alongside price and quality when making purchase decisions.
For enterprises, these findings highlight the necessity of focusing on enhancing the product's perceived value, hedonic motivation, and cultural experience. Optimizing AI algorithms to improve generative quality and personalization is crucial.
A multifaceted approach to marketing, integrating rich cultural elements, leveraging social influence, ensuring reasonable pricing, and streamlining the purchasing process, will effectively boost user purchase intention and market acceptance.
Enterprise Process Flow
Conduct research on AIGC platform to define content characteristics of technical implementation
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Conduct a literature review to integrate relevant theories
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Constructing a Model Framework for User Purchase Intention
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Design of Structured Questionnaires
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Assessing the validity and reliability of questionnaires
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Conduct a questionnaire survey in Gansu region
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Collect data from participants
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Data analysis using SPSS 26
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Structural Equation Modeling (SEM) & Artificial Neural Network (ANN) Analysis
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Calculation of Path Coefficients in SEM & RMSE Values in ANN
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Calculation of the results to ensure matching outputs of the two models
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Research Conclusion & Suggestions
0.916
Kaiser-Meyer-Olkin (KMO) Measure
0.034
RMSEA Fit Index
| Feature | SEM | ANN |
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AI-Assisted Cultural Product Design Success
Midjourney, a leading AI image generation platform, has demonstrated powerful capabilities in assisting cultural and creative product design. Its intuitive interface and advanced algorithms enable designers to rapidly prototype and refine concepts, integrating regional cultural elements and diverse artistic styles. This leads to unique, high-quality outputs that resonate deeply with consumer preferences. Case study results show a significant increase in design efficiency and user engagement when leveraging AI tools.
Key Takeaway: AI tools like Midjourney empower designers to overcome traditional limitations, foster cross-disciplinary innovation, and produce culturally rich, high-quality creative products at an accelerated pace, significantly boosting market acceptance.
0.896
Average Relative Importance of Perceived Value (PV) in Model A
100%
Average Relative Importance of Hedonic Motivation (HM) in Model A
Estimate Your AI-Driven Efficiency Gains
Calculate the potential time and cost savings by integrating AI into your creative workflows.
Annual Savings
$0
Hours Reclaimed
0
Your AI Integration Roadmap
A phased approach to successfully integrate AI into your creative operations and drive consumer purchase intention.
Phase 1: Strategic Assessment & Product Optimization
Identify core product values, enhance hedonic and cultural experience. Focus on unique emotional value and cultural resonance in AI-generated designs. Update AI algorithms for superior quality and personalization. (~1-2 Months)
Phase 2: Market Engagement & Social Amplification
Implement targeted marketing campaigns leveraging social media and influencer partnerships. Showcase user testimonials and real-life product usage scenarios to build social acceptance and trust. Optimize pricing strategy for perceived value. (~2-4 Months)
Phase 3: Experience Refinement & Continuous Innovation
Streamline the entire user journey, from product discovery to post-purchase support, ensuring a seamless and controlled experience. Foster a culture of continuous innovation, actively incorporating user feedback to refine AI models and product offerings. (~3-6 Months and Ongoing)
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