Short answer
Designers should focus on building trust through demonstrable service quality and strong security protocols, while carefully balancing the perceived need and ease of use for AI-driven features in mobile banking.
- Field
- User-Centred Design
- Source
- Future Business Journal (2025)
- Method
- Quantitative research using multiple linear regression.
- Sample
- 452 participants
- Evidence
- Strong effect
Consumers are more likely to adopt AI in mobile banking when they perceive high service quality and robust security, which builds trust and positive attitudes. This user-centred design research insight is drawn from a 2025 study published in Future Business Journal. Using Quantitative research using multiple linear regression. with 452 participants, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Designers should focus on building trust through demonstrable service quality and strong security protocols, while carefully balancing the perceived need and ease of use for AI-driven features in mobile banking.
Perceived service quality and security are paramount for AI adoption in mobile banking
Consumers are more likely to adopt AI in mobile banking when they perceive high service quality and robust security, which builds trust and positive attitudes.
Future Business Journal · 2025
Key Findings
- 01Perceived service quality and security are the primary drivers of trust in AI for mobile banking.
- 02Trust positively influences attitudes towards AI and comfort with its use.
- 03Relative advantage significantly promotes intentions to use AI in mobile banking.
- 04Perceived need and excessive comfort unexpectedly reduce the likelihood of AI adoption.
Application
Design takeaway
Designers should focus on building trust through demonstrable service quality and strong security protocols, while carefully balancing the perceived need and ease of use for AI-driven features in mobile banking.
How to apply
When designing AI features for financial applications, conduct user research to validate perceptions of service quality and security, and test different levels of perceived need and interaction complexity.
Project actions
- 01When researching user needs for a new digital product, consider how perceived quality and security will impact adoption.
- 02Explore the 'sweet spot' for feature complexity; too simple might be as bad as too complex.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Large sample size provides statistical power.
- +Investigates a combination of factors, including counter-intuitive ones.
Limitations
The study's findings are context-specific to Portuguese consumers and mobile banking. The research measures intention to use, which may differ from actual usage.
Reliability & validity
The use of multiple linear regression on a large sample suggests good reliability. Validity would depend on the construct measures used in the survey.
Think critically
How might the 'perceived need' and 'excessive comfort' findings be explained? Could this relate to user anxiety about control or a desire for human interaction in financial matters?
Design Principles
"Trust is a foundational element for user adoption of AI in sensitive applications like mobile banking, built upon perceived service quality and security."
Understanding the drivers of trust is crucial for designing AI-powered banking services that users will readily adopt. Focusing on these core elements can lead to more successful digital product development and user engagement.
What This Means for Your Design
People are more likely to use AI in their banking apps if they think the service is good and their information is safe. Surprisingly, if they think they don't really need it or it's too simple, they might not use it.
How to use in your project
- 1.Reference this study when discussing user adoption barriers and facilitators for technology in your design project, particularly concerning trust and perceived value.
Add to My Project
Quick Cite
Paragraph starter
Research indicates that user adoption of AI in mobile banking is significantly influenced by perceived service quality and security, which are key components of trust. While a clear relative advantage of AI features promotes usage intentions, an overestimation of perceived need or excessive simplicity can paradoxically reduce adoption rates, suggesting a nuanced approach to feature design and user education is required.
Source
Future Business Journal
The role of artificial intelligence in mobile banking: decoding portuguese consumers’ perceptions and intentions to engage
journal · 2025
View sourceQuestions About This Research
- What does the research say about perceived service quality and security are paramount for ai adoption in mobile banking?
- Designers should focus on building trust through demonstrable service quality and strong security protocols, while carefully balancing the perceived need and ease of use for AI-driven features in mobile banking. Evidence: Future Business Journal (2025).
- Why does "Perceived service quality and security are paramount for AI adoption in mobile banking" matter for design?
- Understanding the drivers of trust is crucial for designing AI-powered banking services that users will readily adopt. Focusing on these core elements can lead to more successful digital product development and user engagement.
- How can designers apply this research?
- Designers should focus on building trust through demonstrable service quality and strong security protocols, while carefully balancing the perceived need and ease of use for AI-driven features in mobile banking.
- What were the main findings?
- Perceived service quality and security are the primary drivers of trust in AI for mobile banking.. Trust positively influences attitudes towards AI and comfort with its use.. Relative advantage significantly promotes intentions to use AI in mobile banking.. Perceived need and excessive comfort unexpectedly reduce the likelihood of AI adoption.
- What research method was used?
- Quantitative research using multiple linear regression. with 452 participants.
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2025 journal from Future Business Journal.
- What should I do differently in my next project?
- When designing AI features for financial applications, conduct user research to validate perceptions of service quality and security, and test different levels of perceived need and interaction complexity.
- What are the limitations?
- Findings are specific to Portuguese consumers and may not generalize to other cultural contexts. The study focuses on intentions rather than actual behavior.