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Related Experiment Video

Updated: Jun 5, 2026

Humor or Rationality? The Neural Mechanisms of How Agent Type and Language Style Influence Satisfaction with Ride-Hailing Service Failure Recovery

Humor or Rationality? The Neural Mechanisms of How Agent Type and Language Style Influence Satisfaction with Ride-Hailing Service Failure Recovery

Published on: March 13, 2026

Do Users Really Value Human Warmth and AI Skill? Experiments Reveal a Persistent Preference for Human Nutritionists.

Rukun Zhang¹, Qingqing Xie¹, Bingcan Li¹

¹School of Media and Communication, Shenzhen University, Guangdong,Shenzhen, China.

Journal of Health Communication

|June 4, 2026

Summary

Consumers prefer human nutritionists over AI, even when AI demonstrates high competence. This study highlights the need for AI to show benevolence, not just skill, to build trust and achieve parity in health communication.

Keywords:

Artificial intelligence, Trust, Benevolence, Competence, Health product

Related Experiment Videos

Last Updated: Jun 5, 2026

Humor or Rationality? The Neural Mechanisms of How Agent Type and Language Style Influence Satisfaction with Ride-Hailing Service Failure Recovery

Humor or Rationality? The Neural Mechanisms of How Agent Type and Language Style Influence Satisfaction with Ride-Hailing Service Failure Recovery

Published on: March 13, 2026

Area of Science:

Consumer Psychology
Human-Computer Interaction
Health Communication

Background:

Conventional wisdom suggests consumers value benevolence in humans and competence in AI.
This stereotype is prevalent in trust literature and influences AI adoption.
Algorithmic aversion remains a significant barrier in AI integration, particularly in sensitive fields like healthcare.

Purpose of the Study:

To test the stereotype that consumers prioritize competence in AI over benevolence.
To investigate how perceived benevolence and competence of human versus AI nutritionists influence consumer trust and adoption intention.
To examine the mediating roles of perceived health benefits and trust in the relationship between source type and adoption intention.

Main Methods:

Two between-subjects experiments (N=328; N=329) manipulated benevolence (high/low) and competence (high/low) in health ads featuring human or AI nutritionists.
PROCESS Models 4 and 7 were used to evaluate a dual-mediation pathway.
Mediation analyses assessed the sequential effects of perceived benefits and trust on adoption intention.

Main Results:

Human nutritionists consistently outperformed AI counterparts in perceived benefits, trust, and adoption intention, regardless of competence or benevolence levels.
The advantage of human nutritionists was amplified when AI displayed low benevolence or low competence.
Even when AI exhibited high benevolence and high competence, human nutritionists maintained an advantage.

Conclusions:

The findings challenge the stereotype, demonstrating that human experts are preferred over AI, even when AI signals high competence.
Competence signaling alone is insufficient to overcome algorithmic aversion in healthcare.
AI nutritionists need to convey strong cues of benevolence alongside technical skill to achieve parity with human advisors, informing future AI design in health communication.