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TRUVACE RECORD VERSION
record: TRV-2026-2563
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:54:50.578315Z
status: published
lens: trace
sector: business
headline: When and What Conversational AI Should Ask: Extending the Persuasion Knowledge Model to Explain User Engagement and Purchase Intention
dek: Conversational AI is rapidly expanding in e-commerce, where chatbot questions critically shape users’ cognition and engagement. Yet, the effects of question timing and type on persuasion remain underexplored. Building on the persuasion knowledge model, this study examines how question timing (in-conversation vs. follow-up) and type (informational vs. experience-evoking) influence users’ knowledge structures, flow experience, and purchase intention. We developed a retrieval-augmented generation-based recommendati…
gain_title: In an online experiment with 335 participants, informational chatbot questions asked during the conversation strengthened shoppers' topic knowledge, which positively predicted flow experience and purchase intention.
problem_title: In the same experiment, experience-evoking questions asked during the conversation most strongly activated persuasion knowledge, which negatively predicted flow experience and purchase intention.
trace_subject: Question timing and type in an e-commerce recommendation chatbot
gain_reading: In an online experiment with 335 participants, informational chatbot questions asked during the conversation strengthened shoppers' topic knowledge, which positively predicted flow experience and purchase intention.
gain_evidence: informational questions presented during the conversation and experience-evoking questions presented as follow-up prompts strengthened topic knowledge | Topic knowledge positively predicted flow components and purchase intention
problem_reading: In the same experiment, experience-evoking questions asked during the conversation most strongly activated persuasion knowledge, which negatively predicted flow experience and purchase intention.
problem_evidence: Persuasion knowledge was most strongly activated when experience-evoking questions appeared during the conversation | persuasion knowledge negatively predicted these outcomes
quick_read: Researchers built a recommendation chatbot using retrieval-augmented generation and tested it in an online experiment with 335 participants. They varied when the chatbot asked questions (during the conversation or as follow-up prompts) and what kind it asked (informational or experience-evoking).

Informational questions asked during the conversation, and experience-evoking questions asked as follow-ups, strengthened users' topic knowledge. Topic knowledge in turn positively predicted flow experience and purchase intention.

Experience-evoking questions asked during the conversation most strongly activated persuasion knowledge, meaning users' awareness of being persuaded. That awareness negatively predicted flow and purchase intention, so the same chatbot feature can build or erode buying interest depending on its timing.
limitation: The findings come from a single online experiment with 335 participants using one purpose-built recommendation chatbot, and measure purchase intention rather than actual purchases.
tag: Dual reading
key_points: The study used a retrieval-augmented generation-based recommendation chatbot and an online experiment with 335 participants. | It crossed question timing (in-conversation vs. follow-up) with question type (informational vs. experience-evoking). | Topic knowledge positively predicted flow components and purchase intention. | Persuasion knowledge negatively predicted flow components and purchase intention. | Agent knowledge was activated under certain timing-type combinations but did not directly predict flow experience or purchase intention.
rundown: The work extends the persuasion knowledge model, which describes what people know about a topic, about persuasion tactics and about the agent trying to persuade them. It applies the model to conversational AI in e-commerce, where chatbot questions shape how users think and engage.

The effects depended on pairing timing with type. Informational questions worked best during the conversation, while experience-evoking questions strengthened topic knowledge only when delivered as follow-up prompts. Placed mid-conversation, experience-evoking questions triggered persuasion knowledge most strongly.

Agent knowledge, meaning users' understanding of the chatbot itself, varied across timing-type combinations. It did not directly predict flow experience or purchase intention.
sources:
- peer_reviewed | International Journal of Human–Computer Interaction | https://doi.org/10.1080/10447318.2026.2728933 | 2026-09-11
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