Do human trust models transfer to AI? a necessary condition analysis of interpersonal trust antecedents in large language model-based coaching
Journal
Behaviour & Information Technology (BIT)
Type
Article
Date Issued
2026-05-21
Author(s)
Research Team
IWI6
Abstract
As large language models (LLMs) increasingly serve as a technology basis for conversational health behaviour interventions, understanding how users evaluate and respond to these systems becomes critical for their acceptance. When LLM-based coaches are anthropomorphically designed – e.g. through human names, avatars, and messaging interfaces – they may trigger interpersonal trust expectations traditionally reserved for human relationships. This study investigates whether and to what extent the interpersonal trust antecedents of ability, benevolence, and integrity influence trust-related user responses in LLM-based health coaching for fitness and nutrition. Using a vignette-based survey design and combining partial least-squares structural equation modelling (PLS-SEM) with necessary condition analysis (NCA), we identify which antecedents are essential for perceived usefulness and intention to adopt. Results show that integrity is a necessary condition for both outcomes, with a medium to large effect, and that ability significantly influences perceived usefulness but is not necessary. Benevolence, however, was found to be neither a necessary nor a significant predictor in this context. These findings offer theoretical insights into the selective transfer of interpersonal trustworthiness beliefs to AI and provide practical guidance for the design of trustworthy anthropomorphic coaching systems.
Language
English
Keywords
Large language models
behavior change
digital health
trust
anthropomorphic design
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Taylor and Francis (United Kingdom)
Volume
online first
Start page
1
End page
19
Pages
19
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
Name
JML_1055.pdf
Size
1.18 MB
Format
Adobe PDF
Checksum (MD5)
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