Work

“Word‐By‐Word” or “Line‐By‐Line” Presentation? How Response Generation Dynamics in Generative AI Influence User Acceptance

Year: 2026

Type: article

Abstract: ABSTRACT Generative AI systems are increasingly integrated into various user support and decision‐making contexts. While extensive research has explored user acceptance of generative AI across domains, less is known about how its inherent presentation features influence such acceptance. This study investigates how the presentation format of generative AI responses—high‐dynamism formats (word‐by‐word) versus low‐dynamism formats (line‐by‐line)—affects users' willingness to accept its output. Three experiments, across various contexts, including daily question‐answering, online shopping, and crowdfunding platforms, tested how the dynamic presentation of generative AI responses influences users' willingness to accept its output. The results demonstrate that users exhibit greater receptivity when generative AI responses are presented in high‐dynamism formats. This effect occurs because dynamic presentation enhances users' feelings of immersion. Furthermore, we find that users' situational need for understanding moderates this immersion‐based mechanism. Specifically, the positive indirect effect of dynamic presentation on willingness to accept the output through feelings of immersion is attenuated in high need‐for‐understanding contexts, where users tend to prioritize content quality, accuracy, and diagnosticity. In contrast, this indirect effect is amplified in low need‐for‐understanding contexts, where users are more receptive to the experiential and interactional qualities of dynamic presentation. This research contributes to the literature on the effectiveness of dynamic presentation and provides novel insights into the factors influencing user acceptance of generative AI outputs. Our findings highlight the importance of tailoring output presentation methods based on individual user characteristics and task‐specific cognitive demands.

Source: Journal of Consumer Behaviour

DOI: 10.1002/cb.70256

Authors: Fei Jin, Shuying Gong

Institutions: Sichuan University, Chengdu University

Language: English

Topic: AI in Service Interactions

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, Sichuan University

Awards: G4732719138, G4863735013