Short answer
Focus design efforts on enhancing perceived usefulness, ensuring robust facilitating conditions, cultivating hedonic motivation, and building strong user trust to maximize generative AI adoption.
- Field
- Innovation & Design
- Source
- International Journal of Basic and Applied Sciences (2025)
- Method
- Survey Research
- Sample
- 480 participants
- Evidence
- Strong effect
User adoption of generative AI is significantly influenced by their perceived usefulness, the ease of using supporting systems, the enjoyment derived from using it, their inherent inclination towards new technologies, the perceived value for money, and crucially, their level of trust in the technology. This innovation & design research insight is drawn from a 2025 study published in International Journal of Basic and Applied Sciences. Using Survey research with 480 participants, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Focus design efforts on enhancing perceived usefulness, ensuring robust facilitating conditions, cultivating hedonic motivation, and building strong user trust to maximize generative AI adoption.
Trust and Personal Innovativeness Drive Generative AI Adoption Intentions
User adoption of generative AI is significantly influenced by their perceived usefulness, the ease of using supporting systems, the enjoyment derived from using it, their inherent inclination towards new technologies, the perceived value for money, and crucially, their level of trust in the technology.
International Journal of Basic and Applied Sciences · 2025
Key Findings
- 01Performance expectancy significantly influences behavioral intention.
- 02Facilitating conditions significantly influence behavioral intention.
- 03Hedonic motivation significantly influences behavioral intention.
- 04Personal innovativeness significantly influences behavioral intention.
- 05Price value significantly influences behavioral intention.
Application
Design takeaway
Focus design efforts on enhancing perceived usefulness, ensuring robust facilitating conditions, cultivating hedonic motivation, and building strong user trust to maximize generative AI adoption.
How to apply
When developing generative AI products, conduct user research to understand perceptions of usefulness, ease of use, and enjoyment. Implement features that build trust and clearly communicate the value proposition.
Project actions
- 01When designing a new product, consider how you can make it feel useful and enjoyable for the user.
- 02Think about how you can build trust with your users, perhaps through clear communication or reliable performance.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Utilizes a robust statistical method (PLS-SEM) for analyzing complex relationships.
- +Extends existing technology acceptance models (UTAUT II) with relevant constructs like trust and personal innovativeness.
Limitations
The findings might not apply to users who are not students or who are in different cultural settings. The study relies on what people say they will do, not necessarily what they will actually do.
Reliability & validity
The study likely employed established scales for its constructs, contributing to reliability. PLS-SEM analysis can assess the validity of the model. However, generalizability might be limited by the specific sample.
Think critically
How might the relative importance of these factors shift for different types of generative AI applications (e.g., creative tools vs. analytical tools)?
Design Principles
"User adoption of new technologies is driven by a combination of perceived value, ease of integration, emotional engagement, individual propensity for innovation, economic considerations, and fundamental trust in the system."
Understanding these drivers is essential for designers and product developers aiming to create generative AI tools that users will readily adopt and integrate into their workflows. By focusing on building trust and highlighting the benefits, designers can increase the likelihood of successful product launches and widespread use.
What This Means for Your Design
People are more likely to want to use new AI tools if they think the tools will help them do things better, if they find them fun to use, if they are the kind of person who likes trying new tech, if they think it's worth the cost, and most importantly, if they trust the AI.
How to use in your project
- 1.Reference this study when discussing user acceptance factors for technology in your design project, particularly for AI-related innovations.
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Quick Cite
Paragraph starter
This research highlights that user adoption of generative AI is significantly influenced by factors such as perceived usefulness (performance expectancy), the availability of support systems (facilitating conditions), the enjoyment derived from use (hedonic motivation), an individual's propensity for innovation (personal innovativeness), the perceived cost-benefit ratio (price value), and crucially, user trust. These insights are vital for informing the design of generative AI tools that aim for widespread adoption.
Source
International Journal of Basic and Applied Sciences
Determinants of Behavioral Intention to Use Generative AI: The Role of Trust, Personal Innovativeness, and UTAUT II Factors
journal · 2025
View sourceQuestions About This Research
- What does the research say about trust and personal innovativeness drive generative ai adoption intentions?
- Focus design efforts on enhancing perceived usefulness, ensuring robust facilitating conditions, cultivating hedonic motivation, and building strong user trust to maximize generative AI adoption. Evidence: International Journal of Basic and Applied Sciences (2025).
- Why does "Trust and Personal Innovativeness Drive Generative AI Adoption Intentions" matter for design?
- Understanding these drivers is essential for designers and product developers aiming to create generative AI tools that users will readily adopt and integrate into their workflows. By focusing on building trust and highlighting the benefits, designers can increase the likelihood of successful product launches and widespread use.
- How can designers apply this research?
- Focus design efforts on enhancing perceived usefulness, ensuring robust facilitating conditions, cultivating hedonic motivation, and building strong user trust to maximize generative AI adoption.
- What were the main findings?
- Performance expectancy significantly influences behavioral intention.. Facilitating conditions significantly influence behavioral intention.. Hedonic motivation significantly influences behavioral intention.. Personal innovativeness significantly influences behavioral intention.
- What research method was used?
- Survey Research with 480 participants.
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2025 journal from International Journal of Basic and Applied Sciences.
- What should I do differently in my next project?
- When developing generative AI products, conduct user research to understand perceptions of usefulness, ease of use, and enjoyment. Implement features that build trust and clearly communicate the value proposition.
- What are the limitations?
- The study focused on university students in Indonesia, which may limit generalizability to other demographics or cultural contexts. The reliance on self-reported data through surveys can be subject to response bias.