Short answer

Focus on making AI tools demonstrably useful and enjoyable for users, and tailor adoption strategies based on professional roles and educational backgrounds.

Field
Innovation & Markets
Source
Information (2025)
Method
Quantitative research using a survey-based approach and structural equation modeling.
Sample
297 participants
Evidence
Strong effect

In banking, the perceived usefulness and enjoyable experience of AI systems are more critical for adoption than what others think. This innovation & markets research insight is drawn from a 2025 study published in Information. Using Quantitative research using a survey-based approach and structural equation modeling. with 297 participants, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Focus on making AI tools demonstrably useful and enjoyable for users, and tailor adoption strategies based on professional roles and educational backgrounds.

Study
Innovation & MarketsNew This WeekStrong effect

Performance and Hedonic Motivation Drive AI Adoption in Banking, Not Social Pressure

In banking, the perceived usefulness and enjoyable experience of AI systems are more critical for adoption than what others think.

Information · 2025

01

Key Findings

  • 01Performance Expectancy (perceived usefulness) significantly influences AI adoption.
  • 02Effort Expectancy (perceived ease of use) significantly influences AI adoption.
  • 03Hedonic Motivation (enjoyment) significantly influences AI adoption.
  • 04Social Influence was found to be non-significant in predicting AI adoption.
  • 05Occupation and education level significantly moderate AI adoption attitudes.
02

Application

Design takeaway

Focus on making AI tools demonstrably useful and enjoyable for users, and tailor adoption strategies based on professional roles and educational backgrounds.

How to apply

When developing or implementing AI solutions in banking, conduct user research to understand perceived usefulness and ease of use, and design for engaging and enjoyable interactions. Consider how different professional groups might benefit uniquely from the technology.

Project actions

  • 01When researching user adoption of a new technology, consider measuring perceived usefulness, ease of use, and enjoyment.
  • 02Investigate how different user groups (e.g., by profession, age, or experience) might have different adoption patterns.
03

Method & Evidence

AimWhat are the primary behavioral drivers influencing the adoption of AI technologies within the banking sector, considering different stakeholder groups?
MethodQuantitative research using a survey-based approach and structural equation modeling.
ProcedureA survey was administered to bank employees, digital professionals, and the general public to gather data on their perceptions of AI adoption. This data was then analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) to test the relationships between various technology acceptance constructs.
Sample297 participants
ContextBanking sector in a semi-mature digital economy (Greece)

Variables

IV["Performance Expectancy","Effort Expectancy","Hedonic Motivation","Social Influence","Age","Gender","Occupation","Education"]
DVAI Adoption Behavior
04

Strengths & Limitations

Strengths

  • +Utilizes established theoretical frameworks (TAM and UTAUT-2).
  • +Employs robust statistical analysis (PLS-SEM).
  • +Investigates a relevant and timely topic in the banking sector.

Limitations

The study was conducted in a specific country and may not apply universally. The focus on specific theoretical models might overlook other important adoption factors.

Reliability & validity

The use of established scales within TAM and UTAUT-2, combined with PLS-SEM analysis, contributes to the reliability and validity of the findings regarding the relationships between constructs.

Think critically

Given that social influence was not a significant driver, how might a design team effectively overcome initial user skepticism or resistance to AI adoption without relying on peer endorsement?

05

Design Principles

"Prioritize perceived utility and user experience over social validation when introducing new technologies."

Understanding the true drivers of AI adoption allows organizations to focus their implementation strategies on tangible benefits and user experience, rather than relying on less impactful social factors. This leads to more efficient resource allocation and a higher likelihood of successful technology integration.

06

What This Means for Your Design

People adopt new AI tools in banks when they see how helpful and fun they are, not just because friends or colleagues use them. What job you have and how much education you have matters more than your age or gender.

How to use in your project

  • 1.Use the findings to justify focusing your design efforts on features that enhance performance expectancy and hedonic motivation for your target users.
  • 2.If your design project involves a specific user group, consider how their occupation or education might influence their interaction with your design.
07

Add to My Project

08

Quick Cite

Paragraph starter

This research indicates that for successful AI adoption in banking, design efforts should prioritize enhancing 'Performance Expectancy' (perceived usefulness) and 'Hedonic Motivation' (enjoyment), as these were found to be significant drivers, while 'Social Influence' was not. Furthermore, the study highlights that user attitudes can be significantly moderated by factors such as occupation and education level, suggesting a need for tailored approaches.

09

Source

Information

Behavioral Drivers of AI Adoption in Banking in a Semi-Mature Digital Economy: A TAM and UTAUT-2 Analysis of Stakeholder Perspectives

journal · 2025

View source

Questions About This Research

What does the research say about performance and hedonic motivation drive ai adoption in banking, not social pressure?
Focus on making AI tools demonstrably useful and enjoyable for users, and tailor adoption strategies based on professional roles and educational backgrounds. Evidence: Information (2025).
Why does "Performance and Hedonic Motivation Drive AI Adoption in Banking, Not Social Pressure" matter for design?
Understanding the true drivers of AI adoption allows organizations to focus their implementation strategies on tangible benefits and user experience, rather than relying on less impactful social factors. This leads to more efficient resource allocation and a higher likelihood of successful technology integration.
How can designers apply this research?
Focus on making AI tools demonstrably useful and enjoyable for users, and tailor adoption strategies based on professional roles and educational backgrounds.
What were the main findings?
Performance Expectancy (perceived usefulness) significantly influences AI adoption.. Effort Expectancy (perceived ease of use) significantly influences AI adoption.. Hedonic Motivation (enjoyment) significantly influences AI adoption.. Social Influence was found to be non-significant in predicting AI adoption.
What research method was used?
Quantitative research using a survey-based approach and structural equation modeling. with 297 participants.
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2025 journal from Information.
What should I do differently in my next project?
When developing or implementing AI solutions in banking, conduct user research to understand perceived usefulness and ease of use, and design for engaging and enjoyable interactions. Consider how different professional groups might benefit uniquely from the technology.
What are the limitations?
The findings are specific to a semi-mature digital economy and may not generalize to economies with different levels of digital maturity. The study focused on a specific set of behavioral drivers and may not capture all potential influences.