Explaining the use of AI chatbots as context alignment: Motivations behind the use of AI chatbots across contexts and culture

Drawing from the affordances concept in communication and the advertising research principle of context alignment, this study develops a typology of affordances associated with AI chatbot use across diverse contexts and cultures. We argue that the congruence between perceived affordances and the context of use significantly shapes users’ expectations and engagement with AI chatbots. As a first step, we developed a comprehensive set of AI chatbot affordances. Subsequently, we investigated the predictive value of these affordances for chatbot use across five different contexts, including at home, at work, in public spaces, during commuting, and at school. In a total sample of N = 5,096 AI chatbot users from mainland China, Germany, Hong Kong, Japan, and the United States, parallel analysis identified seven general ( private social presence , accessibility , persistence , anonymity , bandwidth , editability , and conversation control ) and five specific affordances ( writing assistance , emotional assistance , coding assistance , personal assistance, translation assistance ) for the use of AI chatbots. These affordances consistently predicted context-specific AI chatbot use across cultural boundaries. • Identified 7 general and 5 specific affordances of AI chatbot use • Analyzed AI chatbot use across home, work, school, commuting, and public spaces • Surveyed 5,096 users in mainland China, Germany, Japan, Hong Kong, and the U.S. • Context alignment and culture shape AI chatbot perceptions and adoption • Affordance effects vary by context, gender, age, and region

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Publication Details

Journal
Computers in Human Behavior
Published
2025-06-24
DOI
https://doi.org/10.1016/j.chb.2025.108738
Citations
11
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
27.29
Controls
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article

Explaining the use of AI chatbots as context alignment: Motivations behind the use of AI chatbots across contexts and culture

Tetsuro Kobayashi, Sebastian Scherr, Li Jiang, Bolin Cao
11 citations
Computers in Human Behavior
AI in Service Interactions
27.29
article

Explaining the use of AI chatbots as context alignment: Motivations behind the use of AI chatbots across contexts and culture

Tetsuro Kobayashi, Sebastian Scherr, Li Jiang, Bolin Cao
article en
11 citations

Abstract

Drawing from the affordances concept in communication and the advertising research principle of context alignment, this study develops a typology of affordances associated with AI chatbot use across diverse contexts and cultures. We argue that the congruence between perceived affordances and the context of use significantly shapes users’ expectations and engagement with AI chatbots. As a first step, we developed a comprehensive set of AI chatbot affordances. Subsequently, we investigated the predictive value of these affordances for chatbot use across five different contexts, including at home, at work, in public spaces, during commuting, and at school. In a total sample of N = 5,096 AI chatbot users from mainland China, Germany, Hong Kong, Japan, and the United States, parallel analysis identified seven general ( private social presence , accessibility , persistence , anonymity , bandwidth , editability , and conversation control ) and five specific affordances ( writing assistance , emotional assistance , coding assistance , personal assistance, translation assistance ) for the use of AI chatbots. These affordances consistently predicted context-specific AI chatbot use across cultural boundaries. • Identified 7 general and 5 specific affordances of AI chatbot use • Analyzed AI chatbot use across home, work, school, commuting, and public spaces • Surveyed 5,096 users in mainland China, Germany, Japan, Hong Kong, and the U.S. • Context alignment and culture shape AI chatbot perceptions and adoption • Affordance effects vary by context, gender, age, and region

Computers in Human BehaviorVol. 172
Waseda University (JP), City University of Hong Kong (HK), University of Augsburg (DE), Shenzhen University (CN)
Reduced inequalities
Openalex Percentile: Top 1%
AI in Service Interactions
27.29
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