Fit Matters: Format–Distance Alignment Improves Conversational Search

要旨

Existing conversational search systems can synthesize information into responses, but they lack principled ways to adapt response formats to users' cognitive states. This paper investigates whether aligning format and distance, which involves matching information granularity and media to users' psychological distance, improves user experience. In a between-subjects experiment (N=464) on travel planning, we crossed two distance dimensions (temporal/spatial × near/far) with four formats varying in granularity (abstract/concrete) and media (text/image-and-text). The experiment established that format-distance alignment reduced users' risk perceptions while increasing decision confidence, perceptions of information usefulness, ease of use, enjoyment, and credibility, and adoption intentions. Concrete formats imposed higher cognitive load, but yielded productive effort when matched to near-distance tasks. Images enhanced concrete but not abstract text, suggesting multimedia benefits depend on complementarity. These findings establish format-distance alignment as a distinctive and important design dimension, enabling systems to tailor response formats to users' psychological distance.

著者
Yitian Yang
National University of Singapore, Singapore, Singapore
Yugin Tan
National University of Singapore, Singapore, Singapore
Jung-Tai King
National Dong Hwa University, Hualien, Taiwan
Yang Chen Lin
National Tsing Hua University , Hsinchu , Taiwan
YI-CHIEH LEE
National University of Singapore, Singapore, Singapore

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Mental Health Chatbots and Conversational Agents

Auditorium
6 件の発表
2026-04-14 20:15:00
2026-04-14 21:45:00