Short answer

When designing and marketing mental health chatbots, prioritize building user trust and addressing privacy concerns, as these are likely more influential than perceived usefulness or ease of use in driving actual adoption.

Field: Innovation & Markets
Source: Frontiers in Digital Health (2025)
Method: Quantitative survey research with structural equation modelling (SEM).
Sample: 351 participants
Evidence: Moderate effect

While factors like performance expectancy and effort expectancy positively influence the perceived benefits of mental health chatbots, these benefits do not directly translate into a higher intention to use the technology among professional employees. This innovation & markets research insight is drawn from a 2025 study published in Frontiers in Digital Health. Using Quantitative survey research with structural equation modelling (sem). with 351 participants, researchers explored how this design variable affects real-world outcomes. The key design takeaway: When designing and marketing mental health chatbots, prioritize building user trust and addressing privacy concerns, as these are likely more influential than perceived usefulness or ease of use in driving actual adoption.

Study

Innovation & MarketsNew This WeekModerate effect

Perceived benefits alone do not drive adoption of mental health chatbots among professionals.

Frontiers in Digital Health · 2025

Key Findings

01Performance expectancy and effort expectancy positively influenced perceived benefits of mental health chatbots.
02Perceived benefits did not have a significant direct effect on behavioral intention to use mental health chatbots.
03Attitude towards chatbots moderated the relationship between perceived benefits and behavioral intention, but this effect was not statistically significant in the full model.

Application

Design takeaway

How to apply

Before launching a new mental health chatbot, conduct user research focused on trust, privacy perceptions, and perceived credibility, in addition to usability and feature desirability.

Project actions

01When researching user adoption of new technologies, consider factors beyond just perceived usefulness and ease of use.
02Investigate the role of trust, privacy, and credibility in your design project.

Method & Evidence

AimWhat factors influence professional employees' intention to use mental health chatbots, and to what extent do perceived benefits mediate this relationship?

MethodQuantitative survey research with structural equation modelling (SEM).

ProcedureAn online survey was administered to professional employees in Malaysia to collect data on their perceptions of mental health chatbots, their perceived benefits, and their intention to use them. The data was then analyzed using SEM to test the proposed relationships between variables.

Sample351 participants

ContextProfessional employees in Malaysia adopting digital mental health technologies.

Variables

IV["Performance Expectancy","Effort Expectancy","Social Influence","Facilitating Conditions","Attitude Towards Chatbots"]

DV["Behavioral Intention to Use","Perceived Benefits"]

CV["Demographic factors (e.g., age, gender, education)","Previous experience with chatbots"]

Strengths & Limitations

Strengths

+Integrates two established theoretical frameworks (UTAUT and TPB).
+Employs a robust statistical method (SEM) for data analysis.

Limitations

The sample was limited to professional employees in Malaysia, so the findings might not apply to other groups or countries. The study was a snapshot in time, so it doesn't show how opinions might change.

Reliability & validity

The study's reliability and validity would be assessed through measures like Cronbach's alpha for internal consistency of scales and confirmatory factor analysis to ensure construct validity. The use of SEM also contributes to the statistical conclusion validity.

Think critically

If perceived benefits don't directly lead to usage intention, what other psychological or contextual factors are likely playing a more significant role in the adoption of mental health chatbots?

Design Principles

"For novel digital health technologies, adoption is driven by a complex interplay of perceived benefits, trust, privacy, and credibility, not solely by functional advantages."

This finding challenges a common assumption in technology adoption that increased perceived benefits automatically lead to greater usage. It suggests that designers and marketers need to look beyond functional advantages and address other critical factors like trust, privacy, and credibility to effectively drive adoption of new digital health solutions.

What This Means for Your Design

Just because people think a mental health app is good and easy to use doesn't mean they'll actually use it. They also need to trust it and feel their information is safe.

How to use in your project

1.This study provides a framework for investigating user adoption of digital health tools, highlighting the importance of non-functional attributes like trust and privacy.

Add to My Project

Quick Cite

Paragraph starter

This research indicates that while perceived benefits are influenced by factors like performance and effort expectancy, they do not directly predict behavioral intention to use mental health chatbots among professional employees. This suggests that for sensitive digital health technologies, trust, privacy, and credibility may be more significant drivers of adoption than perceived utility alone, necessitating a design approach that prioritizes these aspects.

Source

Frontiers in Digital Health

Factors influencing perceived benefits and behavioral intention to use mental health chatbots among professional employees: an empirical study

journal · 2025

View source

Questions About This Research

What does the research say about perceived benefits alone do not drive adoption of mental health chatbots among professionals?: When designing and marketing mental health chatbots, prioritize building user trust and addressing privacy concerns, as these are likely more influential than perceived usefulness or ease of use in driving actual adoption. Evidence: Frontiers in Digital Health (2025).
Why does "Perceived benefits alone do not drive adoption of mental health chatbots among professionals." matter for design?: This finding challenges a common assumption in technology adoption that increased perceived benefits automatically lead to greater usage. It suggests that designers and marketers need to look beyond functional advantages and address other critical factors like trust, privacy, and credibility to effectively drive adoption of new digital health solutions.
How can designers apply this research?: When designing and marketing mental health chatbots, prioritize building user trust and addressing privacy concerns, as these are likely more influential than perceived usefulness or ease of use in driving actual adoption.
What were the main findings?: Performance expectancy and effort expectancy positively influenced perceived benefits of mental health chatbots.. Perceived benefits did not have a significant direct effect on behavioral intention to use mental health chatbots.. Attitude towards chatbots moderated the relationship between perceived benefits and behavioral intention, but this effect was not statistically significant in the full model.
What research method was used?: Quantitative survey research with structural equation modelling (SEM). with 351 participants.
How strong is the evidence?: Evidence strength is rated Moderate effect, based on a 2025 journal from Frontiers in Digital Health.
What should I do differently in my next project?: Before launching a new mental health chatbot, conduct user research focused on trust, privacy perceptions, and perceived credibility, in addition to usability and feature desirability.
What are the limitations?: The study's findings may be specific to the Malaysian professional context and may not generalize to other cultural settings or employee demographics. The cross-sectional design limits causal inferences.