Short answer

Focus on empowering users with confidence and assuring them of security and privacy when designing and marketing AI technologies for Gen Z.

Field
Innovation & Markets
Source
Journal for STEM Education Research (2026)
Method
Quantitative research using an extended Technology Acceptance Model (TAM) framework, structural equation modelling (SEM), and multigroup analysis.
Sample
349 participants
Evidence
Strong effect

Gen Z university students are more likely to adopt AI technologies when they feel confident in their ability to use them (self-efficacy) and trust the technology's security and privacy, rather than due to social influence. This innovation & markets research insight is drawn from a 2026 study published in Journal for STEM Education Research. Using Quantitative research using an extended technology acceptance model (tam) framework, structural equation modelling (sem), and multigroup analysis. with 349 participants, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Focus on empowering users with confidence and assuring them of security and privacy when designing and marketing AI technologies for Gen Z.

Study
Innovation & MarketsNew This WeekStrong effect

Gen Z AI Adoption Driven by Self-Efficacy and Trust, Not Social Pressure

Gen Z university students are more likely to adopt AI technologies when they feel confident in their ability to use them (self-efficacy) and trust the technology's security and privacy, rather than due to social influence.

Journal for STEM Education Research · 2026

01

Key Findings

  • 01Self-efficacy and perceived trust significantly influence Gen Z's behavioural intention to adopt AI.
  • 02Social influence has a diminishing role in AI adoption among Gen Z.
  • 03Prior experience with technology influences adoption factors, but its impact lessens with increased experience.
  • 04Course major influences the predictive effect of adoption factors, while gender does not.
02

Application

Design takeaway

Focus on empowering users with confidence and assuring them of security and privacy when designing and marketing AI technologies for Gen Z.

How to apply

When developing AI tools for university students, ensure that the onboarding process is highly supportive and that all privacy and security features are clearly communicated and robust.

Project actions

  • 01When researching user adoption, consider psychological factors like confidence and trust alongside social ones.
  • 02If your project involves AI, think about how to make users feel capable and secure.
03

Method & Evidence

AimTo investigate the influence of self-efficacy and perceived trust on Artificial Intelligence adoption among Gen Z university students, and to explore moderating effects of gender, course major, and experience.
MethodQuantitative research using an extended Technology Acceptance Model (TAM) framework, structural equation modelling (SEM), and multigroup analysis.
ProcedureA survey was administered to 349 Gen Z university students to collect data on their self-efficacy, perceived trust, perceived expectancy, social influence, and behavioural intention towards AI adoption. Statistical analysis, including SEM, was used to test the proposed relationships and moderation effects.
Sample349 participants
ContextHigher education, specifically university students (Gen Z) and their adoption of Artificial Intelligence technologies.

Variables

IV["Self-Efficacy","Perceived Trust","Perceived Expectancy","Social Influence","Experience","Gender","Course Major"]
DVBehavioural Intention (to adopt AI)
04

Strengths & Limitations

Strengths

  • +Employs a robust theoretical framework (extended UTAUT).
  • +Utilizes advanced statistical methods (SEM, multigroup analysis) for rigorous analysis.

Limitations

The study was conducted on university students, so results might differ for younger or older users, or those in different educational settings.

Reliability & validity

The study's use of established theoretical models and advanced statistical techniques like SEM suggests a strong focus on reliability and validity. The R² value of 0.751 indicates a high degree of explained variance in behavioural intention, supporting the model's predictive validity.

Think critically

How might the diminishing role of social influence among Gen Z impact the diffusion of future technologies compared to previous generations?

05

Design Principles

"User confidence and perceived trustworthiness are key drivers for technology adoption, particularly among younger demographics."

Understanding the primary drivers of AI adoption among emerging demographics like Gen Z is crucial for technology developers and educational institutions. This insight highlights the need to focus on building user confidence and ensuring robust trust mechanisms rather than relying on social trends.

06

What This Means for Your Design

Gen Z students are more likely to use AI if they feel they can handle it and trust it, not just because their friends do.

How to use in your project

  • 1.Use this study to justify focusing on user confidence and trust in your design process for technology-based projects.
  • 2.Cite this research when discussing the factors influencing user adoption of innovative technologies.
07

Add to My Project

08

Quick Cite

Paragraph starter

Research indicates that for Gen Z university students, the adoption of AI technologies is significantly influenced by their self-efficacy and perceived trust in the system, rather than social influence. This suggests that design efforts should prioritize building user confidence through intuitive interfaces and clear support, alongside robust data security and privacy measures, to encourage uptake.

09

Source

Journal for STEM Education Research

Exploring the Factors Influencing Artificial Intelligence Adoption among Gen Z University Students: the Role of Self-Efficacy and Perceived Trust

journal · 2026

View source

Questions About This Research

What does the research say about gen z ai adoption driven by self-efficacy and trust, not social pressure?
Focus on empowering users with confidence and assuring them of security and privacy when designing and marketing AI technologies for Gen Z. Evidence: Journal for STEM Education Research (2026).
Why does "Gen Z AI Adoption Driven by Self-Efficacy and Trust, Not Social Pressure" matter for design?
Understanding the primary drivers of AI adoption among emerging demographics like Gen Z is crucial for technology developers and educational institutions. This insight highlights the need to focus on building user confidence and ensuring robust trust mechanisms rather than relying on social trends.
How can designers apply this research?
Focus on empowering users with confidence and assuring them of security and privacy when designing and marketing AI technologies for Gen Z.
What were the main findings?
Self-efficacy and perceived trust significantly influence Gen Z's behavioural intention to adopt AI.. Social influence has a diminishing role in AI adoption among Gen Z.. Prior experience with technology influences adoption factors, but its impact lessens with increased experience.. Course major influences the predictive effect of adoption factors, while gender does not.
What research method was used?
Quantitative research using an extended Technology Acceptance Model (TAM) framework, structural equation modelling (SEM), and multigroup analysis. with 349 participants.
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2026 journal from Journal for STEM Education Research.
What should I do differently in my next project?
When developing AI tools for university students, ensure that the onboarding process is highly supportive and that all privacy and security features are clearly communicated and robust.
What are the limitations?
The findings might reflect Gen Z's intention to adopt technology beyond academic contexts, and the study's focus on a specific demographic may limit generalizability to other age groups or populations.