What Drives Consumers to Buy AI-Generated Creative Products? Insights from User Behavior Research

Research on Driving Factors of Consumer Purchase Intention for AI-Generated Creative Products Based on User Behavior

Artificial intelligence (AI) technologies, especially AI-generated content (AIGC), are transforming design and cultural creative sectors. This article examines the key factors that influence consumers' intentions to purchase AI-generated cultural and creative products, focusing on user behavior and experience.

Introduction

The use of AIGC has grown significantly, with millions engaging monthly on AI-powered creative platforms. This growth is reshaping the design industry by enabling more innovative, efficient, and culturally rich creative processes. China, as a major player in cultural and creative product markets, faces challenges like product homogenization and lack of regional uniqueness. AI helps address these by enhancing design efficiency, enabling random cross-disciplinary combinations, and inspiring creativity beyond conventional limits.

While prior studies have focused on user experience and technology acceptance, there is limited research on what drives purchase intentions specifically for AI-generated cultural products. This article fills that gap by integrating multiple behavioral theories to understand these drivers and provide actionable insights for product development and marketing.

Theoretical Framework

This study's model is built on the stimulus-organism-response (SOR) theory, linking external stimuli to internal cognitive/emotional states and then to behavioral responses. It incorporates key theories including:

• Theory of Planned Behavior (TPB): Explains how attitudes, social norms, and perceived control shape intentions.
• Innovation Diffusion Theory (IDT): Focuses on adoption and spread of innovations.
• Unified Theory of Acceptance and Use of Technology 2 (UTAUT2): Highlights factors like hedonic motivation and social influence.
• Value Adoption Model (VAM): Emphasizes perceived value and price as determinants of adoption.

Additional variables include cultural experience, generation quality (how well AI content is generated), and self-innovation—a user’s openness to new technologies—acting as a moderator in the model.

Key Factors Influencing Purchase Intention

• Behavioral Attitude: Positive attitudes toward AI-generated products strongly predict purchase intention.
• Hedonic Motivation: The pleasure and enjoyment derived from the product experience enhance willingness to buy.
• Perceived Price: Users weigh costs carefully; reasonable pricing supports higher purchase intention.
• Perceived Value: The overall benefits versus costs influence user evaluation and decision-making.
• Generation Quality: The quality and creativity of AI-generated content significantly impact user acceptance.

Users consider not just price and quality but also the unique cultural elements and enjoyable experiences these products offer.

Market and Platform Insights

Leading AIGC platforms like Midjourney and Stable Diffusion stand out for their creative output and technical capabilities. Platforms offering intuitive user interfaces, such as Playground and Lucidpic, improve user engagement by allowing real-time content preview. Localized services, like Wenxin Yige for Chinese-speaking users, enhance accessibility. Technical robustness from providers like OpenAI and Adobe Firefly supports diverse creative needs across industries.

Research Methods

The study uses structural equation modeling (SEM) combined with artificial neural networks (ANN) for analysis. SEM helps clarify complex relationships among variables, while ANN captures nonlinear patterns and validates the importance of each factor through cross-validation.

Practical Takeaways for Creatives and Product Developers

• Focus on User Experience: Ensure AI-generated products deliver enjoyable and culturally meaningful experiences.
• Balance Price and Value: Align pricing strategies with perceived value to boost purchase intent.
• Maintain High Generation Quality: Invest in AI models and creative workflows that produce distinctive, high-quality content.
• Leverage Social Influence: Encourage sharing and social proof to enhance product acceptance.
• Support User Innovation: Target early adopters open to new technology to build momentum.

By combining insights from behavioral theories and AI technology capabilities, companies can better design, market, and position AI-generated cultural products for commercial success.

Further Learning

If you're interested in expanding your skills with AI tools for creative product development, consider exploring comprehensive AI courses available at Complete AI Training. They offer tailored programs for creatives and product developers looking to integrate AI into their workflows.

Conclusion

AI-generated cultural and creative products represent a growing market with unique challenges and opportunities. Understanding the factors that drive consumer purchase intention—such as perceived value, price, hedonic motivation, and cultural experience—is critical for success. Integrating AI thoughtfully into design processes can unlock new creative possibilities, improve user satisfaction, and increase market acceptance.